Presentation type:
NH – Natural Hazards

EGU23-2227 | Orals | NH1.2 | Plinius Medal Lecture

Extremes in river flood hydrology: making Black Swans grey 

Alberto Viglione

Black Swans in river flood hydrology are unexpected events that surprise flood managers and citizens, causing massive impacts when they do occur, but that appear to be more predictable in retrospect, after their occurrence. My talk aims at showing how black swans in river flood hydrology can "be made grey", i.e. can be anticipated to a certain degree, in probabilistic terms, and/or made less impactful, by (1) expanding information on flood probabilities by gathering data on floods occurred in other places and at other times; (2) understanding the mechanisms causing heavy tails in flood frequency distributions; (3) understanding the mechanisms causing river flood changes in time; (4) accounting for uncertainties in data, models and flood frequency estimates; (5) accounting for the possible dynamics of coupled human-water systems; and (6) coupling the classical top-down approach to hydrological risk assessment based on predictive modelling with a bottom-up approach that is centered on robustness and resilience.

How to cite: Viglione, A.: Extremes in river flood hydrology: making Black Swans grey, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2227, https://doi.org/10.5194/egusphere-egu23-2227, 2023.

Mountain torrents and debris flows are widely distributed in the mountainous region, threatening the urban development and infrastructure in mountain areas. The adverse effects of these hazards may increase due to the continued socio-economic development and influence of climate change on the frequency and magnitude of the hazards. This lecture introduces an early warning system of mountain hazards based on hazards process simulation and associated risk forecasting. The system identifies the watershed with high susceptibility to mountain hazard occurrences by monitoring the hazard-fostering conditions and real-time meteorological data. Focusing on those watersheds, the formation and movement of the hazards were simulated while different characteristics were captured, such as debris flow scale amplification and flash flood erosion. The risk of the mountain hazards was assessed based on the whole process of disaster formation-movement-deposition/disaster-causing. Compared with traditional early warning systems, which largely rely on rainfall thresholds and expert judgment, this proposed system is fully data-driven and process-based, while little human intervention is required. This system provides more accurate early warning information, and risk forecasting can better support disaster response planning for the government agency. This system is currently under trial in Liangshan Prefecture, Sichuan Province of China. Just in 2022, 15 debris flow and 52 flash flood events were captured and the early warning information was delivered to the residents and government. The accuracy is more than 79% and significantly improved the disaster resilience of the mountainous region.

About the Presenter

 Prof. CUI Peng has long been engaged in research on the formation mechanism, risk assessment, monitoring and early warning, prevention and control technology of debris flows and other mountain hazards. He has given a strong pulse to several topics of major relevance for disaster risk reduction and management, including (1) deepening the understanding of debris flow formation, scale amplification, and disaster-causing mechanisms; (2) providing rigorous insights concerning the formation and evolution of earthquake-induced hazards and multi-hazard chaining effect; (3) development of multi-scale disaster risk assessment model; (4) building of risk-level-based monitoring and early system to support efficient disaster reduction; and (5) creating the mass control and energy-based disaster mitigation theory and technology. He has published more than 400 papers with over 12000 citations and is the world's most published scholar in the field of debris flow.

How to cite: Cui, P.: A data-driven and process-based system for mountain torrent and debris flow early warning and risk forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17044, https://doi.org/10.5194/egusphere-egu23-17044, 2023.

EGU23-8830 | ECS | Orals | NH11.2 | NH Division Outstanding Early Career Scientist Award Lecture

Disentangling the Characteristics and Drivers of Compound Drought and Hot Extremes 

Ankit Agarwal

Compound drought and hot extremes (CDHE) are periods of prolonged dry and hot weather exhibiting adverse impacts on nature and humankind than their counterparts. Understanding compound extremes is in its infancy due to complex dynamical climate systems involving interactions and feedback with the different processes at different scales. Our detailed investigation of the last seven decades of CDHE during the Indian Summer Monsoon has shown alarming observations. Our results confirmed a threefold increase in CDHE frequency for the recent period (1977–2019) relative to the base period (1951–1976), exhibiting a strong spatial pattern. Further investigation revealed CDHE likelihood, and spatial diversity in the CDHE occurrence is a function of the strong negative association between precipitation and temperature and soil moisture-temperature coupling, respectively. Investigation into the temporal evolution of CDHE confirms the strengthening of the negative association between precipitation and temperature, indicating a higher number of CDHE in future.

How to cite: Agarwal, A.: Disentangling the Characteristics and Drivers of Compound Drought and Hot Extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8830, https://doi.org/10.5194/egusphere-egu23-8830, 2023.

NH0 – Inter- and Transdisciplinary Sessions

EGU23-338 | ECS | Posters virtual | ITS1.1/NH0.1

A Stacking Ensemble Deep Learning Approach for Post Disaster Building Assessment using UAV Imagery 

Leon Sim, Fang-Jung Tsai, and Szu-Yun Lin

Traditional post-disaster building damage assessments were performed manually by the response team, which was risky and time-consuming. With advanced remote sensing technology, such as an unmanned aerial vehicle (UAV), it would be possible to acquire high-quality aerial videos and operate at a variety of altitudes and angles.  The collected data would be sent into a neural network for training and validating. In this study, the Object Detection model (YOLO) was utilized, which is capable of predicting both bounding boxes and damage levels. The network was trained using the ISBDA dataset, which was created from aerial videos of the aftermath of Hurricane Harvey in 2017, Hurricane Michael and Hurricane Florence in 2018, and three tornadoes in 2017, 2018, and 2019 in the United States. The Joint Damage Scale was used to classify the buildings in this dataset into four categories: no damage, minor damage, major damage, and destroyed. However, the number of major damage and destroyed classes are significantly lower than the number of no damage and minor damage classes in the dataset. Also, the damage characteristics of minor and major damage classes are similar under such type of disaster. These caused the YOLO model prone to misclassify the intermediate damage levels, i.e., minor and major damage in our earlier experiments. This study aimed to improve the YOLO model using a stacking ensemble deep learning approach with a image classification model called Mobilenet. First, the ISBDA dataset was used and refined to train the YOLO network and the Mobilenet network separately, and the latter provides two classes predictions (0 for no damage or minor damage, 1 for major damage or destroyed) rather than the four classes by the former. In the inference phase, the initial predictions from the trained YOLO network, including bounding box coordinates, confidence scores for four damage classes, and the predicted class, were then extracted and passed to the trained Mobilenet to generate the secondary predictions for each building. Based on the secondary predictions, two hyperparameters were utilized to refine the initial predictions by modifying the confidence scores of each class, and the hyperparameters were trained during this phase. Lastly, the trained hyperparameters were applied to the testing dataset to evaluate the performance of the proposed method. The results show that our stacking ensemble method could obtain more reliable predictions of intermediate classes.

 

How to cite: Sim, L., Tsai, F.-J., and Lin, S.-Y.: A Stacking Ensemble Deep Learning Approach for Post Disaster Building Assessment using UAV Imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-338, https://doi.org/10.5194/egusphere-egu23-338, 2023.

        Since Taiwan is located at the Pacific Ring of Fire, seismic activity of varying magnitudes occurs almost every day. Among them, some of these seismic activities have in turn caused severe disasters, resulting in loss of personal property, casualties and damage to important public facilities. Therefore, investigating the long-term spatiotemporal pattern of seismic activities is a crucial task for understanding the causes of seismic activity and to predict future seismic activity, in order to carry out disaster prevention measures in advance. Previous studies mostly focused on the causes of single seismic events on the small spatiotemporal scale. In this study, the data from 1987 to 2020 are used, including seismic events from the United States Geological Survey (USGS), the ambient environmental factors such as daily air temperature from Taiwan Central Weather Bureau (CWB) and daily sea surface temperature data from National Oceanic and Atmospheric Administration (NOAA). Then the temperature difference between the land air temperature and the sea surface temperature (SST) to the correlation between the occurrence of seismic activities and the abnormal occurrence of temperature difference are compared. The results show that lots of seismic activities often have positive and negative anomalies of temperature difference from 21 days before to 7 days after the seismic event. Moreover, there is a specific trend of temperature difference anomalies under different magnitude intervals. In the magnitude range of 2.5 to 4 and greater than 6, almost all of the seismic events have significant anomalous differences in the temperature difference between land air temperature and SST compared with no seismic events. This study uncovers anomalous frequency signatures of seismic activities and temperature differences between land air temperature and SST. The significant difference in temperature difference between seismic events and non-seismic events was compared by using statistical analysis. Additionally, the deep neural network (DNN) of deep learning model, logistic regression and random forest of machine learning model was used to identify whether there will be a seismic event under different magnitude intervals. It is hoped that it can provide relevant information for the prediction of future seismic activity, to more accurately prevent disasters that may be caused by seismic activity.

How to cite: Chen, Y.-H. and Lin, Y.-C.: Investigating the Correlation between the Characteristics of Seismic Activity and Environmental Variables in Taiwan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2564, https://doi.org/10.5194/egusphere-egu23-2564, 2023.

The 2010-2011 Canterbury Earthquake sequence (CES) led to unprecedented building damage in the Canterbury region, New Zealand. Commercial and residential buildings were significantly affected. Due to New Zealand’s unique insurance setting, around 80% of the losses were covered by insurance (Bevere & Balz, 2012; King et al., 2014). The Insurance Council of New Zealand (ICNZ) estimated the total economic losses to be more than NZ$40 billion, with the Earthquake Commission (EQC) and private insurers covering NZ$10 billion and NZ$21 billion of the losses, respectively (ICNZ, 2021). As a result of the CES and the 2016 Kaikoura earthquake, EQC’s Natural Disaster Fund was depleted (EQC, 2022). This highlighted the need for improved tools enabling damage and loss analysis for natural hazards.
This research project used residential building claims collected by EQC following the CES to develop a rapid seismic loss prediction model for residential buildings in Christchurch. Geographic information systems (GIS) tools, data science techniques, and machine learning (ML) were used for the model development. Before the training of the ML model, the claims data was enriched with additional information from external data sources. The seismic demand, building characteristics, soil conditions, and information about the liquefaction occurrence were added to the claims data. Once merged and pre-processed, the aggregated data was used to train ML models based on the main events in the CES. Emphasis was put on the interpretability and explainability of the model. The ML model delivered valuable insights related to the most important features contributing to losses. Those insights are aligned with engineering knowledge and observations from previous studies, confirming the potential of using ML for disaster loss prediction and management. Care was also put into the retrainability of the model to ensure that any new data from future earthquake events can rapidly be added to the model. 

How to cite: Roeslin, S.: Development of a Rapid Seismic Loss Prediction Model for Residential Buildings using Machine Learning - Christchurch, New Zealand, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2996, https://doi.org/10.5194/egusphere-egu23-2996, 2023.

EGU23-3928 | Orals | ITS1.1/NH0.1

Comparison of deep learning approaches to monitor trash screen blockage from CCTV cameras 

Remy Vandaele, Sarah L Dance, and Varun Ojha

We investigate the use of CCTV cameras and deep learning to automatically monitor trash screen blockage. 

Trash screens are installed to prevent debris from entering critical parts of river networks (pipes, tunnels, locks,...). When the debris piles up at the trash screens,  it  may block the waterway and can cause flooding. It is thus crucial to clean blocked trash screens and avoid flooding and consequent damage. Currently, the maintenance crews must manually check a camera or river level data or go on site to check the state of the screen to know if it needs cleaning. This wastes valuable time in emergency situations where blocked screens must be urgently cleaned (e.g., in case of forecast  heavy rainfall). Some initial attempts at trying to predict trash screen blockage exist. However, these have not been widely adopted in practice.  CCTV cameras can be easily installed at any location and can thus be used to monitor the state of trash screens, but the images need to be processed by an automated algorithm to inform whether the screen is blocked.

With the help of UK-based practitioners (Environment Agency and local councils), we have created a dataset of 40000 CCTV trash screen images coming from 36 cameras, each labelled with blockage information. Using this database, we have compared 3 deep learning approaches to automate the detection of trash screen blockage: 

  • A binary image classifier, which takes as input a single image, and outputs a binary label that estimates whether the trash screen is blocked.
  • An approach based on anomaly detection which tries to reconstruct the input image with an auto-encoder trained on clean trash screen images.  In consequence, blocked trash screens are detected as anomalies by the auto-encoder.
  • An image similarity estimation approach based on the use of a siamese network, which takes as input two images and outputs a similarity index related, in our case, to whether both images contain trash. 

Using performance criteria chosen in discussion  with practitioners (overall accuracy, false alarm rate, resilience to luminosity / moving fields of view, computing capabilities), we show that deep learning can be used in practice to automate the identification of blocked trash screens. We also analyse the strengths and weaknesses of each of these approaches and provide guidelines for their application.

How to cite: Vandaele, R., Dance, S. L., and Ojha, V.: Comparison of deep learning approaches to monitor trash screen blockage from CCTV cameras, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3928, https://doi.org/10.5194/egusphere-egu23-3928, 2023.

EGU23-4455 | ECS | Posters virtual | ITS1.1/NH0.1

Traffic Monitoring System Design considering Multi-Hazard Disaster Risks 

Michele Gazzea, Reza Arghandeh, and Amir Miraki

Roadways are critical infrastructure in our society, providing services for people through and between cities. However, they are prone to closures and disruptions, especially after extreme weather events like hurricanes.

At the same time, traffic flow data are a fundamental type of information for any transportation system.

We tackle the problem of traffic sensor placement on roadways to address two tasks at the same time. The first task is traffic data estimation in ordinary situations, which is vital for traffic monitoring and city planning. We design a graph-based method to estimate traffic flow on roads where sensors are not present. The second one is enhanced observability of roadways in case of extreme weather events. We propose a satellite-based multi-domain risk assessment to locate roads at high risk of closures. Vegetation and flood hazards are taken into account. We formalize the problem as a search method over the network to suggest the minimum number and location of traffic sensors to place while maximizing the traffic estimation capabilities and observability of the risky areas of a city.

How to cite: Gazzea, M., Arghandeh, R., and Miraki, A.: Traffic Monitoring System Design considering Multi-Hazard Disaster Risks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4455, https://doi.org/10.5194/egusphere-egu23-4455, 2023.

Earthquake-induced land deformation and structure failure are more severe over soft soils than over firm soils and rocks owing to the seismic site effect and liquefaction. The site-specific seismic site effect related to the amplification of ground motion, liquefaction, and landslide has spatial uncertainty depending on the local subsurface, surface geological, and topographic conditions. When the 2017 Pohang earthquake (M 5.4), South Korea’s second strongest earthquake in decades, occurred, the severe damages influenced by variable site response and vulnerability indicators were observed focusing on the basin or basin-edge region deposited unconsolidated Quaternary sediments. Thus, nationwide site characterization is essential considering empirical correlations with geotechnical site response and hazard parameters and surface proxies. Furthermore, in case of so many variables and tenuously related correlations, machine learning classification models can prove to be very precise than the parametric methods. This study established a multivariate seismic site classification system using the machine learning technique based on the geospatial big data platform.

The supervised machine learning classification techniques and more specifically, random forest, support vector machine (SVM), and artificial neural network (ANN) algorithms have been adopted. Supervised machine learning algorithms analyze a set of labeled training data consisting of a group of input data and desired output values. They produce an inferred function that can be used for predictions from given input data. To optimize the classification criteria by considering the geotechnical uncertainty and local site effects, the training datasets applying principal component analysis (PCA) were verified with k-fold cross-validation. Moreover, the optimized training algorithm, proved by loss estimators (receiver operating characteristic curve (ROC), the area under the ROC curve (AUC)) based on confusion matrix, was selected.

For the southeastern region in South Korea, the boring log information (strata, standard penetration test, etc.), geological map (1:50k scale), digital terrain model (having 5 m × 5 m), soil map (1:250k scale) were collected and constructed as geospatial big data. Preliminarily, to build spatially coincided datasets with geotechnical response parameters and surface proxies, the mesh-type geospatial information was built by advanced geostatistical interpolation and simulation methods.

Site classification systems use seismic hazard parameters related to the geotechnical characteristics of the study area as the classification criteria. The current site classification systems in South Korea and the United States recommend Vs30, which is the average shear wave velocity (Vs) up to 30 m underground. This criterion uses only the dynamic characteristics of the site without considering its geometric distribution characteristics. Thus, the geospatial information included the geo-layer thickness, surface proxies (elevation, slope, geological category, soil category), and Vs30. For the liquefaction and landslide hazard estimation, the liquefaction vulnerability indexes (i.e., liquefaction potential or severity index) and landslide vulnerability indexes (i.e., a factor of safety or displacement) were also trained as input features into the classifier modeling. Finally, the composite status against seismic site effect, liquefaction, and landslide was predicted as hazard class (I.e., safe, slight-, moderate-, extreme-failure) based on the best-fitting classifier.  

How to cite: Kim, H.: Machine Learning-based Site Classification System for Earthquake-Induced Multi-Hazard in South Korea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4757, https://doi.org/10.5194/egusphere-egu23-4757, 2023.

EGU23-4816 | ECS | Posters on site | ITS1.1/NH0.1

XAIDA4Detection: A Toolbox for the Detection and Characterization of Spatio-Temporal Extreme Events 

Jordi Cortés-Andrés, Maria Gonzalez-Calabuig, Mengxue Zhang, Tristan Williams, Miguel-Ángel Fernández-Torres, Oscar J. Pellicer-Valero, and Gustau Camps-Valls

The automatic anticipation and detection of extreme events constitute a major challenge in the current context of climate change, which has changed their likelihood and intensity. One of the main objectives within the EXtreme Events: Artificial Intelligence for Detection and Attribution (XAIDA) project (https://xaida.eu/) is related to developing novel approaches for the detection and localization of extreme events, such as tropical cyclones and severe convective storms, heat waves and droughts, as well as persistent winter extremes, among others. Here we introduce the XAIDA4Detection toolbox that allows for tackling generic problems of detection and characterization. The open-source toolbox integrates a set of advanced ML models, ranging in complexity, assumptions, and sophistication, and yields spatio-temporal explicit detection maps with probabilistic heatmap estimates. We included supervised and unsupervised methods, deterministic and probabilistic, neural networks based on convolutional and recurrent nets, and density-based methods. The toolbox is intended for scientists, engineers, and students with basic knowledge of extreme events, outlier detection techniques, and Deep Learning (DL), as well as Python programming with basic packages (Numpy, Scikit-learn, Matplotlib) and DL packages (PyTorch, PyTorch Lightning). This presentation will summarize the available features and their potential to be adapted to multiple extreme event problems and use cases.

How to cite: Cortés-Andrés, J., Gonzalez-Calabuig, M., Zhang, M., Williams, T., Fernández-Torres, M.-Á., Pellicer-Valero, O. J., and Camps-Valls, G.: XAIDA4Detection: A Toolbox for the Detection and Characterization of Spatio-Temporal Extreme Events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4816, https://doi.org/10.5194/egusphere-egu23-4816, 2023.

EGU23-5581 | Posters on site | ITS1.1/NH0.1

Vision Transformers for building damage assessment after natural disasters 

Adrien Lagrange, Nicolas Dublé, François De Vieilleville, Aurore Dupuis, Stéphane May, and Aymeric Walker-Deemin

Damage assessment is a critical step in crisis management. It must be fast and accurate in order to organize and scale the emergency response in a manner adapted to the real needs on the ground. The speed requirements motivate an automation of the analysis, at least in support of the photo-interpretation. Deep Learning (DL) seems to be the most suitable methodology for this problem: on one hand for the speed in obtaining the answer, and on the other hand by the high performance of the results obtained by these methods in the extraction of information from images. Following previous studies to evaluate the potential contribution of DL methods for building damage assessment after a disaster, several conventional Deep Neural Network (DNN) and Transformers (TF) architectures were compared.

Made available at the end of 2019, the xView2 database appears to be the most interesting database for this study. It gathers images of disasters between 2011 and 2018 with 6 types of disasters: earthquakes, tsunamis, floods, volcanic eruptions, fires and hurricanes. For each of these disasters, pre- and post-disaster images are available with a ground truth containing the building footprint as well as the evaluation of the type of damage divided into 4 classes (no damage, minor damage, major damage, destroyed) similar to those considered in the study.

This study compares a wide range DNN architectures all based on an encoder-decoder structure. Two encoder families were implemented: EfficientNet (B0 to B7 configurations) and Swin TF (Tiny, Small, and Base configurations). Three adaptable decoders were implemented: UNet, DeepLabV3+, FPN. Finally, to benefit from both pre- and post-disaster images, the trained models were designed to proceed images with a Siamese approach: both images are processed independently by the encoder, and the extracted features are then concatenated by the decoder.

Taking benefit of global information (such as the type of disaster for example) present in the image, the Swin TF, associated with FPN decoder, reaches the better performances than all other encoder-decoder architectures. The Shifted WINdows process enables the pipe to process large images in a reasonable time, comparable to the processing time of EfficientNet-based architectures. An interesting additional result is that the models trained during this study do not seem to benefit so much from extra-large configurations, and both small and tiny configurations reach the highest scores.

How to cite: Lagrange, A., Dublé, N., De Vieilleville, F., Dupuis, A., May, S., and Walker-Deemin, A.: Vision Transformers for building damage assessment after natural disasters, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5581, https://doi.org/10.5194/egusphere-egu23-5581, 2023.

Natural and man-made disasters pose a threat to human life, flora-fauna, and infrastructure. It is critical to detect the damage quickly and accurately for infrastructures right after the occurrence of any disaster. The detection and assessment of infrastructure damage help manage financial strategy as well. Recently, many researchers and agencies have made efforts to create high-resolution satellite imageries database related to pre and post-disaster events. The advanced remote sensing satellite imageries can reflect the surface of the earth accurately up to 30 cm spatial resolution on a daily basis. These high spatial resolutions (HSR) imageries can help access any natural hazard's damage by comparing the pre- and post-disaster data. These remote sensing imageries have limitations, such as cloud occlusions. Building under a thick cloud cannot be recognised in optical images. The manual assessment of the severity of damage to buildings/infrastructure by comparing bi-temporal HSR imageries or airborne will be a tedious and subjective job. On the other hand, the emerging use of unmanned aired vehicles (UAV) can be used to assess the situation precisely. The high-resolution UAV imageries and the HSR satellite imageries can complement each other for critical infrastructure damage assessment. In this study, a novel approach is used to integrate UAV data into HSR satellite imageries for the building damage assessment using a convolution neural network (CNN) based deep learning model. The research work is divided into two fundamental sub-tasks: first is the building localisation in the pre-event images, and second is the damage classification by assigning a unique damage level label reflecting the degree of damage to each building instance on the post-disaster images. For the study, the HSR satellite imageries of 36 pairs of pre- and post natural hazard events is acquired for the year 2021-22, similarly available UAV based data for these events is also collected from the open data source. The data is then pre-processed, and the building damage is assessed using a deep object-based semantic change detection framework (ChangeOS). The mentioned model was trained on the xview2 building damage assessment datasets comprised of ~20,000 images with ~730,000 building polygons of pre and post disaster events over the globe from 2011-2018. The experimental setup in this study includes training on the global dataset and testing on the regional-scale building damage assessment using HSR satellite imageries and local-scale using UAV imageries. The result obtained from the bi-temporal assessment of HSR images for the Indonesia Earthquake 2022 has shown an F1 score of ~67%, while the Uttarakhand flooding event 2021 has reported an F1 score of ~64%. The HSR imageries from the UAV Haiti earthquake event in 2011 have also shown less but promising F1 scores of ~54%. It is inferred that merging HSR imageries from satellite and UAV for building damage assessment using the ChangeOS framework represents a robust tool to further promote future research in infrastructure maintenance strategy and policy management in disaster response.

How to cite: Gupta, S. and Nair, S.: A novel approach for infrastructural disaster damage assessment using high spatial resolution satellite and UAV imageries using deep learning algorithms., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5778, https://doi.org/10.5194/egusphere-egu23-5778, 2023.

EGU23-5913 | ECS | Orals | ITS1.1/NH0.1

Pluto: A global volcanic activity early warning system powered by large scale self-supervised deep learning on InSAR data 

Nikolaos Ioannis Bountos, Dimitrios Michail, Themistocles Herekakis, Angeliki Thanasou, and Ioannis Papoutsis

Artificial intelligence (AI) methods have emerged as a powerful tool to study and in some cases forecast natural disasters [1,2]. Recent works have successfully combined deep learning modeling with scientific knowledge stemming from the SAR Interferometry domain propelling research on tasks like volcanic activity monitoring [3], associated with ground deformation. A milestone in this interdisciplinary field has been the release of the Hephaestus [4] InSAR dataset, facilitating automatic InSAR interpretation, volcanic activity localization as well as the detection and categorization of atmospheric contributions in wrapped interferograms. Hephaestus contains annotations for approximately 20,000 InSAR frames, covering the 44 most active volcanoes in the world. The annotation was performed  by a team of InSAR experts that manually examined each InSAR frame individually. However, even with such a large dataset, class imbalance remains a challenge, i.e. the InSAR samples containing volcano deformation fringes are orders of magnitude less than those that do not. This is anticipated since natural hazards are in principle rare in nature. To counter that, the authors of Hephaestus provide more than 100,000 unlabeled InSAR frames to be used for global large-scale self-supervised learning, which is more robust to class imbalance when compared to supervised learning [5]. 

Motivated by the Hephaestus dataset and the insights provided by [2], we train global, task-agnostic models in a self-supervised learning fashion that can handle distribution shifts caused by spatio-temporal variability as well as major class imbalances. By finetuning such a model to the labeled part of Hephaestus we obtain the backbone for a global volcanic activity alerting system, namely Pluto. Pluto is a novel end-to-end AI based system that provides early warnings of volcanic unrest on a global scale.

Pluto automatically synchronizes its database with the Comet-LiCS [6] portal to receive newly generated Sentinel-1 InSAR data acquired over volcanic areas. The new samples are fed to our volcanic activity detection model. If volcanic activity is detected, an automatic email is sent to the service users, which contains information about the intensity, the exact location and the type (Mogi, Sill, Dyk) of the event. To ensure a robust and ever-improving service we augment Pluto with an iterative pipeline that collects samples that were misclassified in production, and uses them to further improve the existing model. 

 

[1] Kondylatos et al. "Wildfire danger prediction and understanding with Deep Learning." Geophysical Research Letters 49.17 (2022): e2022GL099368.

[2] Bountos et al. "Self-supervised contrastive learning for volcanic unrest detection." IEEE Geoscience and Remote Sensing Letters 19 (2021): 1-5.

[3] Bountos et al. "Learning from Synthetic InSAR with Vision Transformers: The case of volcanic unrest detection." IEEE Transactions on Geoscience and Remote Sensing (2022).

[4] Bountos et al. "Hephaestus: A large scale multitask dataset towards InSAR understanding." Proceedings of the IEEE/CVF CVPR. 2022.

[5] Liu et al. "Self-supervised learning is more robust to dataset imbalance." arXiv preprint arXiv:2110.05025 (2021).

[6] Lazecký et al. "LiCSAR: An automatic InSAR tool for measuring and monitoring tectonic and volcanic activity." Remote Sensing 12.15 (2020): 2430.

How to cite: Bountos, N. I., Michail, D., Herekakis, T., Thanasou, A., and Papoutsis, I.: Pluto: A global volcanic activity early warning system powered by large scale self-supervised deep learning on InSAR data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5913, https://doi.org/10.5194/egusphere-egu23-5913, 2023.

It has become increasingly apparent over the past few decades that environmental degradation is something of a common concern for humanity and it is difficult to deny that the present environmental problems are caused primarily by anthropogenic activities rather than natural causes.

To minimize disaster’s risk, the role of geospatial science and technology may be a terribly helpful and necessary technique for hazard zone mapping throughout emergency conditions. 

This approach can definitively help predict harmful events, but also to mitigate damage to the environment from events that cannot be efficiently predicted.

With detailed information obtained through various dataset, decision making has become simpler. This fact is crucial for a quick and effective response to any disaster. Remote sensing, in particular RADAR/SAR data, help in managing a disaster at various stages. 

Prevention for example refers to the outright avoidance of adverse impacts of hazards and related disasters; preparedness refers to the knowledge and capacities to effectively anticipate, respond to, and recover from, the impacts of likely, imminent or current hazard events or conditions.

Finally relief is the provision of emergency services after a disaster in order to reduce damage to environment and people.

Thanks to the opportunity proposed by ASI (Italian Space Agency) to use COSMO-SkyMed data, in NeMeA Sistemi srl we developed two projects: “Ventimiglia Legalità”, “Edilizia Spontanea” and 3xA.

Their main objective is to detect illegal buildings not present in the land Legal registry.

We developed new and innovative technologies using integrated data for the monitoring and protection of environmental and anthropogenic health, in coastal and nearby areas. 

3xA project addresses the highly challenging problem of automatically detecting changes from a time series of high-resolution synthetic aperture radar (SAR) images. In this context, to fully leverage the potential of such data, an innovative machine learning based approach has been developed. 

The project is characterized by an end-to-end training and inference system which takes as input two raw images and produces a vectorized change map without any human supervision.

More into the details, it takes as input two SAR acquisitions at time t1 and t2, the acquisitions are firstly pre-processed, homogenised and finally undergo a completely self-supervised algorithm which takes advantage of DNNs to classify changed/unchanged areas. This method shows promising results in automatically producing a change map from two input SAR images (Stripmap or Spotlight COSMO-SkyMed data), with 98% accuracy.

Being the process automated, results are produced faster than similar products generated by human operators.

A similar approach has been followed to create an algorithm which performs semantic segmentation from the same kind of data.

This time, only one of the two SAR acquisitions is taken as input for pre-processing steps and then for a supervised neural network. The result is a single image where each pixel is labelled with the class predicted by the algorithm. 

Also in this case, results are promising, reaching around 90% of accuracy. 

How to cite: Pennino, I.: A new approach for hazard and disaster prevention: deep learning algorithms for change detection and classification RADAR/SAR, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6522, https://doi.org/10.5194/egusphere-egu23-6522, 2023.

EGU23-6790 | ECS | Posters on site | ITS1.1/NH0.1

Deep learning for automatic flood mapping from high resolution SAR images 

Arnaud Dupeyrat, abdullah Almaksour, Joao Vinholi, and tapio friberg

 With the gradual warming of the global climate, natural catastrophes have caused billions of dollars in damage to ecosystems, economies and properties. Along with the damage, the loss of life is a very serious possibility. With the unprecedented growth of the human population, large-scale development activities and changes to the natural environment, the frequency, and intensity of extreme natural events and consequent impacts are expected to increase in the future. 

 To be able to mitigate and to reduce the potential damage of the natural catastrophe, continuous monitoring is required. The collection of data using earth observation (EO) systems has been valuable for tracking the effects of natural hazards, especially with their near real-time capabilities for tracking extreme natural events. Remote sensing systems from different platforms also serve as an important decision support tool for devising response strategies, coordinating rescue operations, and making damage and loss estimations.

 Synthetic aperture radar (SAR) imagery provides highly valuable information about our planet that no other technology is capable of. SAR sensors emit their own energy to illuminate objects or areas on Earth and record what’s reflected back from the surface to the sensor. This allows data acquisition day and night since no sunlight is needed. SAR also uses longer wavelengths than optical systems, which gives it the unsurpassed advantage of being able to penetrate clouds, rain, fog and smoke. All of this makes SAR imagery unprecedentedly valuable in sudden events and crisis situations requiring a rapid response.

 In this talk we will be focusing on flood monitoring using our ICEYE SAR images, taking into account multi-satellites, multi-angles and multi-resolutions that are inherent from our constellation and capabilities. We will present the different steps necessary that have allowed us to improve the consistency of our generated flood maps.

How to cite: Dupeyrat, A., Almaksour, A., Vinholi, J., and friberg, T.: Deep learning for automatic flood mapping from high resolution SAR images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6790, https://doi.org/10.5194/egusphere-egu23-6790, 2023.

Increasing climatic extremes resulted in frequency and severity of urban flood events during the last several decades. Significant economic losses were point out the urgency of flood response. In recent years, the government gradually increased the layout of CCTV water level monitoring facilities for the purpose of decision-making in flood event. However, it is difficult for decision makers to recognize multiple images in the same time. Therefore, the aim of this study attempts to establish an automatic water level recognition method for given closed-circuit television (CCTV) system.

In the last years, many advances have been made in the area of automatic image recognition with methods of artificial intelligence. Little literature has been published on real-time water level recognition of closed-circuit television system for disaster management. The purpose of this study is to examine the possibilities in practice of artificial intelligence for real-time water level recognition with deep convolutional neural network. Proposed methodology will demonstrate with several case studies in Taichung. For the potential issue that AI models may lacks of learning target, the generative adversarial network (GAN) may be adopted for this study. The result of this study could be useful to decision makers responsible for organizing response assignments during flood event.

How to cite: Chen, B. and Li, C.-Y.: A study on the establishment of computer vision for disaster identification based on existing closed-circuit television system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7435, https://doi.org/10.5194/egusphere-egu23-7435, 2023.

EGU23-8419 | ECS | Orals | ITS1.1/NH0.1

Synthetic Generation of Extra-Tropical Cyclones’ fields with Generative Adversarial Networks 

Filippo Dainelli, Riccardo Taormina, Guido Ascenso, Enrico Scoccimarro, Matteo Giuliani, and Andrea Castelletti

Extra-Tropical Cyclones are major systems ruling and influencing the atmospheric structure at mid-latitudes. They are characterised by strong winds and heavy precipitation, and can cause considerable storm surges potentially devastating for coastal regions. The availability of historical observations of the extreme events caused by intense ETCs are rather limited, hampering risk evaluation. Increasing the amount of significant data available would substantially help several fields of analysis influenced by these events, such as coastal management, agricultural production, energy distribution, air and maritime transportation, and risk assessment and management.

Here, we address the possibility of generating synthetic ETC atmospheric fields of mean sea level pressure, wind speed, and precipitation in the North Atlantic by training a Generative Adversarial Network (GAN). The purpose of GANs is to learn the distribution of a training set based on a game theoretic scenario where two networks compete against each other, the generator and the discriminator. The former is trained to generate synthetic examples that are plausible and resemble the real ones. The input of the generator is a vector of random Gaussian values, whose domain is known as the “latent space”. The discriminator learns to distinguish whether an example comes from the dataset distribution. The competition set by the game-theoretic approach improves the network until the counterfeits are indistinguishable from the originals.

To train the GAN, we use atmospheric fields extracted from the ERA5 reanalysis dataset in the geographic domain with boundaries 0°- 90°N, 70°W - 20°E and for the period 1st January 1979 - 1st January 2020. We analyse the generated samples’ histograms, the samples’ average fields, the Wasserstein distance and the Kullback-Leibler divergence between the generated samples and the test set distributions. Results show that the generative model has learned the distribution of the values of the atmospheric fields and the general spatial trends of the atmosphere in the domain. To evaluate better the atmospheric structure learned by the network, we perform linear and spherical interpolations in the latent space. Specifically, we consider four cyclones and compare the frames of their tracks to those of the synthetic tracks generated by interpolation. The interpolated tracks show interesting features consistent with the original tracks. These findings suggest that GANs can learn meaningful representations of the ETCs’ fields, encouraging further investigations to model the tracks’ temporal evolution.

How to cite: Dainelli, F., Taormina, R., Ascenso, G., Scoccimarro, E., Giuliani, M., and Castelletti, A.: Synthetic Generation of Extra-Tropical Cyclones’ fields with Generative Adversarial Networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8419, https://doi.org/10.5194/egusphere-egu23-8419, 2023.

EGU23-8944 | ECS | Orals | ITS1.1/NH0.1

Towards probabilistic impact-based drought risk analysis – a case study on the Volta Basin 

Marthe Wens, Raed Hamed, Hans de Moel, Marco Massabo, and Anna Mapelli

Understanding the relationships between different drought drivers and observed drought impact can provide important information for early warning systems and drought management planning. Moreover, this relationship can help inform the definition and delineation of drought events. However, currently, drought hazards are often characterized based on their frequency of occurring, rather than based on the impacts they cause. A more data-driven depiction of “impactful drought events”- whereby droughts are defined by the hydrometeorological conditions that, in the past, have led to observable impacts-, has the potential to be more meaningful for drought risk assessments.

In our research, we apply a data-mining method based on association rules, namely fast and frugal decision trees, to link different drought hazard indices to agricultural impacts. This machine learning technique is able to select the most relevant drought hazard drivers (among both hydrological and meteorological indices) and their thresholds associated with “impactful drought events”. The technique can be used to assess the likelihood of occurrence of several impact severities, hence it supports the creation of a loss exceedance curve and estimates of average annual loss. An additional advantage is that such data-driven relations in essence reflect varying local drought vulnerabilities which are difficult to quantify in data-scarce regions.

This contribution exemplifies the use of fast and frugal decision trees to estimate (agricultural) drought risk in the Volta basin and its riparian countries. We find that some agriculture-dependent regions in Ghana, Togo and Côte d’Ivoire face annual average drought-induced maize production losses up to 3M USD, while per hectare, losses can mount to on average 50 USD/ha per year in Burkina Faso. In general, there is a clear north-south gradient in the drought risk, which we find augmented under projected climate conditions. Climate change is estimated to worsen the drought impacts in the Volta Basin, with 11 regions facing increases in annual average losses of more than 50%.

We show that the proposed multi-variate, impact-based, non-parametric, machine learning approach can improve the evaluation of droughts, as this approach directly leverages observed drought impact information to demarcate impactful drought events. We evidence that the proposed technique can support quantitative drought risk assessments which can be used for geographic comparison of disaster losses at a sub-national scale.

How to cite: Wens, M., Hamed, R., de Moel, H., Massabo, M., and Mapelli, A.: Towards probabilistic impact-based drought risk analysis – a case study on the Volta Basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8944, https://doi.org/10.5194/egusphere-egu23-8944, 2023.

EGU23-9091 | Orals | ITS1.1/NH0.1

Improving near real-time flood extraction pipeline from SAR data using deep learning 

Mathieu Turgeon-Pelchat, Heather McGrath, Fatemeh Esfahani, Simon Tolszczuk-Leclerc, Thomas Rainville, Nicolas Svacina, Lingjun Zhou, Zarrin Langari, and Hospice Houngbo

The Canada Centre for Mapping and Earth Observation (CCMEO) uses Radarsat Constellation Mission (RCM) data for near-real time flood mapping. One of the many advantages of using SAR sensors, is that they are less affected by the cloud coverage and atmospheric conditions, compared to optical sensors. RCM has been used operationally since 2020 and employs 3 satellites, enabling lower revisit times and increased imagery coverage. The team responsible for the production of flood maps in the context of emergency response are able to produce maps within four hours from the data acquisition. Although the results from their automated system are good, there are some limitations to it, requiring manual intervention to correct the data before publication. Main limitations are located in urban and vegetated areas. Work started in 2021 to make use of deep learning algorithms, namely convolutional neural networks (CNN), to improve the performances of the automated production of flood inundation maps. The training dataset make use of the former maps created by the emergency response team and is comprised of over 80 SAR images and corresponding digital elevation model (DEM) in multiple locations in Canada. The training and test images were split in smaller tiles of 256 x 256 pixels, for a total of 22,469 training tiles and 6,821 test tiles. Current implementation uses a U-Net architecture from NRCan geo-deep-learning pipeline (https://github.com/NRCan/geo-deep-learning). To measure performance of the model, intersection over union (IoU) metric is used. The model can achieve 83% IoU for extracting water and flood from background areas over the test tiles. Next steps include increasing the number of different geographical contexts in the training set, towards the integration of the model into production.

How to cite: Turgeon-Pelchat, M., McGrath, H., Esfahani, F., Tolszczuk-Leclerc, S., Rainville, T., Svacina, N., Zhou, L., Langari, Z., and Houngbo, H.: Improving near real-time flood extraction pipeline from SAR data using deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9091, https://doi.org/10.5194/egusphere-egu23-9091, 2023.

EGU23-9426 | ECS | Orals | ITS1.1/NH0.1

Fire hazard modelling with remote sensing data for South America 

Johanna Strebl, Julia Gottfriedsen, Dominik Laux, Max Helleis, and Volker Tresp

Throughout the past couple years, changes in global climate have been turning wildfires into an increasingly unpredictable phenomenon. Many environmental parameters that have been linked to wildfires, such as the number of consecutive hot days, are becoming increasingly unstable. This leads to a twofold problem: adequate fire risk assessment is at the same time more important and more difficult than ever. 

In the past, physical models were the prevalent approach to most questions in the domain of wildfire science. While they tend to provide accurate and transparent results, they require domain expertise and often tedious manual data collection.

In recent years, increased computation capabilities and the improved availability of remote sensing data associated with the new space movement have made deep learning a beneficial approach. Data-driven approaches often yield state of the art performance without requiring expert knowledge at a fraction of the complexity of physical models. The downside, however, is that they are often intransparent and offer no insights into their inner algorithmic workings. 

We want to shed some light on this interpretability/performance tradeoff and compare different approaches for predicting wildfire hazard. We evaluate their strengths and weaknesses with a special focus on explainability. We built a wildfire hazard model for South America based on a spatiotemporal CNN architecture that infers fire susceptibility from environmental conditions that led to fire in the past. The training data used contains selected ECMWF ERA5 Land variables and ESA world cover information. This means that our model is able to learn from actual fire conditions instead of relying on theoretical frameworks. Unlike many other models, we do not make simplifying assumptions such as a standard fuel type, but calculate hazard ratings based on actual environmental conditions. Compared to classical fire hazard models, this approach allows us to account for regional and atypical fire behavior and makes our model readily adaptable and trainable for other ecosystems, too.

The ground truth labels are derived from fusing active fire remote sensing data from 20 different satellites into one active wildfire cluster data set. The problem itself is highly imbalanced with non-fire pixels making up 99.78% of the training data. Therefore we evaluate the ability of our model to correctly predict wildfire hazard using metrics for imbalanced data such as PR-AUC and F1 score. We also compare the results against selected standard fire hazard models such as the Canadian Fire Weather Index (FWI). 

In addition, we assess the computational complexity and speed of calculating the respective models and consider the accuracy/complexity/speed tradeoff of the different approaches. Furthermore, we aim to provide insights why and how our model makes its predictions by leveraging common explainability methods. This allows for insights into which factors tend to influence wildfire hazard the most and to optimize for relatively lightweight, yet performant and transparent architectures.

How to cite: Strebl, J., Gottfriedsen, J., Laux, D., Helleis, M., and Tresp, V.: Fire hazard modelling with remote sensing data for South America, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9426, https://doi.org/10.5194/egusphere-egu23-9426, 2023.

For recent years, Machine Learning (ML) models have been proven to be useful in solving problems of a wide variety of fields such as medical, economic, manufacturing, transportation, energy, education, etc. With increased interest in ML models and advances in sensor technologies, ML models are being widely applied even in civil engineering domain. ML model enables analysis of large amounts of data, automation, improved decision making and provides more accurate prediction. While several state-of-the-art reviews have been conducted in each sub-domain (e.g., geotechnical engineering, structural engineering) of civil engineering or its specific application problems (e.g., structural damage detection, water quality evaluation), little effort has been devoted to comprehensive review on ML models applied in civil engineering and compare them across sub-domains. A systematic, but domain-specific literature review framework should be employed to effectively classify and compare the models. To that end, this study proposes a novel review approach based on the hierarchical classification tree “D-A-M-I-E (Domain-Application problem-ML models-Input data-Example case)”. “D-A-M-I-E” classification tree classifies the ML studies in civil engineering based on the (1) domain of the civil engineering, (2) application problem, (3) applied ML models and (4) data used in the problem. Moreover, data used for the ML models in each application examples are examined based on the specific characteristic of the domain and the application problem. For comprehensive review, five different domains (structural engineering, geotechnical engineering, water engineering, transportation engineering and energy engineering) are considered and the ML application problem is divided into five different problems (prediction, classification, detection, generation, optimization). Based on the “D-A-M-I-E” classification tree, about 300 ML studies in civil engineering are reviewed. For each domain, analysis and comparison on following questions has been conducted: (1) which problems are mainly solved based on ML models, (2) which ML models are mainly applied in each domain and problem, (3) how advanced the ML models are and (4) what kind of data are used and what processing of data is performed for application of ML models. This paper assessed the expansion and applicability of the proposed methodology to other areas (e.g., Earth system modeling, climate science). Furthermore, based on the identification of research gaps of ML models in each domain, this paper provides future direction of ML in civil engineering based on the approaches of dealing data (e.g., collection, handling, storage, and transmission) and hopes to help application of ML models in other fields.

How to cite: Kim, J. and Jung, D.: State-of-the-Art Review of Machine Learning Models in Civil Engineering: Based on DAMIE Classification Tree, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11636, https://doi.org/10.5194/egusphere-egu23-11636, 2023.

EGU23-11756 * | Orals | ITS1.1/NH0.1 | Highlight

Digital twin computing for enhancing resilience of disaster response system 

Shunichi Koshimura and Erick Mas

Digital twin is now recognized as digital copies of physical world's objects stored in digital space and utilized to simulate the sequences and consequences of target phenomena. By incorporating physical world’s data into the digital twin, developers and users have a full view of the target through real-time feedback. Recent advances in high-performance computing and large-scale data fusion of sensing and observations of both natural and social phenomena are enhancing applicability of digital twin paradigm to natural disaster research. Artificial intelligence (AI) and machine learning are also being applied more and more widely across the world and contributing as essential elements of digital twin. Those have significant implications for disaster response and recovery to hold out the promise of dramatically improving our understanding of disaster-affected areas and responses in real-time.

A project is underway to enhance resilience of disaster response systems by constructing "Disaster Digital Twin" to support disaster response team in the anticipated tsunami disaster. “Disaster Digital Twin” platform consists of a fusion of real-time hazard simulation, e.g. tsunami inundation forecast, social sensing to identify dynamic exposed population, and multi-agent simulation of disaster response activities to find optimal allocation or strategy of response efforts, and achieve the enhancement of disaster resilience.

To achieve the goal of innovating digital twin computing for enhancing disaster resilience, four preliminary results are shown;

(1) Developing nation-wide real-time tsunami inundation and damage forecast system. The priority target for forecasting is the Pacific coast of Japan, a region where Nankai trough earthquake is likely to occur.

(2) Establishing a real-time estimation of the number of exposed population in the inundation zone and clarifying the relationship between the exposed population and medical demand.

(3) Developing a reinforcement learning-based multi-agent simulation of medical activities in the affected areas with use of damage information, medical demands, and resources in the medical facilities to fid optimal allocation of medical response.

(4) Developing a digital twin computing platform to support disaster medical response activities and find optimal allocation of disaster medical services through what-if analysis of multi-agent simulation.

How to cite: Koshimura, S. and Mas, E.: Digital twin computing for enhancing resilience of disaster response system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11756, https://doi.org/10.5194/egusphere-egu23-11756, 2023.

EGU23-12240 | ECS | Posters on site | ITS1.1/NH0.1

Classification Seismic Spectrograms from Deep Neural Network: Application to Alarm System of Post-failure Landslides 

Jui-Ming Chang, Wei-An Chao, and Wei-Kai Huang

Daman Landslide had blocked one of the three cross-island roads in Taiwan, and a road section has been under control since last October. During the period, more than thousands of small-scale post-failures occurred whose irregular patterns affected the safety of engineering workers for slope protection construction and road users. Therefore, we installed one time-lapse camera and two geophones at the crown and closed to the toe of the Daman landslide, respectively to train a classification model to offer in-situ alarm. According to time-lapse photos, those post failures can be categorized into two types. One is rock/debris moving and stopping above the upper slope or road, named type I, and the other is the rock/debris going through the road to download slope, named type II. Type I was almost recorded by the crown station, and type II was shown by both stations with different arrival times and the toe station’ high-frequency signals gradually rising (up to 100 Hz). Those distinct features were exhibited by spectrograms. To keep characteristics simultaneously, we merge two stations’ spectrograms as one to indicate different types of post-failures. However, frequent earthquakes affect the performance of the landslide’s discrimination, which should be involved in the classification model. A total of three labels, type I, type II, and earthquake, contained more than 15,000 images of spectrogram, have been used for deep neural network (DNN) to be a two-station-based automatic classifier. Further, user-defined parameters for the specific frequency band within fixed time span windows, including a sum of power spectrogram density, the arrival time of peak amplitude, cross-correlation coefficient, and signal-to-noise ratio, have been utilized for the decision tree algorithm. Both model results benefit the automatic classifier for post-failure alarms and can readily extend to monitor other landslides with frequent post-failures by transfer learning.

How to cite: Chang, J.-M., Chao, W.-A., and Huang, W.-K.: Classification Seismic Spectrograms from Deep Neural Network: Application to Alarm System of Post-failure Landslides, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12240, https://doi.org/10.5194/egusphere-egu23-12240, 2023.

EGU23-12716 | ECS | Posters on site | ITS1.1/NH0.1

Investigating causal effects of anthropogenic factors on global fire modeling 

Nirlipta Pande and Wouter Dorigo

Humans significantly control the natural environment and natural processes. Global fire ignitions are a prime example of how human actions change the frequency of occurrence of otherwise rare events like wildfires. However, human controls on fire ignition are insufficiently characterised by global fire models because impacts are often indirect, complex, and collinear. Hence, modelling fire activity while considering the complex relationships amongst the input variables and their effect on global ignitions is crucial to developing fire models reflecting the real world. 

This presentation leverages causal inference and machine learning frameworks applied to global datasets of fire ignitions from Earth observations and potential drivers to uncover anthropogenic pathways on fire ignition. Potential fire controls include human predictors from Earth observations and statistical data combined with variables traditionally associated with fire activity, like weather, and vegetation abundance and state, derived from earth observations and models.

Our research models causal relationships between fire control variables and global ignitions using Directed Acyclic Graphs(DAGs). Here, every edge between variables symbolises a relation between them; the edge weight indicates the strength of the relationship, and the orientation of the edge between the variables signifies the cause-and-effect relationship between the variables. However, defining a fire ignition distribution using DAGs is challenging owing to the large combinatorial sample space and acyclicity constraint. We use Bayesian structure learning to make these approximations and infer the extent of human intervention when combined with climate variables and vegetation properties. Our research demonstrates the need for causal modelling and the inclusion of anthropogenic factors in global fire modelling.

How to cite: Pande, N. and Dorigo, W.: Investigating causal effects of anthropogenic factors on global fire modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12716, https://doi.org/10.5194/egusphere-egu23-12716, 2023.

EGU23-13083 | Orals | ITS1.1/NH0.1

Machine learning modelling of compound flood events 

Agnieszka Indiana Olbert, Sogol Moradian, and Galal Uddin

Flood early warning systems are vital for preventing flood damages and for reducing disaster risks. Such systems are particularly important for forecasting compound events where multiple, often dependent flood drivers co-occur and interact. In this research an early warning system for prediction of coastal-fluvial floods is developed to provide a robust, cost-effective and time-efficient framework for management of flood risks and impacts. This three-step method combines a cascade of three linked models: (1) statistical model that determines probabilities of multiple-driver flood events, (2) hydrodynamic model forced by outputs from the statistical model, and finally (3) machine learning (ML) model that uses hydrodynamic outputs from various probability flood events to train the ML algorithm in order to predict the spatially and temporarily variable inundation patterns resulting from a combination of coastal and fluvial flood drivers occurring simultaneously.

The method has been utilized for the case of Cork City, located in the south-west of Ireland, which has a long history of fluvial-coastal flooding. The Lee  River channelling through the city centre may generate a substantial flood when the downstream river flow draining to the estuary coincides with the sea water propagating upstream on a flood tide. For this hydrological domain the statistical model employs the univariate extreme values analysis and copula functions to calculate joint probabilities of river discharges and sea water levels (astronomical tides and surge residuals) occurring simultaneously. The return levels for these two components along a return level curve produced by the copula function are used to generate synthetic timeseries, which serve as water level boundary conditions for a hydrodynamic flood model. The multi-scale nested flood model (MSN_Flood) was configured for Cork City at 2m resolution to simulate an unsteady, non-uniform flow in the Lee  River and a flood wave propagation over urban floodplains. The ensemble hydrodynamic model outputs are ultimately used to train and test a range machine learning models for prediction of flood extents and water depths. In total, 23 machine learning algorithms including: Artificial Neural Network, Decision Tree, Gaussian Process Regression, Linear Regression, Radial Basis Function, Support Vector Machine, and Support Vector Regression were employed to confirm that the ML algorithm can be used successfully to predict the flood inundation depths over urban floodplains for a given set of compound flood drivers. Here, the limited flood conditioning factors taken into account to analyse floods are the upstream flood hydrographs and downstream sea water level timeseries. To evaluate model performance, different statistical skill scores were computed. Results indicated that in most pixels, the Gaussian Process Regression model performs better than the other models.

The main contribution of this research is to demonstrate the ML models can be used in early warning systems for flood prediction and to give insight into the most suitable models in terms of robustness, accuracy, effectiveness, and speed. The findings demonstrate that ML models do help in flood water propagation mapping and assessment of flood risk under various compound flood scenarios.

How to cite: Olbert, A. I., Moradian, S., and Uddin, G.: Machine learning modelling of compound flood events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13083, https://doi.org/10.5194/egusphere-egu23-13083, 2023.

EGU23-14126 | ECS | Orals | ITS1.1/NH0.1

ML-based fire spread model and data pipeline optimization 

Tobias Bauer, Julia Miller, Julia Gottfriedsen, Christian Mollière, Juan Durillo Barrionuevo, and Nicolay Hammer

Climate change is one of the most pressing challenges to humankind today. The number and severity of wildfires are increasing in many parts of the world, with record-breaking temperatures, prolonged heat waves, and droughts. We can minimize the risks and consequences of these natural disasters by providing accurate and timely wildfire progression predictions through fire spread modeling. Knowing the direction and rate of spread of wildfires over the next hours can help deploy firefighting resources more efficiently and warn nearby populations hours in advance to allow safe evacuation.
Physics-based spread models have proven their applicability on a regional scale but often require detailed spatial input data. Additionally, rendering them in real-time scenarios can be slow and therefore inhibit fast output generation. Deep learning-based models have shown success in specific fire spread scenarios in recent years. But they are limited by their transferability to other regions, explainability, and longer training time. Accurate active fire data products and a fast data pipeline are additional essential requirements of a wildfire spread early-warning system.
In this study, physical models are compared to a deep learning-based CNN approach in terms of computational speed, area accuracy, and spread direction. We use a dataset of the 30 largest wildfires in the US in the year 2021 to evaluate the performance of the model’s predictions.
This work focuses in particular on the optimization of a cloud-based fire spread modeling data pipeline for near-real-time fire progression over the next  2 to 24 hours. We describe our data pipeline, including the collection and pre-processing of ignition points derived from remote sensing-based active fire detections. Furthermore, we use data from SRTM-1 as topography, ESA Land Cover and Corine Land Cover for fuel composition, and ERA-5 Reanalysis products for weather data inputs. The application of the physics-based models is derived from the open-source library ForeFire, to create and execute physical wildfire spread models from single fire ignition points as well as fire fronts. The predictions of the ForeFire model serve as a benchmark for the evaluation of the performance of our Convolutional Neural Network. The CNN forecasts the fire outline based on a spatiotemporal U-Net architecture. 
The scaling of the algorithms to a global setting is enabled by the Leibniz Supercomputing Centre. It enables large-scale cloud-based machine learning to provide a time-sensitive solution for operational fire spread modeling in emergency management based on real-time remote sensing information. 

How to cite: Bauer, T., Miller, J., Gottfriedsen, J., Mollière, C., Durillo Barrionuevo, J., and Hammer, N.: ML-based fire spread model and data pipeline optimization, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14126, https://doi.org/10.5194/egusphere-egu23-14126, 2023.

EGU23-15711 | Orals | ITS1.1/NH0.1

A globally distributed dataset using generalized DL for rapid landslide mapping on HR satellite imagery 

Filippo Catani, Sansar Raj Meena, Lorenzo Nava, Kushanav Bhuyan, Silvia Puliero, Lucas Pedrosa Soares, Helen Cristina Dias, and Mario Floris

Multiple landslide events occur often across the world which have the potential to cause significant harm to both human life and property. Although a substantial amount of research has been conducted to address the mapping of landslides using Earth Observation (EO) data, several gaps and uncertainties remain when developing models to be operational at the global scale. To address this issue, we present the HR-GLDD, a high-resolution (HR) dataset for landslide mapping composed of landslide instances from ten different physiographical regions globally: South and South-East Asia, East Asia, South America, and Central America. The dataset contains five rainfall triggered and five earthquake-triggered multiple landslide events that occurred in varying geomorphological and topographical regions. HR-GLDD is one of the first datasets for landslide detection generated by high-resolution satellite imagery which can be useful for applications in artificial intelligence for landslide segmentation and detection studies. Five state-of-the-art deep learning models were used to test the transferability and robustness of the HR-GLDD. Moreover, two recent landslide events were used for testing the performance and usability of the dataset to comment on the detection of newly occurring significant landslide events. The deep learning models showed similar results for testing the HR-GLDD in individual test sites thereby indicating the robustness of the dataset for such purposes. The HR-GLDD can be accessed open access and it has the potential to calibrate and develop models to produce reliable inventories using high-resolution satellite imagery after the occurrence of new significant landslide events. The HR-GLDD will be updated regularly by integrating data from new landslide events.

How to cite: Catani, F., Meena, S. R., Nava, L., Bhuyan, K., Puliero, S., Pedrosa Soares, L., Dias, H. C., and Floris, M.: A globally distributed dataset using generalized DL for rapid landslide mapping on HR satellite imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15711, https://doi.org/10.5194/egusphere-egu23-15711, 2023.

EGU23-16626 | ECS | Posters on site | ITS1.1/NH0.1

Danish national early warning system for flash floods based on a gradient boosting machine learning framework 

Grith Martinsen, Yann Sweeney, Jonas Wied Pedersen, Roxana Alexandru, Sergi Capape, Charlotte Harris, Michael Butts, and Maria Diaz

Fluvial and flash floods can have devastating effects if they occur without warning. In Denmark, management of flood risk and performing preventative emergency service actions has been the sole responsibility of local municipalities. However, motivated by the disastrous 2021 floods in Central Europe, the Danish government has recently appointed the Danish Meteorological Institute (DMI) as the national authority for flood warnings in Denmark, and DMI is in the process of building capacity to fulfill this role.

 

One of the most cost-effective ways to mitigate flood damages is a well-functioning early warning system. Flood warning systems can rely on various methods ranging from human interpretation of meteorological and hydrological data to advanced hydrological modelling. The aim of this study is to generate short-range streamflow predictions in Danish river systems with lead times of 4-12 hours. To do so, we train and test models with hourly data on 172 catchments.

 

Machine learning (ML) models have in many cases been shown to outperform traditional hydrological models and offer efficient ways to learn patterns in historical data. Here, we investigate streamflow predictions with LightGBM, which is a gradient boosting framework that employs tree-based ML algorithms and is developed and maintained by Microsoft (Ke et al., 2017). The main argument for choosing a tree-based algorithm is its inherent ability to represent rapid dynamics often observed during flash floods. The main advantages of LightGBM over other tree-based algorithms are efficiency in training and lower memory consumption. We benchmark LightGBM’s performance against persistence, linear regression and various LSTM setups from the Neural Hydrology library (Kratzert et al., 2022).

 

We evaluate the algorithm trained using different input features. This analysis include model explainability, such as SHAP, and the results indicate that simply using lagged real-time observations of streamflow together with precipitation leads to the best performing and most parsimonious models. The results show that the LightGBM setup outperforms the benchmarks and is able to generate predictions with high Klinge-Gupta Efficiency scores > 0.9 in most catchments. Compared to the persistence benchmark it especially shows strong improvements on peak timing errors.

How to cite: Martinsen, G., Sweeney, Y., Pedersen, J. W., Alexandru, R., Capape, S., Harris, C., Butts, M., and Diaz, M.: Danish national early warning system for flash floods based on a gradient boosting machine learning framework, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16626, https://doi.org/10.5194/egusphere-egu23-16626, 2023.

EGU23-39 | Orals | ITS1.12/NH0.2

Deep Convolutional Architectures for Uncertainty Quantification and Forecast in Inundation Problems 

Azzeddine Soulaïmani, Azzedine Abdedou, Yash Kumar, and Pratyush Bhatt

Most science and engineering problems are modeled by time-dependent and parametrized nonlinear partial differential equations. Their resolution with traditional computational methods may be too expensive, especially in the context of predictions with uncertainty quantification or optimization, to allow for rapid predictions.  In this talk, we will overview data-driven methods aimed at representing high-fidelity computational models by means of reduced-dimension surrogate ones.  Different approaches will be presented for the uncertainty quantification for reliable predictions and forecasts in inundation problems.

Particularly, a non-intrusive reduced-order model based on convolutional autoencoders is proposed as a data-driven tool to build an efficient nonlinear reduced-order model for stochastic spatiotemporal large-scale physical problems. The method uses two-level autoencoders to reduce the spatial and temporal dimensions from a set of high-fidelity snapshots collected from an in-house high-fidelity numerical solver of the shallow-water equations. The encoded latent vectors, generated from two compression levels, are then mapped to the input parameters using a regression-based multilayer perceptron. The accuracy of the proposed approach is compared to the linear reduced-order technique-based artificial neural network (POD-ANN) on benchmark tests (the Burgers and Stoker's solutions) and a hypothetical dam-break flow problem over a complex bathymetry river. The numerical results show that the proposed nonlinear framework presents strong predictive abilities to accurately approximate the statistical moments of the outputs for complex stochastic large-scale and time-dependent problems, with low computational cost during the predictive online stage.

The caveat that remains is the long-term temporal extrapolation for problems marked by sharp gradients and discontinuities. Our study explores forecasting convolutional architectures (LSTM, TCN, and CNN) to obtain accurate solutions for time-steps distant from the training domain, on advection-dominated test cases. A simple convolutional architecture is then proposed and shown to provide accurate results for the forecasts. To evaluate the epistemic uncertainties in the solutions, the methodology of deep ensembles is adopted.

REFERENCES

  • Bhatt, Y. Kumar and A. Soulaïmani. Deep Convolutional Architectures for Extrapolative Forecast in Time-dependent Flow Problems, DOI: 10.48550/arXiv.2209.09651.
  • Abdedou and A. Soulaïmani. Reduced-order modeling for stochastic large-scale and time-dependent problems using deep spatial and temporal convolutional autoencoders.
    arXiv:2208.03190[physics.flu-dyn].
  • Jacquier, A. Abdedou, V. Delmas and A. Soulaimani. Non-intrusive reduced-order modeling using uncertainty-aware Deep Neural Networks and Proper Orthogonal Decomposition: Application to flood modeling. Journal of Computational Physics. Volume 424, 1 January 2021, 109854.
  • Abdedou and A. Soulaïmani. A non-intrusive reduced-order modeling for uncertainty propagation of time-dependent problems using a B-splines Bézier elements-based method and proper orthogonal decomposition: Application to dam-break flows. Computers & Mathematics with Applications. Volume 102, 15 November 2021, Pages 187-205.
  • Chaudhry and A. Soulaimani. A Comparative Study of Machine Learning Methods for Computational Modeling of the Selective Laser Melting Additive Manufacturing Process. Appl. Sci. 2022, 12(5), 2324; https://doi.org/10.3390/app12052324.
  • Delmas and A. Soulaimani. Parallel high-order resolution of the Shallow-water equations on real large-scale meshes with complex bathymetries. Journal of Computational Physics. Volume 471, 15 December 2022, 111629

 

How to cite: Soulaïmani, A., Abdedou, A., Kumar, Y., and Bhatt, P.: Deep Convolutional Architectures for Uncertainty Quantification and Forecast in Inundation Problems, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-39, https://doi.org/10.5194/egusphere-egu23-39, 2023.

EGU23-554 | ECS | Orals | ITS1.12/NH0.2

Using machine learning to emulate the hydrodynamic model for flood inundation modelling 

Santosh Kumar Sasanapuri, Dhanya Chadrika Thulaseedharan, and Gosain Ashwini Kumar

Floods are one of the most devastating natural disasters in the world causing loss of human lives and property across the world. These losses can be minimized by accurate prediction of floods well in advance. However, 2D hydrodynamic models which are used for flood inundation modelling require high computational time and hence are unsuitable for development of real-time flood monitoring system in most cases. Therefore, a surrogate machine learning model named XGBoost Regressor (XBGR) is developed for flood inundation modelling. The developed model overcomes the constraint of high computational time required by 2D hydrodynamic models. The XGBR is developed to predict maximum flood depth map and is evaluated with the LISFLOOD-FP hydrodynamic model. The training data for the XGBR model is generated using the LISFLOOD-FP model. The surrogate model is trained on 21 flood events, tested on 4 and validated for 1 flood event. For better development of the surrogate model, physical characteristics of the study area are considered in the form of nine indices referred here as topographic variables along with the flood characteristic variables. However, to refrain the XGBR model from overfitting and decrease the training time, a feed forward feature selection method is used to select the best predictive topographic variables. Four topographic variables are selected after which there is no significant improvement in the model was found. Number of trees and learning rate parameters of XGBR model are parameterized which are having highest impact on the model performance. Mean absolute error (MAE) and root mean square error (RMSE) are used for evaluating model accuracy. For testing period, the average MAE and RMSE are 0.433 m and 0.780 m, respectively and for the validation event MAE and RMSE are 0.595 m and 0.960 m respectively. For evaluating the accuracy of the surrogate model on flood inundation extent, F1 score is used which is the harmonic mean of precision and recall. The F1 score is 0.908 for the testing events and is 0.931 for validation events. The higher value of F1 score (>0.9) indicates good accuracy of the XGBR model when validated using the hydrodynamic model.

How to cite: Sasanapuri, S. K., Chadrika Thulaseedharan, D., and Ashwini Kumar, G.: Using machine learning to emulate the hydrodynamic model for flood inundation modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-554, https://doi.org/10.5194/egusphere-egu23-554, 2023.

EGU23-5068 | ECS | Orals | ITS1.12/NH0.2

Advantages and promises of deep neural operators for the prediction of wave propagation 

Fanny Lehmann, Filippo Gatti, Michaël Bertin, and Didier Clouteau

Physics-based deep learning experienced a major breakthrough a few years ago with the advent of neural operators. Beyond the traditional use of deep neural networks to predict the solution to a fixed Partial Differential Equation (PDE), these novel methods are able to learn the operator solution to a class of PDEs.

Comparisons and analyses of popular neural operators such as Fourier Neural Operator and DeepONet have been conducted for numerical case studies. However, they are still lacking for more realistic problems in complex settings.

In this study, we compare several neural operators to predict the propagation of seismic waves in heterogeneous media. Our database is composed of more than 12 million ground motion timeseries generated from 50,000 media. We quantify the accuracy of the neural operators, their memory requirements, and their dependence towards both the initial condition and the PDE parameters. We also propose insights on their possible extension to 3 dimensions.

How to cite: Lehmann, F., Gatti, F., Bertin, M., and Clouteau, D.: Advantages and promises of deep neural operators for the prediction of wave propagation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5068, https://doi.org/10.5194/egusphere-egu23-5068, 2023.

EGU23-5252 | ECS | Posters on site | ITS1.12/NH0.2

Hybrid generation based on machine learning to enhance numerical simulation for earthquake 

Gottfried Jacquet, Didier Clouteau, and Filippo Gatti

In the last decades, geophysicists have developed numerical simulators to predict earthquakes and other natural catastrophes. However, the more precise the model is, the higher the computational burden and the time to results. In addition, even if we could reproduce the phenomenon with more complex and more representative models, the underlying uncertainty would remain significantly high, affecting the reliability of the final prediction. In response to this challenge, we adopted a hybrid strategy, consisting into mixing physics-based numerical simulations and machine-learning. The goal is to transform synthetic earthquake ground motion, obtained via physics-based simulation, accurate up to a frequency of 5 Hz, into a broader-band prediction that mimics the recorded seismographs. In doing so, we factorize the latent representation of the seismic signal, by forcing an encoding that splits features into two parts: a low frequency one (0-1 Hz) and a high frequency one (1-20 Hz). In the following, we train a convolutional U-Net neural network and apply two different signal-to-signal translation techniques: pix2pix and BiCycleGAN. The latter strategies are compared with the prior work of Gatti et al., 2020, on the Stanford Earthquake Dataset (STEAD) showing their capability of mimicking recorded seismographs. We finally tested the two strategies on the synthetic time-histories obtained for the 2019 Le Teil earthquake (France).

 
  

How to cite: Jacquet, G., Clouteau, D., and Gatti, F.: Hybrid generation based on machine learning to enhance numerical simulation for earthquake, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5252, https://doi.org/10.5194/egusphere-egu23-5252, 2023.

EGU23-7773 | ECS | Orals | ITS1.12/NH0.2

Integration of 3D surveying approaches for critical infrastructure digital twins in natural hazard-prone scenarios 

Federica Gaspari, Federico Barbieri, Francesco Ioli, Livio Pinto, and Paolo Valgoi

The fragile geomorphological context of Italy sets a variety of natural challenges, ranging from seismic to hydrogeological risk. In such a complex territory, documenting the conditions of infrastructures is crucial for planning adequate strategies of maintenance through 3D modelling for structural analysis and digital twins’ implementation of structures like dams (Pagliari et al., 2016) or bridges (Gaspari et al., 2022). Geomatics, through periodical surveys using state-of-the-art technologies, reconstruct accurate 3D models of structures that results in the generation of dense pointclouds from which polygon meshes can be derived as well as in the model integration in Building Information Modeling (BIM) or Finite Element Method (FEM) environments for the computation of simulations and deformation monitoring or structural health assessment analysis in support of decision making.

Such data are generated through different approaches. A traditional methodology first implies the materialization and measurement of a topographic network in a local system with a total station and its subsequent georeferencing in a global coordinate reference system through a roto-translation based on Global Navigation Satellite System observations of ground control points. In the same framework, scans for the acquisition of dense pointclouds are defined through the adoption of a terrestrial laser scanner (TLS). Hence, the execution of planned drone flights, with nadiral and side view of the structure and its surrounding environment, serving as input for the generation of photogrammetric cloud through a robust Structure from Motion data processing.

Implementing open-source WebGL solutions like Potree supports the digital twin and data sharing with audiences of different technical backgrounds, committers concerned with the adoption of a monitoring platform for integrating products in different format as well as experts with non-geomatics expertise interested in further analysis of collected data through computer vision and deep learning approches that enrich the existent documentation. With a user-friendly interactive web platforms users are able to access the 3D model, make measurements and execute simple processing operation like cross-sections and clipping (e.g. https://labmgf.dica.polimi.it/piacenzacs/lugagnano/).

Since 2019, the dams of the Sila mountains in the Calabria region represented the case study for testing the described integrated approach. The present work concerns the integration of data from different sensors (TLS for indoor and outdoor environment, photogrammetric images and lidar from drone) for the generation of the digital twin of the arcuate-plan gravity dam of Trepidò. The dam digital twin of the dam and adjacencies consists of a pointcloud of 2594370 points, with adaptive density and average accuracy of 1-2 cm for the structure and 10 cm for the downstream vegetated sediment. It can be used to increase knowledge of the structure (built in 1930) and for structural analysis.

 

Bibliography:

 

Gaspari, F., Ioli, F., Barbieri, F., Belcore, E., and Pinto, L. (2022): Integration of UAV-LiDAR and UAV-photogrammetry for infrastructure monitoring and bridge assessment, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 995–1002, doi.org/10.5194/isprs-archives-XLIII-B2-2022-995-2022.

Pagliari, D., Rossi, L., Passoni, D., Pinto, L., de Michele, C., and Avanzi, F. (2016). Measuring the volume of flushed sediments in a reservoir using multi-temporal images acquired with UAS, Geomatics, Natural Hazards and Risk, 8(1), 150–166, doi.org/10.1080/19475705.2016.1188423

How to cite: Gaspari, F., Barbieri, F., Ioli, F., Pinto, L., and Valgoi, P.: Integration of 3D surveying approaches for critical infrastructure digital twins in natural hazard-prone scenarios, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7773, https://doi.org/10.5194/egusphere-egu23-7773, 2023.

EGU23-9555 | Orals | ITS1.12/NH0.2

Towards the development of an AI-based early warning system: a deep learning approach to bias correct and downscale seasonal climate forecasts 

Fatemeh Heidari, Qing Lin, Edgar Fabián Espitia Sarmiento, Andrea Toreti, and Elena Xoplaki

Early warning systems protect and support lives, jobs, land and infrastructure. DAKI-FWS, a German national project, aims at developing an early warning system to protect the German society and economy against extreme weather and climate events such as floods, droughts and heatwaves. With a seasonal temporal horizon, DAKI-FWS requires high resolution and bias corrected seasonal forecast of daily minimum and maximum temperatures, daily precipitation and wind speed. To derive such information, we have developed a deep neural network (DNN) approach to downscale and bias correct coarse resolution seasonal forecast ensembles on a 1 degree grid to a 1 arc minute grid.

The proposed DNN approach is here analyzed and compared with other machine learning approaches. Results show that such a deep learning technique can generate realistic, temporally consistent, and high-resolution climate information. The statistical and physical properties of the generated ensembles are analyzed using spatial correlation, cross validation and SVD. The DNN predicts extreme values that are very close to the observed values while preserving the physical relationships in the system as well as the trends in the variables.

How to cite: Heidari, F., Lin, Q., Espitia Sarmiento, E. F., Toreti, A., and Xoplaki, E.: Towards the development of an AI-based early warning system: a deep learning approach to bias correct and downscale seasonal climate forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9555, https://doi.org/10.5194/egusphere-egu23-9555, 2023.

EGU23-11462 | ECS | Orals | ITS1.12/NH0.2

A hybrid approach for declustering of earthquake catalogs 

Jonas Köhler, Wei Li, Johannes Faber, Georg Rümpker, Horst Stöcker, and Nishtha Srivastava

Usually, the earthquake catalog for a given region represents a collection of all detected and localized earthquakes and, thus, contains not only the main shocks, but also fore- and aftershocks. In order to perform an independent seismic event and seismic hazard analysis we require a catalog that, ideally, contains only mainshocks. Thus, the removal of dependent fore- and aftershocks from an earthquake catalogby declustering is a crucial step in seismic hazard analysis. Machine learning methods can potentially offer improvements in speed and accuracy in comparison to classical declustering approaches.

Here, we propose a hybrid approach to identify the temporal clusters of earthquakes from the catalogs of California (USGS) and Japan (ISC). We combine unsupervised 1-D clustering algorithms with seismologically informed methods and machine learning techniques. We use epidemic type aftershock sequence (ETAS) generated catalogs as well as classically declustered catalogs to benchmark the method.

How to cite: Köhler, J., Li, W., Faber, J., Rümpker, G., Stöcker, H., and Srivastava, N.: A hybrid approach for declustering of earthquake catalogs, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11462, https://doi.org/10.5194/egusphere-egu23-11462, 2023.

The present paper focuses on the influence of Rayleigh and Love waves on the seismic structural performance of a simplified nonlinear beam structure representing a bridge column. The impact of surface waves in the structure is quantified directly by a coupled 3D SEM-FEM numerical wave
propagation simulation from the earthquake source to the structure using the Domain Reduction Method.
In the first step, ground motions, including basin-induced surface waves, are generated from a regional model containing the earthquake source and a simplified basin. Surface waves are extracted and characterized with the Normalized Inner Product (NIP) in terms of amplitude and
frequency content from ground motions at different locations inside the basin. In the second step, the seismic wavefield from the SE simulation is imposed in a FE model composed of a nonlinear structure placed over a portion of the basin sediments. The model considers soil-structure
interaction and structural non-linearity through a multifiber beam approach.
By placing the structure in different positions, the extracted surface waves and the structural damage can be linked to a specific location inside the basin. Therefore, the spatial variability of the structural damage and the surface wave characteristics can be quantified. Consequently, this work
evaluates if structural damage can be estimated only from typical ground motion intensity parameters or if other parameters associated with surface wave characteristics are necessary. The results show a correlation between obtained seismic damage with rotational components from
surface waves (torsion for Love waves and rocking for Rayleigh waves).

How to cite: Soto, V. and Lopez-Caballero, F.: Quantification of source- and basin-induced surface waves effects on the seismic performance of nonlinear structures, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11611, https://doi.org/10.5194/egusphere-egu23-11611, 2023.

Solving partial differential equations (PDEs) stably and accurately is essential in simulation analysis of a variety of geophysical phenomena. Designing appropriate discretization schemes for PDEs requires careful and rigorous mathematical treatment and has been a long-term research topic. The computational efficiency is additionally a long-standing challenge when what-if hazard scenario analysis is considered. The data-driven discretization is a hybrid approach to combine machine learning and physics-based simulations, which provides a methodology to derive better discretization schemes from reliable references obtained typically using known stable schemes with higher resolution grids. As the resultant schemes may inherit the physics described by the PDEs, surrogate models employing them are expected to be in good agreement with expensive simulations. It is also argued that the learnt schemes by neural network models can exhibit similar characteristics to known sophisticated algorithms and outperform them in terms of accuracy. However, the method has currently been assessed with only limited examples and the detailed mechanisms of the learnt schemes are not well understood. In this presentation, thorough assessment and investigation of learning discretization schemes are conducted by applying the methodology to several types of differential equations with different learning models for the schemes. Whether the methodology has the potential to derive new schemes is also discussed.

How to cite: Ishikawa, T.: On learning discretization schemes of partial differential equations in geoscience, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11657, https://doi.org/10.5194/egusphere-egu23-11657, 2023.

EGU23-13337 | ECS | Posters on site | ITS1.12/NH0.2

Tropical cyclone storm surge emulation around New Orleans 

Simon Thomas, Dan(i) Jones, Talea Mayo, and Devaraj Gopinathan

Storm surges can have devastating effects on coastal communities. These events, often caused by tropical cyclones, are difficult to simulate due to the challenging nature of process-based modelling and the relative paucity of data covering extreme tropical cyclone conditions. In order to make optimal use of existing physical models, we build an emulator to actively learn the relationship between tropical cyclone characteristics and maximum storm surge height.

 

We used the ADCIRC physical storm surge model, a reliable but costly tool, to simulate a series of representative tropical cyclones that typically affect the coast near New Orleans. These initial storms were sampled using Latin hypercube design, varying tropical cyclone characteristics such as the landfall speed, central pressure, and others. By running the ADCIRC model for each of these events, we were able to determine the maximum sea surface height caused by each simulated storm. Next, we trained a Gaussian process to fit the maximum sea levels at each point along the coast given the tropical cyclones' characteristics as input. Through active learning, we iteratively selected additional tropical cyclones to further improve the emulator’s accuracy. Finally, we evaluated the model's performance using a held-out test set of idealised tropical cyclones.

 

Our emulator approach allowed us to efficiently create a high-quality, low-cost statistical model that can potentially be used to predict the probability of future storm surge heights. Additionally, it allowed us to separate uncertainties in the input distribution of tropical cyclone characteristics from uncertainties in the model itself. By better understanding these sources of uncertainties, we can work towards more accurately assessing the potential impacts of future storms on coastal communities.

How to cite: Thomas, S., Jones, D., Mayo, T., and Gopinathan, D.: Tropical cyclone storm surge emulation around New Orleans, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13337, https://doi.org/10.5194/egusphere-egu23-13337, 2023.

EGU23-15645 | ECS | Orals | ITS1.12/NH0.2

Social & Physics Based Data Driven Methods for Wildfire Prediction 

Jake Lever, Sibo Cheng, and Rossella Arcucci

Twitter is increasingly being used as a real-time human-sensor network during natural disasters, detecting, tracking and documenting events. Current wildfire models currently largely omit social media data, representing a shortcoming in current models, as valuable and timely information is transmitted via this channel. By including this data as a real-time data source, we aim to help disaster managers make more informed, socially driven decisions, by detecting and monitoring online social media sentiment over the course of a wildfire event. This monitoring model is coupled to a real-time forecasting of wildfire dynamics.

Real-time forecasting of wildfire dynamics, which has attracted increasing attention recently in fire safety science, is extremely challenging due to the complexities of the physical models and the geographical features. Running physics-based simulations for large-scale wildfires can be computationally difficult. We propose a novel algorithm scheme, which combines reduced-order modelling (ROM), recurrent neural networks (RNN), data assimilation (DA) and error covariance tuning for real-time forecasting/monitoring of the burned area. An operating cellular automata (CA) simulator is used to compute a data-driven surrogate model for forecasting fire diffusions. A long-short-term-memory (LSTM) neural network is used to build sequence-to-sequence predictions following the simulation results projected/encoded in a reduced-order latent space. 

We implement machine learning in a wildfire prediction model, using social media and geophysical data sources with sentiment analysis to predict wildfire instances and characteristics with high accuracy. The geophysical data is satellite data provided by the Global Fire Atlas, and social data is provided by Twitter. In doing this, we perform our own data collection and analysis, comparing regional differences in online social sentiment expression.

The performance of the proposed algorithm has been tested in recent massive wildfire events in California.

How to cite: Lever, J., Cheng, S., and Arcucci, R.: Social & Physics Based Data Driven Methods for Wildfire Prediction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15645, https://doi.org/10.5194/egusphere-egu23-15645, 2023.

EGU23-16906 | ECS | Orals | ITS1.12/NH0.2

Efficient Probabilistic Tsunami Hazard and Risk Assessment Using a Hybrid Modeling Approach: A Systematic Evaluation 

Naveen Ragu Ramalingam, Alice Abbate, Erlend Briseid Storrøsten, Kendra Johnson, Gareth Davies, Stefano Lorito, Marco Pagani, and Mario Martina

The hybrid modelling approach combining machine learning and physics-based simulation has been used in a variety of ways to study tsunami and improve our understanding of this complex natural hazard. They are broadly applied for (1) Tsunami forecasting and early warning systems and (2) Tsunami hazard and risk assessment including sensitivity, analysis uncertainty studies and inverse modelling for estimating the source. 

Rigorous evaluation of such a hybrid approach is constrained by the limited size of available simulation datasets which is important to guide their usage by practitioners. This study investigates the application of a hybrid tsunami modelling technique (Ragu Ramalingam et al., 2022, Ragu Ramalingam et al., 2022) which offers a computationally efficient approach for hazard assessment where large events-sets must be modelled typical of probabilistic tsunami hazard and risk assessment (PTHA/PTRA). We use a large tsunami simulation dataset for a coastal region of eastern Sicily, Italy and try to address the following question:

  • How to efficiently sample scenarios used to train the ML models?
  • Where and when are such methods accurate? 
  • How do they compare with other traditional modelling methods like Monte Carlo Sampling?

Additionally, the effort will deliver an open tsunami benchmarking dataset that can be utilised for further development, baseline comparison of various ML algorithms, and improved hyperparameter tuning.

References

Ragu Ramalingam, N., Johnson, K., Pagani, M., and Martina, M.: A hybrid ML-physical modelling approach for efficient approximation of tsunami waves at the coast for probabilistic tsunami hazard assessment, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5642, https://doi.org/10.5194/egusphere-egu22-5642, 2022.

Ragu Ramalingam, N., Rao, A., Johnson, K., Pagani, M. and Martina, M. A hybrid ML-physical modelling approach for efficient probabilistic tsunami hazard and risk assessment, Proceedings of the 19th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2022), August 1-5, 2022, Virtual.

How to cite: Ragu Ramalingam, N., Abbate, A., Briseid Storrøsten, E., Johnson, K., Davies, G., Lorito, S., Pagani, M., and Martina, M.: Efficient Probabilistic Tsunami Hazard and Risk Assessment Using a Hybrid Modeling Approach: A Systematic Evaluation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16906, https://doi.org/10.5194/egusphere-egu23-16906, 2023.

Effective support for people´s responses to climate change requires knowledge on the gap between physical climate change science and practices where the responses are realized. Studies have shown that individuals´ strong belief in local impacts of climate change is an important driver of climate change response (e.g. Blennow et al. 2012). Arguably this belief can be fortified by the belief that one has experienced the local impacts of climate change. However, a recent study shows that while responses to climate change correlate positively with the strength of belief that one has experienced negative local impacts of climate change, experience of positive local climate change impacts can either promote or inhibit the response (Blennow and Persson 2021). If the intention is adaptation to the impacts of climate change, positive experiences of climate change promote the response but if the intention is climate change mitigation, experience of positive impacts of climate change inhibit the response.

While strong belief in the local impacts of climate change is a prerequisite of climate change response, for adaptation, the agent also needs detailed knowledge of the causal links between climate change and the negative and positive values of expected climate change related impacts (Blennow et al. 2020). Decision-making in favor of adaptation to climate change generally increases with the absolute value of the net of positive and negative expected impacts in the absence of ‘tipping point’ behavior (Persson et al. 2020; Blennow et al. 2020). Tipping point behaviour occurs when adaptation is not pursued in spite of the strongly negative or positive net value of expected climate change impacts. For mitigation, moreover, it is important that the net value of expected impacts is negative and not positive (Blennow and Persson 2021). We discuss the implications of the results for policies aiming at supporting responses to climate change, such as communications that help the receiver subjectively attribute the causes of an event to climate change.

 

References

Blennow, K. Persson, J., 2021. To Mitigate or Adapt? Explaining Why Citizens Responding to Climate Change Favour the Former. Land, 10, 240. https://doi.org/10.3390/land10030240

Blennow, K., Persson, J., Tomé, M., & Hanewinkel, M., 2012. Climate change: believing and seeing implies adapting. PLOS ONE, 7(11):e50181. http://dx.plos.org/10.1371/journal.pone.0050182

Blennow, K. Persson, J., Gonçalves, L.M.S., Borys, A., Dutcă, I., Hynynen, J., Janeczko, E., Lyubenova, M., Merganič, J., Merganiová, K., Peltoniemi, M., Petr, M., Reboredo, F., Vacchiano, G., Reyer, C.P.O., 2020. The role of beliefs, expectations and values in decision-making favoring climate change adaptation – implications for communications with European forest professionals. Environmental Research Letters,15: 114061.  /doi.org/10.1088/1748-9326/abc2fa

Persson, J., Blennow, K., Gonçalves, L.M.S., Borys, A., Dutca, I., Hynynen, J., Janeczko, E., Lyubenova, M., Martel, S., Merganic, J., Merganicova, K., Peltoniemi, M., Petr, M., Reboredo, F., Vacchiano, G., Reyer, C.P.O., 2020. No polarization – expected values of climate change impacts among European forest professionals and scientists. Sustainability, 12, 2659; doi:10.3390/su12072659

How to cite: Blennow, K. and Persson, J.: The role of beliefs, expectations and values for decision-making in response to climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6880, https://doi.org/10.5194/egusphere-egu23-6880, 2023.

EGU23-8097 | ECS | Orals | ITS4.4/NH0.4

Climate X: Making climate risk data useful and usable for the financial sector 

Sally Woodhouse, Claire Burke, Nick Leach, James Brennan, Graham Reveley, Laura Ramsamy, and Hamish Mitchell

Increasingly the financial sector is interested in understanding their risk to the impacts of climate change. This is driven both by governmental regulation that requires financial services to declare their risks due to climate change, as well as a desire to mitigate risks to profits that climate change poses.

To generate useful and accurate risks assessments users need access to high quality data of the projected changes to hazard due to climate change. However, there is typically a gap between scientific research and what our clients need to understand their risk. Many of the most damaging hazards, such as flooding and subsidence, are not directly modelled by climate models and require specialist hazard knowledge and well as climate data to assess. Scientific studies often focus on large scale changes or small regional studies, whereas clients need consistent high-resolution data across multiple regions. Additionally, a risk portfolio covers a wide range of climate related hazards, which all must be considered when understanding and attempting to mitigate risk. Users will often not have the inhouse knowledge to use data generated by the scientific community directly or the expertise to assess how this relates to the risks posed by different hazards. Therefore, the financial sector is turning to external data providers for this information, such as Climate X.

This talk will cover how at Climate X we make reliable and robust risk assessments of climate hazards that are presented in a way that is usable and useful for the financial sector as well as various other decision makers. The focus will be on how we use open-source climate model data to generate our heat risk metric. This will cover the definition of the metric, how it is calculated and how we how we present the data to users including accuracy and uncertainty. I will also present overview of the other hazards that we provide and the need for an interdisciplinary team to cover the broad range of physical hazards related to climate change.

How to cite: Woodhouse, S., Burke, C., Leach, N., Brennan, J., Reveley, G., Ramsamy, L., and Mitchell, H.: Climate X: Making climate risk data useful and usable for the financial sector, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8097, https://doi.org/10.5194/egusphere-egu23-8097, 2023.

EGU23-10040 | Posters on site | ITS4.4/NH0.4

Communicating impacts of climate change with the RECEIPT storyline visualizer 

Gijs van den Oord, Maarten van Meersbergen, Peter Kok, Jesus Garcia Gonzales, Sander van Rijn, Alessio Ciullo, Elco Koks, Ertug Ercin, Henrique Moreno Dumont Goulart, Esther Boere, Christian Otto, Patryk Kubiczek, Robin Middelanis, Carla Mauricio, Keren Prize Bolter, Dana Stuparu, and Bart van den Hurk

Disseminating the effects of climate change and its potential future impacts to a wider audience is a demanding task, yet of great importance to society. Moreover, quantifying causal chains emerging from global warming is often impeded by the growth of unknown parameters related to modeling socio-economic responses. One method to obtain insights into the complex consequences of climate change is the use of physical climate storylines. Conceptually, storylines correspond to reasonable choices for the unknowns within the modeled impact transmission chain. They allow us to understand and describe the unfolding of climate-induced extreme events, making the impacts of global warming tangible to a wide range of potential stakeholders.

The RECEIPT project develops and applies the concept of climate storylines to provide risk information on climate change effects with a remote origin and an impact on European socio-economic sectors. Sectors that are being addressed within RECEIPT are the European critical infrastructure, manufacturing chains, the food system, financial markets and European international cooperation with (developing) regions. Experts within the consortium construct credible storylines for these sectors, often starting from extreme, disrupting historical events and translating these to counterfactual climate and socio-economic futures. These analyses are being published in scientific journals, but the RECEIPT consortium envisions an alternative dissemination channel to target a larger community.

The storyline visualizer (https://www.climateimpactstories.eu) is an interactive, web-based user interface, aimed at communicating physical climate storylines to an audience of informed stakeholders. The visualizer enables storyline developers in RECEIPT to structure their message into a logical progression of sections, and support each page with text, pictures, geospatial data and interactive charts. The visualizer also allows the user to explore data used within the storyline and browse through counterfactual futures. Currently, five storylines have been visualized with this platform, describing:

  • the future impacts of sea level rise and storm surges upon critical infrastructure around the French Atlantic coast, based upon storm Xynthia;

  • increased impacts of cyclones upon European overseas territories and the sustainability of the European Solidarity Fund within this context;

  • soy production disruptions in a warming climate and their impact on the European food system;

  • multi-breadbasket harvest failures, locust infestations and their impact upon food security in the Greater Horn of Africa;

  • the impact of extreme hurricanes in the Houston metropolitan area for global manufacturing chains and European industry.

Implementing these studies as captivating climate storylines in the visualizer has taught us valuable lessons; one particular challenge has been to handle the growing complexity of the analyses when multiple socio-economic aspects are taken into account. Using a minimalist approach, shifting the focus towards the modeled impacts rather than the full academic reasoning, have appeared to be a useful path forward, resulting in accessible yet credible storylines of climate impacts. In this session, we plan to showcase the capabilities of the storyline visualizer, review lessons learned during the implementation process and discuss possible applications beyond RECEIPT.

How to cite: van den Oord, G., van Meersbergen, M., Kok, P., Garcia Gonzales, J., van Rijn, S., Ciullo, A., Koks, E., Ercin, E., Moreno Dumont Goulart, H., Boere, E., Otto, C., Kubiczek, P., Middelanis, R., Mauricio, C., Prize Bolter, K., Stuparu, D., and van den Hurk, B.: Communicating impacts of climate change with the RECEIPT storyline visualizer, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10040, https://doi.org/10.5194/egusphere-egu23-10040, 2023.

EGU23-11023 | Orals | ITS4.4/NH0.4

AI for Climate Adaptation? 

Tina-Simone Neset, Katerina Vrotsou, Carlo Navarra, Fredrik Schück, Clara Greve Villaro, Magnus Mateo Edström, and Caroline Rydholm

In October 2021, the Swedish Meteorological and Hydrological Institute launched a novel national system for impact-based weather warnings, moving from the traditional format for meteorological, hydrological, and oceanographic warnings towards an assessment process that includes collaboration and consultation with regional stakeholders on the impacts that certain weather events would have for a specific geographic area and time frame. As part of this new system, local and regional administrative efforts are made to create assessment-support documentation drawing on local knowledge and providing support ahead of and during extreme weather events.

We present initial results from the ongoing research project ‘AI4ClimateAdaptation’ (https://liu.se/en/research/ai4climateadaptation), which explores the potential of employing AI-based image and text analysis to support the process and evaluate the precision of impact-based weather warnings. The project collects image and text data appropriate for subsequent use in AI-based analysis from citizen science campaigns and social media. The presentation focuses on the concept of integrating AI-based text and image analysis with the processes of the warning system, as well as the barriers and enablers that are identified by local, regional, and national stakeholders related to the role of AI in weather warning systems. We further discuss to what extent data and knowledge on historical extreme weather events can be integrated with local and regional climate adaptation efforts, and whether these efforts could bridge the divide between long-term adaptation strategies and short-term response measures related to extreme weather events. The results of this study are expected to contribute to the national system for impact-based weather warnings and to increase resilience to extreme climate-related weather events.

How to cite: Neset, T.-S., Vrotsou, K., Navarra, C., Schück, F., Greve Villaro, C., Mateo Edström, M., and Rydholm, C.: AI for Climate Adaptation?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11023, https://doi.org/10.5194/egusphere-egu23-11023, 2023.

EGU23-11292 | Orals | ITS4.4/NH0.4

Challenges and opportunities of knowledge co-creation for the water-energy-land nexus 

Nicu Constantin Tudose, Mirabela Marin, Sorin Cheval, and Cezar Ungurean

The pressure on natural resources including water, energy and land is continuously growing through changes in climate and land use. Representatives of academia, industry, governments and society need to join forces in order to develop new pathways towards sustainable natural resource use and management. Such pathways start from the basic idea that natural resources are finite and interlinked and that human activities can affect these resources and links, with partly irreversible effects. We combine the water−energy−land nexus and the climate services concept and present a cross-sectoral approach of knowledge co-creation to inform natural resource use and management. The approach is tested in three case studies across Europe that face different challenges resulting from climate and socio-economic change. We present the process, applied methods and major results of knowledge co-creation for sustainable natural resource use and management, and we reflect on the challenges and opportunities from engaging multiple stakeholders. Even if a comprehensive, cross-sectoral approach encourages embedding the water−energy−land nexus into climate services and allows the development of pathways towards sustainable natural resource use and management, maintaining these achievements and partnerships beyond the lifetime of a research project remains challenging.

How to cite: Tudose, N. C., Marin, M., Cheval, S., and Ungurean, C.: Challenges and opportunities of knowledge co-creation for the water-energy-land nexus, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11292, https://doi.org/10.5194/egusphere-egu23-11292, 2023.

EGU23-13145 | ECS | Orals | ITS4.4/NH0.4

Implications of governance mechanisms for spanning boundaries and managing risk  

Lydia Cumiskey, Denise McCullagh, Pia-Johanna Schweizer, and Sukaina Bharwani

Managing flood risk and adapting to climate change is complex where multiple actors need to work together across sectoral and disciplinary boundaries to capture synergies and manage trade-offs. A selection of governance mechanisms were found to influence actors’ capacity to work in partnership, break down silos and unlock opportunities.

Results from research conducted within the SYSTEM-RISK project identifies boundary spanning roles as governance mechanisms facilitating integrated flood risk management in England and Serbia (Cumiskey, 2020). Among other characteristics, the ‘reticulist’ was found to utlise networks and diplomacy to access funding, ‘entrepreneurs’ acted creatively to capture funding and test the flexibility of rules, ‘interpreters’ built interpersonal relationships and interpreted different professional languages, ‘organisers’ managed actor partnerships and ‘specialists’ were willing to engage and try new approaches. The availability of rules and resources influenced capacities to hire, train and sustain such boundary spanning staff.  Results highlighting the dynamic interdependencies between such roles and the governance system will be shared.

Place-based adaptation partnerships were found as another governance mechanism, strengthening collaboration, knowledge exchange and joint action across boundaries. The Climate Adaptation Partnership Framework1 was developed through the TalX project (Transboundary Adaptation Learning Exchange) to collate learning from applications in Ireland, Northern Ireland, Scotland, England and Wales and provide guidance for stakeholders interested in implementing such partnerships.  

The RISK-TANDEM framework is being developed within the DIRECTED project (Horizon Europe, 2022 - 2026) to enhance risk governance, knowledge co-production and interoperability across data, models and tools to enable disaster resilience in four Real World Lab regions. An initial version of the framework, which builds upon the existing Tandem Framework2 (among others) will be shared along with plans for implementation.   

The role of such governance mechanisms in integrating research, innovation and science in a collaborative way will be introduced, while opening the discussion on how to improve the application of such mechanisms to facilitate future engaged research.

 

Cumiskey, L. (2020). Embracing boundary spanning roles in Flood Risk Management. PhD Research Briefing Note 2. Middlesex University. Available at: https://eprints.mdx.ac.uk/30418/

1 Climate Adaptation Partnership Framework. Available at: https://talx2020.github.io/

2 The Tandem framework: a holistic approach to co-designing climate services. Available at: https://www.weadapt.org/knowledge-base/climate-services/the-tandem-framework

How to cite: Cumiskey, L., McCullagh, D., Schweizer, P.-J., and Bharwani, S.: Implications of governance mechanisms for spanning boundaries and managing risk , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13145, https://doi.org/10.5194/egusphere-egu23-13145, 2023.

EGU23-14166 * | ECS | Orals | ITS4.4/NH0.4 | Highlight

Reversing the impact chain 

Peter Pfleiderer, Jana Sillmann, Robin Lamboll, Joeri Rogelj, and Carl-Friedrich Schleussner

Climate impacts have been studied intensively and our understanding of changes in climate impacts due to anthropogenic activity is impressive (see IPCC AR6). There is, however, a gap between the physical understanding of changes in climate impacts and availability of information that could directly be used by adaptation planners. We argue that this gap is to a large extent a result of the usual modeling chain that is based on a handful of representative emission scenarios.

Most climate change studies take a small, predefined set of emission scenarios (SSP2-45, SSP1-26, SSP5-85 etc.) and calculate the global and regional climate impacts resulting from these. Focusing on a limited set of emission scenarios allows us to compare results from different modeling groups and lets us run detailed climate models on each scenario. However, this modeling approach does not align with relevant research questions such as: “How much can be emitted to avoid a certain impact?” Or “what are the emission constraints to limit the probability of experiencing a certain event until 2050 to 10%?”

The presented reversal of the impact chain would help to answer these questions. The idea is to start from a clearly defined impact and evolve uncertainties backwards into the emission space. Doing so, we take the perspective of practitioners who know very well what impacts are of relevance and would like to know how these impacts are related to greenhouse gas emissions.

How to cite: Pfleiderer, P., Sillmann, J., Lamboll, R., Rogelj, J., and Schleussner, C.-F.: Reversing the impact chain, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14166, https://doi.org/10.5194/egusphere-egu23-14166, 2023.

EGU23-14580 | ECS | Orals | ITS4.4/NH0.4

Strategic litigation on climate change adaptation: The case of public authorities’ liability in flood risk reduction 

Riccardo Luporini, Marcello Arosio, Emanuele Sommario, and Mario Martina

Strategic climate change litigation is rising on a global scale as a tool to bridge the accountability and enforcement gap that is currently affecting climate change law. The vast majority of strategic climate cases concern mitigation, while adaptation is rarely addressed, and when it is, this is done in a rather residual and vague manner (Setzer and Higham, 2022). However, if it is true that states and corporate actors are lagging behind their emission reduction commitments, at the same time ‘at current rates of adaptation planning and implementation, the adaptation gap will continue to grow’ (IPCC, 2022). Accordingly, once strategic litigation is found to be a suitable tool to advance climate action, opportunities to litigate adaptation strategically should be further explored.

 The role of science in substantiating climate change litigation is very much under the spotlight when it comes to the determination of emission reduction targets, carbon budget and ‘fair shares’ in mitigation cases (BIICL and Sant’Anna, 2021). On the other hand, science does not yet provide accurate indicators of adaptation progress or lack thereof and this contributes to narrowing down opportunities for strategic litigation on adaptation (Berrang-Ford, Biesbroek et al, 2019).

Against this background, this study aims to investigate the role of geosciences in fostering strategic litigation on climate change adaptation. This objective is pursued via a case study. The study builds hypothetical strategic cases concerning public authorities’ liability for flood risk reduction and investigates the potential role of geosciences in such cases. How can geosciences help determine the impacts of climate change on flood risk in a given area and the consequent exposure and vulnerability of specific communities? What does a science-based assessment of given adaptation and flood risk reduction policies and measures look like? To what extent can geosciences determine the activities that public authorities should take to reduce flood risk in a certain area? And, finally, how far can existing commitments in the area of disaster risk reduction and human rights be used in order to distill concrete obligations in terms of adaptation to climate change-induced hazards? The study aims to address these questions by means of an interdisciplinary approach based on combining legal and policy practice with sound geoscience methodology.

References

Joana Setzer and Catherine Higham, ‘Global trends in climate change litigation: 2022 snapshot’, (2022) Grantham Research Institute on Climate Change and the Environment and Centre for Climate Change Economics and Policy, London School of Economics and Political Science

IPCC [Hans-O Pörtner et al. (eds)], Climate Change 2022 Impacts, Adaptation and Vulnerability. Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, Summary for Policy Makers

A Holzhausen, R Luporini (Eds), The Role of Science in Climate Change Litigation: International Workshop Report, (July 2021)

Lea Berrang-Ford, Robbert Biesbroek, et al, Tracking global climate change adaptation among governments, Nature Climate Change 9, 440–449 (2019)

How to cite: Luporini, R., Arosio, M., Sommario, E., and Martina, M.: Strategic litigation on climate change adaptation: The case of public authorities’ liability in flood risk reduction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14580, https://doi.org/10.5194/egusphere-egu23-14580, 2023.

Climate change is posing challenges for operating and designing critical infrastructure. Increasingly, AI has been used to enhance these decision making process. Reinforcement Learning has shown its advantages in dealing with difficult sequential decision making in games. When scaling to real life applications, their complexity and heterogenous nature potentially will require Multi Agent Reinforcement Learning (MARL) to provide adaptive capacity in a distributed manner. However, the human system is also characterised by the diverse belief of each individuals and groups - a feature that was captured in agent based models. AI/agent systems are evolving to work with human and become ubiquitous in real life/applications critical to society (such as health and transport). We argue that allowing belief transfer and full interactions across MARL actors in a three-layer model capturing data uncertainty, logical model and belief will help create a heterogeneous MARL system for better human-AI interaction that better aligns with human thoughts/values for actionable climate decisions.

How to cite: Hoang, L. and Smyrnakis, M.: Towards teaching multi agent system the concept of risks and safety for actionable climate decisions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15960, https://doi.org/10.5194/egusphere-egu23-15960, 2023.

EGU23-16311 | ECS | Orals | ITS4.4/NH0.4

Flood risk assessment in support of the evaluation and selection of risk mitigation measures 

Alice Gallazzi, Francesco Ballio, Daniela Molinari, Marina Credali, and Immacolata Tolone

The purpose of the study is to define how the models available for flood damage assessment in the Italian context can support cost-benefit or multi-criteria analyses of risk mitigation measures, in accordance with current laws and regulations on the subject. On the basis of the present situation in which risk mitigation measures are evaluated mostly according to their capability of reducing the hazard and by considering few simple exposure factors, the study aims at identifying more robust indicators to assess measures effectiveness based on results from flood damage modelling. State of the art flood damage models developed within the context of the project MOVIDA (MOdello per la Valutazione Integrata del Danno Alluvionale – Model for integrated evaluation of flood damage, https://sites.google.com/view/movida-project) were applied to evaluate the expected damage in several flood prone areas within the Lombardia Region (northern Italy), where mitigation actions are planned by the Regional Authority. Then, obtained results for these areas were analysed to define effectiveness indicators as well as their range of values. Finally, specific indicators were developed to evaluate the environmental impact of each intervention according to present policies to promote sustainable investments in the field of soil protection as well as contribute to achieve Green Deal goals. Results show that developed indicators increase the ability of local authorities in the definition of priorities of intervention, leading to a reduction of institutional and legislative inefficiencies and increasing the efficiency of disaster risk reduction policies.

How to cite: Gallazzi, A., Ballio, F., Molinari, D., Credali, M., and Tolone, I.: Flood risk assessment in support of the evaluation and selection of risk mitigation measures, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16311, https://doi.org/10.5194/egusphere-egu23-16311, 2023.

EGU23-17453 | ECS | Orals | ITS4.4/NH0.4

Next Steps for Earth Science Contributions to Community Resilience 

Sruti Modekurty, Arika Virapongse, Rupanwita Gupta, Zachary J. Robbins, Jonathan Blythe, and Ruth E. Duerr

Community resilience increases a place-based community’s capacity to respond and adapt to life-changing environmental dynamics like climate change and natural disasters. Timely access to environmental data is an important factor for community resilience. Most Earth science information is created for a particular science community for a specific scientific purpose, without much thought to who else could benefit from it and how they might use it. New approaches are needed to facilitate better data production and integration for community use.

In this session, we present the findings of a paper published by ESIP’s (Earth Science Information Partners) Community Resilience Cluster. As a convening space for over 150 member organizations across different sectors, ESIP’s biannual meetings, conference calls, and topic-driven clusters provided the infrastructure and expertise to support the Community Resilience cluster’s examination of the role of Earth science data for community resilience. This presentation highlights the challenges communities face when applying Earth science data to their efforts:

• Inequity in the scientific process,

• Gaps in data ethics and governance,

• A mismatch of scale and focus, and

• Lack of actionable information for communities.

Recommendations are made as starting points to address the challenges, along with examples of good practices from across the Earth science community. Given ESIP’s data stewardship efforts with large organizations and across domains, the recommendations are applicable at scale. We offer actionable steps for the Earth science community to help them produce data to better support community resilience.

How to cite: Modekurty, S., Virapongse, A., Gupta, R., Robbins, Z. J., Blythe, J., and Duerr, R. E.: Next Steps for Earth Science Contributions to Community Resilience, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17453, https://doi.org/10.5194/egusphere-egu23-17453, 2023.

EGU23-1988 | ECS | Orals | ITS1.4/NH0.6

Changes in Compound Flood Event frequency in Northern and Central Europe under climate change 

Philipp Heinrich, Stefan Hagemann, and Ralf Weisse

The simultaneous occurrence of increased river discharge and high coastal water levels may cause compound flooding. Compound flood events can potentially cause greater damage than the separate occurrence of the underlying extreme events, making them essential for risk assessment. Even though a general increase in the frequency and/or severity of compound flood events is assumed due to climate change, there have been very few studies conducted for larger regions of Europe. Our work, therefore, focuses on the high-resolution analysis of changes in extreme events of coastal water levels, river discharge, and their concurrent appearance at the end of this century in Northern and Central Europe (2070-2100). For this, we analyse downscaled data sets from two global climate models for the two emissions scenarios RCP2.6 and RCP8.5.

First, we compare the historical runs of the downscaled GCMs to historical reconstruction data to investigate if they deliver comparable results for Northern and Central Europe. Then we study changes in the intensity of extreme events, their number, and the duration of extreme event seasons under climate change. Our analysis shows increases in compound flood events over the whole European domain, mostly due to the rising sea level. This increase is concomitant with an increase in the annual compound flood event season duration.

Furthermore, the sea level rise associated with a global warming of 1.5K will result in a 50% increase in compound flood events for nearly every European river considered.

How to cite: Heinrich, P., Hagemann, S., and Weisse, R.: Changes in Compound Flood Event frequency in Northern and Central Europe under climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1988, https://doi.org/10.5194/egusphere-egu23-1988, 2023.

EGU23-2079 | ECS | Orals | ITS1.4/NH0.6

Hotspots and impacts of present and future compound hot and dry summers in Europe 

Andrea Böhnisch, Elizaveta Felsche, Magdalena Mittermeier, Benjamin Poschlod, and Ralf Ludwig

Compound hot and dry events (such as recent summers of 2015, 2018 and 2022 in Europe) have an impact on a wide range of sectors, including health, transport, energy production, ecology, agriculture and forestry. The co-occurrence of extreme heat and drought poses a risk to water security in particular, since heat exacerbates moisture shortages during dry periods through increased evapotranspiration while at the same time water demand increases (e.g., for drinking water, cooling, irrigation). Current research suggests that climate change will increase the intensity, frequency, and duration of joint hot and dry extreme events in Europe. However, most studies focus on the drivers applying coarse-resolution global climate models.

This study exploits a 50-member single-model initial condition large ensemble (SMILE) of the Canadian Regional Climate Model, version 5, at 12 km resolution (CRCM5-LE, RCP 8.5 from 2006 onwards, driven by the Canadian Earth System Model Version 2 large ensemble, CanESM2-LE). The application of a regional SMILE provides an extensive database of compound events and, subsequently, robust estimations of their occurrence changes across Europe, from current to future states and in high geographical detail.

We define compound hot and dry summers based on joint exceedances of temperature and (negative) precipitation thresholds (2001-2020 JJA 95th percentiles). By considering low soil moisture (below regional 2001-2020 JJA 10th percentile) as an impact indicator, we further show the spatially varying connection between compound hot and dry summers and low water availability in Europe. Compound event occurrences are investigated in a current climate (2001-2020) and future 20-year slices at global warming levels (GWL, derived from the CanESM2-LE) of +2 °C and +3 °C, with each period represented by 1000 model years. Last, we investigate the underlying processes (e.g., heat budget terms) of changing event occurrences and their spatial distribution, and discuss the land use-specific (e.g., urban, agricultural, natural) exposure to impacts on water availability during compound hot and dry summers.

We identify areas in the Mediterranean and northern France as hotspots with a fivefold occurrence frequency of compound hot and dry summers for +2 °C GWL. With +3 °C GWL, the Mediterranean, France, Belgium, southern Germany, Switzerland, and the south of UK and Ireland are affected by a tenfold occurrence frequency with respect to current climate.

This study is an important boundary condition to the development of adaptation strategies for the affected regions.  At the same time, it quantifies the reduction of event occurrence in a +2°C world compared to the higher GWL of +3°C, highlighting the importance of climate mitigation strategies and policies.

How to cite: Böhnisch, A., Felsche, E., Mittermeier, M., Poschlod, B., and Ludwig, R.: Hotspots and impacts of present and future compound hot and dry summers in Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2079, https://doi.org/10.5194/egusphere-egu23-2079, 2023.

EGU23-3133 | Posters on site | ITS1.4/NH0.6

Changes in temperature-precipitation correlations over Europe: Are climate models reliable? 

Mathieu Vrac, Soulivanh Thao, and Pascal Yiou

Inter-variable correlations (e.g., between daily temperature and precipitation) are key statistical properties to characterize probabilities of simultaneous climate events and compound events. Their correct simulations from climate models, both in values and in changes over time, is then a prerequisite to investigate their future changes and associated impacts. Therefore, this study first evaluates the capabilities of one 11-single run multi-model ensemble (CMIP6) and one 40-member single model initial-condition large ensemble (CESM) over Europe to reproduce the characteristics of a reanalysis dataset (ERA5) in terms of temperature-precipitation correlations and their historical changes.

Next, the ensembles’ correlations for the end of the 21st century are compared. Over the historical period, both CMIP6 and CESM ensembles have season-dependent and spatially structured biases. Moreover, the inter-variable correlations from both ensembles mostly appear stationary. Thus, although reanalyses display significant correlation changes, none of the ensembles can reproduce them, with internal variability representing only 30% on the inter-model variability. However, future correlations show significant changes over large spatial patterns. Yet, those patterns are rather different for CMIP6 and CESM, reflecting a large uncertainty in changes. In addition, for historical and future projections, an analysis conditional on atmospheric circulation regimes is performed. The conditional correlations given the regimes are found to be the main contributor to the biases in correlation over the historical period, and to the past and future changes of correlation.

These results highlight the importance of the large-scale circulation regimes and the need to understand their physical relationships with local-scale phenomena associated to specific inter-variable correlations.

How to cite: Vrac, M., Thao, S., and Yiou, P.: Changes in temperature-precipitation correlations over Europe: Are climate models reliable?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3133, https://doi.org/10.5194/egusphere-egu23-3133, 2023.

EGU23-3172 | ECS | Posters on site | ITS1.4/NH0.6

The perfect storm? Concurrent climate extremes in East Africa 

Derrick Muheki, Axel Deijns, Emanuele Bevacqua, Gabriele Messori, Jakob Zscheischler, and Wim Thiery

Concurrent extreme events exacerbate adverse impacts on humans, economy, and environment relative to those from independent extreme events. However, while the effects of climate change on the frequency of individual extreme events have been highly researched, the impacts of climate change on the interaction, dependence and joint occurrence of these extremes have not been extensively investigated, particularly in the East African region. Here, we investigate the joint occurrence of six categories of extreme events in East Africa, namely: river floods, droughts, heatwaves, crop failures, wildfires and tropical cyclones using bias-adjusted impact simulations under past and future climate conditions. We show that the change in the probability of joint occurrence of these extreme events in the region can be explained by the effects of climate change on the frequency, spatial distribution, and dependence of these extreme events. The analysis demonstrates that there is a higher positive correlation between most co-occurring pairs of extremes in the region under end-of-century global warming conditions leading to more frequent concurrence in comparison to the early-industrial period. Our results further highlight the most affected locations in the region by these concurrent events and consequently the main driver(s) in the various co-occurring pairs of extremes. Our results overall highlight that concurrent extremes will become the norm rather than the exception in East Africa under low-end warming scenarios.

How to cite: Muheki, D., Deijns, A., Bevacqua, E., Messori, G., Zscheischler, J., and Thiery, W.: The perfect storm? Concurrent climate extremes in East Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3172, https://doi.org/10.5194/egusphere-egu23-3172, 2023.

EGU23-3273 | ECS | Orals | ITS1.4/NH0.6

Compound drought and heatwave events in the eastern part of the Baltic Sea region 

Laurynas Klimavičius and Egidijus Rimkus

Both droughts and heatwaves cause negative impact on human health, agriculture, economy and other areas while occurring separately. However, in recent years the impact of these phenomena acting together has been increasingly analysed as it was found that such events, called compound drought and heatwave events (CHDE), may induce even more damage. The aim of this research is to identify droughts, heatwaves and CDHE in the eastern part of the Baltic Sea region during the summer months (June-August) from 1950 to 2022 and to assess their frequency and intensity. For the purpose to identify droughts the 1-month Standard Precipitation Index (SPI) values calculated for each day were used. Droughts were distinguished if the SPI values were lower than -1 for at least five or more days in a row and this condition was met in at least one third of the study area.  Heatwaves were defined as a period of five or more consecutive days when daily maximum air temperature (Tmax) was higher than 90th percentile of Tmax of the study period (1951–2022) for each summer day (on a 5-day moving average) and for one or more days covered at least one third of the study area. Daily Tmax data as well as precipitation data that was needed to calculate SPI were obtained from European Centre of Medium-range Weather Forecast ERA-5 reanalysis dataset with a spatial resolution of 0.25° x 0.25°. CDHE events were defined as time periods when heatwave occurs during the drought period. Study showed that the number of heatwaves in the study area since 1950 increased significantly (by 1.25 per decade). The number of droughts during investigation period slightly decreased. The majority of droughts were identified in 1990’s when dry periods were recorded during six summers in a row (from 1992 to 1997). In total, 19 CDHE during the summer months were distinguished, while a lot of them occurred during 1990‘s (5 events). As a consequence, statistically significant increase of such events during the study period was not observed. CHDE of the highest intensity was found in 1994 while the longest CDHE occurred in 2022 and lasted for 19 days (from August 11th to August 29th).

How to cite: Klimavičius, L. and Rimkus, E.: Compound drought and heatwave events in the eastern part of the Baltic Sea region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3273, https://doi.org/10.5194/egusphere-egu23-3273, 2023.

EGU23-5187 | ECS | Orals | ITS1.4/NH0.6

The influence of modes of variability and their interplay on compound extreme wind and precipitation events in the northern hemisphere. 

Khalil Teber, Bastien Francois, Luis Gimeno-Sotelo, Katharina Küpfer, Lou Brett, Richard Leeding, Ahmet Yavuzdogan, Daniela Domeisen, Laura Suarez, and Emanuele Bevacqua

Countless climate-related impacts are caused by compound events, i.e. by the combination of multiple climate processes at different spatial and temporal scales. For example, when precipitation and wind extremes coincide, the resulting impacts on infrastructure and humans can be very destructive. It is established that climate modes of variability, which are known to oscillate from seasonal to decadal timescales, such as the El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Pacific-North American Pattern (PNA) and Atlantic Multidecadal Variability (AMV) favour the occurrence of extreme weather events such as heavy precipitation in several areas worldwide. However, little is known about the effect that these climate modes of variability have on compound events. In this context, understanding the physical modulators of compound events can contribute to an improved comprehension of their dynamics, and ultimately to a better prediction of their impacts. Here, focussing on compound wind and precipitation extremes, we contribute to closing this research gap by using large ensemble climate model simulations (CESM) and reanalysis data (ERA5). We identify hotspot regions in the northern hemisphere where winter (DJF) compound event occurrences are influenced by modes of variability. We also inspect whether particular combinations of modes of variability, e.g., superposition of extreme states of both ENSO and NAO indices, enhance compound event occurrences. Finally, the identified patterns in the observational data are compared to the model simulations. The findings allow us to understand whether climate modes of variability favour the simultaneous occurrence of compound events over different regions worldwide, and how well the current generation of climate model simulations represents these dynamics.  An improved understanding of these oscillating modes of variability could be used to enhance the development of sub-seasonal to seasonal forecasts of compound events, which therefore may reduce their impacts. 

How to cite: Teber, K., Francois, B., Gimeno-Sotelo, L., Küpfer, K., Brett, L., Leeding, R., Yavuzdogan, A., Domeisen, D., Suarez, L., and Bevacqua, E.: The influence of modes of variability and their interplay on compound extreme wind and precipitation events in the northern hemisphere., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5187, https://doi.org/10.5194/egusphere-egu23-5187, 2023.

EGU23-5942 | Posters on site | ITS1.4/NH0.6

Climate Change to Exacerbate the Compounding of Heat Stress and Flooding 

Leonardo Valerio Noto, Dario Treppiedi, and Gabriele Villarini

The role of climate change in exacerbating the impacts of natural hazards has been the focus of extensive interest. However, while the emphasis is generally on a single hazard (e.g., heat stress, extreme precipitation, floods, droughts), their compounding effects under climate change have been the subject of a growing number of studies. Among compound events, heat stress was recently found to be a precursor of summer flooding across the central United States. We show for the first time that heat stress can trigger floods across large areas of North and South America, southern Africa, Asia and eastern Australia. Moreover, using global climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6), we show that the compounding of heat stress and floods is projected to worsen under climate change with effects magnified as we move from the Shared Socioeconomic Pathways (SSPs) 1-2.6 to 5-8.5. Under future conditions, the compounding between heat stress and floods is projected to extend to Europe and Russia due to the increased warming. These results highlight the need towards improved preparation and mitigation measures that account for the compound nature of these two hazards, and how the compounding is expected to be exacerbated because of climate change.

How to cite: Noto, L. V., Treppiedi, D., and Villarini, G.: Climate Change to Exacerbate the Compounding of Heat Stress and Flooding, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5942, https://doi.org/10.5194/egusphere-egu23-5942, 2023.

EGU23-6226 | ECS | Orals | ITS1.4/NH0.6

Recurrence of drought events over Iberia under present and future climate conditions 

Julia Moemken, Benjamin Koerner, Florian Ehmele, Hendrik Feldmann, and Joaquim G. Pinto

Seasonal droughts are a common feature of the Iberian climate. They can have severe socioeconomic and ecological impacts – especially, when recurring in consecutive years. We investigate the recurrence of extreme drought events in the Iberian Peninsula (IP) for the past decades and in regional climate change projections. With this aim, we introduce and apply a new set of indices: the Recurrent Dry Year Index (RDYI) and the Consecutive Drought Year (CDY) Index. For the present climate, different gridded observational and reanalysis datasets at several spatial resolutions (10 to 25 km) are used. To analyse the potential impacts of climate change, we apply the indices to a large EURO-CORDEX multi-model ensemble with 12 km horizontal resolution consisting of 25 different global-to-regional model (GCM-RCM) chains for the historical period and future periods along the RCP8.5 scenario.

Results show that the IP is regularly affected by extreme droughts under present climate conditions, with roughly three individual events per decade. Especially the southern and central parts of IP are exposed to recurrent events, which last between two and six years. Under different global warming levels (GWLs), results reveal a general tendency towards more severe drought conditions. Moreover, recurrent drought events are projected to occur more frequent and last longer (up to 14 years). While the ensemble mean responses are only moderate for a GWL of +2°C (compared to the pre-industrial average), recurrent drought conditions are strongly enhanced for the +3°C GWL. The climate change signals are robust for most of IP and the considered recurrent drought indices, with a larger model agreement for the +3°C GWL. For both present and future climate conditions, results show some sensitivity on the choice of index and dataset.

We conclude that the new indices are suitable for the detection and evaluation of recurrent drought events under present and future climate conditions. With ongoing climate change, the Iberian Peninsula faces an increased risk of recurrent drought events. If global warming should exceed the +3°C threshold, the majority of models projects an almost permanent state of drought – with severe consequences for the Iberian population and ecosystems.

How to cite: Moemken, J., Koerner, B., Ehmele, F., Feldmann, H., and Pinto, J. G.: Recurrence of drought events over Iberia under present and future climate conditions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6226, https://doi.org/10.5194/egusphere-egu23-6226, 2023.

EGU23-6283 | Posters on site | ITS1.4/NH0.6

Compound precipitation and wind extremes under recent and future climate conditions 

Jens Grieger and Uwe Ulbrich

Severe winter wind storms are related with strong impacts. We could show in recent studies that the co-occurrence of extreme wind and precipitation is leading to higher damages of residential buildings in comparison to non-compound events. This was done using ERA5 reanalysis data for the European winter season and daily insurance records of damages for residential buildings over Germany provided by the German Insurance Association (GDV).

This study investigates the representation of co-occurrence of extreme wind and precipitation for climate simulations of the Coordinated Regional Climate Downscaling Experiment (CORDEX) for Europe (EURO-CORDEX). We use multi-model ensemble simulations with horizontal resolution of 0.44° and 0.11°. Individual simulations are analysed with respect to the characteristic of compound events for historical projections. Climate change signals for future scenarios are discussed.

How to cite: Grieger, J. and Ulbrich, U.: Compound precipitation and wind extremes under recent and future climate conditions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6283, https://doi.org/10.5194/egusphere-egu23-6283, 2023.

EGU23-6479 | ECS | Posters on site | ITS1.4/NH0.6

Characterization of compound occurrence of heat waves and drought in Europe and North America 

Natalia Castillo, Marco Gaetani, and Mario Martina

Extreme events such as heat waves and droughts can have major impacts on agriculture, human health, and the energy sector, especially during the co-occurrence of such events. Although there is evidence that heat waves and drought have increased in intensity and frequency in the last decades, the analysis, characterization, and impact assessment of the compound occurrence of drought and heat waves are not well documented yet in a common framework. There are still some open questions related to how changes in midlatitude circulation may transcend in the thermodynamical characteristics of these compound events in the future. Furthermore, the role of some local feedbacks and the relationship with other extremes are still a debating subject.

The purpose of this research is to shed some light and add evidence about the key drivers related to these extreme events. The main atmospheric characteristics of compound heat waves and drought events in Europe and North America are identified through the analysis of the ERA5 dataset during the historical period (1959-2022). Additionally, we evaluate the ability of CMIP6 models with respect ERA5 to reproduce the statistics of these compound events. Specifically, we aim at understanding what are the climatological characteristics of these events in the historical climate and what are the dynamical mechanisms leading to compound occurrence of heat waves and droughts.

How to cite: Castillo, N., Gaetani, M., and Martina, M.: Characterization of compound occurrence of heat waves and drought in Europe and North America, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6479, https://doi.org/10.5194/egusphere-egu23-6479, 2023.

EGU23-7203 | Posters on site | ITS1.4/NH0.6

Co-occurring British flood-wind events (1980-2080): Their anatomy & drivers 

John Hillier, Hannah Bloomfield, Freya Garry, Paul Bates, and Len Shaffrey

In wintertime, infrastructure and property in NW Europe are threatened by multiple meteorological hazards, and it is increasingly apparent that these exacerbate risk by tending to co-occurring in events that last days to weeks. Impacted by Atlantic storms, Great Britain (GB) is a sentinel location for weather that later tracks into NW Europe.   A recent, dramatic storm sequence (Dudley, Eunice, Franklin) demonstrated the need for a multi-hazard view by bringing a mixture of damaging and disruptive extremes including extreme winds and flooding over 7-10 days in Feb 2022.

This work uses a stakeholder inspired, event-based approach to jointly consider these two hazards.  A wind event set (n = 3,426) is created from the 12km regional UK Climate projections (1981-1999, 2061-2079) to match previously created high-flow events (Griffin et al, 2023). Then, the two hazards’ time-series are merged using windows up to a maximum size (Δt = 1-180 days) positioned to maximize the size of the largest events’ impact. The benefits and limitations of this methodology are discussed, anatomy of storm sequences (Δt = 21 days) discussed, and potential drivers of co-occurrence in the multi-hazard sequences (e.g. jet stream position/strength) examined.

How to cite: Hillier, J., Bloomfield, H., Garry, F., Bates, P., and Shaffrey, L.: Co-occurring British flood-wind events (1980-2080): Their anatomy & drivers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7203, https://doi.org/10.5194/egusphere-egu23-7203, 2023.

Heatwave events have been increasing in frequency, duration, and intensity along the past decades, leading to severe impacts on ecosystems, human health and basic resources. These events are projected to continue increasing associated to anthropogenic activity. Moreover, droughts have also been more recurrent and intense, which can significantly impact agriculture and reservoirs’ water level and quality.

Events of high temperature can occur both in the atmosphere and the seas. These warmer conditions, together with extremely dry episodes, have been affecting southern Europe and the Mediterranean region, which appear to be very sensitive to climate change. Additionally, the co-occurrence of droughts and heatwaves increases meteorological fire danger, rising the probability of wildfire occurrence and severity and resulting in economic, ecological, and even human losses.

In this sense, it becomes fundamental to pay special attention to the role of compound events and synergies in fueling extreme fire outbreaks. Therefore, we propose to address this problem by analyzing the occurrence of both marine and atmospheric heatwaves and drought conditions over Southern Europe, East Atlantic and Mediterranean Sea (relying on ERA5 reanalysis), as well as the recorded wildfires (through MODIS burned area product).

This work aims to address the occurrence of heatwaves (marine and atmospheric) and previous and contemporaneous drought episodes on a compound or cascading approach, estimating their contribution to the occurrence of extreme wildfires in the region in the last decades were analyzed on a seasonal scale.

 

Acknowledgments: This study is partially supported by the European Union’s Horizon 2020 research project FirEUrisk (Grant Agreement no. 101003890) and by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020- IDL,  DHEFEUS - 2022.09185.PTDC

How to cite: Santos, R., Russo, A., and Gouveia, C. M.: Assessing the impact of marine and atmospheric heatwaves on droughts and fire activity in the Mediterranean region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7779, https://doi.org/10.5194/egusphere-egu23-7779, 2023.

EGU23-8588 | ECS | Orals | ITS1.4/NH0.6

Detecting dependencies of large-scale heatwaves and droughts with AI-enhanced point process approaches 

Niklas Luther, Andrea Toreti, Jorge Pérez-Aracil, Sancho Salcedo-Sanz, Odysseas Vlachopoulos, Andrej Ceglar, Arthur Hrast Essenfelder, and Elena Xoplaki

Investigating the global connectivities of extreme events is vital for accurate risk reduction and adaptation planning. While human and natural systems have a certain resilience level against single extremes, they may be unable to cope with multiple extreme events whose impacts tend to be amplified in a non-linear relationship. Concurrent droughts and heatwaves are frequently linked to severe damage in socioeconomic sectors such as agriculture, energy, health, and water resources. They can also have detrimental effects on natural ecosystems. Here, we detect global scale dependencies of large-scale droughts and heatwaves using an AI-enhanced point process-based approach, where large-scale events are defined to occur when a certain amount of grid points (e.g., 20%) of a given region of interest experiences heatwave or drought conditions. The classic inhomogeneous and non-stationary J-function can determine whether the occurrence of the events shows clustering, inhibition or independence. However, the analysis and interpretation of this function are usually affected by a high degree of subjectiveness, and its application for large datasets and/or ensembles is challenging. The proposed AI-based automated interpretation tool replaces a subjective and user-dependent approach. Monte Carlo simulations based on standard point process models, reflecting the aforementioned dependence structures, are utilized, allowing the dependence structure to be labeled and the classification problem to be trained using Deep Learning algorithms. To identify the global connectivities of large-scale droughts and heatwaves, we first detect extreme events at the grid scale based on appropriately selected indices. A cluster analysis pinpoints areas with similar drought and heatwave patterns, thus identifying the regions of interest for the large-scale events. For these events we compute the J-functions, and the dependence structure of the large-scale events is then classified by the AI-tool. Links to teleconnections (such as the El Niño-Southern Oscillation and the North Atlantic Oscillation) can be further identified by analyzing the dependencies conditioning on the teleconnection phase under consideration. The proposed tool can be used in diverse research questions where a point process approach is appropriate, and thus has applications beyond climate science.

How to cite: Luther, N., Toreti, A., Pérez-Aracil, J., Salcedo-Sanz, S., Vlachopoulos, O., Ceglar, A., Hrast Essenfelder, A., and Xoplaki, E.: Detecting dependencies of large-scale heatwaves and droughts with AI-enhanced point process approaches, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8588, https://doi.org/10.5194/egusphere-egu23-8588, 2023.

EGU23-8705 | ECS | Orals | ITS1.4/NH0.6

Interdependence among subregional crop production affects global crop failure risk 

Sifang Feng, Jakob Zscheischler, Zengchao Hao, and Emanuele Bevacqua

Synchronous crop failure among multiple breadbaskets worldwide, a typical spatially compound event, may amplify threats to the global food system and food security and has been a growing concern among the scientific community in recent years. While the risk of simultaneous crop loss across multiple breadbasket regions has been analyzed, to date, little is known about how interdependence among regional crop production affects aggregated crop failure at the global scale. Quantifying the impact of dependencies among breadbasket regions on global food production and assessing how the dynamic of spatially compounding crop failures is simulated by climate and crop models is essential for informing the modeling of global food security risk. In this study, focusing on different crop types, we quantify the influence of dependence between crop production of individual regions on global aggregated crop yield based on the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) dataset. We find that spatial dependence between regional crop yields may aggravate global crop deficits and identify a characteristic spatial scale beyond which the dependence between crop production in different regions vanishes. Our results provide valuable information for designing risk strategies for food security at the suited scale.

How to cite: Feng, S., Zscheischler, J., Hao, Z., and Bevacqua, E.: Interdependence among subregional crop production affects global crop failure risk, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8705, https://doi.org/10.5194/egusphere-egu23-8705, 2023.

EGU23-9048 | ECS | Orals | ITS1.4/NH0.6

The joint impact of rainfall events on water- and dike systems in Dutch polders 

Bart Strijker and Matthijs Kok

Polders can be found in coastal and alluvial lowlands all over the world. These polders need an internal drainage system consisting of drainage canals, weirs and/or pumps to discharge the water out of the polder. Next to these drainage canals, dikes can protect the low-lying polder areas that are situated several meters lower than the controlled water levels in these canals. This study investigates the joint impact of extreme rainfall events on water and dike systems within Dutch polders. Previous research has shown that the combined effect of heavy rainfall and storm surge can increase flood risk in coastal polders in the Netherlands. However, the impact of extreme rainfall on multiple water-and-dike systems within a single polder, resulting in multiple hazards, has received little attention. Our analysis uses physical models that are calibrated on measurements and forced by synthetic rainfall and evaporation time series to examine the response time and interdependencies between regional drainage systems and pore-water pressures in canal dikes. Water levels and pore-water pressures and their interrelationships were analyzed as indicators of flood hazards. Our findings demonstrate the importance of considering the joint impact of multiple hazards on flood risk in polders, as the functioning of regional drainage systems and canal dikes can be affected by similar weather events.

How to cite: Strijker, B. and Kok, M.: The joint impact of rainfall events on water- and dike systems in Dutch polders, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9048, https://doi.org/10.5194/egusphere-egu23-9048, 2023.

EGU23-10996 | ECS | Orals | ITS1.4/NH0.6

A bottom-up approach for exploring the role of humidity in high heat-related mortality events: A Multi-City, Multi-Country study 

Sidharth Sivaraj, Samuel Lüthi, Eunice Lo, and Ana Maria Vicedo-Cabrera

Although studies based on physiological models have repeatedly shown that high humidity levels lead to stronger heat stress in humans, findings from epidemiological studies have remained inconclusive on the matter till date. We aim to employ a ‘bottom-up’ strategy of identifying key drivers of compound events to explore the role played by humidity in high heat-related mortality events, spanning across multiple cities in multiple countries. We used daily data on all-cause mortality, mean temperature and mean relative humidity from 11 cities across the world and applied state-of-the-art epidemiological models to compute the daily observed total mortality counts attributable to heat (i.e., limited to days with average temperature exceeding the ‘temperature of minimum mortality’ (MMT) in each city). Each of these days with mean temperature exceeding MMT is considered as an ’event’ and events with highest mortality counts attributable to heat from multiple cities are analysed in a 2D scatter plot of the corresponding percentile rank of temperature and humidity observed during those events. The frequency of such high impact events in the temperature-humidity percentile space across multiple cities, categorised into sub-groups based on the temperature and humidity climatology of the cities, was then studied. It was observed that close to 90% of the high impact events occurred during high temperature (> 90th percentile) and non-high humid (<50th percentile) conditions. The events of high severity, where humidity conditions were comparatively high (> 50th percentile), were mostly representative of cities with prevailing high humidity conditions on average during the warmest months, when compared across all the cities. Based on our preliminary findings, there is no conclusive evidence that high humidity conditions were prevalent during high heat-mortality impact events, but further analysis incorporating more cities and other climatological variables of interests such as absolute humidity, wet-bulb globe temperature etc. are planned. This novel framework provides valuable insights into the role of humidity in heat stress mortality and can be generalised to address other similar complex research questions in environmental epidemiology.

How to cite: Sivaraj, S., Lüthi, S., Lo, E., and Vicedo-Cabrera, A. M.: A bottom-up approach for exploring the role of humidity in high heat-related mortality events: A Multi-City, Multi-Country study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10996, https://doi.org/10.5194/egusphere-egu23-10996, 2023.

Heavy rainfall events and urban flash floods pose a high risk potential for humans and the environment, as a concrete prediction of the regional impacts is difficult. The effects of heavy rainfall events and urban flash floods depend, among other things, on the characteristics of the respective affected area - such as land use, soil type or topographical factors - but also on prior conditions, especially the pre-rainfall index. River floods also pose a similarly high risk, even if they can be predicted more precisely than heavy rainfall events - especially in larger river systems - and thus a more focused flood risk management can be carried out. If these events overlap in the form of compound flooding from river floods and heavy rainfall, the hazards and the risk to people and the environment increase significantly. This was shown in particular by the flood disaster in July 2021 in Rhineland-Palatinate and North Rhine-Westphalia in Germany.

Investigations are carried out into the joint occurrence of river floods and heavy rainfall. Discharge data from various stream gauges in North Rhine-Westphalia (Germany) and precipitation data from radar data of the German Weather Service as well as ERA5-Land reanalysis data of the ECMWF are used for this purpose. First, the respective single events are identified and analysed with regard to various statistical parameters. Then the analysis of the compound events is carried out, considering only events that are identical in time and space. To take this into account, simultaneous series are formed from the time series available. Since not all catchments are equally at risk from compound river flood and heavy rainfall events, one focus is on determining vulnerable areas. Here, various characteristic attributes of the catchments but also weather conditions, such as the pre-rainfall index, are considered. It turns out that special attention must be paid to small to medium-sized catchments and to areas with steep and narrow valleys.

Furthermore, the joint occurrence probability of river floods and heavy rainfall is determined. This is done with archimedean copula functions. A statement on the joint probability of occurrence of river floods and heavy rainfall has not yet been included in practice or in standards but should be adopted for the correct determination of hazards and risks. Furthermore, based on the analyses carried out, a proposal for the preparation of flood hazard maps by compound river floods and heavy rainfall is presented.

How to cite: Simon, F. and Mudersbach, C.: Analysis of compound river flood and heavy rainfall events for a development of combined flood maps, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11000, https://doi.org/10.5194/egusphere-egu23-11000, 2023.

EGU23-11029 | ECS | Posters on site | ITS1.4/NH0.6

Unfolding the role of active/break spells in compound hot and dry extremes (CHDE) in India 

Iqura Malik and Vimal Mishra

Abstract

The co-occurrence of temperature and precipitation extremes can have profound consequences than either individual extremes. The role of increasing warm spells in increasing CHDEs has been studied in various studies, but the role of active and break spells on CHDEs during monsoon has not been studied. As a result, in this study, we investigated the fraction of CHDEs in both active and break spells in India. We used copula and threshold-based methodology to characterize CHDE to investigate the uncertainty in the frequency of CHDEs during active and break spells. We also looked at how CHDEs in two different spells will impact society differently. We further investigated the changes in CHDEs to future projections of active-break spells of the Indian Summer Monsoon. The findings of the study may help to mitigate the severe impacts of compound hot and dry extremes in the future.

Keywords: Climate change, Compound extremes, active spells, dry spells

How to cite: Malik, I. and Mishra, V.: Unfolding the role of active/break spells in compound hot and dry extremes (CHDE) in India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11029, https://doi.org/10.5194/egusphere-egu23-11029, 2023.

EGU23-11220 | ECS | Orals | ITS1.4/NH0.6

Compound storm surge and river flood events in the coastal zone: Exploring the influence of data sources and compound approach on extreme recurrence levels 

Kevin Dubois, Morten Andreas Dahl Larsen, Martin Drews, Erik Nilsson, and Anna Rutgersson

Floods are among the most impactful disasters especially in terms of economy in affecting humans’ activities and damaging infrastructures. This is particularly the case along the coast where coastal floods happen. Such floods can be due to three different factors: meteorological (precipitation), hydrological (river runoff) and oceanographic (storm surge). A single factor but also a combination of two or more of such factors happening at the same time can lead to coastal floods also called compound floods. Flood hazards can then be underestimating when compound effects are not considered. 

This study focuses on coastal compound floods from oceanographic and hydrological phenomena at the coastal city of Halmstad (Sweden). It aims to quantify the risk of such flood events at Halmstad and to analyse the sensitivity of data sources and copula’ approaches.

Here, the copula method is used to analyse compound floods based on annual maxima of river discharge and corresponding sea level and vice-versa. A comparison is carried out with the commonly used Extreme Value theory on a single factor and the compound approach. Effects from different data time-series available from observations and models for both river discharge and sea level are studied.

This paper concludes the presence of a higher risk of flooding when compound effects are not considered and that the choices made on input datasets and copulas can have a significant impact.

How to cite: Dubois, K., Andreas Dahl Larsen, M., Drews, M., Nilsson, E., and Rutgersson, A.: Compound storm surge and river flood events in the coastal zone: Exploring the influence of data sources and compound approach on extreme recurrence levels, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11220, https://doi.org/10.5194/egusphere-egu23-11220, 2023.

EGU23-12245 | Orals | ITS1.4/NH0.6

Projecting the occurrence of extreme heat-related mortality using long short-term memory networks in cities of Switzerland 

Saeid Ashraf Vaghefi, Veruska Muccione, Ana Vicedo-Cabrera, Raphael Neukom, Christian Huggel, and Nadine Salzmann

Climate change increases the frequency and severity of heat waves, which can negatively impact human health. Extreme heat can lead to heat stroke, dehydration, and other heat-related illnesses. Heatwaves are more severe for vulnerable populations such as older adults, young children, and people with pre-existing medical conditions. In this study, we analyze the occurrence of compound extreme heat-related mortality in five Swiss cities using neural networks.

To define the excess mortality due to compound heat extremes (Hot day, Tmax>30oC, followed by a tropical night, Tmin>20oC) we compared mortality during the four hot summers of 2003, 2015, 2018, and 2019 with long-term average mortality rates (1981-2020). We trained long short-term memory (LSTM) neural networks on 40-year time series of maximum and minimum temperatures, hot day / tropical night compound events, and mortality in Basel, Bern, Geneva, Lugano, and Zürich.  LSTM neural networks learn the important parts of the sequence seen so far and forget the less important ones. This makes these models predict with greater accuracy than traditional time series analysis methods.

In general, we found that over the past 40 years, more than six percent of deaths were caused by compound extreme heat waves in the five Swiss cities. Geneva and Lugano are the most affected cities by compound heat, but the risk of heat-related mortality has decreased in these two regions over time, which could be a result of the action plans that exist in the Latin regions of Switzerland.

We further used Switzerland's future climate model scenarios (CH2018), to predict mortality rates in Swiss cities in the near-future (2020–2050) and far-future (2070–2100). We projected that the number of people affected by mortality risks associated with heat could increase by three folds by the end of the century in most cities if no further adaptation is taken place.

Our results show how important it is for governments, public health agencies, and individuals to be aware of the potential impacts of climate change on heat-related mortality and to take steps to mitigate and adapt to these impacts.

How to cite: Ashraf Vaghefi, S., Muccione, V., Vicedo-Cabrera, A., Neukom, R., Huggel, C., and Salzmann, N.: Projecting the occurrence of extreme heat-related mortality using long short-term memory networks in cities of Switzerland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12245, https://doi.org/10.5194/egusphere-egu23-12245, 2023.

EGU23-12427 | ECS | Orals | ITS1.4/NH0.6

Compounding hydro-meteorological drivers of forest damage over Europe 

Pauline Rivoire, Daniela Domeisen, Antoine Guisan, and Pascal Vittoz

Extreme meteorological events such as frost, heat, and drought can induce significant damage to vegetation and ecosystems. In particular, heat and drought events are projected to become more frequent under a changing climate. It is therefore crucial to predict the frequency (on climate timescales) and the occurrence (on timescales of weeks to months) of such extremes.

The subseasonal-to-seasonal (S2S) forecasting timescale refers to forecasting timescales from two weeks to a season. Skillful S2S forecasts of hydro-meteorological hazards can be of crucial importance to prevent large-scale vegetation damage. The utility of S2S forecasts for vegetation is very broad (agriculture, biodiversity and flora protection, wildfire risk management, forest management, etc.).

We focus here on forest damage, defined as negative anomalies of the normalized difference vegetation index (NDVI). We use the AVHRR dataset, providing NDVI data over Europe. Compound droughts and heat waves are known to trigger low NDVI events in summer. A dry summer combined with moist conditions during the previous autumn can also have a negative impact. The idea is to find, among all the hydrometeorological variables available as S2S forecast in the ECMWF model, the most relevant ones to predict forest damage. For that, we establish an automated procedure to identify the compound hydro-meteorological conditions leading to low NDVI events, up to several seasons before the impact. We train a model using ERA5 and ERA5-Land reanalysis datasets for the explicative variables. These variables include temperature, precipitation, dew point temperature, surface latent heat flux, soil moisture, snow water equivalent, soil temperature, etc. Several space and time aggregations are considered in order to find the optimal scales and most relevant combinations of variables to predict low NDVI events. The overall goal of this research project is to bridge the research gap between the S2S forecast of hydrometeorological variables and vegetation damage in general. For that, we assess the forecast skill of variables identified as responsible for compound low NDVI events and vegetation biodiversity loss.

How to cite: Rivoire, P., Domeisen, D., Guisan, A., and Vittoz, P.: Compounding hydro-meteorological drivers of forest damage over Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12427, https://doi.org/10.5194/egusphere-egu23-12427, 2023.

EGU23-13562 | ECS | Orals | ITS1.4/NH0.6

Defining compound extreme events on objective spatiotemporal scales 

Nina Schuhen, Jana Sillmann, Julien Cattiaux, and Carley Iles

Compound extreme events describe the simultaneous occurrence of two or more individual extreme weather or climate events that often have a significant impact on environment, society or economy. Many studies have investigated such events, often using different spatiotemporal scales for the same event, depending on e.g., the country or region of interest. Although appropriate from an impact point of view, this practice might lead to conflicting or inconsistent results. It is therefore necessary to find objective definitions of extreme events for attribution studies or to investigate how likelihoods of certain extreme events change over time.

Building on previous work for single extreme events, we propose a roadmap for obtaining objective compound event definitions, especially with regards to their spatiotemporal characteristics, by estimating multivariate probability distributions via copulas and then maximizing the rarity of the event across several scales. We present applications to past compound extreme events with considerable impact on e.g., human health and agriculture, such as the European heat wave/high ozone event in summer 2003, and also investigate how probabilities of these events change under different emission scenarios.

How to cite: Schuhen, N., Sillmann, J., Cattiaux, J., and Iles, C.: Defining compound extreme events on objective spatiotemporal scales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13562, https://doi.org/10.5194/egusphere-egu23-13562, 2023.

EGU23-14152 | ECS | Posters on site | ITS1.4/NH0.6

How compound can a compound event be? Mapping the compoundness of the Gloria storm 

Maria Aguilera Vidal, Jose A Jimenez, Montserrat Llasat, Salvador Castan, and Carmen Llasat

From a risk management perspective, compound events are very relevant because they can significantly increase the intensity and/or the spatial and temporal extension of the impact. Thus, depending on their magnitude, they may overwhelm the capability of emergency-response services to cope with “unusual” situations of major damage and respond to a large number of emergency situations throughout the region at the same time, and/or have to maintain the level of response during a relatively long period of time. When an extreme compound event occurs, its characteristics depart from the idealized conditions that are usually analyzed and, from the risk management perspective, the problem becomes highly multidimensional. This will be illustrated with the impact of the Gloria storm on the Spanish Mediterranean coast in January 2020. During five days extreme conditions (with some record breakings) of multiple hazards (wind, waves, rainfall, river discharge and surge) were recorded. In places such as the mouth of the Tordera River, they occurred simultaneously, but the most common situation was that different extreme conditions of univariate hazard occurred in remote areas of the territory, although they had to be managed simultaneously. In addition, the storm caused massive damage of various kinds, affecting transportation infrastructure, railway services, breakwaters, docks, urban services, housing, agricultural land and four fatalities in Catalonia. As a result of this, although the storm lasted about five days, the management of its impacts was much more extended, so that several months later some repairs were still being carried out. Looking to the event, the analysis of its probability of occurrence will be significantly affected by the adopted perspective. Thus, from the “physical” point of view, the analysis would range from the simplest joint probability of some hazards occurring in a given location (classical 2-drivers multivariate events) to multiple hazards over the whole territory (spatially compound with up to four concurrent hazards). From a "management" point of view, the analysis would focus on the probability of different types of damage (and their corresponding services) occurring at the same time, and on the probability of providing services in remote parts of the territory (and, consequently, dividing the available services) within a short period of time. To illustrate this possible multidimensional study plane, we will map the compoundness of the Gloria storm encompassing its induced hazards, impacts, damage and response. 

This work was supported by the Spanish Agency of Research in the framework of the C3RiskMed project (PID2020-113638RB-C21/ AEI / 10.13039/501100011033).

How to cite: Aguilera Vidal, M., Jimenez, J. A., Llasat, M., Castan, S., and Llasat, C.: How compound can a compound event be? Mapping the compoundness of the Gloria storm, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14152, https://doi.org/10.5194/egusphere-egu23-14152, 2023.

EGU23-15290 | ECS | Posters on site | ITS1.4/NH0.6

Quantifying climate change induced shifts in the risk of jointly and individually occurring drought and late-spring frost 

Benjamin F. Meyer, Marija Tepegjozova, Anja Rammig, Claudia Czado, and Christian S. Zang

Global climate change is altering the frequency, intensity, and timing of drought and late-spring frost (LSF). European beech, an ecological and economical cornerstone of European forestry, has been shown to be susceptible to both extremes. Since recovery from both drought and frost damage requires access to stored carbohydrate reserves, the joint occurrence of drought and late-frost exacerbates the deleterious effects on forest health. Both extremes are projected to increase in frequency with increasing temperatures, yet, a statistical model for compound drought and late-spring frost events over time is still lacking. Thus, in order to facilitate forest risk assessment, we quantify the joint probability of drought and spring late-frost risk in the historic domain and identify shifts in this dependency across multiple, future climate change scenarios. Analogously, we determine the individual probability of both drought and LSF to determine the contribution of each extreme to the joint probability. 

We determine frost risk based on the minimum temperature during the period of leaf flushing as predicted by a phenological model. Drought risk is quantified using the Standardized Precipitation Evapotranspiration Index (SPEI). To quantify the joint risk of these two extremes while accounting for climatic and topographical covariates, we use vine copula based models. Specifically,  we apply a novel, regular vine copula based regression model, Y-vine copula regression, designed for a two-response regression setting.

We establish a historical baseline for the joint probability of drought and LSF and identify critical climatic and topographic covariates. Subsequently, we repeat the analysis with climate projections for three different scenarios (RCP 2.6, RCP 4.5, RCP 8.5). We identify differences in the joint probability of drought and LSF across the three climate change trajectories, yet note, that the critical covariates remain constant across scenarios. To further disentangle the coupling between drought and LSF, we use a single response, D-vine copula to determine probability and critical covariates for each extreme separately. Consequently, we are able to determine whether the risk of frost and drought change in concert, how this differs between climate change scenarios, and which covariates drive each extreme. 

How to cite: Meyer, B. F., Tepegjozova, M., Rammig, A., Czado, C., and Zang, C. S.: Quantifying climate change induced shifts in the risk of jointly and individually occurring drought and late-spring frost, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15290, https://doi.org/10.5194/egusphere-egu23-15290, 2023.

Compound climate-related events are impactful extreme events in which the interactions between multiple variables amplify the final impact. They may be classified depending on the types of interaction and the scales involved. For example, temporal compounding events are characterized by the occurrence of subsequent events in time, as in case of a temporal clustering of precipitation. This last trigger is of great importance when the antecedent soil saturation shapes the intensity or occurrence of a given natural hazard, like for floods or deep landslides. Here, we focus on the characteristics of temporal clustering of precipitation over the Italian territory and its link with landslides occurrence. First, we investigate the spatial and temporal distribution of temporal clustering and the synoptic conditions more prone to it, using Era5-Land dataset. Second, we link the identified clusters with the occurrence of different movements’ types (complex, debris flow, fall, flow, and sliding), using a shuffling procedure to assess the significance. Regarding the first point, clear differences emerged between the Italian regions and the four seasons. Clusters were more widespread in autumn and spring and more localized in winter and summer. During winter, we observed a negative link between the number of clusters and the Mediterranean oscillation index in south-central Italy. Regarding the second point, differences were found between the five landslide typologies: fall events were mostly preceded by an intense precipitation event, debris flow by a temporal clustering over small windows and complex, flow, and sliding with a temporal clustering over long windows.

How to cite: Banfi, F. and De Michele, C.: Temporal compounding of precipitation and its occurrence before landslide events over the Italian territory, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15427, https://doi.org/10.5194/egusphere-egu23-15427, 2023.

EGU23-16867 | Orals | ITS1.4/NH0.6

Characterization of Lagged Compound Floods and Droughts Under Climate Change 

Mohammad Reza Najafi, Wooyoung Na, Reza Rezvani, and Melika Rahimi Movaghar

Increases in the frequency and intensity of hydroclimatic extremes (floods and droughts) and their temporal swings have led to severe consequences in many regions around the world. Traditionally, these contrasting extremes have been assessed in isolation without considering their spatial and temporal interactions, implications for infrastructure design and management and the overall compounding risks. Nonetheless, understanding the changing characteristics of such lagged compound events is critical to developing effective mitigation and adaptation strategies. In this study, we propose a novel framework to identify and characterize the hydroclimatic whiplash events and investigate their spatiotemporal projections under climate change. Multiple hydroclimate variables such as precipitation, evapotranspiration, soil moisture, runoff, and streamflow are used to identify dry and wet extremes and their transitions. Different scenarios for nonstationary hydrological swings between flood and drought are investigated based on streamflow data. Meteorological wet and dry conditions are investigated using standardized drought indices calculated based on the downscaled and statistically bias-adjusted simulations of CMIP5 for 1.5°C-4 °C global warming levels over three major river basins in northwest North America. Further, three dry-wet spell indices estimated by precipitation, soil moisture, and runoff simulations are merged into an integrated indicator to provide a thorough perspective on the changing risks of such transitions across North America using the Canadian Regional Climate Model version 4 Large Ensemble. We apply an ensemble pooling approach to enhance the sample size for index estimation, which enables projecting the characteristics more robustly. Frequency, intensity, transition time, spatial fraction, aggregation index, and seasonality are quantified for each warming period and compared with those of the baseline period to investigate their projected changes. In addition, we assess the contribution of external forcing and internal variability to the historical and projected changes of the lagged compound events. The results of this study suggest that hydroclimatic whiplash across North America is expected to become more frequent and intensified in a warmer climate. Projections show overall increases in the frequency of hydroclimatic whiplash and a decrease in the corresponding transition times as the climate gets warmer. In addition, the magnitude, intensity, and duration of wet and dry components of such lagged compound events are projected to increase based on the analyses with streamflow. Increasing trends of spatial fraction and spatial aggregation during both transitions between dry and wet spells also imply higher risks and future challenges for water resources management. The findings of this study support the necessity of developing appropriate mitigation measures targeting lagged compound floods and droughts that can lead to severe environmental and socio-economic disasters in North America.

How to cite: Najafi, M. R., Na, W., Rezvani, R., and Rahimi Movaghar, M.: Characterization of Lagged Compound Floods and Droughts Under Climate Change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16867, https://doi.org/10.5194/egusphere-egu23-16867, 2023.

EGU23-17187 | Posters virtual | ITS1.4/NH0.6

Trends in the Frequency and Intensity of Compound Coastal Flooding Events along the Indian coastline during 1980-2020 

Diljit Dutta, V Vemavarapu Srinivas, and Govindasamy Bala

The Bay of Bengal and the Arabian Sea adjoining the coastline of India are breeding grounds for depressions and tropical cyclones, with 2 to 3 cyclones making landfall every year on average. The frequency and intensity of compound coastal flooding events are expected to increase as the world continues to warm. The impact of these events will also be more due to the growing exposure and vulnerability of human settlements in the coastal areas of India. The compound coastal flooding events are primarily driven by extreme sea levels and heavy rainfall during tropical storms and depressions making landfall near the coast. However, there is no comprehensive study on the trends in compound flooding scenarios with reference to Indian coastline. This study presents results from an analysis of compound extreme flood events in the Indian coastal region and assesses the change in frequency and intensity of these events based on in-situ data for the period 1980-2020. The hourly sea-level data was obtained from 9 Tide Gauge stations (TGs) operated by the Survey of India. The daily rainfall data at these stations are extracted from 0.25° resolution gridded rainfall product of the India Meteorological Department (IMD). Harmonic analysis is carried out on the detrended sea-level data to separate the astronomical tide component and obtain skew surge time series at predicted high tide timesteps. The extremes corresponding to 90th, 95th and 98th percentile thresholds are identified for both skew surge and rainfall time series, and the co-occurrence probability of the two extreme events is analysed for the historical data. The evolution of frequency and intensity of the potential compound flood days over the historical period is also investigated.

How to cite: Dutta, D., Srinivas, V. V., and Bala, G.: Trends in the Frequency and Intensity of Compound Coastal Flooding Events along the Indian coastline during 1980-2020, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17187, https://doi.org/10.5194/egusphere-egu23-17187, 2023.

EGU23-17600 | ECS | Orals | SM1.6

Permanent Displacement Distribution From Strong Ground Motion Records of the 2023 Mw7.7 Earthquake. 

Emrecan Adanır and Gülüm Tanırcan

One of the most damaging earthquake effects occurring in the vicinity of the fault trace is fling step, also known as permanent displacement. However, due to the fact that the standard filtering techniques eliminate the low frequency portions of the motion, the permanent displacements are not seen on the displacement time histories derived from the accelerogram records. Thus, fling step is neglected in many engineering practices. To reveal the permanent displacements, special data processing schemes based on removal of the baseline shifts in separated time windows were proposed. In this study, the most recently proposed data processing scheme eBASCO (Schiappapietra et al., 2021) is improved and the effectiveness of the new scheme is tested by comparing the obtained displacements on the processed records with those derived from nearby GPS data for 25 records from worldwide earthquakes.

 Preliminary site screening efforts and geodetic observations demonstrated that the earthquake sequence of February 6, 2023 in Turkey caused remarkable permanent displacements, which might be one of the reasons for severe damage and collapse of the structures, especially those which have long fundamental periods such as pipelines, roadways and high-rise buildings. In this study, near fault records of the earthquake sequence are processed with the proposed scheme and the obtained permanent displacements are evaluated with those predicted by existing models.

How to cite: Adanır, E. and Tanırcan, G.: Permanent Displacement Distribution From Strong Ground Motion Records of the 2023 Mw7.7 Earthquake., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17600, https://doi.org/10.5194/egusphere-egu23-17600, 2023.

EGU23-17601 | Orals | SM1.6

Evidence of Early Supershear Transition in the Mw 7.8 Kahramanmaraş Earthquake From Near-Field Records 

Ahmed Elbanna, Mohamed Abdelmeguid, and Ares Rosakis

The Mw7.8 Kahramanmaraş Earthquake was larger and more destructive than what had been expected for the tectonic setting in Southeastern Turkey. By using near-field records we provide evidence for early supershear transition on the splay fault that hosted the nucleation and early propagation of the first rupture that eventually transitioned into the East Anatolian fault. The two stations located furthest from the epicenter show a larger fault parallel particle velocity component relative to the fault normal particle velocity component; a unique signature of supershear ruptures that has been identified in theoretical and experimental models of intersonic rupture growth. The third station located closest to the epicenter, while mostly preserving the classical sub-Rayleigh characteristics, it also features a small supershear pulse clearly propagating ahead of the original sub-Rayleigh rupture. This record provides, for the first time ever, field observational evidence for the mechanism of intersonic transition. By using the two furthest stations we estimate the instantaneous supershear rupture propagation speed to be ~1.55 Cs and the sub-Rayleigh to supershear transition length to be around 19.45 km, very close to the location of the station nearest to the epicenter. This early supershear transition might have facilitated the continued propagation and triggering of slip on the nearby East Anatolian Fault leading to amplification of the hazard. The complex dynamics of the Kahramanmaraş earthquake warrants further studies.

How to cite: Elbanna, A., Abdelmeguid, M., and Rosakis, A.: Evidence of Early Supershear Transition in the Mw 7.8 Kahramanmaraş Earthquake From Near-Field Records, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17601, https://doi.org/10.5194/egusphere-egu23-17601, 2023.

EGU23-17602 | ECS | Orals | SM1.6

Preliminary Results of Dynamic Rupture Simulations of the Mw7.8 Kahramanmaras Earthquake 

Yasemin Korkusuz Öztürk, Nurcan Meral Özel, Jean-Paul Ampuero, and Elif Oral

It is essential to investigate how ruptures develop and propagate dynamically along the East Anatolian Fault (EAF) and what conditions explain the rupture propagation patterns observed for recent earthquakes. The northeast motion of the Arabian plate with respect to the Anatolian microplate and the African plate is accommodated along the left-lateral East Anatolian and Dead Sea faults.  The slip-rate along the northern Dead Sea Fault is about 4 mm/yr while the slip rate along the EAF increases from 5 mm/yr to ~12 mmm/yr towards the northeast where it connects to the North Anatolian Fault. The Mw7.8 Kahramanmaras earthquake on 6th of February 2023 initiated along a splay called the Narli fault and proceeded along the EAF bilaterally, rupturing a total of more than 300 km. The earthquake ruptured a significant portion of the EAF and a section of the Amanos Fault which connects to the Cyprus Arc offshore. One interesting point is that the rupture along the EAF was dynamically triggered by a splay which is at an acute angle of ~30°. This raises the question of how the slip distribution and rupture parameters were affected by the rupture initiation at a splay fault. Initial models indicate that the rupture propagated faster toward northeast and slower toward southwest, which might indicate that the directivity of the splay fault played an important role in the rupture dynamics of this earthquake. Remarkably, this complex event triggered another destructive earthquake with magnitude Mw7.6, west of the epicenter of the first mainshock, nine hours later. The second event caused a relatively short surface rupture (~80 km) with high stress drop. The analysis of 3D dynamic earthquake rupture simulations contributes to a comprehensive understanding of the effects of material properties and initial stresses on dynamic triggering and ground motion intensity. In this study we will show our preliminary results of the dynamic modeling of the Mw7.8 earthquake using the Finite Element community code Pylith. East and south Anatolia contain many faults which are capable of generating M>7.0 earthquakes in the near future. Therefore, understanding the dynamics of the Kahramanmaras earthquakes and stress transfer to neighboring faults is important in order to understand the potential for new destructive earthquakes in the surrounding area, and to generate scenarios of damage, shaking and PGA distributions.

How to cite: Korkusuz Öztürk, Y., Meral Özel, N., Ampuero, J.-P., and Oral, E.: Preliminary Results of Dynamic Rupture Simulations of the Mw7.8 Kahramanmaras Earthquake, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17602, https://doi.org/10.5194/egusphere-egu23-17602, 2023.

EGU23-17603 | Posters on site | SM1.6

Geodetically and seismically informed rapid 3D dynamic rupture modeling of the Mw7.8 Kahramanmaraş earthquake 

Alice-Agnes Gabriel, Thomas Ulrich, Mathilde Marchandon, and James Biemiller

The destruction unfolding after the February 6, 2023 Turkey-Syria Earthquake sequence is devastating. First observations reveal complex earthquake dynamics challenging data-driven efforts. We present rapid, data-informed and physics-based 3D dynamic rupture simulations of the puzzling Mw7.8 Kahramanmaras earthquake providing a first-order mechanical explanation of this earthquake’s complexity and its implications for the Mw7.5 doublet event.

By incorporating detailed fault geometries constrained by satellite geodetic observations into 3D dynamic rupture simulations, we show how dynamic interactions between fault geometric complexity and the heterogeneous regional stress field generated the unique and unexpected rupture behaviors observed, including localized supershear, backwards rupture branching, and locally strong shaking.

Our supercomputing empowered simulations that tightly link earthquake physics with interdisciplinary observations can provide a direct understanding of the fault system mechanics, reconcile competing interpretations and serve as a constraint to understand the short- and long-term Eastern Anatolian Fault system interaction.

How to cite: Gabriel, A.-A., Ulrich, T., Marchandon, M., and Biemiller, J.: Geodetically and seismically informed rapid 3D dynamic rupture modeling of the Mw7.8 Kahramanmaraş earthquake, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17603, https://doi.org/10.5194/egusphere-egu23-17603, 2023.

EGU23-17604 | ECS | Posters on site | SM1.6

Delineation of Regional High Seismic Risk Zone for More Targeted Seismic Risk Mitigation 

Danhua Xin, Zhenguo Zhang, Bo Chen, and Friedemann Wenzel

Despite global efforts to reduce seismic risk, earthquake remains one of the most destructive natural disasters in the world, especially for seismic active zones when they are characterized by high densification of fixed assets and population. For a specific country or region, the most effective way to achieve earthquake resilience is preparedness prior to an earthquake. To mitigate potential seismic risk, it is important to understand where high seismic risk zone locates, since the budgetary resources available from the local government are always limited and they should be allocated to such zone with priority. This paper proposes a strategy to delineate regional high seismic risk zone by combing different seismic risk assessment results, aiming to make the seismic risk mitigation practice more targeted and operable. Our analyses show that while the delineated high seismic risk zone occupies only ~10% of the case study area, it accounts for ~90% of the total seismic risk in terms of economic loss. To achieve more targeted seismic risk mitigation, we recommend that such zone should be given top priority in seismic risk mitigation.

How to cite: Xin, D., Zhang, Z., Chen, B., and Wenzel, F.: Delineation of Regional High Seismic Risk Zone for More Targeted Seismic Risk Mitigation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17604, https://doi.org/10.5194/egusphere-egu23-17604, 2023.

A devastating earthquake sequence occurred on February 6, 2023, within the East Anatolian fault system. Two main shocks, the Mw 7.7 Sofalaca-Şehitkamil-Gaziantep, and Mw 7.6 Ekinözü-Kahramanmaraş earthquakes occurred nine hours apart and affected 10 cities and more than an area of 100,000 km2 (PGA>0.08g). The earthquake-affected area mainly exhibits arid/semi-arid climatic conditions where approximately 15% of the landscape is characterized by steep topography (slope steepness>20°). Initial estimates of globally available predictive landslide models indicated extensive landslide distribution over the area.

We examined high-resolution satellite images and aerial photos to provide a better insight into this co-seismic landslide event and its possible post-seismic consequences. These observations are going to be validated and enriched by detailed field surveys. This research presents our preliminary findings as a result of these investigations. Our observations carried out in the first two weeks after the sequence showed that rock fall and lateral spreading are the dominant landslide types, and the overall landslide population could be less than expected. Therefore, the resultant co-seismic landslide event seems unexpected, given the intensity of ground shaking and landscape characteristics. Based on the preliminary investigations, lithology, topographic relief, and climatic conditions appear to be the main variables causing these below expectations for landslide distribution. We should stress that our historical records mostly lack landslide events in arid/semi-arid conditions, as we observed in this event. In this context, this event is going to be recorded as one of a few of its kind. Our observations also showed intense ground shaking and strongly deformed numbers of hillslopes, although most have not failed yet. In particular, heavy rain and snowmelt may result in a considerable number of failures on those hillslopes that are prone to cracking and deformation due to strong ground shaking. In this respect, this area needs to be monitored for a long time to understand the earthquake legacy effect and post-seismic hillslope response.

How to cite: Gorum, T. and Tanyas, H.: Less than expected? Landslides triggered by the 2023 Mw 7.7 and 7.6 Kahramanmaras (Türkiye) earthquake sequence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17606, https://doi.org/10.5194/egusphere-egu23-17606, 2023.

EGU23-17607 | ECS | Posters on site | SM1.6

Strong Ground Motion Simulations of the 2023 Turkey–Syria Earthquake Sequence Using CGFDM3D-EQR 

Tianhong Xu, Wenqiang Wang, and Zhenguo Zhang

On February 6th 2023, a large Mw 7.8 earthquake struck Turkey and Syria near the border area. Only 9 hours later, another Mw 7.5 earthquake occurred about 90 km northeast of the epicenter of the first earthquake. Up to now, the two earthquakes have killed at least 43,000 people and injured 120,000. Preliminary inversion results from USGS show that the geometric structure of the seismogenic fault is rather complex, and the rupture propagates through multiple sub-faults.

Massive casualties show the necessity and urgency of an earthquake rapid emergency response system, and ground motion simulation is a key component of this system. Empirical ground-motion prediction equations (GMPEs), which are widely used, can quickly provide the distribution of ground motion and seismic intensity. Unfortunately, the calculated seismic intensity is not accurate enough due to its incomplete consideration of the earthquake source and the complicated seismic wave propagation process(Paolucci et al., 2018; Infantino et al., 2020; Stupazzini et al., 2021). In contrast, the physics-based ground motion simulation method has more advantages. In this study, we employ the USGS's finite fault inversion results as kinematic source input to model the two earthquakes' strong ground motion using the CGFDM3D-EQR platform (Wang et al., 2022). The platform can quickly run an earthquake simulation while taking into account the three-dimensional complexity of topography, underground medium, and source, providing timely reliable distribution of ground motion and seismic intensity. Preliminary findings indicate that the first earthquake's maximum intensity is XI, the second earthquake's maximum intensity is X, which is consistent with the report issued by AFAD, and that the simulated intensity's spatial distribution range is also consistent. The simulation completely considers the effects of the source, geological environment, and topography, and the seismic intensity distribution exhibits complex non-uniform properties that are closer to the reality.

The rapid ground shaking simulations of the Turkey–Syria earthquake allows for the quick, accurate, and scientific assessment of earthquake damage. To reduce lives and financial losses, these results can serve as a scientific foundation and point of reference for the relevant authorities as they decide how best to respond in an earthquake and conduct out rescue operations.

 

 

 

 

References

Infantino M, Mazzieri I, Özcebe A G, et al. 3d physics-based numerical simulations of ground motion in istanbul from earthquakes along the marmara segment of the north anatolian fault[J]. Bulletin of the Seismological Society of America, 2020, 110(5): 2559-2576.

Paolucci r, Gatti F, Infantino M, et al. Broadband ground motions from 3d physics-based numerical simulations using artificial neural networksbroadband ground motions from 3d pbss using anns[J]. Bulletin of the Seismological Society of America, 2018, 108(3A): 1272-1286.

Stupazzini M, Infantino M, Allmann A, et al. Physics-based probabilistic seismic hazard and loss assessment in large urban areas: A simplified application to istanbul[J]. Earthquake Engineering & Structural Dynamics, 2021, 50(1):99-115.

Wang, W., Zhang, Z., Zhang, W., Yu, H., Liu, Q., Zhang, W., & Chen, X. (2022). CGFDM3D‐EQR: A Platform for Rapid Response to Earthquake Disasters in 3D Complex Media. Seismological Research Letters, 93 (4): 2320-2334.

How to cite: Xu, T., Wang, W., and Zhang, Z.: Strong Ground Motion Simulations of the 2023 Turkey–Syria Earthquake Sequence Using CGFDM3D-EQR, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17607, https://doi.org/10.5194/egusphere-egu23-17607, 2023.

EGU23-17608 | Orals | SM1.6

Dynamic Triggering of Tremor and Earthquakes along the Dead Sea Transform by the 2023 Kahramanmaraş Earthquake Doublet 

Asaf Inbal, Itzhak Lior, Alon Ziv, and Ran Novitsky Nof

The Kahramanmaraş earthquake doublet, which struck south-eastern Turkey, imparted stress changes that dramatically affected neighboring regions: northern Israel, located about 600 km to the south of the epicenters, experienced roughly a hundred-fold increase in seismicity rates during the first week following the M>7 earthquakes. Here, we study seismic records along the Dead Sea Transform (DST) in order to identify, locate, and determine the characteristics of seismic sources triggered by seismic waves due to the M>7 earthquakes. We take advantage of a dense near-fault accelerometer network recently installed along the DST in Israel, and scan high- and low-pass filtered seismograms to look for body- and surface-wave triggering. We find that Love waves generated by the Mw7.5 earthquake triggered a small-magnitude earthquake in the northern Dead Sea lake area. Importantly, we find the first evidence of deep tectonic tremor along the DST, also triggered by the Mw7.5 Love waves. This tremor episode is composed of two 10 s bursts aligned with the strongest Love wave energy. Preliminary tremor envelope cross-correlation location results suggest it resides in the Jordan Valley, north of the Dead Sea lake, at 10 to 20 km depth, within the San Andreas Fault tremor depth range. Despite its larger magnitude, we do not find evidence for dynamic triggering due to the Mw7.8. The lack of dynamic triggering due to the Mw7.8, and the fact that waves from both earthquakes travel along similar paths to Israel, allow us to establish a threshold for dynamic earthquake triggering in the Dead Sea area.

How to cite: Inbal, A., Lior, I., Ziv, A., and Novitsky Nof, R.: Dynamic Triggering of Tremor and Earthquakes along the Dead Sea Transform by the 2023 Kahramanmaraş Earthquake Doublet, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17608, https://doi.org/10.5194/egusphere-egu23-17608, 2023.

EGU23-17609 | Orals | SM1.6

Rupture processes of the 2023 Türkiye earthquake sequence: Main- and aftershocks 

Gesa Petersen, Pinar Büyükakpinar, Felipe Vera, Malte Metz, Joachim Saul, Simone Cesca, Torsten Dahm, and Frederik Tilmann

On February 6, 2023, southeastern Turkey was hit by two of the most devastating earthquakes in the instrumental period of the country, with Mw 7.7-7.8 and Mw 7.6, respectively. Both earthquakes caused massive damage and in total tens of thousands of casualties in Turkey and Syria. In this study, we analyze the rupture processes of main- and aftershocks by combining different seismic source characterization techniques using teleseismic, regional and local data. We perform finite source inversion and back projection-based analyses for the two main shocks and invert for probabilistic centroid moment tensor solutions of both main and aftershocks (M≥4). The first earthquake was bilateral and ruptured a seismic gap along the East Anatolian Fault Zone, with rupture first propagating to the north-east for ~200 km, and in a latter phase propagating to the SSW, probably coming to a halt only on a branch extending into the Mediterranean Sea. The total length of the rupture likely exceeds 500 km. The second event ruptured the EW oriented Sürgü-Misis Fault Zone to the NW of the first event. It shows a highly concentrated rupture near the epicenter, Rupture directivity analyses for M≥5.3 earthquakes provide additional insights into dynamic source aspects. Preliminary moment tensor solutions of numerous aftershocks indicate a remarkable variability of rupturing mechanisms, suggesting stress changes and the activation of multiple faults in the vicinity of the main ruptures. With our work, we aim to shed light onto multiple aspects of the complex rupture evolution and hope to provide new insights towards a better understanding of the devastating 2023 Türkiye earthquake sequence.

How to cite: Petersen, G., Büyükakpinar, P., Vera, F., Metz, M., Saul, J., Cesca, S., Dahm, T., and Tilmann, F.: Rupture processes of the 2023 Türkiye earthquake sequence: Main- and aftershocks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17609, https://doi.org/10.5194/egusphere-egu23-17609, 2023.

EGU23-17610 | Orals | SM1.6

The 6 February 2023 Türkiye Earthquakes: Insights for the European Seismic Hazard and Risk Models 

Graeme Weatherill, Fabrice Cotton, Helen Crowley, Laurentiu Danciu, Karin Sesetyan, Eser Cakti, M. Abdullah Sandikkaya, Ozkan Kale, and Elif Türker and the Members of the 2020 European Seismic Hazard Model and 2020 European Seismic Risk Model Core Teams

The earthquakes that struck eastern Türkiye and Syria on 6 February 2023, first with a Mw 7.8 shock then followed only hours later by a second Mw 7.6 event, will have profound and long-lasting consequences for those living in this highly seismically active region. From the perspective of the European earthquake science and engineering communities, however, these events also force us to evaluate our models of seismic hazard and risk for the region, specifically the 2020 European Seismic Hazard Model (ESHM20, Danciu et al.. 2021) and European Seismic Risk Model (ESRM20, Crowley et al.. 2021), to identify potential shortcomings and focus on areas where improvement is needed. A single event such as this can neither validate nor invalidate probabilistic models, but as data emerge, we can compare these with components of our models and verify the extent to which the events themselves and their consequences are described.

We first verified that the ruptures associated to the two main earthquakes are present within the inventory of ruptures and associated probabilities within the source model (the earthquake rupture forecasts or ERFs) for the East Anatolian Fault (first event) and Sürgü-Cardak Fault (second event). These earthquakes are larger than those in the historical earthquake catalogue, but ruptures close in magnitude and dimension to those observed were present in the ESHM20 ERFs. Both magnitudes were between 0.2 – 0.4 Mw units lower than those defined for their respective faults on the different logic tree branches.

Preliminary ground motion observations allowed us to compare the observed shaking to that predicted by the ESHM20 ground motion model (GMM) and others in the literature. These were found to be consistent in their prediction of the expected shaking and its attenuation. The 6th February earthquakes do show that future models must address issues of time-dependence between earthquakes and allow for short-term clustering of large events on nearby ruptures. Recorded near-fault ground motions also suggest strong pulse-like behaviour, indicating the need for such phenomena to be better captured in the GMMs.

A complete assessment of the actual damage and consequences is not yet available from which we could compare the seismic risk model. We have run scenario risk calculations using the ESRSM20 site, exposure and vulnerability models for the two main earthquakes, along with other scenario ruptures on neighbouring faults. Expected fatalities were lower than those reported at the time of writing; however, many factors contribute to this. Further analysis is needed to understand the difference, but critical areas for future improvement to the risk models should include state-dependent fragility, modelling of further epistemic uncertainty in exposure and vulnerability, and inclusion of spatial- and temporal correlations in ground motions across a region. Future efforts by the seismic hazard and risk modelling community to address these issues considering the February 2023 earthquakes may have a lasting impact on risk mitigation, both in Türkiye and across Europe.

How to cite: Weatherill, G., Cotton, F., Crowley, H., Danciu, L., Sesetyan, K., Cakti, E., Sandikkaya, M. A., Kale, O., and Türker, E. and the Members of the 2020 European Seismic Hazard Model and 2020 European Seismic Risk Model Core Teams: The 6 February 2023 Türkiye Earthquakes: Insights for the European Seismic Hazard and Risk Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17610, https://doi.org/10.5194/egusphere-egu23-17610, 2023.

EGU23-17611 | ECS | Posters on site | SM1.6

Fault slip and fault-zone damage of the 6 February 2023 Kahramanmaraş earthquake duplet estimated from 3D displacement derivations of Sentinel-1 radar images 

Jihong Liu, Xing Li, Adriano Nobile, Yann Klinger, and Sigurjón Jónsson

We report on the surface displacements of the 6 February 2023 Kahramanmaraş earthquake duplet derived from pixel-offset tracking of Sentinel-1 radar images. From both ascending and descending orbit images, along-track (azimuth) and across-track (range) pixel offsets were derived, yielding four different offset images from which we inverted for three-dimensional surface displacements. The resulting horizontal surface displacements clearly show the left-lateral motion across the two main faults, with the vertical displacements small in comparison, confirming the almost pure strike-slip mechanism of both events. Comparison with GPS data indicates that an accuracy of ~10 cm can be achieved for the horizontal displacements. From the offset results, we mapped the main surface rupture of the first event along the East Anatolian Fault (EAF) for ~300 km and the surface rupture of the second mainshock for over 100 km, i.e., somewhat shorter than illuminated by the aftershocks. Using multiple profiles across the faults, of the fault-parallel displacements derived from the offset results, we find three slip maxima along the EAF, with the largest slip (6-7 m) found northeast of the epicenter, ~30 km east of the city of Kahramanmaraş. Another slip maximum (~4 m) is found further southwest, near Islahiye, with fault slip abruptly decreasing near Antakya at the southwestern end of the rupture. The maximum surface offset of the second fault is even larger than for the first rupture, or about 8 m, and it is found near the epicenter. In addition to localized deformation along the main rupture, across-fault profiles of both fault-parallel and fault-perpendicular displacement components also show deformation gradients that might be evidence for off-fault damage extending several km away from the surface ruptures. From the derived coseismic 3D displacements and GNSS observations, we inverted for spatially variable fault slip, revealing that most of the fault slip occurred above 15 km with maximum slip of both quakes reaching almost 10 m. The spatially variable slip model of the first mainshock has primarily three areas of high slip, like what is seen at the surface. Together the results have provided a quick and a complete overview of surface fault offsets and what faults were activated in the earthquake and will help assessing the influence these large earthquakes have had on other faults in the region.

How to cite: Liu, J., Li, X., Nobile, A., Klinger, Y., and Jónsson, S.: Fault slip and fault-zone damage of the 6 February 2023 Kahramanmaraş earthquake duplet estimated from 3D displacement derivations of Sentinel-1 radar images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17611, https://doi.org/10.5194/egusphere-egu23-17611, 2023.

EGU23-17612 | Posters on site | SM1.6

Mapping the ruptures of the Mw7.8 and Mw7.7 Turkey-Syria Earthquakes using optical offset tracking with Sentinel-2 images 

Floriane Provost, Jérôme Van der Woerd, Jean-Philippe Malet, Alessia Maggi, Yann Klinger, David Michéa, Elisabeth Pointal, and Fabrizio Pacini

Monday February 6, 2023, two large Mw7+ earthquakes struck Turkey and North-Syria. The first event occurred along the N60 striking East Anatolian Fault (EAF) and its prolongation towards the Dead Sea Fault, the N25 striking Karazu fault, with an epicenter 30 km south-east off the main rupture zone. The second event is located to the north of the first one, along the N100 Sürgü-Çartak fault. Focal mechanisms of both shocks exhibit a dominant left-lateral strike-slip component on sub-vertical faults. These ruptures and mechanisms are compatible with Anatolia westward extrusion between the North and East Anatolian faults in response to Arabia-Eurasia convergence. The complex geometry of the activated faults during this earthquake sequence sheds light on how strain is partitioned and distributed among the faults of this triple-junctions linking Nubia, Arabia and Anatolia.

The current constellation of Earth Observation satellites allowed for rapid acquisition of the whole impacted area shortly after the mainshocks. On February 9, 2023, the Copernicus Sentinel-2 satellite captured a set of optical images while the region was mostly cloud free. This dataset offers a complete coverage of the system of faults activated during these events at 10 m spatial resolution. Although this resolution is not sufficient to map surface ruptures directly from the images, image correlation (also known as offset tracking) techniques can be applied on these images to retrieve the distribution of the surface displacement. In the present work, we used the GDM-OPT-ETQ service of the ForM@Ter solid Earth data hub to measure (with the open source photogrammetry library MicMac) the co-seismic displacement between images of January 25, 2023 and February 9, 2023. The massive processing was performed on the Geohazards Exploitation Platform (GEP). The final products of the processing are East-West and North-South displacement maps covering an area of  300 km x 300 km at 10 m resolution and further 2D strain maps are also derived. Spatial offsets in the range of 3 to near 10 m are identified with large geographic variability along the faults. 

These maps significantly contribute to identify and map the ruptures of the Turkey-Syria earthquakes and determine the along fault displacement. The spatial distribution of the displacement will be discussed together with a first order cluster analysis of the seismic sequence using an aftershock catalogs. The combined datasets should allow us to better understand the complexity of the on-fault and off-fault deformation pattern.

How to cite: Provost, F., Van der Woerd, J., Malet, J.-P., Maggi, A., Klinger, Y., Michéa, D., Pointal, E., and Pacini, F.: Mapping the ruptures of the Mw7.8 and Mw7.7 Turkey-Syria Earthquakes using optical offset tracking with Sentinel-2 images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17612, https://doi.org/10.5194/egusphere-egu23-17612, 2023.

The double earthquakes of 6 February 2023 in central Turkey and their associated seismic activity show a composite cloud of thousands of epicenters that mimic the number 7 and extend from central Turkey to the east Mediterranean shoreline. The lower limb of this mighty “7” spreads along a trend that matches the onshore continuation of the Latakia ridge, which is one of the most prominent seafloor structures of the east Mediterranean region. This structure extends for about 200 km along the subduction zone of the Cyprus arc where compressional forces are dominant. We interpreted a major and active reverse fault system underneath the Latakia ridge using 3D seismic interpretation. The ridge’s reverse faults rupture the seafloor and display a relief up to 500 m in height. The fault system underneath this prominent seafloor rupture is capable of generating a high magnitude earthquake and can be considered a very plausible source of the 9 July 551 M 7.2 earthquake and its associated tsunami along the Levant coast. The magnitudes of the 6 February 2023 double earthquakes and the density and trend of their associated seismic activity highlight the importance of understanding the interconnection of the seismogenic structures in the east Mediterranean region, both onshore and offshore, with additional attention to those that are potentially tsunamigenic.

How to cite: Nemer, T., Faysal, R., and Sarieddine, K.: The double earthquakes of 6 February 2023 in central Turkey: a mighty “7” from continental strike-slip to subduction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17613, https://doi.org/10.5194/egusphere-egu23-17613, 2023.

Temporal seismic velocity changes have been reported to occur before, during and after major earthquakes. We applied seismic ambient noise interferometry to analyse transient velocity changes (dv/v) in the vicinity of the fault segment affected by the 6th of February East Anatolia earthquake sequence. The dataset consists of 5 months of continuous seismic records (from October 1st 2022 to February 15 2023)   recorded by three triaxial broadband stations deployed on the shoulders of the reactivated fault system. The open-access stations are operated by the Kandilli Observatory and Earthquake Research Institute of Turkey. Cross-correlation changes over time between station pairs reveal a large velocity co-seismic drop of about 2%   in the apparent velocity. We also examine the velocity variations in single-station cross-component analysis finding a co-seismic velocity variation of 1% more prominent on horizontal cross-components. These variations may be associated with changes in the effective stress of the upper crust and may be identified before and during the occurrence of important events. We are currently investigating precursory cross-correlations and auto-correlations of the signal in comparison to long-term seasonal trends. We show the importance of seismic interferometry as an additional method to monitor active fault systems.

How to cite: Muñoz-Burbano, F., Savard, G., and Lupi, M.: Temporal seismic Velocity variations prior and during the 7.8 and 7.5 MW earhquakes occurred in south-central Turkey implementing ambient noise interferometry, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17614, https://doi.org/10.5194/egusphere-egu23-17614, 2023.

EGU23-17616 | Posters on site | SM1.6

Surface Displacement and Source Parameters of the Mw 7.7 and Mw 7.6 Kahramanmaraş Earthquakes 

Haluk Özener, Çağkan Serhun Zoroğlu, Egehan Vardar, Emre Havazlı, Tülay Kaya Eken, and Mahyat Shafapour Tehrany

On February 6th, 2023, a devastating earthquake with a magnitude of Mw7.7 occurred in the Kahramanmaras region of Türkiye. The earthquake is caused by the rupture of a NE-SW oriented left lateral strike-slip Pazarcık fault segment located between the East Anatolian Fault (EAF) and Dead Sea Fault (DSF) fault systems. The aftershock sequence of the earthquake indicated that post-seismic deformation continued along the EAF and DSF toward the NE and SW. Just 9 hours later, another earthquake with a magnitude of Mw7.6 occurred along the EW-oriented left lateral Sürgü Fault, located approximately 100 km north of the first event. These two earthquakes released a significant amount of energy and affected ten provinces in southeastern Türkiye. The earthquake region is characterized by a complex tectonic structure actively deforming through a network of strike-slip, thrust, and normal faults formed by the convergence of the Arabian Plate to the Eurasian Plate and the westward movement of the Anatolian Plate. It is of utmost importance to understand the co-seismic and post-seismic surface deformation behavior to make reliable seismic hazard assessments.

To better understand the deformation patterns during and after the Kahramanmaraş earthquakes, we processed Interferometric Synthetic Aperture Radar (InSAR) data sets obtained before and after the earthquakes. We used both ascending and descending track SAR images of the ESA Sentinel-1 to detect the surface displacement. Then, we incorporated the post-seismic deformation patterns from the relocated aftershock events to the InSAR derived deformation field to gain insight into the source properties of the events. Our preliminary results revealed several meters of displacement across the faults.

How to cite: Özener, H., Zoroğlu, Ç. S., Vardar, E., Havazlı, E., Kaya Eken, T., and Shafapour Tehrany, M.: Surface Displacement and Source Parameters of the Mw 7.7 and Mw 7.6 Kahramanmaraş Earthquakes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17616, https://doi.org/10.5194/egusphere-egu23-17616, 2023.

EGU23-17617 | Posters on site | SM1.6

Multiple effects contributed to the intensive shaking recorded in the 6 February 2023 Kahramanmaraş (Türkiye) earthquake sequence 

Sigurjón Jónsson, Theodoros Aspiotis, Tariq Aquib, Eduardo Cano, David Castro-Cruz, Armando Espindola-Carmona, Bo Li, Xing Li, Jihong Liu, Rémi Matrau, Adriano Nobile, Kadek Palgunadi, Laura Parisi, Matthieu Ribot, Cahli Suhendi, Yuxiang Tang, Bora Yalcin, Ulaş Avşar, Yann Klinger, and P. Martin Mai

The Kahramanmaraş earthquake sequence caused strong shaking and extensive damage in central-south Türkiye and northwestern Syria, making them the deadliest earthquakes in the region for multiple centuries. The rupture of the first mainshock (M7.8) initiated just south of the East Anatolian Fault (EAF) and then ruptured bilaterally hundreds of km of the EAF, causing major stress changes in the region and triggering the second mainshock (M7.6) about 9 hours later. We mapped the surface ruptures of the two mainshocks using pixel-offset tracking of Sentinel-1 radar images and find them to be ~300 km and 100-150 km long. The distribution of aftershocks indicates that the fault ruptures may have been even longer at depth, or about ~350 km and ~170 km, respectively. The pixel-tracking results and finite-fault modeling of the spatially variable fault slip show up to 7 and 8 m of surface fault offsets at the two faults, respectively, and that fault slip was shallow in both events, mostly above 15 km. In addition, our back-projection analysis suggests the first mainshock ruptured from the hypocenter to the northeast towards the EAF (first ~15 sec), then continued along it to the northeast (until ~55 sec), and also to the southwest towards the Hatay province, later at high rupture speeds (until ~80 sec). Furthermore, strong motion recordings show PGA values up to 2g and are particularly severe in Hatay, where multiple stations show over 0.5g PGA values. Both events are characterized by abrupt rupture cessation, generating strong stopping phases that likely contributed to the observed high shaking levels. Together the results show that directivity effects, high rupture speed, strong stopping phases, and local site effects all contributed to the intensive shaking and damage in the Hatay province.

How to cite: Jónsson, S., Aspiotis, T., Aquib, T., Cano, E., Castro-Cruz, D., Espindola-Carmona, A., Li, B., Li, X., Liu, J., Matrau, R., Nobile, A., Palgunadi, K., Parisi, L., Ribot, M., Suhendi, C., Tang, Y., Yalcin, B., Avşar, U., Klinger, Y., and Mai, P. M.: Multiple effects contributed to the intensive shaking recorded in the 6 February 2023 Kahramanmaraş (Türkiye) earthquake sequence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17617, https://doi.org/10.5194/egusphere-egu23-17617, 2023.

EGU23-17618 | Posters on site | SM1.6

Global Distributed Fibre Optic Sensing recordings of the February 2023 Turkey earthquake sequence. 

Philippe Jousset, Andreas Wuestefeld, Charlotte Krawczyk, Alan Baird, Gilda Currenti, Martin Landrø, Andy Nowacki, Zack Spica, Sandra Ruiz Barajas, Fabian Lindner, Özgün A. Konca, Pascal Edme, Voon Hui Lai, Vladimir Treshchikov, Lena Urmantseva, Jan Peter Morten, Werner Lienhart, Bradley Paul Lipovsky, Martin Schoenball, and Kuo-Fong Ma and the “DAS-month” team (sample only!)

As part of a global distributed acoustic sensing (DAS) campaign, multiple DAS interrogators (from academia and industry) recorded simultaneously from 1st till 28th February 2023 in different regions of the globe. The objective is to define if and how a global monitoring system based on DAS could perform for teleseismic event record and analysis. Each participant uploaded triggered data window from earthquakes with magnitude larger than 5, as defined by global seismological networks, to a central storage location. Data was pre-processed following common filtering parameters (spatial and temporal sampling). Bottle-necks in data format, storage, and legal issues are identified and reviewed to pose the basis for a common DAS data archive strategy.

In this study, we present a selection of DAS records of the Turkey earthquake sequence, from borehole, surface, on-land, submarine telecommunication or dedicated cables all over the globe. They comprise a few kilometers long railroad track (Switzerland), an 0.8 km long deployed cable in the Limmat river, near Zürich (Switzerland), a 1 km deployed cable at Mt. Zugspitze in the Alps (Germany/Austria), a 21 km telecom cable in the forest around Potsdam (Germany), a 17 km telecom cable surface geothermal field (north Iceland), a 0.2 km borehole at Etna volcano (Italy), a telecom cable in the city of Istanbul (Turkey), a 25 km telecom cable in Melbourne (Australia), in the inner city line in Graz (Austria), in the city of Seattle, WA (USA), a submarine cable in the North Sea, a submarine cable connecting Ny Ålesund and Longyearbyen at Svalbard (Norway), a 0.8 km dedicated fibre in a quick clay area in Norway, amongst many others.

We show that signals from the two destructive earthquakes in Turkey were recorded all over the globe. We discuss the signal quality and their potential use to study teleseism signals. We analyze recorded strain amplitudes according to the different array geometries and the differing sensitivities to wave types (body, surface waves, possibly others) and deployment conditions. When available, comparison with other sensors located in the same place is performed. Finally, we analyze the influence of local geological conditions due to the passing large amplitudes waves.

With the increasing availability, reduced cost and improved simplicity of DAS systems and the wide spread existing fibre optic networks, we believe fibre-optic sensing will play an ever-increasing role in the global seismic monitoring.

How to cite: Jousset, P., Wuestefeld, A., Krawczyk, C., Baird, A., Currenti, G., Landrø, M., Nowacki, A., Spica, Z., Barajas, S. R., Lindner, F., Konca, Ö. A., Edme, P., Lai, V. H., Treshchikov, V., Urmantseva, L., Morten, J. P., Lienhart, W., Lipovsky, B. P., Schoenball, M., and Ma, K.-F. and the “DAS-month” team (sample only!): Global Distributed Fibre Optic Sensing recordings of the February 2023 Turkey earthquake sequence., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17618, https://doi.org/10.5194/egusphere-egu23-17618, 2023.

EGU23-17619 | Posters on site | SM1.6

Preliminary Seismic and Geodetic Observations of the Mw7.8 and Mw7.6 Earthquakes in Eastern Turkey 

Sezim Ezgi Güvercin, Ali Özgün Konca, Hayrullah Karabulut, Figen Eskiköy, James Hollingsworth, and Semih Ergintav

On 6 February 2023, Mw7.8 Kahramanmaraş earthquake sequence ruptured a section of ~300 km of the East Anatolian Fault (EAF). The rupture was initiated with a relatively small ~Mw7.0 event on the Narli Fault, a subparallel prolongation to the Amanos segment breaking ~50 km of its length to the north before reaching to the EAF, ~20s later. The Mw7.8 earthquake was followed by a Mw7.6 event rupturing E-W oriented Çardak Fault on the north of the EAF, ~9 hours later. The initial part of the rupture along the Narlı fault with Mw7.0 earthquake has significant normal component while the rest of the rupture is mostly left-lateral strike slip consistent with the EAF. The pixel correlation of satellite images shows that the rupture of the Mw7.8 event extends for 300 km along the EAF with a maximum slip of ~9 m near Kahramanmaraş Junction. Preliminary finite-fault models show that average rupture velocity toward north-east is faster (~ 3km/s) compared to the southwest (~2 km/s). The north-east extent of the rupture almost reached to the termination of the 2020, Mw6.8 Sivrice earthquake, while to the southwest, it extends to the east of the city of Antakya. The Mw7.6 earthquake has surface offset of ~10 m extending E-W for ~100 km between the EAF in the east and Savrun Fault in the west. The aftershock zone expanded over a wide region during the first few days, all over the eastern Anatolia. The seismic activities triggered on Malatya, Savrun and Göksun Faults are consistent with Coulomb stress increases. Earthquake focal mechanisms solutions are consistent with the kinematics of the ruptured faults with strike slip solutions. Normal fault solutions are observed at the terminations of the ruptures with Coulomb stress increases.  The normal fault is activated on the southern border of the Hatay Graben, with a continuation to the Cyprus Arc.  In this presentation we present the preliminary results of the seismicity, slip model including GNSS, seismic and InSAR data as well as the satellite obtained surface offsets.

How to cite: Güvercin, S. E., Konca, A. Ö., Karabulut, H., Eskiköy, F., Hollingsworth, J., and Ergintav, S.: Preliminary Seismic and Geodetic Observations of the Mw7.8 and Mw7.6 Earthquakes in Eastern Turkey, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17619, https://doi.org/10.5194/egusphere-egu23-17619, 2023.

EGU23-17620 | ECS | Posters on site | SM1.6

Strong ground motions due to directivity and site effects inflicted by the February 6 2023 earthquake doublet, along the East Anatolian Fault 

Theodoros Aspiotis, Tariq Anwar Aquib, David Castro-Cruz, Bo Li, Xing Li, Jihong Liu, Remi Matrau, Kadek Hendrawani Palgunadi, Laura Parisi, Cahli Suhendi, Yuxiang Tang, Yann Klinger, Sigurjon Jonsson, and Paul Martin Mai

Two powerful earthquakes (magnitudes 7.8 and 7.6) struck south-central Türkiye on February 6, 2023, causing significant damage across an extensive area of at least ten provinces in Türkiye as well as in multiple cities in northwestern Syria, making them one of the deadliest earthquakes in Türkiye for multiple centuries. The first mainshock started close to the well-known East Anatolian Fault (EAF) and then rupturing more than 300 km of that fault, whereas the second large earthquake occurred nine hours later around 90 km north of the first mainshock, on an east-west trending fault. In this study, we analysed recorded strong ground motions from the two events to better understand the factors contributing to the devastation caused by the earthquakes.

 

For this, we collected 250 and 200 strong ground motion records for the first and the second event, respectively, from the Disaster and Emergency Management Authority (AFAD) in Türkiye. Maximum peak ground accelerations (PGA) of 2g were observed at a distance of 31 km northeast of the first mainshock epicenter and 0.6g for the second event 65km west to its epicenter. In addition, we find particularly high amplitude ground motions in the Hatay province for the first event, which is consistent with the extent of damage reported in that region. High shaking levels in Antakya and other parts of Hatay can be explained by a combination of strong directivity and local site effects.

 

The results of our analysis imply that the PGA values derived from two local ground motion models (GMMs), adopted for the 2018 Turkish hazard map, are underestimated in comparison to observed strong motion recordings. In addition, we also compared observed peak and spectral ground motion characteristics with estimated seismic hazard values (10% probability to exceed in 50 years) in the East Anatolian Fault region (extracted from the 2018 Turkish seismic hazard map). Furthermore, we compare the recorded response spectra with the Turkish design code for several locations around the main faults.  The results show that the observations greatly exceed the hazard values and code guidelines in the Hatay province.

How to cite: Aspiotis, T., Aquib, T. A., Castro-Cruz, D., Li, B., Li, X., Liu, J., Matrau, R., Palgunadi, K. H., Parisi, L., Suhendi, C., Tang, Y., Klinger, Y., Jonsson, S., and Mai, P. M.: Strong ground motions due to directivity and site effects inflicted by the February 6 2023 earthquake doublet, along the East Anatolian Fault, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17620, https://doi.org/10.5194/egusphere-egu23-17620, 2023.

EGU23-17621 | Posters on site | SM1.6

The Tsunami Warning triggered in the Mediterranean Sea by the 2023 February 6 Mw 7.8 Türkiye-Syria earthquake: from the present Decision Matrix (DM) to Probabilistic Tsunami Forecasting (PTF). 

Stefano Lorito, Jacopo Selva, Alessandro Amato, Andrey Babeyko, Basak Bayraktar, Fabrizio Bernardi, Marinos Charalampakis, Louise Cordrie, Nikos Kalligeris, Alessio Piatanesi, Fabrizio Romano, Antonio Scala, Roberto Tonini, Manuela Volpe, Musavver Didem Cambaz, and Doğan Kalafat

The 2023 February 6 Mw 7.8 earthquake was the first one of a doublet which shook Türkiye and Syria causing, as per the estimates at the time of writing of this abstract, more than 45,000 casualties.

The current standard operating procedures of the NEAMTWS (Tsunami Warning System in the North-Eastern Atlantic, the Mediterranean and connected seas, coordinated by UNESCO/IOC) for the initial tsunami warning message following an earthquake are based on a Decision Matrix (DM), whose input parameters are hypocentre and magnitude of the earthquake. Since the epicentre of this earthquake was located at a depth between 15-35 km at almost 100 km from the coast, both KOERI (Türkiye) and INGV (Italy) Tsunami Service Providers (TSPs) of the NEAMTWS issued a Tsunami Watch message (i.e., runup expected to exceed 1 m) for the whole Mediterranean Sea. NOA (Greece) did not issue any alert, because its initial location was more than 100 km from the coast.

In response to the tsunami warning, trains were stopped in different locations in Southern Italy for several hours, and evacuation of some coastal areas was enforced. However, only a relatively small tsunami was recorded by Turkish close-by tide-gauges in the Eastern Mediterranean, with a maximum recorded amplitude of less than 50 cm. Based on these measurements and on others showing little to no tsunami at increasing distances, the alert was then ended after 5 and 9 hours by INGV and KOERI, respectively, based on the available tide-gauge recordings and interaction with Civil Protection Officers.

This event has highlighted that NEAMTWS is an asset for the coastal communities. It can provide rapid alerts, which can save lives if the last-mile of the procedures is in place and the communities are “Tsunami Ready”, that is aware and prepared to respond with evacuations and other appropriate countermeasures. On the other hand, while it is reasonable – and dutiful based on current standard operation procedures – to issue a basin-wide, or at least a local alert, for an inland earthquake of unknown mechanism and of such a large magnitude, it is perhaps possible to improve the DM, which is totally heuristic and characterized by hard-thresholds, with consideration of numerical tsunami simulations and quantitative uncertainty treatment with more continuous variations. Moreover, there is no procedure currently in place to differentiate among locations where the expected time of arrival differs by many hours across the Mediterranean basin, nor a sufficient instrumental coverage that could make cancellation/ending faster due to a more solid observational basis.

We will discuss some of the scientific and operational aspects with the aim of identifying which lessons can be learned to improve the NEAMTWS efficiency. We will also compare the DM-based alerts with those that would be produced with the recently introduced Probabilistic Tsunami Forecasting (PTF, Selva et al., 2021, Nature Communications), presently in pre-operational testing at INGV.

How to cite: Lorito, S., Selva, J., Amato, A., Babeyko, A., Bayraktar, B., Bernardi, F., Charalampakis, M., Cordrie, L., Kalligeris, N., Piatanesi, A., Romano, F., Scala, A., Tonini, R., Volpe, M., Cambaz, M. D., and Kalafat, D.: The Tsunami Warning triggered in the Mediterranean Sea by the 2023 February 6 Mw 7.8 Türkiye-Syria earthquake: from the present Decision Matrix (DM) to Probabilistic Tsunami Forecasting (PTF)., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17621, https://doi.org/10.5194/egusphere-egu23-17621, 2023.

EGU23-17624 | Orals | SM1.6

February 6, 2023, Mw 7.8 and 7.6 Kahramanmaraş (Turkiye) Earthquake Sequence: Insights from Co-seismic and Post-seismic Surface Deformation 

Seda Özarpacı, Alpay Özdemir, Efe Turan Ayruk, İlay Farımaz, Muhammed Turğut, Yusuf Yüksel, Figen Eskiköy, Uğur Doğan, Semih Ergintav, Cengiz Zabcı, Rahşan Çakmak, Mehmet Köküm, and Ziyadin Çakır

On 6 February 2023, 04:17 in local time, Mw 7.8 earthquake and nine hours later, 13:24 in local time, Mw 7.7 earthquake struck the same region resulting a massive destruction with loss of lives more than 41,000 in Türkiye and 4,000 Syria.  The earthquake took place on the East Anatolian Fault Zona (EAFZ) which is a plate boundary (~600 km) between the Anatolian and Arabian plates from Karlıova triple junction between Arabian, Anatolian and Eurasian plates to the Dead Sea Fault Zone (DSFZ) and parts of another triple junction at the south end between Adana block, Anatolian and Arabian plates at Kahramanmaraş. Secular plate velocities between Arabia and Anatolia range from 6 to 10 mm/yr and has resulted in destructive earthquakes in eastern Turkey as documented by historical records. The largest known earthquakes along the EAFZ occurred on November 29, 1114 (M > 7.8), March 28, 1513 (M > 7.4) and March 2, 1893 (M > 7.1).  The activity of these large devastating historical earthquakes contrasts with the low-level activity during the 20th century. The quiescence ended with the Mw 6.9 1971 Bingöl earthquake, which is followed about 50 year later by the Mw 6.9 January 24, 2020 Sivrice, Elazığ earthquake that ruptured only 45 km of the 95 km long Sivrice-Pütürge segment. With the latter event, seismicity accelerated along the rupture zone and activity moved towards to the SW.

Our aim is to monitor and estimate the co- and post- deformation field from geodetic measurements (InSAR and GNSS). While maximum co-seismic displacement at the ANTE GNSS station was 0.4 m in the first event (KMRS, Kahramanmaras), the biggest co-seismic displacement observed in the second event was 4.5 m in EKIZ (Ekinozu) station which is ~1.5 km away from the epicenter of the second earthquake. This co-seismic deformation field was estimated from open station of TUSAGA-Active GNNS Network. Following the earthquakes, we established three new continuous GNSS stations to monitor the postseismic deformation in Hatay province close to Türkoğlu segment of the East Anatolian Fault. Preliminary analysis indicates about 20 mm of postseismic deformation 10 days following the earthquakes. We have also conducted a GNSS campaign and occupied nearfield benchmarks. We will also monitor postseismic deformation using Sentinel and CosmoSkyMed SAR data field.

This work is supported by TUBITAK project number 121Y400 and 1002-C project “Mw 7.7 Pazarcik (Kahramanmaras) Earthquake Sequence”.

Keywords: 06.02.2023 Turkiye Earthquake Sequence, Kahramanmaras Earthquake, GNSS, InSAR, Coseismic and Postseismic Deformation

How to cite: Özarpacı, S., Özdemir, A., Ayruk, E. T., Farımaz, İ., Turğut, M., Yüksel, Y., Eskiköy, F., Doğan, U., Ergintav, S., Zabcı, C., Çakmak, R., Köküm, M., and Çakır, Z.: February 6, 2023, Mw 7.8 and 7.6 Kahramanmaraş (Turkiye) Earthquake Sequence: Insights from Co-seismic and Post-seismic Surface Deformation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17624, https://doi.org/10.5194/egusphere-egu23-17624, 2023.

EGU23-17626 | Posters on site | SM1.6

The contribution of Total Variometric Approachto the 2023 Türkiye earthquake sequences 

Michela Ravanelli, Federica Fuso, Elvira Astafyeva, and Mattia Crespi

The 2023 Türkiye earthquake sequences are among the most devastating events of recent years. The earthquake occurred on February 6th, 2023 and was characterized by several foreshocks starting from 1:17UT. The Mw 7.8 shock was the strongest and was caused by a shallow strike-slip faulting.
We applied the Total Variometric Approach (TVA) methodology to fully characterize the 2023 Turkey earthquake sequences from ground to the ionosphere [1].
The TVA technique simultaneously employs two variometric algorithms, VADASE (Variometric Approach for Displacement Analysis Stand-alone Engine) and VARION (Variometric Approach for Real-Time Ionosphere Observation), to retrieve earthquake ground shaking, co-seismic displacements and ionospheric Total Electron Content (TEC) variations in real-time. TVA was already successfully applied to the 2015 Mw 8.3 Illapel earthquake and tsunami.
In this case, we used IGS observations from 6 GNSS located in Turkey, Greece and Israeli and data from 60 GNSS receivers belonging to the Turkish network TUSAGA-Akitf [2].

Our first results show very strong ground shaking up to 10 cm/s in the East direction and up to 25 cm in the North direction. We notice great displacements especially in the horizontal plane (up to 30 cm). This is coherent with a strike-slip earthquake. Nonetheless, we also observe great displacements in the Up component (up to 1m). This could be the reason why we see this earthquake signature also in the ionosphere, although it is a strike-slip shock.
Indeed, preliminary ionospheric analyses reveal the signature of acoustic-gravity waves epicenter (AGWepi) especially for satellites G03, G04, G31 and E09.
A 30cm tsunami wave was also registered in Erdemil, along the Turkish coastline.

This study shows how the TVA can contribute to the complete understanding and rapid characterization of the seismic event, from ground to the atmosphere, and to manage and real-time earthquake hazard assessment.

[1] Ravanelli, M., Occhipinti, G., Savastano, G., Komjathy, A., Shume, E. B., & Crespi, M. (2021). GNSS total variometric approach: first demonstration of a tool for real-time tsunami genesis estimation. Scientific Reports, 11(1), 1-12.

[2] https://www.tusaga-aktif.gov.tr/

How to cite: Ravanelli, M., Fuso, F., Astafyeva, E., and Crespi, M.: The contribution of Total Variometric Approachto the 2023 Türkiye earthquake sequences, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17626, https://doi.org/10.5194/egusphere-egu23-17626, 2023.

EGU23-17628 | Posters on site | SM1.6

Surface deformation retrieval of the February 2023 South-East Turkeyand Northern Syria Mw 7.8 and Mw 7.5 seismic events through Sentinel-1and SAOCOM-1 co-seismic SAR image analysis 

Francesco Casu, Fernando Monterroso, Yenni Lorena Belen Roa, Pasquale Striano, Simone Atzori, Manuela Bonano, Claudio De Luca, Marianna Franzese, Michele Manunta, Giovanni Onorato, Muhammad Yasir, Ivana Zinno, and Riccardo Lanari

On 6 February 2023 two Mw 7.8 and Mw 7.5 seismic events struck the South-East Turkey and Northern Syria regions, close to the cities of Gaziantep and Ekinözü, causing more than 50 thousands of fatalities and above 120 thousands of injured, with incalculable, widespread damage to the surrounding villages. Such earthquakes are related to the main geodynamic regime controlled by the triple junction between the Anatolian, Arabian and African Plates, and by the tectonic context associated with a shallow strike-slip faulting, including the East Anatolian Fault zone and the Dead Sea Transform. Immediately after the occurrence of these earthquakes, we started investigating the surface deformation field induced by the considered seismic events by applying the Differential SAR Interferometry (DInSAR) and the Pixel Offset (PO) techniques, within the framework of EPOS (European Plate Observing System), which is the European research infrastructure for the study of the solid Earth.

To this aim, we exploited several co-seismic SAR data pairs that have been collected by different satellite constellations. First of all, we exploited C-band (5.6 cm of wavelength) SAR data acquired by the Sentinel-1A sensor of the European Copernicus program from both ascending (Track 14) and descending (Track 94 and 21) orbits. Moreover, we benefited from the availability of a number of L-band (23 cm of wavelength) SAR images acquired by the twin satellites of the Argentine SAOCOM-1 constellation, programmed in collaboration with the Italian and Argentine Space Agencies.

The main focus of this work regards the joint exploitation of the Sentinel-1 and SAOCOM-1 SAR products to retrieve the 3D co-seismic deformation field. Further analysis is envisaged in order to model the co-seismic sources.

This work is supported by: the 2022-2024 IREA-CNR and Italian Civil Protection Department agreement, and by the H2020 EPOS-SP (GA 871121) and Geo-INQUIRE (GA 101058518) projects. The authors also acknowledge ASI for providing the SAOCOM data under the ASI-CONAE SAOCOM License to Use Agreement. Sentinel-1 data were provided through the European Copernicus program.

How to cite: Casu, F., Monterroso, F., Roa, Y. L. B., Striano, P., Atzori, S., Bonano, M., De Luca, C., Franzese, M., Manunta, M., Onorato, G., Yasir, M., Zinno, I., and Lanari, R.: Surface deformation retrieval of the February 2023 South-East Turkeyand Northern Syria Mw 7.8 and Mw 7.5 seismic events through Sentinel-1and SAOCOM-1 co-seismic SAR image analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17628, https://doi.org/10.5194/egusphere-egu23-17628, 2023.

EGU23-17632 | Posters on site | SM1.6

Direct Rupture Speed Estimation from "Rupture Phase" of the 2023 Turkey Mw 7.8 Earthquake 

Suli Yao and Hongfeng Yang

Rupture speed is a fundamental dynamic characteristic of earthquakes, which can be inferred by multiple approaches such as the back projection (BP) and kinematic fault slip inversion with near-field or far-field data as constraints. Here we propose a rapid estimate for rupture speed directly from the strong motion records along the southern segment of the Mw 7.8 Turkey earthquake on 6 Jan 2023. We collect data on 12 strong motion stations that are located within 3 km from the major fault trace. Due to the short distances to the fault, the ground motions on these stations can approximate a very local rupture phase, with the peak amplitudes of fault-parallel velocities corresponding to the rupture front passage. We pick peak velocities on three components and obtain an apparent propagation speed of ~ 3 km/s. To validate the correlation between the rupture speed and the apparent rupture-phase speed, we conduct 3-D dynamic rupture simulations for this Mw 7.8 event and compare the synthetic rupture front with the rupture phase. We find that the rupture speed is slightly higher than the rupture-phase speed with a difference of 5% - 10%. Based on our modeling results, we infer the actual rupture speed of ~3.2 km/s along the south segment in the Mw 7.8 Turkey earthquake. Different from those waveform-fitting methods that require certain assumptions on the earthquake source, such as the relation between the rupture front and the radiation process or the slip rate function, our approach provide a fast and robust rupture speed estimation that can be done in real time.

How to cite: Yao, S. and Yang, H.: Direct Rupture Speed Estimation from "Rupture Phase" of the 2023 Turkey Mw 7.8 Earthquake, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17632, https://doi.org/10.5194/egusphere-egu23-17632, 2023.

EGU23-17634 | Posters on site | SM1.6

Sequence-specific updating of European ETAS model: Application to the 2023 Türkiye-Syria earthquake sequence 

Marta Han, Leila Mizrahi, Stefan Wiemer, and Irina Dallo

We analyse the spatio-temporal evolution of the aftershock sequence to the 2023 M7.8 Türkiye-Syria earthquake. Recently, we have calibrated a generic ETAS-based operational forecasting model for Europe, using the unified earthquake catalog developed within the European Seismic Hazard Model (ESHM20; Danciu et al., 2022) for data between 1990 and 2015. Focusing on the earthquake sequence that started in February 2023 in Türkiye, we analyse how our model would have forecasted the temporal and spatial evolution of the sequence. We observed that the productivity of the sequence is substantially higher than forecasted by our generic model. Similar observations have been made in earlier studies on other sequences, and strategies have been proposed to improve existing models based on sequence-specific data (e.g., Omi et al., 2015). Therefore, we conclude that sequence-specific updating is required to achieve an acceptable fit between model and observations.

Here, we investigate the best way to visualize the results of aftershock forecasting and operational earthquake forecasting, and propose a new strategy for sequence-specific updating of model parameters to accurately describe the productivity and the spatial aftershock distribution, while leveraging on the parameters obtained from larger amounts of data within the European model. Our approach strives to avoid biases in the description of the temporal decay due to relying on short-term data. This is done by keeping the parameters describing the temporal decay fixed to the values inverted with our baseline model and calibrating the remaining parameters, using data of the ongoing sequence. As an alternative way to better control productivity, we test model variants for which the a value is fixed to be equal to the GR law exponent b, as proposed by Hainzl et al. (2008). The variants with both fixed and calibrated temporal kernel and productivity are fitted to varying time periods of the Turkish sequence.

We assess the model’s consistency with observations by comparing the forecasts issued by the basic and modified models to the observed events. Preliminary results suggest that keeping the temporal kernel and the productivity parameter a fixed provides better forecasts than the baseline model, already when small amounts of data from the sequence are available. Having identified a promising strategy for sequence-specific model updating, we plan to test whether it is systematically successful by applying it to all earthquake sequences in Europe that occurred after the end of the baseline model training period in 2015. Moreover, we will develop prototypes of communication products that should support professional, societal stakeholders (e.g., decision makers, first responders) to take informed decisions, for example during rescue investigations. Thereby, we will follow evidence-based recommendations derived from the research efforts in the European Horizon-2020 project RISE (Freeman et al., 2023).

How to cite: Han, M., Mizrahi, L., Wiemer, S., and Dallo, I.: Sequence-specific updating of European ETAS model: Application to the 2023 Türkiye-Syria earthquake sequence, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17634, https://doi.org/10.5194/egusphere-egu23-17634, 2023.

NH1 – Hydro-Meteorological Hazards

Extreme heat under the warming environment has direct societal implications for developing and highly urbanized populations. Previous studies have shown that extreme wet-bulb temperature, a multivariate measure of temperature-humidity, has led to anomalously high convective inhibition and increased precipitation intensity over the tropics. However, little is known about the linkage of humid heat stress characteristics, such as duration and peak heat stress, versus the sub-daily precipitation extremes over urban and periurban locations in the tropics. Leveraging ground-based meteorological records of around five decades from the 27 hydrometric observatories of the India Meteorological Department, I investigate the compound occurrence of humid heat stress versus sub-daily precipitation extremes across the Indian subcontinent (4 - 40° latitude and 65 - 100° longitude). Here heatwaves are identified when three or more consecutive days of extreme wet-bulb temperature, Tw, is above the 90th percentile daily variable threshold for each day of the year.  I show the impact of heat stress and its duration on sub-daily precipitation extremes using a novel conditional probabilistic approach. The risk of sub-daily precipitation extremes at each urbanized location is modelled considering the nonstationarity of underlying drivers. The relative timings between each driver and duration overlap between heatwaves and above-average precipitation extremes (wet spells) are also shown. The results show that the extremal upper dependence between peak Tw and sub-daily precipitation extremes are significantly positive and lies in the range of 0.12 to ≥0.20 across the central northeast region that housed part of the Indo-Gangetic Plains. More than 40% of sites report the most coinciding occurrence of humid-heat stress versus sub-daily precipitation extreme, where each of these drivers readily overlapped each other with a lag time of fewer than two days. Further, I show that considering the magnitude of heat stress as a 10-year return period, even a moderate increase in duration will increase the probability of sub-daily precipitation extremes by a range of 1.7 to 20%, with a notable increase across coastal cities. These results are supported by the physically consistent theory suggesting an increase in sub-daily rainfall extremes in response to climate warming over lands of the tropics because of the combination of “positive thermodynamic” and “dynamic contributions.” The observational evidence of increased sub-daily precipitation extremes in response to humid heat stress would help stakeholders and international organizations build resilient strategies to mitigate the impacts of such consecutive hazards.

How to cite: Ganguli, P.: Observational Evidence Reveals Growing Spatial Scales of Compound Occurrence of Humid Heat Stress-Extreme Rainfall in India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-509, https://doi.org/10.5194/egusphere-egu23-509, 2023.

EGU23-2491 | Posters on site | NH1.1

Minimum rest time for outdoor workers exposed to summer heat stress in South Korea 

Seung-Wook Lee, Young-San Park, and Gwangyong Choi

Continuous work in high-temperature environments can lead to occupational injuries, illnesses, and even deaths. Thus, mandatory rest time for appropriate heat management programs must be provided for the safety of workers. In this study, we figured out the minimum rest time for the heat safety of outdoor workers in South Korea. To quantitatively calculate the minimum rest time, the wet-bulb globe temperature (WBGT) index estimated by 27 synoptic weather stations in South Korea and the national work-rest regimens were used. We assumed that the minimum rest time is the same as the rest time of the work-rest regimens recommended by the WBGT. To examine the intra-seasonal evolution patterns of the minimum rest time, summer seasons are divided into several sub-periods. The average of the hourly WBGT values during summer months from June to August (2009–2018) shows that outdoor workers with a moderate workload (200–350 kcal/h) are exposed to heat stress during approximately 30% of their daytime working hours (06:00–18:00). In the whole summer period, the minimum rest time required for each hour was about 5 minutes for moderate work. But in the mid-summer period from late July to early August, the daily minimum rest time for moderate workload noticeably increases to 20 minutes of mid-day (11:00–15:00). Regionally, no significant increase in rest time was found in areas with high urbanization rates.

How to cite: Lee, S.-W., Park, Y.-S., and Choi, G.: Minimum rest time for outdoor workers exposed to summer heat stress in South Korea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2491, https://doi.org/10.5194/egusphere-egu23-2491, 2023.

EGU23-2554 | Orals | NH1.1 | Highlight

Impact of Persistent Heatwave on Heat-Related Illness 

JongHyeon Baek, Sungsu Lee, and Jeong Ah Um

Since 2000, numerous people have suffered from heat-related illness and even died of the heatwave, and more than thousands of people in Korea share the similar pain and loss since 2010. The extremely high temperature and humid are known to be responsible for illness and death; however, the spatial correlation between highest temperature and occurrence of heat-related illness seems relatively low according to the previous studies. There can be many reasons for this, one of which is social aspect. Another reason of the varying probability of occurrence can be the duration of the temperature. Therefore, in this paper, in order to analyze the impacts of persistent high temperatures on the occurrence of heatwave disease, we analyzed the number of days of heatwave days and tropical night days immediately preceding the date of the onset of heat-related illness. This research was supported by a grant(2020-MOIS35-002) of Policy-linked Technology Development Program on Natural Disaster Prevention and Mitigation funded by Ministry of Interior and Safety(MOIS, Korea).

How to cite: Baek, J., Lee, S., and Um, J. A.: Impact of Persistent Heatwave on Heat-Related Illness, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2554, https://doi.org/10.5194/egusphere-egu23-2554, 2023.

EGU23-3460 | ECS | Orals | NH1.1

Heat waves monitoring over West African cities: uncertainties, characterization and recent trends 

Cedric Gacial Ngoungue Langue, Christophe Lavaysse, Mathieu Vrac, and Cyrille Flamant

Heat waves can be one of the most dangerous climatic hazards affecting the planet; having dramatic impacts on the health of humans and natural ecosystems as well as on anthropogenic activities, infrastructures and economy. Based on climatic conditions in West Africa, the urban centers of the region appear to be vulnerable to heat waves. The goals of this work is firstly to assess the potential uncertainties encountered in heat waves detection; and secondly analyze their recent trend in West Africa cities during the period 1993-2020. This is done using two state-of-the-art reanalysis products, namely ERA5 and MERRA, as well as two local station datasets, namely Yoff Dakar in Senegal and Aéroport Félix Houphouët Boigny Abidjan in Ivory Coast. An estimate of station data from reanalyses is processed using an interpolation technique : the nearest neighbor to the station with a land sea mask >=0.5; the interpolated temperatures from local station in Dakar and Abidjan, show slightly better correlation with ERA5 than MERRA. Three types of uncertainties are discussed: the first type of uncertainty is related to the reanalyses themselves, the second is related to the sensitivity of heat waves frequency and duration to the threshold values used to monitor them; and the last one is linked to the choice of indicators and the methodology used to define heat waves. Three sorts of heat waves have been analyzed, namely those occurring during daytime, nighttime and both daytime and nighttime concomitantly. Four indicators have been used to analyze heat waves based on 2-m temperature, humidity, 10-m wind or a combination of these. We found that humidity plays an important role in nighttime events; concomitant events detected with wet-bulb temperature are more frequent and located over the north Sahel. For all indicators, we identified 6 years with a significantly higher frequency of events (1998, 2005, 2010, 2016, 2019 and 2020) possibly due to higher sea surface temperatures in the equatorial Atlantic ocean corresponding to El Nino events for some years. A significant increase in the frequency, duration and intensity of heat waves in the cities has been observed during the last decade(2012-2020); this is thought to be a consequence of climate change acting on extreme events

How to cite: Ngoungue Langue, C. G., Lavaysse, C., Vrac, M., and Flamant, C.: Heat waves monitoring over West African cities: uncertainties, characterization and recent trends, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3460, https://doi.org/10.5194/egusphere-egu23-3460, 2023.

EGU23-3523 | Orals | NH1.1 | Highlight

Varying drivers of humid heat extremes over Africa 

Cathryn Birch, Richard Keane, and John Marsham

Africa is particularly vulnerable to present day and future temperature extremes due to its (sub)tropical location, its growing population and the challenges of adapting to extreme heat in many of its regions. Globally, the vast majority of past research on the drivers of heatwaves is focused on dry bulb temperature extremes. The drivers of humid heat extremes vary by location and there is limited understanding of the drivers in all parts of the world, but particularly over Africa. Previous published research by the authors showed increased humidity, cloud, rainfall and/or evaporation drive events over most of Africa. However, across the central African equatorial belt, where absolute values of wet bulb temperature are highest, humid heat extremes are driven by both increased temperature and humidity, and cloud and rainfall anomalies are less important. Here we use ERA5 reanalysis to identify multi-day, large-scale humid heat events over different regions of Africa and quantify the roles of moisture transport, cloud, rainfall and atmospheric circulation. We compare and contrast the different sub-tropical and tropical climatic regions of Africa and present a detailed case study of coastal East Africa. East Africa is of particular interest due to its high climatological wet bulb temperatures, its high population, and its coastal location where the land-sea breeze may be a key control on humid heat extremes. We identify the time of day and locations in coastal East Africa that experience the highest daily maximum wet bulb temperatures and discuss the controlling factors.

How to cite: Birch, C., Keane, R., and Marsham, J.: Varying drivers of humid heat extremes over Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3523, https://doi.org/10.5194/egusphere-egu23-3523, 2023.

EGU23-4145 | ECS | Orals | NH1.1

Using Apriori Algorithm to Find the Number of Frequent Heat Wave Days Affecting Cities in Europe Over the Future Period 

Mahesh Ramadoss, Christopher Kadow, Meyyappan Thirunavukkarasu, Samuel Chellathurai, Shameema Begum, Narmatha Duraisamy, Akbar Bhadushah, and Abdul Rasheed

Heatwave episodes have severe consequences in the forms of excess mortality in many regions around the world, shortage of agricultural products, drastic changes in ecosystem function and health risks. Due to the global mean temperature rising, the acceleration of extreme temperature disturbing highly at the local scale level, particularly in urban areas. From an economic growth point of view, Major cities are contributing in terms of GDP more. Heatwaves have impacted European GDP significantly in recent years. Our work is to find the number of frequent heat wave days affecting cities which are contributing to the growth of the economy in terms of GDP and density of population wise in Europe over the near future, mid future and long future using the Apriori algorithm. The features of the heat wave and their attributes have been defined according to the criteria explained in ETCCDI. The dataset that contains heat wave days in Europe derived from EURO-CORDEX climate projections is used in this work.

References

  • Copernicus Climate Change Service (C3S): Heat waves and cold spells in Europe derived from climate projections, Climate Change Service Climate Data Store (CDS),  DOI:10.24381/cds.9e7ca677
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  • S. E. Perkins and L.V.Alexander, On the Measurement of Heat Waves, DOI: https://doi.org/10.1175/JCLI-D-12-00383.1
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How to cite: Ramadoss, M., Kadow, C., Thirunavukkarasu, M., Chellathurai, S., Begum, S., Duraisamy, N., Bhadushah, A., and Rasheed, A.: Using Apriori Algorithm to Find the Number of Frequent Heat Wave Days Affecting Cities in Europe Over the Future Period, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4145, https://doi.org/10.5194/egusphere-egu23-4145, 2023.

In the recent years, under the influence of land cover changes and climate changes, more frequent extreme heat events have occurred and make people face severer heat stress than before, especially for the people of low socioeconomic status, elderly or other vulnerable groups. Moreover, although land cover changes in rural areas are milder than in urban areas, the rotation of crop fields and expansion of non-vegetation areas in rural areas will alter the landscape and further influence the thermal environment. However, the issues of thermal comfortability in aging rural areas have been rarely studied compared to the urban areas in the past. To quantify and mitigate the risk of heat exposure of the elderly in rural areas, the goal of this study is to analyze the spatial-temporal characteristics of thermal environment and heat-related comfortability in an aging rural areas, Yunlin County, in central Taiwan.

To characterize the spatial-temporal patterns of the thermal environment in Yunlin, this study estimated the spatial distribution of different meteorological parameters from seasonal to annual scales and analyzed the land cover compositions from the satellite remote-sensing images. Furthermore, to evaluate the effects of heat stress on the human comfort in aging rural areas, a thermal comfort index, physiological equivalent temperature (PET), was quantified using the meteorological data from weather stations in Yunlin and surrounding area with the Python package of pythermalcomfort. In addition, the statistical methods will be used to analyze how land use affects the microclimate and comfort in the Yunlin area from the community to regional scales. In brief, the anticipated results from this study are expected to characterize factors that affect the thermal environment in aging rural areas, and further provide management and policy suggestions for the reduction in the risk of heat exposure in the future.

Key words: Thermal comfortability, Physiologically Equivalent Temperature (PET), Heat stress, Elderly group 

How to cite: Wang, T.-Y. and Juang, J.-Y.: Investigation Effects of Environmental and Geographical Factors on Thermal Environment in Aging Rural Areas in Taiwan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4904, https://doi.org/10.5194/egusphere-egu23-4904, 2023.

We implement the Wet Bulb Globe Temperature (WBGT), a standardized heat stress metric, into the Community Land Model (CLM5), the land surface component of the Community Earth System Model (CESM2). This includes the notoriously difficult calculation related to measuring human heat stress: radiation. Following the International Organization for Standardization (ISO) 7243, physical representations of the instruments, a globe thermometer and natural wet bulb thermometer, simulate where humans work and live in non-urban environments. By using ISO 7243 within CLM5, we create a common framework within Earth system models to calculate the impact of radiation on temperature-moisture covariance.

 

We demonstrate the capabilities of the WBGT using a default configuration of CLM5. We output 4x daily temporal resolutions globally, showing the advantage of simulating the WBGT within each environment. The WBGT outdoor and indoor calibration is simulated in an averaged grid cell, above the vegetation canopy, below the vegetation canopy, and bare ground environments. We examine the 1995 Chicago Heatwave, specifically the rural regions impacted by the heatwave, and demonstrate that the grid cell average calculated at the CLM5 30-minute time step is a poor representation of human environments and can differ by multiple degrees. In high heat stress environments following ISO 7243, a 0.5C change in WBGT can lead to a >10% reduction in labor capacity. This difference in temperature and labor capacity shows that assumptions about calculating a non-linear algorithm — even utilizing high temporal frequency grid cell averages that drive non-linear labor capacity impact models — is a flawed approach that can grossly over or underestimate the impact of heat stress on future climate change projections. To accurately assess the direct exposure, risk, and damage of climate change on people, it is critical to implement diagnostics directly into Earth system models.

How to cite: Buzan, J. and Joos, F.: Substantial Errors Revealed When Calculating Heat Stress Using Grid Cell Averages as Compared to Sub-Grid Cell Environments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5245, https://doi.org/10.5194/egusphere-egu23-5245, 2023.

EGU23-5468 | Orals | NH1.1

Effect of anomalous high-pressure in Eastern Europe on the prediction of 2018 East Asian heatwave 

Jinhee Kang, Jieun Wie, Sang-Min Lee, Johan Lee, Baek-Jo Kim, Semin Yun, and Byung-Kwon Moon

In 2018, a severe and long-lasting heatwave in East Asia resulted in significant socio-economic damage. To possibly reduce losses, it is necessary to understand the mechanisms of heatwaves and increase their predictability. In this study, we identify the patterns of geopotential height responsible for the 2018 East Asian heatwave from ERA5 observation and compare them with simulations using Global Seasonal Forecasting System version 6 (GloSea6). The K-means clustering analysis reveals an anomalous high-pressure pattern in Eastern Europe, which is mainly associated with the 2018 East Asian heatwave. GloSea6 experiments were then conducted with various initial conditions. Notably, GloSea6 runs reproducing the observed high-pressure anomaly in Eastern Europe shows a good prediction of the 2018 East Asian heatwave. Sensitivity experiments further highlight the lack of soil moisture in Eastern Europe seems to be a key factor for the anomalous high-pressure pattern there, resulting in the 2018 East Asian heatwave. Our results imply that model- and observation-consistent representations of soil moisture in Eastern Europe are required to reduce the uncertainty in predicting the East Asian heatwaves.

This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2020-01212. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2022R1A2C1008858).

How to cite: Kang, J., Wie, J., Lee, S.-M., Lee, J., Kim, B.-J., Yun, S., and Moon, B.-K.: Effect of anomalous high-pressure in Eastern Europe on the prediction of 2018 East Asian heatwave, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5468, https://doi.org/10.5194/egusphere-egu23-5468, 2023.

EGU23-7581 | ECS | Posters on site | NH1.1

The impact of extreme heat during pregnancy and childbirth in Johannesburg, South Africa 

Chloe Brimicombe, Annika Sachs dos Santos, Ijeoma Solarin, Gloria Maimela, Matthew Cherish, Katharina Wieser, and Ilona M. Otto

Heatwaves and Heat Stress are an increasing risk on a global scale with our changing climate. For Southern Africa, it has been demonstrated that the number of heatwaves and heat stress days have increased since the 1980s. Consequently, a greater proportion of people in the region are exposed to extreme heat for longer periods of time. Extreme heat has been shown to have negative effects on maternal health and birth outcomes and is compounded by existing vulnerabilities such as age and lower socio-economic status. Limited previous research in Africa has demonstrated that exposure to extreme heat in the first weeks of pregnancy can cause complications during and after pregnancy such as pre-eclampsia and gestational diabetes. In addition, it has been found that exposure to extreme heat in the region increases the risk of low birth weight, pre-term birth and in some cases stillbirth. In this study, maternal health data from tertiary hospitals in Johannesburg is analysed against local weather station observations for temperature and heat stress. We assess the threshold at which extreme heat has adverse health outcomes during pregnancy and childbirth (intra-partum) and suggest potential interventions to mitigate against this. This work contributes to calls to improve the understanding of the impacts of extreme heat on maternal and child health in Africa. It also supports work to create global maternal and child climate change health indicators, to better inform adaptation and mitigation efforts.

This research is part of HIGH horizons which is funded by the European Union’s Horizon Research and Innovation programme under grant agreement no 101057843

How to cite: Brimicombe, C., Sachs dos Santos, A., Solarin, I., Maimela, G., Cherish, M., Wieser, K., and Otto, I. M.: The impact of extreme heat during pregnancy and childbirth in Johannesburg, South Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7581, https://doi.org/10.5194/egusphere-egu23-7581, 2023.

EGU23-7995 | ECS | Orals | NH1.1 | Highlight

A Novel Framework for the Assessment of Heat-Wave Risks and Nature-Based Solutions (NBS) Impacts 

Luigi Brogno, Francesco Barbano, Laura Sandra Leo, and Silvana Di Sabatino

Current climate change projections show that the probability of occurrence and the magnitude of heat-wave events are increasing worldwide. These events have to be considered as higher risks for territories and ecosystems, especially where vulnerability is high. The occurrence of heat waves translates into several potential damages such as an increase in fatalities and production losses, degradation of natural and cultural heritages, or the triggering of other hazards such as wildfires. The overlap of all these consequences may lead to both relevant economic losses and additional CO2 emissions affecting our resilience and exacerbating in turn climate change.
In this context, we propose a novel framework for the assessment of risks resulting from heat waves with the aim of quantifying the main contributions to economic losses and CO2 emissions. This framework follows the conceptual definition of risk provided by the Intergovernmental Panel on Climate Change (IPCC) as the product of hazard, exposure, and vulnerability components. The newly-proposed formulation of these components includes the concept of Nature-Based Solutions (NBS) as strategies carried out to enhance our adaptive capacity in a sustainable and cost-effective way. Since NBS consist of natural features that are also exposed to heat waves, the entire life cycle of NBS is considered (i.e., the implementation, maintenance, and possible restorations). The proposed framework stands as a tool for assessing the local impacts of already-implemented or designed NBS in the current and future climate scenarios.

How to cite: Brogno, L., Barbano, F., Leo, L. S., and Di Sabatino, S.: A Novel Framework for the Assessment of Heat-Wave Risks and Nature-Based Solutions (NBS) Impacts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7995, https://doi.org/10.5194/egusphere-egu23-7995, 2023.

EGU23-8146 | ECS | Orals | NH1.1 | Highlight

Seasonal forecasts of the nighttime heat waves in Europe 

Verónica Torralba, Stefano Materia, Leone Cavicchia, M.Carmen Álvarez-Castro, Enrico Scoccimarro, and Silvio Gualdi

Extreme climate events such as heat waves cause enormous stress on human health and ecosystems and economic losses in agriculture, energy, or water management activities. In particular, the combined effect of above-normal nighttime temperatures and high humidity poses a high risk to human health. This is related to the thermal discomfort which prevents the human body’s recovery from daytime high-heat exposure. Seasonal forecasts of the nighttime heat waves might be used as a tool to anticipate these risks and to better manage their social and economic impacts. However, the ability of the seasonal forecast systems to predict these extreme events has not been explored so far. This work provides insight into the potential of four seasonal forecasting systems (CMCC Seasonal Prediction System 3.5, DWD System 2.1, ECMWF SEAS5, and Météo-France System 7) to provide skillful and reliable predictions of the nighttime heat waves in Europe during the boreal summer season. Different potential proxies for the assessment of nighttime heat waves have been considered: nighttime apparent temperature computed from temperature and humidity at night, the temperature at night, or daily minimum temperature. There are different indices that can be used to investigate extreme temperatures, but the one chosen in this study is very suitable for seasonal forecast analysis because it is invariant to the mean biases and provides an integrated view of the nighttime heat waves for the entire season with information on their duration, frequency, and intensity. The forecast quality assessment has revealed that state-of-the-art seasonal forecast systems are able to provide useful information on the nighttime heat waves in Southern Europe, which is a particularly vulnerable region where timely climate information can benefit the decision-making processes. 

How to cite: Torralba, V., Materia, S., Cavicchia, L., Álvarez-Castro, M. C., Scoccimarro, E., and Gualdi, S.: Seasonal forecasts of the nighttime heat waves in Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8146, https://doi.org/10.5194/egusphere-egu23-8146, 2023.

EGU23-10533 | ECS | Orals | NH1.1 | Highlight

Stickiness: A New Variable to Characterize the Temperature and Humidity Contributions toward Extreme Humid Heat 

Catherine Ivanovich, Colin Raymond, Radley Horton, and Adam Sobel

Extreme values of wet bulb temperature are often used as indicators of heat stress for humans and other animals. However, humid heat extremes are fundamentally compound events, and a given wet bulb temperature can be generated by various combinations of temperature and humidity. Differentiating between extreme humid heat driven by anomalous temperature versus anomalous humidity is essential to identifying these extremes’ distinct physical drivers and preparing for their individual impacts. Extreme dry heat tends to occur due to processes such as blocking events and land surface feedbacks, and it has the potential to prime regions for wildfires and crop damage. In contrast, extreme humid heat depends more on strong moisture fluxes and vertical stability to moist convection, and it poses high risk for human health through its influence over heat stress.

Here we explore the variety of combinations of temperature and humidity contributing to heat extremes across the globe. In addition to using traditional metrics, we derive a novel thermodynamic state variable named “stickiness.” Directly analogous to oceanographic spice (which quantifies the relative contributions of temperature and salinity to a given seawater density), stickiness quantifies the relative contributions of temperature and specific humidity to a given wet bulb temperature.

Consistent across metrics, we find that extreme humid heat — that is, the occurrence of wet bulb temperatures sufficiently high to impact human health — tends to occur in the presence of anomalously high humidity. Although theoretically humid heat extremes can be achieved at low humidities if temperature is high enough, this tends not to happen in practice. Using stickiness allows for the direct evaluation of the spatial and temporal variability in the temperature- and humidity-dependence of humid heat events, a task that is more complicated and subjective using traditional variables. We identify locations with high variability in stickiness: these include the Persian Gulf, the western United States, and southeast Australia. These locations are key areas where the predictive skill for heat stress-related mortality may improve by considering fluctuations in atmospheric humidity in addition to dry bulb temperature.

How to cite: Ivanovich, C., Raymond, C., Horton, R., and Sobel, A.: Stickiness: A New Variable to Characterize the Temperature and Humidity Contributions toward Extreme Humid Heat, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10533, https://doi.org/10.5194/egusphere-egu23-10533, 2023.

EGU23-11659 | Posters on site | NH1.1

How unexpected was the Spring 2022 South Asian Heatwave 

Waqar ul Hassan and Munir Ahmad Nayak

Persistent heatwaves cause severe impacts on the ecosystem and society, including increased mortality, and widespread snow and glacier melting. These impacts are expected to escalate in a warmer world, which is likely to witness more frequent and intense heatwaves. South Asia (SA), home to one-fifth of the global population and the largest freshwater resource on the earth, is a hotspot of extreme heatwaves and vulnerable to severe impacts. The region recently experienced its hottest March and April of the century in the year 2022. Here, we use high-resolution, long-term ERA5 (1959–2022) and CPC (1979–2022) data to show that the temperatures in Northwestern South Asia were about 5°C higher than the climatology, which corresponds to about 2.5 standard deviations above the mean. Using maximum temperature-based CTX90pct definition of heatwaves, we show the 42-day-long heatwave in the month of March and April 2022 ranked the most severe heatwave recorded in the available observation period of 65 years. The heatwave engulfed half of Northwest SA, approximately 1.6 million km2, with an average intensity of 1.8°C. The high-temperature driven snow melting during the heatwave nearly vanished the year’s snowpack, which normally lasts till June. With further analysis, we find that the heatwave was initiated by a persistent anticyclonic blocking associated with a sub-tropical Rossby wave, while it was exacerbated by strong positive land-atmosphere feedback due to lack of soil moisture and latent heat. Our findings provide valuable insights into understanding the changes and impacts of heatwaves in the mountainous areas of SA.

How to cite: ul Hassan, W. and Ahmad Nayak, M.: How unexpected was the Spring 2022 South Asian Heatwave, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11659, https://doi.org/10.5194/egusphere-egu23-11659, 2023.

EGU23-12382 | ECS | Orals | NH1.1 | Highlight

Quantifying impact-relevant heatwave durations 

Kelley De Polt, Philip J. Ward, Marleen de Ruiter, Ekaterina Bogdanovich, Markus Reichstein, Dorothea Frank, and René Orth

Heatwaves are weather hazards which can influence societal and natural systems. Recently, heatwaves have increased in frequency, duration, and intensity, and this trend is projected to continue as a consequence of climate change. This has triggered extensive research aiming at a better understanding of their impacts and underlying processes. However, the study of heatwaves is hampered by the lack of a common definition, which limits comparability between studies. This applies in particular to the considered time scale. 

Here, we determine impact-relevant temporal scales of heatwaves. For this purpose we characterise societal metrics related to health (heat-related hospitalizations, mortality) as well as public attention (Google trends, news articles) in Germany. We calculate country-averaged temperatures and select the warmest periods of varying durations between 1 and 90 days. For each time scale, the societal response is assessed to find the heat wave durations with the most pronounced impacts. This way, we yield impact-relevant heat wave durations for Germany. The results differ slightly between the considered societal metrics but indicate overall that heat waves are most relevant at weekly to monthly time scales. Finally, we also compare impact-relevant heat wave durations between moderate and extreme heat waves, as well as between heat waves occurring individually or jointly with droughts.

Our methodology can be extended to other societal indices, countries, and hazard types to form more meaningful definitions of climate extremes in order to guide future research on these events.  An improved understanding of weather and climate hazards with their impacts on society, economy and environmental systems will support better communication for preparation, response, and future adaptation.

How to cite: De Polt, K., Ward, P. J., de Ruiter, M., Bogdanovich, E., Reichstein, M., Frank, D., and Orth, R.: Quantifying impact-relevant heatwave durations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12382, https://doi.org/10.5194/egusphere-egu23-12382, 2023.

EGU23-13758 | ECS | Posters on site | NH1.1

Investigating Potential Risk of Thermal Hazards Along Race Routes of Taipei Marathon By Mobile Monitoring and Quantitative Analysis 

Cheng-En Lin, Shiuh-Shen Chien, and Jehn-Yih Juang

In the recent years, the long-distance endurance running races, such as half-marathon or marathon, are becoming much more popular in Taiwan. However, due to the frequent hot and humid weather in this low-latitude country, runners in these races usually face the risk of thermal hazards. In order to analyze the heat stress for the runners in such environment, the main objectives of this study are to characterize the thermal environment in road race events and to quantify the risk of thermal hazard for athletes.

This study chose the route of half-marathon of Taipei Marathon, a World Athletics Elite Label race, as the research object.  The necessary environmental parameters for risk of thermal hazards along the route were collected by means of mobile monitoring, and the heat stress on the route was evaluated through the Heat Strain Decision Aid model (HSDA) and the thermal index, Wet Bulb Globe Temperature (WBGT). To quantify the impact of heat stress on different groups from beginner to elite runners, the spatiotemporal variations of WBGT and body core temperature along the route were further estimated. The results from this study could help the race organizer to identify the high-risk areas during the race planning and help the participants to understand the potential risk of heat stress in the race.

How to cite: Lin, C.-E., Chien, S.-S., and Juang, J.-Y.: Investigating Potential Risk of Thermal Hazards Along Race Routes of Taipei Marathon By Mobile Monitoring and Quantitative Analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13758, https://doi.org/10.5194/egusphere-egu23-13758, 2023.

EGU23-16703 | Posters on site | NH1.1

Predictability of heat waves over West African main cities 

Christophe Lavaysse, Cedric Gacial Ngoungue Langue, Cyrille Flamant, and Mathieu Vrac

Heatwaves are one of the most dangerous climatic hazards affecting the health of humans and ecosystems around the world. Accurate forecasts of these dramatic events can be relevant for policy makers, climate services and the local population. In this perspective, the present study addresses the predictability of heatwaves in sub-seasonal to seasonal forecasts in the West Africa region over the recent period from 2001 up to 2020. Two models from the S2S Prediction project namely ECMWF and UKMO have been analyzed. Heatwaves have been detected using minimum/maximum values of 2-m temperature as indicators over a period of at least 3 consecutive  days. The validation of the model outputs is processed using ERA5 as reference. The global skill of the models in reproducing 2-m temperature is done by calculating the Continuous Rank Probability Score (CRPS). ECMWF model shows more skill in the Guinean region for minimum and maximum values of 2-meter temperatures.  The predictability of heatwaves in the models is estimated by the computation of some probabilistic metrics such as : hit-rate and false alarm ratio (FAR).  Models show predictive skill of heatwave days greater than the climatology up to 3 weeks lead time in the 3 regions. The FAR values are high and increasing with the lead time. This suggests that the models used to predict heat wave days which are not observed in the reanalysis (ERA5) more than real events. ECMWF shows more predictive skill at weekly time scale with high hit_rate values up to 3 weeks lead time. The accurate representation of the heatwaves intensity in the models remains very challenging at any lead time.

How to cite: Lavaysse, C., Ngoungue Langue, C. G., Flamant, C., and Vrac, M.: Predictability of heat waves over West African main cities, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16703, https://doi.org/10.5194/egusphere-egu23-16703, 2023.

EGU23-283 | ECS | Orals | NH1.2

Impact of climate change on temperature and precipitation in Toruń, Poland, based on CMIP6 under SSP scenarios 

Babak Ghazi, Rajmund Przybylak, and Aleksandra Pospieszyńska

The latest projection of temperature under Shared Socioeconomic Pathways (SSPs) scenarios from Coupled Model Intercomparison Project Phase-6 (CMIP6) indicates that, by the 21st century, the global average temperature will increase by over 5.4 °C in the highest-emission scenario and 1.1 °C in the highest mitigation scenario. Climate change is mainly described by changes in two main meteorological variables, i.e., temperature and precipitation. Observed and projected changes in temperature and precipitation significantly influence various hydroclimatic events such as droughts and floods. Therefore, a precise projection of those variables, including at local and regional scales, is crucial and urgently needed. In Poland, the negative impact of the observed warming on the frequency and intensity of droughts and floods has been detected.

In this research, we present a projection of temperature and precipitation variations in Toruń, Poland, for future periods (2015–2100). To accomplish this, several general circulation models (GCMs) are employed under two SSP scenarios, namely SSP1-2.6 and SSP5-8.5 from NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6) datasets. In these models, the historical reference period is 1950–2014, and future projections are for 2015–2100.

The results indicated that the mean annual air temperature will increase from 8.1 °C in the reference period to 8.9 °C in SSP1-2.6 scenario and 10.1 °C in SSP5-8.5 scenario. Precipitation will increase slightly under both scenarios. It is projected that the average annual precipitation in Toruń will change from 514.38 mm in the reference period to 533.15 mm and 522.37 mm during 2015–2100 according to the SSP1-2.6 and SSP5-8.5 scenarios, respectively. It is evident that an increase in precipitation and heavy rainfall will culminate in extreme occurrences such as floods, which will further threaten lives, properties and the environment within the heart of Toruń.

How to cite: Ghazi, B., Przybylak, R., and Pospieszyńska, A.: Impact of climate change on temperature and precipitation in Toruń, Poland, based on CMIP6 under SSP scenarios, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-283, https://doi.org/10.5194/egusphere-egu23-283, 2023.

In 2016, the World Meteorological Organization declared that lightning is an essential climate variable. To date, global change studies have only considered the effect of warming on lightning flash frequency and the global distribution of lightning activity. Furthermore, none of these studies considered the effects of climate change on lightning flash intensity. In our previous studies we suggested based on laboratory experiments that lightning intensity over water surfaces may be influenced by their chemical properties, including salinity (S), pH and total alkalinity (TA). In this study we tested the combined effects of changes in S, TA and pH in Mediterranean Sea surface water on the intensity of laboratory generated electrical sparks, which are considered to be analogous to cloud to sea-surface intensity of lightning discharges. The range of values tested in the lab correspond to changes in S, pH and TA of Mediterranean surface water that were caused by the anthropogenic climate change, ocean acidification and damming of the Nile in the 1960s. Where, the damming of the Nile is generally accepted to have caused nearly 30% of the total salination of Mediterranean surface water until now. The experimental results were used to develop a multivariate linear model of Lightning Flash Intensity (LFI) as a function of S, TA/S, which  and pH. The model was validated with wintertime (DJF) LFI measurements along a Mediterranean Sea zonal profile during the period 2009-2020 compared to corresponding climate model outputs of S, TA and pH. Based on this model, the combined effects of climate change, ocean acidification and the damming of the Nile, may have increased LFI in the Levantine Sea by 16±14% until now relative to the pre-Aswan Dam period. Furthermore, assuming that salinization and acidification of the Levantine Sea will continue at current trends, the LFI is predicted to increase by 25±13% by the year 2050.

How to cite: Asfur, M. and Silverman, J.: Climate mediated changes in seawater chemistry and their potential effects on marine lightning intensity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-334, https://doi.org/10.5194/egusphere-egu23-334, 2023.

EGU23-647 | ECS | Orals | NH1.2

Understanding the role of climate change in disaster mortality: Empirical evidence from Nepal 

Dipesh Chapagain, Luna Bharati, Reinhard Mechler, Samir Kc, Georg Pflug, and Christian Borgemeister

Climatic disaster impacts, such as loss of human life as its most severe consequence, have been rising globally. Several studies argue that the growth in exposure, such as population, is responsible for the rise and the role of climate change is not evident. While disaster mortality is highest in low-income countries, existing studies focus mostly on developed countries. Here we address this impact attribution question in the context of the Global South using disaster-specific mixed-effects regression models. We show that the rise in landslide and flood mortality in a low-income country Nepal between 1992-2021 is attributable primarily to the increased precipitation extremes. An increase in one standardized unit in maximum one-day precipitation increases flood mortality by 33%, and heavy rain days increase landslide mortality by 45%. A one-unit increase in per capita income decreases landslide and flood mortality by 30% and 45%, respectively. Population density does not show significant effects.

How to cite: Chapagain, D., Bharati, L., Mechler, R., Kc, S., Pflug, G., and Borgemeister, C.: Understanding the role of climate change in disaster mortality: Empirical evidence from Nepal, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-647, https://doi.org/10.5194/egusphere-egu23-647, 2023.

EGU23-666 | ECS | Orals | NH1.2

Drought-to-flood transitions – When and where do they occur? 

Jonas Götte and Manuela Brunner

Drought-to-flood transitions are both a challenge and opportunity for water management. While the two extremes are often studied separately, their close succession can have severe impacts. The timespan between events can range from rapid transitions happening within a few days to long transitions taking many years. Still, the drivers and frequency of those transitions in specific river basins remain unknown. Therefore, we ask ‘when, where and how often do transitions from streamflow droughts to floods occur?’

To answer these questions, we analyse over 1000 catchments in the contiguous US from the GAGES-II database, identify streamflow droughts and floods, and calculate transition times between both types of extremes. Then, we relate the time and frequency of occurrence and the timespan between extremes to local climate and topographic characteristics. We distinguish between winter and summer transitions to identify hydro-meteorological processes important in different seasons and focus on particularly rapid transitions. 

We find that the duration and frequency of transitions show large spatial variability. Regionally, rapid transitions occur during a typical time of the year which is often related to the presence of snow and melt processes. Snow also dictates seasonal differences in rapid transition frequencies between summer and winter. Snow-free catchments have a lower frequency but higher variability of transitions which makes the phenomenon less predictable. Additionally, reservoirs reduce the occurrence of snow-affected rapid transitions. We conclude that management challenges related to drought-to-flood transitions are particularly pronounced in natural and rainfall-dominated catchments.

How to cite: Götte, J. and Brunner, M.: Drought-to-flood transitions – When and where do they occur?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-666, https://doi.org/10.5194/egusphere-egu23-666, 2023.

EGU23-797 | ECS | Orals | NH1.2

Projections and uncertainties of future winter windstorm damage in Europe 

Luca Severino, Chahan M. Kropf, Hilla Afargan-Gerstman, Christopher Fairless, Andries Jan De Vries, Daniela I.V. Domeisen, and David N. Bresch

Extratropical winter windstorms are among the most significant natural hazards in Europe in terms of fatalities and economic losses, and windstorm impacts projections under climate change in Europe are considerably uncertain. This study combines state-of-the-art climatic projections from 29 global climate models participating in CMIP6, with the open-source weather and climate impact-risk assessment model CLIMADA to obtain a set of relevant projections for future windstorm-induced damages over Europe. Spatial patterns of the future changes in windstorm damages projected by the multi-model ensemble show a median increase in the damages in northwestern and northern-central Europe, and a median decrease over the rest of Europe, in agreement with an eastward extension of the North Atlantic storm track into Europe. We combine all 29 available climate models in an ensemble of opportunity approach and find evidence for an overall increase in future windstorm loss events, with events with return periods of 100 years under current climate becoming events with return periods of less than 20 years under future SSP585 climate. Using an uncertainty-sensitivity quantification analysis, we find that the climate model uncertainty dominates the uncertainty in the projections of damages related to frequent events, but that stochastic uncertainty hinders the uncertainty quantification for more extreme events. Our findings demonstrate the importance of climate model uncertainty for the CMIP6 projections of extratropical winter windstorms in Europe, and emphasize the increasing need for risk mitigation and management due to extreme weather in the future.

How to cite: Severino, L., Kropf, C. M., Afargan-Gerstman, H., Fairless, C., De Vries, A. J., Domeisen, D. I. V., and Bresch, D. N.: Projections and uncertainties of future winter windstorm damage in Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-797, https://doi.org/10.5194/egusphere-egu23-797, 2023.

EGU23-827 | ECS | Orals | NH1.2

An insight into the severe 2019-2021 drought over Southeast South America from a daily to decadal and regional to large-scale variability perspective 

João Lucas Geirinhas, Ana Russo, Renata Libonati, Diego Gonzalez Miralles, Alexandre Miguel Ramos, and Ricardo Machado Trigo

An acceleration in the global water cycle with severe rainfall and drought episodes is expected to occur in many regions due to climate change1. Spatial and temporal disturbances in the atmospheric water budget encompassing changes in precipitation and evaporation rates, soil moisture levels, groundwater recharge and water available for runoff are foreseen, posing great challenges to the global freshwater availability2 , food security3 and the sustainability of natural ecosystems4 . Thus, the assessment of hydro-meteorological extremes is crucial, particularly in regions such as South America (SA) that are extremely vulnerable to climate change and lack a comprehensive assessment of this extremes. Moreover, SA has two main watersheds (Amazon and La Plata basin) essential for the regional hydroclimate and local and remote precipitation in a global scale, through moisture recycling, transport and convergence.

Regions of Southeast SA, particularly over the La Plata basin and the Pantanal, have witnessed severe drought conditions in recent years. Pronounced soil dryness started to be recorded during mid-2018 over Southeast Brazil, but rapidly spread to areas in Paraguay, Bolivia, and northern Argentina. This abnormal situation lasted until 2021, leading to huge agricultural losses. Extremely low streamflow levels in the Paraná and Paraguay rivers caused serious constraints in the hydropower generation and water supply, and led to disruptions in the waterways that are fundamental for the fluvial transport and economy of these countries. Moreover, the Pantanal biome was also dramatically affected, particularly during 2020, when pronounced soil dry-out conditions concurred with several heatwaves, leading to devastating fires that resulted in catastrophic burned area levels7.

This study presents a detailed analysis of the 2019–2021 drought episode over Southeast SA from a climate change and variability context aiming: to (1) evaluate the exceptionality of the soil dry-out conditions within a historical record of 70 years; (2) provide a detailed spatiotemporal evolution (from daily to decadal and regional to large-scale) of soil moisture anomalies across the southeast SA; and (3) assess the large-scale tropical and subtropical atmospheric mechanisms that were responsible for pronounced disturbances in the normal processes of moisture transport and convergence, and that, ultimately, explained the observed soil moisture deficits over southeast SA.

References

  • [1] Chagas, V. B. P., Chaffe, P. L. B. & Blöschl, G. Climate and land management accelerate the Brazilian water cycle. Nat. Commun. 13, 5136 (2022).
  • [2] Konapala, G., Mishra, A. K., Wada, Y. & Mann, M. E. Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nat. Commun. 11, 1–10 (2020).
  • [3] Lesk, C., Rowhani, P. & Ramankutty, N. Influence of extreme weather disasters on global crop production. Nature 529, 84–87 (2016).
  • [4] Seddon, A. W. R., Macias-Fauria, M., Long, P. R., Benz, D. & Willis, K. J. Sensitivity of global terrestrial ecosystems to climate variability. Nature 531, 229–232 (2016).

Acknowledgments:

JG, AR, RT are grateful to Fundação para a Ciência e a Tecnologia for the PhD Grant 2020.05198.BD, I.P./MCTES for the national funding (PIDDAC) – UIDB/50019/2020 and for Dhefeus (2022. 09185.PTDC). RL is grateful to CNPq (Grant 311487/2021-1) and FAPERJ (Grant E26/202.714/2019).

How to cite: Geirinhas, J. L., Russo, A., Libonati, R., Miralles, D. G., Ramos, A. M., and Trigo, R. M.: An insight into the severe 2019-2021 drought over Southeast South America from a daily to decadal and regional to large-scale variability perspective, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-827, https://doi.org/10.5194/egusphere-egu23-827, 2023.

Flood disaster resilient design of the bridges is the lifeline of the transport infrastructure. Design of flood disaster resilient bridges is the major requirement for construction of the main highways and railway networks as well as for developing the transport networks in hilly regions and remote areas. Inadequate hydrologic and hydraulic design of the bridges results in failure of the bridges and during the current rainy season, a number of bridges were washed away in the India and other parts of the world, mainly due to it. In the present era, the construction technology is in fairly well advanced state and a major challenge associated in construction of the disaster resilient bridge infrastructure is to estimate the accurate design flood and using it for determination of the highest flood level (HFL) of the bridges incorporating the growing climate-change-induced threats of the intensifying extreme weather events. In this research, a procedure for design flood estimation for the bridges will be developed based on the L-moments approach of flood frequency analysis and its superiority will be demonstrated over the existing procedures. The data will be screened using the discordancy measure (Di) in terms of the L-moments. Homogeneity of the region will be tested using the L-moments based heterogeneity measure, H. For computing the heterogeneity measure H, 500 simulations will be  performed using the four parameter Kappa distribution. Comparative regional flood frequency analysis studies ill be performed using the L-moments based frequency distributions: viz. Extreme value, General extreme value, Logistic, Generalized logistic, Normal, Generalized normal, Uniform, Pearson Type-III, Exponential, Generalized Pareto, Kappa, and five parameter Wakeby. Based on the L-moment ratio diagram and Zidist -statistic criteria, the robust distribution will be identified and design flood will be estimated using the robust frequency distribution. Effect of climate change will be studied using the CMIP-5 scenarios and the fixed percentage increases in the design flood. The research will create a climate-resilience-centred procedure leading to policy framework comprising of exhaustive methodology, guidelines and tools for design of flood disaster resilient bridges for the road and railway networks to make the transport infrastructure more resilient in the face of future climate change induced uncertainties of the extreme rainfall events.

How to cite: Kumar, R.: Design flood estimation for flood disaster Resilient bridges exposed to climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-882, https://doi.org/10.5194/egusphere-egu23-882, 2023.

EGU23-1669 | Posters on site | NH1.2

Assessment of meteorological drought characteristics during 1980-2020 over the Marathwada Region, India 

Surendra Kumar Mishra, Sabyasachi Swain, and Ashish Pandey

Drought is a meteorological phenomenon that occurs when there is a prolonged period of below-average precipitation, leading to a shortage of water. It can have serious consequences, particularly for agriculture, as plants and crops depend on water for their growth and survival. In this study, we conducted a spatiotemporal assessment of drought trends and variabilities in the Marathwada Region of Maharashtra, India, which is dominated by agriculture. We used precipitation data from the India Meteorological Department for 1980-2020 and characterized drought occurrences using the Standardized Precipitation Index (SPI) at different time frames (1-, 3-, 6-, and 12-months moving windows). Further, we used non-parametric tests, such as the modified Mann–Kendall (MMK) and Sen's slope (SS) tests, to detect trends in precipitation as well as in Evaporative Stress Index (ESI) and actual evapotranspiration (ET). The results of the study indicate that the Marathwada region is prone to droughts, and the SPI at a 12-monthly moving frame is more effective at capturing drought occurrences than shorter time frames due to the lesser randomness in the time series. We also found a mix of positive and negative trends in the SPI series for the monsoonal months, with more concentration towards negative trends, thereby indicating an increased tendency or severity of drought events. A detailed discussion is also provided on the seasonal variations of precipitation, ESI and ET. The information from this study can be used to develop water management strategies to mitigate the effects of drought in the region.

How to cite: Mishra, S. K., Swain, S., and Pandey, A.: Assessment of meteorological drought characteristics during 1980-2020 over the Marathwada Region, India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1669, https://doi.org/10.5194/egusphere-egu23-1669, 2023.

EGU23-1697 | Posters virtual | NH1.2

Intensity Detection methods of Tropical Cyclone in Western North Pacific with Deviation Angle Variance Technique 

Wei Zhong, Qian Qian, Yao Yao, Yuan Sun, Hongrang He, and Shilin Wang

In this paper, standardized infrared cloud images from Fengyun (FY) Series geostationary satellites and Best-Track Data from China Meteorological Administration (CMA-BST) within 2015-2017 are used to investigate the effects of two multi-factor models, generalized linear model (GLM) and Long Short-Term Memory (LSTM) model, for tropical cyclone (TC) intensity estimation. The typical single-factor Sigmoid function model (SFM) with map minimum value (MMV) of deviation angle variance (DAV) is also reproduced for comparison. Through applying the sensitivity experiments to DAV calculation radius and different training data groups, the estimation precision and their optimum calculation radius for DAV in Western North Pacific (WNP) are analyzed. The results show that the root mean square error (RMSE) of single-factor SFM is between 8.79 and 13.91 by using individual years as test sets and the remaining two years as training sets with the optimum calculation radius of 550 km. However, after selecting and using high-correlation factors by GLM, the RMSE of GLM and LSTM model decreases to 5.93~8.68  and 4.99~7.00 , respectively with their own optimum calculation radius of 350 km and 400 km. All sensitivity experiments indicate that the estimation results of SFM can be significantly influenced by DAV calculation radius and the characteristics of training set data, while the results of multi-factor models appear more stable. Furthermore, the multi-factor models reduce the optimum radius within the process of DAV calculation and improve the precision of TC intensity estimation in WNP, which can be an effective way for TC intensity estimation in marine area.

How to cite: Zhong, W., Qian, Q., Yao, Y., Sun, Y., He, H., and Wang, S.: Intensity Detection methods of Tropical Cyclone in Western North Pacific with Deviation Angle Variance Technique, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1697, https://doi.org/10.5194/egusphere-egu23-1697, 2023.

EGU23-2227 | Orals | NH1.2 | Plinius Medal Lecture

Extremes in river flood hydrology: making Black Swans grey 

Alberto Viglione

Black Swans in river flood hydrology are unexpected events that surprise flood managers and citizens, causing massive impacts when they do occur, but that appear to be more predictable in retrospect, after their occurrence. My talk aims at showing how black swans in river flood hydrology can "be made grey", i.e. can be anticipated to a certain degree, in probabilistic terms, and/or made less impactful, by (1) expanding information on flood probabilities by gathering data on floods occurred in other places and at other times; (2) understanding the mechanisms causing heavy tails in flood frequency distributions; (3) understanding the mechanisms causing river flood changes in time; (4) accounting for uncertainties in data, models and flood frequency estimates; (5) accounting for the possible dynamics of coupled human-water systems; and (6) coupling the classical top-down approach to hydrological risk assessment based on predictive modelling with a bottom-up approach that is centered on robustness and resilience.

How to cite: Viglione, A.: Extremes in river flood hydrology: making Black Swans grey, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2227, https://doi.org/10.5194/egusphere-egu23-2227, 2023.

EGU23-3160 | Orals | NH1.2

MEF application- the extreme floods are already in maps! 

Libor Elleder and Jolana Šírová

The usual goal of palaeoflood hydrology and historical hydrology is, according to the classic concept, to extend the flood records over centuries or millennia back. Many compilations of documentary sources on floods in Western and Central Europe were collected in last two centuries. The palaeoflood records were presented for hundreds of localities in Europe. Numerous individual flood event case studies were carried out and published. On the PAGES Flood Working Group (FWG) website hundreds of flood records are collected in one database. The overall goals of the FWG are "to integrate and analyse existing palaeoflood data at the regional and global scales and to promote and disseminate palaeoflood science and data at different levels". The aim of this contribution is to present the recently created “Map of Extreme Floods” (MEF) ESRI application focused on European floods.  Apart from the FWG goals stated above, the MEF application aims also to the future geographic characterization and mapping of hydrological extreme events. The MEF places some of interpreted documentary sources for Central and Western Europe into relevant spatial and temporal frameworks. Actually, the MEF application more event oriented approach enables to put the fundamental information on European historical floods, i.e. the exact location and datum, into broader spatial and temporal context. The maps created by this tool form the reliable fundament for detailed exploration and including of additional data. The principal MEF application aims are: (i) archiving, (ii) verification, (iii) corrections, (iv) addition of further data and information, (v) exchange of data and last but not least (vi) providing information for both scientists and public. 24 large floods from 1432 to 2002 are now at disposal and next 20 are under preparation. The estimated extremities, economic losses, infrastructural damages, water levels, flood marks etc. are attributed to individual localities. The performance of the MEF application is documented by selected historical extreme flood events, possibly analogical to these recent of 1997, 2002, and 2013.

 

How to cite: Elleder, L. and Šírová, J.: MEF application- the extreme floods are already in maps!, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3160, https://doi.org/10.5194/egusphere-egu23-3160, 2023.

EGU23-3751 | ECS | Posters virtual | NH1.2

Lightning activity in Mexico under climate change scenarios 

Alejandro Jaramillo, Brian Gustavo Pérez Juárez, and Christian Dominguez

Lightning has an important role in the Earth's energy balance, atmospheric chemistry, the initiation of natural forest fires, and a close relationship with the development of deep convection. On the other hand, lightning is also a significant meteorological hazard, particularly in Mexico, during the rainy season, causing deaths and disrupting socio-economic activities. According to the IPCC report, it is expected an increase in the frequency and severity of extreme events in North America due to climate change in the forthcoming decades. To understand the impacts of climate change on lightning in the Mexican territory, it is necessary to explore the future changes in the regional patterns of lightning activity in the region. We use different parameterizations of lightning activity over Mexico, using data from reanalysis and coupled models from CMIP6. We also evaluate the parameterization performance in representing the historical period against available lightning observations. Later, we obtain the projected changes at the end of the 21st Century in the selected CMIP models under the most extreme climate change scenario. At the end of this preliminary study, we provide insides into how climate change will impact extreme events and lightning activity over Mexico. 

How to cite: Jaramillo, A., Pérez Juárez, B. G., and Dominguez, C.: Lightning activity in Mexico under climate change scenarios, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3751, https://doi.org/10.5194/egusphere-egu23-3751, 2023.

EGU23-3938 | ECS | Posters on site | NH1.2

Flood inundation mapping along a downstream river segment 

Sofia Sarchani and Ioannis Tsanis

According to the IPCC, Eastern Canada is an area where heavy precipitation events are likely to intensify. The Humber River basin is a medium-sized basin located in the Greater Toronto Area, in Southern Ontario, Canada, which is exposed to severe storms resulting in flash floods. A severe storm that passed by the city of Toronto on July 8, 2013 caused a flood with damages across the area, including blackouts and citizens trapped in public transportation and vehicles. Hydro-meteorological stations close to the basin’s outlet, in the urban section, recorded 60-63 mm of rain in two-three hours. The analysis of the examined river segment, including several bridge structures, is performed with two hydraulic models (1D and 2D) by using a high-resolution DTM and two flow hydrographs as input boundary conditions. The 2D hydraulic model provides more detailed results regarding the maximum flood depths, flood wave velocities, and arrival times of maximum depths, at every grid cell of the computational mesh. In comparison, the 1D model provides results at cross-sectional level, and interpolates them in the intermediate positions. The differences between the two models in low-height bridge locations are considerable. The 2D model can be improved by enforcing grid cells at bridges’ locations. However, there is a risk of possible instabilities in solving the shallow water equations by assuming a Courant number kept in low levels. Moreover, during storm events, water level gauges in situ measurements can improve calibrating both hydraulic models. The probable increase in precipitation heights due to climate change indicates the necessity for effective flood risk management in the urban area of the city of Toronto. On-going research concerns the effect of projected extreme precipitation on peak runoff and downstream flood impacts via climate model datasets.

How to cite: Sarchani, S. and Tsanis, I.: Flood inundation mapping along a downstream river segment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3938, https://doi.org/10.5194/egusphere-egu23-3938, 2023.

EGU23-4255 | Orals | NH1.2

Hail in the Kvemo Kartli Region (Georgia) 

Elizbar Elizbarashvili, Mariam Elizbarashvili, and bela Kvirkvelia

The research aimed at studying the characteristics of hail in the Kvemo Kartli region (Georgia). Hail (30%) is the most frequent natural hydrometeorological event in the territory of Georgia after flash floods (37%). Hail is typical for the Kvemo Kartli region, where agricultural production is the leading industry. It damages farm lands. The study of hail is very important for the development and introduction of hail prevention methods in the region.

We used the observation materials of 7 weather stations of the region for the years of 1961-2022, a catalog was compiled and the following characteristics of hailfall were calculated: probability, number of days, intensity, frequency, duration, and distribution areas.

Hail is observed in the warm spell of the year; especially active processes develop in spring and the first half of summer, which are associated with convective clouds.

The highest number of hailfall days in the region is 12-14 days a year. In the Kvemo Kartli region, hail damage the territory with an area of ​​1 to 5 square kilometers in 38% of its cases; in 33% of cases, it damages an area of ​​less than 1 km2. An area of ​​more than 5 km2 is damaged in approximately 30% of cases of hail. Rarely, hail damages much larger areas, for example, more than 50 km2 is damaged in 3% of cases. The average duration of hailfall is 9-10 minutes. In 60% of cases, hailfall lasts less than 5 minutes, in 80% of cases, the duration of hailfall is less than 10 minutes. In 3% of cases, hail can last for an hour and a half.

Thus, the main climatic characteristics of hail in the Kvemo Kartli region have been identified.

The research results can be used to reduce the negative impact of hail and implement measures to prevent hail.

This work was supported by Shota Rustaveli National Science Foundation of Georgia (SRNSFG) Grant № FR-19-14993.

How to cite: Elizbarashvili, E., Elizbarashvili, M., and Kvirkvelia, B.: Hail in the Kvemo Kartli Region (Georgia), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4255, https://doi.org/10.5194/egusphere-egu23-4255, 2023.

Spain is a territory with spatial variations highly influenced by its distance from the sea and its complex orography, where it is possible to note an uneven distribution of both temperature and precipitation. This study presents an analysis of trends in maximum temperature and precipitation by zone over the period 1951-2021 using monthly data. The database used includes 16156 multivariate time series (maximum temperatures and precipitation) corresponding to different areas of the Spanish territory, distributed over a grid of 5x5km2. The methodology used starts by reducing the dimensionality of the time series and with this version are clustered using an approach based on multiscale analysis using a clustering algorithm. In the following, the prototypes of each group are defined, which allows to identify and analyse patterns of change in maximum temperatures and precipitation by zones. An increase in average maximum temperature has been identified in eight zones distributed in Spain from 1951 to 2021. The rate of change of maximum temperature was between 0.060ºC and 0.2155ºC per decade. Areas further south showed a higher rate of increase than areas found in the north. It has been observed that May was the month with the highest variation for all areas in maximum temperature, nevertheless, differences in seasonal variation are evident when passing from one zone to other, as in some there is greater variation in spring months and in others in winter months. An analysis of trends and seasonal variations of precipitation in the identified zones will be carried out and the correlation between patterns of maximum temperature and precipitation will be studied in each of the eight zones.

How to cite: Palacios Gutiérrez, A. and Valencia Delfa, J. L.: Identification of precipitation and maximum temperature patterns in Spain during 1951 to 2021 using clustering based on multiscale analysis of time series, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4466, https://doi.org/10.5194/egusphere-egu23-4466, 2023.

When tropical cyclones (TCs) move to the mid-latitudes, they encounter the baroclinic environment where many of them experience extratropical transition (ET) by which they lose the symmetric and warm-core characteristics and transform into extratropical cyclones (ETCs). ETCs are usually faster than TCs and oftentimes destructive to coastal cities with strong wind and heavy precipitation. Climate models predict that the mean intensity of TCs would become stronger fundamentally due to the increase in atmospheric moisture contents in response to global warming. However, whether the destructiveness of ETCs originated from TCs will change in the future has not been explored with a high-resolution fully-coupled model. To understand the future changes in ET events and the destructive potential of these ETCs, we analyzed the high-resolution Community Earth System Model (CESM) simulations (0.25 degrees for the atmosphere and 0.1 degrees for the ocean) with present-day, doubling, and quadrupling CO2 concentrations.

The high-resolution model well captures the frequency and annual cycle of the ET events compared to observation with underestimated frequency in the North Atlantic and West Pacific while overestimating them in the East Pacific, South Indian, and South Pacific. Our results show that the frequency and ratio of ET events do not change significantly in both CO2 doubling and quadrupling experiments. An increase in 10-m wind speed at ET completion is observed mainly in North Atlantic and South Indian. We used the total integrated kinetic energy, which depends on the wind speed and the area covered by the high wind region of a storm, to represent the destructive potential of a storm upon ET completion. It is found that the relative ratio of the strongly destructive ETCs to weaker ETCs increase in response to greenhouse warming.

Our study highlights the destructive potential of transitioned TCs. Since ETCs usually have greater spatial coverage than TCs, the former can impact a larger population and region, albeit with lower intensity. Therefore, accurate prediction of future changes in ET events can have significant socio-economic implications.

How to cite: Cheung, H. M. and Chu, J.-E.: Increasing destructive potential of extratropical transition events in response to higher CO2 concentration in global climate model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4736, https://doi.org/10.5194/egusphere-egu23-4736, 2023.

EGU23-6087 | Posters on site | NH1.2

Multidisciplinary reconstruction of the May 1853 flood episode 

Josep Carles Balasch, Feliu Izard, Jaume Calvet, Jordi Tuset, David Pino, Mariano Barriendos, and Josep Barriendos

Climate variability conditioned by the effects of climate change justifies the study of historical periods in order to identify and characterise episodes of high severity and low frequency. The increase in the irregularity of the rainfall regime in some regions justifies the study of these events for a better assessment of their presence in the immediate future. In this regard, the study of extreme hydrometeorological episodes that happen in unusual seasons of the year for these extreme episodes is of particular interest.

One of these unusual episodes was the torrential rainfall and floods of May 1853 in Catalonia (NE Iberian Peninsula). The whole month of May 1853 is a unique hydrometeorological anomaly, being the second most rainy month of May in the whole instrumental series of precipitation of the city of Barcelona (period 1786-2022).

This work reconstructs this episode of heavy rainfall and floods using a multidisciplinary approach. Old instrumental meteorological data are used to obtain the daily pluviometric behaviour in Barcelona. Surface atmospheric pressure data from different points of Western Europe allow its synoptic description.

Historical information allows the identification of the different river overflow points and the floods caused by this episode. These points are represented cartographically together with the documented impacts on infrastructures. For this episode, there are 38 cases with historical information on impacts caused by floods or overflows. These occurred in eight different river basins which are included in the hydrographic demarcations of the Ebro River and the Catalan Coastal Basins. 

In order to appreciate the magnitude of the event, a limnimark (or floodmark) located in Tres Ponts Canyon on the Segre River is used. This record enables us to assimilate the episode with the most severe episode measured on the Segre River, one of the main tributaries of the Ebro River, in November 1982, with 1900 m3/s. This confirms the magnitude of the event of 1853, which is one of the most severe episodes of the lower Ebro basin for the last 500 years. 

How to cite: Balasch, J. C., Izard, F., Calvet, J., Tuset, J., Pino, D., Barriendos, M., and Barriendos, J.: Multidisciplinary reconstruction of the May 1853 flood episode, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6087, https://doi.org/10.5194/egusphere-egu23-6087, 2023.

EGU23-6192 | Posters on site | NH1.2

Impacts of extreme wind speeds and other factors on tree fall alongside railway lines 

Rike Lorenz, Nico Becker, and Uwe Ulbrich

High wind speeds are a major cause for tree fall. However, the impact of wind on trees can depend on other co-occurring or antecedent meteorological conditions. Tree fall can also have an impact on surrounding infrastructure like the railway system. Here, it may damage railway infrastructure and lead to train disruptions. The intensity and frequency of windstorms and other meteorological factors is expected to change in the course of a changing climate. Thus, their impacts on trees and tree damage might change as well. To understand these changes an examination of impact-relevant weather factors and sequences is needed.
We obtained a dataset for tree and branch fall events alongside German railway lines from the Deutsche Bahn for the years 2017 to 2021. We use logistic regression to model tree fall probabilities and to identify relevant current and antecedent weather factors, their combinations and their impact on tree fall risk during winter. We use meteorological predictors derived from the ERA5 reanalysis and RADOLAN radar data.
High wind speed is identified as the strongest risk increasing factor. However, high daily precipitation and high soil water volume during the tree fall event as well as an antecedent year with high precipitation also increase tree fall risk. A small decreasing effect was found for warm and wet soil conditions in the three preceding months. We found no or only minor effects for daily mean air temperature, storm duration and wind direction.
In further steps these factors and their combinations will be assessed in terms of their effect on occurrence probabilities of tree fall events under recent and future climate conditions based on regional climate simulations.

How to cite: Lorenz, R., Becker, N., and Ulbrich, U.: Impacts of extreme wind speeds and other factors on tree fall alongside railway lines, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6192, https://doi.org/10.5194/egusphere-egu23-6192, 2023.

EGU23-6322 | ECS | Orals | NH1.2

Investigating the relationship between drought and clay-shrinkage-induced subsidence damage at the town scale over France 

Sophie Barthelemy, Séverine Bernardie, Bertrand Bonan, Gilles Grandjean, Dorothée Kapsambelis, David Moncoulon, and Jean-Christophe Calvet

Clay shrink-swell consists in volume changes of clayey, smectite-rich soils as a function of their soil water content. Building foundations can be affected by soil shrinkage during droughts, entailing what is called subsidence damage. This is the second costliest peril covered by the French national natural disaster compensation scheme, the losses amounting to more than 16B€ between 1989 and 2021 (CCR, 2021). As illustrated by the 2022 drought in France, these costs are likely to increase as a result of climate change and of the related amplification of annual soil moisture cycles.

In this context, we investigate the relationship between drought and subsidence damage, using the ISBA land surface model developed by the French meteorological service (Météo-France), geotechnical data from the French geological survey (BRGM) and data from a national claim database operated by the French state-owned national reinsurance company (CCR). We compute several yearly drought indices based on multi-layer soil moisture time series simulated by the ISBA model. Different configurations of the indices are considered, varying in particular the ISBA model settings, and the soil drought definition through a threshold value accounting for a given temporal frequency, for each model soil layer. We assess a large range of configurations by using the Kendall rank correlation of the indices with yearly town-scale insurance claim data from 2000 to 2018, processed using the geotechnical data. The analysis is repeated for five sets of four towns with an important damage history distributed throughout France, in contrasting climate conditions.

Highest rank correlation coefficients are obtained for soil layers deeper than 60 cm, and with temporal frequency threshold values corresponding to intense droughts. Under these conditions, the indices are able to fairly represent the occurrence of damages. The relationship between drought indices and the number of claims is non-linear. This study benefits from the latest improvements in land surface modeling and is a step forwards in climate risk modeling since the indices investigated can be considered as new predictors for subsidence damage. Climate change impact studies will be conducted in a next phase.

[References] CCR: Les Catastrophes naturelles en France, Bilan 1982-2021, 2021.

How to cite: Barthelemy, S., Bernardie, S., Bonan, B., Grandjean, G., Kapsambelis, D., Moncoulon, D., and Calvet, J.-C.: Investigating the relationship between drought and clay-shrinkage-induced subsidence damage at the town scale over France, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6322, https://doi.org/10.5194/egusphere-egu23-6322, 2023.

Upper Bavaria has comparatively higher mean rainfall amounts than other regions in Germany when the long-term rainfall data analyzed. Those heavy rainfall events cause severe human and economic impacts in the region. However, when the precipitation patterns (i.e. intensity, duration, etc.) were examined for this region, it is possible to see that similar rainfall patterns might cause different impacts in different districts of Oberland. This situation underlines the importance of understanding the different aggravating pathways of heavy rainfall events in the region. For this aim, flood-aggravating pathways such as topographic features, land use types, soil moisture and infiltration properties of the events in Oberland between the years 2001-2021 were analyzed. To determine the dominant influencing mechanisms, aggravating pathway factors are classified using hierarchical clustering for the study area. The classification results are compared with the heavy rainfall events defined by the German Weather Service (DWD), Catalogue of Radar-based Heavy Rainfall Events (CatRaRE catalogue) in terms of precipitation duration and amount, as well as fire brigade operations in the area regarding the impacts.

Herewith, direct or indirect relevance of rainfall patterns and catchment properties on the flood events in Oberland (Upper Bavaria), Germany between the years 2001 and 2021 are investigated with this study. The outcomes could provide beneficial information on different aggravating mechanisms in different districts in Oberland and could be used for future land-use planning and flood risk prevention studies.

How to cite: Koc, G.: Clustering of flood-aggravating pathways in the forelands of Pre-Alpine Region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6846, https://doi.org/10.5194/egusphere-egu23-6846, 2023.

EGU23-7244 | ECS | Orals | NH1.2

A first insight into the hail distribution over Germany 

Tabea Wilke, Markus Schultze, and Katharina Lengfeld

Since major hail events are quite rare in Germany, there is a lack of information in hail occurrence, size and its spatio-temporal distribution. As hailstorms are often locally very limited events, the hail distribution is hard to analyze precisely. Hail reports can only give a first intuition about the amount of hail overall. There might be a bias in the amount of reports towards too many reports in highly populated areas, which could lead to an underrepresentation of reports in rural and sparsely populated areas. Areal information from weather radar networks can overcome this issue with a high spatio-temporal resolution. As an addition, data from the German Insurance Association (GDV) about damages through hail serve as a very certain source for hail occurrence.

The German radar network consists of 17 dual-polarimetric radar systems, which cover Germany more or less completely. For the analysis of the hail distribution, the Maximum Expected Size of Hail (MESH) and a method based on Vertical Integrated Ice (VII) are used to estimate the hail size. Those sizes are reduced to thresholds to obtain where hail is reasonable or have a significant large size. The results of MESH and VII are finally compared to the eyewitness reports sent to the European Severe Weather Database and the WarnWetter-App. An important comparison are the loss data by the GDV. It can give further insides into the amount and the size of hail.

How to cite: Wilke, T., Schultze, M., and Lengfeld, K.: A first insight into the hail distribution over Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7244, https://doi.org/10.5194/egusphere-egu23-7244, 2023.

Record-breaking heatwaves, both atmospheric or marine, occur regularly throughout the world, leading to a variety of sectoral and societal impacts. According to the WMO, since 1970 there were more than 11 000 reported disasters attributed to these hazards globally, with just over 2 million deaths and US$ 3.64 trillion in losses. Heatwaves are among the top 4 disasters in terms of human losses during the 50-yr long period, with uneven impacts throughout the world.

Madagascar, which is well known for its vast biodiversity, and abundant and unique natural resources, has been affected by successive droughts and hot events with disastrous consequences for the agriculture sector, and consequently increasing food insecurity. During the last decades, climate change and environmental degradation contributed to an escalation of the ecosystem’s fragility, therefore decreasing, even more, food security in subsistence farming regions.

Considering that the association between higher temperatures and water scarcity increases the risk of food insecurity compared to the sole occurrence of individual hazards, it is very important to address extreme events on a compound approach, identifying synergies, driving mechanisms, and dominant atmospheric modes controlling single and combined hazards.

Here the focus is placed on a particularly sensitive region,  the Madagascar Island, which shows significant positive trends in heatwaves metrics over the period 1982–2022 (frequency, intensity, duration, and intensity composite index). The combined occurrence of both marine and atmospheric heatwaves and drought conditions along the Mozambique Channel and Madagascar relying on ERA5 reanalysis was also performed and ranked according to the referred metrics. Of the two zones considered, there is considerable differences between trends when addressing separately the northern and southern regions, particularly in the case of the intensity of marine and atmospheric heatwaves.  

 

This work was funded by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020- IDL ,  DHEFEUS - 2022.09185.PTDC and ROADMAP - JPIOCEANS/0001/2019.

How to cite: Russo, A., Santos, R., and Gouveia, C. M.: Compound occurrence of marine and atmospheric heatwaves with drought conditions over the Mozambique Channel and Madagascar Island, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8156, https://doi.org/10.5194/egusphere-egu23-8156, 2023.

EGU23-8704 | Posters on site | NH1.2

Multidisciplinary reconstruction of the flood episode of September 1793 

Pablo Gimenez-Font, Josep Barriendos, Jorge Olcina, Mariano Barriendos, Josep Carles Balasch, and Jordi Tuset

Climate variability conditioned by the effects of climate change justifies the study of past periods in order to identify and characterise episodes of high severity and low frequency. The increase in the irregularity of the precipitation regime in some regions justifies the study of these events for a better assessment of their occurrence in the immediate future. In this regard, the climatic framework of the Maldà Oscillation (1760-1800) offers hydrometeorological anomalies of low frequency and high severity, especially in the dimension of catastrophic floods. One of the episodes that occurred in this period affected the region of the present-day Valencian Community, on the eastern coast of the Iberian Peninsula, between 6 and 8 September 1793.The aim of this work is to reconstruct, in as much detail as possible, the meteorological and hydrological behaviour of an extraordinary rainfall event. At the same time, it also aims to reconstruct the impacts caused on human activity. In order to achieve this objective, data from old instrumental meteorological observation and the result of an extensive collection of information from historical documentary sources are used. Although the meteorological data are scarce, they allow the synoptic characterisation of the episode. The hydrological approach of the episode is only qualitative, but it allows the identification of the affected river basins and the occurrence of river floods and overflows. The social impacts of the episode are significant and occurred due to the exceptional nature of the episode. For example, the overflowing of the top of a hydraulic dam built at the end of the 16th century (Tibi dam, 43 metres above the river course). Despite the presence of hydraulic infrastructures that were able to control the floods, there were numerous catastrophic damages that are represented in a detailed thematic cartography. During 3 days of flooding, 7 river basins belonging to the Júcar and Segura hydrographic demarcations were affected, with 11 towns and villages suffering catastrophic damages. Likewise, the social response to these events is analyzed, basically characterized by the celebration of religious ceremonies.

How to cite: Gimenez-Font, P., Barriendos, J., Olcina, J., Barriendos, M., Balasch, J. C., and Tuset, J.: Multidisciplinary reconstruction of the flood episode of September 1793, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8704, https://doi.org/10.5194/egusphere-egu23-8704, 2023.

EGU23-9729 | ECS | Posters on site | NH1.2

Development of a flood events database for the Spanish Mediterranean coast and its application to improve flood risk awareness 

Montserrat Llasat-Botija, Erika Pardo, Laura Esbrí, Raül Marcos, and María Carmen Llasat

Floods are the natural risk that causes the most damage in Mediterranean coastal areas. In Spain, for instance, more than 60% of disaster compensations correspond to floods. Consequently, it is essential to characterize these phenomena to obtain information that can be useful to improve preparedness and future response by generating effective and efficient adaptation strategies.

In the context of the C3RiskMed project, all the flood events that have affected the Spanish Mediterranean coast between 1980 and 2020 have been identified. To this aim, the INUNGAMA flood database (Llasat et al., 2014) has been used as starting point. This database contains all the flood events that have occurred in Catalonia since 1900 and includes hydrometeorological and impact information for each event.  Once this database has been updated until 2020, flood events from the Valencian Community, the Region of Murcia and Mediterranean Andalusia have been searched and classified. This has been achieved by using the Civil Protection Catalog of Historical Floods and other sources such as newspaper archives and reports. This base allows us to characterize the different regions in terms of the impact of events and to identify differences and commonalities to take into account in the design of adaptation measures. It will be also used to identify and characterize compound events.

Hence, in this communication we present the update of this database as well as its application as an adaptation tool for Catalonia: the AGORA Flood Observatory. This Observatory consists of an online portal (agora.ub.edu) that contains multiple resources related to floods such as reports of historical events with different sections adjusted to different target audiences (i.e. the general population, schools, expert and/or technical audiences). This observatory also includes the AGORA viewer. This viewer allows interactive consultation of flood events by municipality, county, and river basin, either on a map or in a table (with event details). The observatory also offer technical and pedagogical material about floods for different target groups. The role of this Observatory as an adaptation tool is based on its potential as a decision support and planning tool and its contribution to the improvement of risk awareness of the population. This research has been done in the framework of the C3Riskmed project (MICINN-AEI/PID2020-113638RB-C22) funded by the Spanish Ministry of Science and Innovation and the AGORA project financed by the Water Catalan Agency.

Llasat, M.C., Marcos, R., Llasat-Botija, M., et al. (2014). Flash flood evolution in North-Western Mediterranean. Atmospheric Research, 149: 230–243.

How to cite: Llasat-Botija, M., Pardo, E., Esbrí, L., Marcos, R., and Llasat, M. C.: Development of a flood events database for the Spanish Mediterranean coast and its application to improve flood risk awareness, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9729, https://doi.org/10.5194/egusphere-egu23-9729, 2023.

EGU23-10893 | Posters on site | NH1.2

Maximum wind radius approximation for parametric typhoon model combining operational forecast model and its application to storm surge and wave predictions 

Jae-Il Kwon, Sang-Hun Jeong, Yeong-Yeon Kwon, Jung-Woon Choi, Hojin Kim, Jin-Yong Choi, Ki-Young Heo, and Deoksu Kim

Parametric typhoon models reproduce realistic atmospheric pressure and wind fields during a typhoon. They require much less computation than planetary boundary layer models, allowing rapid coastal hazard estimations such as storm surges and waves. In these models, the maximum wind radius (Rmax) is a crucial parameter determining a wind field with the typhoon’s track and central pressure. This study proposes a new approach to Rmax estimation to generate accurate wind and pressure fields using numerical model data. We use the parametric typhoon model based on the basic vortex model. The track and central pressure of typhoons are obtained from the best track archives of the Joint Typhoon Warning Center (JTWC). Rmax was estimated hourly using circle fitting methods from the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) data. We tested this approach on five typhoons that passed near the Korean peninsula from 2016 to 2020. We evaluated the timeseries of Rmax with the JTWC archives. In addition, the prediction accuracy of storm surge and wave was compared using the reproduced wind field with Korea Operational Oceanographic System (KOOS). We also performed sensitivity tests for Rmax. This approach was tested on Typhoon Hinnamnor in 2022 with typhoon information from the Korea Meteorological Administration (KMA).

How to cite: Kwon, J.-I., Jeong, S.-H., Kwon, Y.-Y., Choi, J.-W., Kim, H., Choi, J.-Y., Heo, K.-Y., and Kim, D.: Maximum wind radius approximation for parametric typhoon model combining operational forecast model and its application to storm surge and wave predictions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10893, https://doi.org/10.5194/egusphere-egu23-10893, 2023.

EGU23-14453 | Orals | NH1.2

Extreme wind gusts assessment in France 

Nathalie Bertrand, Adrien Gaonac'h, Hugues Delattre, Maeva Sabre, and Laurent Li

Extreme wind phenomena generated during convective events are reported every year in France and cause significant material losses. In 2022, several significant events were observed, for example in Normandy in June where a kite surfer lost his life or in August in Corsica where a new wind gust record was recorded (225km/h). However, convective wind gusts are not considered in the design process of residential and industrial infrastructures. Therefore, our study assesses the available databases on convective wind speeds monitored or estimated by MeteoFrance and Keraunos for the period 2000-2019 in order to define a methodology to estimate the wind gusts return level.

Since all these convective events occur during a thunderstorm, the originality of this work was to use lightning data to separate convective from non-convective events. In addition, we considered all available data by projecting them onto the same grid (Meteorage lightning grid with 25 km resolution). A first probability analysis and conditional probabilities knowing the occurrence of a thunderstorm were deduced over the period and by seasons.

Then, we will focus on a very convective region (North-East of France) to evaluate the possibility to estimate the return level of wind gusts with the designed available dataset.

How to cite: Bertrand, N., Gaonac'h, A., Delattre, H., Sabre, M., and Li, L.: Extreme wind gusts assessment in France, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14453, https://doi.org/10.5194/egusphere-egu23-14453, 2023.

EGU23-14921 | Orals | NH1.2

The April 2022 South Africa Flood event 

Carlotta Scudeler, Giulia Giani, and Alexandra Tsioulou

Over April 2022, heavy rain in KwaZulu-Natal province in South Africa, followed by intense flooding and mudslides, caused one of the deadliest natural disasters in the country, mostly hard-hitting the areas in and around Durban. The intense rainfall was caused by a cut-of-low mid-latitude depression, known as Storm Issa, which is a common weather system in South Africa, particularly in the spring and summer months. In this study we present an analysis which investigates 1) the reasons why this event has been so impactful in terms of damage and loss compared to other similar events in South Africa and 2) if the intensity and frequency of such a storm is increasing as a result of a changing climate.  The analysis has been carried out at different levels: ERA-5 reanalysis data and rain gauge data have been used to characterize at different temporal and spatial scales the precipitation relative to the event and compared to other similar events; DWS discharge data have been used to analyse the event in terms of hydrological response and flow; and finally the footprint of the event has been reconstructed, following the flow analysis and by means of UNOSAT satellite-detected flood, landslide and damaged structures taken as reference. Among the major outcomes of the analysis we found that the duration and antecedent conditions, most probably also exacerbated by La Nina effect, made the event exceptional, resulting in a flash flood among the highest recorded in the last 70 years. The reconstructed event footprint whilst could be improved in the areas which were mostly affected by landslides, captures well the flooding in the major floodplain.     

How to cite: Scudeler, C., Giani, G., and Tsioulou, A.: The April 2022 South Africa Flood event, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14921, https://doi.org/10.5194/egusphere-egu23-14921, 2023.

EGU23-15054 | ECS | Posters virtual | NH1.2

Numerical Analysis of a Spanish Supercell Outbreak 

Carlos Calvo-Sancho, Javier Díaz-Fernández, Juan Jesús González-Alemán, Yago Martín, Lara Quitián-Hernandez, Pedro Bolgiani, Daniel Santos-Muñoz, José Ignacio Farrán, Mariano Sastre, and Maria Luisa Martín

On July 31 2015, a supercell outbreak occurred in Spain, causing significant damage and disruption. More than 20 supercells were responsible for producing multiple large hail, flash floods, and severe wind gusts. The outbreak was driven by a deep shortwave trough over the Iberian Peninsula, bringing with it a strong geopotential height gradient and instability in the Iberian Peninsula. On the front side of the trough axis, positive vorticity advection and divergence helped to promote and strengthen the upper-level forcing favoring thunderstorm episodes.

The event was simulated using the WRF-ARW model, in which several convective variables and instability indices were studied. To track the supercells, a python-based supercell tracking tool was used. This tool identified and tracked every supercell resolved by the model, and these results were verified with the supercell database. Reanalysis and sounding data revealed pre-convective environments favorable for supporting supercells development (i.e., high-level instability coupled with strong deep-layer shear). The results indicate large interaction between topography, convective initiation and supercell life-cycle, inducing their dynamics and the growth of mesocyclones.

The use of the WRF-ARW model and python-based supercell tracking tool allowed for a better understanding of the event and can help improve future forecasting and warning efforts.

How to cite: Calvo-Sancho, C., Díaz-Fernández, J., González-Alemán, J. J., Martín, Y., Quitián-Hernandez, L., Bolgiani, P., Santos-Muñoz, D., Farrán, J. I., Sastre, M., and Martín, M. L.: Numerical Analysis of a Spanish Supercell Outbreak, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15054, https://doi.org/10.5194/egusphere-egu23-15054, 2023.

EGU23-15352 | ECS | Orals | NH1.2

Intensifying Hydrometeorological Extreme Events and Compound Anomalies in a Temperate Region, Germany 

Haiyan Chen, Ye Tuo, and Markus Disse

Hydrometeorological extremes (HMEs) pose immense challenges and hazards to communities in a warming world, and this is particularly true for compound extremes (CHMEs) associated with deadly wet and dry events. More attention has been paid to extremes happening in arid and wet regions, nonetheless, temperate regions are understood poorly where more and more HMEs are striking fragile social-ecological systems. Therefore, the study shines a light on a proper temperate region of Europe, Germany. Not only the spatial-temporary variation of individual HMEs (IHMEs) but also compound events are fully investigated over the past seven decades. Notably, we propose a new insight to explore the concurrent extreme wet and dry events (CEDWs). A comprehensive framework is devised here, it combines the percentile and standardized index methods to explore compound extremes first. Different time scales are utilized to identify extreme wet (EWs) and dry events (EDs) separately considering their different evolving processes and impacting patterns on human society. The research presents the spatiotemporal distribution of the number, magnitude, and intensity of IHMEs and CHMEs in the wet and dry regions of Germany. Moreover, the changing tendency and spatial clustering of these events are further discussed by Ordinary Least Squares and Moran Index methods. Our study provides an important perspective on the changes in the spatiotemporal distribution of HMEs in the temperate region, especially the novel discussion of compound events. On the other hand, the research results regarding phases and areas of severe extremes facilitate planners and decision-makers to prioritize disaster management with limited resources and produce effective risk-mitigation plans.

How to cite: Chen, H., Tuo, Y., and Disse, M.: Intensifying Hydrometeorological Extreme Events and Compound Anomalies in a Temperate Region, Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15352, https://doi.org/10.5194/egusphere-egu23-15352, 2023.

EGU23-15638 | ECS | Posters on site | NH1.2

Bivariate modeling of flood peak and volume for assessing the hydrological safety of dams 

Epari Ritesh Patro, Greta Cazzaniga, and Carlo De Michele

The dam failure can be caused by multiple factors such as slope instability, presence of structural faults, or overflow. The latter is one of the most frequent causes and accounts for more than 40 % of them worldwide. Checking the hydrological safety of dams means assessing the ability of the dam, and thus of its outlets, to dispose of intense flood events without overflow. In Italy, the assessment of the hydrological safety of dams is a key and urgent issue in Italy. About the 8% of the large dams were built more than one century ago, and such a percentage is expected to increase up to 23% in a decade. Traditionally, such assessment is performed by means of the millennial quantile of flood peak. However, in literature, it has been shown that the determination of critical flood events should consider the statistical dependence between flood peak and flood volume. In the present work, we assess the hydrological safety of three Italian dams (namely, Ceppo Morelli, Mignano, and Molato) exploiting a bivariate approach, which stems from the method presented by De Michele et al., 2005. The statistical dependence between flood peak and volume is firstly estimated and modelled using copula models. Massive synthetic simulations are afterward performed to estimate the rate of overtopping of each dam, and consequently the return period. Results show that all the three dams result as hydrologically safe, even if Ceppo Morelli dam needs to be regularly monitored. Furthermore, for each dam, we also define a critical region, where the couples flood peak-flood volume may lead to overtopping. It is observed that the shape of such regions strictly depends both on flood peak and volume. Eventually, the routing effects of the three dams are compared, with respect to the return period and assuming three different dependence behaviors. It is proved that an overestimate of the dependence degree would result in an underestimate of the dam routing effect, and viceversa, leading to improper assessment of the risk.

How to cite: Patro, E. R., Cazzaniga, G., and De Michele, C.: Bivariate modeling of flood peak and volume for assessing the hydrological safety of dams, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15638, https://doi.org/10.5194/egusphere-egu23-15638, 2023.

EGU23-16269 | ECS | Posters on site | NH1.2

Combining the Weights of Evidence model, the Strahler/Shreve hydrographic model, and the HEC-RAS analysis for the assessment of flood susceptibility in Essaouira Province, Morocco 

Abdellah Khouz, Jorge Trindade, Sérgio Oliveira, Pedro Pinto Santos, Fatima El Bchari, Blaid Bougadir, Ricardo Garcia, Eusébio Reis, Mourad Jadoud, and Andreia Alves Silva
The most frequent disasters indiced by natural hazards in Morocco's northern and central regions are floods, namely flashfloods. Determining the areas covered by the maximum extent of floodwaters from estimated flood flows is how flood-prone areas are often defined. The primary goal of the current study was to develop a map of flood susceptibility using a weights-of-evidence (WofE) model. To confirm it, compare it to a simplified hydrographic model that was constructed based on the hierarchy of drainage system characteristics, adhering to the Strahler stream order criteria and the magnitude of the drainage networks based on Shreve’s magnitude, considering both approaches are widely used in the literature review. The most susceptible area defined by the two approaches was thoroughly analysed through hydraulic modelling using HEC-RAS, providing the most accurate results. Digital elevation models (DEMs) created from 12.5 m high-resolution orthophoto images, were used for the investigation in this study.The Essaouira provincial Watersheds in Morocco mapped around 95 flood locations in a GIS system, during the last 20 years. From the flood locations inventory, 70% were randomly chosen for training the flood susceptibility model and the remaining 30% were deployed for independent validation goals. 18 flood-conditioning factors were considered:  elevation, aspect, slope angle, curvature plan, curvature profile, Stream Power Index (SPI), Topographic Wetness Index (TWI), Normalized Difference Vegetation Index (NDVI), distance to rivers, lithology, rainfall, land use and land cover (LULC), drainage density, valley depth, Topographic Position Index (TPI), Terrain Ruggedness Index (TRI), Geomorphons and permeability. The final flood susceptibility map was produced by using the weights-of-evidence (WofE) model, for which the receiver operating characteristic curve and the area under the curve (AUC) were generated. The validation findings demonstrated the WofE model's robustness and effectiveness. Additionally, the results of both approaches revealed a linkage in terms of susceptible locations, with the most susceptible area being nearer to the city of Essaouira on Ksob oued. HEC-RAS analysis was performed on the cited location, helped to determine the local susceptible area with higher specificity, comparing to the two previous approaches. Managers, academics, and planners can use the study's findings to manage flood-prone areas and decrease damage. Acknowledgements: The work has been financed by national funds through FCT (Foundation for Science and Technology, I. P.), in the framework of the project “HighWaters – Assessing sea level rise exposure and social vulnerability scenarios for sustainable land use planning” (EXPL/GES-AMB/1246/2021).
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

How to cite: Khouz, A., Trindade, J., Oliveira, S., Santos, P. P., El Bchari, F., Bougadir, B., Garcia, R., Reis, E., Jadoud, M., and Silva, A. A.: Combining the Weights of Evidence model, the Strahler/Shreve hydrographic model, and the HEC-RAS analysis for the assessment of flood susceptibility in Essaouira Province, Morocco, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16269, https://doi.org/10.5194/egusphere-egu23-16269, 2023.

EGU23-16301 | Posters on site | NH1.2

How will climate change affect spatial coherence of streamflow and groundwater droughts in Great Britain? 

Amulya Chevuturi, Maliko Tanguy, Ben P Marchant, Jonathan D Mackay, Simon Parry, and Jamie Hannaford

How climate change will affect spatial coherence of droughts is a key question that water managers must answer in order to adopt strategies to mitigate impacts on water resources. For example, water transfers between regions have long been considered as a possible water management option. Conjunctive use of surface water and groundwater is another common water management practice. However, in both cases, these solutions are only viable if both regions or stores are not in drought simultaneously. These relationships might change under the influence of climate change.

The recently published ‘enhanced Future Flows and Groundwater’ (eFLaG) dataset of nationally consistent hydrological projections for the UK, based on the latest UK Climate Projections (UKCP18), provides the opportunity to explore the future evolution of drought spatial coherence in detail. Here, we use eFLaG future simulations of streamflows and groundwater levels to analyse the projected change in drought spatial coherence in Great Britain, over its seven different water regions, using joint and conditional probabilities of occurrence. Some key findings are: an increase in coherence in summer everywhere in the country; in winter, however, it will only increase in the South-East; and, in most regions, the coherence between groundwater and streamflow droughts will increase, one exception being the South-East in summer.

These results provide valuable insight to water managers to inform their long-term strategies to overcome future impacts of droughts. The methodology has the potential to be applied to other parts of the world to help shape strategic regional and national investments to increase resilience to droughts.

How to cite: Chevuturi, A., Tanguy, M., Marchant, B. P., Mackay, J. D., Parry, S., and Hannaford, J.: How will climate change affect spatial coherence of streamflow and groundwater droughts in Great Britain?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16301, https://doi.org/10.5194/egusphere-egu23-16301, 2023.

Flooding is one of the most prevalent and disruptive natural hazards that affect livelihoods worldwide, especially in lower-income countries where proper drainage and flood protection measures tend to be less developed. Floods have become more frequent and intense over the recent decades and are expected to worsen their negative impacts in the future. Managing flood risk requires the evaluation of potential flood hazards and their consequences. Hydrodynamic models are generally employed to predict flood hazards (inundation extent, depth, velocity, etc.). One of the major concerns in flood hazard mapping is selecting an appropriate model structure. This study examines the flood predictions by a one-dimensional (1-D) hydrodynamic model for two geomorphologically distinct river reaches, the Adyar River, Chennai, India, and the Brazos River, Texas, USA. The results are compared against the simulation results of a two-dimensional (2-D) hydrodynamic model. An open-source model, HEC-RAS, with both 1-D and 2-D modeling capabilities, is employed for flood inundation modeling. The inundation patterns predicted by the 1-D model are found to vary significantly in the case of the Brazos River compared to those for the Adyar river. The study suggests that the simulations of flood inundation extent and maximum flow depth are influenced by the 1-D modeling assumptions on flood plains in river reaches characterized by wide flood plains with complex local terrain variations. The 1-D model simulations are also found sensitive to the magnitude of the flood event with respect to the hydraulic capacity of the reach.

How to cite: Jesna, , Sm, B., and Kp, S.: Investigating the potential of a 1-D hydrodynamic model for flood inundation modeling and hazard mapping, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-362, https://doi.org/10.5194/egusphere-egu23-362, 2023.

EGU23-832 | ECS | Posters virtual | NH1.3

Assessment of future fluvial floods damages on buildings based on different climate scenarios: a case study in France 

Emmanuelle Itam, Thomas Boidot-Doremieux, and Mohammad Ali Iravani

Today, there is no doubt climate change leads to more frequent and severe climate extremes in the future. Like other extreme climate events, fluvial flooding, which is already the most damaging climate extreme in France, will be more frequent, endangering the entire economy and financial system. As essential economic actors, financial institutions must be prepared to face higher resulting economic impacts caused by extreme fluvial floods, even in the close future. However, it is still difficult to analyze and quantify this physical risk and its resulting direct losses, particularly in the building sector. In this context, the necessity to integrate flood-related risks into financial risks (credit, market, and liquidity risks) can be done through the quantification of predicted climate-related damages.

In this study, we emphasize the results of the application of a new framework to calculate the direct damages from fluvial flooding on residential buildings and its future worsening due to climate change. The originality of our work is to develop a frequency analysis of the prediction of flood damages at the building scale by combining specific depth-damage functions (Grelot and Richert, 2019) and fine-resolution hazard maps for river flooding.

We calculate damages on buildings located in the center of Paris (close to Seine river) for different fluvial flood frequencies. The damage modeling is performed using national depth-damage functions that give relationships between flood depth, flood duration, and subsequent damage. The latter concerns the cost of repair or replacement of each elementary component of the buildings that will be damaged or destroyed depending on the flooding scenario. We consider three different types of buildings collective buildings, multi-storey individuals, and single-storey individuals. The water depths due to flooding defines exposed areas of buildings and are based on data extracted from maps provided by the Joint Research Centre (Alfieri et al., 2015). Those maps depict flood-prone areas for river flood events for six different flood frequencies (from 1-in-10-years to 1-in-500-years) and are based on the high-emissions “RCP8.5” global warming scenario.

For each return period, we detect the impacted buildings by crossing the building map created from the French National Building Database with the corresponding fluvial flood map. Total damages are then computed as the sum of damages predicted for each building type associated with the closest water depth value.

By using the expected annual damage (EAD) methodology, we have investigated the effects of climate change caused by decreasing the return period (increasing the frequency of events). The results show that an increase in the frequency of occurrence of flooding due to climate change (decreasing the return period) led to increasing in the value of annual damage.

REFERENCES :

Alfieri, L., Feyen, L., Dottori, F. and Bianchi, A., 2015. Ensemble flood risk assessment in Europe under high end climate scenarios. Global Environmental Change, 35, pp.199-212.

Grelot, F. and Richert, C., 2019. Floodam: Modelling Flood Damage functions of buildings. Manual for floodam v1. 0.0 (Doctoral dissertation, irstea).

How to cite: Itam, E., Boidot-Doremieux, T., and Iravani, M. A.: Assessment of future fluvial floods damages on buildings based on different climate scenarios: a case study in France, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-832, https://doi.org/10.5194/egusphere-egu23-832, 2023.

EGU23-1316 | ECS | Orals | NH1.3

RESCUE a new physically-based large scale flood model 

Luciano Pavesi, Elena Volpi, and Aldo Fiori

Flood mapping is an essential step in flood risk assessment to reduce losses. Through flood mapping, we can detect vulnerable areas, assess flood impacts and create mitigation plans.
From the literature, we have two consolidated approaches to delineating flood maps. The first one is the hydrologic-hydraulic approach. Its strength relies on the possibility of simulating scenarios for different probabilities of occurrence (return period scenarios), considering the physics of the phenomenon. At the same time, the weaknesses of this approach regard the required amount of input data and high computational costs. The second approach is the geomorphological one, which allows to delineate flood-prone areas directly from some topographic features derived from a Digital Terrain Model (DTM), i.e. elevation, distance to the channel, etc.. Thanks to the limited request of input data and its rapidity in terms of computational efficiency, this approach is particularly appealing for large scale analyses. However, the geomorphological approach does not allow for the delineation of flood maps for different return period scenarios; further, the output is strongly linked to the quality of the input DTM.
Here we propose a model that combines the two approaches to enable preliminary mapping of flood areas for different scenarios at the regional scale; the model is named RESCUE, laRgE SCale inUndation model. RESCUE takes advantage from coupling geomorphological analysis and simplified hydrologic-hydraulic modeling, providing simple and reliable large scales inundation estimates. Like geomorphological models, it requires few data in input and has a high computational efficiency; while like hydrological-hydraulic models, it is physically-based and linked to a return period scenario.
Noteworthy, RESCUE allows for parameter uncertainty estimation through Monte-Carlo analysis, leading to a probabilistic assessment of flooded areas. Here, we show the potentialities and limitations through two examples: The Paglia-Chiani River system, and Central Apennines District (Central Italy).

How to cite: Pavesi, L., Volpi, E., and Fiori, A.: RESCUE a new physically-based large scale flood model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1316, https://doi.org/10.5194/egusphere-egu23-1316, 2023.

Accurate extraction of high-resolution digital elevation models (DEMs) is critical for many flood-sensitive rivers regarding landform change monitoring, hydraulic modeling, sediment transport tracking, and evaluation of river channel morphodynamics. Multi-temporal repeat monitoring of flood-vulnerable rivers is crucial due to rapid alteration of morphological properties of in-channel landforms. Thus, in this study the three-dimensional (3D) DEMs of the study region were acquired by unmanned aerial vehicle (UAV) based surveys in order for continuous tracking of stream channel morphology for the rivers sensitive to floods. Repeated high-resolution topography of the Bogacay basin, Antalya, Turkey was obtained in this study by means of UAV-based Structure from Motion (SfM) photogrammetry. The acquired topography during two consecutive years allows analysis of the relations between the main geomorphic processes related to landform alterations and their role in sediment transfer. In conjunction with the flood simulation, the scour depths at bridge piles after a probable flood (Q500) were predicted by HEC-RAS software. The flood analysis was conducted for a maximum runoff of 2560 m3/s with a calculated return period of 500 years (Q500). The results of HEC-RAS flood and scour analyses indicated that a maximum scour depth of 2.49 m could be expected during a probable flood with a maximum water depth of 8.2 m measured from the scoured depth. This water level corresponded to a 0.46 m of submersion of the bridge deck since the pier height was 5.25 m and the maximum flood velocity was predicted as 5.7 m/s. The results indicated that the alterations in the river channel after an expected flood event, allowed reliable evaluation of riverbed morphodynamics, while verifying that UAV-SfM and Dem of Difference (DoD) are useful tools in geomorphological dynamic mapping and in change monitoring studies.

How to cite: Özcan, O. and Özcan, O.: UAV-based monitoring of river bed morphodynamics for multi-hazard vulnerability assessment of bridges, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1858, https://doi.org/10.5194/egusphere-egu23-1858, 2023.

EGU23-2133 | Posters on site | NH1.3

Riverine flood risk in municipalities of Slovakia 

Matej Vojtek and Jana Vojteková

Integrated flood risk assessment is based on a multidimensional definition of flood risk, i.e. the extent of flood losses depends not only on the flood hazard itself, but also on the vulnerability of the social, economic, and environmental system to flooding. This study aims at the riverine flood risk mapping and assessment in municipalities of Slovakia. The riverine flood risk index (RFRI) was determined for 2,927 municipalities of Slovakia as a synthesis of the riverine flood hazard index (RFHI) and the riverine flood vulnerability index (RFVI) using the spatial multi-criteria analysis and geographic information systems (GIS). The RFHI was calculated based on eight indicators representing the riverine flood potential: number of flood events, slope, curvature, average annual maximum 5-day rainfall, river density, lithological rock types, soil texture, and land cover. Moreover, the RFVI was calculated based on seven indicators representing the social and economic vulnerability of municipalities: population density of urban areas of municipalities, share of population included in the age category 65+ from the total population of municipality, share of unemployed persons from the total number of economically active population in municipality, share of the Roma ethnicity from the total population of municipality, number of buildings within 100 m from a river, length of roads within 100 m from a river, and number of bridges in a municipality. The result of the Pearson correlation between individual indicators and the number of flood events in municipalities was used to determine the importance of indicators, which was subsequently used for assigning the indicator weights applying the rank sum method. The RFHI and RFVI for each municipality were calculated as the aggregation of the respective weighted indicators. The multiplication of the RFHI and RFVI resulted in the final RFRI. Based on the results obtained, the very high and high classes of RFHI contained 839 municipalities, which are located mostly in northern and eastern Slovakia and partly also in western and central Slovakia. The very high and high classes of RFVI included 817 municipalities, mainly, in northern and central Slovakia and partly also in western and eastern Slovakia. The highest RFRI values were recorded mostly by the municipalities in northern, central, and eastern Slovakia and partly also in western Slovakia. The very high and high risk of riverine flooding was recorded in 700 municipalities, i.e. these municipalities are included in the very high and high classes of RFRI. The results achieved in this study are useful, on one hand, for local self-governments and actors responsible for flood risk management, but more importantly for cyclic updating of the Preliminary Flood Risk Assessment in Slovakia under the EU Floods Directive. This work was supported by the VEGA agency under the grant number 1/0103/22 through the project entitled "Spatio-temporal Changes and Prediction of Flood Risk in Municipalities of Slovakia".

How to cite: Vojtek, M. and Vojteková, J.: Riverine flood risk in municipalities of Slovakia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2133, https://doi.org/10.5194/egusphere-egu23-2133, 2023.

EGU23-3239 | ECS | Orals | NH1.3

Accelerating urban flood modelling using a GPU-parallel non-uniform structured grid and sub-grid approach 

Youtong Rong, Paul Bates, and Jeffrey Neal

Remote sensing technology and the resulting high resolution geospatial data now allow for a detailed description of urban landscape, advancing the development of raster-based flood models. Previous studies have highlighted the critical role of finely resolved and accurate terrain data (5m or less) in capturing flow patterns in urban areas. However, using a uniform fine grid resolution over a rectangular domain generally results in dense grids and leads to large computational costs. The small cell size is often an overspecification for rural regions where the flow processes are changing much less rapidly. Unstructured grid models resolve this issue and trade off more complex programming and slower operation against being able to represent a given problem with fewer computational elements. An alternative solution has been recently proposed to apply the non-uniform structured grid, with fine grids covering only the regions where this detail is a necessity, for example to capture the preferential flow paths influenced by small-scale topographic features or man-made structures (river channels, buildings, roads, defences, etc.). Without this, the smoothing effect of mesh coarsening upon input topographical data in urban areas leads to a uncertain prediction of the inundation extent and the timing of inundation due to the simplified wetting process. Flow connectivity formed by the river channels and the road network, which has a strong control on urban floodplain hydraulics, is also better represented by mixing grid resolutions. Considering the large consequences in terms of economic losses caused by urban flooding, here we develop a GPU-accelerated non-uniform sub-/super-grid channel model (river channels with width below or above the fine grid resolution) for accurate and efficient urban flood modelling. Urban areas and the river channel network are forced to keep fine resolution, while a coarse representation, depending on the terrain gradient, is allowed for rural regions. This model allows the utilization of available sub-/super-grid scale bathymetric information for 1D in-channel flow representation, and a 2D model for floodplain with variable grid resolution, minimising the computational costs and below water line data requirements in the river channel. Three tests are set up to validate the model performance, and the results show that modelling the urban area with fine resolution improved the model reliability and accuracy, and reduces computational cost in rural areas where a coarse grid may be used.

How to cite: Rong, Y., Bates, P., and Neal, J.: Accelerating urban flood modelling using a GPU-parallel non-uniform structured grid and sub-grid approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3239, https://doi.org/10.5194/egusphere-egu23-3239, 2023.

Flood susceptibility assessment for identifying flood-prone areas plays a significant role in flood hazard mitigation. Machine learning is an optional assessment method because of its high objectivity and computational efficiency, but how to get enough and accurate information of historical flood locations to train the machine learning models has been a key problem. In recent years, news media data from both news websites and social media authentication accounts has emerged as a promising source for natural science studies. However, the application of news media data in urban flood susceptibility assessment is still inadequate. This study proposed an approach of three tasks to use news media data on this topic. Firstly, flood locations were extracted from news media data based on a named entity recognition (NER) model. Then, a frequency or distance-based data quality control method was employed to improve the representativeness of the extracted flooded locations. Finally, flood conditioning factors with information of historical flood locations were input into a Support Vector Machine (SVM) model for flood susceptibility assessment. We took the central city of Dalian, China, as a case study. The results show that there was no significant difference of a T-test between the distributions of most flood conditioning factors at the flood locations from the news media data and the official planning report. In the obtained flood susceptibility map, the high flood susceptibility areas got a recall of 90% compared with the high flood hazard areas in the planning report. Performing data quality control in the frequency-based method can improve the precision of the flood susceptibility map by up to 5%, while the distance-based method is ineffective. This study provides an example and offers the value of applying new data sources and modern deep learning techniques for urban flood management. 

How to cite: Fu, S., Lyu, H., Wang, Z., and Hao, X.: Extracting flood locations from news media data by the named entity recognition (NER) model to assess urban flood susceptibility, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3286, https://doi.org/10.5194/egusphere-egu23-3286, 2023.

EGU23-4812 | ECS | Posters virtual | NH1.3 | Highlight

Daily Streamflow Forecasting in the Mahanadi River Basin using a Novel Deep Learning-based Model 

Amina Khatun, Chandranath Chatterjee, and Bhabagrahi Sahoo

Flood is one of the most devastating natural disasters accounting for the loss of life and property of millions of people every year. Since 2000s, floods have become more frequent in some parts of the world, especially in the tropical region. In India, many frequent extreme floods are found to occur recently. While the structural measures of flood management are not always feasible, the non-structural measures, such as flood forecasting plays a vital role in developing early flood warning systems. In the present study, a novel deep learning model, namely Smoothing-based Long Short-Term Memory (Smooth-LSTM) model is developed for daily streamflow forecasting at the head of the delta region in the Mahanadi River basin, eastern India. This modelling framework integrates smoothing filters and the traditional LSTM networks to predict the daily streamflow foreacasts up to 5-days lead-time. This model follows a sequence-to-single output approach, with the time-lagged streamflows as the only input variable. The Smooth-LSTM model is able to predict the streamflows reasonably well with a Nash-Sutcliffe Efficiency of 0.87–0.82 up to a lead-time of 5-days. The overall model performance is found to be satisfactory with the ability to capture the observed streamflows within the 90% uncertainty bands.

How to cite: Khatun, A., Chatterjee, C., and Sahoo, B.: Daily Streamflow Forecasting in the Mahanadi River Basin using a Novel Deep Learning-based Model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4812, https://doi.org/10.5194/egusphere-egu23-4812, 2023.

EGU23-6693 | ECS | Posters on site | NH1.3

Transferability of data-driven models to predict urban pluvial floodwater depth in Berlin, Germany. 

Omar Seleem, Georgy Ayzel, Axel Bronstert, and Maik Heistermann

Hydrodynamic models are considered the best representation of the physical process of runoff generation and concentration. However, they are computationally expensive. Data-driven models are raising as a potential alternative to surrogate them but the models’ transferability in space is still a major challenge. This study compared the performance of random forest (RF) and convolutional neural networks (CNN) based on the U-Net architecture for predicting urban pluvial floodwater depth, the models’ transferability in space and whether using transfer learning techniques could improve the models’ performance outside the training domains. The results showed that RF models were better for predictions among the training domains, though this may be due to overfitting. The CNN models had a better potential to generalize beyond the training domains and were able to benefit from transfer learning techniques to improve their performance outside the training domains than RF models.

How to cite: Seleem, O., Ayzel, G., Bronstert, A., and Heistermann, M.: Transferability of data-driven models to predict urban pluvial floodwater depth in Berlin, Germany., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6693, https://doi.org/10.5194/egusphere-egu23-6693, 2023.

EGU23-7583 | ECS | Orals | NH1.3

City of Pemba: development of an automatic prediction tools for pluvial hazard assessment 

Giacomo Fagugli, Flavio Pignone, Alessandro Masoero, Simone Gabellani, Umberto Morra di Cella, Lauro Rossi, and Federico Munaretto

Mozambique is one of the countries in Africa most seriously affected by tropical cyclones, which bring heavy rains and flooding causing severe damage and often exacerbating human-driven emergencies. Coastal cities, like Pemba (Cabo Delgado), are exposed to cyclone-triggered urban flooding events. 

In the framework of the ECHO funded project “REDE-EDUCAMA Disaster Reduction and Education in Cabo Delgado and Manica)”, an innovative (open-source) hydraulic modelling tool was adapted to recreate flooding scenarios caused by heavy rainfall in the peninsula of Pemba (85 square kilometres) with the aim of identify the area most prone to pluvial flooding and implementing an operational tool to inform Disaster Risk Management Authorities (DRMA) with reliable forecasts to issue timely early warnings (EWs) in case of cyclones and heavy rain affecting this area. For improving the sustainability of the tool, the operational chain implemented in co-operation with local authorities, is based on the use of open-source free software and models. The hydrodynamic model of rainfall-runoff (Broich et al., 2019), available in Telemac-2D and adapted to deal with time-variant grid-based rainfall input, was used.  

A preliminary collection of available data was carried out for the definition of the inputs needed to feed the model: a topographical base-map and precipitation. The map was derived integrating the results of a high-resolution drone survey (performed together with local authorities on 14 km2) with the Copernicus DSM satellite product (30m), to ensure the hydrological continuity needed.  

Concerning the rainfall input, the historical precipitation data series from the Pemba weather station, provided by INAM Cabo Delgado was analysed to identify the maximum rainfall depth for certain hourly intervals (24, 48 and 72 hours). Following this analysis,  33 rainfall events (hyetographs), different in timing and intensity, were generated and used to feed the ponding model, to produce 33 urban flooding scenarios. For warning purposes, 2 representation modalities of the outputs were investigated: a 200-metre grid aggregation (selecting medium-high percentiles) and a neighborhood-scale aggregation (selecting high percentiles and using the neighborhood map provided by the Municipality of Pemba). 

Modelled inundation maps were shared and commented with the local community in Pemba, with the dual objective of receiving feedback and increasing flood risk awareness. 

The full pluvial flooding forecasting chain for the Pemba urban area was then operationally implemented by connecting the flooding scenarios with the operational weather forecasts, by means of FloodPROOFS open-source modelling system (https://github.com/c-hydro). Daily forecasts of rainfall over Pemba are extracted from freely available global models (GFS 0.25), considering a set of pixels surrounding Pemba to account for uncertainty. A tailored tool connects the forecast rainfall with the most similar rainfall scenario, activating the corresponding urban flooding scenario, was developed. Operational forecasts are made available to DRMA officers through the www.myDEWETRA.world EW platform. 

The application in Pemba demonstrated the goodness of the approach based on innovation and co-operation with local authorities, enabling the replication on other cities of the country. 

How to cite: Fagugli, G., Pignone, F., Masoero, A., Gabellani, S., Morra di Cella, U., Rossi, L., and Munaretto, F.: City of Pemba: development of an automatic prediction tools for pluvial hazard assessment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7583, https://doi.org/10.5194/egusphere-egu23-7583, 2023.

On a global scale, the frequency and magnitude of flooding are getting worse. Although hydrologists around the globe have developed sophisticated flood models, their performance, especially over mountainous regions, is not comprehensively understood. The situation is challenging for flood-affected low-income nations, as sufficient resources are needed to procure commercial flood models with appropriate technical know-how. For instance, Bhutan, a mountain-dominated landscape in Asia, has been experiencing unprecedented flooding due to its fragile topography and climate change impacts. Unfortunately, a comprehensive data-driven modeling approach to determining flood hazard zones is missing in this region. The present study quantifies flood risks while considering a robust hydrodynamic flood model over Bhutan’s Chamkhar Chu River basin, a severely flood-prone area. The recently released open-source HEC-RAS v6.3 by the U.S. Army corps of Engineers, whose efficacy for flood inundation modeling is less explored, is considered to derive a set of flood risk maps. The coupled 1D-2D flood model setup is developed to simulate various flooding scenarios corresponding to design discharge and rainfalls for 50-yr, 100-yr, and 200-yrs. A corrected high-resolution Digital Elevation Model (DEM) from the ALOS-PALSAR product was utilized to reduce uncertainties in the final flood risk values. The simulated flood hazard maps for the settlements along the Chamkhar chu river are quantified in terms of flood depth, velocity, and a product of depth and velocity. A set of performance statistics are derived from testing the model performance while comparing the simulated inundation maps with the past inundation maps from MODIS satellite imagery. It was noticed that a significant portion of the central region is at a potential threat of very high flood risk as the simulated depth exceeds 3 m and velocities surpassing over 1.6 m/s. Such research will assist flood management agencies in prioritizing affordable structural and non-structural flood mitigation measures for the public that will reduce the impact of flood hazards in the future. Given the efficient computational performance of HEC-RAS v6.3 over a sensitive terrain, the study encourages the adoption of the model for accurately identifying flood risks over global mountainous regions for effective flood management.

How to cite: Namgyal, T., Mohanty, M. P., and Thakur, D. A.: How fitting are open-source flood models in capturing flood risks over mountainous regions: A prudent analysis over Chamkar Chu Basin, Bhutan using HEC-RAS v6.3, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11334, https://doi.org/10.5194/egusphere-egu23-11334, 2023.

EGU23-11448 | ECS | Orals | NH1.3

Challenges for flood hazard and risk assessment in Mozambique: the case of Megaruma and Muaguide rivers 

Sara Rrokaj, Benedetta Corti, Giorgio Cancelliere, Alice Costa, Anna Giovannini, Daniela Molinari, Charlie Dayane Paz Idarraga, Alessio Radice, and Ana Maria Rotaru

As a consequence of climate change and rapid urbanization, floods have increased both in terms of intensity and frequency, impacting especially the less developed countries of the World, and particularly sub-Saharan Africa. In such contexts, reliable flood risk assessments are of primary importance to support local authorities and stakeholders in emergency management and planning, and in the definition of effective risk mitigation measures. Still, their implementation is often hampered by lack of suitable data and resources. The present study has the main objectives of presenting challenges and identified solutions of performing flood hazard and risk analysis for the Megaruma and Muaguide rivers in Cabo Delgado, the northern province of Mozambique and also the poorest one. The downstream paths of the rivers cross the districts of Mecufi and Metuge, rural areas covered by fields cultivated by inhabitants who live on subsistence agriculture. During the wet season, some of the villages are completely isolated, with no access to adequate health services due to the floods that periodically affect the local population and their activities. As for many developing countries, data scarcity was the first limiting factor for quantitative analysis; therefore, much effort was primarily invested into data research. The hydrologic and hydraulic modelling to determine the flood hazard in the areas rely on free or at least cheap, global data (rainfall, terrain elevation and soil cover), meeting the second requirement of low available budget. On the contrary, an intensive field survey was required to collect data on the vulnerability of exposed assets at the base of damage assessment. Particular attention was also paid in the choice of free softwares and modelling tools. The resulting approach and methods can be easily exported to similar contexts, enabling robust flood risk analyses in the support of sustainable development.

How to cite: Rrokaj, S., Corti, B., Cancelliere, G., Costa, A., Giovannini, A., Molinari, D., Paz Idarraga, C. D., Radice, A., and Rotaru, A. M.: Challenges for flood hazard and risk assessment in Mozambique: the case of Megaruma and Muaguide rivers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11448, https://doi.org/10.5194/egusphere-egu23-11448, 2023.

EGU23-12263 | Posters on site | NH1.3

Flood forecasting for everywhere-PUB in flood forecasting 

Thomas Skaugen, Zelalem Mengistu, Ivar Peerebom, and Wai Wong

In this study the Prediction in Ungauged Basins has been taken very literally in that we present a system that enables setting up a rainfall-runoff model, the Distance Distribution Dynamics (DDD) model for any catchment in Norway. The system can be used in operational flood forecasting since hydrological simulation results for an arbitrary catchment are obtained in a few minutes. A GIS map tool is used to calculate catchment boundaries, a hypsographic curve and other catchment characteristics such as vegetation and mean annual discharge needed to estimate DDD model parameters. Derived terrain information and catchment boundaries are furthermore used to extract meteorological information from gridded (1 x 1 km) maps for both historical and forecast periods. The historical period may be of such length (>30 years, daily resolution) that the mean annual flood can be reasonably estimated and compared to forecasted runoff values for hazard assessments. In this way a flood forecaster is no longer limited to only be looking at hydrological simulation results from calibrated models set up for a few gauged catchments. Rather, she can set up a model for ungauged catchments where the forecasted precipitation is the most intense or where vulnerable infrastructure is located. The relative comparison between simulated forecasted runoff and simulated mean annual flood is of value for hazard assessments. Regarding absolute values, the DDD model has been tested for prediction in ungauged basins for 25 gauged catchments and obtains an average Kling-Gupta efficiency (KGE) of 0.77. The mean annual flood is, however underestimated by 40 %. Better results are expected when improved gridded meteorology and estimates of mean annual discharge are available. Future developments include higher temporal and spatial resolutions so that flood forecasting and flood estimation can be carried out for smaller and faster responding ungauged catchments.

How to cite: Skaugen, T., Mengistu, Z., Peerebom, I., and Wong, W.: Flood forecasting for everywhere-PUB in flood forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12263, https://doi.org/10.5194/egusphere-egu23-12263, 2023.

EGU23-12397 | ECS | Orals | NH1.3

Pluvial flooding in urbanscapes: a full-coupled flood modelling approach 

Paolo Tamagnone, Guy Schumann, and Ben Suttor

Are your properties located far enough from rivers, sea shorelines or water bodies? If the answer is yes, this does not mean that they are fully safe from flooding.

In an era governed by continuous climate instability and unstoppable expansion of cities, the exacerbation of hydrometeorological events is increasing the occurrence of pluvial floods. Pluvial flooding is induced by the combination of two factors: extreme precipitations and the incapability of the ground/drainage systems to effectively handle excessive rainwater.

In an urban environment, the runoff generated by localized and intense rainstorms may quickly inundate streets and buildings undermining the safety of people and assets. The characteristic of being hardly predictable has inspired the definition of pluvial flood as an ‘invisible hazard’ and the related damages and losses are increasingly weighing on the budget of municipalities and private citizens.

Looking at the upsetting climate projections, experts are resolute in developing comprehensive methodologies and strategies for flood risk assessment and management.

In this work, we present the attempt of accomplishing a high-resolution pluvial flood risk assessment at the city scale. The city of Differdange (Luxembourg's third largest city) is used as case study in which the extreme rainfall-related impacts and hazards are analyzed through the implementation of a fully coupled 1D/2D dual drainage model. This type of hydrodynamic model closely mimics the complexity of an urban landscape allowing to simulate all hydraulic phenomena occurring both on the surface and through the sewer network. Despite the digital accuracy of these models, they are rarely implemented due to the vast amount of detailed information required; which are often unavailable.

The implementation of the hydraulic model follows two main steps: the bi-dimensional discretization of the surface and the 1D modelling of the whole drainage network.

Nowadays, many countries provide open-access high-resolution digital elevation models of their territories (50 cm for Luxembourg) and up-to-date cadastral planimetries from which essential information for the 2D component are extrapolated. Ground data is enriched by land use/cover and soil maps for the estimation of roughness and infiltration parameters.

The drainage network contemplates all pipes carrying rainwater, meaning the newer storm-water system and the old combined sewer network. The geometric specifications required are size, shape, elevation, material of pipes, manholes and tanks. Important infrastructures, such as flooding barriers, have been systematically added to the model.

 

The fully-distributed hydrological engine allows operating the rainfall-runoff transformation on each cell of the domain and the exchange of water between the surface and drainage network occurs through the nodes of the network (storm drains and manholes).

The model’s outcomes allow for assessing the level of hazard to which each building is exposed, identifying the critical nodes within the drainage network, and proposing mitigation strategies.

Furthermore, these insights may help authorities to improve their warning systems and emergency plans.

How to cite: Tamagnone, P., Schumann, G., and Suttor, B.: Pluvial flooding in urbanscapes: a full-coupled flood modelling approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12397, https://doi.org/10.5194/egusphere-egu23-12397, 2023.

EGU23-13130 | ECS | Posters on site | NH1.3 | Highlight

Hydrodynamic simulations of the flash flood event in July 2021 in the Wesselbach catchment in Germany, and the effects of land use changes 

Franziska Tügel, Lizanne Eckmann, Lennart Steffen, Reinhard Hinkelmann, and Eva Paton

Flash floods are among the most dangerous natural hazards and the associated risks are likely to increase due to climate change and increased urbanization. Observations of the last decades and projections of the future climate show an increase in the frequency and intensity of heavy rainfalls for many land surfaces. In July 2021, many European countries have been severely affected by large-scale heavy rainfalls. In Germany, the federal states of North Rhine-Westphalia and Rhineland-Palatinate have been particularly affected with at least 180 fatalities, hundreds of injuries, lots of heavily damaged buildings, and extensive infrastructural damages. The modeling of flash floods is essential for effective risk management to produce hazard and risk maps, investigate the effects of land use changes, and plan mitigation measures.

This works aims to investigate the flash flood event in the Wesselbach catchment in North Rhine-Westphalia (Germany), which was generated by an extreme, short rainfall event of 118 mm within less than two hours in the late evening of 13th July 2021. The catchment is part of the city of Hagen, and the considered model domain of approximately 3 km² is characterized by steep slopes, a main soil type of silty loam, and a main land use type of forest, with settlements along the main watercourse in the downstream half of the domain. Large portions of coniferous areas in the catchment have exhibited decreasing vitality since 2018, up to complete dead or cleared areas. The in-house robust shallow water model hms++ is used to simulate the flash flood event using the measured rainfall data of a nearby rainfall gauge as input. Spatially distributed Manning’s roughness coefficients are used to account for the different land use types. Infiltration is neglected as the soils in that area show limited infiltration capacity, and the worst-case is considered that the soils are already saturated. Building heights have been included in the digital elevation model.

The results include the temporal development of flooding areas, spatial distributions of maximum water depths, and flow velocities in the Wesselbach catchment as well as hydrographs at different cross-sections of the main water course. Furthermore, the effects of forest damage on the discharge behavior and flooding areas will be investigated. Later on, structural mitigation measures will be included in the model to study their effectiveness for different heavy rainfall events.

How to cite: Tügel, F., Eckmann, L., Steffen, L., Hinkelmann, R., and Paton, E.: Hydrodynamic simulations of the flash flood event in July 2021 in the Wesselbach catchment in Germany, and the effects of land use changes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13130, https://doi.org/10.5194/egusphere-egu23-13130, 2023.

EGU23-14829 | Orals | NH1.3 | Highlight

Thermal imaging and vegetation detection through UAV survey for large scale hazard monitoring of river levee 

Giorgia Dalla Santa, Lorenzo Picco, Francesca Ceccato, Simonetta Cola, and Paolo Simonini

Levees are linear structures that can be thousands of kilometers long and play a very important role in flood protection. They are usually monitored by traditional direct survey techniques, such as CPTU or coring, or piezometers, which provide high accuracy, but are localized and performed in predetermined locations.

As a result, long distances between investigated sections limit the detailed analysis of the entire structure. In addition, predetermined locations may not cover areas of actual potential weakness.

Recently, new survey technologies from aerial media (drones) have been successfully applied to obtain a first level of levee investigation in order to identify the location of possible weak areas or potential locations of levee failure, so as to plan further local investigations in those areas.

Usually, levee failures are localized in the presence of:

(i) concrete structures passing the levee;

(ii) large trees, which can be dangerous because their roots are a preferred route for water infiltration and, therefore, potential seepage pipes. In addition, at higher erosion levels of the river bank, large trees can promote bank collapse due to their weight;

(iii) sandy soils, which are characterized by high permeability. From previous experience, we have noticed that levee failures have occurred at sections previously vegetated by reeds. Reed canes usually grow on sandy soils and, in addition, are characterized by very deep and large roots, possible routes of localized infiltration through the body of the levee. From these observations comes the idea of using reedbeds as indicators of sandy soils and possible weak levee sections;

(iv) sections where unfavorable conditions of the levee body, such as soils with high permeability or the presence of animal burrows crossing the levee or obstructed drains, prevent proper drainage and bring the phreatic surface close to the levee surface.

Thus, the idea is to test different innovative UAV-supported survey approaches on the same test area, in combination with local on-site surveys, to compare and combine the obtained results. Firstly, we would test the possibility of using vegetation maps as an indicator of weak sections of the embankment. Up to now, a first drone survey data has been performed and the obtained RGB orthophotos have been elaborated to determine the Green Red Vegetation Index (GRVI), in order to acquire a vegetation cover map of the embankment. The obtained data have been calibrated with on-site surveys conducted by vegetation experts. To facilitate the identification of reedbeds, the campaign has been carried out in winter, when reedbeds are yellowish in color, unlike short grass. In areas identified as reedbed vegetated, the soil has been sampled by coring and fully classified in the geotechnical laboratory to check if reedbed can effectively be an indicator of sandy soils. Further characterization may be carried out in order to investigate the relationship between reedbeds and soil characteristics.

The final aim is to develop an innovative method of low-cost aerial monitoring of levee structures that can provide an initial state of information and identify areas in need of further direct investigation in order to define the necessary maintenance works, decreasing associated risks.

How to cite: Dalla Santa, G., Picco, L., Ceccato, F., Cola, S., and Simonini, P.: Thermal imaging and vegetation detection through UAV survey for large scale hazard monitoring of river levee, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14829, https://doi.org/10.5194/egusphere-egu23-14829, 2023.

EGU23-15342 | Posters on site | NH1.3

Integrated Reservoir Operations using coupled Hydro-Met Multi-Model system for flood forecasting and mitigations for Pune, India 

Pallavi Gavali, Srujan Gavhale, Mohamed Niyaz, Sahidul Islam, Sumita kedia, Sagar Pokale, Arun Dwivedi, Gouri Kadam, Akshara Kaginalkar, Manoj Khare, and Abhinav Wadhwa

Meteorology and Hydrological extreme events, such as heavy rainfall and associated Flooding is one of the increasing disasters in India for last two decades. Due to heavy reservoir discharge, Impact of rapid Urbanization, unauthorized encroachments across riverbanks extreme flood events are likely to be more common and severe in the future, potentially impacting millions of people.

Pune one the fastest growing megacities in India facing frequent riverine flooding and associated disaster causing huge property losses in millions and causalities. The city is located at the leeward side of Sahyadri mountain range, with 7 reservoirs on the upstream side of the catchment, which control the flows in the rivers impacting the downstream Urban catchment. The reservoirs spillway discharges causes riverine flooding along with contribution from free catchment runoff, which usually occur concurrently. Estimation of reservoir inflows and subsequent spillway discharges is needed for integrated reservoir operations to execute effective flood control measures. To understand these severe flood disasters associated with reservoir operation ensemble multi model simulations were carried for Pune catchment for flood mitigation.

In current study, coupled meteorology model WRF with integrated high resolution (10m) hydrology model HEC-HMS and Hydraulic Model HEC-RAS was developed. High resolution CartoSAT, Digital Elevation Model (DEM) and generated 1m DTM was used to develop both hydraulic and hydrology models. The geometric data for dam structures and gates/spillways have been incorporated in developed models. Gates were operated based on reservoir rule curves for spillway discharge and riverine flood simulations. Spatially distributed high-resolution WRF (1.5 Km) forecasted (72 Hrs.) gridded rainfall data with temporal resolution of 15 mins has been used for forecasting the flood condition in the city. 3D buildings have been incorporated in the terrain to recognize water depth and flooding in the city, which can be visualized through 2-dimensional Rasmapper and 3-dimensional viewer.  The performance of the models has been validated on the basis of statistical error functions (NSE, RSR, PBIAS and R2). Pune flood disaster events for the year 2019 and 2022 were simulated by developed flood forecasting system with reservoir operations. The model output (water level, spread and discharge) were validated using observed flood data from Pune Municipal corporation and dam discharges from water Resource department.

The developed multi-model flood forecasting framework will help the reservoir authorities to perform reservoir operations effectively in future to minimize the downstream flood conditions. Also the disaster management authorities will plan flood mitigation plans with sufficient lead time.

How to cite: Gavali, P., Gavhale, S., Niyaz, M., Islam, S., kedia, S., Pokale, S., Dwivedi, A., Kadam, G., Kaginalkar, A., Khare, M., and Wadhwa, A.: Integrated Reservoir Operations using coupled Hydro-Met Multi-Model system for flood forecasting and mitigations for Pune, India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15342, https://doi.org/10.5194/egusphere-egu23-15342, 2023.

EGU23-16719 | Posters on site | NH1.3

MICA: A fast and flexible solution for real-time flood mapping – An application on 2015 Cannes Antibes flood 

Alexandre Bredimas and Tristan Cambonie

Fast-flood prediction challenges both scientists and stakeholders. Flood evolution is extremely sensitive to input data regarding rain forecasts and the actual status of infrastructures and soil. Accurate and quick modelling is critical to the responsiveness and decision-making of all stakeholders for an optimal allocation of the resources required to limit the damages to the infrastructure and the risk to the population.

BlueMapping develop a decision-support tool called MICA. MICA is a cellular automata model building on previous academic models, especially CADDIES, with tailored adaptations.

The code of MICA has been industrialised into a high-performance calculation algorithm based on parallel computing using GPUs. It has been deployed on the Amazon Web Services cloud. It provides an efficient, scalable and flexible solution for pluvial flood prediction.

MICA's potential will be illustrated with the test case of fast-flood inundations that hit the watershed of Cannes and Antibes (South of France) in October 2015. The code runs in a few minutes on this 149 km2 watershed. The result will be benchmarked with a standard model and the actual maximum depth measurements.

How to cite: Bredimas, A. and Cambonie, T.: MICA: A fast and flexible solution for real-time flood mapping – An application on 2015 Cannes Antibes flood, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16719, https://doi.org/10.5194/egusphere-egu23-16719, 2023.

EGU23-16893 | Orals | NH1.3 | Highlight

Disseminating Flood Risk Information in the USA through Risk Rating 2.0 

Md Adilur Rahim, Rubayet Bin Mostafiz, and Carol Friedland

Federal Emergency Management Agency (FEMA) introduced Risk Rating 2.0, a new risk-based premium approach, on October 1, 2021, for new policies and on April 1, 2022, for existing policies. Risk rating 2.0 considers the geographic attributes (e.g. distance to the lake, river, coast), building attributes (e.g. foundation type, first-floor height), and policy attributes (e.g. coverage and deductible limit) by coverage (i.e., building and contents) and perils (i.e., pluvial and fluvial flooding, storm surge, tsunami, great lake, and coastal erosion) to estimate risk premiums. In this review study, we conduct exploratory data analysis and visualization of the rating factors released by FEMA to better understand the risk premium. The associated rating factors are multiplied and summed by coverage to get the initial premium without fees for each structure. As the rating factors are multiplicative, lower factors contribute to lower risk premiums. The rating factors decrease with increasing distance from flood sources.

The states in the USA are categorized into five segments (e.g. Gulf coast states are categorized as segment 1). A base rate is applied to each state by single-family home indicator and perils for levee and non-levee protected areas. The factors are then distributed by territory where each HUC12 is assigned a factor by peril. Inland flood from pluvial and fluvial sources is applicable for all the states where single-family homes are not levee protected. The effect of the inland flood is considered for structures in segment 1 where the distance to the river is less than 13,500 meters. Storm Surge flooding is considered within 11,000 meters of the Gulf coast for non-barrier islands. Tsunami flooding is considered for structures located in coastal CA, OR, WA, AK, AS, GU/MP, and HI. Great Lake flooding is considered for structures located within 8,500 meters of the Great Lake. Coastal erosion is considered for structures located within 100 meters of the coastline.   

The elevation of a structure is an important indicator for estimating risk premium. The higher the elevation of the structure relative to flood sources, the lower the risk factors. The occupancy affects the premium where single-family home masonry structure has a lower rating factor than frame structure. A higher floor of interest has lower factors, lowering the premium for all perils except coastal erosion. The foundation type also affects the factors where Slab foundation has lower factors than Crawlspace foundation, hence lower risk premium. Another addition is elevating the machinery and equipment above the first floor which reduces the initial premium without fees by 5%.  

Individual and community level flood mitigation reduces risk rating 2 insurance premium. Elevating first-floor height (FFH) to 1, 2, and 3 feet above ground reduces the initial premium without fees by 10, 19, and 27.1 percent, respectively, compared to FFH of 0 feet. Community Rating System (CRS) discount reduces the initial premium without fees between 5% to 45% based on CRS class. The information presented in this study will help homeowners, community developers, and government agencies to understand the effect of each attribute on risk premiums.

How to cite: Rahim, M. A., Mostafiz, R. B., and Friedland, C.: Disseminating Flood Risk Information in the USA through Risk Rating 2.0, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16893, https://doi.org/10.5194/egusphere-egu23-16893, 2023.

Flooding is among the most prevalent natural hazards worldwide, with increasing frequency and devastating impact. Urban floods are severe in Indian cities due to the culmination of changing climate, rapid urbanization, and intensive population growth. Transport infrastructure such as roads underpins economic activity enabling goods and human mobility. Evaluating the response against urban flooding is critical as disruption of the road system can result in cascading effects. The recent advancements in assessing the direct impact of urban flooding on road infrastructure are well explored. However, we lack a systematic approach to model and evaluate the direct, intangible, and indirect effects of extreme precipitation-induced urban flooding on road infrastructure systems in urban areas with unplanned drainage systems. Here in this study, we model the interaction between urban flooding and road transportation systems by integrating a hydrodynamic model with a network science approach for the coastal city of Kozhikode, India. We evaluated the response of the combined sewer drainage system against extreme precipitation events through the 1D-2D coupled flood model. While also identifying the resulting flood inundation characteristics- extent, propagation, and depth. Flood modeling results indicate the inundated roads and functionality loss of the road system for extreme precipitation events. Our initial assessment highlights that highly localized road network submergence due to flood inundation has a widespread and prolonged disruption in the system. The integrated framework and network functionality measures could help in future resilience assessment and in devising effective planning strategies for hazard mitigation in urban areas.

How to cite: Dave, R. and Bhatia, U.: Investigating the impact of extreme precipitation induced urban flooding on road network disruption, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-738, https://doi.org/10.5194/egusphere-egu23-738, 2023.

Increasing urban pluvial flood disasters due to climate change and rapid urbanisation have been a great challenge worldwide. Timely and effective emergency evacuation is important for reducing casualties and losses. This has become a bottleneck for emergency management. This study aimed to develop a commonly used Agent-Based Mode (ABM) for pluvial flood emergency evacuation at the city scale, exploring the cascading impacts of pluvial flooding on human behaviour and emergency evacuation. The July 2021 pluvial flood event in Zhengzhou, Henan Province, claiming 380 lives and 40.9 billion yuan in direct losses, was selected as this case study. A raster-based hydraulic model (ECNU Flood-Urban) was used to predict flood inundation (extent and depth) during an event in Zhengzhou’s centre. Moreover, a comparative analysis of emergency evacuations was conducted before and after the pluvial flood event. The results showed that crowd behaviour plays an important role in emergency evacuation, and extensive flooding leads to an 11–83% reduction in the number of evacuees. This study highlights the importance of risk education and contingency plans in emergency response. The ABM model developed in this study is proven to be effective and practical and will provide support for decision-making in urban flood emergency management.

How to cite: Yang, Y.: ABM-based emergency evacuation modeling during urban pluvial floods: A “7.20” pluvial flood event study in Zhengzhou, Henan Province, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1800, https://doi.org/10.5194/egusphere-egu23-1800, 2023.

EGU23-3616 | ECS | Posters on site | NH1.4

Mapping the impact of levee failure on flood risks: A Toronto case study 

Florence Mainguenaud, Usman T Khan, Laurent Peyras, Claudio Carvajal, Bruno Beullac, and Jitendra Sharma

Urban areas are frequently built along rivers and earthen levees are commonly used to protect areas from fluvial floods. Levees are designed to protect assets from flooding, however, they deteriorate over time. Maintenance checks are required to maintain their efficacy but even in good condition, a levee structure may fail during a flood, hence flood risk assessments in fluvial areas require an investigation of levee failures, e.g. by overtopping, erosion, or sliding. In this research, we investigate the failure probability due to backward erosion of an adapted levee in the Etobicoke Creek watershed, in Toronto, Canada. The study proposes an adapted levee as the residential area is often flooded. Backward erosion is the most probable and challenging failure mechanism for our case study based on the levee shape and soil type. For this probabilistic study, the levee was modelled using GeoStudio, which produces seepage analysis from geotechnical and hydrological parameters. The seepage analysis provides hydraulic gradients from which we determine the failure probability of backward erosion based on a critical hydraulic gradient value. To obtain the flood hazard, we use a steady flow hydraulic model (HEC-RAS) to simulate the 350-years return period flow through the River. We compare two backward failure scenarios: one with a levee breach and one without, to better understand how failure of the levee will impact flood risks, and therefore, highlighting the importance of on-going levee maintenance. To obtain flood risk maps, the flood hazard (i.e., flood extent) is combined with flood exposure. The flood exposure includes land-use type (residential, commercial, etc.) and demographic information. Flood hazard and exposure data are combined using ArcGIS. The flood hazard and exposure rasters are reclassified in a new scale to determine flood risk. We then overlay the rasters to determine the spatial distribution of flood risk for both scenarios. We compare the resulting flood risk maps and calculate the change in flood risks for the area protected by the levee. Accounting for potential failure of infrastructure in flood risk mapping results in more accurate risk estimations. We also demonstrate the positive impact of the levee.

How to cite: Mainguenaud, F., Khan, U. T., Peyras, L., Carvajal, C., Beullac, B., and Sharma, J.: Mapping the impact of levee failure on flood risks: A Toronto case study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3616, https://doi.org/10.5194/egusphere-egu23-3616, 2023.

EGU23-3883 | Posters on site | NH1.4

Smart city disaster prevention platform information integration displays and practical application in New Taipei City Taiwan 

Sheng-Hsueh Yang, Der-Ren Song, Jyh-Hour Pan, Xi-Jun Wang, Sheau-Ling Hsieh, Keh-Chia Yeh, Cheng-Wei Li, and Wen-Feng Wu

Urban areas are gradually being affected by climate change. It is difficult to avoid urban flooding caused by heavy rainfall. Especially road flooding occurs 2-3 times a year in urban areas in the summer of Taiwan, when the regional weather is convective rainfall strong, it is difficult for general weather forecasting models to predict the amount of rainfall in the city in a short period of time. Rainfall areas in urban areas are prone to road flooding. Therefore, the intensity management value (>50dBz) of the radar reflectivity around the city is used to estimate the rainfall and urban flood warning, and the IoT water level monitoring instrument can monitor the water level in the urban rainwater sewer and set the urban flood warning based on the management value. The local low-lying areas of the city can also use CCTV images to identify flooding situation as a tool through AI's CCN deep learning technology and CCTV's flooding big data database that according to CNN's learning, training, and testing, after the completion, CCTV inspection and flood image recognition can be used for urban disaster prevention and relief. Finally, the monitoring data related to urban flooding is collected and displayed through the urban smart flood prevention platform, which provides efficient data collection, increases the response time for disaster relief, and quickly eliminates road flooding in the city. This study takes the urban smart flood prevention platform in New Taipei City, Taiwan as an example.

How to cite: Yang, S.-H., Song, D.-R., Pan, J.-H., Wang, X.-J., Hsieh, S.-L., Yeh, K.-C., Li, C.-W., and Wu, W.-F.: Smart city disaster prevention platform information integration displays and practical application in New Taipei City Taiwan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3883, https://doi.org/10.5194/egusphere-egu23-3883, 2023.

EGU23-5027 | ECS | Orals | NH1.4

Flood Simulation and Population Impact Analysis of Short-Duration Intense Rainfall in Urban Area 

Hsiao-Ping Wei, Yuan-Fong Su, Chih-Hsin Chang, and Keh-Chia Yeh

A report from World Bank in 2022 reveals that about 1.81 billion people (23% of the world population) are directly exposed to flood with depths greater than 0.15 meters. In this study, we evaluate the impact of extreme rainfall events on population in urban areas in Taiwan using SOBEK models. The validation results of the SOBEK models are promising with photos collected from social media for historical storm events. To further assess the impact of extreme rainfall events, we used design rainfall with hourly rainfall of 80mm/hr, 90mm/hr, and 100mm/hr derived from Simple Scaling Gaussian Markov (SSGM) method for single rainfall gauge within major urban area. These results are provided for disaster prevention authority to reinforce the flooding management in urban area.

How to cite: Wei, H.-P., Su, Y.-F., Chang, C.-H., and Yeh, K.-C.: Flood Simulation and Population Impact Analysis of Short-Duration Intense Rainfall in Urban Area, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5027, https://doi.org/10.5194/egusphere-egu23-5027, 2023.

EGU23-5522 | ECS | Orals | NH1.4

2D Flood Analysis considering Buildings in Urban Areas 

Kyewon Jun, Sunguk Kim, Minjin Jung, and Seunghee Lee

Due to climate change, the scale of flood damage by localized torrential rains in urban areas is on an increase. Meanwhile, the existing flood runoff analysis methods do not consider buildings in urban areas, resulting in an overestimation of the degree of flood damage. Therefore, this study presents a method to consider buildings when applying XP-SWMM for flood analysis in downtown areas where buildings are concentrated, in order to accurately simulate the flood spread pattern around the building. To propose an optimal method which considers buildings, water depth, maximum flooded area, and the flow pattern around the building were compared according to whether or not the building was applied. As a result of the study, the average flooded area was 172,900㎡ when the building was set as an inactive area, which was 64% of the average flooded area (271,000㎡) when the building was not considered. The average water depth was 0.32m when buildings were considered, which was 1.78 times deeper than the average water depth (0.15m) when buildings were considered. This is the reflection of the blocking effect of the building in the model analysis, resulting in a significant reduction of the flooded area. In addition, since the flood simulation considered the flow rate of the same volume, flow velocity and average submerged depth relatively increased. This study is expected to contribute to the establishment of optimal downtown flood measures, by presenting a method for accurate flood analysis using the XP-SWMM model considering the influence of buildings in urban areas. For further improvement in the accuracy of flood analysis, it would be necessary to develop flood simulation methods suitable for different basins with flood records.

 

This research was support by a (2022-MOIS63-002) of Cooperative Research Method and Safety Management Technology in National Disaster funded by Ministry of Interior and Safety(MOIS, Korea).

How to cite: Jun, K., Kim, S., Jung, M., and Lee, S.: 2D Flood Analysis considering Buildings in Urban Areas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5522, https://doi.org/10.5194/egusphere-egu23-5522, 2023.

EGU23-7707 | ECS | Orals | NH1.4 | Highlight

A modelling framework to assess urban flood risk on the city scale 

Stefan Reinstaller, Albert König, and Dirk Muschalla

A city-wide approach to reduce the uncertainty regarding the spatial variability of urban flooding events is required in urban catchments. The goal of this study is the development of a modelling framework independent of the spatial scale to address the most hazardous areas in the current state and the future. The framework starts with the definition of the study objectives (e.g. reducing flood risk), which have a direct impact on the spatial and temporal scale, the used model approach, the data requirement and the level of detail. Furthermore, potentially hazardous areas will be identified with the potential flood risk index (PFRIi). The determination of this is a risk-based approach (R=E*V*H) which combines the exposition (E) with the vulnerability (V) and the hazard (H). The population density of each object and the total number of persons in the catchment will quantify the exposition. The vulnerability includes the number of past damage events and the object use. How accurate the modelled hazard is considered, depends on the used model approach: i) GIS-based; ii) only 1D; iii) only 2D; iv) 1D/2D models. The combination of H and V resulted in the risk factor (RFi) in four levels of detail depending on the used model approach. This allows both, the quantification of hazardous areas at the current state and the change of the PFRIi by future scenarios such as climate change and urbanization.

PFRIi = nP,k * RFi / (Ak * ∑P)                                                                                                                                                                                     

PFRIi=Potential Flood Risk Index; nP,k= number of Persons on a private ground k; Ak=total object area; =total number of persons in the catchment; Rfi= risk factor depending on the used model approach k

The GIS-based flow path analysis as the first level of detail can be used to identify the urban flooding hot spots. This allows the identification of hazardous sub-catchments in a city or high-risk private ground in a catchment quantified by the PFRIGIS. This is useful for further detailed analysis with other model approaches (e.g. 1D/2D model). The next steps are the implementation of the demonstrated framework for each level of detail in the city of Graz in Austria. Furthermore, the framework will integrate different climate scenarios based on a high-resolution climate model to address the impact of climate change on the urban drainage system quantified by the PFRIi.


Keywords: urban flooding, urban flood modelling, risk assessment, future changes

How to cite: Reinstaller, S., König, A., and Muschalla, D.: A modelling framework to assess urban flood risk on the city scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7707, https://doi.org/10.5194/egusphere-egu23-7707, 2023.

EGU23-9359 | ECS | Posters on site | NH1.4

Evaluating the design relevance of the choice of flood frequency analysis technique in an urban coastal watershed 

Daniel Lassiter, Julianne Quinn, and Daniel Wright

The central challenge to flood risk management is in designing flood mitigation practices and strategies that avoid the human and economic costs of under- and over-investment. Designing to avoid these costs requires accurate recurrence probability estimates for decision-relevant flood impacts such as water depths, financial damages, and water volumes. The challenge of estimating flood impact probabilities is exacerbated in coastal settings with non-independent compound flood drivers such as rainfall and storm surge. While techniques for compound flood impact probability assessments have been proposed, insight into the decision relevance of the choice of methodology has not been explored. Our work begins to address this gap by comparing flood-volume exceedance curves resulting from three approaches in a 1.9km2 coastal urban watershed in Norfolk, Virginia. Watershed runoff and storm sewer flow are represented by 1144 links, 1128 nodes, and 869 subcatchments in a U.S. Environmental Protection Agency Stormwater Management Model (SWMM).

The first flood impact probability assessment technique follows a traditional design storm approach in which the joint probability of storm surge and rainfall are mapped directly onto the modeled flood volumes. In contrast, the second and third techniques involve modeling many years of stochastically generated rainfall and storm surge time series and empirically estimating the probability distribution of the resulting flood volumes. These two techniques differ in their approach to stochastic weather generation, one fitting a probability distribution to local rainfall observations to allow for extrapolation outside the record, and the other using only historical rainfall observations but across a wider regional domain. Each approach is grounded in statistical and physical theory but leads to different estimates in flood-volume exceedance curves and their associated uncertainty. Since these estimates would influence flood mitigation design, we show that the choice of technique has design implications.

How to cite: Lassiter, D., Quinn, J., and Wright, D.: Evaluating the design relevance of the choice of flood frequency analysis technique in an urban coastal watershed, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9359, https://doi.org/10.5194/egusphere-egu23-9359, 2023.

EGU23-10415 | Posters on site | NH1.4

Development of Guidelines on the Application of Urban Flood Sensors 

Sunguk Kim, Kyewon Jun, and Minjin Jung

In recent years, the frequency and intensity of localized torrential rains in Korea have increased due to climate change, thereby increasing human and property damage through frequent urban flooding. Research on urban flood forecasting is mainly focused on numerical modeling and rainfall-based flood prediction, but the analysis technology of quantitative flood measurement data is lacking. In addition to flood mapping and verification of flood prediction results, it is necessary to develop urban flood management technologies using sensor-based quantitative flood depth measurements. The existing flood sensors have different management regulations depending on the development entities, and there are no set standard or basic performance standards, causing inefficiency in their budget and maintenance. Therefore, in order to improve the efficiency and prevent trial and error, this study proposes the performance standards and installation methods as guidelines, necessary for the installation and operation of flood sensors. To this end, firstly, domestic and foreign cases for urban flood sensors were reviewed for their installation procedures, installation location selection, measurement intervals, inspection and management plans, etc. The table of contents of the guidelines was derived through case analysis, consisting of a standard model installation plan that describes the detailed composition and operation principle of flood sensors, sensor installation plans for each measurement point such as the surface and sub-surface, on-site installation procedures, and instructions on a test run. These guidelines are expected to be followed to strengthen a proactive urban flood response system by effectively operating flood sensors.

 

Acknowledgment: This research was support by a (2022-MOIS63-002) of Cooperative Research Method and Safety Management Technology in National Disaster funded by Ministry of Interior and Safety(MOIS, Korea).

How to cite: Kim, S., Jun, K., and Jung, M.: Development of Guidelines on the Application of Urban Flood Sensors, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10415, https://doi.org/10.5194/egusphere-egu23-10415, 2023.

The most frequent catastrophic natural disaster is thought to be flooding. Due to climatic uncertainty, variable rainfall, a lack of river carrying capacity, illegal settlement along river banks, and dense population, flood magnitudes and vulnerabilities increased in the past ten years globally. There are 11,356 records of hydrological and meteorological occurrences from 1900 to 2020, according to the United Office of Disasters Risk Reduction's (UNDRR) International Database, with 8.6 million fatalities and 2600 million US dollars in economic damage. In the current climate, floods cannot be prevented, but their damages can be reduced with a thorough flood assessment. The identification of the flood inundation area, flood arrival time, and flow velocity in flood-prone areas can be accomplished using a variety of hydrodynamic models; however, the limited resolution of DEM (Digital Elevation Model) makes it impossible to determine the actual flooding state. To remedy this shortcoming, we developed a high-resolution DEM from UAV (Unnamed Aerial Vehicle) for this case study, which involves the well-known Sabarmati of Gujarat State, one of India's principal west-flowing rivers with a length of 371 kilometres, which was impacted by a flood in 2006. The 4RTK (Real-Time Kinematic) Phantom, a UAV survey, was used to acquire aerial pictures of a portion of the river Sabarmati. The image was then processed with 75% mosaicking using the Pix4D mapper tool for better accuracy. Later, with the aid of Global Mapper, various DEMs with grid sizes ranging from 0.5 m x 0.5 m to 10 m x 10 m are created with near precision of 3 cm spatial resolution. These generated DEMs are then used as input for the hydrodynamic simulation using Civil Geo-HECRAS. Hence, the hydrological data required for the hydrodynamic model has been assumed from past floods and the geometrical data for the study is derived from the UAV survey with four Manning's roughness coefficients—0.025, 0.030, 0.033, and 0.035 have been assumed for this case study considering the local conditions. The analysis of Manning's roughness value's influence reveals that when roughness increases, discharge reduces, and velocity and Froude's number decrease.

Keywords: Flood, DEM-Digital Elevation Model, UAV-Unnamed Aerial Vehicle, Hydrodynamic modeling, Manning’s roughness

How to cite: Rana, M., Patel, D., and Vakhria, V.: UAV based High-resolution DEM for 1D Hydrodynamic modeling - A case of Flood Assessment of Sabarmati River, Gujarat, India., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11379, https://doi.org/10.5194/egusphere-egu23-11379, 2023.

EGU23-12624 | Orals | NH1.4 | Highlight

Multi-parameter flood risk assessment towards efficient flood management in highly dense urban river basins in the Region of Attica, Greece 

Alexia Tsouni, Stavroula Sigourou, Panayiotis Dimitriadis, Vasiliki Pagana, Theano Iliopoulou, G.-Fivos Sargentis, Romanos Ioannidis, Efthymios Chardavellas, Dimitra Dimitrakopoulou, Nikos Mamasis, Demetris Koutsoyiannis, and Charalampos (Haris) Kontoes

Flood risk assessment in vulnerable areas is crucial for efficient flood risk management, including the analysis and design of civil protection measures and the implementation of studies with proper interventions towards mitigating flood risk. This is even more crucial in highly dense urban river basins such as the ones in the region of Attica, which is hosting Athens, the capital of Greece, as well as critical infrastructures and important social economic activities. In the framework of the Programming Agreement with the Prefecture of Attica, the Operational Unit BEYOND Centre of EO Research and Satellite Remote Sensing of the Institute of Astronomy, Astrophysics, Space Applications & Remote Sensing (IAASARS) of the National Observatory of Athens (NOA), in cooperation with the Research Group ITIA of the Department of Water Resources and Environmental Engineering of the School of Civil Engineering of the National Technical University of Athens (NTUA), study five flood-stricken river basins in the region of Attica, which affect 23 Municipalities. The research teams collect all available data, conduct detailed field visits, run hydrological and hydraulic models, and assess flood hazard, flood vulnerability and eventually flood risk in every area of interest. Furthermore, high-risk critical points are identified, and mitigation measures are proposed, both structural and non-structural, in order to achieve effective crisis management for the protection of the population, the properties and the infrastructures. In addition, the BEYOND Centre has developed a web GIS platform where all the collected and produced data, the flood hazard, vulnerability and risk maps, as well as the identified critical points, the refuge areas and escape routes are stored and made available. All the relevant stakeholders and the competent authorities, who are directly or indirectly involved in civil protection, participate in dedicated workshops designed for their needs, and moreover, the studies’ general outcomes are disseminated to the wider public for raising awareness purposes. The response of the end users is very positive, and their feedback very constructive. The methodology and the outputs of the project are in line with the requirements for the implementation of the EU Floods Directive 2007/60/EC, the Sendai Framework for Disaster Risk Reduction, the UN SDGs, as well as the GEO’s Societal Benefit Areas.

How to cite: Tsouni, A., Sigourou, S., Dimitriadis, P., Pagana, V., Iliopoulou, T., Sargentis, G.-F., Ioannidis, R., Chardavellas, E., Dimitrakopoulou, D., Mamasis, N., Koutsoyiannis, D., and Kontoes, C. (.: Multi-parameter flood risk assessment towards efficient flood management in highly dense urban river basins in the Region of Attica, Greece, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12624, https://doi.org/10.5194/egusphere-egu23-12624, 2023.

EGU23-15092 | ECS | Orals | NH1.4

Urban Flood Resilience Assessment Using Arc GIS Based AHP Approach: A Case Study of Gyor City, Hungary 

Ibrar Ullah, Gabor Kovacs, and Tibor Lenner

Urban flooding has gained great attention in recent years since population in urban areas have become more vulnerable to climatic extremes. The rate of urban flooding has increased around the globe mainly due to climate change. To cope with an increasing flooding issue, there has been an increased effort to manage flood management in urban areas. Similarly in this study, an attempt was made to develop a GIS based map to access flood resilience for the Gyor city. The Gyor city is particularly vulnerable to flooding due to its geographical proximity at the confluence of Raba, Rabca, Mosoni, Marcal and the great Danube rivers. Three elements i.e., hazard, Exposure, and coping capacity with each having pre-determined parameters were selected and processed through Analytic Hierarchy Process (AHP) technique. The product value map was then analyzed in ArcGIS using Specialized Flood Resilience Model (S-FRESI). The resultant product map shows that the majority of Gyorszentivan, Menfocsanak and Ipari Park districts have the very high resilience to floods, while most area of the  districts of Kismegyer, Nadorvaros,  Sziget, and Belvaros have very low resilience to floods. Similarly, the districts of Bacsa, Saras, Pinnyed, Gyimot and Likocs have most of the areas in medium resilience, while the remaining 6 districts possess areas with low, medium and high resilience. The study is very beneficial for future studies in assessing the areas that are more vulnerable to flooding and have low resilience and can help the decision makers to prepare a better urban flood management system.

How to cite: Ullah, I., Kovacs, G., and Lenner, T.: Urban Flood Resilience Assessment Using Arc GIS Based AHP Approach: A Case Study of Gyor City, Hungary, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15092, https://doi.org/10.5194/egusphere-egu23-15092, 2023.

EGU23-15752 | ECS | Posters on site | NH1.4

Contrasting levels of surprise and levee effect between municipalities in the 2021 flood in Belgium 

Daniela Rodriguez Castro, Solène Roucour, Pierre Archambeau, Christophe Dessers, Sébastian Erpicum, Michel Pirotton, Mario Cools, Jacques Teller, and Benjamin Dewals

In July 2021, the Bernd low-pressure system induced disastrous floods over part of Germany, the Netherlands and Belgium. Relatively small catchments were mostly affected. In Belgium, nine out of the ten most impacted municipalities are situated in a single catchment, namely river Vesdre (700 km2). Considering this catchment as a case study, we investigate whether available data enable detecting surprise and levee effects and, if so, whether the distribution of such effects shows a particular spatial pattern.

To explore this, we apply relatively simple data analysis based on official flood hazard maps, field surveys, as well as outcomes of hydrological and hydrodynamic modelling. The field surveys are twofold. On one hand, inundation depths were registered for 8,000 buildings and infrastructures in the considered catchment. On the other hand, detailed interviews were conducted with flood victims. Information was collect on: flow characteristics, building features, damage and monetary losses, as well as implemented precautionary measures and warning.

Data analysis shows that the mismatch between the observed inundation extent and the official hazard maps varies strongly from one section of the river to another, particularly between municipalities. These variations could be related to the presence of flood defense constructed along specific sections of the river, and the associated levee effects. Another quantity which varies enormously from one municipality to another is the ratio between the number of flooded buildings in a municipality to the total number of buildings in the same municipality. This quantity may reflect the degree of overwhelming of local authorities and first respondents, though it is not accounted for in current flood damage modelling.

The outcomes of the data analysis contribute to explain differences in how local authorities and communities reacted during this unprecedented flood. Overall, the results highlight the relevance of initiatives undertaken since the event for updating the official flood hazard maps based on more extreme scenarios aiming at enhancing risk awareness. It also emphasizes the need for improved management of residual risk in the case of channelized rivers, or rivers equipped with high-standard flood defences.

We are currently exploring to which extent the differences in the level of surprise and levee effects contribute to explain differences in damage and monetary losses between municipalities.

How to cite: Rodriguez Castro, D., Roucour, S., Archambeau, P., Dessers, C., Erpicum, S., Pirotton, M., Cools, M., Teller, J., and Dewals, B.: Contrasting levels of surprise and levee effect between municipalities in the 2021 flood in Belgium, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15752, https://doi.org/10.5194/egusphere-egu23-15752, 2023.

Urban flooding occurs to  populated and build environments when there is excess water due to intense rainfall, extreme river flows, or storm occurance. Flood protection procedures and processes are  important and critical tools in flood-vulnerable and flood-prone areas. The consequences of the occurrence of such phenomena can have a seriously negative impact on a social, economic and environmental level. The first two categories are particularly affected in urban environments, where flooding might lead to severe casualties. The assessemnt for  optimal use of mobile systems of mobile flood protection dams/barrires as  short-term flood prevention and non-permanent/ nonstructural measures in combination with the permanently existing protection works and infrastructures in the urban environment is the subject of this study.

As a field of application (case study) of this research and the evaluation of different flooding and intervention scenarios, a stream section of an important transboundary watercourse that flows through the city of Serres, Greece was chosen. For this stream, the river bed and the surrounding areas as well as the built environment and all the technical works along the stream were measured by land observation methods (topographic and remote sensing data).

In order to draw into conclusions, the assessment of the hydrological characteristics and the water flow characteristics of the stream and the catchment area was carried out. Then, the simulation of the hydraulic characteristics for the current state of the stream and for various different flooding scenarios through the use of mobile flood barriers/small dams of different types and geometrical characteristics was applied.

The result of the study has led to a “roadmap” of how, when and where non-permanent protection measures and can be implemented in urban environments, useful to local authorities and civil protection in charge.

The evaluation of the capacity and performance of mobile barrier systems (based on their characteristics) was carried out, in order to be  effectively used in varying flooding events, with different characteristics and in  site-specific locations in various scenarios, through hydraulic simulations. The results of the hydraulic simulations resulted in the barrier systems’ evaluation and the formation of a methodology, which concerns their application efficiency and their inter-operability in the pilot area, while determining the optimal management and the overall cost at the same time.

How to cite: Tzanou, E., Chatzigiannis, A., and Piliouras, M.: Pilot-scale application of mobile barrier systems for flood protection of urban areas. Assessment and evaluation of their interoperability in the urban area of Serres, Greece., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16291, https://doi.org/10.5194/egusphere-egu23-16291, 2023.

EGU23-17553 | ECS | Orals | NH1.4 | Highlight

Urban textures and flood hazard impacts from 2008 to 2018 in Nairobi, Kenya 

Bernard Majani, Bruce D Malamud, and James Millington

This research develops a methodology to examine the change over time of urban textures for Nairobi in relation to flood hazard impact on infrastructure. We use three Landsat 7 (30 m resolution) images of Nairobi (2008, 2013, 2018). ‘Urban textures’ are the spatial distribution, shape and relative arrangement of urban elements such as green spaces, trees, roads and height of buildings and their geometry in a given urban city. Here, revising Stewart and Oke’s classifications for built-up areas and land cover types, we classify each of the three Landsat images into 14 urban textures using maximum likelihood under supervised classification. The building structure types were then examined using local knowledge, YouTube videos, Google Street View and ground truthing. We find that from 2008 to 2018 the urban textures with the largest total increases in area were compact mid-rise by 49.9km2 (6.9%) and compact high-rise by 11.3 km2 (1.5%). In contrast, the compact low-rise residential urban texture decreased greatly (29.2 km2). This suggests that for non-industrial land uses, Nairobi has grown upward. Accuracy assessments for the 2008 [2018] map were 83.6% [87.9%] with 95% confidence interval of 75.4–90.0% [80.6–93.2%] and kappa statistic 0.777 [0.834]. We then examine the spatial temporal change of intensive (high severity – low frequency) and extensive (low severity – high frequency) flood hazard events in terms of pattern, trend and impact in relation to rainfall, elevation, and urban textures. We find that urban textures for 2018 have reduced area coverage of the urban texture lightweight low-rise, having partly changed to compact midrise. The impact of change in land use through the development of urban areas greatly affects flooding and impacts in terms of severity. Flooding is more prevalent close to the major rivers in Nairobi, some of which occur in the non-informal settlements. Flood water flows from the higher areas of Ngong and Kikuyu towards the town centre, Nairobi west into industrial area going towards east lands. Rivers in Nairobi regularly overflow their banks and inundate low-lying areas like T-Mall, Nairobi west, industrial area and Mathare valley. These are the flood hotspots of Nairobi that also have high severity of fatalities and impact on infrastructure. We believe that our methodology of examining urban textures over time, using remote sensing images, combined with flood hazard impact information, will help scientists and hazard managers better understand, and prepare for, the interlinked nature of urban change with the flood hazard.

How to cite: Majani, B., Malamud, B. D., and Millington, J.: Urban textures and flood hazard impacts from 2008 to 2018 in Nairobi, Kenya, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17553, https://doi.org/10.5194/egusphere-egu23-17553, 2023.

EGU23-17580 | Posters on site | NH1.4

Development of Buoyancy Type Urban Water Level Gauge 

Hyunsuk Lee, Ki-Won Lee, and Ho-Jeong Jo

The water level observation technology, which converts the change in weight due to buoyancy into the water level, has a long history of Archimedes' buoyancy experiment. A buoyancy type water level gauge that can provide a resolution of 0.1mm or more using Archimedes buoyancy was developed by Lee (2001) under the model name BYL-EV250. Currently, the above technology has been used since 2013 for the purpose of observing evaporation from the water surface with a resolution of 0.03 mm or more. Recently, various observation techniques have been developed to quantitatively monitor urban flooding. The Department of Homeland Security (DHS) Science and Technology Directorate (S&T) (2020) provides guidelines for the use of low-cost urban flood monitoring sensors. In this study, the element technology necessary for urban flood monitoring was developed. The first is a waterproofing technology developed to minimize equipment damage even when the equipment is completely submerged as urban flooding progresses. The second is a power-saving technology developed to provide smooth monitoring power while minimizing installation space. In addition, case protection technology that can provide smooth communication while protecting the device has been developed. In the future, these technologies can be used for developing technologies to minimize damage and prevent disasters by quantitatively monitoring urban flooding.

How to cite: Lee, H., Lee, K.-W., and Jo, H.-J.: Development of Buoyancy Type Urban Water Level Gauge, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17580, https://doi.org/10.5194/egusphere-egu23-17580, 2023.

EGU23-58 | ECS | Orals | NH1.5

Seasonal and regional distribution of lightning fraction over Indian Sub-continent 

Rakesh Ghosh, Sunil D Pawar, Anupam Hazra, and Jonathan Wilkinson

The three years of IITM LLN lightning observation data are used to determine the seasonal and spatial (over different geographical locations) distribution of the ratio of intra-cloud lightning (IC) to cloud-to-ground lightning (CG) in thunderstorms over the Indian sub-continent. The ratio is high (8-10) in the north-western parts and low (0.3-3) in the north-eastern parts. There is not a prominent latitudinal variation of IC and CG ratio, but a climatological seasonal variability exists all over the regions. In the Pre-monsoon (March to May), the mean ratio is observed at 3.87 with a standard deviation of 0.74, and during Monsoon (June to September), that is 3.01 with a standard deviation of 0.52. Pre-monsoon thunderstorm exhibits more IC discharge comparatively monsoonal thunderstorms; hence IC:CG ratio is also high in pre-monsoon. We have observed that CG lightning is approximately 20% of total lightning in pre-monsoon whereas 25% of total lightning in monsoon all over the Indian region. High CAPE associated with a stronger vertical updraft enhances the cold cloud depth and expands the mixed phase region, which can broaden and uplift the size of the upper positive charge center inside a thunderstorm while the middle negative charge center remains at the same temperature level. Therefore it enhances the occurrence of IC discharge between the upper positive charge center and middle negative charge center, hence increasing the IC:CG ratio of a thunderstorm. The implication of these observed results has the importance of separating CG lightning flash from total and can be used in the numerical model to give a proper prediction of CG lightning in hazard mitigation.

 

How to cite: Ghosh, R., Pawar, S. D., Hazra, A., and Wilkinson, J.: Seasonal and regional distribution of lightning fraction over Indian Sub-continent, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-58, https://doi.org/10.5194/egusphere-egu23-58, 2023.

EGU23-657 | ECS | Orals | NH1.5

Multi-station observation of periodic variations in long-term Schumann resonance records 

José Tacza, Tamás Bozóki, Gabriella Satori, József Bór, Anne Neska, Tero Raita, Ciaran Beggan, Mike Atkinson, Ashwini Kumar Sinha, and Rahul Rawat

Lightning has been declared as a new Essential Climate Variable by the World Meteorological Organization. Schumann resonance is a valuable parameter to monitor the global lightning activity, thus, the Atmospheric Observation Panel for Climate accepted Schumann resonance (SR) measurements as an emerging tool for studying lightning-related large-scale processes in the atmosphere. Previous studies showed a clear extraterrestrial influence on the SR parameters at different time scales (e.g., solar cycle). For all these reasons, a growing new interest arises in the scientific community to exploit the potential of SR better in gaining more information on electrodynamic coupling mechanisms taking place in the atmosphere. This has motivated the installation of new instruments worldwide to monitor SR measurements.

We performed a multi-station spectral analysis of the SR parameters (frequency and intensity) by using wavelet transformation. SR records from different monitoring sites around the globe were analyzed simultaneously for the first time: Hornsund (~12 years of data) and Belsk (~7 y.) managed by Poland, Rovaniemi and Ivalo in Finland (~16 y.), Eskdalemuir in Scotland (~10 y.), Nagycenk in Hungary (~22 y.), Boulder Creek in USA (~4 y.) and Shillong in India (~9 y.). For all SR sites, the periodicities of 0.5, 1, ~180 and 365-day appeared both in the frequency and the intensity of SR modes. Evidence was also found for the ~27- and ~45-day periods at specific time intervals. Cross-wavelet transform and wavelet coherence analyses were made between SR frequencies and the Kp index, and between SR intensities and Madden-Julian Oscillation index. Time periods of highly coherent 27-day as well as 45-day periodicities were found in the time series of these parameters intermittently. These preliminary results suggest that these periodicities are likely related to the solar rotation and Madden-Julian Oscillation, respectively. A detailed analysis about our findings will be presented and discussed.

How to cite: Tacza, J., Bozóki, T., Satori, G., Bór, J., Neska, A., Raita, T., Beggan, C., Atkinson, M., Kumar Sinha, A., and Rawat, R.: Multi-station observation of periodic variations in long-term Schumann resonance records, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-657, https://doi.org/10.5194/egusphere-egu23-657, 2023.

EGU23-795 | ECS | Orals | NH1.5

Changes in thunderstorm activity at high latitudes observed at WMO weather stations 

Daniel Kępski and Marek Kubicki

Knowledge about the occurrence of thunderstorms in polar regions is still limited. Lightning detection systems have varying detection efficiency over time and space, which makes climatological analysis difficult. This is especially problematic in areas where lightning strikes are relatively rare. Traditional observations carried out at weather stations are therefore still a very important source of information about the occurrence of thunderstorms in the polar and circumpolar regions. Scientific studies usually predict that these phenomena will be more frequent in high latitudes in a warmer world. To check whether the number of thunderstorms changes as projected, we summarize SYNOP data from manned World Meteorological Organization (WMO) stations operating in the years 2000-2019 located at latitudes above 60° of both hemispheres. According to this source, the changes in thunderstorm frequency are only visible in certain areas and mostly during the summer months. The regional Kendall test revealed a statistically significant increase in the number of thunderstorm days north of 60°N in Interior Alaska, northwestern Canada, much of Siberia and European Russia. However, a decrease in thunderstorm frequency has also been detected in some regions. This was the case on the shores of the southern Norwegian Sea and seasonally in spring in the northern Urals. The largest increase in thunderstorm days exceeded 5 per decade in the highly continental regions of central Siberia and interior Alaska. For the entire high-latitude area, the change in the number of days with thunderstorms was statistically insignificant. However, the statistically relevant increase in the number of thunderstorm days is visible for inland weather stations located 250 – 1,000 km from the coastline, where it was on average 1 day per decade.

How to cite: Kępski, D. and Kubicki, M.: Changes in thunderstorm activity at high latitudes observed at WMO weather stations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-795, https://doi.org/10.5194/egusphere-egu23-795, 2023.

The ground-level potential gradient atmospheric electric field, the air conductivity, and concentration of cloud condensation nuclei have been recorded at Stanislaw Kalinowski Geophysical Observatory in Świder, Poland (52°07' N, 21°14' E), for several decades. A new digitisation project of Świder atmospheric electric data published in the observatory year books provides an opportunity to review the results of studies of the long-term variation of the electric parameters. New results of an analysis of both short-term and long-term variations in the positive conductivity and related component of the air-Earth current density are presented, and implications for the Global Electric Circuit studies using the Świder dataset are discussed. This work is supported by Poland National Science Centre grant no 2021/41/B/ST10/04448.

How to cite: Odzimek, A., Pawlak, I., and Kępski, D.: Analysis of long-term variations in fair-weather PG, the positive air conductivity and conduction current density at Geophysical Observatory in Świder, Poland, and implications for the Global Electric Circuit, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-977, https://doi.org/10.5194/egusphere-egu23-977, 2023.

The ground-level atmospheric potential gradient (PG) has been measured with a radioactive collector method in Stanislaw Kalinowski Geophysical Observatory in Świder, Poland, for several decades. The observations have been previously analysed by Kubicki et al. (ICAE 2003, ICAE 2007) revealing rather typical behaviour in the diurnal and seasonal variations of the PG of a land station controlled by pollution. Electric field measurements at such station usually show a maximum at local winter months which are mostly affected by anthropogenic pollution. The whole series has been newly analysed to describe the Świder PG variations in greater detail, also in connection with an analysis of simultaneous measurements of cloud condensation nuclei. Fair-weather potential gradient course is calculated in different time scales (annual, seasonal and diurnal) with taking into account local meteorological and air pollution conditions. An attempt is made to calculate the diurnal and seasonal variations at very low cloud condensation nuclei counts. The work is supported by Poland National Science Centre grant no 2021/41/B/ST10/04448.

How to cite: Pawlak, I., Kępski, D., Tacza, J., and Odzimek, A.: New analysis of diurnal and seasonal variations in fair-weather atmospheric potential gradient and cloud condensation nuclei measured in S. Kalinowski Geophysical Observatory in Świder, Poland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-978, https://doi.org/10.5194/egusphere-egu23-978, 2023.

EGU23-1480 | Orals | NH1.5

Employing Optical Lightning Data to identify lightning flashes associated to Terrestrial Gamma-ray Flashes 

Christoph Köhn, Matthias Heumesser, Olivier Chanrion, Victor Reglero, Nikolai Østgaard, Hugh Christian, Timothy Lang, Richard Blakeslee, and Torsten Neubert

Terrestrial gamma-ray flashes (TGFs) are bursts of energetic X- and gamma-rays emitted from thunderstorms and observed by the Atmosphere-Space Interactions Monitor (ASIM) mounted onto the International Space Station (ISS) detecting TGFs and optical signatures of lightning. ISS-LIS (Lightning Imaging Sensor) detects lightning flashes allowing for simultaneous measurements with ASIM. Whilst ASIM measures ~300-400 TGFs per year, ISS-LIS detects ~ 106 annual lightning flashes giving a disproportion of four orders of magnitude. Hence, based on the temporal evolution of lightning flashes and their spatial pattern, we present an algorithm to reduce the number of flashes potentially associated with TGFs by ~90%, and we use the ASIM TGF list to ensure that the resulting flashes are those associated with TGFs and thus benchmark our algorithm. We will compare how the radiance, footprint size and the global distribution of lightning flashes of the reduced set relates to the average of all measured lightning flashes. Finally, we will present a parameter study of our algorithm and discuss which parameters can be tweaked to maximize the reduction efficiency whilst keeping those flashes associated to TGFs. In the future, this algorithm will hence facilitate the search for TGFs in a reduced set of lightning flashes.

How to cite: Köhn, C., Heumesser, M., Chanrion, O., Reglero, V., Østgaard, N., Christian, H., Lang, T., Blakeslee, R., and Neubert, T.: Employing Optical Lightning Data to identify lightning flashes associated to Terrestrial Gamma-ray Flashes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1480, https://doi.org/10.5194/egusphere-egu23-1480, 2023.

EGU23-1492 | Posters on site | NH1.5

Measurements of PG during rain, hail, snow and lightning 

Konstantinos Kourtidis, Stergios Misios, Athanassios Karagioras, and Ioannis Kosmadakis

We present an analysis of the evolution of PG during the course of rain, hail and snow events at the Xanthi site, N. Greece. In particular, using data from eight rain events in 2021, four hail events in the period 2018-2021 and four snow events during the same period, we examine how the PG frequency distribution changes during the progression of these events and discuss potential implications for the charge of the hydrometeors and the clouds that produce them. We also present some first results from measurements of PG and lightning at the high altitude (2340 m ASL) site of Helmos Observatory, Peloponnese, Greece.

How to cite: Kourtidis, K., Misios, S., Karagioras, A., and Kosmadakis, I.: Measurements of PG during rain, hail, snow and lightning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1492, https://doi.org/10.5194/egusphere-egu23-1492, 2023.

EGU23-1742 | ECS | Orals | NH1.5 | Highlight

Experimental volcanic lightning under conditions relevant to the early Earth: Discharges as a possible prebiotic synthesis mechanism 

Christina Springsklee, Bettina Scheu, Christoph Seifert, Corrado Cimarelli, Damien Gaudin, Donald B. Dingwell, and Oliver Trapp

Far from being a recent development of the Earth System, volcanism has accompanied the Earth, terrestrial planets and countless exoplanets since their origins. Volcanism is a material mechanism whereby planets evolve to their differentiated states that are potentially capable of hosting life. Explosive volcanic eruptions are commonly accompanied by volcanic lightning, modulated by charging and discharging mechanisms within the eruption column. As discharges have been proposed as a potential prebiotic synthesis mechanism for forming first organic molecules, the behaviour of volcanic lightning at early Earth conditions could yield further insights into likely environments for the origin of life.

Earth´s atmosphere has changed significantly in composition and pressure since its early beginnings. Here, we would like to investigate how volcanic lightning might have operated and was influenced by changes in those environmental conditions. For this purpose, we have developed an experimental device, which consists of a gas-tight modification of a shock-tube apparatus, to investigate experimental discharges in decompressed jets of gas and volcanic ash particles under varying atmospheric conditions. The setup acts as a Faraday cage, capable of measuring discharges close to the vent. The gas inside the particle collector tank is sampled by crimp cap bottles and analysed by gas chromatography. We modified the enveloping atmospheric composition and pressure (200 mbar – 4 bar) and the transporting gas phase (argon and nitrogen).

We have tested atmospheres containing carbon dioxide, nitrogen and carbon monoxide to mimic early Earth conditions and obtained discharges with similar magnitude to those achieved in an air atmosphere. We have also varied the atmospheric pressure and observed that decreasing the atmospheric pressure results in less discharges. The results of the experiments demonstrate that it is the coupling between gas and ash particles which largely governs the occurrence and magnitude of discharges close to the jet nozzle. Nitrogen as transporting gas results in fewer discharges compared to argon, emphasizing the importance of the composition of the transporting gas phase in the jet charging and discharging mechanisms. The preliminary results point to active volcanic settings under varying atmospheric conditions as multivariate environment for the emergence of life and thus our experiments continue.  

How to cite: Springsklee, C., Scheu, B., Seifert, C., Cimarelli, C., Gaudin, D., Dingwell, D. B., and Trapp, O.: Experimental volcanic lightning under conditions relevant to the early Earth: Discharges as a possible prebiotic synthesis mechanism, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1742, https://doi.org/10.5194/egusphere-egu23-1742, 2023.

EGU23-1888 | Orals | NH1.5

Short term forecast and monitoring of thunderstorms - status and recent developments at DWD. 

Richard Müller, Axel Barleben, Stephane Haussler, and Matthias Jerg

During the last few years, DWD has developed a pioneering nowcasting procedure (NCS-A) for thunderstorms and strong convection based on  intelligent combination of lightning data, satellite information and Numerical Weather Prediction. The atmospheric motion vectors needed for the nowcasting are derived with the optical flow method TV-L1. Version 1 of the method NCS-A is operated 24/7 by DWD, covers the complete geostationary ring and has been very well received by aviation customers. The current developments of the nowcasting method focus on the analysis of life cycles in order to be able to improve the prediction of formation and decay of thunderstorms. This includes analysis of lightning activity. Further, work is also being done to seamlessly extend the forecast times by up to 6-8 hours through ensemble analysis of the Lightning Potential Index, provided by the DWD NWP model ICON. In addition to the mentioned developments of physical methods,  research is being also carried out on AI-based methods (neural networks) in cooperation with the University of Mainz. The presentation will start with an overview of the current 24/7 thunderstorm nowcasting. This will be followed by a presentation and discussion of the current developments at DWD aimed at providing accurate 6-8 hour forecasts of thunderstorms. Links for further readings and software will be provided as well.

References: 

Müller R, Haussler S, Jerg M. The Role of NWP Filter for the Satellite Based Detection of Cumulonimbus Clouds. Remote Sensing. 2018; 10(3):386. https://doi.org/10.3390/rs10030386

Urbich I, Bendix J, Müller R. Development of a Seamless Forecast for Solar Radiation Using ANAKLIM++. Remote Sensing. 2020; 12(21):3672. https://doi.org/10.3390/rs12213672.

Müller R, Haussler S, Jerg M, Heizenreder D. A Novel Approach for the Detection of Developing Thunderstorm Cells. Remote Sensing. 2019; 11(4):443. https://doi.org/10.3390/rs11040443

Zach, Christopher & Pock, Thomas & Bischof, Horst. (2007). A Duality Based Approach for Realtime TV-L1 Optical Flow. Pattern Recognition. 4713. 214-223. 10.1007/978-3-540-7

Müller, R.; Barleben, A.; Haussler, S.; Jerg, M. A Novel Approach for the Global Detection and Nowcasting of Deep Convection and Thunderstorms. Remote Sens. 2022, 14, 3372. https://doi.org/10.3390/rs14143372

Brodehl, S.; Müller, R.; Schömer, E.; Spichtinger, P.; Wand, M. End-to-End Prediction of Lightning Events from Geostationary Satellite Images. Remote Sens. 2022, 14, 3760. https://doi.org/10.3390/rs14153760 

 

How to cite: Müller, R., Barleben, A., Haussler, S., and Jerg, M.: Short term forecast and monitoring of thunderstorms - status and recent developments at DWD., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1888, https://doi.org/10.5194/egusphere-egu23-1888, 2023.

EGU23-2026 | Orals | NH1.5

Worldwide distributions and key properties of Blue LUminous Events (BLUEs) as detected by ASIM 

Francisco J. Gordillo-Vazquez, Sergio Soler, Francisco J. Pérez-Invernón, Alejandro Luque, Dongshuai Li, Torsten Neubert, Olivier Chanrion, Victor Reglero, Javier Pérez-Navarro, and Nikolai Ostgaard

The presence of transient corona discharges occurring in thunderclouds has been suspected for a long time. Thunderstorm coronas can be observed as Blue LUminous Events (BLUEs) formed by a large number of streamers characterized by their distinct 337 nm light flashes with negligible (or absent) 777.4 nm optical emission (typical of lightning leaders). The Modular Multispectral Imaging Array (MMIA) of the Atmosphere-Space Interaction Monitor (ASIM) has successfully allowed us to map and characterize BLUEs worldwide. The results presented here include a global analysis of key properties of BLUEs such as their characteristic rise times and duration, their depth with respect to cloud tops, vertical length and number of streamers. We present two different global annual average climatologies of BLUEs depending on considerations about the rise time and total duration of BLUEs worldwide [1-3].

We found that around 10 % of all detected BLUEs exhibit an impulsive single pulse 337 nm light curve shape. The rest of BLUEs are unclear (impulsive or not) single, multiple or with ambiguous pulse shapes. BLUEs exhibit two distinct populations with peak power density < 25 μWm−2 (common) and ≥ 25 μWm−2 (rare) with different rise times and durations. The altitude (and depth below cloud tops) zonal distribution of impulsive single pulse BLUEs indicate that they are commonly present between cloud tops and a depth of ≤ 4 km in the tropics and ≤ 1 km in mid and higher latitudes. Impulsive single pulse BLUEs in the tropics are the longest (up to about 4 km height) and have the largest number of streamers (up to approximately 3 × 109).

 

[1] S. Soler, F. J. Pérez-Invernón, F. J. Gordillo-Vázquez, A. Luque, D. Li, A. Malagón-Romero, T. Neubert, O. Chanrion, V. Reglero, J. Navarro-González, G. Lu, H. Zhang, A. Huang, N. Ostgaard.: "Blue optical observations of narrow bipolar events by ASIM suggest corona streamer activity in thunderstorms" (Editor's Hightlight), Journal of Geophysical Research - Atmospheres, vol. 125, 2020, doi: 10.1029/2020JD032708.

[2] S. Soler, F. J. Gordillo-Vázquez, F. J. Pérez-Invernón, A. Luque, D. Li, T. Neubert, O. Chanrion, V. Reglero, J. Navarro-González, N. Ostgaard.: "Global Frequency and Geographical Distribution of Nighttime Streamer Corona Discharges (BLUEs) in Thunderclouds", Geophysical Research Letters 2021, 48, doi: 10.1029/2021GL094657.

[3] S. Soler, F. J. Gordillo‐Vázquez, F. J. Pérez‐Invernón, A. Luque, D. Li, T. Neubert, O. Chanrion, V. Reglero, J. Navarro-González, N. Østgaard.: "Global distribution of key features of streamer corona discharges in thunderclouds". Journal of Geophysical Research: Atmospheres, vol. 127, 2022, doi: 10.1029/2022JD037535.

How to cite: Gordillo-Vazquez, F. J., Soler, S., Pérez-Invernón, F. J., Luque, A., Li, D., Neubert, T., Chanrion, O., Reglero, V., Pérez-Navarro, J., and Ostgaard, N.: Worldwide distributions and key properties of Blue LUminous Events (BLUEs) as detected by ASIM, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2026, https://doi.org/10.5194/egusphere-egu23-2026, 2023.

EGU23-3116 | Orals | NH1.5

The ALOFT mission: a flight campaign for TGF and gamma-ray glow observations over Central America and the Caribbean in July 2023 

Nikolai Ostgaard, Martino Marisaldi, Kjetil Ullaland, Shiming Yang, Bilal Hasan Qureshi, Jens Søndergaard, Andrey Mezentsev, David Sarria, Nikolai Lehtinen, Timothy Lang, Hugh Christian, Mason Quick, Richard Blakeslee, J. Eric Grove, and Daniel Shy

The Airborne Lighting Observatory for FEGS and TGFs (ALOFT)  is a field campaign focused on observing Terrestrial Gamma-ray Flashes (TGFs) and gamma-ray glows from thunderclouds. ALOFT will be flown on a NASA ER-2 research aircraft, flying at 20 km altitude, and the payload  includes:

1) Fly’s Eye GLM Simulator (FEGS), an array of imaging photometers as well as different wavelengths, and electric field change meters.
2) Lightning Instrument Package (LIP), giving three component electric field measurements.
3) Several gamma-ray detectors covering four orders of magnitude dynamic range in flux as well as the full energy range for TGF/gamma-ray glow detection.

ALOFT is scheduled for July 2023, with 50 flight hours based out of Florida.  Flying over thunderstorms in Central America and Caribbean, one of the most active TGF regions on the planet during the most optimal season, the ALOFT campaign will help us to answer the questions:

1) How and under what conditions are TGFs produced?
2) How extended in space and time are the gamma-ray glows?

To answer question 1), the ALOFT campaign will be supported by ground based radio measurements from different locations in Central America and Caribbean.

To answer question 2), with realtime downlink of data we will know when the ER-2 encounters gamma-ray glowing thunderclouds, and we will instruct the pilot to have the aircraft perform have repeated overflights over this cloud as long as the glow exists, to answer question 2).  This will also help us understand whether gamma-ray glows and TGFs are interrelated.

The full set of observational goals of ALOFT are:

1. Observe TGFs in one of the most TGF-intense regions on the planet.
2. Observe gamma-ray glows in thunderstorms and their relation to TGFs.
3. Perform International Space Station Lightning Imaging Sensor (ISS LIS) and Global Lightning Monitor (GLM) validation using improved suborbital instrumentation (including upgraded FEGS).
4. Evaluate new design concepts for next-generation spaceborne lightning mappers.
5. If relevant instrumentation is available, make measurements useful to advance convection science from a suborbital platform.

In this presentation we will give the status and plans for the ALOFT mission.

How to cite: Ostgaard, N., Marisaldi, M., Ullaland, K., Yang, S., Hasan Qureshi, B., Søndergaard, J., Mezentsev, A., Sarria, D., Lehtinen, N., Lang, T., Christian, H., Quick, M., Blakeslee, R., Grove, J. E., and Shy, D.: The ALOFT mission: a flight campaign for TGF and gamma-ray glow observations over Central America and the Caribbean in July 2023, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3116, https://doi.org/10.5194/egusphere-egu23-3116, 2023.

EGU23-3124 | Orals | NH1.5

Electrodynamic model of K-changes 

Petr Kaspar, Thomas Marshall, Maribeth Stolzenburg, Ivana Kolmasova, and Ondrej Santolik

K-changes are step-like electrostatic field changes, which occur during the final part of cloud flashes or between the return strokes in cloud-to-ground discharges. We numerically solve the full set of Maxwell’s equations coupled to the electrostatic Poisson’s equation for a given thundercloud charge structure to model the K-changes. We simulate the K-changes by a sequential increase of conductivity of the decayed vertical channel. This process creates a current pulse which attenuates as it propagates downward. We show how the modeled linear charge densities and electric potentials connected to K-changes evolve in time. We successfully compare our model with the electric field measured by a flat-plate E-change antenna with a sensor having a decay time constant of 1 s, a bandwidth of 0.16 Hz –2.6 MHz, and a sampling rate of 5 MS/s. The experimental data used for comparison with our model were obtained at KSC Florida in 2011.

How to cite: Kaspar, P., Marshall, T., Stolzenburg, M., Kolmasova, I., and Santolik, O.: Electrodynamic model of K-changes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3124, https://doi.org/10.5194/egusphere-egu23-3124, 2023.

EGU23-3281 | Posters on site | NH1.5

Library of simulated gamma-ray glows and application to previous airborne observations 

David Sarria, Nikolai Østgaard, Martino Marisaldi, Nikolai Lehtinen, and Andrey Mezentsev

Gamma-Ray Glows (GRGs) are bursts of high-energy radiation that are emitted by thunderclouds and have a duration of seconds to minutes. These radiation sources are extended over several to tens of square kilometers. GRGs have been observed from detectors on the ground, in aircraft, and on balloons. In this paper, we present a Monte-Carlo model that can be used to study the production and propagation of GRGs. We compare our model to one developed by Zhou et al. (2016) and find small differences between the two. We have also created a library of simulations that is available to the community. Using this library, we were able to reproduce five previous GRG observations from five airborne campaigns: balloons from Eack et al. (1996) and Eack et al. (2000), and aircraft from the ADELE (Kelley et al. 2015), ILDAS (Kochking et al. 2016), and ALOFT campaigns (Østgaard et al. 2019).

Our simulation results confirm that the flux of cosmic-ray secondary particles at a given altitude can be enhanced by several percent or even several orders of magnitude due to the effect of thunderstorms' electric fields. These results explain the five observations we studied and will be useful for the upcoming ALOFT-2023 campaign. While some GRGs can be explained solely by the MOS process, the strongest GRGs observed require electric fields significantly larger than the RREA threshold value (E_th). Some of the observations also came with in-situ electric field measurements that were always lower than E_th, but these measurements may not have been taken from the regions where the glows were produced. This study supports the idea that some thunderstorms must have electric fields with magnitudes of at least E_th on a kilometer scale.

 

References :

-Effect of near-earth thunderstorms electric field on the intensity of ground cosmic ray positrons/electrons in tibet. Zhou et al. (2016). https://doi.org/10.1016/j.astropartphys.2016.08.004

-Balloon-borne x-ray spectrometer for detection of x-rays produced by thunderstorms. Eack et al. 1996. https://doi.org/10.1063/1.1146959

-Gamma-ray emissions observed in a thunderstorm anvil. Eack et al. 2000. https://doi.org/10.1029/1999GL010849

-Relativistic electron avalanches as a thunderstorm discharge competing with lightning. Kelley et al. 2015. https://doi.org/10.1038/ncomms8845

-In-Flight Observation of Gamma Ray Glows by ILDAS. Kochkin et al. 2017. https://doi.org/10.1002/2017JD027405 -Gamma Ray Glow Observations at 20-km Altitude. Østgaard et al. 2019. https://doi.org/10.1029/2019JD030312

How to cite: Sarria, D., Østgaard, N., Marisaldi, M., Lehtinen, N., and Mezentsev, A.: Library of simulated gamma-ray glows and application to previous airborne observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3281, https://doi.org/10.5194/egusphere-egu23-3281, 2023.

EGU23-3432 | Posters on site | NH1.5

The effects of fog on the atmospheric electrical field close to the surface 

Yoav Yair, Roy Yaniv, and Colin Price

For almost a decade, ground-based measurements of the electric field (Ez) have been conducted continuously at Tel-Aviv University's Wise astronomical observatory, located in the Negev desert highland in southern Israel. The data enabled identifying the characteristics of Ez in fair weather, during dust storms, lightning activity and the passage of different cloud types overhead. We present new results of observations of the variability of the atmospheric electric field during several foggy days along with meteorological data of wind speed and relative humidity. The results show a substantial increase of the electric field (up to 400-650 V m-1) compared with the mean fair-weather values at the site (180-190 V m-1) during times of high values of relative humidity (>95%) and low wind speed (<3 m s-1). This increase is a consequence of the reduction in the conductivity at low levels due to the attachment of ions to fog droplets. We suggest that closely monitoring the electric field when there is a forecast for the occurrence of fog can offer a precise indication when fog begins and ends.

How to cite: Yair, Y., Yaniv, R., and Price, C.: The effects of fog on the atmospheric electrical field close to the surface, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3432, https://doi.org/10.5194/egusphere-egu23-3432, 2023.

EGU23-3546 | ECS | Posters on site | NH1.5

Lightning activity over central Europe in years 2017-2022 (analysis of ISS-LIS data) 

Andrea Kolínská, Ivana Kolmašová, Colin Price, and Ondřej Santolík

We analyze the lightning activity over central Europe from 2017 to 2022 using the optical data from the Lightning Imaging Sensor (LIS) on board the International Space Station (ISS). The area of interest covers a central European region limited by 54.5° N, 7.5° E and 44.5° N, 22.5° E. A total number of 68192 lightning flashes was detected during 1805 ISS orbital overpasses. This study compares the lightning activity in central Europe to the global lightning activity and investigates the impact of the COVID-19 pandemic. While there is a global reduction of the lightning activity during the lockdowns in 2020, no significant decrease is observed in central Europe.

On the territory of Czechia, the highest density of flashes was detected in the northwestern part of the country. We combine the ISS-LIS data with measurements of the Shielded Loop Antenna with Versatile Integrated Amplifier (SLAVIA) detectors located in this region. The measurements of the ISS-LIS and SLAVIA detectors are combined with data from the World Wide Lightning Location Network (WWLLN) or Global Lightning Dataset (GLD360) in order to understand the correlation between electromagnetic radiation from selected lightning flashes and their optical characteristics observed from space.

How to cite: Kolínská, A., Kolmašová, I., Price, C., and Santolík, O.: Lightning activity over central Europe in years 2017-2022 (analysis of ISS-LIS data), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3546, https://doi.org/10.5194/egusphere-egu23-3546, 2023.

EGU23-3801 | Orals | NH1.5

A strong pulsing nature of negative recoil leaders accompanied by regular trains of microsecond-scale pulses 

Ivana Kolmašová, Olaf Scholten, Ondřej Santolik, Brian M. Hare, Ningyu Y. Liu, Joseph R. Dwyer, and Radek Lán

A presence of regular sequences of microsecond-scale pulses has been occasionally reported in the lightning literature for more than forty years. Due to a fine time resolution of modern electromagnetic receivers, the properties of these pulse trains are now well described. Nevertheless, the conditions for their occurrence are still not understood, and the information needed for their proper modelling is not sufficient.  

To contribute to this effort, we report for the first time properties of negative recoil stepped leaders accompanied by regular trains of microsecond-scale pulses simultaneously seen by the broadband magnetic loop antenna SLAVIA (Shielded Loop Antenna with a Versatile Integrated Amplifier; 5 kHz-90 MHz), and the radio telescope LOFAR (Low Frequency Array; 30-80MHz). We investigate four pulse trains that occurred during complicated intracloud flashes on 18 June 2021, when heavy thunderstorms hit Netherlands.

The pulses within the trains are unipolar, a few microseconds wide with an inter-pulse interval of about ten microseconds. The pulse trains last from 100 µs to 800 µs. After a careful time alignment of both magnetic field and LOFAR time series, we found that the broadband pulses perfectly match with regularly distributed and relatively isolated bursts of VHF sources localized by the LOFAR impulsive imager. All trains were generated by negative recoil stepped leaders propagating downward (two events) or upward (two events) at altitudes between 5.5 km and 8.5 km. Their tracks were formed by positive leaders occurring within the same flash several hundreds of milliseconds previously. The peak powers of VHF sources seen by the LOFAR electric antennas closest to the investigated discharges were about one order of magnitude higher than the power of signals emitted by normal negative leaders. These stepped recoil leaders propagate at a relatively low speed of about 2-5x10^6 m/s, when similar recoil leaders often reach speeds of 10^7 m/s. The velocity and inter-pulse intervals decrease towards the end of trains.

We show that observed pulse trains are due to stepping recoil leaders. However, we consider this strong pulsing nature of the examined recoil leaders to be quite unusual. The physical mechanism giving rise to the energetic VHF bursts and accompanying regular microsecond-scale pulses remains unclear.

How to cite: Kolmašová, I., Scholten, O., Santolik, O., Hare, B. M., Liu, N. Y., Dwyer, J. R., and Lán, R.: A strong pulsing nature of negative recoil leaders accompanied by regular trains of microsecond-scale pulses, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3801, https://doi.org/10.5194/egusphere-egu23-3801, 2023.

EGU23-4086 | Posters on site | NH1.5 | Highlight

Effects of cloud-to-water lightning strokes on open sea fish cages during eastern Mediterranean winter thunderstorms 

Mustafa Asfur, Roy Lavie, Jacob Silverman, Colin Price, Menahem Korzets, and Yoav Yair

Based on data obtained by the Earth Networks Total Lightning Network (ENTLN) for 5 winter seasons (DJF, 2018-2022), the flash density of lightning striking the water surface of the eastern Mediterranean Sea up to 50 km from the Israeli coastline is on average 3 strokes/km2. Out of the total lightning that strike the sea surface in the said area, about 0.05% on are superbolts with peak current > 200 kA. Cloud-to-water strikes generate thunder and underwater acoustic noise that can propagate for a few km from the strike location. While anthropogenic noises have been shown to cause negative stress responses in the marine environment and specifically in aquaculture fish cages, no stress response of cultured fish due to lightning strikes have been recorded yet.  New areas in the Israeli territorial waters are allocated to fish farms. These commercial farms will be using net cages, with high fish density expecting large yields.

This research aims to find out how cultured fish respond to the acoustic noises generated by lightning strikes. This hypothesis meets a growing awareness in the aquaculture field to research fish stress that, in this case, stay trapped in the water body without the ability to effectively respond and flee lightning strikes. Continual stress of cultured fish can economically adversely affect the fish farm due to high mortality rates and decreased growth rates. By monitoring sea bream (Sparus aurata) cages, with cameras and hydrophone, during winter months of years 2021-2023, we have found several cases of stress related behavior. These cases were correlated with precise lightnings data, videos of surveillance cameras pointed toward the fish farm, audio records of underwater sound and indications of abnormal fish behavior (sudden dive or direction changes). We will present results from newly developed image processing algorithm that reads underwater fish videos files and automatically finds abnormal behavior events.

How to cite: Asfur, M., Lavie, R., Silverman, J., Price, C., Korzets, M., and Yair, Y.: Effects of cloud-to-water lightning strokes on open sea fish cages during eastern Mediterranean winter thunderstorms, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4086, https://doi.org/10.5194/egusphere-egu23-4086, 2023.

    Existing literature indicates that volcanic lightning occurs during a devastating volcano eruption. However, it is still limited to understanding the volcanic electrification mechanism in nature because of the rarity of the explosive volcano eruption and visible spectrum obstacles from plumes full of dirty ashes. The eruption of the Hunga Tonga–Hunga Haʻapai (HT-HH) submarine volcanoes in the Lau Basin, South Pacific, had an extremely violate surtseyan type eruption on January 15th and generated numerous volcanic lightning. This eruption event provides a great opportunity to explore the electrification and evolution of volcanic lightning. 

    In this work, more than 40,000 lightning events were detected by the World Wide Lightning Location Network (WWLLN) during the primary eruption on January 15th. At the first stage of the eruption, the geographic distribution of lightning strikes expanded rapidly and isotropically while the eruption column reached a specific altitude. Then a lightning tranquility period occurred subsequently, implying explosive erupting was intermittent. Several explosive sub-eruptions were detected from 04:00Z to 07:00Z, and sub-eruptions' timestamps are highly consistent with seismic data analysis from IRIS. Lightning footprint provided evidence that the HT-HH eruption was a surtseyan eruption unsteady with several quiescent phases separating the explosive stages.

    HT-HH is one of the most powerful eruptions of the 21st century and provides a favorable environment for volcanic lightning research. The result of this work can track the immediate eruption by using lightning activities. Moreover, volcanic lightning has a different charging mechanism than general tropospheric lightning. Therefore, many interesting issues can be discussed, such as the volcano eruption's contribution to global electrical circuits or whether volcanic lightning can generate other atmospheric electricity events like TLEs or TGFs.

How to cite: Lin, Y.-C. and Chen, A.: Characteristics of Volcanic Lightning Distribution Generated by Hunga Tonga–Hunga Haʻapai on January 15th, 2022, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4714, https://doi.org/10.5194/egusphere-egu23-4714, 2023.

EGU23-5231 | ECS | Posters on site | NH1.5

Simultaneous detection of long continuing current lightning with space and ground-based detectors 

Pablo A. Camino-Faillace, Francisco J. Pérez-Invernón, Francisco J. Gordillo-Vázquez, Torsten Neubert, Víctor Reglero, and Nikolai Ostgaard

Long continuing current (LCC) lightning flashes contain a discharge in which a continuing electrical current flows for more than 40 ms. They represent about 10% of the total cloud-to-ground lightning flashes and have been associated with lightning-ignited wildfires. LCC flashes can be detected by different terrestrial- and space-based instruments. However, those instruments simultaneously detect all kinds of lightning across the globe, including those with long continuing current, which hinders the analysis of LCC-only events.

We present a method to match every single flash from the Geostationary Lightning Mapper (GLM), the Atmosphere-Space Interactions Monitor (ASIM) and the Earth Networks Total Lightning Network (ENTLN) by using a proximity index. In turn, we analyze the optical signal of LCC flashes simultaneously detected by GLM and ASIM.

According to preliminary results, we found an average of 15 LCC events per month in the continental United States simultaneously detected by the three mentioned sensors (GLM, ASIM and ENTLN). Moreover, this method can be used to match other atmospheric electricity phenomena simultaneously detected by different ground and/or space-based instruments.

How to cite: Camino-Faillace, P. A., Pérez-Invernón, F. J., Gordillo-Vázquez, F. J., Neubert, T., Reglero, V., and Ostgaard, N.: Simultaneous detection of long continuing current lightning with space and ground-based detectors, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5231, https://doi.org/10.5194/egusphere-egu23-5231, 2023.

EGU23-5302 | ECS | Orals | NH1.5

Global occurrence of continuing currents in lightning and lightning-ignited wildfires predicted for the next century 

Francisco J. Pérez-Invernón, Francisco J. Gordillo-Vázquez, Heidi Huntrieser, and Patrick Jöckel

Lightning flashes can produce a discharge in which a continuing electrical current flows for more than 40 ms. Such flashes have been proposed to be the main precursors of lightning-ignited wildfires.

In this work, we used lightning measurements provided by the Geostationary Lightning Mapper (GLM) over the continental United States of America during the summer of 2018 to confirm the role of lightning with continuing currents in the ignition of wildfires. We investigated projections in the occurrence of lightning with continuing currents and in the meteorological conditions that favor wildfires over the next century by applying a new parameterization of continuing currents based on the updraft strength. The simulations are performed by using the European Center HAMburg general circulation (ECHAM) / Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model [1]. We found a 41% increase in the occurrence of lightning with continuing currents worldwide. Increases are largest in South America, the western coast of Northern America, Central America, Australia, Southern and Eastern Asia, and Europe, while only regional variations are found in northern polar forests, where wildfires can affect permafrost soil carbon release.

We obtained a possible increase in the risk of lightning-ignited fires in Europe, Eastern Asia, North America, the Western coast of South America, Central Africa and Australia. In turn, the simulations suggest a decrease in the risk of lightning-ignited wildfires in polar regions of Eurasia and North America. Finally, projections do not show any clear tendency in the Amazon rainforest during the typical fire season.

[1] Pérez-Invernón, F. J., Huntrieser, H., Jöckel, P., and Gordillo-Vázquez, F. J.: A parameterization of long-continuing-current (LCC) lightning in the lightning submodel LNOX (version 3.0) of the Modular Earth Submodel System (MESSy, version 2.54), Geosci. Model Dev., 15, 1545–1565, https://doi.org/10.5194/gmd-15-1545-2022, 2022.

How to cite: Pérez-Invernón, F. J., Gordillo-Vázquez, F. J., Huntrieser, H., and Jöckel, P.: Global occurrence of continuing currents in lightning and lightning-ignited wildfires predicted for the next century, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5302, https://doi.org/10.5194/egusphere-egu23-5302, 2023.

Negative streamers play an important part in propagation of a negative stepped leader. They are emitted from the tip of a space stem or, as a streamer burst, from the tip of the space leader right after its attachment to the main leader.

In the laboratory conditions, it was shown that negative streamers need a significantly higher voltage for inception than positive streamers [e.g., Briels et al, 2008, doi:10.1088/0022-3727/41/23/234004]. The higher negative threshold is in agreement with the higher field measured inside streamer channels, namely 13±2 kV/cm for negative streamers versus 5 kV/cm for positive streamers.

We obtain the conditions for propagation of negative streamers using the Streamer Parameter Model (SPM) [Lehtinen, 2021, doi:10.1007/s11141-021-10108-5]. In this model, we calculate various streamer parameters from relationships between them, with the assumption of maximization of streamer velocity. This model, in the positive streamer case, was shown to agree well with both experimental measurements and hydrodynamic simulation results [Lehtinen and Marskar, 2021, doi:10.3390/atmos12121664]. In the negative streamer case, we show that the parameter equations have no solution below certain background electric fields. The threshold at which the negative streamer appears is around 12-14 kV/cm for 5-10 cm streamer length, which agrees with the experimental data. We also perform hydrodynamic simulations of negative streamers as another way to calculate the conditions for negative streamer propagation.

There is an important difference from positive streamers, for which the propagation threshold is determined by the rate of free electron removal from the streamer channel (i.e., attachment): namely, we find that the negative streamer threshold field is finite even in the absence of the electron removal.

How to cite: Lehtinen, N.: Conditions for inception and propagation of negative streamers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5529, https://doi.org/10.5194/egusphere-egu23-5529, 2023.

EGU23-6089 | Orals | NH1.5

Evolution of lightning activity observed during rapid intensity changes of tropical cyclones 

Kateřina Rosická, Ivana Kolmašová, and Ondřej Santolík

We study evolution of lightning activity accompanying rapid intensity changes of tropical cyclones worldwide. We use a dataset of 400 tropical cyclones occurring between 2012 and 2017. We use the cyclones tracks from the International Best Track Archive for Clime Stewardship. The lightning data are provided by the World Wide Lightning Location Network (WWLLN). We inspect the lightning activity and median stroke energies accompanying rapid intensifications (RI) of cyclones, defined as increases of the wind speed by more than 30 kt in 24 hours, and their rapid weakenings (RW), defined as decreases of the wind speed by more than 40 kt in 24 hours.

In an area of radial wind maximum (RWM), we observe a stroke density of 15.1 strokes/(100 km)2/hour for RI and 21.8 strokes/(100 km)2/hour for RW, respectively, which is much higher than average RWM density 7.9 strokes/(100 km)2/hour over the duration of the cyclone. A median stroke energy is 0.3 kJ during RI and 0.7 kJ during RW. It means that during rapid intensification of cyclones, there are less strokes with slightly higher energies and during rapid weakening there are more strokes with slightly lower energies. When analyzing the cyclones in both hemispheres separately, we obtain 0.3 kJ for RI and 0.6 kJ for RW in the northern hemisphere, and 0.8 kJ for RI and 0.9 kJ for RW in the southern hemisphere. The difference in the stroke density during RI and RW was observed larger in the northern hemisphere (19.7 vs 34.1 strokes/(100 km)2/hour), when in the southern hemisphere the stroke density is much lower and differs less (4.4 strokes/(100 km)2/hour for RI and 5.1 strokes/(100 km)2/hour for RW).

 

How to cite: Rosická, K., Kolmašová, I., and Santolík, O.: Evolution of lightning activity observed during rapid intensity changes of tropical cyclones, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6089, https://doi.org/10.5194/egusphere-egu23-6089, 2023.

EGU23-6099 | Posters on site | NH1.5

Analyses of thunderstorm structures using data of a Ka-band Doppler polarimetric vertical cloud profiler 

Zbyněk Sokol, Jana Popová, and Kateřina Skripniková

This study investigates the structure of strong convective storms to determine the difference between the structure of storms inducing or not lightning discharges. The structure of strong convective storms is investigated using a Ka-band Doppler polarimetric vertical cloud profiler operating at a frequency of 35 GHz. The profiler is located at the Milešovka meteorological observatory in Czechia (Central Europe). To study the structure of storms, we used the basic radar measurements of phase and power spectra of the co- and the cross-channel. We analysed the data from all the storms that occurred close to the Milešovka observatory during 2018-2022 and we performed statistical and correlation analyses of vertical profiles of phase and power spectra in the co- and the cross-channel in dependence on the distance of lightning discharges observed and recorded by the EUCLID lightning network.

How to cite: Sokol, Z., Popová, J., and Skripniková, K.: Analyses of thunderstorm structures using data of a Ka-band Doppler polarimetric vertical cloud profiler, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6099, https://doi.org/10.5194/egusphere-egu23-6099, 2023.

EGU23-6148 | Orals | NH1.5 | Highlight

Immediate effects of the Hunga Tonga - Hunga Ha’apai volcanic eruption on the AC and DC Global Electric Circuits 

József Bór, Tamás Bozóki, Gabriella Sátori, Earle R. Williams, Sonja Ann Behnke, Michael Rycroft, Attila Buzás, Hugo Gonçalves Silva, Marek Kubicki, Ryan Said, Chris Vagasky, Péter Steinbach, Karolina Szabone André, and Mike Atkinson

Observed responses of the AC and DC parts of the Global Electric Circuit (GEC) to the large eruption of the Hunga Tonga - Hunga Ha’apai (HT-HH) volcano on 15 January, 2022 are presented. The AC-related investigation is based on Schumann resonance (SR) measurements from the Nagycenk Geophysical Observatory (NCK), Hungary as well as from distant stations on the globe belonging to the HeartMath Institute (https://www.heartmath.org/gci/). The DC-related investigation is based on atmospheric electric potential gradient measurements (PG) from six recording stations in Europe and in the USA. The GLD360 and the WWLLN lightning detection networks were used to characterize lightning activity in the vicinity of the HT-HH island on the investigated day. The peak lightning stroke rate reached 80/s (5000/minute), whereas the average global rate is ~44/s. Lightning discharges occurred in rings around the vent of the volcano. Peak currents and the diameter of the ring of positive and negative polarity lightning strokes varied differently in the main phase of the eruption. At its peak, negative lightning dominated the electric activity in the volcanic cloud.

A global intensification of SR is apparent in connection with the enhanced lightning activity caused by the eruption. The SR data together with the global network observations indicate that the lightning activity in the eruption dominates the naturally occurring global activity for a period of at least one hour. The highly localized increase in lightning activity over HT-HH provides a unique point source of excitation for the SR.

In contrast with the dramatic response of the AC global circuit, the response of the DC GEC to this exceptional eruption is not readily unambiguous in the PG measurements. The observations suggest that impulse-like charging of the GEC by ~15% via -CG lightning strokes took place two times during the eruption. A time constant of 7 or 8 minutes has been inferred for near-surface electric field changes from these enhancements. This could be the first direct measurement of the time constant of the GEC near the Earth’s surface, as well as the first observation of the direct charging of the DC GEC by a single atmospheric electrified source.

How to cite: Bór, J., Bozóki, T., Sátori, G., Williams, E. R., Behnke, S. A., Rycroft, M., Buzás, A., Silva, H. G., Kubicki, M., Said, R., Vagasky, C., Steinbach, P., Szabone André, K., and Atkinson, M.: Immediate effects of the Hunga Tonga - Hunga Ha’apai volcanic eruption on the AC and DC Global Electric Circuits, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6148, https://doi.org/10.5194/egusphere-egu23-6148, 2023.

EGU23-6342 | ECS | Orals | NH1.5

Localization and quantification of the acoustical power of lightning flashes 

Damien Bestard, Thomas Farges, and François Coulouvrat

Lightning is a ubiquitous source of infrasound, and an essential climate variable. Acoustic measurements have been carried out by the CEA over the last ten years to characterize thunder within the framework of the HyMeX project. First, during the fall of 2012 in the south of France in the Cévennes region during the intensive measurement campaign (SOP1) and more recently, in the fall of 2018 in Corsica (France), as part of the EXAEDRE campaign. During both the SOP1 and EXAEDRE campaigns, mini-arrays (“AA” for “Acoustic Array”) of four microphones (respectively disposed on a 50m and a 30m-wide triangle) were used. Lightning information were available thanks to three kinds of electromagnetic detection systems. Firstly, classical Lightning Location Systems (LLS) measured the low frequency range (1-350 kHz), giving the flash emission time and location, as well as its peak current. Secondly, a network of 12 antennas, Lightning Mapping Array (LMA), detecting in the very high frequency range (60-66 MHz) was used. It measured the radiation from leaders and intracloud discharges, which occur mostly inside the thundercloud, providing the 3D location of these discharges. Thirdly, the Charge Moment Change (CMC) was provided by broadband Extremely Low Frequency (< 1.1 kHz) measurements.

Time delays between AA sensors inform on the direction of sound arrival, while the difference between emission time and sound arrival provides the source distance. Combining the two allows a geometrical reconstruction of individual lightning flashes, each viewed as a set of point sound sources. Co-localization of acoustic sources with in-cloud detections provided by the LMA and with ground impacts provided by the LLS shows the efficiency and precision of the method. The measured sound amplitude can also be back-propagated, compensating for absorption and density stratification. This allows to evaluate the acoustical power of each detected source, and then the total power of an individual flash.

In both campaigns, very heterogeneous geometrical distributions of source sound powers within a single flash are frequently observed. Most of the power is frequently located in only one portion of the lightning, most of the time in the return stroke, but also sometimes in the intracloud part. A few homogeneous cases are observed, especially in SOP1. The total acoustical power of the flashes turns out to be also extremely variable, extending over at least 4 orders of magnitude with a median value of 3 MW. It correlates quite good with the peak current or the CMC, and the nature of the correlation differs strongly with the category of lightning considered, either typical return strokes or very energetic positive flashes generating sprites. However, a high dispersion of the data is observed, so that it is not possible to correctly predict any electrical parameter using only the total acoustic power of an event, although a trend is statistically observed. This could be overcome by finding other variables to fully explain the relationship between acoustical and electrical parameters, and improving our propagation model to better account for acoustic variability.

How to cite: Bestard, D., Farges, T., and Coulouvrat, F.: Localization and quantification of the acoustical power of lightning flashes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6342, https://doi.org/10.5194/egusphere-egu23-6342, 2023.

Over the last few decades, lightning has been one of the fatal extreme weather phenomena in the Indian subcontinent. Aerosols which act as cloud condensation nuclei (CCN) and ice nuclei (IN) can modify the cloud properties and alter the thermodynamic processes within the deep convective clouds in a way that eventually affects the lightning flash rates associated with thunderstorms. Long-term satellite observations suggest that a maximum number of lightning strikes (40-45 flashes/km2) occur during the pre-monsoon (March-May) and monsoon (June-September) seasons over the Indian subcontinent. We analyzed the lightning data available from satellite observations over two distinctly different climatological regions namely, northeast India and western India. In this study, we evaluate the performance of a numerical weather research and forecasting model (WRF) in reproducing the lighting characteristics over these two regions and further try to understand the sensitivity of simulated lightning flash rates to aerosol characteristics and aerosol-cloud interactions considered in the model.

Two severe lightning episodes which occurred on 5-6 May 2013 and 16 April 2019 over northeast India and western India respectively are chosen as case studies for our model sensitivity experiments. We used Morrison, NSSL & SBM microphysics schemes to understand the capability of bulk and bin schemes in simulating these events. Our results show that SBM (bin) scheme affects lightning flash events more accurately than the other two bulk schemes. Increasing aerosol concentrations, increases the cloud droplet number concentrations, thus influences the collision-coalescence processes thereby increase lightning activity over both regions. To further understand the influence of aerosol size, we used a spectral bin microphysics method with a dry radius range of (0.7nm-12µm), which modified the cloud microphysical features. Changing the number concentration and default size of aerosols also influenced the meteorology and hence the deep convection and thunderstorms occurring over the two selected case study regions. More results with greater details will be presented.

How to cite: Ghoshal Chowdhury, S., Ganguly, D., and Dey, S.: A modeling study on the role of aerosols in modulating the lightning flash rates over two different climatological regions of India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6397, https://doi.org/10.5194/egusphere-egu23-6397, 2023.

The generation and variation of the atmospheric electric field (hereafter E-field), which exists under all meteorological conditions and drives the charge flow around the Earth globally, and many natural phenomena such as lightning, thunderstorms, and even earthquakes have been observed accompanied by surface E-field disturbances; therefore, E-field observations are also used in disaster warnings. Since 2021, a ground E-field network consisting of three stations using in-house electric field mills has been deployed in the Tainan area, covering two known seismic faults, to monitor the characteristics of the e-field variation caused by diurnal cycle, thunderstorms, and earthquake precursor.

The results indicated that the small-area E-field variation did not follow the Carnegie curve because local effects (aerosols, weather conditions, and environment) masked the variations of the global electrical circuit. In addition, the analysis of the disturbed E-field showed that more than 90% of the single-cell thunderstorms observed in the surface E-field could be classified as mature and dissipating stages. Each disturbance lasted approximately 34 minutes and was accompanied by an average of 1.4 times E-field phase reversals. Among them, the negative reversal of the surface electric field caused by the negative charge layer was relatively strong and frequent. Eventually, triangulation was used to reconstruct the charge structure of four distinctive single-cell thunderstorm events and restore the surface E-field responses during the passage of clouds. The correlation coefficients between the simulation and the observation were higher than 85%, and the trajectory and speed of the thunderclouds is also successfully reproduced by the recorded e-field data. Furthermore, some preliminary conclusions about earthquake precursors were drawn by analyzing the surface E-field.

How to cite: Chen, A. B.-C. and Chuang, C.-W.: The charge structure of thunderstorms revealed by the ground electric field monitoring network deployed in Tainan, Taiwan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6527, https://doi.org/10.5194/egusphere-egu23-6527, 2023.

EGU23-6741 | ECS | Orals | NH1.5

Long-range Lightning Interferometry (A Simulation Study) 

Xue Bai and Martin Fullekrug

Traditional long-range lightning detection and location networks use Time-of-Arrival (TOA) differences, and a single timestamp to locate lightning events. For long propagation distances, the amplitude of ground waves decays faster with distance than sky waves as a result of the ground conductivity and the effects of Earth curvature (Caligaris et al., 2008, Cooray, 2009, Hou et al., 2018). This can lead the skywaves to interfere with their large amplitudes when locating lightning.

Coherency, which is short for phase coherency of the analytic signal, is used here, which exhibits lightning characteristics (Bai & Fullekrug, 2022). This work introduces a simulation study to lay the foundation for new lightning location concepts. A novel interferometric method using coherency is presented here, which expands the use of more data points of recorded lightning sferics to map the lightning into an area in a long-range network. In this map, each pixel corresponds to a lightning location with different coherency and time of arrival differences, simulated by shifting the complex lightning waveforms. In long-range networks, the coherency of the 1st skywave is larger than the ground wave, and it is difficult to distinguish them due to the short time delay between them. One solution is to use a small network so that the recorded waveforms are associated with short propagation distances which can eliminate the interferences caused by the first skywave. Another solution is to filter the data such that a lightning waveform is represented by an impulse. In this case, only one maximum coherency area exists for each event at the lightning occurrence time.

In the future, the data collected with a real-time lightning detection network will be analysed to map the lightning events using the complex interferometric method for use in long-range lightning location networks.

 

References

Bai, X., & Füllekrug, M. (2022). Coherency of Lightning Sferics. Radio Sci., 57(5), e2021RS007347. doi: 10.1029/2021rs007347

Caligaris, C., Delfino, F., & Procopio, R. (2008). Cooray–Rubinstein Formula for the Evaluation of Lightning Radial Electric Fields: Derivation and Implementation in the Time Domain. IEEE Trans. Electromagn. Compat., 50(1), 194-197. doi: 10.1109/temc .2007.913226

Cooray, V. (2009). Propagation Effects Due to Finitely Conducting Ground on Lightning-Generated Magnetic Fields Evaluated Using Sommerfeld’s Integrals. IEEE Trans. Elec-tromagn. Compat., 51(3), 526-531. doi: 10.1109/temc.2009.2019759

Hou, W., Zhang, Q., Zhang, J., Wang, L., & Shen, Y. (2018). A New Approximate Method for Lightning-Radiated ELF/VLF Ground Wave Propagation over Intermediate Ranges. Int. J. Antennas Propag., 2018(6), 1-10. doi: 10.1155/2018/9353294

How to cite: Bai, X. and Fullekrug, M.: Long-range Lightning Interferometry (A Simulation Study), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6741, https://doi.org/10.5194/egusphere-egu23-6741, 2023.

EGU23-7176 | Orals | NH1.5

Study of multiple ELVES at the Pierre Auger Observatory 

Adriana Vásquez Ramírez, Roberto Mussa, and Luis A. Núñez and the Pierre Auger Collaboration

ELVES are transient ring-shaped emissions occurring in the ionosphere above thunderstorms. Multi-ELVES are events consisting of two and up to four rings of light separated temporally by tens of microseconds. The Fluorescence Detector (FD) at the Pierre Auger Observatory has been detecting ELVES with a dedicated trigger since 2013. The high temporal resolution of 100 ns of the FD allows us to record the phototraces of the events in great detail. From the improved processing of the phototraces, we have observed ELVES with double and triple peaks. In fact, during the period 2014-20, about 27% of the events detected at Auger are multi-ELVES. The origin of multi-ELVES is still not fully understood, therefore in this work, we tested two models: the first one relates the temporal difference between two peaks (ΔT) to the rise (tr) and fall (tf) times of the current density pulse of the source beam; the second one relates the height of the intra-cloud lightning source (hb) to ΔT and is used to study events with three or more peaks. From the first model, we can obtain combinations of tr and tf where ΔT tends to zero, i.e. the origin of the simple ELVES can also be explained. For this analysis, we compare the ELVES parameters measured in Auger with the lightning properties detected by Earth Networks, i.e. the location, waveform, and height of these sources. 

How to cite: Vásquez Ramírez, A., Mussa, R., and Núñez, L. A. and the Pierre Auger Collaboration: Study of multiple ELVES at the Pierre Auger Observatory, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7176, https://doi.org/10.5194/egusphere-egu23-7176, 2023.

EGU23-7235 | ECS | Posters virtual | NH1.5

Towards a GPU based particle model for streamer discharges 

Elloïse Fangel-Lloyd, Saša Dujko, Sven Karlsson, Matthias Gammelmark, Anton Rydahl, Kenishi Nishikawa, and Christoph Köhn

Terrestrial gamma-ray flashes (TGFs), bursts of X- and gamma-rays, are emitted from thunderstorms and are produced through relativistic electrons through the Bremsstrahlung process. Despite recent progress through measurements and simulations, the specific mechanism of electron acceleration remains unknown. As the processes inside thunderclouds occur on a multiscale level, we need to develop models that cover a wide range of temporal and spatial scales. As a first step, we here present a GPU based Monte Carlo particle-in-cell code to simulate electron avalanches and streamers, benchmarked against existing particle models. We will present this benchmarking as well as details on the GPU-code implementation as well as first results of electron avalanches and streamers and compare runtimes with previous models. This code will form the basis for a fully hybrid code running on the newest generation of pre-exascale computers. In the future, such a code will allow us to gain insight on the mechanisms responsible for TGFs.

How to cite: Fangel-Lloyd, E., Dujko, S., Karlsson, S., Gammelmark, M., Rydahl, A., Nishikawa, K., and Köhn, C.: Towards a GPU based particle model for streamer discharges, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7235, https://doi.org/10.5194/egusphere-egu23-7235, 2023.

EGU23-9041 | ECS | Orals | NH1.5

Optical properties of the shallow and exposed lightning discharges observed by ASIM 

Dongshuai Li, Torsten Neubert, Olivier Chanrion, Lasse Skaaning Husbjerg, Alejandro Luque, Yanan Zhu, Nikolai Østgaard, and Víctor Reglero

The Atmosphere-Space Interactions Monitor (ASIM) on the International Space Station (ISS) observes lightning and Transient Luminous Events (TLEs) above the thunderstorm clouds. ASIM includes three photometers that sample at 100 kHz and two cameras that image at 12 frames per second. The photometers measure part of the far ultraviolet (FUV) and middle ultraviolet (MUV) band at 180 – 300 nm, a line of the second positive system of N2 at 337nm (blue) and an atomic oxygen line at 777.4 nm (red). The cameras measure in the blue and red bands of the photometers with a spatial resolution on the ground around 400 m × 400 m. When ASIM is in a nadir-viewing configuration, photometer signals in the blue and red are sometimes associated with coincident UV signals, indicating that the UV photons originated from lightning discharges at cloud altitudes and not from the TLEs at higher altitudes. Here, we analyse the optical properties of these events by combining data from ASIM, the global lightning network GLD360, Lightning Mapping Arrays (LMAs) and NEXRAD radars. Of the 12 cases identified with such data coverage, 5 are Cloud-to-Ground (CG) and 7 are Intra-Cloud (IC) lightnings. The lightning leaders are located nearby the cloud top boundaries or partly exposed outside the cloud. Both the CG and IC lightnings are associated with the exposed lightning leaders. The 5 CG lightnings are identified as the “bolts from the blue”, and the 7 IC lightnings are “cloud-to-air” lightning. The altitudes of the sources vary from 5 km to 7 km for the CG lightnings and from 7 km to 15 km for the IC lightnings. The optical properties for the events, such as their irradiance, rise time and duration in the different optical bands are summarized and discussed. The results provide information that allows to estimate the global occurrence of “bolts from the blue” and “cloud-to-air” lightning.

How to cite: Li, D., Neubert, T., Chanrion, O., Husbjerg, L. S., Luque, A., Zhu, Y., Østgaard, N., and Reglero, V.: Optical properties of the shallow and exposed lightning discharges observed by ASIM, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9041, https://doi.org/10.5194/egusphere-egu23-9041, 2023.

EGU23-9381 | Posters on site | NH1.5

The scientific payload of the ALOFT mission to chase Terrestrial Gamma-ray Flashes and gamma-ray glows 

Martino Marisaldi, Nikolai Østgaard, Kjetil Ullaland, Shiming Yang, B. Hasan Qureshi, Jens Søndergaard, Andrey Mezentsev, David Sarria, Nikolai Lehtinen, Timothy J. Lang, Hugh Christian, Mason Quick, Richard Blakeslee, J. Eric Grove, and Daniel Shy

ALOFT (Airborne Lightning Observatory for FEGS and TGFs) is a flight campaign designed to observe Terrestrial Gamma-ray Flashes (TGF) and gamma-ray glows close to their production source. The campaign consists of 50 flight hours of a NASA ER-2 research aircraft taking off from Florida and is scheduled for July 2023. The ER-2 cruise altitude of 20 km allows flying over active thunderstorms in the Gulf of Mexico and Caribbean region, one of the most TGF-active region on the planet. The main challenge for TGF detection at close distance is the large variability in the expected gamma-ray flux, spanning four orders of magnitude depending on the radial distance from the source. To cope with this challenge, the ALOFT gamma-ray payload consists of several detectors of different size, made of different materials and readout sensors, designed to cover 4 orders of magnitude dynamic range on the typical TGF/gamma-ray glow energy range (~100 keV - ~40 MeV). In addition, the payload includes the Fly’s Eye GLM Simulator (FEGS), an array of imaging photometers sensitive at different wavelengths, and electric field change meters, and the Lightning Instrument Package (LIP), giving three component electric field measurements. The synergy between airborne gamma-ray, optical and electric field measurements, combined with ground-based radio observations, will provide a unique set of observations to constrain the source properties and their physics. This presentation will focus on the ALOFT scientific payload and the system architecture.

How to cite: Marisaldi, M., Østgaard, N., Ullaland, K., Yang, S., Qureshi, B. H., Søndergaard, J., Mezentsev, A., Sarria, D., Lehtinen, N., Lang, T. J., Christian, H., Quick, M., Blakeslee, R., Grove, J. E., and Shy, D.: The scientific payload of the ALOFT mission to chase Terrestrial Gamma-ray Flashes and gamma-ray glows, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9381, https://doi.org/10.5194/egusphere-egu23-9381, 2023.

EGU23-9415 | ECS | Orals | NH1.5

Detections of high peak current lightning and observations of Elves 

Ingrid Bjørge-Engeland, Nikolai Østgaard, Martino Marisaldi, Alejandro Luque, Andrey Mezentsev, Nikolai Lehtinen, Olivier Chanrion, Torsten Neubert, and Victor Reglero

Elves are produced when electromagnetic pulses from lightning interact with the lower parts of the ionosphere and are observed from space as expanding rings of light in the UV and visible optical bands. Elves are known to be associated with high peak current lightning. Using data from the Modular Multi-spectral Imaging Array (MMIA) instrument of the Atmosphere-Space Interactions Monitor (ASIM) payload, we search for observations of Elves when high peak currents (>70 kA) are detected by the global ground-based lightning detection network GLD360. We identify two types of events; high peak current detections associated with Elves, and high peak current detections not associated with Elves. To understand why some high peak current discharges do not generate observable Elves, we explore the number of lightning discharges and their peak currents leading up to the events. Preliminary results indicate that for current pulses with peak currents below 100 kA we observe a significant number of Elves, but this quantity depends on the lightning activity within 5 minutes before. Current pulses with peak currents above 120 kA nearly always produce Elves, regardless of the preceding lightning activity.

 

How to cite: Bjørge-Engeland, I., Østgaard, N., Marisaldi, M., Luque, A., Mezentsev, A., Lehtinen, N., Chanrion, O., Neubert, T., and Reglero, V.: Detections of high peak current lightning and observations of Elves, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9415, https://doi.org/10.5194/egusphere-egu23-9415, 2023.

EGU23-9459 | ECS | Posters on site | NH1.5

Characterization of Thunderstorm Cells Producing Observable Terrestrial Gamma Ray Flashes 

Lasse Husbjerg, Torsten Neubert, Olivier Chanrion, Martino Marisaldi, Martin Stendel, Eigil Kaas, Nikolai Østgaard, and Victor Reglero

We present the largest catalogue compiled to date of TGFs and associated lightning activity, geostationary satellite cloud images and ERA5 reanalysis data. The TGFs are observed from AGILE, ASIM, FERMI and RHESSI, and the lightning activity by the WWLLN and GLD360 networks. The 1582 TGF events identified are analysed and contextualized relative to lightning flashes. In our analysis, we consider the proportion of TGFs and lightning coming from overshooting tops, and the dependencies on Cloud Top Temperature (CTT) and the Convective Available Potential Energy (CAPE). We find that TGFs come from primarily higher cloud tops than lightning flashes, consistent with previous studies. We also find that CAPE is similar for TGF and lightning-producing cells, and that the proportion of TGF and lightning-producing cells in the overshooting phase are similar. We analyse the regional and seasonal differences between TGFs and lightning and see that regional meteorological effects dominate.

How to cite: Husbjerg, L., Neubert, T., Chanrion, O., Marisaldi, M., Stendel, M., Kaas, E., Østgaard, N., and Reglero, V.: Characterization of Thunderstorm Cells Producing Observable Terrestrial Gamma Ray Flashes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9459, https://doi.org/10.5194/egusphere-egu23-9459, 2023.

EGU23-9989 | Orals | NH1.5

The nonlinear interactions of whistlers in the ionospheric plasma during strong thunderstorms 

Jan Blecki, Roman Wronowski, and Paweł Jujeczko

  It is not new knowledge that whistler are always present in the ionosphere during the thunderstorms. The terrestrial ionosphere is mainly a plasma region which is very sensitive for different disturbances. A wide range of plasma instabilities can develop  in this region, which are often nonlinear processes and leading to the development of plasma turbulence.  Turbulence is one of the most universal events phenomena in nature. It plays a crucial role in the dynamics of the space plasma processes. The turbulence appears when some physical parameter exceeds a certain level. It can have place during strong thunderstorms. The ionosphere is sometimes treated as plasma physics laboratory with unique possibility to study fundamental plasma processes. The use of ionospheric satellite  gives the chance to perform insitu measurement of plasma parameters during dynamic processes. For our analysis we used set of selected data  of the electric and magnetic fields variations in ELF and VLF ranges originating from the all French microsatellite DEMETER which was operating on the circular orbit with inclination of about 800 at altitude of 660 km from July 2004 until December 2010.

The  Fourier, wavelet and bispectral analysis of these signals has been performed. The 3 waves processes has been identified during few very strong strokes. In some cases the nonlinear interactions of whistlers with VLF signals of ground based transmitters. The character of spectra suggests the presence of Richardson’s cascade. Our conclusion is that these results are related to whistler turbulence.

How to cite: Blecki, J., Wronowski, R., and Jujeczko, P.: The nonlinear interactions of whistlers in the ionospheric plasma during strong thunderstorms, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9989, https://doi.org/10.5194/egusphere-egu23-9989, 2023.

The use of very low frequency (VLF) radio waves for monitoring the lower part of ionosphere (D region) has contributed immensely to explore this unique domain as satellites and other ground-based instrumentations have not been able to physically assess and characterized it. Several deployed ground- and space-based observational techniques not only enhance a robust capability to monitor, model and predict processes in the atmosphere-ionosphere-magnetosphere coupled regions, but also act as key feature to perform scientific studies in geo-sciences related areas. Here, we present preliminary results from multi-dimensional analyses of LF broadband measurement conducted from Ariel University (AU), Israel, as a complementary useful data source to other available ground- and space- based observational tools, already deployed. The AU LF (0.50 – 470 kHz) observational site, receives electromagnetic waves from different worldwide VLF transmitters as well as other natural sources such as lightning discharges. The station data is mainly used for diagnostic probing of ionospheric irregularities, caused by space weather events such as gamma-ray burst and EUV radiation, along with additional atmospheric electricity measurements. Additionally, different Machine Learning (ML) are used to study spheric waveforms in order to infer their exact location along with different physical characteristics.

How to cite: Ajakaiye, M. P., Reuveni, Y., and Romano, B.: Multi-dimensional Analyses of the First Measurement from the Low Frequency (LF) Radio Waves Receiving Station at Ariel University, Israel, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10903, https://doi.org/10.5194/egusphere-egu23-10903, 2023.

EGU23-11467 | Posters virtual | NH1.5

Discerning TGF and Leader Current Pulse in ASIM Observation 

Andrey Mezentsev, Nikolai Østgaard, Martino Marisaldi, Torsten Neubert, Olivier Chanrion, and Victor Reglero

TGFs being the bursts of high energy photons shot from Earth’s atmosphere to space, are known to be produced during the initial upward propagation of the +IC lightning leader. VLF and LF radio sferics can often be found in association with the short duration TGFs. The Atmosphere-Space Interactions Monitor (ASIM) instrument provides synchronous X- and gamma-ray measurements with optical recordings in 180-240 nm, 337 nm and 777.4 nm wavelength. This allows for simultaneous detection for TGFs and the lightning processes associated with them.

ASIM TGF observations have shown that TGFs within the FOV of the optical instruments are always accompanied by the prominent optical pulse which starts the lightning flash. TGFs have a clear tendency to slightly precede the optical pulse, but the short duration of TGFs together with the optical delay of the lightning light propagating through the cloud do not allow to confidently resolve the true sequence of these events.

The same problem is present in radio measurements: radio signature from TGF current is usually mixed with lightning current in the recordings due to temporal proximity of the processes involved.

Here we report a remarkable, high fluence and long duration TGF, together with its associated optical recordings. This observation shows clear distinction between the TGF and the associated optical pulse: the optical pulse is subsequent to the TGF, as it starts after the TGF is terminated. This allows to conclude that strong current surges inside the leader channel are not responsible for the TGF generation, and, in turn, the current surge producing the optical pulse can be conditioned by the generated TGF, or even be responsible for TGF termination.

How to cite: Mezentsev, A., Østgaard, N., Marisaldi, M., Neubert, T., Chanrion, O., and Reglero, V.: Discerning TGF and Leader Current Pulse in ASIM Observation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11467, https://doi.org/10.5194/egusphere-egu23-11467, 2023.

EGU23-11504 | Orals | NH1.5

Searching for the VHF signature of the tip of an intra-cloud positive leader 

Olaf Scholten, Brian Hare, Joe Dwyer, Ningyu Liu, and Christopher Sterpka

We have used the LOw-Frequency ARray (LOFAR) to search for the growing tip of an intra-cloud (IC) positive leader. LOFAR is an extended astronomical radio telescope consisting of many (thousands antennas arranged is stations operating at very-high frequencies (VHF). For these lightning observations we have used about 170 dual polarized antennas in the Netherlands with baselines up to 100 km.  

Even with our most sensitive beamforming method, where we coherently add the signals of all 170 antenna pairs, we were not able to detect any emission from the tip of an IC positive leader. Instead, we put constraints on the emissivity of VHF radiation from the tip at 1 aJ/MHz at 60 MHz, well below the intensity of the galactic background.

We conclude that these IC positive leaders propagate in a continuous process which is in sharp contrast to what is seen to the step-wise propagation seen in some cloud-to-ground positive leaders and for negative leaders.

How to cite: Scholten, O., Hare, B., Dwyer, J., Liu, N., and Sterpka, C.: Searching for the VHF signature of the tip of an intra-cloud positive leader, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11504, https://doi.org/10.5194/egusphere-egu23-11504, 2023.

EGU23-12033 | ECS | Orals | NH1.5

CASPER: A Space Mission Concept to Investigate Transient Luminous Events and Terrestrial Gamma Ray Flashes 

Manuel Maurer, Louí Byrne, Ulrik Falk-Petersen, Ali Hamdoun, Gwendal Hénaff, Kilian Huber, Andreea Ilas, Nadja Reisinger, Jonas Sinjan, Crisel Suarez, András Szilágy-Sándor, Vertti Tarvus, Marialinda Tsindis, and Mikhail Vaganov

As part of the Alpbach Summer School, a collaboration between FFG, ESA and ISSI, a team of students developed the F-class CASPER mission concept to investigate Transient Luminous Events (TLEs) and Terrestrial Gamma Ray Flashes (TGFs). These lightning-related plasma phenomena, first detected on Earth in 1989, typically occur in the mesosphere at an altitude between 50-100 km. The UVS instrument onboard the JUNO mission detected several similar events on Jupiter, and they are expected to also occur on other planets.

The CASPER mission consists of two identical spacecraft, each of which will be equipped with three cameras in different wavelengths and four high speed sensors, the latter will function as triggers to start the data acquisition of higher resolution images. A system chosen to combat the transient characteristic of the events (lifetime < 300 ms). While three sensors will be taking measurements of photons, one will quantify the electron flux in order to constrain the role of TLEs and TGFs in the global electric circuit.

The second great area of interest is the vertical structure of TLEs as well as their global distribution and occurrence rates. To achieve this, data will be captured using a two-satellite train in a sun-synchronous low earth orbit. The orbit is inclined at 98° and the satellites are phased at an angle of 5.2° to observe these events from two points of view simultaneously. The operational mission lifetime is five years, with a possible extension.

How to cite: Maurer, M., Byrne, L., Falk-Petersen, U., Hamdoun, A., Hénaff, G., Huber, K., Ilas, A., Reisinger, N., Sinjan, J., Suarez, C., Szilágy-Sándor, A., Tarvus, V., Tsindis, M., and Vaganov, M.: CASPER: A Space Mission Concept to Investigate Transient Luminous Events and Terrestrial Gamma Ray Flashes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12033, https://doi.org/10.5194/egusphere-egu23-12033, 2023.

EGU23-12591 | Orals | NH1.5

The propagation and 3D VHF polarization properties of recoil leaders 

Brian Hare, Olaf Scholten, Stijn Buitink, Joseph Dwyer, Ningyu Liu, Chris Sterpka, and Sander ter Veen

Lightning dart and recoil leaders are difficult to understand, as they have a different (often smoother) propagation mode than stepped leaders, and re-ionize a previously ionized channel. In order to understand them better, we have imaged recoil leaders with the LOFAR radio telescope (30-80 MHz), and will present 3D polarization, speed, and intensity data from multiple recoil leaders. We will show that many recoil leaders with high VHF intensity have VHF polarization that is very parallel to the recoil leader channel, with an opening angle as small as 15 degrees. Recoil leaders with lower VHF intensity have larger polarization opening angles, but it is not clear if this is physical or instrumental. In addition, VHF emission from recoil leaders comes from a sub-meter thin channel. Finally, we will show that the propagation speed and VHF intensity are strongly correlated; almost following a power-law or exponential relationship. These results probe the streamer behavior of recoil leaders, and thus provide significant clues to how recoil and dart leaders propagate. The fact that recoil leaders are very VHF thin is consistent with small polarization opening angles, and demonstrates that recoil leaders have significant streamer activity in their core and their corona sheath is VHF silent. The power-law/exponential relationship between speed and VHF intensity, however, is very difficult to explain.

How to cite: Hare, B., Scholten, O., Buitink, S., Dwyer, J., Liu, N., Sterpka, C., and ter Veen, S.: The propagation and 3D VHF polarization properties of recoil leaders, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12591, https://doi.org/10.5194/egusphere-egu23-12591, 2023.

EGU23-13117 | Posters on site | NH1.5

Modelling of cloud electrification 

Jana Popová and Zbynek Sokol

We developed a cloud electrification model (CEM) which describes the evolution of the electric field in clouds, including electric discharges. Our CEM simulates evolution of charge of individual hydrometeors (cloud droplets, rain droplets, ice, snow, graupel) and models the distribution of positive and negative ions. Using this model, we compared the evolution of electric charge and electric field for selected winter and summer thunderstorms that occurred close to the Milešovka meteorological observatory. We analysed the dataset of thunderstorms using measurements of Ka-band cloud profiler and X-band weather radar, both located at the Milešovka observatory, and standard meteorological measurements. The analyses include a comparison of the structure of the modelled thunderstorms with the structure derived from radar and satellite observations. 

How to cite: Popová, J. and Sokol, Z.: Modelling of cloud electrification, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13117, https://doi.org/10.5194/egusphere-egu23-13117, 2023.

EGU23-13197 | ECS | Orals | NH1.5

The Role of Global Thunderstorm Activity in Modulating Global Cirrus Clouds 

Joydeb Saha, Colin Price, and Anirban Guha

Cirrus clouds provide a significant radiative forcing on the Earth's climate system. The net cloud radiative forcing for cirrus clouds results a warming of the climate.  More/less cirrus clouds result in more/less warming of the planet. The moisture for the formation of cirrus clouds in the upper atmosphere is transported there in large part via deep convective storms, many associated with lightning activity and hence defined as thunderstorms.  An increasing in cirrus clouds in a warmer atmosphere will amplify the initial warming. This paper looks at the connection in space and time between monthly mean lightning activity observed from the Lightning Imaging Sensor on board the International Space Station (LIS-ISS), and the global monthly mean cirrus cloud cover obtained from the MERRA-2 reanalysis product. The correlation coefficient between the global monthly mean cloud optical thickness (COT) of the cirrus clouds (clouds at altitudes above the 400hPa pressure levels) with the monthly mean lightning flash counts is 0.84, implying that monthly mean  lightning can explain 70% of monthly variability of the global high cloud optical thickness. In addition, lightning amount explains nearly 60% of the monthly mean global area coverage of cirrus clouds.  Given these statistically significant connections between lightning and cirrus clouds, we propose using global lightning data as an additional tool for monitoring monthly variability of cirrus clouds.

 

How to cite: Saha, J., Price, C., and Guha, A.: The Role of Global Thunderstorm Activity in Modulating Global Cirrus Clouds, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13197, https://doi.org/10.5194/egusphere-egu23-13197, 2023.

EGU23-13985 | Posters virtual | NH1.5

Development and testing of a modernised Programmable Ion Mobility Spectrometer 

Karen Aplin, Alan Meaney, József Bór, and Attila Buzás

The atmosphere is made slightly electrically conductive by cosmic rays and natural radioactivity, which generate ions. Air conductivity is a key component of the global electric circuit and influences droplet and cloud charging [1]. Further, atmospheric ions may affect the radiative balance through particle formation and infra-red absorption [2], [3]. Both considerations motivate the need for accurate atmospheric ion measurements. The Programmable Ion Mobility Spectrometer (PIMS) is a computer-controlled instrument based on the Gerdien measurement principle in which a cylindrical capacitor, across which a voltage is applied, is aspirated to sample air ions [4]. Computer control of a switchable multimode electrometer [5] offers the capability to measure ions in two modes, offering self-calibration, which removes the difficulties with providing a well-characterised environment for calibration [6]. The PIMS can independently monitor internal leakage currents which can be a significant source of thermally dependent error, especially in outdoor use. First developed in the early 2000s, the PIMS has recently been modernised with a new electrometer and advanced microcontroller, leading to significantly miniaturised electronics and opportunities for more sophisticated interfacing. The modernised PIMS was tested at Nagycenk Geophysical Observatory (47.632°N,16.718°E), Hungary in summer 2022, alongside a full range of meteorological and atmospheric electrical measurements for comparison.

 

References

[1]      R. G. Harrison and K. A. Nicoll, “The electricity of extensive layer clouds,” Weather, vol. 77, no. 11, pp. 379–383, Nov. 2022, doi: 10.1002/wea.4307.

[2]      K. L. Aplin, “Composition and measurement of charged atmospheric clusters,” Space Sci Rev, vol. 137, no. 1–4, 2008, doi: 10.1007/s11214-008-9397-1.

[3]      K. L. Aplin and M. Lockwood, “Cosmic ray modulation of infra-red radiation in the atmosphere,” Environmental Research Letters, vol. 8, no. 1, 2013, doi: 10.1088/1748-9326/8/1/015026.

[4]      K. L. Aplin and R. G. Harrison, “A computer-controlled Gerdien atmospheric ion counter,” Review of Scientific Instruments, vol. 71, no. 8, 2000, doi: 10.1063/1.1305511.

[5]      R. G. Harrison and K. L. Aplin, “Multimode electrometer for atmospheric ion measurements,” Review of Scientific Instruments, vol. 71, no. 12, 2000, doi: 10.1063/1.1327303.

[6]      K. L. Aplin and R. G. Harrison, “A self-calibrating programable mobility spectrometer for atmospheric ion measurements,” Review of Scientific Instruments, vol. 72, no. 8, 2001, doi: 10.1063/1.1382634.

How to cite: Aplin, K., Meaney, A., Bór, J., and Buzás, A.: Development and testing of a modernised Programmable Ion Mobility Spectrometer, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13985, https://doi.org/10.5194/egusphere-egu23-13985, 2023.

Being one of the natural hazards and an indicator of severe weather, studying and evaluating lightning activity has a well recognized role in scientific research. The detection of lightning activity with a good efficiency is crucial not only to the protection of human lives and minimizing economic losses, but to get a better understanding of Earth’s climate system as well.

There are several solutions for lightning detection implemented both on ground (e.g., Earth Networks, EUCLID, LINET, WWLLN, etc.) and in space (e.g., GLM, LIS, OTD) providing a big amount of reliable data. The BlitzOrtung (BO) is a dynamically developing and community-based lightning detection network (Wanke et al., 2014). By 2018, the BO had circa 2000 stations around the globe (Narita et al., 2018) and their data are used widely in Europe. However, there is a need to evaluate the detection efficiency and compare the parameters of the detected lightning strokes with the ones derived from other networks (Narita et al., 2018).

In this study, we aim at evaluating the performance of the BO network on a statistical basis. First, the detected lightning strokes are paired with those reported by the LINET and WWLLN systems using the time point and location information. Then the geographical distribution as well as the temporal stability of the number of detected events and the percentage of paired events are examined. The first results of a pilot analysis over Hungary (45.5°-49° N, 16°-23° E) in Central Europe will be presented. This project serves to establish a comparison-based method for the evaluation of the lightning climatology of a region.

 

Narita, T. et al. (2018): A study of lightning location system (Blitz) based on VLF sferics, 34th International Conference on Lightning Protection, 978-1-5386-6635-7/18/$31.00,

Wanke, E., Andersen, R., and Volgnandt, T. (2014): A World-Wide Low Cost Community-Based Time-Of-Arrival Lightning Detection and Lightning Location Network, http://www.blitzortung.org/Documents/TOA_Blitzortung_RED.pdf

How to cite: Buzás, A., Bozóki, T., and Bór, J.: Community-based lightning detection in Europe: studying the detection efficiency of the BlitzOrtung network - a case study concerning lightning climatology over Hungary, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14243, https://doi.org/10.5194/egusphere-egu23-14243, 2023.

EGU23-14440 | Orals | NH1.5

Removing local variability from Potential Gradient data – the Carnegie filter 

R.Giles Harrison, Keri Nicoll, Manoj Joshi, and Ed Hawkins

Measurements of atmospheric electricity have been made at many sites over a long time, with the vertical Potential Gradient (PG) the most commonly observed quantity. In general, the PG responds to local influences from weather, aerosol effects on charge exchange, and variability in the global atmospheric electric circuit. Different methods have been used to classify PG data, for example through identifying days when conditions were considered relatively undisturbed, or by using meteorological information to identify days on which weather-related variability was negligible. Nevertheless, local effects can persist, especially in data obtained at continental sites. Hence, if long term changes in the global atmospheric electric circuit are to be investigated, the local effects need first to be reduced or, ideally, removed.

Recent work has demonstrated a close relationship between the PG at some sites and ocean temperatures modulated by the El Niño Southern Oscillation, through the associated changes in the global atmospheric electric circuit ([1],[2], [3]). The expectation of such a relationship can be used to test methods of removing and reducing local effects in PG data. A method based on the Carnegie curve – the hourly variation known to be present in the global circuit – is discussed here. Through comparison of hourly PG data from a site with the Carnegie curve, outlier values lying beyond the usual range of global circuit changes can be identified and removed. The remaining data can then be used to construct new daily or monthly averages with reduced local variability, evaluated by comparison with global circuit changes associated with the El Niño Southern Oscillation.

 

References

[1] R.G. Harrison, K.A. Nicoll, M. Joshi, E. Hawkins: Empirical evidence for multidecadal scale Global Atmospheric Electric Circuit modulation by the El Niño-Southern Oscillation Environ Res Lett 17, 124048 (2022) https://iopscience.iop.org/article/10.1088/1748-9326/aca68c

[2] N.N. Slyunyaev, N.V.I lin, , E.A. Mareev,.G. Price: A new link between El Nino - Southern Oscillation and atmospheric electricity, Environ. Res. Lett., 16, (2021) https://doi.org/10.1088/1748-9326/abe908 

[3] R.G. Harrison, M. Joshi, K. Pascoe: Inferring convective responses to El Niño with atmospheric electricity measurements at Shetland Environ Res Lett 6 (2011) 044028  http://iopscience.iop.org/1748-9326/6/4/044028/ 

How to cite: Harrison, R. G., Nicoll, K., Joshi, M., and Hawkins, E.: Removing local variability from Potential Gradient data – the Carnegie filter, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14440, https://doi.org/10.5194/egusphere-egu23-14440, 2023.

EGU23-14579 | Orals | NH1.5

Increase in Lightning and Upper Tropospheric Water Vapour Over the Arctic Circle 

Anirban Guha, Joydeb Saha, and Colin Price

Sea ice in the Arctic grows during each hemisphere’s winter and it retreats in the summer. The highly reflective white surface of sea ice reflects solar energy, cooling the planet. When it melts, the darker ocean absorbs more heat, reinforcing the cycle of melting sea ice. Sea ice plays a critical role in regulating Earth’s climate, and it influences global weather patterns and ocean circulations. One essential feedback in the Arctic is the rise in upper tropospheric water vapor (UTWV) or the specific humidity (SH) that acts as an intense greenhouse gas trapping in additional heat released from the Earth's surface.   While temperature change is driven by increasing greenhouse gases, the interannual variability in sea ice can be explained by changes in the UTWV (ASO) at 400mb in the Arctic. Where is this increase in UTWV (400mb) coming from in the Arctic?  Thunderstorm activity appears to be increasing in the Arctic in the last decades, and could be a source of the increasing UTWV, and hence the decrease in Arctic sea ice.

How to cite: Guha, A., Saha, J., and Price, C.: Increase in Lightning and Upper Tropospheric Water Vapour Over the Arctic Circle, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14579, https://doi.org/10.5194/egusphere-egu23-14579, 2023.

EGU23-14736 | ECS | Posters on site | NH1.5

Validation of the ASIM MXGS performance using cosmic Gamma-Ray Bursts 

Andreas Ramsli, Martino Marisaldi, Anastasia Tsvetkova, Cristiano Guidorzi, David Sarria, Andrey Mezentsev, Anders Lindanger, Nikolai Østgaard, Torsten Neubert, Victor Reglero, Dmitry Svinkin, Alexandra Lysenko, and Dmitry Frederiks

The Atmosphere-Space Interactions Monitor (ASIM) is a mission of the European Space Agency launched in April 2018 and hosted onboard the International Space Station (ISS). ASIM is dedicated to study the physics of Transient Luminous Events (TLEs) and Terrestrial Gamma-ray Flashes (TGFs) and their relation to lightning. TGFs are X- and Gamma-ray flashes associated to lightning discharges, with average duration of few tens of microseconds and energies up to 40 MeV. ASIM detects TGFs by means of the Modular X- and Gamma-ray Sensor (MXGS). So far, the MXGS performance (efficiency, effective area) have been evaluated by Monte Carlo simulations only, while energy calibration is monitored using built-in radioactive sources and background lines. TGFs are local events, very rarely observed by more then one spacecraft simultaneously, therefore it is difficult to use them to validate the MXGS performance. Goal of this study is to use cosmic Gamma-ray Bursts (GRBs) simultaneously detected by ASIM and other spacecraft as calibration sources to validate the spectral performance of MXGS. GRBs are the brightest explosions in the universe, associated to the collapse of massive stars or the merger of compact objects, involving at least one neutron star, at cosmological distances. During the period from June 2018 to December 2021, 12 GRBs were detected by ASIM and by one or more other spacecraft. Here we use data from the Konus-WIND mission and from the Fermi Gamma Burst Monitor (GBM), both considered as benchmarks in the field of GRB analysis. We cross-correlate the light curves of the three instruments, and we perform simultaneous spectral analysis using the forward-folding approach. In some cases, we find good consistency between the detectors, indicating an overall validation of the MXGS performance. In other cases, we identified discrepancies, possibly due to absorption from structures of the ISS, currently under investigation. In this presentation, we show our data sample, the methodology used and the preliminary joint spectral analysis results. This work is relevant because it will provide an independent assessment of the MXGS performance, with clear implications for ASIM TGF results.

How to cite: Ramsli, A., Marisaldi, M., Tsvetkova, A., Guidorzi, C., Sarria, D., Mezentsev, A., Lindanger, A., Østgaard, N., Neubert, T., Reglero, V., Svinkin, D., Lysenko, A., and Frederiks, D.: Validation of the ASIM MXGS performance using cosmic Gamma-Ray Bursts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14736, https://doi.org/10.5194/egusphere-egu23-14736, 2023.

EGU23-15378 | Orals | NH1.5

Studying downward TGFs with the largest ground array of gamma-ray detectors 

Roberta Colalillo, Joseph Dwyer, David M. Smith, and John Ortberg and the Pierre Auger Collaboration

The Pierre Auger Observatory, the largest cosmic-ray detector in the world, has been
observing peculiar events which are very likely downward TGFs. Their experimental
signature and their time evolution are very different from those of a shower produced
by an ultra high energy cosmic ray. The TGF-like events happen in coincidence with
lightning and low clouds and their deposited energy at the ground is compatible with
that of a standard downward TGF with the source at few kilometers above the
ground. The surface detector (SD) of the Auger Observatory consists of 1660 water-
Cherenkov detectors (WCDs) spread over 3000 km2 in the Argentinian pampa. The
WCD height of 1.2 m makes them highly sensitive to gamma rays and the large area
covered with SD allows us to sample the TGF beam from different points. The
timing shape of WCD signals can be very important to constrain different TGF source
models. Cold runaway from the high fields near the leader tips or relativistic
feedback produce the same energy spectrum but predict a different rise and fall of the
counts versus time, and they could produce a different angular distribution.
Comparisons between simulations and data will be shown.
Moreover, first results from a preliminary analysis of the available meteorological
data at the time of Auger TGF-like events will be presented. Little is known about the
TGF-producing storms. The characteristics of these thunderstorms are being
investigated by studying meteorological data in coincidence with upward TGFs. A
similar analysis is important to better understand downward TGF production
mechanisms and investigate if are the same as those producing upward TGFs.

How to cite: Colalillo, R., Dwyer, J., Smith, D. M., and Ortberg, J. and the Pierre Auger Collaboration: Studying downward TGFs with the largest ground array of gamma-ray detectors, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15378, https://doi.org/10.5194/egusphere-egu23-15378, 2023.

EGU23-15873 | Posters on site | NH1.5

THOR-DAVIS: A neuromorphic camera to observe thunderstorms from inboard ISS. 

Olivier Chanrion, Nicolas Pedersen, Andreas Stokholm, Benjamin Hauptmann, and Torsten Neubert

The technical purpose of THOR-DAVIS is to test a new camera concept in space for observations of thunderclouds and their electrical activity at up to a resolution of 10 µs. The scientific purpose is to conduct video camera observations of thunderclouds and their electrical activity. The focus is on altitude-resolved measurements of activity at the top of the clouds and the stratosphere above. The camera type is a so-called neuromorphic camera (or event camera) where pixels are read out asynchronously when the pixel illumination changes. The goal is to understand, under realistic conditions, the use of such a camera for future use in space for observations of processes in severe electrical storms. The camera has a high temporal resolution 100.000 equivalent frame per second and a huge dynamic range of about 120 dB and is particularly well suited for this kind of observations. The camera weights about 200g and consumes about 1.5A in operation and is particularly well suited for space applications.

In this presentation we will give the status of the development of the THOR-DAVIS experiment to be conducted by the Danish astronaut Andreas Mogensen during his upcoming ESA mission Huggin onboard the International Space Station (ISS). We’ll present the design of the payload based on a Davis 346 neuromorphic camera mounted on top of a Nikon D5 camera for handheld operation. The 2 cameras are controlled by an AstroPi unit based on a Raspberry Pi computer board.

Finally, we’ll give preliminary results of laboratory measurements made with the flight model.

 

How to cite: Chanrion, O., Pedersen, N., Stokholm, A., Hauptmann, B., and Neubert, T.: THOR-DAVIS: A neuromorphic camera to observe thunderstorms from inboard ISS., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15873, https://doi.org/10.5194/egusphere-egu23-15873, 2023.

EGU23-15876 | Orals | NH1.5

TGFs - "Storm Activity" relationship  

Javier Navarro-González, Paul Connell, Chris Eyles, Víctor Reglero, Jesús Alberto López, Joan Montanyà, Martino Marisaldi, Andrey Mezentzev, Anders Lindanger, David Sarria, Nikolai Østgaard, Olivier Chanrion, Freddy Christiansen, and Torsten Neubert

In the first two years of ASIM operations from June 2018 till the end of 2019 486 TGFs have been observed, with a TGF rate of 0.84 per day. Their geographical distribution is consistent with the three main lightning chimneys Central America, Central Africa, and South East of Asia. Figure 1 displays the ISS footprint positions when the TGFs were detected.

Figure 1: ISS position for the 2018-2019 ASIM TGFs. Red circles marked those within 4 minutes of the previous TGF detected.

If the TGF occurrence follows a stochastic process (each TGF is not related to the next one), the time-difference distribution between a TGF detection and the next one should fit an exponential distribution. For a Δt < 4 minutes the number of TGFs following the exponential distribution is 16. Opposite we got 85 in groups of 2-3 TGFs displayed in Figure 1 in red circles. Analyzing the apparent strong discrepancy in the number of detection in less than 4 minutes (Figure 2) and the number derived from the exponential distribution is one of the motivations of this study.

We build a grid of variable dimension cell size to keep the same ISS observing time for each cell in a Monte Carlo code to simulate the TGF generation that has into account the frequency and the anisotropy distribution of the TGFs over the earth.

To preserve the total number of TGF observed in Δt < 4 minutes we need to add a parameter related to the “Storm Activity” defined as the time in a cell available to generate a TGF. The model fits observations when this parameter is 7%±1%. The good correlation between model/observation is displayed in Figure 2.

Figure 2: The predicted distribution of the TGF pairs (Orange) in 15s bins fits the observations (Blue).

The scope of this work is to check the adopted “Storm Activity” value using WWLLN sferics database as a good indicator of storm activity.

 

 

 

 

 

 

 

How to cite: Navarro-González, J., Connell, P., Eyles, C., Reglero, V., López, J. A., Montanyà, J., Marisaldi, M., Mezentzev, A., Lindanger, A., Sarria, D., Østgaard, N., Chanrion, O., Christiansen, F., and Neubert, T.: TGFs - "Storm Activity" relationship , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15876, https://doi.org/10.5194/egusphere-egu23-15876, 2023.

EGU23-16046 | Orals | NH1.5

Investigating ELVE photometric waveforms with elementary electromagnetics 

Alejandro Luque Estepa, Ingrid Bjørge-Engeland, Dongshuai Li, Nikolai Østgaard, and Martino Marisaldi

ELVEs are quickly expanding rings of light emissions excited in the lower ionosphere by the electromagnetic pulse of an electric discharge in a thundercloud. They are commonly observed from space platforms and have been reported in conjunction with other atmospheric-electricity events. One motivation to investigate ELVEs is that their signal may provide insight into the discharge that created them. Until now the modeling of ELVES has either relied on strong simplifications or on the Finite-Difference Time-Domain (FDTD) method to directly solve the Maxwell equations. One limitation of the latter is that non-axisymmetrical discharges (with a slanted channel or a non-vertical magnetic field for example) require computationally expensive, fully three-dimensional meshes, which makes parametric studies of the ELVE features slow and cumbersome.  We show here that elementary electromagnetic theory allows one to model ELVEs, even non-axisymmetrical ones, with sufficient accuracy at little computational cost. We then apply our methods to the parametric study of ELVE photometric waveforms as recorded by space-based instruments.

How to cite: Luque Estepa, A., Bjørge-Engeland, I., Li, D., Østgaard, N., and Marisaldi, M.: Investigating ELVE photometric waveforms with elementary electromagnetics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16046, https://doi.org/10.5194/egusphere-egu23-16046, 2023.

EGU23-16868 | ECS | Posters on site | NH1.5

Project JetNet: Hemispheric-scale gigantic jet detection network 

Levi Boggs, Jeffrey Smith, Douglas Mach, Steve Cummer, John Trostel, Jeffery Burke, and Jessica Losego

In this presentation we will provide an overview and present preliminary results from a multi-institutional collaborative project, which seeks to detect gigantic jets over hemispheric scales using a combination orbital and ground-based sensors and machine learning. Gigantic jets are a type of transient luminous event (TLE, Pasko 2010, doi: 10.1029/2009JA014860) that escape the cloud top of a thunderstorm and propagate up to the lower ionosphere (80-100 km altitude), transferring tens to hundreds of Coulombs of charge. Our detection methodology primarily uses the Geostationary Lightning Mapper (GLM), which is a staring optical imager in geostationary orbit that detects the 777.4 nm (OI) triplet commonly emitted by lightning (Goodman et al. 2013, doi: 10.1016/j.atmosres.2013.01.006).  Gigantic jets have been shown to have unique signatures in the GLM data from past studies (Boggs et al. 2019, doi: 10.1029/2019GL082278; Boggs et al. 2022, doi: 10.1126/sciadv.abl8731). Thus far, we have built a preliminary, supervised machine learning model that detects potential gigantic jets using GLM, and begun development on a series of vetting techniques to confirm the detections as real gigantic jets. The vetting techniques use a combination of low frequency (LF) and extremely low frequency (ELF) sferic data, in combination with stereo GLM measurements. When our detection methodology grows in maturity, we will deploy it to all past GLM data (2018-present), with the potential to detect thousands of events each year, allowing correlation with other meteorological and atmospheric measurements. We will share the database of gigantic jet detections publicly during and at project conclusion (2025), allowing other researchers to use this data for their own research.

How to cite: Boggs, L., Smith, J., Mach, D., Cummer, S., Trostel, J., Burke, J., and Losego, J.: Project JetNet: Hemispheric-scale gigantic jet detection network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16868, https://doi.org/10.5194/egusphere-egu23-16868, 2023.

EGU23-16886 | ECS | Orals | NH1.5

Earth Networks Lightning System Update 

Elizabeth DiGangi, Jeff Lapierre, Yanan Zhu, and Michael Stock

Global lightning location data has long been a critical tool for lightning research and safety. The Earth Networks Total Lightning Network (TLN) incorporates advanced lightning location technology delivering competitive lightning detection efficiency, location accuracy, and classification (intracloud vs cloud-to-ground). It consists of over 1800 wideband sensors deployed in 40+ countries to detect lightning and generate real-time localized storm alerts. TLN is constantly evolving through network expansion, as well as hardware and software development. In this presentation, we will cover some of the recent advances to the TLN hardware and processor. The new TLN sensor has been redesigned to use a dipole sensing element to help reduce the requirements of a strong ground. These new sensors are currently being used operationally and produce comparable waveforms to the previous monopole antenna. New upgrades to the lightning location algorithm have increased the detection efficiency, location accuracy, and classification accuracy of the network. Globally, TLN is locating approximately 50% more pulses than it was before. In moderately remote regions of the world, performance gains can be higher. TLN continues to use data from the World Wide Lightning Location Network (WWLLN), enhanced via raw signals from approximately 200 TLN sensors, to locate lightning in extremely remote regions like the deep oceans. However, how WWLLN data is incorporated into the TLN feed has changed, leading to significantly reduced false alarm rates in some regions. Location accuracy was improved by developing a new propagation model for signals produced by lightning, resulting in a reduction in location error by as much as a factor of 2. As a result of the improved location accuracy, as well as enhancements to the false alarms rates, there is improved clustering of lightning, which directly impacts downstream products such as lightning alerting and Dangerous Thunderstorm Alerts.

How to cite: DiGangi, E., Lapierre, J., Zhu, Y., and Stock, M.: Earth Networks Lightning System Update, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16886, https://doi.org/10.5194/egusphere-egu23-16886, 2023.

EGU23-17102 | ECS | Orals | NH1.5

ELF-transients detected in the broadband recordings at the Hylaty station in Poland 

Tamas Bozoki, Janusz Młynarczyk, Jozsef Bor, Jerzy Kubisz, Istvan Bozso, Andras Horvath, Lukacs Kuslits, and Mate Timko

Lightning acts as a natural antenna radiating electromagnetic (EM) waves in a wide frequency range. In the extremely low frequency (ELF) band (3 Hz - 3 kHz), lightning-induced EM waves suffer very weak attenuation while they propagate in the waveguide formed by the Earth’s surface and the lowest part of the ionosphere. These EM waves can travel around the Earth several times before losing most of their energy. This allows ELF-transients generated by powerful lightning discharges from around the globe to be detected at any observation site. We developed an algorithm that identifies ELF-transients in the broadband recordings at Hylaty, Poland (sampling frequency: 3004.81 Hz, antenna bandwidth: 0.02 Hz to 1.1 kHz) and finds their most probable source lightning discharge in the lightning database of the Word Wide Lightning Location Network (WWLLN) based on the technique described by Bór et al. (2022).

Between July 2020 and April 2021 about 270,000 ELF-transients were found in the records from Hylaty. The most probable source of 160,000 transients  was identified in the WWLLN database. Using this data set, we show that the propagation speed of broadband ELF-transients differ significantly when the propagation path is on the dayside or on the nightside of the Earth. It is also demonstrated that for lightning discharges close to Hylaty (d<2Mm), the timing and location accuracy of WWLLN has a large impact on the identification of the lightning source and on the inferred propagation speed. A convolutional neural network, trained with ELF-transients of known source location, was used to determine the distance to the lightning source in cases where the source lightning discharge could not be found in the WWLLN database. The average accuracy of the distance provided by the neural network is 700 km. No significant difference can be seen between the distribution of distances obtained by matching the source lightning stroke in the WWLLN database and that obtained using the neural network-based approach.

 

Reference:

Bór, J., Szabóné André, K., Bozóki, T., Mlynarczyk, J., Steinbach, P., Novák, A., and Lemperger, I. (2022): Estimating the Attenuation of ELF-Band Radio Waves in the Earth’s Crust by Q-Bursts. IEEE Transactions on Antennas and Propagation, 70, 8. https://doi.org/10.1109/TAP.2022.3161504

How to cite: Bozoki, T., Młynarczyk, J., Bor, J., Kubisz, J., Bozso, I., Horvath, A., Kuslits, L., and Timko, M.: ELF-transients detected in the broadband recordings at the Hylaty station in Poland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17102, https://doi.org/10.5194/egusphere-egu23-17102, 2023.

EGU23-636 | Posters on site | NH1.6

Flood Risk Mitigation in Highly Urbanized Area by Nature Based Solutions: The Case of Istanbul Esenyurt 

Buse Özer, Egemen Fırat, Koray K. Yılmaz, Gülçin Türkkan Karaoğlu, Esra Fitoz, Özlem Yıldız Yüksekol, Tuba Alphan, and Görkem Önder

As in the rest of the world, floods have devastating socio-economic effects in Turkey as well. Especially, highly urbanized areas do not enable the implementation of structural measures, properly. In addition, in recent years, structural measures have been replaced by nature-based and eco-friendly approaches. Therefore, it is essential to investigate the effectiveness of nature-based solutions (NBSs) in such places in order to eliminate the negative effects of floods. This study covers the area of Esenyurt District in Istanbul, which is highly urbanized and frequently affected by floods. The main channel of the Haramidere Creek and its six branches were studied with a total length of approximately 25 km. Firstly, 2, 5, 10, 50, 100, 500 and 1000 year recurrence interval of flow rates were calculated by using a hydrological model and extreme value analysis. Next, flood inundation area and depths were determined using 1D and 2D hydrodynamic models. Social and economic risks were estimated by combining the related studies mentioned above with the road, vehicle, building etc. inventory. Following this, the basin was divided into regions according to its NBS characteristics. The approaches such as rainwater harvesting in areas with insufficient infrastructure and permeable pavement in suitable areas having gentle slopes were modeled both individually and in combinations. In the literature, the effects of NBS have been revealed by examining the flood volume and peak flow values. In some similar studies, there is more decrease in peak flow rates while implementing a combination of NBSs rather than applied alone as a solution. In our study, in addition to the changes in flood volume and peak values, changes in flood inundation boundary, depth, social and economic risk will also be quantitatively revealed for the relevant recurrence interval of flow rates.

How to cite: Özer, B., Fırat, E., Yılmaz, K. K., Türkkan Karaoğlu, G., Fitoz, E., Yıldız Yüksekol, Ö., Alphan, T., and Önder, G.: Flood Risk Mitigation in Highly Urbanized Area by Nature Based Solutions: The Case of Istanbul Esenyurt, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-636, https://doi.org/10.5194/egusphere-egu23-636, 2023.

Nature-Based Infrastructure (NBI) should be designed to evolve over time with the ability to adapt to changing conditions while providing ecosystem benefits to form culturally valuable landscapes. There are substantial differences between NBI and traditional infrastructure - whose forms specifically do not change over time and do not migrate or evolve. Given these differences as well as the substantial operational scale of NBI, how do we monitor and determine their management needs?

Professors Basener and Luegering’s research examines the role of plants as vital elements of NBI, whose capacity to indicate climate change impacts as well as respond to varying conditions through existing genetic capacities such as rapid resprouting, rhizomatous or tillering root systems, present an immense opportunity to track, induce and manage these changes.  With plants at the center of the research, our work builds on emerging remote sensing techniques, to develop ‘Fundamental Signatures’ (FS), which are signatures whose development methods anticipate atmospheric material interference, increase and vary resolution, and increase seasonal collection frequency with sensitivity towards species habits and growth patterns. FS engage the chemistry and geometry of plants through the intentional usage of emerging technology in the form of Hyperspectral and LiDAR sensors.

We argue that the 2020 spectral research survey performed by Hennessy, Clarke and Andrew Hennessy in Hyperspectral Classification of Plants: A Review of Waveband Selection Generalizability, points to a need for a greatly expanded capture and classification of signatures associated with seasonal variation as well as environmental disturbance.  As such, we are working to develop controlled studies at our test plots at the University of Virginia’s Morven Sustainability Lab. With these test plots, we can track spectral changes with regular intervals, but further, we will create controlled inundations with salt and fresh water as well as manipulate the pH and soil composition to track a full range of spectral signatures within each species. We have teamed with the United States Department of Agriculture Natural Resource Conservation Service (USDA-NRCS) to assist in study development as well as plant and knowledge exchange.

Our scale of study stretches from the single plant and test plot scale (CM scale) to the Chesapeake Bay (KM scale). As we develop Fundamental Signatures for indicator and disturbance invigorated species, we will begin to test them against seasonal large-scale data collections performed by the University of Vermont, including LiDAR and Hyperspectral data collected via manned aircraft. At the scale of the Chesapeake Bay, we can study the effectiveness of fundamental signatures in identifying existing plant communities as well as the identification of stressors through variegated portions of the fundamental signature.

The project continues to work towards several key outcomes, including the construction of a public fundamental signature library, the development of workflows for incorporating and updated landcover and Manning’s classifications for hydrodynamic modeling and design studies as well as field techniques for the propagation and manipulation of plants in Nature-Based Infrastructure. 

How to cite: Luegering, M. and Basener, W.: Monitoring Landscape Change: Fundamental Spectral Signatures and the Adaptive Management of Nature-Based Infrastructure , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1922, https://doi.org/10.5194/egusphere-egu23-1922, 2023.

EGU23-2894 | ECS | Posters virtual | NH1.6

Investigating the effectiveness of Nature-Based Solutions (NBS) for climate change adaptation: The case study of Aa of Weerijs catchment, The Netherlands. 

Irfan Nazar, Muhammad Haris Ali, Claudia Bertini, Ioana Popescu, Andreja Jonoski, and Schalk Jan van Andel

Anthropogenic Climate Change has caused an increase in frequency, intensity and impact of hydro-meteorological-hazards (HMHs) such as floods, droughts, wildfires, and sea level rise. Prior to the 21st century, most policies and strategies to deal with water-related climate risks were based on conventional or grey solutions without considering Nature-Based Solutions (NBS) as potential measures. In the recent past, NBS has gained prominence over conventional measures, in the long run, owing to multi-functionality, flexibility, and cost-effectiveness, providing inter-related and multi-scale benefits to deal with water-related climate hazards.

However, the efficiency and robustness of NBS are still under question because of the lack of specialized models and tools to assess them throughout the life cycle and under varying climate patterns. A solid framework of Key Performance Indicators is needed to progress further in promoting NBS at larger scales.

In this study, we have explored a set of potential NBS for the Aa of Weerijs catchment, in the Netherlands, which is currently under water stress. The performance of NBS to deal with water-stress-related challenges in the catchment is investigated using a fully distributed physical coupled MIKE SHE-MIKE11 model previously developed. A different set of scales and extents and combinations of NBS have been modelled in the MIKE SHE model of the catchment, ranging from wetlands, detention ponds and river meandering. The performance of NBS is evaluated both for the present and for future climate change conditions, using two sets of climate change projections, the KNMI ’14 scenarios, developed by the Koninklijk Nederlands Meteorologisch Instituut (KNMI), and the RCP 6.0 and RCP 8.5 scenarios, provided by Copernicus.

To assess the performance of each NBS set-up and support informed decision-making for stakeholders, a suite of defined KPIs, including surface and groundwater availability in the catchment, water stress ratio, and soil moisture deficit index, is being calculated for each NBS simulation run and used for comparison with base results.

The study results are intended to support NBS impact evaluation as an adaptation strategy for the long term.

How to cite: Nazar, I., Ali, M. H., Bertini, C., Popescu, I., Jonoski, A., and van Andel, S. J.: Investigating the effectiveness of Nature-Based Solutions (NBS) for climate change adaptation: The case study of Aa of Weerijs catchment, The Netherlands., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2894, https://doi.org/10.5194/egusphere-egu23-2894, 2023.

Flood is one of the most frequent and costly natural disasters worldwide. In many cases, implementing flood protection measures is limited due to land or resource availability. Natural Water Retention Measures (NWRMs), a more cost-effective approach, have recently drawn the attention of many researchers. Instead of infrastructure construction, NWRMs aim to reduce the risk of flooding and economic loss by land use and water management practices without many construction applications. Much previous literature qualitatively investigates the mechanisms of NWRMs, however, only a few focus on the hydraulic characteristic and the effectiveness of flood reduction of NWRMs. To improve the understanding of NWRMs, this study clarifies and analyzes the hydraulic performance of NWRMs. We consider the triangular inflow hydrograph based on the continuity equation with the Muskingum-Cunge method to derive the outflow of the channel, as well as the weir equation to the outflow of the retention area. Following, the continuity equations are formulated as a first-order ordinary differential equation in dimensionless form. The conceptual model built from the equations could denote the primary hydraulic mechanism in the original channel and the additional retention area. Two important parameters include the ratio of peak maximum outflow and peak inflow, and the ratio of maximum storage and total flood volume can be obtained by solving the equations. The results show that the relationship between two dimensionless parameters are nonlinear. Also, in the channel, the relationship is sensitive to the shape factor in the Muskingum-Cunge method, especially in a lower ratio of maximum outflow and peak inflow. With this model, the study following examined the different proportional of flood volume flowing in retention areas and calculate the downstream outflow. The result shows the effectiveness of flood reduction and the proportional of flood volume in retention areas are nonlinear relationships. Briefly, there is an optimal operation of NWRMs by balancing the flood volume in the river and retention could induce the minimum outflow. The findings in this study represent the hydraulic performance of NWRMs. The results can also improve the design and operation of NWRMs appropriately.

How to cite: Huang, Y.-S. and You, J.-Y.: Application Continuity Equation to Analyze the Hydraulic Performance of Nature Water Retention Measures, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4335, https://doi.org/10.5194/egusphere-egu23-4335, 2023.

Sustainable urban drainage systems (SUDS) have been increasingly implemented as a low-impact, cost-effective stormwater control measure (SCM). SUDS include a diverse set of infiltration-based measures designed to maintain the pre-development hydrological cycle, reduce runoff, and enhance water quality via infiltration. However, these functions are prone to deterioration during winter, especially when soil frost is present. Unlike inland cold regions, maritime cities are particularly vulnerable to the negative winter impacts due to frequent freeze-thaw cycles, rain-on-snow events, and intermittent midwinter snowmelt. To date, the hydrological efficacy of SUDS at catchment-scale under cyclical cold conditions is still lacking. The goal of this study, therefore, was to evaluate the runoff and volume reduction achieved by a SUDS network in a small, 1.5 ha catchment in Garðabær, Iceland. In addition to assessing the seasonal and spatial variability of infiltration performance of different SUDS elements with varied soil properties and vegetation covers in an urban area. To that end, a total of 18 soil water content reflectometers to measure soil temperature and moisture were implemented in three SUDS components (i.e., densely vegetated rain garden, sparsely vegetated rain garden, and a front lawn with a grass vegetation cover receiving stormwater from a roof through a drain into a soakaway) in the study area at different depths (5–20 cm). An area-velocity flowmeter was installed at the outfall of the catchment to monitor runoff from the SUDS system as well as from the impervious surfaces that include streets, parking lots, and walking paths. Preliminary assessment at the beginning of the freezing period (i.e., November and December) showed that the densely vegetated rain garden was less susceptible to frost formation (frost reached 15 cm depth; min. -1.6 °C) compared to the sparsely vegetated rain garden (20 cm frost depth; min. -3.8 °C at 15 cm). In the front lawn, on the other hand, frost penetrated down to 10 cm depth (the depth at which soil was monitored and the minimum soil temperature dropped to -5.4 °C). The preliminary results show that the SUDS system was very effective during summer/fall and successfully infiltrated a total of 58% (n=14) of the storm events, especially small events (< 2 mm). The runoff coefficient for the events that produced surface runoff ranged between 0.011 and 0.19 (n=24) with an average volume reduction of 92% of the incoming runoff. However, further assessment of the system’s efficiency in terms of volume and runoff reduction during winter is still needed.

How to cite: Zaqout, T. and Andradóttir, H. Ó.: Catchment-scale hydrological performance of sustainable urban drainage systems in a cold maritime climate undergoing soil freeze-thaw cycles and rain-on-snow events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5220, https://doi.org/10.5194/egusphere-egu23-5220, 2023.

Fluvial floods in recent years (e.g., 2002 and 2013) have caused high financial losses in Germany. Floodplains are under extensive use and dikes disconnect two thirds of natural flood retention areas from rivers. Moreover, floodplains serve as hotspots of biodiversity and floodplain habitats as well as ecosystems are categorized as endangered. Additionally, the goal of the German National Strategy on Biodiversity in increasing retention areas along rivers by at least 10% by 2020 failed. In summary, urgent actions need to be taken to reduce flood risk on the one hand, and increase floodplain area for ecological improvement – often synergies are not considered. Dike Relocation (DR) or levee setback is considered as nature-based flood protection measure whereby flood water levels can be lowered by reconnecting floodplain areas to rivers and improving nature conservation. Although DRs are being implemented already, an integrated and systematic approach is needed to consider the synergies between fields, nature conservation and flood protection.

Using dike lines, Basic European Assets Map (BEAM), Natura 2000 protected sites, and EU Copernicus land use map, a GIS-based method was developed. Four criteria were considered to evaluate effective DR; (1) The narrowness of flood channels, (2) flood-exposed assets and population, (3) floodplain habitats, and (4) urban land use and infrastructure behind the dikes. Narrow width in flood channels (between dike lines) were identified as bottlenecks. Using BEAM, flood-exposed population and asset values were calculated upstream of the identified bottleneck. The area behind dikes was searched for Special Areas of Conservation (SACs) with typical floodplain habitats. By ranking and rescaling, indices were provided for each criterion. The indices were combined with equal weights to reach DR effectiveness index. The share of urban land use and transport infrastructure was calculated behind the dikes, and DRs were grouped based on the potential of socio-economic conflicting interests.

The developed method was applied to the German part of the river Elbe. Along the 195 km river reach between Tangermuende and Geesthacht, 29 critical bottlenecks were identified. Because of high urban land use and existing transport infrastructure behind the dikes, no DR is possible at 13 of those bottlenecks. As an example of recommended DRs, the highest effectiveness index was reached for a 72% width contraction and flood-exposed assets of 16 million Euro/km2 with high share of habitat area behind the dikes (93%). The results were confirmed by a comparison of this approach with the German Federal Institute of Hydrology (BfG) 2D hydraulic analysis of bottlenecks at the Lower Middle Elbe.

The GIS-based method can be used especially in the initial phase of decision making instead of time-consuming hydraulic models. Hereby, priority is given to DRs with higher synergy and low socioeconomic restrictions. Application of freely available data makes the method transferable to other European countries.

How to cite: Kazemi, H., Natho, S., and Thieken, A.: GIS-based identification of effective dike relocations: considering the synergy between nature conservation and flood risk reduction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5241, https://doi.org/10.5194/egusphere-egu23-5241, 2023.

EGU23-5974 | Orals | NH1.6

Monitoring and modeling the hydrological performance of a rain garden installation for flood risk mitigation at a urban site 

Adriano Magliocco, Arianna Cauteruccio, Katia Perini, and Luca Lanza

The Rain Garden was built as part of "Proterina 3 Evolution", a strategic project of the Interreg Maritime Italy France program, between 2018 and 2019, commissioned by the partner Città Metropolitana di Genova to the Department of Architecture and Design of the University of Genoa, with the project's scientific directors prof. Adriano Magliocco and prof. Katia Perini, involving arch. Paola Sabbion, as landscape architect and, subsequently as regards to hydrological monitoring, prof. Luca Lanza and dr. Arianna Cauteruccio.

The rain garden was built in a free area facing a school building in the municipality of Campomorone (Genoa, Italy). The goal was to verify the functioning of a NBS in a climatic context characterized by rainfall concentrated in short periods of time, with particularly dry summer seasons.

The rain garden is of the non-infiltrating type. It receives the rainwater directly and from the pavement of a parking lot. The water passes through a container equipped with an overflow and is supplied to the rain garden via a micro-perforated pipe. The Rain Garden is waterproofed on the bottom and has a drainage pipe that takes the water to the measuring device placed inside a control pit.

The pilot site is equipped with a tipping bucket rain gauge, calibrated according to the European Standard EN 17277:2019. The rain gauge provides the inter-tip time stamp as a measurement of precipitation intensity at high temporal resolution. Both direct precipitation over the raingarden area and the flow rate drained from the nearby impervious parking surface act as the forcing input. The output from the raingarden is measured using a water level gauge located in the output control pit, at a one-second resolution. The input and output measurements are then aggregated at the one-minute resolution for post-processing.

In this work, the hydrological behaviour of the raingarden is simulated using a conceptual model involving a cascade of three linear reservoirs: the first one representing the fast response of the impervious surface, the second one the shallow soil layer used by the vegetation and the third one the deep drainage layer. Each reservoir is characterized by a retention and a storage coefficient. Precipitation and outflow events recorded during one year of measurements in the wet period, from autumn to spring, allowed characterizing the hydrological performance of the system. The value of each parameter was calibrated using part of the measured precipitation and outflow events. The remaining events were used to validate the reliability of the conceptual model using the same parameters. The aim of this work is to verify the role of the implemented NBS to reduce direct runoff in an urban environment for flood risk mitigation purposes.

Results are expressed in terms of non-dimensional performance indices: flow peak attenuation, dead time and retention coefficient. The validation of the model parameters allows extending this NBS model to other sites characterized by a similar rainfall climatology. In that case, performance indices can be derived by measuring the precipitation alone.

How to cite: Magliocco, A., Cauteruccio, A., Perini, K., and Lanza, L.: Monitoring and modeling the hydrological performance of a rain garden installation for flood risk mitigation at a urban site, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5974, https://doi.org/10.5194/egusphere-egu23-5974, 2023.

EGU23-6780 | ECS | Orals | NH1.6 | Highlight

Evaluating the impact of urban wetlands as nature-based solutions at the catchment scale 

Fangjun Peng, Leyang Liu, Yuxuan Gao, Vladimir Krivtsov, Barnaby Dobson, and Ana Mijic

During COP14 in 2022, Ramsar Convention commended 25 cities around the world for their efforts to protect urban wetlands. With the development of cities and the increase in land demand, the trend is to reduce the number of open blue spaces. Yet when preserved and sustainably used, urban nature-based solutions in the form of constructed wetlands could provide water management benefits including water quality regulation and flood mitigation. However, these water management benefits have rarely been evaluated at a catchment scale, and the mechanisms behind them are not fully understood, both of which hinder effective integrated constructed wetlands planning. We aim to explore the impact of wetland changes on water quality and quantity at the catchment scale. This study firstly evaluates the benefits by analysing the monitoring water quantity and quality datasets before and after the wetland construction in Enfield catchment, London. To understand the mechanisms behind the benefits, we build a Water Systems Integration Modelling framework (WSIMOD) to simulate the catchment-scale water cycle. This model is validated against monitoring river flow and water quality data. The constructed wetlands are then conceptualised and integrated into the WSIMOD, and their interactions with the catchment water cycle are simulated. Scenarios are constructed to analyse the impacts of different configurations and sizes of the constructed wetlands on the catchment water cycle. The results show that urban wetlands play a role in flood detention and water quality purification of watershed water resources at the catchment scale. Scattered small wetlands can more effectively reduce the impact of a flood under the same total wetland area. The results provide useful insights into the planning of constructed wetlands for maximising the water management benefits at a catchment scale. Future studies could focus on representing the interaction between the quantity and quality of water in a wetland with biodiversity and leveraging this representation to design interventions to improve biodiversity.

How to cite: Peng, F., Liu, L., Gao, Y., Krivtsov, V., Dobson, B., and Mijic, A.: Evaluating the impact of urban wetlands as nature-based solutions at the catchment scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6780, https://doi.org/10.5194/egusphere-egu23-6780, 2023.

Urbanization and climate change are increasing the frequency and severity of urban pluvial flooding. The traditional urban modelling approaches do not take infiltration from sealed surfaces into account, leading to an overestimation of excess runoff. Still, the conventional centralized sewage systems are often overburdened. While municipalities are taking initiatives to utilize green infrastructure as a sustainable way to manage stormwater, the performance of the implemented measures varies from region to region. This study uses the WaSim-ETH physically based hydrological model to investigate runoff and infiltration processes in urban areas and determine how much rainfall contributes to runoff and infiltrates through different types of land use surfaces. It also evaluates the efficiency of green infrastructure to reduce the generated runoff from different rainfall events. The model is applied to study areas in Berlin and Würzburg, both cities have experienced frequent pluvial flooding in the last decades.

How to cite: Dobkowitz, S. and Seleem, O.: Evaluating the impact of infiltration from sealed surfaces and green infrastructure on urban pluvial flooding, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6841, https://doi.org/10.5194/egusphere-egu23-6841, 2023.

Tonkin, B.Ra., Marti-Cardona, Ba., Hughes, S.Ja., Ru, Ta., Philpott, Nb.

a Civil and Environmental Engineering Department, University of Surrey, UK.

b Environment Agency, England.

b.tonkin@surrey.ac.uk

Nature-based solutions (NBS) are increasingly being recognised as a tool for flood mitigation, particularly relevant in the face of increased storm severity due to climate change. The simulation of NBS functioning is of great interest for their effective location and design, and there are substantial ongoing efforts in developing strategies to underpin their catchment scale modelling.  Leaky Barriers (LB) are a type of NBS interventions which consist of placing logs across a channel to hold back runoff during storm events and to slow down its travel. Despite their common adoption in the UK, there are relatively few studies that have addressed the best hydraulic representations of LB’s through calibrated and validated measurements during flood events. 

This study is based within the Thames basin in the Southeast of England and encompasses the 21km2 headwater catchment of the Pipp Brook. In 2019, the Environment Agency of England installed over thirty LB’s in the Pipp Brook as a trail study. Monitoring data has been continuously acquired by sensors installed at the site (water level gauges, ultrasonic flow gauge, fixed-point infrared cameras) and during periodic inspections (structural monitoring) since 2019. This enhanced monitoring programme, one of the most comprehensive in the UK, provides rigorous evidence to understand and assess the effectiveness of the NFM measures installed in the catchment. To date, this has included the capture of multiple high flow events, up to a peak magnitude of 1 in 20 years.

This research seeks to address a gap in the strategy to simulate individual leaky barriers using 1-dimensional hydraulic models. To this aim, one LB in the Pipp Brook was simulated with an industry leading hydraulic modelling software package (Flood Modeller, Version 6.10 by Jacobs) using six different 1D modelling strategies reported in the literature: i) Orifice, ii) Bridge, iii) Weir, iv) Increased roughness (Manning’s n), v) Bernoulli loss, vi) Blockage. High-flow records from a double-peak event in October 2021 were used to calibrate and assess these hydraulic representations. Upstream boundary conditions were produced by ReFH2 rainfall-runoff modelling, using the precipitation records from a nearby meteorological station.

The comparison of calibrated models to the gauged data revealed a maximum difference of circa 0.20m to the measured upstream water elevation, for a maximum water depth of 0.75m. Our results showed that the best approximation was achieved by using the bridge unit. A common approach in the literature is to represent LB’s with a high roughness coefficient (Manning’s n), which in our case resulted in the poorest performance. The results of this ongoing research will improve the ability of flood practitioners to predict the effectiveness of leaky barrier configurations in a catchment, hence informing their optimal design.

How to cite: tonkin, B.: Nature-based solutions for flood mitigation:Monitoring and modelling leaky barriers. A case study from the South East of England., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7126, https://doi.org/10.5194/egusphere-egu23-7126, 2023.

EGU23-7494 | ECS | Orals | NH1.6

Drought mitigating nature-based solutions: a critical state-of-the-art review at global and regional scales 

Estifanos Addisu Yimer, Lien De Trift, Jiri Nossent, and Ann Van Griensven

Natural disasters are creating a major collapse in human existence. Among the many, drought is becoming more frequent and intense. Therefore, mitigation/adaptation measures have to be set to reduce the impact. This can be achieved via the application of Nature-based solutions (NBSs). This concept is now gaining more attention than ever but with fewer applications. The primary goal of this review paper is to analyze the different NBSs targeted for drought impact mitigation. The study constitutes the application of NBS at a global, continental and regional scale. Extensive literature was made to assess; NBS type, location, start and ending period of implementation, status, and level of effectiveness, recommendations set by researchers, and insight for future applications.

The comprehensive review revealed that there are only a few scientific publications, hence, grey and non-scientific literature need to be included. Only four papers included a quantitative assessment for evaluating the effectiveness of NBS targeting drought. However, the continental and regional performance of NBS is not mentioned. Therefore, a common effectiveness evaluation framework shall be created to give policymakers a clear view of the different NBS’s. Furthermore, a more collaborative approach, including different stakeholder groups, is recommended, with specific attention to the local communities. In Flanders, most projects are in the pilot project stage. Moreover, the few successfully implemented projects are only very local and have a long realization time which the earlier limits to acquire visible impact at a larger scale. Finally, the loss of wetlands at a global scale and in Flanders (70% are lost), increases the vulnerability of catchments to drought. Therefore, future research should increase the evidence base and implementation of NBS, such as wetlands, in Flanders.

How to cite: Yimer, E. A., De Trift, L., Nossent, J., and Van Griensven, A.: Drought mitigating nature-based solutions: a critical state-of-the-art review at global and regional scales, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7494, https://doi.org/10.5194/egusphere-egu23-7494, 2023.

EGU23-8034 | ECS | Orals | NH1.6 | Highlight

Indicators and metrics to evaluate the effectiveness of nature-based solutions for climate risk management and adaptation: A systematic review 

Fabienne Horneman, Silvia Torresan, Elisa Furlan, Diep Ngoc Nguyen, Asrat Telke Asresu, and Andrea Critto

In recent years Nature-based Solutions (NbS) have received increasing attention in coastal areas due to their ability to counteract Hydro-Meteorological Hazards (HMHs) and adverse climate change effects through habitat restoration and the re-establishment of Ecosystem Services (ES). Regardless of the wide adoption of NbS, there remain gaps and barriers in the effective uptake and implementation. There is an urgent need to define the specifics of NbS outcomes, measures of success and appropriate evaluation metrics. To bridge this knowledge gap this review focuses on: i) the terminology of NbS applied in coastal archetypes; ii) the ecosystem services delivered; iii) the HMHs targeted by NbS; and iv) the effectiveness indicators and metrics applied to monitor the impact of NbS implementation, including the tools and technologies employed. The NbS terminology applied addresses a range of different approaches included under the umbrella term NbS, e.g., building with nature, nature-based adaptation, or mitigation, and ecosystem-based management. Yet most of the included approaches mention the provisioning of ES as part of the main objective, relying on habitats and ecosystems to provide these services. In the scope of this paper 87.1% of the included ES can be attributed to regulating services such as reduction of erosion rates, coastal protection, carbon sequestration and water quality improvement.  The ES also clearly align with the climate change hazards addressed by NbS which include, e.g., flood and erosion risk, sea level rise, eutrophication, and extreme weather. These hazards are addressed through the implementation of NbS which aim to, e.g., reduce wave energy, anticipate storm surges, achieve good ecological/environmental status of water, re-establish carbon sinks and mitigate storm risk. To evaluate the effectiveness of NbS in counteracting these hazards and mitigate the impact of climate change this work identified 28 indicators. The indicators reflect mainly habitat characteristics and ES, e.g., geomorphology, vegetation cover and composition, risk reduction, carbon sequestration, and storm surge attenuation, complemented by socio-economic indicators such as willingness to pay and stakeholder perception. They are supported by multitude of metrics, evaluated through a variety of monitoring methods encompassing historical records (to create a baseline using, e.g.,  salinity records, seagrasses, vegetation status, or habitat size), questionnaires (to evaluate stakeholder values), in-situ measurements and remote sensing (to assess change in, e.g., bed level dynamics, vegetation presence, carbon stock, bird species, and marsh surface following NbS interventions) and modelling (the impact of NbS through, e.g., UVVR, morphology, vegetated leading edge, and habitat distribution). The results of this review will support the upcoming monitoring activity of saltmarsh restoration in the Venice lagoon (Italy) as part of the REST-COAST project and will pave the way for the creation of a methodological framework to systematically evaluate NbS effectiveness under current and future climate change scenarios. The project leading to these results has received funding from the European Union’s Horizon2020 research and innovation programme under grant agreement No 101037097.

How to cite: Horneman, F., Torresan, S., Furlan, E., Ngoc Nguyen, D., Telke Asresu, A., and Critto, A.: Indicators and metrics to evaluate the effectiveness of nature-based solutions for climate risk management and adaptation: A systematic review, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8034, https://doi.org/10.5194/egusphere-egu23-8034, 2023.

EGU23-9844 | Orals | NH1.6

Multiple criteria-based assessments of Nature-Based Solutions for flood management: a review 

Lorette Gallois, Marc van den Homberg, and Marco Cinelli

Increased flooding frequency and intensity threaten vulnerable populations’ lives and livelihoods worldwide. Fitting into the preparedness and mitigation phases of the Disaster Risk Management framework used by humanitarian and conservation organisations, Nature-Based Solutions (NBS) have been advanced as effective alternatives to traditional grey infrastructures in order to mitigate flooding impacts. By reproducing natural processes, NBS have shown to provide multiple environmental, social, and economic benefits in addition to their technical performance in mitigating floods. However, a framework to systematically assess these co-benefits is not readily available, which is an obstacle to the effective implementation of NBS on a larger scale. This paper develops such a framework using a Systematic Literature Review (SLR) based on the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) method. The framework includes a set of descriptors to characterize and analyse NBS consistently. These include:

  • Type of NBS;
  • Type of protection area (coastal, urban, rural, mountainous, riverine);
  • Provided environmental/technical/social/economic benefits;
  • Location of applicability;
  • Scale of implementation;
  • Inclusion of stakeholders’ preferences for NBS implementation.

The  SLR is shaped using a combination of scholarly literature (via Web of Science) and grey literature from reputable organizations in the NBS domain and beyond, including the WWF Nature-based Solutions Accelerator, the United Nations Office for Disaster Reduction, the Disaster Risk Management Knowledge Centre, and the Geneva Environment Network. The resulting framework can support decision-making and facilitates the deployment of sustainable infrastructure. The Red Cross Red Crescent Movement and WWF will test the framework in a case study in the Zambezi river basin in Zambia.

How to cite: Gallois, L., van den Homberg, M., and Cinelli, M.: Multiple criteria-based assessments of Nature-Based Solutions for flood management: a review, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9844, https://doi.org/10.5194/egusphere-egu23-9844, 2023.

EGU23-9875 | ECS | Posters on site | NH1.6 | Highlight

Where to locate large-scale nature-based solutions? Finding suitable locations for rainwater harvesting 

Yared Abayneh Abebe, Beatriz Emma Gutierrez Caloir, and Zoran Vojinovic

Nature-Based Solutions (NBS) are suitable responses for hydrometeorological hazard reduction, integrating the hydrology, geomorphology, hydraulic and ecological dynamics of a catchment. Large-scale NBS are implemented on a catchment scale and may provide more co-benefits than small-scale NBS. Literature shows a methodological gap in finding suitable locations to implement large-scale NBS. Developing spatial analysis tools in a GIS environment is essential to generate information for decision support. In this research, we developed a method for finding suitable rainwater harvesting locations (RWH) and applied it to the Municipality of Santiago de Machaca, part of the La Paz Department in Bolivia. Large-scale RWH is the collection of rainwater from ground surfaces and streams and its storage in depressions made for that purpose. RWH is implemented to store and provide water supply in stressed regions and mitigate the impacts of floods by diverting and storing runoff. The raster datasets required to map suitable locations for RWH implementation are annual precipitation depth, coefficient of variation of the monthly precipitation, runoff coefficient, aridity index, population density and slope. The datasets are normalized to generate a standard scale between 0 and 100, and the analytic hierarchy process (AHP) was used to weigh, prioritize and rank alternatives. Built-up areas and buffered roads and rivers should be removed from the raster file generated after the AHP analysis. For the Santiago de Machaca case study, we categorized the final raster cells as no, low, medium and high suitability for implementing RWH. The areas highly suitable for RWH are located in the valleys, but some are small patches fragmented by the road network. It should be noted that the final result is dependent on the matrix built to obtain the raster weights in the AHP analysis and the final suitability ranking categories. However, the developed method is a generic one that can be applied in any site and is a step forward in general for planning the implementation of large-scale RWH as an NBS.

How to cite: Abebe, Y. A., Gutierrez Caloir, B. E., and Vojinovic, Z.: Where to locate large-scale nature-based solutions? Finding suitable locations for rainwater harvesting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9875, https://doi.org/10.5194/egusphere-egu23-9875, 2023.

EGU23-9957 | Orals | NH1.6

PHUSICOS – Nature-Based Solutions to Reduce Risk in Rural Areas and Mountain Landscapes 

Amy Oen, Bjørn Kalsnes, Anders Solheim, Vittoria Capobianco, James Strout, and Farrokh Nadim

The H2020 project PHUSICOS has from 2018-2022 aimed to demonstrate how nature-based solutions (NbS) reduce the risk of extreme weather events in rural areas and mountain landscapes. Mountains amplify risks and therefore the impacts of extreme hydro-meteorological events such as flooding and landslides in mountain areas often affect entire river basins. However, NBS in rural areas and mountain regions have not received the same amount of attention as urban areas. This presentation highlights the lessons learned in order to tackle the challenges of selecting, designing and implementing NbS at the landscape spatial scale in rural areas.  

The PHUSICOS case study sites in Norway, France, Spain, Italy, Germany and Austria represent a broad range of natural hazards, including snow avalanches, erosion, rockfall, flooding and debris flows. The demonstrator sites have undergone a co-creation process with stakeholders to select and plan the NbS interventions. The specific location and NbS selection were based on a rigorous process considering the following selection criteria: risk reduction, technical feasibility, co-benefits, effectiveness, efficiency, potential negative impacts, stakeholder involvement, and compliance with international and EU agreements and directives.

Innovation actions have framed the project activities as an approach to fill NbS knowledge gaps. These innovation actions have included: service innovation to engage stakeholder participation through a Living Labs approach, technical innovation to design an NbS assessment framework in the context of natural hazard risk mitigation to document the effectiveness of NbSs, governance innovation to explore planning and policy frameworks as enablers for the design and implementation of NbS, learning arena innovation to facilitate knowledge exchange through Virtual Reality and Serious Gaming as training programs as well as product innovation establishes an evidence-base and data platform for NbS in mountains.

For example, the assessment framework as a flexible disaster risk management support tool for NbS is viewed as especially relevant. It has been applied to three different NbS interventions to document the baseline scenario and subsequently compared to the NbS design scenario. After completion, the assessment framework will be used to develop the monitoring programs to assess the long-term effectiveness of the NbS interventions. Improved processes and services related to governance innovation outputs focus on exploring ways to improve the planning policy and implementation mechanisms for sustainable use and management of land, water, and natural resources in rural areas and their impacts at the local and wider watershed scale. The most critical governance innovation enablers for successful NbS interventions include polycentric governance arrangements in public administration, participatory co-design processes, as well as financial incentives.

The different innovation actions will be further showcased to share project outputs and outcomes, to reflect on the lessons learned as well as to weigh in on their significance towards long-term impacts.

How to cite: Oen, A., Kalsnes, B., Solheim, A., Capobianco, V., Strout, J., and Nadim, F.: PHUSICOS – Nature-Based Solutions to Reduce Risk in Rural Areas and Mountain Landscapes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9957, https://doi.org/10.5194/egusphere-egu23-9957, 2023.

EGU23-10108 | Orals | NH1.6

The MANCOGA Project - co-designing NbS using mangroves against coastal hazards in Ghana 

Holger Brix, Edem Mahu, David Kaiser, Christiane Eschenbach, Donatus Yaw Atiglo, and Joanna Staneva

Coastal hazards such as erosion, flooding and pollution are major problems globally, exacerbated by increasing frequency and severity of hydro-meteorological extremes amidst inadequate technology and adaptive capacity. The Ghanaian coast is an example of a region impacted by such problems. Factors hampering the management and improvement of these issues include the lack of data, insufficient communication structures between stakeholders and missing pathways to informed decisions with sustained impact.

In this context, the MANCOGA project stands out by employing a co-design approach to develop a robust and participatory Nature-based Solution (NbS) to coastal hazards. The co-design pilot phase has drawn the focus onto steps for restoring wetlands, mangroves in particular, to provide sustainable livelihoods by protecting and reinvigorating coastal systems and environmental health.

In the implementation phase, MANCOGA will evaluate mangrove ecosystem services for their potential as NbS to a number of pressing local issues. A Digital Twin will use What-If scenarios to predict the role of mangroves as NbS for flood mitigation and erosion prevention. Being a dominant Blue Carbon ecosystem, mangroves will also contribute to climate change adaptation strategies as well as provide socio-economic value (e.g., through carbon credits). The wider effects on water quality, through the reduction of eutrophication, is critical for local economics, including fisheries. We employ aerial photography and remote sensing to identify possible nature-based solution areas.

The comprehensive community involvement of stakeholders from all societal and administrative levels facilitates frameworks to understand and evaluate effectiveness of NbS applications. The relationships and collaborative approach developed during the co-design phase will guarantee continued involvement of stakeholders. MANCOGA will provide a digital toolbox of intuitive, interactive tools to analyze and disseminate archived and new observational data, which will enable ecosystem service quantification before and after the application of NbS, and lead to knowledge-based decision-making.

We envision MANCOGA as the start of sustained collaboration, knowledge transfer and capacity building. Therefore, we warmly invite researchers and stakeholders, from Africa and elsewhere, to connect to MANCOGA and share experiences and efforts.

How to cite: Brix, H., Mahu, E., Kaiser, D., Eschenbach, C., Atiglo, D. Y., and Staneva, J.: The MANCOGA Project - co-designing NbS using mangroves against coastal hazards in Ghana, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10108, https://doi.org/10.5194/egusphere-egu23-10108, 2023.

EGU23-10360 | ECS | Orals | NH1.6

Optimisation of Urban-Rural Nature-Based Solutions for Integrated Catchment Water Management 

Leyang Liu, Barnaby Dobson, and Ana Mijic

Nature-based solutions (NBS) have co-benefits for water availability, water quality, and flood management. However, searching for optimal integrated urban-rural NBS planning to maximise co-benefits at a catchment scale is still limited by fragmented evaluation. This study develops an integrated urban-rural NBS planning optimisation framework based on the CatchWat-SD model, which is developed to simulate a multi-catchment integrated water cycle in the Norfolk region, UK. Three rural (runoff attenuation features, regenerative farming, floodplain) and two urban (urban green space, constructed wastewater wetlands) NBS interventions are integrated into the model at a range of implementation scales. A many-objective optimization problem with seven water management objectives to account for flow, quality and cost indicators is formulated, and the NSGAII algorithm is adopted to search for optimal NBS portfolios. Results show that rural NBS have more significant impacts across the catchment, which increase with the scale of implementation. Integrated urban-rural NBS planning can improve water availability, water quality, and flood management simultaneously, though trade-offs exist between different objectives. Runoff attenuation features and floodplains provide the greatest benefits for water availability. Regenerative farming is most effective for water quality and flood management, though it decreases water availability by up to 15% because it retains more water in the soil. Phosphorus levels are best reduced by expansion of urban green space to decrease loading on combined sewer systems, though this trades off against water availability, flood, nitrogen and suspended solids. The proposed framework enables spatial prioritisation of NBS, which may ultimately guide multi-stakeholder decision-making, bridging the urban-rural divide in catchment water management. 

How to cite: Liu, L., Dobson, B., and Mijic, A.: Optimisation of Urban-Rural Nature-Based Solutions for Integrated Catchment Water Management, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10360, https://doi.org/10.5194/egusphere-egu23-10360, 2023.

EGU23-10930 | ECS | Posters virtual | NH1.6

Nature Based Solutions (NBS) to achieve food security and SDGs in drought prone subtropical area 

Chanda Kumari and Roopam Shukla

Drought is a slow onset natural disaster but affects most of the facets of human life in a very large scale. Climate change is causing droughts to become more severe. Due to the drought's impact on agricultural productivity and the fact that most of the rural towns depend on farming and  on an agriculture-based economy, rural populations appear to be more susceptible to this disaster. As a result, they are the main victim. The current study attempts to evaluate our knowledge of how agricultural productivity in drought-prone areas is being impacted by climate change. Low crop yield raises the risk of food insecurity, including the risk of hunger and malnutrition. Hence, a more lasting and sustainable approach is needed to lessen the negative effects of droughts. This can be more effectively accomplished by incorporating Nature Based Solutions (NBS) into agricultural practices like Globally Important Agricultural Heritage Systems (GIAHS) and Nature Climate Solutions (NCS), a subset of NBS. And with NBS, we may further advance our step toward sustainability and be able to accomplish some of the Sustainable Development Goals while also enhancing and improving traditional and technological expertise in agriculture. Additionally, a suitable adaptation strategy is suggested for the local communities to adapt to the changing environment, which will aid in the development of the society's potential. In summary, the results of this study will give all the stakeholders deep insights that they may use to revise their plans and policies for managing the drought.

Aim:

The aim of this research is to deal with the food insecurities due to drought (due to climate change) and mitigating it through Nature based Solutions to achieve food security and some SDGs in a drought prone area.

Objectives:

  • To monitor the present and future data of drought and key climatic variables in sub-tropical region due to climate change in drought prone area.
  • To analyse the risk of food insecurity in terms of hunger and malnutrition in the targeted population.
  • To inculcate the NBS against drought prone area to achieve high productivity through agriculture practices by the local community and accomplish several Sustainable Development Goals (SDGs) through it.
  • To support the capacity building and adaptation strategies by the local communities against the drought and climate change.

    Significance:

    The study's goal was to evaluate the drought circumstances, which are influenced by all of the above listed aspects. After evaluating these aspects, one may determine the precise steps that should be taken to address the low agricultural productivity while also putting the appropriate remedies to use against the same. Farmers who use nature-based solutions will not only see an increase in crop yield, but also a continued commitment to use exclusively natural solutions. By doing this, we can easily address the issue of food insecurity. The local communities' adaptation plans will help the society to become stronger, be able to handle any crisis in the future, move toward sustainability, and aid in the achievement of the SDGs.

How to cite: Kumari, C. and Shukla, R.: Nature Based Solutions (NBS) to achieve food security and SDGs in drought prone subtropical area, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10930, https://doi.org/10.5194/egusphere-egu23-10930, 2023.

EGU23-11417 | Orals | NH1.6 | Highlight

Exploring nature-based solutions to droughts and floods in the Limpopo basin 

Anne Van Loon, Alessia Matanó, Sithabile Tirivarombo, Luis Artur, Syed Mustafa, Melanie Rohse, Rosie Day, and Jean-Christophe Comte

Southern Africa faces both severe droughts and strong floods. Communities describe how they are impacted by both extremes, but do not regard them as connected. They prepare for droughts by implementing water-saving measures and crop changes, but report doing little to prepare for floods. Governance actors instead try to manage both extremes, for example by installing dams that can capture floodwater to increase water availability during dry seasons. In the Connect4WR project, we combined community and governance interviews and workshops with scenario modelling to explore more nature-based solutions focusing on subsurface storage and infiltration. The governance actors in the four countries of the Limpopo (Botswana, Zimbabwe, South Africa and Mozambique) were keen to explore effects of afforestation, sand dams, managed aquifer recharge, and rainwater harvesting. The coupled surface-water-groundwater model we set up, showed that these measures can successfully reduce both droughts and floods. Especially measures that increase groundwater levels both increase water availability and reduce flood peaks throughout the basin. Although downstream communities benefit from the decreased flooding, they could be negatively affected if measures that increase (ground)water storage are combined with high abstraction for irrigation in the upstream part of the basin. In a transboundary river basin like Limpopo, international cooperation and information sharing is crucial. Also, these measures are often too costly and large-scale for the resource-limited rural communities, who can often only respond to extremes by relocating to less drought- or flood-prone areas. Training and government support can help with the implementation of nature-based solutions, but measures need to be resonating with local cultural practices to be adopted and effective land- and water management is important. In this presentation I will discuss the benefits and challenges related to the implementation of nature-based solutions in low- and middle-income countries with fragile populations.

How to cite: Van Loon, A., Matanó, A., Tirivarombo, S., Artur, L., Mustafa, S., Rohse, M., Day, R., and Comte, J.-C.: Exploring nature-based solutions to droughts and floods in the Limpopo basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11417, https://doi.org/10.5194/egusphere-egu23-11417, 2023.

EGU23-12470 | Orals | NH1.6 | Highlight

Eco-Hydrology Engineering Design Tool - ClearWater Capabilities - General Constituents, Nutrients, and Contaminants 

Todd Steissberg, Billy Johnson, and Zhonglong Zhang

The U.S. Army Corps of Engineers (USACE) has a major responsibility for the regulation of the Nation’s streams, rivers, and waterways. This often requires developing water quality models to resolve issues and concerns with regard to the environment and ecosystems. USACE Engineer Research and Development Center (ERDC) is currently developing an Eco-Hydrology Engineering Design Tool supported by years of research and development. This tool development integrates ERDC’s Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model and the Corps Library for Environmental Analysis and Restoration of Watersheds (ClearWater), a suite of water quality and ecosystem models.

 

Weather is not the only cause of flooding, stream erosion, and pollution. These problems usually occur due to human impacts on watersheds, including urban development, construction activities, hydrologic modifications, forestry, mining, and agricultural practices.

 

To help evaluate the impact of these serious economic and environmental issues, experts in watershed, riverine, and reservoir engineering at the ERDC developed a suite of water quality, contaminant, and vegetation modules that can be integrated with existing hydraulic and hydrologic models.

 

ClearWater, developed by the ERDC, LimnoTech, and Portland State University, is a library of environmental simulation software that leverages capabilities of existing water resource simulation models (e.g., HEC-RAS-1D/2D,  HEC-ResSim, GSSHA, AdH, and SWAT) to assess environmental impacts (e.g., changes in water temperature and constituent concentrations) and design solutions (e.g., constructed wetlands) to manage (e.g., modifications to reservoir operations rules) and restore aquatic ecosystems (e.g., fisheries and bird habitat). The following water quality modules are included: NSMs (Nutrient Simulation Modules I and II), TSM (Temperature Simulation Module), MSM (Mercury Simulation Module), CSM (Contaminant Simulation Module), GCSM (General Constituent Simulation Module), SSM (Solids Simulation Module), and RVSM (Riparian Vegetation Simulation Module).

 

This presentation will discuss current development efforts and future directions in support of an Eco-Hydrology Engineering Design Tool to support U.S. Army Corps of Engineers (USACE) ecosystem restoration and management.

How to cite: Steissberg, T., Johnson, B., and Zhang, Z.: Eco-Hydrology Engineering Design Tool - ClearWater Capabilities - General Constituents, Nutrients, and Contaminants, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12470, https://doi.org/10.5194/egusphere-egu23-12470, 2023.

EGU23-12565 | Orals | NH1.6

Nature-based Restoration Planning using Spatial-Temporal Simulation Modeling 

kihwan Song and Jinhyung Chon

Natural hazards such as typhoons and earthquakes caused by climate change cause enormous damage to the social-ecological system and result in the degradation of ecosystem services. This has suggested the necessity of considering the concept of resilience along with the limitations of existing methods in disaster management and has been linked to restoration plans connected to nature-based solutions. The Republic of Korea suffers from natural disasters caused by typhoons and torrential rains every summer and the damage is worsened because of insufficient spatial management and the failure to predict disasters. Therefore, to cope with these damages and maintain ecosystem services, a nature-based restoration plan should be presented using the concept of resilience. In the process, it is necessary to understand the changes that have happened in ecosystem services over time and plan a space that can respond to natural disasters. The purpose of this study is to simulate changes in ecosystem services for natural disaster damage through spatial-temporal models and present the improvement effects of ecosystem services through nature-based restoration scenarios. Accordingly, we first searched for areas to which the resilient ecosystem service restoration planning could be applied within Pohang, which suffered significant flood damage throughout 2022. Then, a spatial-temporal model of the target area was constructed to simulate changes in the ecosystem services due to floods. Finally, the ecosystem service improvement effect of the spatial-temporal simulation model was analyzed by constructing and applying a nature-based restoration scenario. Based on the results of this study, a nature-based restoration plan was conceived of as a method to improve ecosystem services for the long term by simulating changes in the target area affected by natural disasters in terms of time and space. In addition, by presenting the preceding process as a nature-based restoration plan, it is possible to maintain resilience to the damage caused by natural disasters in terms of the social-ecological system.

This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(NRF-2021R1A6A3A01087973). This research was also supported by OJEong Resilience Institute (OJERI).

How to cite: Song, K. and Chon, J.: Nature-based Restoration Planning using Spatial-Temporal Simulation Modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12565, https://doi.org/10.5194/egusphere-egu23-12565, 2023.

EGU23-14216 | Posters on site | NH1.6

Proposal of UAV-Lidar and photogrammetry-based modeling for assessing soil loss using nature-based solutions as countermeasure in urban areas in DRC 

Carles Raïmat, Christian Van Eghoff, Ana Campos, Laurent Corroyer, Sergio Mora-Castro, Javier Saborío, and Arabela Vega

Mass movements due to soil erosion, intense rains and water runoff in sandy soils represent a major socio-ecological problem in the Democratic Republic of Congo (DRC). Kinshasa, Kananga and other urban areas in DRC are currently challenged by severe forms of land degradation.

Natural resources are being exploited at high rates due to unplanned human settling pressed by migration and population growth. Consequently, demanding energy, forestry, agricultural goods and services that undermine tropical forests, savannas and monsoon forests, which are fragile and high value ecosystems.

The effects of forest lost are exacerbated by growing urban areas with drainage mismanagement in anarchic urban environments due to concentrated and disorganized flow. Rain events between 2019 and 2022 have compromised or destroyed basic structure such as railroads and main streets, considered key lifelines that have already caused human, environmental and infrastructural losses.

Reliable rain data records are scarce or inexistent; however, intense, heavy, punctual rain events can be identified throughout the dry and rainy season. No particular erosive effect can be attributed to these events without consistent Intensity-duration-frequency (IDF) data. On the other hand, slope instability has been well documented through satellite imagery and demonstrated exponential growth since 2010. Aero transported Lidar, photogrammetry and cloud point technique have been used to map urban growth, vegetation cover and soil management practices in four study sites, in between seasons.

Hence, this study proposes to systemize a methodology that assesses soil degradation and stability risks; evaluating the effectiveness of NBS to reduce soil erodibility through the combination of agroecological solutions, engineering risk management in urban and peri-urban environments facing climate change challenges. This is a project currently under research and execution by the Government of DRC with the support of the World Bank.

 

 

 

How to cite: Raïmat, C., Van Eghoff, C., Campos, A., Corroyer, L., Mora-Castro, S., Saborío, J., and Vega, A.: Proposal of UAV-Lidar and photogrammetry-based modeling for assessing soil loss using nature-based solutions as countermeasure in urban areas in DRC, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14216, https://doi.org/10.5194/egusphere-egu23-14216, 2023.

EGU23-14281 | ECS | Orals | NH1.6

Hydrologic characterization of sponge-city systems for urban trees based on monitoring and modelling 

Anna Zeiser, Sebastian Rath, Peter Strauss, and Thomas Weninger

Sponge-city sites for urban trees based on the model of Stockholm promise to improve the chances of trees surrounded by sealed or condensed surfaces for root growth and therefore vital tree development tremendously. The system furthermore helps to conquer the urban heat island effect, aids in stormwater management as an underground retention basin saving soil water for transpiration and hence supports the ambition to approach a (more) natural hydrologic cycle. The actual capacity of the system to fulfil these services is determined by a variety of design criteria which need to be optimized based on detailed knowledge about the hydrological functionality of the different elements of the system. The aim of several monitoring and modelling studies in Austria is to gain such knowledge to ensure a proper performance of the system in terms of tree growth and rainwater retention.

First of all, the substructure construction consists of unconsolidated fine substrate flushed into the voids of edged stones that serve as load-bearing structure, assuring root-favouring pore distribution. Technical components like an inlet and surface water distribution system accompany the substrate. Further influencing parameters are e.g. properties of the existing substrate underneath, design of urban surface as well as origin and treatment of fed surface water. Based on monitoring sites in the shape of lysimeters, field scale projects in real urban settings, and laboratory experiments, modelling approaches for the hydrological functionality of the sponge-city systems are generated. Crucial system elements and target values for design properties are derived from these simulations. In the longer term, the results should serve as a supportive planning tool for engineering projects in urban environments.

How to cite: Zeiser, A., Rath, S., Strauss, P., and Weninger, T.: Hydrologic characterization of sponge-city systems for urban trees based on monitoring and modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14281, https://doi.org/10.5194/egusphere-egu23-14281, 2023.

EGU23-14949 | ECS | Orals | NH1.6

Nature-based solutions for drought resilient forests 

Lucas Alcamo, Karl Broich, and Markus Disse

Climatic extremes are the new normal for large parts of the world. Even temperate places, such as northern Bavaria, have experienced an abundance of extreme weather with significant impacts on the local ecology. Heavy torrential rainstorms are observed more often, while simultaneously, the precipitation required for a healthy ecology does not occur for increasing periods. These droughts, in combination with anthropogenic influences, have severely weakened the vitality of vast stretches of forest in northern Bavaria. In consequence, secondary pests were able to cause wide spread tree mortality. This indicates the need for innovative water management strategies to increase the resilience of forest ecosystems with regard to an increased occurrence of droughts.

This study aims at exploring the potential of nature-based solutions to increase the infiltration of surface runoff in forest in order to increase the plant-available soil moisture and therefore the drought resilience during dry periods. Specifically two measures are investigated, which alter the micro-topography of the forest floor. These are:

  • “Dead-wood” left in the forest after timber-harvest and aligned along slopes to act as flow barriers during runoff events and,
  • Small-scale basins of shallow depth that mimic the natural topography of the forest floor and act as retention basins.

To be able to understand and evaluate the effectiveness of the nature-based solutions and investigate the relevant hydrological and hydrodynamic processes, a small, forested slope in Northern Bavaria was modeled using an innovative coupling of the 2-dimensional hydrodynamic TELEMAC Model with Green & Ampt infiltration. In preparation of setting up the model, state-of-the-art drone LiDAR measurements were used to produce a high-resolution (10 cm resolution) Digital Elevation Model of the area. This enabled us to set up the model with a high enough resolution to capture and simulate the micro-topographic changes of the measures. We simulated various scenarios representing different implementations of the nature-based solutions and used the change of runoff coefficient as compared to the current state simulations as a measure of efficiency. In general, our findings show a clear link between the implementation of the measures and decreased runoff coefficient. While the aligning of dead wood along the slope reduced the runoff coefficient more as compared to a random distribution of dead-wood, the shallow retention-basins showed a significantly higher impact on the runoff coefficient. However, it is likely that the distribution of soil types, vegetation and soil animal activity are very crucial because they significantly affect the infiltration and therefore the efficiency of these measures for drought resilience. Theses aspects were not considered. Altogether, the results of this study should be considered as qualitative as compared to quantitative, due to the simplifications done, especially with regard to the soil and infiltration processes.

 

Keywords: Drought; Forest ecosystem; TELEMAC; Greene & Ampt; LiDAR

How to cite: Alcamo, L., Broich, K., and Disse, M.: Nature-based solutions for drought resilient forests, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14949, https://doi.org/10.5194/egusphere-egu23-14949, 2023.

EGU23-1239 | ECS | Posters on site | HS7.6

Impact of urbanization and climate change on spatial patterns of precipitation 

Marika Koukoula, Herminia Torelló-Sentelles, and Nadav Peleg

More than half of the world’s population now resides in cities and the amount of urban population is expected to further increase during the coming decades. Urbanization and the associated changes in land use/land cover can have a notable impact on the climate at local and regional scales. Specifically, several studies recently concluded that urbanization can modify the temporal and spatial properties of precipitation. On top of that, global warming is expected to enhance the magnitude and frequency of short-duration heavy precipitation, with consequential effects on the severity and frequency of urban pluvial flood events. Therefore, improving our understanding of the separate and combined effects of urbanization and climate change on short-duration precipitation is imperative for flood risk assessments and planning of future cities. To this end, we investigate the impact of climate change and urbanization on the space-time properties of precipitation by conducting current and future simulation scenarios over cities with different climates using the Weather Research and Forecasting (WRF) physically-based climate model. The results of this study elucidate the important role of urban land cover on the spatial stucture of precipitation under a changing climate.

How to cite: Koukoula, M., Torelló-Sentelles, H., and Peleg, N.: Impact of urbanization and climate change on spatial patterns of precipitation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1239, https://doi.org/10.5194/egusphere-egu23-1239, 2023.

EGU23-1276 | ECS | Orals | HS7.6

Changing spatial patterns of convective rainfall across urban areas 

Herminia Torelló-Sentelles, Francesco Marra, and Nadav Peleg

Observations using remote sensing data reveal that urban areas affect the intensities and spatial structure of rainfall fields on small scales (i.e., at sub-hourly and sub-kilometer resolutions). However, there is currently disagreement regarding the precise pattern of change and the driving dynamic and thermodynamic forces behind it. As the hydrological response in urban areas is fast and highly sensitive to space-time rainfall variability, it is crucial to understand how urban areas change the intensity and spatial structure of rainfall to improve our abilities to nowcast rainfall and urban floods. We used high-resolution weather radar data to analyze the intensity, spatial structure, and motion of convective rainfall events that crossed several urban areas with diverse characteristics (e.g., Milan, Italy; Phoenix, US). We present an automatic methodology  (i.e., does not require an expert’s interpretation of rainfall fields) that can be applied to different urban areas worldwide. We first tracked convective rainfall events using a storm-tracking algorithm (from a Lagrangian perspective) and investigated changes to the properties of the rainfall fields (e.g., mean intensity, area, and intensity distribution profile) at varying upwind and downwind distances relative to each urban center. We also investigated changes to storms’ trajectories and to the frequency of storm initiations, terminations, splitting and merging events. We validated our results by repeating the analyses in control regions, that were adjacent to each study region and did not contain large urban areas within them. Our results show a general intensification of rainfall over cities, conserved spatial structures (instead of an expected weakening), as well as, increased storm initiations downwind of urban areas. Our findings also suggest that urban areas might be acting as barriers, by increasing storm terminations upwind of urban areas and deflecting incoming storms leftwards; possibly as a result of roughness-induced frictional turning.

How to cite: Torelló-Sentelles, H., Marra, F., and Peleg, N.: Changing spatial patterns of convective rainfall across urban areas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1276, https://doi.org/10.5194/egusphere-egu23-1276, 2023.

EGU23-1354 | Posters on site | HS7.6

Enhanced intensification of hourly rainfall extremes due to urban warming in Phoenix, Arizona 

Jamie Huang, Simone Fatichi, Giuseppe Mascaro, Gabriele Manoli, and Nadav Peleg

The main cause of flash and pluvial floods in cities is short-duration extreme rainfall events. The built environment can either intensify or weaken extreme rainfall intensity depending on the urban fabric that controls the local environmental and climatic conditions. From 2000 through 2018, we examined how the built area affected hourly extreme rainfall intensities in the large metropolitan area of Phoenix, Arizona, characterized by open low-rise buildings, using a large and dense rain-gauge network of 168 ground stations. We found that hourly extreme rainfall intensities increased both in the city and its surroundings but the increase in the built area was significantly greater (3 times greater) - the mean trend in annual hourly rainfall maximum in the urban area was 0.6 mm h-1 y-1 while in the rural surrounding the mean was 0.2 mm h-1 y-1. We calculated a negative trend in aerosol concentration (−0.005 AOD y−1) but a positive trend in near-surface air temperature that was considerably larger in the urban areas (0.15 °C y−1) as compared to the rural counterpart (0.09 °C y−1). Even though built surfaces and low-rise buildings contributed to an increase in air temperature, they did not affect air humidity. Generally, rainfall extremes follow the Clausius–Clapeyron relationship with an increase at a rate of 7% °C−1. Our results demonstrate that the warming effect associated with a low-rise urban area can result in increased rainfall extremes that are significantly greater than in the surrounding areas of the city.

How to cite: Huang, J., Fatichi, S., Mascaro, G., Manoli, G., and Peleg, N.: Enhanced intensification of hourly rainfall extremes due to urban warming in Phoenix, Arizona, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1354, https://doi.org/10.5194/egusphere-egu23-1354, 2023.

EGU23-2673 | ECS | Posters on site | HS7.6

Impact of Climate Change on Non-stationary IDF Curves for Urban Areas 

Naman Kishan Rastogi, Abhinav Wadhwa, and Pradeep P. Mujumdar

High-intensity rainfall in a short duration has become the primary reason for the flooding of urban areas, and quantifying this may help to reduce the destruction caused by the floods. Continuous human interventions, change in land use land-cover and urbanization have significantly altered the climate patterns in many places of the world. Urban infrastructure, economic activity, and social well-being are greatly affected by the increase in rainfall intensity resulting in more runoff, drainage system overflow, and subsequent flooding disasters. Water infrastructure planners and designers have traditionally used Intensity-Duration-Frequency (IDF) curves as tools for urban flood assessment and management. However, IDF curves created based on the stationarity hypothesis are inaccurate and may underestimate the present or future results due to continuous changes in climatic conditions. This study investigates the non-stationary behavior of IDF curves due to climate change. It is assumed that the likelihood of quantile occurrence changes with time. An optimal solution is determined by comparing Generalized Extreme Value (GEV) parameters with a stationary GEV incorporating time, space, location, and shape as covariates. These covariates are associated with the most significant physical processes, such as urbanization, local temperature changes, and global warming, that make the time series non-stationary. In addition, for downscaling the climate change model data to station-level data, a modified K-Nearest Neighbour (KNN) approach is used, incorporating non-stationarity wherever appropriate. The method is applied to 100 Telemetric Rain Gauge (TRGs) stations that are spatially dispersed throughout the urban catchment of Bangalore city, India. According to the findings, the spatial plots for IDFs can capture the current patterns and translate them into predictions of future rainfall intensities. The return period can be shortened by more than one-tenth of its length in the estimations of future rainfall intensities. These analyses along with a comparison study with the existing and future IDFs will help raise awareness and provide potential warnings to the existing water infrastructure systems.

How to cite: Kishan Rastogi, N., Wadhwa, A., and P. Mujumdar, P.: Impact of Climate Change on Non-stationary IDF Curves for Urban Areas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2673, https://doi.org/10.5194/egusphere-egu23-2673, 2023.

EGU23-2704 | ECS | Posters on site | HS7.6

A bivariate rainfall frequency analysis framework in urban areas by coupling copula theory and stochastic storm transposition 

Qi Zhuang, Shuguang Liu, Zhengzheng Zhou, and Daniel Wright

Extreme rainfall is a critical “agent” driving flash floods in urban areas. In rainfall frequency analysis (RFA), however, storms are usually assumed to be uniform in space and fixed in time. Spatially and temporally uniform design storms and area reduction factors are oftentimes used in conjunction with RFA results in engineering practice for infrastructure design and planning. The consequences of such assumptions are poorly understood. This study examines how spatiotemporal rainfall heterogeneity impacts RFA, using a newly-introduced bivariate framework consisting of copula theory and stochastic storm transposition (SST). A large number of regionally-extreme storms with specific features—rainfall depth, duration, intensity, and level of intra-storm spatial organization—were collected. Rainfall intensity-duration-frequency (IDF) estimates exhibiting these bivariate features were then generated by synthesizing long records of rainfall via SST. The results show that dependencies exist among spatiotemporal storm characteristics. Bivariate frequency results exhibit smaller uncertainties but more complex physical meanings that the results from conventional methods. In particular, the highly spatially-organized storms play a leading role in frequency estimates.

How to cite: Zhuang, Q., Liu, S., Zhou, Z., and Wright, D.: A bivariate rainfall frequency analysis framework in urban areas by coupling copula theory and stochastic storm transposition, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2704, https://doi.org/10.5194/egusphere-egu23-2704, 2023.

Stochastic rainfall modeling has been a useful tool to generate long rainfall time series for hydrological applications. One of the widely-used stochastic rainfall generators in the UK water industry to support drainage system design is the Bartlett-Lewis Rectangular Pulse model (BLRP). In practice, there are two main challenges that need to be addressed in the development of BLRP models: 1) capacity of preserving standard and extreme rainfall properties across a wide range of timescales, e.g. from sub-hourly to monthly; 2) ability to reflect the variations in the underlying climate/weather.

For the first challenge, some breakthroughs have been achieved over the past few years. Onof and Wang (2020) reformulated the original BLRP model to overcome its deficiency in underestimating rainfall extremes at sub-hourly timescales. Kim and Onof (2020) further extended Onof and Wang’s work by introducing an additional parameter to enable reproducing rainfall properties across a wide range of timescales –from sub-hourly to monthly or longer. 

The second challenge is however yet to be addressed. The concept of weather analogs is often adopted in the literature to incorporate the impact of climate dynamics. A set of atmospheric variables, which are assumed to be able to well represent the underlying weather/climate condition, are selected and associated with the co-located local rainfall properties. Cross (2020), e.g., proposed a regression method to associate the monthly temperature with the parameters of the BLRP model. However, the concept of ‘calendar month’ –a man-made period of time–  was still used in this method, which hindered the capacity of resembling the natural variations in seasons between years. To better resemble nature, Dai (2021) proposed a moving-window approach Dynamic Time Warping (DTW) method. Dai’s method sliced the original rainfall time series with a 30-day width and 10-day step moving window to reduce the impact of artificial separation of seasons. In addition, the DTW was employed to provide a more robust metric than the eulerian distance for quantifying the similarity between any two climate conditions. Dai’s work suggests that an unconventional metric may be required to better identify weather/climate analogs. 

Hoffmann and Lessig (2022) proposed a deep-learning method, called AtmoDist, that transforms the original atmospheric variables into a number of high-dimensional features and computes the distance from the extracted features. The result showed that the AtmoDist outperforms the traditional distance in identifying weather analogs. In this research, we extend the moving-window DTW based analog method proposed in Dai (2021) by replacing the DTW with the AtmoDist. Similarly to Dai (2021), selected atmospheric variables from the ERA5 hourly data on pressure levels are used for model training and validation. The local rainfall properties derived from the periods of the identified weather analogs resulting from the AtmoDist and the DTW methods will be first compared to evaluate their ability to identify weather analogs. Then, the derived local rainfall properties will be used as input to the BLRP model. This will enable the quantification of the impact of large-scale atmospheric variations to the local rainfall properties. 

How to cite: Chen, P.-C. and Wang, L.-P.: Modeling rainfall with a Bartlett–Lewis process: incorporating climate co-variate using a deep learning method, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3733, https://doi.org/10.5194/egusphere-egu23-3733, 2023.

EGU23-5619 | Posters on site | HS7.6

How to consistently adapt soil parameters to express urban growth in physically based precipitation modeling ? 

Etienne Leblois, Silvia-Patricia Salas Aguilar, Sandrine Anquetin, and Enrique Gonzalez Sosa

Atmospheric limited-area models are superb tools built by atmospheric scientists, and can also be used by scientists from other disciplines. As hydrologists interested in urban rainfall hazard, we want to study possible changes in local-scale precipitation intensities and patterns under urban growth scenarios.

Unfortunately, the parameterization of ground properties appears scattered in many datasets. These differ by their spatial resolution, computational type (exclusive categories expressed as integers, categories expressed as percentages in the patchwork/tile approach, continuous parameters as real numbers, month-dependent real numbers), and of course by their semantic (land use/land cover, radiative properties such as LAI according to one or another sensor, orography, soil type according to one or another research institute).

From the above, the basic way to deal with expected land use changes in impact simulation changes would involve reading the scientific literature exhaustively - literally: to the point of exhaustion - to establish which parameter must be changed, and to hope that no inconsistencies will be introduced in the individual values or in their interdependence.

We propose another, easier, and above all safer strategy. The first step is to recognize the "ground properties" are not a list of individual parameters, but a compound object where many parameters are related in a hierarchy of aspects  : parameters related to land use, parameters related to orography, etc. The determination of this hierarchy is quite easy using multivariate statistics, individuals being locations sampled in the domain of interest and data being the parameters values at these locations. This approach helps to establish the list of parameters connected to the intended change.

Armed with this list, a "geographic cut-and-paste" strategy can be safely adopted to express intended land use change: the relevant parameter values of a representative (donor) location will be used at the target (modified) location, while leaving all other local parameters untouched.

We illustrate this approach with the specific case of prescribing variable levels of urban development for the city of Querétaro, Mexico, in the technical context of using WRF's UEMS distribution (89 datasets distributed as 25633 files distributed in 219 directories).

How to cite: Leblois, E., Salas Aguilar, S.-P., Anquetin, S., and Gonzalez Sosa, E.: How to consistently adapt soil parameters to express urban growth in physically based precipitation modeling ?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5619, https://doi.org/10.5194/egusphere-egu23-5619, 2023.

Urban flooding is a critical disaster resulting in the malfunction of the city and the loss of properties. Furthermore, urban flood prediction often requires a combined modeling process due to the complicated drainage system. In this study, the water levels and relevant inundation areas were estimated by the radar rainfall estimations and the SWMM model. Regarding the radar rainfall estimation, the joint relationship between reflectivity, phase (i.e, ZH, ZDR, KDP) of dual-polarization radar and ground rainfalls was explored through the copula function. The copula is a function that effectively joins marginal distribution functions to form a multivariate distribution function. Finally, the water level and inundation areas of Gangnam district were estimated using hourly mean areal precipitation (MAP) through radar rainfall estimations and the coupled 1D/2D urban hydrological model. The coupled model consists of a 1D conduit network model based SWMM (i.e., the RUNOFF and EXTRAN modules) and a 2D overland flow model, which links the surcharging flows at the manholes of the 1D sewer network model.

 

Acknowledgement

This work was supported by Korea Environment Industry & Technology Institute (KEITI) through the Aquatic Ecosystem Conservation Research Program, funded by the Korea Ministry of Environment(MOE). (No. 2021003030001)

How to cite: Kim, H.-J., Jung, M.-K., Cho, H., and Kwon, H.-H.: Estimation of mean areal precipitation based on dual-polarization radar using copula function and Its use for urban drainage modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6299, https://doi.org/10.5194/egusphere-egu23-6299, 2023.

EGU23-9127 | ECS | Posters on site | HS7.6

Comparitive performance of two quality control algorithms for personal weather station rainfall data in Amsterdam Metropolitan Area 

Lotte de Vos, Abbas El Hachem, Jochen Seidel, and András Bárdossy

The accurate estimation of precipitation is still one of the major challenges in hydrology. One fairly new approach to improve rainfall quantification is the use of so-called opportunistic sensors (OS), i.e. sensors that were not designed to provide high-quality rainfall data at a larger scale, but can be used for that purpose. One type of OS are personal weather stations (PWS) that are owned by private users. They typically comprise one or a set of low-cost devices that record meteorological variables such as air temperature and rainfall. The number of PWS has increased over the past years and the high number of rain gauges offers potential to improve rainfall estimates. 
OS have also raised scientific interest in the recent years. In October 2021, the EU COST Action CA 20136 “Opportunistic Precipitation Sensing Network” (OPENSENSE) was launched with the aim to bring together researchers in the field of OS and to build a global opportunistic sensing community. Furthermore, EUMETNET recently released a dataset containing data of PWS in Europe for 2020 from MetOffice WOW and Netatmo to support the development of PWS quality control tools.
Compared to traditional rain gauge networks, PWS provide data in high temporal and spatial resolution but with low quality, since they are often not installed and maintained according to professional standards. Therefore, these data require a thorough quality control (QC) and filtering before they can be used for applications such as areal precipitation estimates. Two different QC algorithms have been published by de Vos et al. (2019) and Bárdossy et al. (2021). These are available in the OPENSENSE GitHub environment (https://github.com/OpenSenseAction). 
In this study, we apply these two aforementioned QC algorithms on four 24-hour periods, containing convective or homogeneous rain events, from the same PWS dataset for the Amsterdam Metropolitan Area, and validate the outcome using a gauge-adjusted radar product as reference. The characteristics and relative performance of the QC algorithms are presented, thus providing aid for prospective users to decide which of these QC algorithms is best suited for their purpose.

References:
Bárdossy, A., Seidel, J., & El Hachem, A. (2021). The use of personal weather station observations to improve precipitation estimation and interpolation. Hydrology and Earth System Sciences, 25(2), 583-601
de Vos, L. W., Leijnse, H., Overeem, A., & Uijlenhoet, R. (2019). Quality Control for Crowdsourced Personal Weather Stations to Enable Operational Rainfall Monitoring. Geophysical Research Letters, 46(15), 8820-8829.

How to cite: de Vos, L., El Hachem, A., Seidel, J., and Bárdossy, A.: Comparitive performance of two quality control algorithms for personal weather station rainfall data in Amsterdam Metropolitan Area, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9127, https://doi.org/10.5194/egusphere-egu23-9127, 2023.

EGU23-9498 | ECS | Posters on site | HS7.6

Effects of urban structures on spatial and temporal flood distribution 

Marlin Shlewet, Daniel Caviedes-Voullième, Karl Kästner, and Christoph Hinz

Urban pluvial flooding is a modern, growing global disaster, particularly in developing countries with inadequate infrastructure. It remains a challenge to accurately model the runoff behavior in urban areas with a complex topography and to quantify the impact of spatial urban patterns on changing urban rainfall-runoff response. The question to be addressed is how varying the urban spatial configurations can quantitatively influence the overland flow response in relation to the spatiotemporal hydrodynamic variables such as water depth, velocity, and outflow discharge. We use a 2D shallow water model to indicate the influence of changing spatial urban factors (such as the orientation of streets and buildings, and adding sidewalks) in small idealized (synthetic) urban catchments during a single pluvial flood event. The domain layout extends over a size of 267.5m*267.5m with a 3% longitude slope. We differentiate mainly between two street networks: i) the two-way main street with of 14-m width with sidewalks, and ii) side streets of 10m width (Fig.1). We then define novel spatially integrated indicators over the domain at the steady state to analyze quantitatively runoff variables in correlation with the urban features (Fig.1). Additionally, local hotspot maps were created to assess the flood-risk thresholds, such as human stability and failure of buildings. Hotspots are defined as the places with the highest flow velocity magnitudes and water depths (> 90%). The results of the modeling showed that, with respect to the flow velocities in small-scale urban catchments, the main street layout is the dominant urban factor, followed by the side street widths, which were decisively determined by the geometry of the sidewalks. The comparison with real flood risk thresholds shows that the lower part of the main road is the most sensitive to flood risk in the domain with a high-risk hazard for human stability. However, the riskiest case is not corresponding to the fastest hydrograph response. Varying the spatial urban configurations, especially the rotation of the main roads, changes the flood risk thresholds and delays runoff. On the other hand, spatially integrated indicators of the flow variables in the domain are showing low sensitivity to the spatial urban features. Our findings offer a new important perspective on the development of urban flood risk assessment, especially for rapidly urbanizing cities, and provide a better understanding of the spatiotemporal rainfall-runoff generation in a small urban catchment considering the spatial layout of the urban structures.

Fig.1 Overview of the modelling approach and evaluation of the runoff data

How to cite: Shlewet, M., Caviedes-Voullième, D., Kästner, K., and Hinz, C.: Effects of urban structures on spatial and temporal flood distribution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9498, https://doi.org/10.5194/egusphere-egu23-9498, 2023.

EGU23-9567 | Orals | HS7.6

Opportunistic rain sensors and flood modelling to assess the risk of failure of surface drainage in urban areas 

Luca G. Lanza, Arianna Cauteruccio, and Enrico Chinchella

High-resolution space-time measurements of rain fields in urban areas are crucial to support the assessment of the risk of failure of urban drainage systems. In this work, opportunistic rain sensors based on optical principles and mounted on board moving vehicles are tested and used as an input to a hydraulic model to assess the risk of flooding of selected urban areas. Opportunistic sensors can be joined with other innovative measurement techniques (satellite links) and traditional instruments (radars and rain gauges at the ground) to provide the best real-time estimate of the space-time rain field for selected events. Synthetic hyetographs based on the local DDF curves are also used to assess the return period of flooding scenarios.

The focus of this work is on the impact of the inlet number, positioning, and efficiency on the risk of flooding. Detailed information about the inlet characteristics, including the potential degree of clogging, were obtained from the archives of the company in charge of the street and inlet maintenance, corroborated by a dedicated survey in the study area. This allowed obtaining a complete definition of the geometric and hydraulic characteristics of the surface drainage system (inlets), connecting the runoff produced during rain events with the underground storm sewers. It is assumed here that the capacity of the storm sewers is sufficient to drive away the water conveyed through the inlets, therefore no backflow is considered.

Hydraulic modelling is performed by using the HEC-RAS 2D software code (v. 6.3.1) and inlets are simulated as pumping stations with a customised stage-discharge relationship based on the available literature studies. Results are presented in the form of maps of the water depth and velocity over the study areas, and critical regions are identified based on the observed frequency (return period) of the expected flooding.

This study aims at providing suitable information to plan priorities in the maintenance interventions (cleaning and repairing of inlets) and possible expansion of the surface drainage system. The model is applied to a case study of an urban district of the town of Genoa (Italy), to support the activities of the project RUN – “Urban Resilience: Now-casting of the risk of flooding with IoT sensors and Open Data”, funded within the ROP-ERDF (Regional Operational Programme of the European Regional Development Fund).

How to cite: Lanza, L. G., Cauteruccio, A., and Chinchella, E.: Opportunistic rain sensors and flood modelling to assess the risk of failure of surface drainage in urban areas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9567, https://doi.org/10.5194/egusphere-egu23-9567, 2023.

EGU23-10806 | Posters on site | HS7.6

Sensitivity Analysis of the Effect of Rainfall on Road Traffic Speed in Bangkok, Thailand 

Tsuyoshi Takano, Shinichiro Nakamura, Hiroyoshi Morita, Napaporn Piamsa-nga, and Varameth Vichiensan

Rainfall affects urban traffic flow. In rapidly urbanizing megacities in Asian countries, heavy rainfall causes roads to flood and traffic congestion to worsen due to weak drainage systems. This study statistically quantified the impact of rainfall on urban traffic speed in Bangkok, using probe vehicle data and rainfall data from 2018 to 2020. Traffic speeds are calculated based on the travel distance and travel time between districts, taking into account the detouring of flooded sections.

Results show that both the rainfall intensity at the time of driving as well as the amount of previous rainfall affect the traffic speed reduction. In particular, the impact of previous rainfall increases at times and areas where traffic is concentrated, such as during the weekday morning and evening peak hours and travel to/from the city center. The results of the analysis based on regional characteristics show that low-lying districts are more affected by the previous rainfall because the flood water tends to stay on the road surface, while districts with high vegetation index (NDVI) are less affected by the previous rainfall. In addition, the impact of previous rainfall increases with population density and the ratio of narrow streets. In Bangkok, urbanization has progressed while leaving behind a city block configuration with many narrow streets, called Soi, connecting to arterial roads. This result means that limited road space is prone to flooding, and once flooding occurs, combined with the concentration of traffic on adjacent roads, traffic congestion becomes more severe.

The results of this study showed the impact of rainfall on urban traffic in different areas and at different times of the day in the target site. Integrated improvements to the transport and drainage systems could have a greater benefit.

How to cite: Takano, T., Nakamura, S., Morita, H., Piamsa-nga, N., and Vichiensan, V.: Sensitivity Analysis of the Effect of Rainfall on Road Traffic Speed in Bangkok, Thailand, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10806, https://doi.org/10.5194/egusphere-egu23-10806, 2023.

EGU23-11115 | ECS | Posters on site | HS7.6

Developing a SMART flood early warning system for a mountain watershed: experiences from the Lesser Himalayas 

Sudhanshu Dixit, Tahmina Yasmin, Kieran Khamis, Antony Ross, Subir Sen, Debashish Sen, Wouter Buytaert, David M. Hannah, and Sumit Sen

In the current context of climate change, urban areas in the Himalayas frequently experience flash floods. During high-intensity rainfall events in the catchments, due to hilly terrain and steep slopes, headwater streams cause flash floods and destroy life and property downstream. Increased encroachment along riverbanks and unplanned urban settlements expose financially distressed communities to the elevated risk of floods. This requires developing a reliable warning/alert system to ensure better preparedness for flood hazards and improve disaster resilience. Adequate hydrometeorological monitoring is a key element of such a system to generate knowledge on catchment/watershed characteristics as part of a broader disaster mitigation framework to reduce flood risk. 

The Bindal river in Dehradun (the capital city of Uttarakhand state in India) lies in the Doon valley on the foothills of the Himalayas, having a significant elevation difference of 450m with an area of 44.4 km2. The downstream settlements of the Bindal river experience flash floods during the monsoon season. Utilizing a SMART approach (developing shared understanding, monitoring, and awareness of the associated risks for preplanning response actions on time), this study aims to leverage and test a low-cost sensor network to provide information of hydrological variability and runoff response in the Bindal catchment. The SMART sensor network consists of 3 LiDAR river water level sensors and 4 tipping-bucket rain gauges at 15-minute intervals. The observed data showcases a substantial variability at both spatial and temporal scales within the small catchment of the Bindal river. The correlation coefficient (p value<0.05) between the rainfall observations at different stations varied from 0.82 to 0.20, with distance between their locations varying from 2.74 to 8.24km. The difference in total monthly rainfall recorded in two rain gauges 8.24 km apart in September is 187 mm. Additionally, the preliminary data suggests urban settlements in the downstream receive heavy rainfall within a short duration, while upper-catchment regions receive low-intensity rainfall for a longer duration. Future work will focus on developing a correlation between rainfall intensity and streamflow to define Intensity-Duration (ID) thresholds for early warning of flash floods. Similar systems in mountain landscapes with long-term rainfall and discharge data can contribute to establishing effective and low-cost flood warning systems for vulnerable riverine communities, particularly in developing countries.

How to cite: Dixit, S., Yasmin, T., Khamis, K., Ross, A., Sen, S., Sen, D., Buytaert, W., Hannah, D. M., and Sen, S.: Developing a SMART flood early warning system for a mountain watershed: experiences from the Lesser Himalayas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11115, https://doi.org/10.5194/egusphere-egu23-11115, 2023.

EGU23-12555 | ECS | Posters on site | HS7.6

The sensitivity of urban surface water flood modelling to the temporal structure of rainfall 

Molly Asher, Mark Trigg, Cathryn Birch, Steven Böing, and Roberto Villalobos-Herrera

The risk posed globally by surface water flooding to people and properties is growing due to rapid urbanisation and the intensification of rainfall due to climate change. Whilst tools to model urban flood risk have also been rapidly developing, there remains a knowledge gap around the sensitivity of urban hydraulic modelling methods to the temporal structure of rainfall. In the UK, the industry standard process considers rainfall events to always be symmetrical, and with a singular peak in intensity. Previous studies of observed UK extreme rainfall events suggests that loading of rainfall towards the start or end of events is in fact more common. In this study, the sensitivity of an urban catchment in the north of England is tested using fifteen realistic rainfall profiles derived from these observed extremes. Additionally, idealized systematic variations are made to the industry standard profile to shift the single peak towards the start or end of the event, and to split the rainfall volume over multiple peaks. We demonstrate that the positioning of the peak, as well as its magnitude, influences the severity, timing and nature of the associated flooding. The profile with the peak nearest the end of the event is associated with an 18% larger flooded area than the early peaking profile which is associated with the smallest flooded area.

How to cite: Asher, M., Trigg, M., Birch, C., Böing, S., and Villalobos-Herrera, R.: The sensitivity of urban surface water flood modelling to the temporal structure of rainfall, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12555, https://doi.org/10.5194/egusphere-egu23-12555, 2023.

EGU23-12626 | Orals | HS7.6

Real-time Rainfall Estimation Using Binarized Rain Streak Images in Surveillance Cameras 

Jongyun Byun, Jinwook Lee, Hyeon-Joon Kim, and Changhyun Jun

Real-time monitoring and analysis of rainfall are important in reducing potential damage and losses in water-related disasters. Nowadays, IoT sensor data is being widely used in weather observation because of cost-effectiveness with high spatiotemporal resolutions. This study proposes a novel approach to estimate rainfall intensity from binarized rain streak images in surveillance cameras. Here, several background subtract algorithms are considered to extract rain streak images from raw video data recorded by surveillance cameras installed in six different points in Seoul, Korea. Various ranges of binarization threshold values are also used to calculate the number of white pixel values from rain streak images. As results, it indicates that rainfall intensity is properly estimated from binarized rain streak images with a relation equation between the number of white values and observation rainfall intensity data, which shows high dependence on the amount of illumination and recording environment characteristics (e.g. rainfall type, camera parameter, etc.).

Keywords: Rainfall Estimation, Rain Streak, CCTV, Computer Vision, Korea

Acknowledgement

This work was funded by the Korea Meteorological Administration Research and Development Program under Grant KMI2022-01910 and in part supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. NRF-2022R1A4A3032838).

How to cite: Byun, J., Lee, J., Kim, H.-J., and Jun, C.: Real-time Rainfall Estimation Using Binarized Rain Streak Images in Surveillance Cameras, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12626, https://doi.org/10.5194/egusphere-egu23-12626, 2023.

EGU23-13977 | ECS | Orals | HS7.6

Simulating rainfall and drainage response using CON-SST-RAIN - a stochastic areal rainfall generator 

Christoffer B. Andersen, Søren Thorndahl, and Daniel B. Wright

Stochastic rainfall generators have been commonly used in the field of hydrological and hydrodynamic modeling for a long time. These generators allow for an extensive ensemble of rainfall scenarios and continuous time series that is applicable for risk assessment and response variability studies under current and future climate conditions. Most rainfall generators simulate rainfall at daily scale and at point values. Recently some generators have been developed to produce gridded rainfall products. With advancement in weather radar technology a much more detailed representation of rainfall fields is now possible. This is especially needed in the field of urban hydrology.

We developed the stochastic rainfall generator CON-SST-RAIN that is based on traditional dry/wet sequencing using Markov Chains and rainfall field generation by Stochastic Storm Transposition (SST), a time-in-space resampling method. CON-SST-RAIN was developed utilizing a 17-year long C-band radar dataset, with a spatio-temporal resolution of 500m x 500m and 10 minutes, discontinuous in time (discard of data) and Markov Chains are derived from rain gauges.

CON-SST-RAIN can recreate continuous areal time series that captures the mean annual precipitation while also retaining seasonal and inter-annual variances. Extreme rain rates are likewise preserved and comparable to rain gauge data with +40 years of record.

We test the CON-SST-RAIN on stochastically generated artificial hydrological networks to examine the importance of spatio-temporal dynamic rainfall fields. The networks are generated by a Gibbs sampling approach where the modeler can choose the extent and complexity of the generated network. Runoff from these networks is coupled with a simple detention pond model to estimate return periods for rainfall storage.

How to cite: Andersen, C. B., Thorndahl, S., and Wright, D. B.: Simulating rainfall and drainage response using CON-SST-RAIN - a stochastic areal rainfall generator, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13977, https://doi.org/10.5194/egusphere-egu23-13977, 2023.

Flood damage is not only caused by river floods. In particular, highly sealed urban areas are repeatedly affected by flooding as a result of convective heavy precipitation, regardless of their proximity to surface waters. Floods are often very localized due to the small spatial extent of the heavy precipitation cells. However, the spatial and temporal prediction of these precipitation cells is subject to great uncertainty due to the multitude of meteorological influences. In many cases, only the affected large areas in which convective heavy precipitation events can occur are known. The spontaneous implementation of safety measures by municipalities and residents is therefore rarely effective, which has already led to high damages in the past.

Hydrodynamic numerical (HN) models for simulating runoff, water levels and water velocity for heavy precipitation events require a high spatial and temporal resolution. Therefore, computational costs for pure HN models are high, so that a novel coupling approach with a hydrological rainfall-runoff (RR) model, which computes comparatively fast, is suggested. To represent the flooding events resulting from convective heavy precipitation events in highly heterogeneous inner-city areas, surface runoff can be simulated using RR models. Overloads of the existing drainage system are also identified. Averaging of, for example, sealing values, as is the case with conventional RR modelling, is dispensed with using high-resolution area information. A particularly detailed analysis of the study area at street level is thus possible as long as the flow directions are unambiguous. Subsequent coupling of the RR-simulated runoff to an HN model represents flooding of the area away from the fixed RR model runoff pathways. Due to the model concept developed for our study, runoff is represented with high temporal and spatial resolution and very short response times in the RR model. In the case of identified flooding of a road section, the flooding is then followed up with a non-uniform and transient HN model for the respective area. The combined approach reduces the model area of the HN model, which simulates dynamic flooding into the area, to the flood critical areas. In addition, this approach increases the accuracy of hindcasts compared to observations and delivers the opportunity to assess weak spots in the drainage system of complex urban areas. Municipalities may use the knowledge to create adapted and adequate risk management approaches for heavy precipitation events and make structural adjustments to reduce the now known risks.

How to cite: Sauer, C., Nagrelli, S., and Fröhle, P.: High-resolution modelling of heavy precipitation runoff behavior in urban areas using a coupled rainfall-runoff and hydrodynamic modelling approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14225, https://doi.org/10.5194/egusphere-egu23-14225, 2023.

EGU23-14790 | Orals | HS7.6

Anthropogenic intensification of life-threatening rainfall extremes: Implications for flash floods in urban areas 

Hayley Fowler, Stephen Blenkinsop, Steven Chan, Abdullah Kahraman, Haider Ali, Elizabeth Kendon, and Geert Lenderink

Short-duration (1 to 3 hour) rainfall extremes can cause serious damage to infrastructure and ecosystems and can result in loss of life through rapidly developing (flash) flooding. Short-duration rainfall extremes are intensifying with warming at a rate consistent with atmospheric moisture increase (~7%/K) that also drives intensification of longer-duration extremes (1day+). Evidence from some regions indicates stronger increases to short-duration extreme rainfall intensities related to convective cloud feedbacks but their relevance to climate change is uncertain. This intensification has likely increased the incidence of flash flooding at local scales, particularly in urban areas, and this can further compound with an increased storm spatial footprint to significantly increase total event rainfall. These findings call for urgent climate-change adaptation measures to manage increasing flood risks, including rethinking the way climate change is incorporated into flood estimation guidance.

How to cite: Fowler, H., Blenkinsop, S., Chan, S., Kahraman, A., Ali, H., Kendon, E., and Lenderink, G.: Anthropogenic intensification of life-threatening rainfall extremes: Implications for flash floods in urban areas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14790, https://doi.org/10.5194/egusphere-egu23-14790, 2023.

Rainfall observations with high spatio-temporal resolutions are required for a wide range of urban hydrological applications. The requirements on rainfall data are particularly high when predicting discharges in catchments with short lag times between rainfall and runoff peaks. Commercial microwave links (CMLs) can help in this regard, as they densely cover urban areas and can provide quantitative precipitation estimates (QPEs) at high temporal resolution. This study i) investigates how to reduce systematic errors in CML QPEs using rainfall and runoff observations commonly available in urban areas and ii) evaluates the potential of CML QPEs for modeling discharge and its uncertainty in a small urban catchment.

The catchment is located in a suburb of Prague (CZ), has an area of 1.3 km2 (35 % impervious surfaces) and is drained by a stormwater sewer system. Rainfall data are retrieved from 16 CMLs operated between 25 and 39 GHz, four municipal rain gauges located outside of the catchment, and three temporarily deployed rain gauges located at the border of the catchment. Discharge is measured at the outlet of the catchment. The dataset spans the period between July 2014 and October 2016 during which we observed 46 rainfall events with the average rainfall depth exceeding 2 mm. We randomly selected 23 events and used them for optimizing CML QPEs, whereas the remaining 23 events were used in the subsequent validation stage for evaluating the CML performance. CML QPEs are optimized using rainfall data observed by rain gauges at different distances from the catchment. Furthermore, we investigate how to optimize CML QPEs by comparing simulated and observed discharges. Rainfall data are propagated through the rainfall-runoff model and the simulated discharges are compared to the those observed at the outlet of the catchment. Finally, uncertainties in the simulated discharge are estimated by extending the deterministic hydrodynamic model by a stochastic error model explicitly accounting for model bias (Pastorek et al., 2022).

The results show that discharge simulations with CML QPEs outperform simulations with the rain gauges used alone and are only slightly worse than the benchmark simulations with three rain gauges located in the catchment (1 gauge per 0.5 – 1 km2). The best performance is achieved with CML QPEs optimized by the three closest municipal rain gauges (about three km from the catchment); CML QPEs optimized by the observed discharges achieve only slightly worse performance. The estimated discharge uncertainty reflects well different quality of the input rainfall data, i.e. the width of uncertainty bands increases when more distant RGs are used to optimize CML QPEs. We also show that even a single rain gauge located 8 km from the catchment, which is simply too far to be used alone for rainfall-runoff modeling, can efficiently reduce systematic errors in CML QPEs. Overall, the results show that CMLs can complement existing monitoring networks and significantly improve rainfall-runoff modeling including uncertainty estimation.

References:

Pastorek, J., Fencl, M., Bareš, V., 2022. Uncertainties in discharge predictions based on microwave link rainfall estimates in a small urban catchment. Journal of Hydrology 129051. https://doi.org/10.1016/j.jhydrol.2022.129051

How to cite: Fencl, M., Pastorek, J., and Bareš, V.: Improving discharge predictions and uncertainty estimates in a small urban catchment using commercial microwave links, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16044, https://doi.org/10.5194/egusphere-egu23-16044, 2023.

Pluvial urban flood events are prone to cause huge damages to infrastructures and can also endanger human lives. A strategy for dealing with natural disasters like urban flood events is to build up detailed models to predict potential implications of an event. These models are commonly physically based hydrodynamic models. Using such models for gaining better understanding of historical and possible future events can be beneficial. For damage mitigation during a storm event, the computational demand of these models is, however, too high. Therefore, substitute models have been developed in recent years, which are fast enough to allow for real time prediction. We present a machine learning model for real-time urban flood prediction with spatial and temporal resolution. The model was tested with promising results for a flat urban catchment. The model is based on a combination of autoencoders and a NARX neural network structure. The spatial resolution is 6 x 6 meters and the temporal resolution is 5 minutes. During the present research we applied the model to a steep urban catchment. Database for training the model was generated with the 1D/2D bidirectional coupled hydrodynamic model Hystem Extran 2D. As input we used design storm events with return periods of up to 100 years.  

How to cite: Berkhahn, S. and Neuweiler, I.: Real-time pluvial urban flood prediction with high spatial and temporal resolution – a case study for a steep catchment., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16652, https://doi.org/10.5194/egusphere-egu23-16652, 2023.

Rainfall is the driving force of hydrological events. In order to predict Pluvial Flooding in cities modelling approaches make use of rainfall data of various sources: radar-based observations and predictions, high-precision rain gauges (like the OTT Pluvio² types used in the Brussels monitoring network Flowbru.be). The first have the advantage of being area-covering and having predictive power, the latter providing more precise absolute and ground-based rainfall measurements but potentially lacking spatial representativity. In an urban setting , high-density rainfall measurements are important as a little shift in rainfall may lead to a significantly different hydrological response (peak flow at different location in sewer network). The main objective is to explore the potential of low-cost rain sensors as complement for extreme peak rainfall monitoring in Brussels, Belgium. Within the frame of the FloodCitiSense project (www.floodcitisense.eu) rainfall data has been collected during 2 years (2019-2021) using low-cost acoustic rain sensors, installed via citizen observatories. For the data analysis we focus mainly on convective rain storms typically occurring during summer time, which are most often very localized and challenging to measure and/or predict.

The research questions were as following: (1) What is the performance of the low-cost sensors compared to the existing high-precision rain gauges of the FLOWBRU monitoring network in network? (2) Can we improve the quantitative estimation of extreme rainfall distribution using the measurements of the low-cost sensors?

A comparative analysis, focusing on rainfall events with a return period of 10 years (T10), between a local low-cost acoustic rain sensor and a high-precision FLOWBRU rain gauge, installed at the same location (Royal Meteorological Institute) revealed a relative strong correlation between both rainfall timeseries, but a significant under estimation of cumulative rainfall during the events. A regression analysis enabled to develop a dynamic multiplier, varying in function of the rainfall intensity per 5-min timestep, improving the rainfall estimated by the low-cost sensor. Therefore the multiplier has been used to re-calibrate all low-cost measurements. In order to answer the second research question a spatial interpolation (Inverse Distance Weighted) using the cumulative rainfall per T10 event from FLOWBRU stations WITH and WITHOUT the low-cost stations has been applied. As a reference radar QPE images were used (cumulative rainfall per T10 event). Although yielding variable results, the use of the low-cost sensor data shows clearly an added value for (extreme) peak rainfall monitoring in Brussels.

How to cite: Verbeiren, B. and Lemmens, J.: Exploring the added value of low-cost sensors via citizen observatories for peak rainfall monitoring in cities (Case study: Brussels), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16721, https://doi.org/10.5194/egusphere-egu23-16721, 2023.

EGU23-746 | ECS | Orals | HS5.14

Functional response evaluation of hard and soft adaptation strategies in urban flooding 

Angana Borah, Raviraj Dave, and Udit Bhatia

Intensified climate extremes in changing climate scenarios with rapid urbanization make urban floods a global concern since the population in the cities is increasing. One way to manage urban floods is the adoption of various adaptation measures. The existing infrastructures for flood adaptation are classified as 'hard,' 'soft,' and 'hybrid' adaptation strategies, which constitute the conventional Stormwater Drainage Network (SWD),  Green Infrastructures (GI) practices, and a combination of soft and hard strategies, respectively. As infrastructures are vulnerable to damage because of exceedance in design life, capacity, or any adverse situation, all adaptation methods are likely to become non-functional in the event of a disaster. Under such circumstances, the flood response of an urban region on account of the non-functionality of both soft and hard adaptation strategies is not well understood. We develop a coupled 1D-2D hydrodynamic model using MIKE+ and generate scenarios to compare the damages in the functional capacity of all three adaptation strategies. We implement this model for Ahmedabad city, India, and our Initial results show the hotspots which are highly prone to urban flooding. Here, we evaluate the hydrodynamic interaction between flood propagation on the surface with components of SWD structures and GI facilities and determine the consequence of their functional damages. Our analysis unfolds all the aspects of utilizing certain adaptation pathways, including the merits and demerits of the success and failure of a project. Our framework could aid in determining the trade-offs between different adaptation pathways from the perspective of building flood-resilient cities. 

How to cite: Borah, A., Dave, R., and Bhatia, U.: Functional response evaluation of hard and soft adaptation strategies in urban flooding, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-746, https://doi.org/10.5194/egusphere-egu23-746, 2023.

EGU23-2223 | ECS | Orals | HS5.14

Societal interest and willingness to pay for green roofs in Sardinia 

Elena Cristiano, Roberto Deidda, and Francesco Viola

Among the different nature-based solutions proposed for the sustainable development of urban areas, green roofs are becoming more and more popular, thanks to their multiple benefits. Indeed, these nature-based solutions reduce the pluvial flood risk during rainfall events, contribute to the thermal insulation of buildings, mitigate the urban heat island effect, and improve the air quality. The knowledge that citizens have about green roofs, the interest and willingness to pay for their installation are still poorly investigated and quantified, although this meta-information could be a valid support and guidance for policy makers and urban planners. In this work, we investigated, through an anonymous online survey, the perception of people living in Sardinia on the most common urban environmental issues (i.e., urban flood, increase of temperature, energy consumption, air pollution and lack of green spaces), and the willingness to pay for green roof installation on both public and private roofs. We estimated the empirical relation among environmental issues awareness and the willingness to pay for a specific green solution while trying to relate the latter to socio demographic characteristics. Results show that citizens are very interested in having green roofs on public building, and on average they are willing to pay around 35 euro per year for their installation and maintenance. The interest for green roofs on private building is, on the other hand, lower than on public ones, due to the high installation and maintenance costs. Moreover, when possible, citizens would rather have solar panels instead of green roofs, since they fully perceive the economic advantages deriving from the installation and are not fully aware of the green roof benefits.

How to cite: Cristiano, E., Deidda, R., and Viola, F.: Societal interest and willingness to pay for green roofs in Sardinia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2223, https://doi.org/10.5194/egusphere-egu23-2223, 2023.

Nature-based Sustainable Drainage Systems (SuDS) have been promoted for enhancing urban drainage, as well as offering additional benefits to urban greening and amenity, and engaging communities in the design and adoption of schemes. However, a lack of data on the efficacy of nature-based options means that schemes often use traditional engineering approaches instead of nature-based designs. Where nature-based options are used, most schemes lack long-term monitoring to understand their effectiveness; interventions are rarely designed to maximise their potential and often underperform once constructed. Existing practices also mean that most schemes are led by technical expertise and hence proceed with token public engagement, and lack support for community acceptance and adoption. This is unsustainable and undermines SuDS as a crucial tool for climate adaptation and sustainable urban development.

The SuDS+ approach argues for a radical rethink of the benefits of SuDS, de-prioritising drainage as their primary driver, and instead conceptualising ‘SuDS+’ as a multi-benefit urban development tool with a range of co-, not additional, benefits. In this approach SuDS become a vehicle for enhancing urban design, amenity, and health and wellbeing which can be adapted to meet community needs and aspirations.

The SuDS+ project, a 5-year Defra funded study in the Northeast of England, aims to develop and deliver community-centred SuDS, embedding innovation in collaborative design, as well as pushing forward new technologies and approaches for nature-based urban water management, and co-developing our understanding of what and how to monitor interventions to develop a robust evidence-base for the future.

This paper outlines the key challenges and how the project will aim to tackle these as a call to reimagine SuDS as a vehicle for delivering greener, healthier, more sustainable, and more resilience urban communities.

How to cite: Starkey, E. and Rollason, E.: SuDS+: establishing a new vision for sustainable drainage in delivering  sustainable and resilient urban communities, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3381, https://doi.org/10.5194/egusphere-egu23-3381, 2023.

EGU23-4190 | Posters on site | HS5.14

On the effectiveness of green infrastructure to reduce stormflow at catchment scale 

Julian Klaus, Paulina Busch, and Michael McHale

Population growth and climate change alter the urban water cycle resulting in increasing frequency and magnitude of urban floods. In this study, we compared stormwater response in an urban drainage system between two adjacent urban sewersheds in Buffalo, NY, USA. At the first site (DEL), comprehensive installations of green infrastructure (GI) (i.e. bioretention cells) were carried out, while the second site (SQ) was minimally influenced by GI practices. Stormflow was monitored as pipeflow at both sites for an observation period of five years, three pre-construction and two post-construction years. We identified storm events and calculated event runoff, as excess flow above baseflow. Additionally, we evaluated annual total flow and peakflow (annual and seasonal) between the sites and between pre- and post-construction. Our analyses were confined to snow-free seasons because storage of precipitation in the snowpack confounds the evaluation of the precipitation-runoff relation. The analysis showed that the GI implementation was highly effective in reducing stormflow. Total annual flow was reduced at DEL between pre- and post-construction, while no trend was observable at the minimally influenced by GI SQ. Also, event-based stormflow was reduced through GI implementation across all snow-free seasons. Last, median event peakflow was clearly reduced through GI, especially in spring and summer, whereas results during fall were less clear. Through this hydrometric analysis, this study is among the first that provided evidence for the efficiency of GI in reducing stormflow beyond the plot-scale and thus provides future guidance on flood mitigation in urban environments.       

How to cite: Klaus, J., Busch, P., and McHale, M.: On the effectiveness of green infrastructure to reduce stormflow at catchment scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4190, https://doi.org/10.5194/egusphere-egu23-4190, 2023.

EGU23-4666 | ECS | Orals | HS5.14

Integrated urban water management modeling under future water demand and climate scenarios for the city of Bangalore, India 

Snigdha Sarita Mohapatra, Meenakshi Arora, Wenyan Wu, and Manoj Kumar Tiwari

Climate change and population growth have a significant impact on urban water supplies. This is due to the fact that meeting urban water demand with the available water resources is quite challenging due to ever-growing water demand, variable supply as a result of climate uncertainties, and water pollution. In many urban areas around the world, the concept of integrated urban water management (IUWM) has become quite prominent in recent decades to tackle the challenges of urban water supply and management. The main principle of IUWM is to incorporate non-conventional water supply sources, such as stormwater, rooftop rainwater, and recycled wastewater, to augment the water supply and provide fit-for-purpose water. IUWM, if implemented successfully, has the potential to mitigate multiple challenges outlined above including enhanced water security during droughts, reduced waste streams, reduced floods, and enhanced groundwater recharge as well as reduced water pollution.

In this research, an IUWM principles incorporated water balance model (i.e., developed using eWater Source Version 5.4.0.11797) was used to identify the most suitable supply options from multiple water sources to satisfy the water demands under future demand and climate scenarios for the city of Bangalore, India. Five different water supply configurations were generated based on available water sources and within the policy framework to meet water demand. The effect of climate change has been incorporated into the IUWM model configurations through the runoff responses from future precipitation and temperature changes. Future climate change scenarios for four IPCC emission scenarios i.e., ssp126, ssp246, ssp323, and ssp586 have been incorporated from thirteen Coupled Model Intercomparison Project-6 (CMIP6) models (i.e., 0.25° spatial resolution available at the study location). Three water demand scenarios i.e., low (150 liters per capita per day), average (175 liters per capita per day), and high (200 liters per capita per day) for the projected population were considered as per the Indian Standards. The selected configurations were evaluated for water supply reliability (i.e., time and volumetric reliability) in the study area. Further, as multiple future scenarios resulted in multiple water supply reliability solutions under five IUWM model configurations, the robust solution was identified using robustness metrics.

How to cite: Mohapatra, S. S., Arora, M., Wu, W., and Tiwari, M. K.: Integrated urban water management modeling under future water demand and climate scenarios for the city of Bangalore, India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4666, https://doi.org/10.5194/egusphere-egu23-4666, 2023.

Floods have devastated many urban socio-ecological systems, adding to urban planners' concerns. Floods caused by typhoons and heavy rain are common in South Korea during the summer, and especially Seoul has experienced urban flooding due to unusually localized heavy rains since 2010. According to the Intergovernmental Panel on Climate Change scenarios (IPCC, 2014), flood damage in Korea is expected to increase due to summer-concentrated precipitation. As an example of what happened, record-breaking rainfall in the summer of 2022 caused severe damage in the Gangnam, a prime district in Seoul, Korea, that has been most vulnerable to flood damage due to drainage problems.

Green infrastructure's socio-ecological system aspect has been recognized for its ability to improve the provision of urban ecosystem services and is increasingly being used for stormwater management. Flood resilience necessitates the ability of urban socio-ecological systems to maintain their structures and functions during and after flooding events. In terms of achieving sustainable outcomes for municipalities, green infrastructure has practical limitations, such as a limited capacity for storing and infiltrating stormwater. As an interdisciplinary approach, green infrastructure necessitates the involvement of multiple stakeholders with conflicting interests, and it is critical to identify the best measures to apply in each context for effective flood mitigation strategies. There is, however, a knowledge gap in investigating an urban water system as a social-ecological system that coevolves because of interactions between actors, institutions, and water systems.

Gangnam district has quickly become the focal point for discourses on socio-economic inequality in Korea, consolidating both socio-economic segregation and political conservatism, making social-economic-ecological context critical for any urban planning to be sustainable. The aim of this research is to develop a system for selecting appropriate green infrastructure for resilient urban stormwater management in Seoul's Gangnam district using simulation-based modeling.

The first step will be to identify suitable green infrastructure practices for Gangnam district’s socio-economic context based on a co-benefits analysis, which will include incorporating co-benefits and human well-being into flood management decision-making while taking stakeholders' perceptions into account using a multi-criteria decision support system. The second step involves using the "Green Values Stormwater Management" model (Jaffe et al., 2010) to assess the green infrastructure's ability to adhere to the "4R" principles of resilience: robustness, rapidity, redundancy, and resourcefulness based on simulation results.

The volume of rain captured or retained by the area's green infrastructure, providing feedback on construction and maintenance costs, as well as an estimate of the percentage of the desired volume retention goal being met will be estimated by the simulation model. Additionally, co-benefits such as cost savings and increased real estate value will be calculated and presented. This research framework will assist city planners decide which green infrastructure practices to use for resilient urban flood management.

References

IPCC (2014). Climate Change 2014: Synthesis Report. IPCC, Geneva, Switzerland.

Jaffe, M., Zellner, M., Gonzalez-Meler, M., Cotner, L. A., Massey, D., Ahmed, H., & Elberts, M. (2010). USING GREEN INFRASTRUCTURE TO MANAGE URBAN STORMWATER QUALITY: A Review of Selected Practices and State Programs.

How to cite: Rahman, M. R., Kim, H., Kwon, D., and Lee, J.: A Simulation-based Modeling Approach to Adapt Social-Ecological Green Infrastructure System for Resilient Urban Flood Management, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4830, https://doi.org/10.5194/egusphere-egu23-4830, 2023.

Green roofs are beneficial in urban drainage systems due to their role in mitigating the hydrological response of the largely impervious surfaces to intense rainfall events. Such benefit is often assumed to hold also in case RainWater Harvesting (RWH) is implemented to exploit the collected rainwater for non-potable usages and to save valuable potable water resources. However, the role of green roofs on the RWH efficiency is not obvious and requires detailed investigation by accounting for the local rainfall climatology.   

On the one hand, retention of rainwater operated by the vegetation would reduce the total volume of collected water made available for exploitation. On the other hand, rainwater detention in the green roof substrates would add to the storage capability of the RWH system, therefore improving the delayed supply of water during inter-event dry periods. The resulting efficiency at the annual scale depends on the distribution of precipitation within the year (duration of dry periods, intensity of rain events, frequency of extremes, etc.).

In this work, a behavioural model is developed to investigate the impact of the inflow modulation due to an interposed green roof on the efficiency of a generic RWH system located in the Mediterranean environment (Cauteruccio and Lanza, 2022). Various configurations of both the green roof characteristics (retention and detention performance) and the RWH system (rainwater collection area and storage volume) are compared with the collection from impervious surfaces in terms of non-dimensional reliability indices.

Furthermore, the annual usage volume per unit tank capacity is used as an indicator of the economic benefit associated with the exploitation of the resource, and its variation in case of the various green roof/RWH system design configurations is assessed. In particular, the reduction of the significant overflow ratio that is typical of RWH systems in the Mediterranean climate is calculated, which is interpreted as a positive feature since overflow represents the unused portion of the collected water.

Cauteruccio, A. and L.G. Lanza (2022). Rainwater harvesting for urban landscape irrigation using a soil water depletion algorithm conditional on daily precipitation. Water, 14(21), 3468. https://doi.org/10.3390/w14213468.

How to cite: Cauteruccio, A. and Lanza, L. G.: Competing roles of green roof in rain water harvesting systems: accounting for retention and detention in a behavioural model simulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8630, https://doi.org/10.5194/egusphere-egu23-8630, 2023.

EGU23-8836 | ECS | Posters on site | HS5.14

Engaging local communities in planning Nature-Based-Solutions for urban drainage systems - the MUDAR project 

Livia Serrao, Susanna Ottaviani, Corrado Diamantini, Alessandra Marzadri, Marco Ragazzi, Wilson Alberto Munguita Paulino, Félix Cândido Cláudio Eduardo Macueia, Harold Juvenal Chate, Americo Da Stela Valdimir Msopela, Alfredo Manhota Antonio, and Guido Zolezzi

Urban population has been increasing worldwide in recent decades and it is expected to continue growing in the coming years. Cities are facing the effects of the climate crisis, which primarily impact the most vulnerable contexts, first and foremost informal settlements. In this context, the growth of informal neighborhoods, home to one billion people1, poses complex challenges for the cities of today and tomorrow. In these urban areas traditional, informal and formal social dynamics coexist, strengthened by strong community identities and bonds. Major problems are due to the lack of basic services and infrastructure, making these areas more vulnerable to the increasingly frequent and intense extreme rainfall events. 

In this work, we present the recently launched Europeaid-funded project MUDAR (Mozambique integrated Urban Development by Actions and Relationships), and specifically focus on its component that addresses the dynamics and effects of flooding  in an informal urban area: the Macuti neighborhood in the city of Beira, Mozambique. Macuti is situated on the coast, making it particularly vulnerable to frequent cyclones, one of all Idai, which damaged 49% of its buildings in March 20192. Moreover, it is located on a marshy, purely flat area at the end of an inadequate open drainage network serving the entire city, which is unable to drain the flow at high tide. Macuti, with its almost 17 thousand people (2017), since the early 2000s has been experiencing a rapid growth in spontaneous settlements, which has resulted in a higher population density, with the unbuilt area decreasing by 40% from 2004 to 2022, and soil permeability further reducing in a context where the clayey soil composition already strongly limits rainfall infiltration. These changes, in addition to the inadequate water infrastructure, have exacerbated flooding problems associated with heavy rainfall events (the maximum daily precipitation of the 1990-2020 period was 288.5 mm/day). Investigating the socio-hydrology of flooding in these informal settlements is particularly complex because its requirements for high-resolution topographic, soil, land use and meteorological data, which are very limited in these informal settlements. 

More specifically, we present preliminary outcomes and the proposed project strategy to cope with the intrinsic data scarcity of such context, which is based on carefully designed participatory surveys with local actors. To fill this data gap, a multi-disciplinary approach has been adopted by combining elaborations from satellite image processing (SAR) with in-situ measurements and interviews to inhabitants and professionals. In addition to being involved in providing information about the area, the inhabitants are a crucial actor in the decision-making process for choosing the technical solutions to be implemented. Preliminary results on  flooding dynamics in Macuti neighborhood, as well as on three Nature-Based-Solutions scenarios emerging from the participatory process highlight promising factors that can allow adapting the participatory procedure in similar contexts.

 

1French, M., Trundle, A., Korte, I., Koto, C. (2020). Climate Resilience in Urban Informal Settlements: Towards a Transformative Upgrading Agenda. Climate Resilient Urban Areas, 129-153

2UNOSAT-REACH (2019). Mozambique- Beira City -Macuti - Neighbourhood Damage Assessment- As of 26 March 2019. URL: https://m.reliefweb.int/report/3056948

How to cite: Serrao, L., Ottaviani, S., Diamantini, C., Marzadri, A., Ragazzi, M., Paulino, W. A. M., Macueia, F. C. C. E., Chate, H. J., Msopela, A. D. S. V., Antonio, A. M., and Zolezzi, G.: Engaging local communities in planning Nature-Based-Solutions for urban drainage systems - the MUDAR project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8836, https://doi.org/10.5194/egusphere-egu23-8836, 2023.

EGU23-10523 | ECS | Posters virtual | HS5.14

Sensitivity analysis of green roof design parameters in SWMM for its improved understanding of hydrological performance 

Husnain Tansar, Huan-Feng Duan, and Ole Mark

Improved understanding of dynamic hydrological performance of green roof (GR) design parameters towards different model responses is important for maximizing its target design goals at the unit-scale. Replication of an optimally designed GR unit at the catchment-scale significantly contributes to achieving its target design goals (i.e., surface runoff reduction, urban flood reduction, peak flow control, etc.). Moreover, adequate efforts are required to explore and provide appropriate knowledge about the categorization of influential and non-influential design parameters with their suitable design spaces to guide researchers, drainage engineers, and stormwater management practitioners for effective and efficient planning, designing and optimization of GR at catchment-scales.

This study employs a robust and comprehensive global sensitivity analysis (GSA) method known as the variogram analysis of response surfaces (VARS) for sensitivity analysis of GR design parameters. Firstly, a total of 13,999 sample points for 14 GR parameters of three layers (i.e., surface, soil and drainage mat) are generated by using the latin hypercube sampling technique and their factor spaces are decided based on design guidelines in current SWMM manuals. Following that, the PySWMM is used to simulate these design samples in a Monte-Carlo-type setting on a conceptual catchment of 0.01km2 (100m2 × 100m2) with 50% treatment area of GR, and the model responses (e.g., surface infiltration, surface outflow, storage volume, and peak flow) are estimated and applied for sensitivity analysis. Finally, VARS evaluates different sensitivity analysis metrics by using different model responses corresponding to their designed samples.

Overall, the senstivity analysis results demonstrate that 8 out of 14 design parameters are highly influential on different model responses, however, the parameters’ sensitivity varies towards different model responses under different perturbation scales and rainfall conditions. Moreover, the selection of an effective range of design space of design parameters is necessary as it has a higher influence on model responses, while the parameters’ rankings and contributions to total sensitivity indices change with the range of design spaces. Furthermore, this research also provides an opportunity through VARS directional variogram index (an integrated sensitivity index) to study and understand the underlying mechanisms of design parameters under different perturbation scales with no extra computational burden. Senstivity analysis results will be presented with insights and recommendations for other regions, which will be helpful for decision-makers for effective planning, designing and implementation of GR. The findings of this parametric study would be helpful for the calibration and optimization of design parameters of GR for different case studies.

 

How to cite: Tansar, H., Duan, H.-F., and Mark, O.: Sensitivity analysis of green roof design parameters in SWMM for its improved understanding of hydrological performance, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10523, https://doi.org/10.5194/egusphere-egu23-10523, 2023.

Green infrastructure (GI) has become a common solution to mitigate stormwater-related problems such as water quality and flooding hazards. Despite  widespread acknowledgement of GI benefits, there is a lack of decision support methods that allow practitioners to identify optimal locations and evaluate the costs and benefits of numerous spatially distributed small GI practices at larger scales (subwatershed to entire watershed) under uncertainty. To address these needs, an online Cloud-based interactive tool coupling SWMM (Storm Water Management Model) and the Water Research Foundation LID life cycle model, , called Interactive DEsign and Assessment System for Green Infrastructure (IDEAS_GI), is optimized using a noisy genetic algorithm (GA) with life cycle costs and stormwater volume reduction as the primary objectives. To overcome the computational challenge of probabilistic sampling with the noisy GA and to identify significant features for preferable locations, the GA  is merged with an artificial neural network, which acts as a meta-model (surrogate) for the numerical simulation model (SWMM). Post-optimization, machine learning decision trees are also generated that classify the numerous potential solutions generated by the noisy GA into GI coverage classes based on sub-watershed parameters. This framework is applied to a watershed in Baltimore, Maryland, U.S., under multiple budgetary scenarios. The results suggest that the greatest GI investments under the highest and lowest budgetary scenarios should be allocated to subwatersheds closest to the watershed outlet. For the lowest scenario, GI practices should be installed only in subwatersheds closest to the watershed outlet. When the budgetary scenario is highest, GI is sited across the watershed but highest priority is still given to subwatersheds closest to the watershed outlet. On the other hand, the importance of total distance to the watershed outlet is lower for the medium budgetary scenario. In fact, the impacts of different features for preferable GI coverage for these solutions are more complex, don’t follow a consistent pattern, and require more depth to capture the patterns in their corresponding classifier decision trees. In addition to these GI findings, the results showed that the addition of meta-models decreases average computational time required to reach Pareto frontiers similar to the ones generated by the noisy GA by more than 95%.

How to cite: Minsker, B. and Heidari Haratmeh, B.: Optimization of Green Infrastructure Networks to Maximize Stormwater-Related Benefits and Minimize Life Cycle Costs Using a Noisy Genetic Algorithm and Machine Learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10670, https://doi.org/10.5194/egusphere-egu23-10670, 2023.

Infiltration-based green infrastructures (GIs) are commonly constructed to effectively storage the excessive stormwater runoff. These GIs exploit infiltration process, as the key natural phenomenon in the hydrological water balance, to detain excessive stormwater volume, especially at the outlet of the peri-urban watershed. Beside many factors playing significant roles in the performance of the infiltration-based GIs, implementing them in shallow groundwater area still represents a challenge that can restrict their widespread adoption. In fact, the groundwater level, if close to the bottom of infiltration-based GIs, can strongly influence the infiltration process. Basically, the shallow groundwater may theoretically play as a boundary conduction and subsequently reduces the infiltration rate.

The present study investigated the activation of an infiltration-based GI located at the outlet of the combined sewer system in the municipality of Sedriano (12,000 inhabitants in province of Milan, North Italy), monitoring the inflow and the water depth over a period of almost two years. Meantime, groundwater level and meteorological measurements were observed (including precipitation, air temperature, solar radiation, wind velocity, and relative humidity). Using these observations, a Water-Balance Model (WBM) was calibrated on the hydrological response of the infiltration-based GI and then, used to simulate how much time is required to empty under a specific precipitation event, and to understand the spatial distributed performances of these measures under different groundwater levels.

The implementation of an accurate WBM can be a useful tool for designing and assessing the performance of the infiltration-based GIs in shallow groundwater environments in peri-urban areas. This study is an integral part of the project Smart-Green (www.smartgreen.unimi.it) that developed online tools for supporting the water utilities to accelerate the transition towards the sponge cities utilizing GIs techniques.

How to cite: Masseroni, D., Niazkar, M., and Cislaghi, A.: Implementing Water Balance Model for Stormwater Management: the case of an Infiltration-Based Green Infrastructure Under Shallow Groundwater Levels, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11430, https://doi.org/10.5194/egusphere-egu23-11430, 2023.

EGU23-13446 | ECS | Orals | HS5.14

Modelling reference evapotranspiration for vertical green (in urban areas) 

Karin A. Hoffmann, Rabea Saad, Björn Kluge, and Thomas Nehls

Vertical green is promoted as climate change mitigation and adaptation measure, and it provides green space for the urban population. However, it could be used in urban water management as well if its evapotranspiration, thus its water demand would be predictable.

For optimal performance, plants need to be provided with water, nutrients, and rooting space. But irregular precipitation, drought periods, and lack of natural water storage necessitate additional irrigation preferably by local water sources (such as rainwater runoff and greywater).

The amount of water needed for irrigation can be calculated using the Penman-Monteith approach which quantifies evapotranspiration of vegetated horizontal surfaces. For Vertical Green, the Penman-Monteith equation has already been tested. In that way, water demand of VGS can be calculated for hourly time steps based on radiation, wind speed, and vapor pressure deficit expressed by air temperature and relative humidity data.

The needed meteorological data can be measured on-site or derived, thus adapted – verticalized - from remote climate stations, depending on data availability, and needed accuracy of the results. This study models water demand using (1) on-site measured meteorological data, (2) ‘verticalized’ remote station data, and (3) remote station data. We then compare simulated evapotranspiration with measured lysimetry data for a ground-based Vertical Greenery system of Fallopia baldschuanica monitored in Berlin, Germany.

This study finds radiation and vapor pressure deficit to have the highest impacts on the variance of the results while wind speed has the lowest impact. In this contribution, we present the developed model, verticalization methods for the input parameters and validate the performance of the model based on measured water demands.

How to cite: Hoffmann, K. A., Saad, R., Kluge, B., and Nehls, T.: Modelling reference evapotranspiration for vertical green (in urban areas), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13446, https://doi.org/10.5194/egusphere-egu23-13446, 2023.

EGU23-13499 | ECS | Posters on site | HS5.14

Have roofs in Berlin become greener? Evaluation of Berlin's green roof subsidy program performance using geodata and deep learning 

Siling Chen, Margaux Antonia Huth, and Andrea Cominola

Green roofs are one of the most widely applied blue-green infrastructure in urban regions to serve several purposes moving towards climate change mitigation and urban adaptation. Their large-scale adoption is critical in enhancing resilience against urban hazards, such as urban flooding, urban heat island effects, and biodiversity loss. Currently, the most popular policy format to encourage their roll-out is subsidy programs. However, the success of such programs is oftentimes evaluated based on siloed governmental data, local evaluation reports, and non-recurrent monitoring campaigns, which may become inconsistent and incomparable across temporal scales and different geographical regions. Due to the lack of open data, complementary metadata, and standard quantitative evaluation tools, monitoring and consistently comparing the effectiveness of different green roof incentivization policies is a challenge in practice. This lack of data and high cost of frequent large-scale monitoring campaigns also hinders city-wide spatial distribution analysis of green roofs and identification of green roof development potential, which could support policymakers in devising effective and sustainable urban management strategies.

Moving towards an automated frequent monitoring of green roof development, previous work by Wu and Biljecki developed “Roofpedia”, an open-source deep learning algorithm for green roof mapping and urban sustainability evaluation using satellite imagery. In this work, we validate Roofpedia and evaluate its accuracy in automatically identifying and classifying green roofs from satellite images with public ground truth data in Berlin, Germany. Furthermore, we develop a Berlin-based case study where Roofpedia is applied using geospatial data across temporal scales to assess the efficacy of Berlin’s green roofing subsidy program "GründachPLUS", which has provided 2.7 Million Euros of funding for green roof construction since 2019. We first retrieve open-access orthoimagery data, then extract green roof coverages in Berlin across two temporal steps (i.e., before and after subsidy program instigation), and finally evaluate how effectively and promptly the subsidy program fostered the development of green roofs. This study contributes a Machine Learning-based add-on to the current evaluation protocol of the Berlin municipality, which is implemented via threshold-based spectral analysis. We analyze the spatial distribution of green roofs and provide insights into further green roof potentials in the city of Berlin, by identifying interesting hotspots for future green roof development. Upon imagery availability, this automated assessment may be extended to multiple cities to enable comparative studies of various green roofing incentivization policies and offer a transferrable and scalable policy evaluation framework.

How to cite: Chen, S., Huth, M. A., and Cominola, A.: Have roofs in Berlin become greener? Evaluation of Berlin's green roof subsidy program performance using geodata and deep learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13499, https://doi.org/10.5194/egusphere-egu23-13499, 2023.

EGU23-13613 | ECS | Posters on site | HS5.14

The role of urban trees in water cycle restoration 

Giacomo Marrazzo and Anita Raimondi

Urban development leads to an increment of impervious cover that drastically reduces infiltration rates and increases the risk of stormwater floods, also reinforced by the rise of extreme events due to climate change.

In this context, urban trees represent a valid system for sustainable stormwater management. They decrease the runoff discharged in the sewer network and/or in the receiving water bodies.

Trees impact the hydrological cycle through the processes of interception, evapotranspiration and infiltration strictly depending on several factors such as tree features, soils properties, climate, and storm event characteristics.

The objective of the study is to propose an analytical-probabilistic approach to model the contribution of urban trees to the restoration of the water cycle, with particular focus on the evapotranspiration component.

How to cite: Marrazzo, G. and Raimondi, A.: The role of urban trees in water cycle restoration, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13613, https://doi.org/10.5194/egusphere-egu23-13613, 2023.

EGU23-14713 | Posters on site | HS5.14

Investigation of thermal cooling potential of Permeable Paving at an urban trial site in London, UK 

Adrian Butler, Thomas Rowan, and Athanasios Paschalis

The built environment is being forced to adapt to rising global temperatures and severe weather events such as more intense storms, longer heatwaves etc. The proliferation of impermeable surfaces has over time led to many urban design problems, such as storm surges overwhelming sewers. Increasing urban temperatures are also caused by the built environment, the Urban Heat Island (UHI) effect. These impacts can be tackled through better infrastructure. Permeable paving offers an alternative to many impermeable surfaces, providing a robust surface with the advantage of drainage. Its ability to mitigate heat, however, remains poorly understood.

To address this, a detailed performance evaluation of two permeable paving pads, one a control and the other actively (mains supply) and passively (rainwater retention) watered, was undertaken. The 16 m2 permeable paving pads were installed at Imperial College London’s White City campus (London, UK) and monitored over 4 months (July to October 2021). The pads were bounded by a raised impermeable barrier and consisted of a block layer with foundations of grit underneath. Both pads were placed on a slope enabling them to be drained, a weir prevented flooding and a tap allowed for complete drainage. The pads were instrumented with internal heat and water content sensors, as well as surface thermal sensing, and a dedicated weather station. Several artificial wetting events were conducted during the summer of 2021 alongside controlled laboratory work. A significant cooling effect was found (average of 1, and up to 5 of cooling), which was around half that computed for well-watered green space. It was found that the evaporation rate of the wetted pad was dependent on the degree of saturation, with the greatest heat loss efficiency occurring when the grit layer was partially saturated. A variety of secondary observations were also made, including issues around water fouling, and porous bricks. Whilst permeable paving can assist with flood alleviation, is it hoped, through minor design modifications, that it can also help tackle extreme urban heat impacts.

How to cite: Butler, A., Rowan, T., and Paschalis, A.: Investigation of thermal cooling potential of Permeable Paving at an urban trial site in London, UK, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14713, https://doi.org/10.5194/egusphere-egu23-14713, 2023.

EGU23-15672 | ECS | Orals | HS5.14

The effect of Nature Based Willow system deployment at a catchment scale for flood control 

Arunima Sarkar Basu and Laurence Gill

Extreme hydro-meteorological events have caused massive devastations in European territories. The rising frequency and severity of hydro-meteorological events such as floods appear to be associated with climate change and land cover change. Flooding can be broadly classified into three types, fluvial flooding, pluvial flooding and coastal flooding. Fluvial flooding occurs when rivers and streams break their banks and water flows out onto the adjacent low-lying areas (the natural floodplains). Many factors are responsible in understanding the impact of rainfall events to fluvial flooding. The factors are size and slope of catchment, permeability of the soil, urbanization and soil compaction, presence of dams upstream to the floodplain and degree to which water can be stored in the dam and the rate of water release.

Pluvial flooding occurs when the amount of precipitation received exceeds the capacity of storm water drainage systems or the capacity of ground to absorb it.

Due to urbanization process, the surface cover of the land alters leading to increasing impervious areas and decreasing infiltration of the soil

The main focus of the research is to understand the effect of willow plantation at a catchment scale for improving pervious areas for flood control. Willow plants have shown high rate of evapotranspiration and improved infiltration. Willow based systems are used to understand the improvement in the rate of evapotranspiration and infiltration in the presence of appropriate climate and representative soil conditions in Ireland.

The willow systems are being monitored in the western, eastern and northern catchments in Ireland which are regulating the evapotranspiration and also the rate of infiltration at a catchment scale. A statistical rainfall runoff model has been deployed to understand the rainfall-runoff relationship. The evapotranspiration has been estimated based on the Penman–Monteith equation, which requires values of mean temperature, wind speed, relative humidity and solar radiation at daily scale. An inter-comparison for rainfall-runoff relationship is made for estimating the percentage change for improvement in runoff in the presence and absence of the willow plantations.

How to cite: Sarkar Basu, A. and Gill, L.: The effect of Nature Based Willow system deployment at a catchment scale for flood control, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15672, https://doi.org/10.5194/egusphere-egu23-15672, 2023.

EGU23-15822 | ECS | Posters virtual | HS5.14

Sludge valorisation to obtain high quality water from WWTP 

Nuria Oliver, Miguel Año, Pura Almenar, Angela Baeza, Carmen Hernández-Crespo, and Miguel Martín

In the face of insufficient water resources and the intensification of extreme events caused by climate change, the generation of non-conventional water sources is an option that should become a priority. Wastewater treated is a water resource that with proper post-treatment can be suitable for maintaining the environmental quality of rivers and wetlands or be used for productive activities such as agricultural uses.

Returning water to the environment in similar conditions to its original state is vital to promote its reuse and to help maintain biodiversity. In this sense, the project Integrating circular economy and biodiversity in sustainable water treatments based on constructed wetlands LIFE RENATURWAT aims to demonstrate that it is possible to obtain high quality water from Waste Water Treatment Plants (WWTP) effluents by combining Nature-Based Solutions (NBS) and industrial wastes.

One of the disruptive issues of this project is exactly the use of a waste generated in the integral water cycle itself, concretely during the production of drinking water, to produce quality water from WWTP. This sludge (DWTS) has inert and non-toxic properties, so usually is disposed in landfills, not taking profit of the economic and environmental benefits derived from its valorisation. Nevertheless, the DWTS has adsorbent capacities due to the coagulant used in the drinking water treatment process.

LIFE RENATURWAT plans to use the DWTS as an active substrate in constructed wetlands (CWs) aimed at upgrading treated urban wastewater. This sludge is dewatered and milled to obtain a grain size similar to sand. The DWTS reinforces the wetland technology so that it can be more efficient and can efficiently remove phosphorus and other pollutants at the same time as generating a habitat in itself.

The solution includes two kinds of CWs operating in series. The first is a vertical subsurface flow constructed wetland with DWTS as a filter medium and the second one is a free water surface constructed wetland. The described system is able to remove phosphorus from wastewater even at very low concentrations, achieving an average total phosphorus concentration in the effluent below than 0.1 mgP/l. This is considerably lower than the legal limit set by Directive 91/271/EEC, UWWTD (1 or 2 mg P/l), as well as the so-called sensitive area 0.6 mg P/l. In this way, a wastewater effluent with a very low phosphorus concentration is obtained, without additional consumption of reagents, addressing one of the main problems faced by WWTP managers, which is the eutrophication of the natural environment and compliance with phosphorus discharge limits. Within the framework of this project, two pilot projects have been implemented, one in the Valencian town of Carrícola, and the other in the Los Monasterios urbanisation (Puçol).

How to cite: Oliver, N., Año, M., Almenar, P., Baeza, A., Hernández-Crespo, C., and Martín, M.: Sludge valorisation to obtain high quality water from WWTP, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15822, https://doi.org/10.5194/egusphere-egu23-15822, 2023.

Unprecedented famine and rocketing food prices are expected to grow as the emerging shocks continue to reshape our world. Lying at the interface of the resource nexus, the agri-food systems are identified as a primary consumer of global freshwater resources and the main contributor to food security. As a result of external shocks, limitations on human activities have resulted in unexpected disturbances in the global agri-food chain, decreasing the functionality and efficiency of agri-food systems and raising the alarm for a need to transform our food systems. Candidating Peri-urban Green Belts as agents of transformation, this research investigates the potential of adding decentralized and coupled Citizen Science and Nature-Based Sanitation Solutions (CS-NBSS) to cause a transformation in urban and peri-urban contexts. Utilizing existing knowledge from researchers and practitioners in the field, alternative NBSs have been identified which interconnect the WASH sector to the food sector, e.g., evapotranspiration tanks (TEvap). We hypothesize that adding such systems to the existing grey infrastructure can increase food and urban resilience and promote marginalized communities' participation in urban governance. CS and NBS have been prominently highlighted in literature due to their merits in constructing and promoting sustainable attitudes and contexts, causing the underlying systems to behave sustainably. Considering the vital role of governance in steering the technical, economic, social, and environmental dimensions of transformation, a critical question remains on how to go beyond existing public policy research on the participation variable. Current research primarily emphasizes ‘what is (status quo) and what needs to be’ rather than proposing methodological approaches towards the latter. With this objective in mind and focused on the food and WASH sector as primary concerns of peri-urban communities, their local governments, and academia, this project will apply mixed-method research to collaboratively design, implement, monitor, and evaluate CS-NBSS living lab experiences in three case studies, incorporating and assessing the effect of such systems on the participation variable, food, and urban resilience, as well as their potential to cause a transformation.

How to cite: Loghmani Khouzani, S. T.: Peri-Urban Green Belts: Introduction of Decentralized and Coupled Citizen Science and Nature-Based Sanitation Solutions in the Context of Urban Transformation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16391, https://doi.org/10.5194/egusphere-egu23-16391, 2023.

Since the settlement of the São Miguel Island (Azores-Portugal), in the middle of the fifteenth century, there is a record of occurrence of landslides, some with high socio-economic impact. In this work, we carried out a spatial, temporal and impact analysis of landslide events that were registered in the NATHA (Natural Hazards in Azores) database for the period 1900-2020, based on newspapers descriptions. A total of 236 landslide events (a day with one or more landslides identified) that caused human losses, damage to houses or obstruction of roads on São Miguel Island were catalogued. Based on the recorded events, it is verified that there is not a regular increment and/or pattern in the distribution of the events over time, although two main periods can be distinguished: 1900–1994 (1.0 events per year) and 1995–2020 (5.3 events per year). The events were responsible for 82 fatalities, 41 injuries, 66 houses partially or totally destroyed and 305 homeless people. The municipality of Povoação registered 76 landslide events, followed by the municipalities of Ribeira Grande (71 events), Ponta Delgada (69 events), Vila Franca do Campo (47 events), Nordeste (26 events) and Lagoa (21 events). Although there is a relative homogeneity on the distribution of landslide events in the municipalities of Povoação, Ribeira Grande and Ponta Delgada, the same does not apply to the impact caused. In the municipality of Povoação were counted 48 fatalities, 20 injuries, 17 houses destroyed and 109 homeless people, in Ponta Delgada 14 fatalities, 14 injuries, 24 houses destroyed and 173 homeless people and in Ribeira Grande 8 fatalities, 5 injuries, 16 houses destroyed and 21 homeless people. In the municipality of Vila Franca do Campo were counted 7 fatalities and 2 houses destroyed, in Nordeste 3 fatalities and 2 injuries, and in Lagoa 2 fatalities, 7 houses destroyed and 2 were homeless people. Rainfall was the triggering factor responsible for 70% of the catalogued landslide events, followed by sea erosion (8%), anthropogenic actions (4%) and earthquakes (2%). The triggering factor was not possible to identify in 16% of the landslide events. Landslides occurred mostly during the rainiest season (from November to March), which comprise about 78% of the catalogued landslide events.

How to cite: Silva, R. F., Marques, R., and Zêzere, J. L.: Landslides on São Miguel Island (Azores-Portugal) in the period 1900-2020: Analysis of the spatio-temporal distribution, triggering factors and impact based on newspapers press articles, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-445, https://doi.org/10.5194/egusphere-egu23-445, 2023.

EGU23-1542 | ECS | Orals | HS7.5

Triggering rainfall conditions of post-fire debris flows in Campania, Southern Italy 

Stefano Luigi Gariano, Giuseppe Esposito, Rocco Masi, Stefano Alfano, and Gaetano Giannatiempo

The Campania region, in Southern Italy, is affected by hundreds of wildfires every year, mainly during the summer season. Starting from the month of September, mountain watersheds including those hit by wildfires are impacted by even more frequent intense rainstorms. In such conditions, the high sediment availability, lack of recovered vegetation and a likely stronger soil water repellency increase the likelihood of surface runoff and soil erosion, leading to potential post-fire debris flows downstream.

This work provides information on more than 100 post-fire debris flows (PFDFs) that occurred in Campania between 2001 and 2021, with a particular focus on the triggering rainfall conditions. Rainfall measurements at a high temporal resolution (10 min) were gathered from a dense rain gauge network, with an average distance between sensors and PFDFs initiation areas of 2.6 km. Information on the occurrence of PFDFs was obtained from web news, social networks, and reports produced by the Fire Brigades. The collection of accurate information related to the debris flow timing and location allowed retrieving and analyzing properties of the triggering rainfall inputs, by identifying the minimum triggering conditions with rainfall thresholds. Moreover, to evaluate the temporal structure and type of the storms associated with the PFDFs (e.g., convective or frontal systems), the standardized rainfall profiles of the triggering events were defined. The return times of the peak cumulative rainfall of the bursts in 10, 20, and 30 minutes were also calculated.

Results show that the triggering rainfall events are very short (37 minutes on average), with high average intensity (73.2 mm/h and 49 mm/h in 10 and 30 minutes, respectively), and mostly associated with severe convective systems (i.e., thunderstorms). The estimated return times are quite low, with 75° percentiles of the related distribution ranging from 2.7 to 3.2 years, indicating that these rainfall events are neither rare nor extreme, as also observed by other authors worldwide. Differences are observed in return times and the spatial distribution of the events that occurred in July-September (higher rainfall magnitudes and longer return times) rather than in October-December. The time window in which PFDFs are more likely to occur in the study area has an extension of four months, from September to December. According to the defined triggering rainfall threshold, a rainfall of 11.4 mm in 30 minutes (corresponding to an average intensity of 22.8 mm/h) is likely sufficient to trigger a PFDF in the study area.

These research outcomes provide reliable and effective support to inform decision-makers engaged in hazard assessment and risk management, in order to implement suitable countermeasures in terms of monitoring and early warning systems. It is worth noting that PFDFs often occur in small-scale watersheds characterized by very short concentration times, in response to intense bursts of less than 60 minutes. This means insufficient lead time to fully develop an effective emergency response. This and other criticalities represent serious challenges requiring additional work.

How to cite: Gariano, S. L., Esposito, G., Masi, R., Alfano, S., and Giannatiempo, G.: Triggering rainfall conditions of post-fire debris flows in Campania, Southern Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1542, https://doi.org/10.5194/egusphere-egu23-1542, 2023.

EGU23-2000 | ECS | Orals | HS7.5

Foreseeing the propensity of rivers to extreme floods 

Stefano Basso, Ralf Merz, Larisa Tarasova, and Arianna Miniussi

Notwithstanding hundreds of years of efforts, flooding is still the most common natural disaster. A reliable assessment of the impending flood hazard is indeed an outstanding challenge with severe consequences. Mistaken estimates of the odds and magnitude of extreme floods especially result in huge economic losses due to widespread destruction of infrastructure and properties.

We show here that we can infer the propensity of rivers to generate extreme floods by means of two hydroclimatic and geomorphic descriptors of watersheds, which embody the spatial organization of the stream network and the characteristic streamflow dynamics of the river basin. These features are main determinants of a sharp increase of the magnitude of the rarer floods and of the flood value for which this marked growth of magnitude occurs, which we term flood divide as it separates ordinary from extreme floods. Their relevance is suggested by a novel ecohydrological approach to flood hazard assessment and confirmed by observations from hundreds of watersheds in the USA and Germany.

We first ascertained the capability of the method to distinguish between basins which do not and exhibit a flood divide, and its ability to dependably estimate its magnitude. We then applied a dimensional reduction tool to pinpoint key physioclimatic controls of the occurrence of flood divides, verifying our results against data. Finally, we utilized descriptors of these controls (namely the hydrograph recession exponent and streamflow variability) within binary logistic regression to predict the possible occurrence of flood divides and extreme floods in river basins. Repeated analyses for independent realizations of subsets of data indicate good prediction accuracy.

The identified controls of the propensity of rivers to generate extreme floods are readily estimated from primary hydroclimatic variables. The tool thus allows for inferring cases where extreme events shall be expected from short records of ordinary events, providing valuable information to raise awareness of the peril of floods in river basins.

This study summarizes results of the DFG-funded project "Propensity of rivers to extreme floods: climate-landscape controls and early detection - PREDICTED" (Deutsche Forschungsgemeinschaft - German Research Foundation, Project Number 421396820).

How to cite: Basso, S., Merz, R., Tarasova, L., and Miniussi, A.: Foreseeing the propensity of rivers to extreme floods, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2000, https://doi.org/10.5194/egusphere-egu23-2000, 2023.

EGU23-2240 | ECS | Posters on site | HS7.5

Large-scale dynamical drivers associated with sub-daily extreme rainfall in Europe 

Anna Whitford, Hayley Fowler, Stephen Blenkinsop, and Rachel White

Short-duration (3hr) extreme rainfall events can cause significant socioeconomic and structural damage, alongside loss of life, due to their ability to generate dangerous flash floods, particularly in urban areas and small catchments. With the projected future increase in the frequency and intensity of these events due to global warming, it is imperative to improve our ability to provide warning to communities that may be impacted by these floods. Large-scale atmospheric dynamics play a role in generating the conditions conducive to the development of local-scale sub-daily extremes, but our current understanding of these processes is limited. Additionally, large-scale circulations are inherently more forecastable than small-scale features such as convection, therefore, this project focuses on finding connections between the large-scale dynamics and sub-daily extremes.

This study uses the quality-controlled Global Sub-Daily Rainfall dataset to identify past extreme events in western Europe. The atmospheric circulation pattern present on the day of each event is extracted from the UK Met Office’s set of 30 weather patterns (WPs) based on mean sea level pressure. This information is then used to examine the intensity and frequency of extreme events under each WP, leading to analysis of the spatial connections between the WPs and sub-daily extremes.

Results indicate just 5 of the 30 WPs account for 53% of recorded 3hr events above the 99.9th percentile in Europe in summer. The important WPs are a mixture of those showing a cyclonic system (cut-off low) close to or over western Europe and those representing a transitional environment. There are also distinct spatial patterns to the relationships in some cases, for example WP11 (isolated low pressure centred over the south-west UK), is associated with very high frequency of extremes over the UK and Portugal but much lower frequencies elsewhere in Europe. The identification of a select group of WPs as important for the generation of sub-daily extremes has implications for forecasting these events at longer lead times, as the large-scale WPs can be predicted further ahead than local conditions.

The WP-based analysis is supplemented by investigation of the links between the sub-daily rainfall extremes and synoptic scale Rossby wave patterns. The Local Finite Amplitude Wave Activity (LWA) metric is used to identify regions of anomalous cyclonic or anticyclonic wave activity both prior to and during the extreme events. This analysis indicates anomalous cyclonic wave activity at certain locations, including over Alaska, to the west of the British Isles and over northern Siberia, is significantly correlated with extreme rainfall over Europe. It is also possible to trace the LWA in days leading up to the extreme events, enabling identification of wave patterns that evolve into conditions associated with the extremes.

These results offer new evidence on the role of large-scale dynamics associated with sub-daily extreme rainfall, whilst also providing powerful information that could be used in the forecasting of these events.

How to cite: Whitford, A., Fowler, H., Blenkinsop, S., and White, R.: Large-scale dynamical drivers associated with sub-daily extreme rainfall in Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2240, https://doi.org/10.5194/egusphere-egu23-2240, 2023.

EGU23-2462 | ECS | Orals | HS7.5

Towards a method of rapid flood scenario mapping using hybrid approaches of hydraulic modelling and machine learning 

Andrea Pozo, Matthew Wilson, Emily Lane, Fernando Méndez, and Marwan Katurji

Floods are the most common hazard in New Zealand, the second most costly and they will change rapidly in frequency and intensity, become more extreme as the impacts of climate change become realized. At the same time, we are undergoing an intense urban development and growing population lives in floodplains, increasing the risk for people’s households and wellbeing. Additionally, computers have limited power and capacity, so there is a limitation in the number of flood scenarios that can be assessed and in the complexity of the hydrodynamic modelling process. This research project, which is part of the 5-year multi-stakeholder research programme “Reducing flood inundation hazard and risk across Aotearoa/New Zealand”, supported by the New Zealand Government and led by the National Institute of Water and Atmospheric Research (NIWA); investigates the feasibility of using a hybrid hydrodynamic/machine learning model to reduce the numerical modelling load and enable probabilistic modelling. The study site is the Wairewa catchment (Little River, Canterbury, New Zealand), working closely with the Wairewa Rūnanga based there. A sample of flooding scenarios is constructed based on the characteristics of the main inundation driver (spatial and temporal characteristics of rainfall extreme events) and other inundation drivers (lake level and antecedent conditions in the catchment). Selected scenarios from this sample will be modelled through a previously calibrated hydrodynamic model and the resultant inundation maps (maximum water depth map for each event) will be used to train a machine learning algorithm to produce the maps for the remaining events. The hybrid model would provide for any flooding scenario (defined by a specific number of variables) the corresponding inundation map in a fast and accurate way, avoiding the hydrodynamic modeling process (very time and computationally expensive). Results from this research will be used to develop a Mātauranga Māori approach to flood resilience and flood related policies by the local and central governments.

How to cite: Pozo, A., Wilson, M., Lane, E., Méndez, F., and Katurji, M.: Towards a method of rapid flood scenario mapping using hybrid approaches of hydraulic modelling and machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2462, https://doi.org/10.5194/egusphere-egu23-2462, 2023.

EGU23-3005 | ECS | Posters virtual | HS7.5

Variations in floods associated with Tropical Cyclones over Mexico under ENSO conditions 

Christian Dominguez and Alejandro Jaramillo

Tropical cyclones (TCs) are among the most hazardous hydrometeorological phenomena. Mexico is affected by TCs from the North Atlantic and Eastern Pacific oceans, and they originate 86.5% of domestic disasters. The natural hazards associated with TCs are extreme precipitation events, floods, storm surges, and landslides. In the present preliminary study, we focus on exploring how El Niño-Southern Oscillation (ENSO) modulates the frequency and magnitude of extreme precipitation events and floods caused by TCs. We use the CHIRPS dataset for determining the extreme precipitation events (defined by the 95th percentile of daily precipitation) and Mexican rain gauge stations from May to November during the 1981-2013 period. We find that TCs are responsible for ~60% of floods in coastal regions, but this percentage decreases inland. Under El Niño conditions, most floods occur over southwestern Mexico. During neutral conditions, the western coast of Mexico is mainly affected. Under La Niña conditions, most floods occur over the eastern coast of Mexico. Additionally, trends in floods are explored. We conclude that local decision-makers need this information to decrease the hydrometeorological risk before the tropical cyclone season begins. Implementing this information on Early Warning Systems for TCs is also discussed.

How to cite: Dominguez, C. and Jaramillo, A.: Variations in floods associated with Tropical Cyclones over Mexico under ENSO conditions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3005, https://doi.org/10.5194/egusphere-egu23-3005, 2023.

EGU23-3073 | Posters on site | HS7.5

Flooding Hazard of Union Station and Impact of Ridership due to Climate Change-an Example of Banqiao Main Station 

Yong-Jun Lin, Hsiang-Kuan Chang, Kai-Yuan Ke, Jihn-Sung Lai, and Yih-Chi Tan

This study adopts the rainfall scenario generated by TCCIP (The Taiwan Climate Change Projection Information and Adaptation Knowledge Platform) based on IPCC AR5, which provides the 95th percentile of Taipei’s maximum 24-hour cumulative rainfall due to climate change. The baseline of this scenario is 404 mm for 1979-2008, and the projected rainfall is 517 mm for the future mid-century (2039-2065).

The flooding potentials of the Taipei Mass Rapid Transit (MRT) stations are obtained by applying the scenarios of rainfalls and the corresponding rainfall patterns of each rainfall station to a two-dimensional flood model. The flooding simulations of baseline and future scenarios show that Jingan Station and Fu-Jen University Station have the highest flooding potential, with a maximum flooding depth of 2 meters. The flooding hazard factors include flooding depth, flow velocity, and rising rate of water surface level. We adopted those factors to analyze the flooding hazard at Banqiao Main Station, which unites Banqiao Railway Station, a high-speed rail station, and Banqiao MRT station. It has a severe flooding potential and a large traffic volume. Because the mid-century rainfall is 1.43 times that of the baseline, the corresponding flooded area of the future scenario is also increased. As a result, the flooding hazards around the exits of Banqiao Main Station are high within the 300 m buffer for the baseline. In contrast, the very high flood hazard was found in a 200m-300m buffer for the future scenario.  

MRT Banqiao Station has 5 entrances/exits, while Banqiao Railway Station has 6 entrances/exits, a total of 11. The average daily ridership at this union station before Covid-19 is 159,239 people/day. The impact ratio of the ridership is set by the degree of flood hazard for each entrance/exit. In the future scenario, the number of affected people is roughly estimated to be 11,611 people/day, which is about 7% of daily ridership before Covid-19.

How to cite: Lin, Y.-J., Chang, H.-K., Ke, K.-Y., Lai, J.-S., and Tan, Y.-C.: Flooding Hazard of Union Station and Impact of Ridership due to Climate Change-an Example of Banqiao Main Station, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3073, https://doi.org/10.5194/egusphere-egu23-3073, 2023.

EGU23-3734 | ECS | Orals | HS7.5

Modeling risk to infrastructure due to episodic debris fan aggradation 

Yuan-Hung Chiu, Colin P. Stark, and Hervé Capart

In many mountain valleys, communities and infrastructure are exposed to high risks of damage due to debris fan aggradation. To assess such risks, two questions must be addressed: (1) What will be the extent and thickness of deposition over the fan for a given volume of debris delivered from the upstream catchment? (2) How large could debris flow volumes be for a single event or a sequence of events? In this contribution, we propose a methodology to address both questions. Its first component is a simplified model of debris fan morphology, based on assuming a fan-slope-distance relationship along paths affected by topographic obstacles like steep valley sides. Using a computationally efficient algorithm, this model can be used to reconstruct past fan volumes from terrace remnants resolved on high resolution DEM topography, and to simulate large numbers of possible future events. Its second component is a stochastic model for the evolution of fan volume framed as a form of random walk. To take into account the episodicity of debris delivery, we model this random walk as a gamma-subordinated Wiener process aka a variance-gamma process. To calibrate the model parameters, we exploit both short-term and long-term data: for the slope-distance relationship, topographic data from recent and Holocene debris-fan remnants; for the stochastic process parameters, reconstructed fan-volume changes associated with recent flood events and with older radiocarbon-dated fan surfaces. We illustrate the approach with an application to the Laonong River in southern Taiwan. In this valley, an important roadway link has been repeatedly damaged by debris-flow aggradation. To guide road and bridge reconstruction, it is essential to assess fan aggradation risk for different design alternatives on a decadal time scale or more. The model provides a basis for optimizing the layout and height of such infrastructure.

How to cite: Chiu, Y.-H., Stark, C. P., and Capart, H.: Modeling risk to infrastructure due to episodic debris fan aggradation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3734, https://doi.org/10.5194/egusphere-egu23-3734, 2023.

EGU23-4243 | ECS | Posters on site | HS7.5

Do CMIP6 climate models capture rapid shifts between dry and wet extremes? 

Rong Gan and Yuting Yang

Do CMIP6 climate models capture rapid shifts between dry and wet extremes?

Authors: Rong Gan1, Yuting Yang1,*

Affiliations: 1State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, China

*Correspondence to: Yuting Yang (yuting_yang@tsinghua.edu.cn)

Keywords: CMIP6, climate extremes, compound events

Abstract:

Rapid shifts between dry and wet extremes may impose higher socioeconomic and environmental pressure than single extremes. Whether the sixth phase of the Coupled Model Intercomparison Project (CMIP6) models are capable of capturing the abrupt alternations between dry and wet periods remain elusive. Here we examine such compound events simulated by CMIP6 models based on the state-of-the art reanalysis datasets, namely ERA5, NCEP-NCAR and MERRA-2. The 1-month Standard Precipitation-Evapotranspiration Index (SPEI) were first calculated to identify dry spells (SPEI≤1) followed by wet spells (SPEI≥1), and vice versa. Event characters including frequency, duration and intensity were then evaluated across all CMIP6 models and reanalysis datasets spanning 1980-2014. We find the following:

  • CMIP6 multimodel-ensemble median and reanalysis ensemble give close estimates of event characters on global average, with frequency being about 4.1 and 3.67 (No. events/20-year), duration of 2.50 and 2.55 (months), and intensity around 3 (SPEI mean) for dry-wet events, respectively. Similar values were found for wet-dry events.
  • During 1980-2014, CMIP6 and reanalysis indicate roughly 10% increase in event frequency comparing the first and last 20-year periods, and less than 1% increase in duration and intensity for both dry-wet and wet-dry events.
  • Spatial distribution for event frequency tends to overlap for dry-wet and wet-dry events, as shown by both CMIP6 models and reanalysis. Hot spots were found in North-eastern America, Europe, Eastern Asia, South-western America, and Middle Africa. Higher latitude regions were shown to experience more events. Despite general spatial agreement between CMIP6 and reanalysis, discrepancies can be seen on finer scales within each region.
  • Common spatial patterns for duration were also found between the two types of events based on CMIP6 models, where the events tend to last longer in middle and southern Eurasia, Eastern Africa, northwest of South America and west of Northern and Central America. However, reanalysis indicates longer events also happened in Middle Africa and eastern Australia. Both CMIP6 models and reanalysis indicate longer event duration roughly around the equator.
  • CMIP6 models give much higher dry-wet intensity compared to wet-dry, especially in Australia and Southern and Western Asia. Reanalysis agrees well on this pattern, yet greater magnitude differences were found in eastern South America.

Overall, CMIP6 models are capturing the variations of abrupt dry and wet alternations well when compared to reanalysis. The models are more skilful in simulating event frequency than duration and intensity in general. Caution should be paid assessing such compound events especially on smaller spatial scales and sensitive regions such as Africa for frequency and Australia for duration and intensity. Our results can be further employed to support climate risk adaptation and mitigation.

How to cite: Gan, R. and Yang, Y.: Do CMIP6 climate models capture rapid shifts between dry and wet extremes?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4243, https://doi.org/10.5194/egusphere-egu23-4243, 2023.

EGU23-4417 | ECS | Posters on site | HS7.5

Hazard index applied to natural rivers – Preliminary result from a case study of mountain trails in southern Brazil 

Marina Refatti Fagundes, Fernando Mainardi Fan, Gean Paulo Michel, Karla Campagnolo, Masato Kobiyama, Ronald Pöppl, and Bruno Henrique Abatti

Trails are one of the main places for ecotourism practitioners’ activities. Many of them are located close to watercourses, and it is often necessary for practitioners to cross them. This often leads to dangerous situations, since critical conditions of water stages and flow velocity can make people lose their walking stability. One way to quantify these hazards is the hazard index (HI) which, in general, is defined as the product of the flow velocity by its depth (Stephenson, 2002). Although many studies have been carried out to determine the HI values as safety limits for people exposed to water flows, none of them analyzed the natural river conditions like those encountered during an ecotourism trail. In these environments, locomotion is hampered due to the surface which is usually highly irregular and often contains slippery rocks and sediments. Thus, that there is a gap related to the HI analysis in natural rivers, and more research becomes necessary, since more people have sought to carry out activities related to ecotourism. The main objective of this research is to apply HI approach in natural rivers so that its results can be utilized in the management of trails containing watercourses crossing. Initially, a bibliographic review was carried out, where some important concerns related to people's loss of stability were analyzed. The results of the bibliographic review were organized within a summary table which permits verifying variables with stronger influence on people's stability, during these walks. After this first stage, three mountain trails located in the Aparados da Serra National Park, in southern Brazil, were selected for field measurements. In all of these trails, measurements of flow depth and velocity were carried out using a small current meter and the granulometry of the river sediments was measured through an adaptation of the Pebble Count method. The measurements were taken at all points where tourists cross the riverbed during the trails, i.e., 23 measurement sites in total. The analysis of these data resulted in preliminar information: (i) an easy-to-interpret diagram that indicates the thresholds values of HI in natural rivers, named Hazard Index Diagram of Natural River (HIDNR); and (ii) list of the main variables responsible for people's loss of stability, in order to contribute to the safety of ecotourism practitioners. One of the next steps of the work is to analyze how the sediment transport and connectivity behaviour could give us insights about hazard levels.

REFERENCES

STEPHENSON, D. (2002). Integrated flood plain management strategy for the Vaal. Urban Water, v. 4, n. 4, p. 423-428.

How to cite: Refatti Fagundes, M., Mainardi Fan, F., Michel, G. P., Campagnolo, K., Kobiyama, M., Pöppl, R., and Abatti, B. H.: Hazard index applied to natural rivers – Preliminary result from a case study of mountain trails in southern Brazil, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4417, https://doi.org/10.5194/egusphere-egu23-4417, 2023.

EGU23-4537 | ECS | Orals | HS7.5

What controls physical vulnerability to geo-hydrological hazards? A contribution to quantitative assessment of landslide and flood risk in western Uganda 

John Sekajugo, Grace Kagoro-Rugunda, Rodgers Mutyebere, Clovis Kabaseke, David Mubiru, Esther Namara, Violet Kanyiginya, Bosco Bwambale, Liesbet Jacobs Jacobs, Olivier Dewitte, and Matthieu Kervyn

Geo-hydrological hazards (landslides and floods) are often associated with significant damages on physical infrastructure like buildings and roads. Understanding the factors controlling the extent of damage is a prerequisite for quantitatively estimating risk and its spatial distribution, and advising on measures to reduce vulnerability. In this study we document the impact of 64 landslide and six flood events in four selected districts in western Uganda for the period May 2019 - March 2021 through extensive fieldwork. We quantify in economic value the physical damage of landslide and flood hazards on exposed buildings, roads and bridges. We then analyse the physical vulnerability based on damage ratios and determine the factors  (building material, hazard characteristics and age of the building) that control the degree of damage using fractional logistic regression. Out of the 91 buildings affected by landslides, 54% were totally destroyed, and only 10% not or minorly damaged, for an average damage cost of 3,179 USD/building. For the 212 documented buildings affected by floods, 35% were totally destroyed, 28% had severe to moderate damage and the rest were minorly or not affected, with an average damage costs of 1,755 USD/building. The physical vulnerability of buildings to landslides depends on the size of the landslide, age of the building, type of building wall material and the steepness of the slope cut to establish an artificial foundation platform. On the other hand, the physical vulnerability of buildings to flood hazards is largely controlled by the flood depth, the distance from the river channel, slope, size of flooded area and type of floor material. The physical vulnerability functions developed in this study are being used as a new inputs into a regional quantitative model of geo-hydrological risks. Combining the hazard estimates with the most accurate information on exposure of physical infrastructure, will facilitate the identification of the types of events and the locations that require most attention for risk reduction.

How to cite: Sekajugo, J., Kagoro-Rugunda, G., Mutyebere, R., Kabaseke, C., Mubiru, D., Namara, E., Kanyiginya, V., Bwambale, B., Jacobs, L. J., Dewitte, O., and Kervyn, M.: What controls physical vulnerability to geo-hydrological hazards? A contribution to quantitative assessment of landslide and flood risk in western Uganda, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4537, https://doi.org/10.5194/egusphere-egu23-4537, 2023.

EGU23-5513 | ECS | Posters on site | HS7.5

Global IDF curves created from local observations using machine learning 

Jannis Hoch, Izzy Probyn, Joe Bates, Oliver Wing, and Christopher Sampson

Intensity–duration–frequency (IDF) curves are representations of the probability that a given rainfall intensity will occur within a given period. At the global scale, however, only for a few locations sub-daily rain gauge data is available from which global IDF curves could be derived. This poses a major challenge for simulations of global pluvial flood hazard and risk which require information of intensity, duration, and probability as boundary conditions. Therefore, efficient yet accurate means for scaling the locally available data to the global extent need to be found.

Consequently, we use available quality-controlled sub-daily precipitation data from the GSDR data set to derive growth curve parameters at around 10,000 locations world-wide. After combining these scale and shape parameters with globally available data of main precipitation drivers, a regionalized machine learning model is first trained and tested and then applied to produce global IDF maps.

Finally, we evaluated these maps against an ensemble of openly available local IDF curves found in literature. By selecting locations spread across the globe, we try to ensure to include as much variability as possible in the evaluation. Additionally, the global IDF curves were benchmarked against available more bespoke IDF data in the USA and UK.

While such data-driven approaches clearly depend on the quality and quantity of available sub-daily rainfall observations, the method still shows to capabilities of current data-driven modelling approaches to scale local data to global data applicable in both flood risk research and practice.

How to cite: Hoch, J., Probyn, I., Bates, J., Wing, O., and Sampson, C.: Global IDF curves created from local observations using machine learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5513, https://doi.org/10.5194/egusphere-egu23-5513, 2023.

EGU23-6689 | ECS | Orals | HS7.5 | Highlight

Global analysis of emergency service provision to vulnerable populations during floods of various magnitude under climate change 

Sarah Johnson, Robert Wilby, Dapeng Yu, and Tom Matthews

In a world of increasing global flood hazards, vulnerable populations (very young and elderly) are disproportionately affected by flooding due to their low self-reliance, weak political voice and insufficient inclusion in climate adaptation and emergency response plans. These individuals account for most flood casualties and often rely on emergency services due to flood-induced injuries, exacerbated medical conditions, and requiring evacuative assistance. However, emergency service demand often exceeds the potential capacity whilst flooded roads and short emergency response timeframes decrease accessibility, service area, and population coverage; but how does this compare across the globe and what will the future hold?

To answer this question, a global analytical framework has been created to determine the spatial, temporal, and demographic variability of emergency service provision during floods. This is based on global fluvial and coastal flooding (at 10-year and 100-year return periods), and present and future flood conditions (present-day and 2050, under RCP 4.5 and RCP 8.5 climate scenarios). The framework includes an accessibility analysis to identify emergency service accessibility to vulnerable populations based on restrictions of flood barriers and response-time frameworks, a vulnerability analysis to compare the difference in emergency service provision between key demographic groups, and a hotspot analysis to identify the extent and distribution of flood hazards and at-risk vulnerable populations.

Research findings include the identification that (based on the scenario of 2050 riverine flooding at a 100-yr return period under RCP8.5 and a 30-minute response time):

  • Globally, 64% of schools are always accessible to the ambulance service and 56% of schools are always accessible to the fire service
  • Globally, 29% of schools are never accessible to the ambulance service and 38% of schools are never accessible to the fire service.
  • Globally, approximately 20% fewer people are accessible to emergency services than under non-flood conditions.
  • Africa and Asia experience the greatest accessible population reductions (14-27% and 24-25%) whilst Europe experiences the least accessible population reductions (8-9%).
  • Priority hotspot countries are primarily located in central North America (e.g., Belize), northern South America (e.g., Guyana) and west-central Africa (e.g., Liberia).

The highlighted geographical and temporal differences in emergency service provision globally and between regions, in addition to the framework itself, can be used by national and international organisations to inform strategic planning of emergency response operations and major investments of infrastructure, services, and facilities to maximise the benefit to the disproportionately affected vulnerable populations. This includes the production of more detailed flood hazard and evacuation maps that highlight vulnerability hotspots, the prioritisation of vulnerable population groups in emergency response plans to minimise geographic and population disparities of flood injuries and fatalities, and the allocation of emergency service hubs in regions of high vulnerability but low emergency response provision.

How to cite: Johnson, S., Wilby, R., Yu, D., and Matthews, T.: Global analysis of emergency service provision to vulnerable populations during floods of various magnitude under climate change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6689, https://doi.org/10.5194/egusphere-egu23-6689, 2023.

EGU23-7000 | Orals | HS7.5

Areal reduction factor assessment for extreme rainfalls through a new empirical fixed-area formulation 

Alessia Flammini, Jacopo Dari, Carla Saltalippi, and Renato Morbidelli

In the hydraulic structures design against extreme events a proper estimate of the areal reduction factor (ARF) is required. Specifically, rainfall-runoff models widely used need to be fed with information on areal-average rainfall over a watershed surface, while rainfall data is typically available at a point scale. The ARF allows to convert rainfall data from point to areal scale.

In this work, a new fixed-area and deterministic approach for estimating the ARF is proposed; it involves ratios between observed annual maxima with specific duration of average rainfall occurring in a specific area and those referring to all the available point rainfalls in the same area. The approach was applied to the Umbria region in Central Italy where, using high-quality and validated rainfall data (with a temporal resolution of 1 minute), a parametric relation expressing ARFs as function of duration and area was found. The outcomes were then compared with those of the most widespread empirical approaches available in literature, often applied when rainfall data are lacking, obtaining substantial over- or underestimation of empirical ARFs. This confirms that the transposition of ARF relations from a geographic area to another could have not-negligible impacts on the design of hydraulic structures. In addition, indications aimed at selecting the most suitable method to be applied for ARF estimation are provided. Specifically, the proposed approach is suitable when a limited number of years of rainfall observations is available. In this regard, the robustness of the methodology was tested by varying the length of the rainfall observation period; a minimum number of about 6 years was found to make the derived empirical formulation sufficiently accurate in a specific area.

How to cite: Flammini, A., Dari, J., Saltalippi, C., and Morbidelli, R.: Areal reduction factor assessment for extreme rainfalls through a new empirical fixed-area formulation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7000, https://doi.org/10.5194/egusphere-egu23-7000, 2023.

EGU23-7194 | ECS | Posters on site | HS7.5

Urban Flood Risk in Dhaka, Bangladesh 

Farzana Mohuya, Claire Walsh, and Hayley Fowler

Dhaka, the capital city of Bangladesh, is one of the most densely populated cities in South Asia. Urban flooding from extreme rainfall is a recurring phenomenon, with historic floods in 1988, 1998, and 2004 amongst the most catastrophic events in Dhaka. Prolonged urban flooding or water logging is a major concern for both Dhaka North City Corporation (DNCC) and Dhaka South City Corporation (DSCC) areas. This research investigates how “Citizen Science (CS)” could help individuals, communities, and stakeholders understand and manage the risk of current and future urban flooding, integrating formal flood risk management along with the affected area’s respondents’ self-perceived perception, concerns, experience, awareness, and opinions about flood risk management, and ability to cope with the flood risk. Fieldwork data were collected through the administration of a purposely designed questionnaire to 500 respondents in the water logging affected wards of the two city corporations’ areas in Dhaka. Preliminary findings from the fieldwork revealed that every year approximately 45.6% and 29.4% respondents in the study area experienced 1-3 days of urban flooding/water logging, mostly during the monsoon season (June – September), with a work time loss of 3-4 hours respectively. Respondents in the study area are aware and concerned about flooding and its associated risk, and approximately 36.9% respondents think that the frequency of urban flooding will increase in Dhaka in the next 10 years. In terms of the vulnerability, approximately 51.5% respondents mentioned that they are vulnerable to urban flooding and small business holders (Entrepreneurs) are most affected (61.5% respondents) by flooding. Although almost 61.2% respondents were not familiar with the “Citizen Science” concept, but approximately 42.8% of respondents expressed an eagerness to involve themselves in any Citizen Science based project to promote awareness and mitigation of urban flood risk/water logging issues in their community or in Dhaka City. In addition, preliminary findings from Key Informant Interviews (KII) and Focus Group Discussion (FGD) Meetings suggested that unplanned urbanisation, poor and inadequate drainage system management, and recent extreme rainfall events were the major drivers behind the urban flooding/water logging situation in Dhaka.

The study also explored annual and seasonal trends of rainfall in Dhaka (using observed datasets from the Bangladesh Meteorological Department) over the period from 1953-2019 using extreme precipitation indices [Climate Change Detection and Indices (ETCCDI)]. It is revealed that over these 67 years, Annual Maximum Daily Rainfall has increased during winter (0.021 mm/year) but statistically significantly decreased during the monsoon (-0.636 mm/year). The overall annual rainfall has significantly decreased (-0.718 mm/year). Trends in Consecutive Dry Days, heavy, and very heavy precipitation days indicate an annual increasing rate of 0.158 days/year for CDD, 0.077 days/year with >= 10 mm rainfall and 0.019 days/year with >= 20 mm rainfall, respectively. Results from the rainfall datasets are now being integrated with the fieldwork findings and other secondary datasets to set up a Hydrodynamic Model (CityCAT) to investigate current and future flood risk in Dhaka in more detail.

How to cite: Mohuya, F., Walsh, C., and Fowler, H.: Urban Flood Risk in Dhaka, Bangladesh, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7194, https://doi.org/10.5194/egusphere-egu23-7194, 2023.

EGU23-7772 | ECS | Orals | HS7.5 | Highlight

Societal Flood Risk in Italy 

Mina Yazdani, Paola Salvati, Mauro Rossi, Cinzia Bianchi, and Fausto Guzzetti

Flood events are among the most damaging natural disasters, with billions of people being directly exposed to the risk of intense flooding worldwide. The economic and societal consequences of these events are expected to increase in the coming years. Flood societal risk can be determined by analyzing the relationship between the frequency of fatal flood events and the magnitude of the resulting consequences to the population (evaluated by the number of fatalities due to the event). Here, we test an approach previously proposed for landslides to estimate the flood societal risk in Italy, using historical sparse data on flood fatalities, available through national catalogues. Such an approach is based on the use of the Zipf distribution, which has previously been widely adopted for the modeling of societal risk for different natural hazards. The model allowed the evaluation of the spatial and temporal distribution of societal flood risk over the Italian territory over a regularly spaced grid. Different risk scenarios are presented and discussed.  

How to cite: Yazdani, M., Salvati, P., Rossi, M., Bianchi, C., and Guzzetti, F.: Societal Flood Risk in Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7772, https://doi.org/10.5194/egusphere-egu23-7772, 2023.

EGU23-10096 * | ECS | Orals | HS7.5 | Highlight

Could the 2019-20 Australia bushfires or 2020-22 floods be predicted using CMIP decadal prediction? 

Ze Jiang, Dipayan Choudhury, and Ashish Sharma

Over the past six years, Australia has experienced significant fluctuations in rainfall, including prolonged dry conditions and extensive bushfires, followed by two consecutive years of heavy rainfall in the east. Could such anomalies be predicted many years in advance is the question this study hopes to answer. A prediction framework that combines empirical and physically-based approaches using CMIP decadal prediction, and a novel spectral transformation approach is presented. When tested in a hindcast experiment, this framework shows significant prediction skill for rainfall up to five years in the future across all regions and climate zones in Australia. This framework was used to project from 2018 to 2022, covering the years of bushfires and extreme floods in Australia, as an added blindfolded validation of the prediction approach used. Following this, a blind projection of the precipitation anomalies over the continent for the coming five years is presented, to assess whether the anomalies for the past five years were, indeed, anomalies, or part of a pattern of what can be expected into the future. It is shown that this decadal framework has great potential for predicting whether the next few years will be wetter or drier, extending the predictive accuracy beyond a few months into the future. This can be valuable for managing water resources, prioritizing demands, protecting vulnerable systems, and reducing uncertainty in hydrological decision-making.

How to cite: Jiang, Z., Choudhury, D., and Sharma, A.: Could the 2019-20 Australia bushfires or 2020-22 floods be predicted using CMIP decadal prediction?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10096, https://doi.org/10.5194/egusphere-egu23-10096, 2023.

EGU23-10255 | Orals | HS7.5

Cascading flood hazards: the role of large wood transport 

Virginia Ruiz-Villanueva

Floods are one of the most relevant natural hazards, causing significant socio-economic damage every year globally. They will likely continue to increase for various reasons: the climate and global changes, two relevant ones. More importantly, our still limited capability to predict river response to flooding and anticipate the consequences by designing proper and sustainable risk mitigation measures. A recent example was Europe's floods in July 2021, the highest recorded. They led to many casualties and economic losses (i.e., 180 fatalities and billions of Euros). Extreme long, high-intensity rainfall resulted in extreme flows, particularly in small tributaries, but this could not solely explain the devastating impacts. Geomorphological changes, bank erosion and channel widening, sediment erosion and transport, and uprooted and transported trees and instream large wood accumulated at bridges played a significant role. However, these cascade processes are rarely quantified or considered in flood hazard and risk analysis. This is the focus of this talk. Case studies showing a combination of modelling approaches will illustrate how quantifying the supply and transport of instream large wood is essential in river reaches crossing infrastructures like bridges to assess flood-related hazards and risks.

How to cite: Ruiz-Villanueva, V.: Cascading flood hazards: the role of large wood transport, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10255, https://doi.org/10.5194/egusphere-egu23-10255, 2023.

Characterizing the upper tail of flood peak distributions remains a challenge due to the elusive nature of extreme floods, particularly the key elements of flood-producing storms that are responsible for them. Here I examine the upper tail of flood peaks over China based on a comprehensive flood dataset that integrates systematic observations from 1759 stream gaging stations and 14,779 historical flood surveys. I show that flood peak distributions over China are associated with a mixture of rainfall-generation processes. The storms responsible for the upper-tail floods (with the recurrence intervals beyond 50 years) are characterized with anomalous moisture transport and/or synoptic configurations, with respect to those responsible for annual flood peaks. Anomalous moisture transport (in terms of intensity, pathways, and durations) dictates the space-time rainfall dynamics (relative to the drainage networks) that subsequently lead to anomalous basin-scale flood response. I provide physical insights into extreme flood processes based on downscaling simulations using the Weather Research and Forecasting model driven by the 20th Century Reanalysis fields. Modeling analyses for a collective of extreme flood events highlight the role of interactions between complex terrain and large-scale environment in determining the spatial and temporal variability of extreme rainfall. My analyses contribute to improved understanding of the unprecedented flood hazards over China by establishing the nexus between atmospheric processes and basin-scale flood response. These knowledge gains can be potentially used to the upper tail of flood peak distributions.

How to cite: Yang, L.: Hydrometeorological processes and controls of the upper-tail floods over China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10453, https://doi.org/10.5194/egusphere-egu23-10453, 2023.

EGU23-10474 | ECS | Posters on site | HS7.5

A hydrological and socioeconomic risk assessment of tropical cyclone disasters by leveraging space-based Earth observations 

Gigi Pavur, Venkataraman Lakshmi, and James H Lambert

On September 28, 2022, Hurricane Ian made landfall in Florida as the 5th strongest tropical cyclone on record for the United States of America. Preliminary damage assessments conducted by the National Oceanic and Atmospheric Administration (NOAA) estimated over $50 billion USD in insured and uninsured losses from the event. The extensive environmental and socioeconomic consequences of recent hydrometeorological extremes in Florida indicate an urgent need to improve understanding of hydrological and socioeconomic vulnerability in the region to inform future investments to increase resilience to events like Hurricane Ian. This study conducts an interdisciplinary risk analysis of both hydrological and socioeconomic variables before and after Hurricane Ian to improve understanding of the region’s hydrological and socioeconomic vulnerability to hydrometeorological extremes. A variety of publicly available satellite-based remote sensing data are leveraged for the hydrological analysis, specifically precipitation data from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), soil moisture data from Soil Moisture Active Passive (SMAP), synthetic aperture radar data from Sentinel-1, optical imagery from Landsat 8, and Global Navigation Satellite System Reflectometry (GNSS-R) data from the Cyclone Global Navigation Satellite System (CYGNSS) are utilized. Additionally, high-resolution commercial satellite data from Planet, Maxar, and Capella are used to further identify infrastructure damages from Hurricane Ian. To support the socioeconomic risk analysis, publicly available demographic and economic data are used from the U.S. Census Bureau and State of Florida. Results from this work can be used to improve understanding of hydrological and socioeconomic risk in Florida due to hydrometeorological extremes. Additionally, this work can be used to inform priorities and strategy aimed to decrease risk and increase resilience in this region towards major tropical cyclones. 

How to cite: Pavur, G., Lakshmi, V., and Lambert, J. H.: A hydrological and socioeconomic risk assessment of tropical cyclone disasters by leveraging space-based Earth observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10474, https://doi.org/10.5194/egusphere-egu23-10474, 2023.

EGU23-11439 | ECS | Posters on site | HS7.5 | Highlight

Assessing floods impacts on population displacement in Sudan 

Eleonora Panizza, Yared Abayneh Abebe, and Roberto Rudari

The frequency and intensity of floods in the Intergovernmental Authority on Development (IGAD) region in Eastern Africa have increased over the years because of climate variability and change. Sudan is one of the IGAD countries most affected by these extreme events. In August 2022, the country experienced the fourth consecutive year of major flooding, which extensively damaged buildings and impacted people’s livelihoods. Floods also cause the displacement of thousands of people every year in Sudan due to direct damage to houses and impacts on livelihoods, critical services, and infrastructure. The effects of these events on people’s lives are worsened by contextual socio-economic, political, and individual vulnerabilities. In this regard, assessing flood impacts on displacement is crucial to increase people’s resilience and risk reduction capacities.

In this poster, we present the design, execution, and results of a data collection campaign focused on a pilot area in the Khartoum State of Sudan. These data will support the next phase of research, which is an agent-based modeling (ABM) study. The aims of the broader study are to better understand the nexus between flood events and displacement patterns in the area, including flood perception, preparedness, and displacement duration, and to evaluate the impact of different risk reduction policies. The overall goal of the effort is to strengthen local resilience and capacity, and to support policymakers in identifying effective mitigation and management strategies.

Considering that there could not be a one-size-fits-all solution for different contexts, first-hand data were collected at the local level to capture specific information about the area and its population. Questionnaires were administered to a statistically significant sample of residents in the pilot area, focusing on household characteristics, their experience regarding floods and displacement, and their risk perception. Among the results, it was found that 67% of the surveyed population was displaced due to flooding at least once, most of them for a period ranging from 1 to 5 months. The main reason for the decision to move was the damage to the house, followed by flood impacting livelihood. Displacements occurred most often during the event itself, showing a lack of preparedness. Data showed that 81% of the respondents perceived that they lived in a flood-prone area, while 56% of them believed they were at high risk of being displaced due to flood events. To gain a broader understanding of flood risk reduction policies and implementation contexts, representatives of Sudanese institutions and relevant organizations were interviewed. Policy options were explored, including housing policy and Early Warning Systems.  Both questionnaires and interviews are being used to inform the construction of the ABM.

The research is therefore relevant to understand the main elements that affect displacement decisions and to support the design of strategies for mitigating the risk of involuntary mobility in the area, and for increasing people’s resilience and capacity to cope with flood events and displacement risks.

How to cite: Panizza, E., Abebe, Y. A., and Rudari, R.: Assessing floods impacts on population displacement in Sudan, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11439, https://doi.org/10.5194/egusphere-egu23-11439, 2023.

EGU23-11966 | Orals | HS7.5

Spatially consistent flood risk assessment for Germany 

Bruno Merz, Mostafa Farrag, Xiaoxiang Guan, Björn Guse, Li Han, Heidi Kreibich, Dung Nguyen, Nivedita Sairam, Kai Schröter, and Sergiy Vorogushyn

Flood risk assessments are an important basis for risk management. For larger regions, these assessments are often based on small-scale modelling, which is subsequently compiled into a large-scale picture. However, this approach neglects spatial interactions, such as decreasing risk due to upstream dike breaches, and does not provide realistic risk statements for larger regions. This paper presents the ‘derived flood risk analysis’ as an alternative approach and its implementation for Germany. A model chain consisting of hydrological, hydraulic, and damage models simulates the occurrence of extreme runoff, inundation, and direct economic damages. This model chain is driven by a weather generator that provides spatially consistent fields of climate variables. The generation of very long (several thousand years) time series with daily resolution allows the estimation of extreme runoff and corresponding damages. The consideration of the spatial relations in all model components, from the weather generator to the damage model, is able to provide consistent large-scale risk statements. This avoids the significant overestimates typical of many large-scale flood risk assessments.

How to cite: Merz, B., Farrag, M., Guan, X., Guse, B., Han, L., Kreibich, H., Nguyen, D., Sairam, N., Schröter, K., and Vorogushyn, S.: Spatially consistent flood risk assessment for Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11966, https://doi.org/10.5194/egusphere-egu23-11966, 2023.

EGU23-12932 | ECS | Orals | HS7.5 | Highlight

Communicating the return period of extremes 

Elisa Ragno and Amir AghaKouchak

The concept of return period (recurrence interval) of extreme events is widely used in engineering practice and in the media. In engineering design and risk assessment, the concept of return period is used to determine the expected magnitude(s) of one or more extreme weather events – i.e., the expected magnitude of an event that, if occurred, might cause the failure of a structure. In the media, the concept of return period is used to communicate to the general public the severity of an event. For example, the 2021 summer flood in Northwestern Europe was reported in the news as a one-in-400-year event – an event expected on average once in 400 years. The strength of return period as a metric (in years) to describe the severity of events resides in the straightforward comparison between the average occurrence in years of an event with the average number of years a person can experience and recollect events.

Generally, the return period of a rare event and its magnitude (known as return level) is inferred from limited observations - often derived by extrapolating from a distribution function fitted to the available observations. The distribution is often greatly influenced by the length of observations. These factors make the concept of return period prone to misinterpretation as extreme events are rarely observed in existing records.

Here we provide a new perspective on the return period of extremes determined not only by its exceedance probability but also in relation to the observations used to describe the underlying distribution. Our method offers a straightforward metric, independent of the type of statistical distribution adopted, to quantify and communicate the likelihood of having observed the event of interest in the available observations, ranging from unlikely to very likely. This metric can provide a measure of confidence in the statistical inference of return periods based on the length of record used for inference. We argue that this additional information on likelihood offers important information for designers, planners, and decision-makers.

How to cite: Ragno, E. and AghaKouchak, A.: Communicating the return period of extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12932, https://doi.org/10.5194/egusphere-egu23-12932, 2023.

EGU23-14062 | Orals | HS7.5

Suitability of near-real time precipitation products for Flood Risk Forecasting 

Jose Luis Salinas Illarena, Ludovico Nicotina, Shuangcai Li, and Arno Hilberts

Accurate real and near-real time forecasting of extreme flood events has lately become more and more important for the insurance and re-insurance industry (e.g., for claims allocations, Insurance Linked Securities and Catastrophe Bonds…). Examples of such events triggering significant losses in recent years are low-pressure system Bernd (July 2021, eastern Belgium, western Germany, and north-eastern France), hurricane Ida (August-September 2021, Louisiana and Northeastern United States), or hurricane Ian (September 2022, Florida). In order to estimate overall flood risk and flood losses in near-real time, a precipitation product released with a short latency is necessary.

This study analyses the use of the near-real time precipitation products NOAA’s Climate Prediction Center (CPC) and Multi-Radar/Multi-Sensor System (MRMS) for flood forecasting, the latter having a higher spatial and temporal resolution than the former. We investigate and compare their different rainfall characteristics in terms of their ability to capture rainfall extremes, their suitability as input for hydrological/inundation models, and the effect that they have on overall economic losses for a series of selected historical events over the Conterminous United States. Finally, we include in the comparison the more stablished, long-latency dataset North American Land Data Assimilation System (NLDAS), more frequently used for event reconstruction c.a. 1 week after the event.

How to cite: Salinas Illarena, J. L., Nicotina, L., Li, S., and Hilberts, A.: Suitability of near-real time precipitation products for Flood Risk Forecasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14062, https://doi.org/10.5194/egusphere-egu23-14062, 2023.

EGU23-14903 | ECS | Posters on site | HS7.5

Modelling severe hail events over Austria using the metastatistical extreme value distribution 

Marc-André Falkensteiner, Gregor Ehrensperger, Thorsten Simon, and Tobias Hell

Knowledge about extreme values of severe hail plays an important role in engineering and insurance. The estimation of return levels of severe hail events is challenging, as hail is locally rare and documentation about hail events is not available in a unified way. For instance for the state of Austria GeoSphere provides radar based probabilities of hail (POH) and maxima of expected hail size (MEHS) that only span a period from 2010 onward.

Based on this sparse data the application of classical extreme value theory, such as Block-Maxima or Peak over Threshold might be invalid. Instead we use a version of the metastatistical extreme value distribution (MEVD), which was shown to work reasonably well in the context of extreme precipitation events, even with a rather small number of available years used for the estimation in comparison to the recurrence time. More precisely we make an assumption about the underlying probability distribution of the daily maximum POH values. The parameters of the distribution are then modeled as smooth functions of the day of the year and the year of observation, thus employing the framework of generalized additive models for location, scale and shape (GAMLSS). Furthermore we add topographic information (longitude, latitude, altitude) to our model, resulting in a full spatiotemporal model across the whole domain of Austria, from which the return values of the POH, respectively MEHS are calculated.

This framework allows for the incorporation of an arbitrary number of additional covariables, as long as they are available on the same grid as the desired output. To illustrate this we use the information of daily precipitation extremes to enrich the model with additional atmospheric information.

How to cite: Falkensteiner, M.-A., Ehrensperger, G., Simon, T., and Hell, T.: Modelling severe hail events over Austria using the metastatistical extreme value distribution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14903, https://doi.org/10.5194/egusphere-egu23-14903, 2023.

In Indian Himalayas, many hydroelectric projects are now under construction due to the availability of a perennial water source and a natural head for hydropower generation. Hydropower plants often require significant investments, design lifetimes, and lengthy repayment. Indian Himalayan states are now developing State Action Plans on Climate Change, with policies for climate change mitigation and adaptation at the subnational level. These plans recognize GLOFs as a significant climate change-related flood to be considered for the safety of River Valley Projects. The snow-fed catchment area of these projects has many glacial lakes, and there is a high likelihood of breaching for lakes located at the glacier's snout. In general, potentially dangerous lakes are located near the end of a glacier in the lower part of the ablation area. A large mother glacier can create potentially hazardous lakes. These moraine dams could likely breach due   to   piping   or   overtopping   due   to   their porous soil content inside dam body. A sudden discharge of significant magnitude could endanger the safety of the downstream HE hydroelectric project. It is suggested, the glacial lake outburst flood (GLOF) and the design flood be simultaneously considered while assessing the spillway capacity of new hydropower projects to ensure that they are hydrologically secure.

Bajoli-Holi Hydroelectric Project, located on river Ravi in the Himachal Pradesh state of India, is studied, to analyze its spillway capacity considering both GLOF and Inflow Design flood. BIS published the guidelines for fixing spillway capacity. As per the codal provisions, the Bajoli-Holi dam qualifies for PMF as its Inflow design flood.

The hydrology of a particular basin or project undergoes certain changes due to factors such as climate change, urbanization, deforestation, soil erosion, a heavy spell of short-duration rainfall, etc. With the aid of the most recent methods, including hydrodynamic modeling and a hydro meteorological approach, the design flood and GLOF for the dam have been evaluated in this study.

There are a total of 83 glacial lakes identified and mapped in this catchment area. It is further critically analysed to find the effect of the most critical glacial lake which is glacial Lake-52 having an area of 14.5 ha at a distance of 26.5km from the project location. River cross sections spaced 400 m apart has been considered. The upper envelope of the PMF is calculated to be 15,303 cumecs, average envelope is 6247cumecs and the lower envelope value is 2551 cumecs. The combined GLOF peak attenuated after hydrodynamic channel routing at the project site and the PMF analysed, will be taken as the inflow flood for analyzing the spillway requirements for the Bajoli-Holi project. The study results can be applied to similar hydro-meteorologically similar basins of the Himalayas in India which are under the influence of glacial lake outbursts and PMF.

How to cite: Issac, I., Goel, D. N. K., and Rai, N.: Approach and methodology for estimating combined glacial lake outburst flood (GLOF) and PMF design flood for Bajoli Holi hydro-electric project in the Indian Himalayas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15819, https://doi.org/10.5194/egusphere-egu23-15819, 2023.

Flash droughts are generally considered a subset of seasonal drought events. In the present study, we have characterized the flash drought events based on soil moisture index (SMI) using daily ERA5 reanalysis data having a spatial resolution of 0.250 * 0.250 from 1960 till 2021. Flash drought events were identified when SMI drops below the 20th percentile within less than 3 next pentads, and it terminates when SMI goes above the 20th percentile and stays there for the next 2 pentads. Flash drought time series was prepared and the Mann-Kendall trend test was applied to investigate the evidence of the statistically significant trends. To assess the atmospheric drivers (precipitation, PET) of flash drought, standardized precipitation index (SPI), and standardized precipitation evapotranspiration index (SPEI) were calculated during the occurrence of each flash drought event at each grid pixel. For calculating SPI and SPEI, ERA5 reanalysis data of precipitation and PET (potential evapotranspiration) was used. Seasonal analysis of results showed that the flash drought frequency observed during the pre-monsoon season (March-April-May) shows considerable variation when compared to the monsoon (July-August-September) and post-monsoon (October-November-December) seasons. Results of Mann-Kendall statistics show the increasing trend of flash drought over semi-arid regions like Marathwada and Vidarbha. Both SPI and SPEI shows spatially varying similarity with the flash drought events. When observed on a seasonal scale, it is observed that SPEI shows a higher degree of similarity with flash drought events during pre-monsoon season as compared to SPI as evaporative demand is high during this period.  

How to cite: Remesan, R. and Pachore, A.: Analysis of Spatio-temporal variability and atmospheric drivers of the flash drought over Godavari river basin., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15836, https://doi.org/10.5194/egusphere-egu23-15836, 2023.

EGU23-16630 | ECS | Orals | HS7.5

Projection of future rainfall erosivity over China under global warming 

Wenting Wang, Shuiqing Yin, Zeng He, Deliang Chen, Hao Wang, and Andreas Klik

Five CMIP6 models were selected to project changes in rainfall erosivity of China for two future periods (the near-term in 2041-2065, the long-term in 2076-2100) under SSP1-RCP2.6 and SSP5-RCP8.5 scenarios. Models’ capacity in estimating two erosivity indices, annual average rainfall erosivity (R-factor) and the storm erosivity at 10-year return level (10-year storm EI) were evaluated by comparing the model derived indices for the historical period with the state-of-the-art reference erosivity maps of China interpolated with hourly observations. Results show that GFDL-ESM4, IPSL-CM6A-LR, and UKESM1-0-LL outperform the other two models with higher NSEs and better spatial correlation, especially in the water erosion regions. R-factor and 10-year storm EI estimated using MMEs (the arithmetic means of the aforementioned three models) for the historical period are generally underestimated, and the median biases are 0.80 and 0.66, respectively. Biases for each grid were determined as the bias-correction factors for future erosivity projection. Generally, most areas in eastern and central China are expected to experience larger rainfall erosivity. Under SSP1-RCP2.6 and SSP5-RCP8.5 scenarios, R-factor over mainland China is projected to increase by 18.9% and 19.8% for the near-term and 26.0% and 46.5% for the long-term, respectively; and 10-year storm EI is projected to increase by 14.2% and 17.4% for the near-term, and 14.9% and 45.0% for the long-term, respectively. The projected increases in rainfall erosivity are mainly due to the increasing probability of extreme precipitation. This implies that soil and water conservation measures in China need to be further strengthened to meet the challenges brought by the increasing number and magnitude of extreme events in the context of global warming.

How to cite: Wang, W., Yin, S., He, Z., Chen, D., Wang, H., and Klik, A.: Projection of future rainfall erosivity over China under global warming, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16630, https://doi.org/10.5194/egusphere-egu23-16630, 2023.

EGU23-16753 | Orals | HS7.5

Dry and wet climatic change and its driving factors in China 

Jie Tang, Wenting Wang, and Yun Xie

Evaluating the characteristics of long-term dry and wet climate changes under the background of global climate change is important for regional water resources security, ecosystem security and socio-economic development. Based on the daily meteorological data of 1680 meteorological stations in China from 1971 to 2019, the reference evapotranspiration (ET0) was estimated with the FAO-56 Penman–Monteith equation. Based on which, the temporal and spatial variations of humidity index (HI), precipitation (P), reference evapotranspiration (ET0) and the driving factors of which were further analyzed. Results showed that HI significantly increased in the northwest China of arid area, the northeast China of subhumid area and the Huang-Huai region of humid area, while it significantly decreased in the southwest and southeast China of humid areas. The change of HI can be mainly attributed to the change of ET0 while no significant trends has been detected for P for most regions of China. During the past 50 years, the increasing rate of ET0 was 3.76 mm/10a. But the temporal variation of ET0 are different from regions, and the increasing and decreasing area were mainly dominated by climate different factors. For region of Huang-huai and northern Northeast China, ET0 showed significant downward trend. Among factors that relating to ET0, wind speed contributes most to the significant decrease of ET0. For all rest regions of China, ET0 showed significant upward trends, and relative humidity contribute most to the increase.

 

Key words: Dry and wet climatic change, humidity index, reference evapotranspiration, contribution, climatic factors.

How to cite: Tang, J., Wang, W., and Xie, Y.: Dry and wet climatic change and its driving factors in China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16753, https://doi.org/10.5194/egusphere-egu23-16753, 2023.

EGU23-17047 | Orals | HS7.5 | Highlight

A just map: community and fluvial science working together for flood hazard vulnerability mapping in Massachusetts 

Christine Hatch, Seda Salap-Ayca, Christian Guzman, and Eve Vogel

In the Northeastern U.S., the most costly damages from intense storm events were impacts to road-stream crossings.  In steep post-glacial terrain, erosion by floodwater and entrained sediment is the largest destructive force during intense storms, and the most likely driver of major morphological changes to riverbanks and channels.  Steam power analysis is a tool that can successfully quantify floodwater energy that caused damages, however, prediction of which reaches or watersheds may experience future impacts remains uncertain. Downstream, in urban areas, floodwaters increasingly occupy larger geographic extents that spill well beyond traditionally mapped flood and hazard zones. Limiting these maps are critical biases: Often more information is available for coastal and urban areas (missing steeper terrain geomorphic hazard zones), base functional assumptions (that flood risk is dominantly inundation risk from a specific depth of water, ignoring the force of moving water, sediment or erosion), their concentration around the highest-value infrastructure (lower-value and lower-density development or undeveloped areas have little or no map coverage) and how these maps are utilized for regulatory purposes (e.g. mortgage and insurance requirements). Compounding the physical destruction of flooding is the unequal distribution of these impacts on socially vulnerable populations that are least able to recover from them.  We strive to improve the co-generated mapping of social vulnerability and flood risk by (1) utilizing measures of social vulnerability with greater social and geographical insight and nuance, including self-organizing maps (SOM) that cluster overlapping metrics, (2) applying modified flood hazard maps that accurately represent fluvial geomorphic hazards, urban flooding hazards, and climate change considerations, and (3) overlapping these to understand what factors influence current maps and policy practice; what populations and places may be overlooked or under-resourced relative to vulnerability; and use this collective insight to help inform and develop improved map products and policy approaches.  Integration of this information directly with practitioners’ resources allows communities to prioritize and make land-use decisions and flood-response and preparedness decisions that are informed by the specific vulnerabilities of their populations as well as the fluvial geomorphic workings of the larger watershed, and that have powerful local implications.  Outreach and educational programs focused on social vulnerability and fluvial systems for river practitioners and politicians at all levels align communities’ attitudes about flooding and rivers can ultimately result in ecologically sound, socially just, and more flood resilient policies and practices.

How to cite: Hatch, C., Salap-Ayca, S., Guzman, C., and Vogel, E.: A just map: community and fluvial science working together for flood hazard vulnerability mapping in Massachusetts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17047, https://doi.org/10.5194/egusphere-egu23-17047, 2023.

The Sikkim Himalaya, similar to other mountain regions, has lost considerable ice cover over the years owing to the changing climatic factors leading to enlargement of glacier-fed lakes, and thus posing a potential threat to downstream communities in the mountain and Tarai (foothills) region in case of breach anytime in the future. The Chhombo Chhu Watershed (CCW) of the Tista Basin in the Sikkim Himalaya, located between the Greater Himalayan Range and the Tethyan Sedimentary Sequence, is the storehouse of number of glacial lakes with large areas and volumes. In this study, we mapped the glacial lakes' changes between 1975–2018 and assessed their dynamics based on manual analysis of optical satellite images using KeyHole-9 Hexagon (∼4 m), Landsat Series (∼15-30 m), and Sentinel 2A-MSI (∼10-20 m) imagery and verified during field surveys. The results show that the number of lakes has increased from 62 to 98, and its total area expanded significantly by 34.6 ± 5.4%, i.e., from 8.5 ± 0.2 km2 in 1975 to 11.4 ± 0.6 km2 by 2018, at an expansion rate of 0.8 ± 0.1% a–1. Lake outburst susceptibility result reveals that a total of twenty-seven potentially dangerous glacial lakes exist in the watershed; 5 have a status of ‘high’ outburst probability, 17 ‘medium’ and 5 ‘low’. The majority of the proglacial lakes in the watershed have significantly enlarged due to the faster melting and calving processes as a result of accelerating increasing long term average annual trend of temperature (+0.283° Ca–1; 95% confidence level) and homogeneous or slightly declining precipitation.

How to cite: De, S. K., Chowdhury, A., and Sharma, M. C.: Inventory, Classification, Evolution, and Potential Outburst Flood Assessment of Glacial Lakes in the Chhombo Chhu Watershed (Sikkim Himalaya, India), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17459, https://doi.org/10.5194/egusphere-egu23-17459, 2023.

EGU23-529 | ECS | Posters on site | HS3.8

How consistent are citizens in their observation of temporary streams? 

Mirjam Scheller, Ilja van Meerveld, Sara Blanco, and Jan Seibert

Half of the global river network dries up from time to time. However, these so-called temporary streams are not represented well in traditional gauging networks. One reason is the difficulty in measuring zero flows. Therefore, new approaches, such as low-cost sensors and citizen science, have been developed in the past few years. CrowdWater is such a citizen science project, in which citizens can submit observations of the state of temporary streams with the help of a smartphone app. The flow state of the stream is assessed visually and assigned to one of the following six classes: dry streambed, wet/damp streambed, isolated pools, standing water, trickling water, and flowing.

To determine the consistency of observations by different citizens, we asked questions regarding the flow state to more than 1200 people, who passed by temporary streams of various sizes in Switzerland and Germany. The survey consisted of 19 multiple-choice questions (with 14 being yes/no questions), three rating scale questions, two open-ended questions and five demographic questions, and was available in German and English. Most participants were interested in the topic and happy to participate. We estimate that about 80% of the people we approached participated in the survey.

Over 90% of the participants were native German speakers. When the expert assessment of the flow state was dry streambed, isolated pools or flowing water multiple surveys (4-6) could be done for up to four streams. Other states (standing water and trickling water) were assessed at only one stream. The surveys covered all six flow state classes: dry streambed: 4 times with a total of 244 participants; wet/damp streambed: 3 times with 179 participants; isolated pools: 5 times with 265 participants; standing water: 3 times with 177 participants; trickling water: 2 times with 106 participants; flowing: 6 times with 297 participants.

The answers of the participants were consistent for the driest and wettest states (dry streambed and flowing water) but differed for the in-between states. For example, half of the participants at one stream chose the wet streambed category, while the other half decided on standing water. This suggests that visual assessments of flow states for multiple classes are more complicated than could be assumed initially, but still give additional information beyond the flowing or dry categories. Above all, it provides information for streams that otherwise would be unmonitored.

How to cite: Scheller, M., van Meerveld, I., Blanco, S., and Seibert, J.: How consistent are citizens in their observation of temporary streams?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-529, https://doi.org/10.5194/egusphere-egu23-529, 2023.

Floods are one of the most common and catastrophic natural events worldwide, making studies on the magnitude, severity and frequency of past events essential for risk management. On this wise, remote sensing techniques have been widely used in flooding diagnoses, where Sentinel-2 images are one of the main resources employed in surface water mapping. These studies have developed single band, spectral indexes and machine learning-based methods, which have typically been applied to large water bodies. However, one of the issues in identifying water surfaces remains their size. When water surfaces have sizes close to the spatial resolution of satellite images, they are difficult to detect and map. To improve remotely sensed images' spatial resolution, an algorithm for super-resolving imagery has been developed, giving good results, especially in areas covered by agricultural land with large uniform surfaces. Although this method has proved effective on Sentinel-2 images, it has not been tested for enhancing flood mapping. Thus, flood mapping is still considered an open research topic, as no suitable method has been found for all datasets and all conditions. Consequently, the present study has developed a methodology for flood delineation in small-sized water bodies. The method leverages the advantages of Sentinel-2 MSI data, image preprocessing techniques, thresholding algorithms, spectral indexes and an unsupervised classification method. Thus, this framework includes a) the generation of super-resolved Sentinel-2 images, b) the application of seven spectral indexes to highlight flood surfaces and evaluation of their effectiveness, c) the application of fifteen methods for flood extent mapping, including fourteen thresholding algorithms and one unsupervised classification method and, d) the evaluation and comparison of the performance of flood mapping methods. The technique was applied in the Carrión River, located in the Duero basin, province of Palencia, Spain. This river is classified as a narrow water body, which presents recurrent flood events due to its morphometric characteristics, fluvial dynamics, and land uses. The results obtained show optimal performances when highlighting flood zones by combining AWE spectral indices with methods such as those of Huang and Wang, Li and Tam, Otsu, and momentum-preserving thresholding algorithms and EM cluster classification.

How to cite: Lombana, L.: Flood mapping in small-size water rivers: Analysis of spectral indexes using super-resolved Sentinel-2 images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-690, https://doi.org/10.5194/egusphere-egu23-690, 2023.

EGU23-2029 | ECS | Posters on site | HS3.8

Showcasing the Potential of Crowd-sourced Observations for Flood Model Calibration 

Antara Dasgupta, Stefania Grimaldi, Raaj Ramsankaran, Valentijn Pauwels, and Jeffrey Walker

Floods are one the costliest natural disasters, having caused global economic losses worth over USD 51 million and >6000 fatalities just in 2020. Hydrodynamic modelling and forecasting of flood inundation requires distributed observations of flood depth and extent to enable effective evaluation and to minimize uncertainties. Given the decline of in situ hydrological monitoring networks, Earth Observation (EO) has emerged as a valuable tool for model calibration and evaluation in data scarce regions, as it provides synoptic observations of flood variables. However, low temporal frequencies and the (currently) instantaneous nature of EO, still limits the ability to characterize fast moving floods. The concurrent rise of smartphones, social media, and internet access has recently led to the emerging discipline of citizen sensing in hydrology, which has the potential to complement real-time EO and in situ flood observations. Despite this, methods to effectively utilise crowd-sourced flood observations to quantitatively assess model performance are yet to be developed. In this study the potential of crowd-sourced flood observations for hydraulic model evaluation is demonstrated for the first time. The channel roughness for the hydraulic model LISFLOOD-FP was calibrated using just 32 distributed high-water marks and wrack marks collected by the community and provided by the Clarence Valley Council for the 2013 flood event. Since the timings of acquisition of these data points were unknown, it was assumed that these provide information on the peak flow. Maximum model simulated and observed water levels were thus compared at observation locations for each model realization, and errors were quantified through the root mean squared error (RMSE) and the mean percentage difference (MPD), respectively. Peak flow information was also extracted from the 11 available hydrometric gauges along the Clarence River and used to constrain the roughness parameter, to enable the quantification of value addition from the citizen sensed observations. Identical calibrated parameter values were obtained for both data types resulting in a mean RMSE value of ∼50 cm for peak flow simulation across all gauges. Outcomes from this study demonstrate the utility of uncertain crowd-sourced flood observations for hydraulic flood model calibration in ungauged catchments.

How to cite: Dasgupta, A., Grimaldi, S., Ramsankaran, R., Pauwels, V., and Walker, J.: Showcasing the Potential of Crowd-sourced Observations for Flood Model Calibration, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2029, https://doi.org/10.5194/egusphere-egu23-2029, 2023.

Due to the influence of climate change, the range of change in precipitation and regional variation have increased over the past 10 years, and the occurrence of local drought is increasing. The existing water supply and demand analysis system in Korea is produced by each management department, so there are limitations in data collection and decision-making on water distribution. For efficient water management, integration of water information should be prioritized. Based on this, actual water use monitoring, evaluation and water shortage prediction technology can be developed.

In this study, the DB of water-cycle system was constructed focusing on domestic water and transfer function model was developed. DB construction was classified into 3 stages (pre-preparation/investigation and analysis/application and evaluation), and the first stage was defined as the concept of water inflow/delivery/outflow from the urban perspective and the current status of data by point was identified. In the second stage, research directions were established through expert consultation and undisclosed data were collected through cooperation with related organizations. The third stage was applied to Gongju-si and Nonsan-si in Korea, which are the study sites, and the supplementations were reviewed. A transfer function model was developed using the constructed DB. It is expected that it will be possible to construct a useful transfer function model when analyzing the performance index by learning the outflow of the Singwan sewage treatment equipment based on the water intake amount of the Hyeondo intake station and confirming the autocorrelation of the non-significant residual.

In the future, additional considerations (outlet location, service area, and sewage treatment area subdivision) are required in national reports on river basins and droughts, and precipitation is also considered as a major input factor for outflow.

 

(This work was supported by a grant from the Korea Environmental Industry & Technology Institute (KEITI), funded by the Ministry of Environment (ME) of the Republic of Korea (2022003610003))

How to cite: Lee, S. and Lee, S.: Construction of integrated DB for domestic water-cycle system and development of transfer function model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2201, https://doi.org/10.5194/egusphere-egu23-2201, 2023.

EGU23-2493 | ECS | Posters virtual | HS3.8

pyRCIT - A rainfall nowcasting tool based on a synthetic approach 

Ting He and Thomas Einfalt

Operating precise rainfall nowcasting with the help of observations from weather radar can give an effective warning before hydrometerological hazards occur. A common radar based rainfall nowcasting procedure includes: rain cell identification and tracking, spatial and temporal analysis of rain cell, rainfall nowcasting and nowcasting results evaluation.

In this study, an open source rainfall nowcasting tool - pyRCIT is designed and developed which is purely based on qualified weather radar data. It have four main modules: (1) weather radar data processing; (2) rainfall spatial and temporal analysis; (3) deterministic rainfall nowcasting and (4) ensemble rainfall nowcasting. In pyRCIT, rainfall is firstly obtained from weather radar data sets with a series of data quality adjustment procedures. Secondly, rain cells are identified and their spatial and temporal properties are analyzed by the RCIT algorithm. Thirdly, deterministic rainfall nowcasting is operated following the extrapolating schema using lagrangian persistence and semi-lagrangian methods separately, nowcasting results are evaluated by the object oriented verification method - SAL (Structure-Amplitude-Location). Finally, nowcasting uncertainties are analyzed by the random field theory and the quantified uncertainties are implemented as the aid of ensemble rainfall nowcasting.

Nowcasting quality of pyRCIT are evaluated by comparing it with some main rainfall nowcasting methods: TREC, SCOUT and pySTEPS. Comparative results showed that deterministic nowcasting score of pyRCIT were higher than the TREC and SCOUT methods but is nearly equal to the score of pySTEPS, for the ensemble nowcasting, score of pyRCIT is higher than all three methods for the selected cases. The pyRCIT can serve as the basis for further hydro-meteorological applications such as spatial and temporal analysis of rainfall events and flash flood forecasting.

The code of pyRCIT is available at https://github.com/greensubriane/PYRCIT.git

How to cite: He, T. and Einfalt, T.: pyRCIT - A rainfall nowcasting tool based on a synthetic approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2493, https://doi.org/10.5194/egusphere-egu23-2493, 2023.

EGU23-5818 | Posters on site | HS3.8

Application of multimodal deep learning using radar and water level data for water level prediction 

Seongsim Yoon, Seyong Kim, and Sangmin Bae

In general, water level prediction models using deep learning techniques have been developed using time-series water level observation data from upstream water level stations and target water level stations even though many of physical data are necessary to predict water level. The changes of the water level are greatly affected by rainfall in the basin, therefore rainfall information is needed to more accurately predict the water level. In particular, radar data has the advantage of being able to directly acquire the amount of rainfall occurring within a watershed. This study aims to develop the multimodal deep learning model to predict the water level using 2D grid radar rainfall data and 1D time-series water level observation data. This study proposed two multimodal deep learning models which have different structures. Both multimodal deep learning models predict the water level by simultaneously using the observed water level data up to the present time and the radar rainfall data that affects the water level in the future. The first proposed model consists of a deep learning network that links 2D Average Pooling (AvgPool2D), which compresses 2D radar data to 1D data, and Long Short-Term Memory (LSTM), which predicts 1D time series water level data. The second proposed model consists of a deep learning network that predicts water levels by linking Conv2DLSTM and LSTM, which can reflect the characteristics of 2D radar data without deformation.  The two proposed multimodal deep learning models were learned and evaluated in the upper basin of Hantan River. In addition, it was compared with the results of single-modal LSTM using only water level data. There are three water level stations in the study area, and the objective was to predict the water level of the downstream station up to 180 minutes in advance. For learning and verification of the deep learning model, 10-minute water level and radar rainfall data were collected from May 2019 to October 2021. For the radar data used as input, the grid data included in the target watershed were extracted and used among composite radar data with a resolution of 1 km operating by Ministry of Environment. As a result of evaluating each learned deep learning model, two multimodal models had higher prediction accuracy than the single-modal using only water level data. In particular, second proposed model (Conv2dLSTM+LSTM) had better predictive performance than first proposed model (AvgPool2D+LSTM) at the time of the sudden rise in water level due to rainfall.

Acknowledgments

Research for this paper was carried out under the KICT Research Program (project no. 202200175-001, Development of future-leading technologies solving water crisis against to water disasters affected by climate change) funded by the Ministry of Science and ICT.

How to cite: Yoon, S., Kim, S., and Bae, S.: Application of multimodal deep learning using radar and water level data for water level prediction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5818, https://doi.org/10.5194/egusphere-egu23-5818, 2023.

EGU23-7700 | ECS | Orals | HS3.8

Improved flush detection and classification in combined sewer monitoring 

Markus Pichler and Dirk Muschalla

During rain events, rainwater reaches the combined sewer system and causes additional hydraulic and pollutant load. Due to the limited capacity of the sewer system and the wastewater treatment plant, overflow structures are constructed to reduce the discharge and thus create a potential hazard for the environment. For optimal management of these structures, it is necessary to know the runoff and pollutant load of the events and their distribution over time. When these distributions have a significant peak, they are often referred to as a flush, the best-known phenomenon being the first flush at the beginning of a rainfall event. This knowledge can be used for the design of retention facilities and the calibration of sewer models. The flush phenomena are mainly caused by the erosion of contaminants on the surface as well as the remobilisation of sediments in the sewer network.

Although many papers have investigated the first flush, no common pattern for the occurrence of these flushes has been identified. While the concentration of the flushes in rainwater sewers can be measured directly, the rain flushes in combined sewers are mixed with more polluted wastewater, which leads to a reduction in signal strength.

The sensor site for the used measurement data is located in a combined sewer overflow in the western part of Graz, Austria with a catchment area of 460 ha, consisting mainly of residential areas and with about 19500 inhabitants.

This work aims to separate and classify pollution flush signals from rainfall events in combined sewer systems to better understand the relationship between these signals and rainfall event characteristics.

For this reason, the continuous hydraulic and pollution data are first analysed to determine the representative dry weather contribution. By subtracting the dry weather contribution from the combined wastewater volume and the mass flux, the stormwater contribution and thus the flushes can be estimated. In addition, automatic event detection of combined sewer events was done.

Next, the wet weather events are classified by clustering the stormwater runoff-induced pollutant distribution (flush signals) and the event parameters. For the clustering of the temporal pollutant load distribution of events of different duration, the events are normalised by the mass-volume curves. To obtain the best possible clustering result, the dimension of the mass-volume curves is reduced by a principal component analysis. Different clustering methods, such as partitioning or hierarchical methods, are applied and compared.

How to cite: Pichler, M. and Muschalla, D.: Improved flush detection and classification in combined sewer monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7700, https://doi.org/10.5194/egusphere-egu23-7700, 2023.

EGU23-8802 | Orals | HS3.8

Improving Early Warning System for Urban Flooding in Chinese Mega Cities using Advanced Active Phased Array Radar 

Dehua Zhu, Yunqing Xuan, Richard Body, Dongming Hu, and Xiaojun Bao

This two-year trial aims to bring together academics and industrial partners from UK and China to conduct a pilot study on the use of the active phased array radar to provide early urban flood warnings for Chinese mega cities, which facing challenging urban flood issues. This is the first in the world of cascade modelling using the cutting-edge active phase array radar (APRA) to provide rainfall monitoring and nowcasting information for a real-time two-dimension urban drainage model. The collaboration built up by this project and the first-hand experiment data will serve well to further catalyse the taking-up of state-of-the-art weather radars for urban flood risk management, and to tackle the innovation in tuning the radar technology to fit the complex urban environment as well as advanced modelling facilities that are designed to link the observations, providing decision making support to the city government. Recommendations for applying high spatial-temporal resolution precipitation data to real-time flood forecasting on an urban catchment are provided and suggestions for further investigation are discussed.

How to cite: Zhu, D., Xuan, Y., Body, R., Hu, D., and Bao, X.: Improving Early Warning System for Urban Flooding in Chinese Mega Cities using Advanced Active Phased Array Radar, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8802, https://doi.org/10.5194/egusphere-egu23-8802, 2023.

EGU23-9494 | Orals | HS3.8

A Nonstationary Multivariate Framework for Modelling Compound Flooding 

Yunqing Xuan and Han Wang

 Flooding is widely regarded as one of the most dangerous natural hazards worldwide. It often arises from various sources either individually or combined such as extreme rainfall, storm surge, high sea level, large river discharge or the combination of them. However, the concurrence or close succession of these different source mechanisms can lead to compound flooding, resulting in larger damages and even catastrophic consequences than those from the events caused by the individual mechanism. Here, we present a modelling framework aimed at supporting risk analysis of compound flooding in the context of climate change, where nonstationary joint probability of multiple variables and their interactions need to be quantified.The framework uses the Block Bootstrapping Mann-Kendall test to detect the temporal changes of marginals, and the correlation test associated with the Rolling Window method to estimate whether the correlation structure varies with time; it then evaluates various combinations of marginals and copulas under stationary and nonstationary assumptions. Meanwhile, a Bayesian Markov Chain Monte Carlo method is employed to estimate the time-varying parameters of copulas.

How to cite: Xuan, Y. and Wang, H.: A Nonstationary Multivariate Framework for Modelling Compound Flooding, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9494, https://doi.org/10.5194/egusphere-egu23-9494, 2023.

EGU23-9546 | ECS | Orals | HS3.8

DeepRain: a separable residual convolutional neural algorithm with squeeze-excitation blocks for rainfall nowcasting 

Ahmed Abdelhalim, Miguel Rico-Ramirez, and Dawei Han

Precipitation nowcasting is critical for mitigating the natural disasters caused by severe weather events. State-of-the-art operational nowcasting methods are radar extrapolation techniques that calculate the motion field from sequential radar images and advect the precipitation field into the future. However, these methods assume the motion field's invariance, and prediction is based solely on recent observations, rather than historical radar sequences. To overcome these limitations, deep learning methods such as convolutional neural networks have recently been applied in radar rainfall nowcasting. Although, the promising progress of using deep learning techniques in rainfall nowcasting, these methods face some challenges. These challenges include producing blurry predictions, inaccurate forecasting of high rainfall intensities and degradation of the prediction accuracy with rising lead times. Therefore, the aim of this study is to develop a more performant deep-learning model capable of overcoming these challenges and preventing information loss in order to produce more accurate nowcasts. DeepRain is a convolutional neural network that uses a spatial and channel Squeeze & Excitation Block after each convolutional layer, local importance-based pooling, and residual connections to improve model performance. The algorithm is trained and validated using the UK Met Office's radar rainfall mosaic, which is produced by the UK Met Office Nimrod system. Using verification metrics, the model's predictive skill is compared to another deep learning model and a range of extrapolation methods.

Keywords: deep learning; rainfall nowcasting; radar; convolutional neural networks; Squeeze-and-Excitation

How to cite: Abdelhalim, A., Rico-Ramirez, M., and Han, D.: DeepRain: a separable residual convolutional neural algorithm with squeeze-excitation blocks for rainfall nowcasting, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9546, https://doi.org/10.5194/egusphere-egu23-9546, 2023.

EGU23-9588 | ECS | Orals | HS3.8

Comparative performance of recently introduced Deep Learning models for Rainfall-Runoff Modelling 

Yirgalem Gebremichael, Gerald Corzo Perez, and Dimitri Solomatine

Machine learning and specifically deep learning has been applied in solving numerous hydrology related problems in the past. Furthermore, extensive research has been done on the evaluation and comparison of performances of different Machine learning techniques applied in solving hydrology related problems. In this research, the possible reasons behind these performance variations are being assessed. The performance of recently introduced deep learning techniques for rainfall-runoff modelling are being evaluated by looking in to the possible modelling set-up and training procedures. Therefore, model set-up and training procedures such as: normalization techniques, input variable selection (feature selection), sampling techniques, model complexity, optimization techniques and random initialization of weights are being examined closely in order to improve the performances of different deep learning techniques for rainfall-runoff modelling. As a result, this study is trying to answer whether these factors have significant effect on the model accuracy.

The experiments are being conducted on different deep learning models such as: LSTMs, GRUs and MLPs as well as non-deep learning models such as: XGBoost, Random Forest, Linear Regression and Naïve models. Deep learning frameworks including TensorFlow and Keras are being implemented on Python. For better generalization, study areas from three different climatic zones namely: Bagmati catchment in Nepal, Yuna catchment in Dominican Republic and Magdalena catchment in Colombia are chosen to implement this experimental research. Additionally, in situ meteorological and stream flow data are being used for the rainfall-runoff modelling.

The preliminary model results show that model performances in case of Bagmati catchment are higher as compared to the other catchments. The LSTMs and MLPs are performing good with NSE values of 0.71 and 0.72 respectively. Most importantly, the linear regression model was outperforming the other models with NSE up to 0.75 in case of considering 6 days lagged rainfall input. This implies the relationship between daily rainfall and runoff data from Bagmati catchment may not be as complex. On the contrary, the 3-hourly data from Yuna catchment shows results with lower values for the performance metrics. This may be an indication of more complex relationships within the Yuna catchment.

This research provides key elements of the modelling process, especially in setting up and training deep learning models for rainfall-runoff modelling. The comparative analysis performed here, provides a basis of performance variations on different basins. This work contributes to the experiences in understanding machine learning requirements for different types of river basins.

How to cite: Gebremichael, Y., Corzo Perez, G., and Solomatine, D.: Comparative performance of recently introduced Deep Learning models for Rainfall-Runoff Modelling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9588, https://doi.org/10.5194/egusphere-egu23-9588, 2023.

EGU23-10491 | ECS | Posters on site | HS3.8

Addressing discoverability, trust and data quality in peer-to-peer distributed databases for citizen science 

Julien Malard-Adam, Sheeja Krishnankutty, Anandaraja Nallusamy, and Wietske Medema

Peer-to-peer distributed databases show promise for lowering the barrier to entry for citizen science projects. These databases, which do not require a centralised server to store and exchange data, instead use participants’ devices (phones or computers) to store and transfer data directly between project participants. This offers concrete advantages in terms of avoiding usually very costly and time-consuming server maintenance for the research team, as well as improving data access and sovereignty for the participating communities.

However, several technical challenges remain to the routine use of distributed databases in citizen science projects. In particular, indexing data and discovering peers who hold data of interest or from the same project; managing safety, trust and permissions; and ensuring data quality all without relying on a central server to perform these functions requires a rethinking of the standard paradigms of database and user account management.

This presentation will give a brief overview of the Constellation software for distributed scientific databases before presenting several novel approaches (concentric recursive data search, user network-centric trust, and multiple data quality verification layers) it has adopted to respond to the above-mentioned challenges. Examples of concrete applications of Constellation for data sharing in the fields of hydrology and agronomy will be included.

How to cite: Malard-Adam, J., Krishnankutty, S., Nallusamy, A., and Medema, W.: Addressing discoverability, trust and data quality in peer-to-peer distributed databases for citizen science, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10491, https://doi.org/10.5194/egusphere-egu23-10491, 2023.

Population growth and economic development increase water demand, while human activities degrade the quality of available water resources along the adjacent rivers. The U.S. state of Alabama has been suffering from floods causing the degraded water quality by scouring pollutants into the water. In recent decades, Alabama has been experiencing persistent precipitation deficits and unusual severe droughts, resulting in limited economic and water-based recreation activities within downstream states. Since 2020, The COVID-19 pandemic aroused a series policies like quarantine and lock down, which slowed down the economic development and reduced chances of people going outside to witness the water pollution accidents.

In this study, we conducted a sentiment analysis of over 9,900 water pollution complaints (2012-2020) from residents in Alabama. Overall, it is found that complaints are dominated by negative and objective complaints no matter what extremes events or environmental accidents. Results show that sentiment alteration during climate extremes and COVID period was detected. Potential causes of the sentimental alteration in the public water pollution complaint reports were explored. Results show more complaints during summer seasons, which can be explained as higher temperature and intensive precipitation at that time. More complaints are distributed in the counties that are higher socioeconomically developed, to be more specific, counties with more population and higher GDP level. The severity of antecedent extreme events can affect the sentiment of environmental pollution complaints related to on-going extreme events due to limited human judgements. Key words extracted from the complaints point out the pollution resources and locations, which provide important clues from local government to resolved problems.

This study provides an example of how unstructured data such as public complaints can be used as a technology to improve the water pollution and public health monitoring with the help of big data and artificial intelligent technologies. While the results of this study were based water pollution complaints from residents of Alabama state, it is applicable to other environmental pollutions (like air and land) and other regions with available long-term textual data.

 

How to cite: Liu, A. and Kam, J.: Observed Sentimental Alteration in the Public Water Pollution Complaints during Climatic Extremes and the COVID-19 Pandemic, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10886, https://doi.org/10.5194/egusphere-egu23-10886, 2023.

EGU23-10937 | ECS | Orals | HS3.8

Feature engineering strategies based on GIS and the SCS-CN method for improving hydrological forecasting in a complex mountain basin 

María José Merizalde, Paul Muñoz, Gerald Corzo, and Rolando Célleri

Hydrological modeling and forecasting are important tools for adequate water resources management, especially in complex systems (basins) characterized by high spatio-temporal variability of runoff driving forces, landscape heterogeneity, and insufficient hydrometeorological monitoring. Yet, during the last decades, the use of machine learning (ML) techniques has become popular for runoff forecasting, and the current research trend focuses on performing feature engineering (FE) strategies aimed both at improving forecasting efficiencies and allowing model interpretation. Here, we employed three ML techniques, the Random Forest (RF) algorithm, traditional Artificial Neural Networks (ANN), and specialized Long-Short Term Memory (LSTM) networks, assisted by FE strategies for developing short-term runoff forecasting models for a 3300-km2 complex basin representative of the tropical Andes of Ecuador. We exploited the information of two readily-available satellite products, the IMERG and GSMaP to overcome the absence of ground precipitation data, and the FE strategies proposed were based on GIS and the Soil Conservation Service Curve Number (SCS-CN) method to synthesize the use of land use and land cover, soil types, slope, among other hydrological concepts. To assess the forecasting improvement, we contrasted a set of efficiency metrics calculated both for the developed specialized models and for referential models without the application of  FE strategies. In terms of results, we were first able to develop accurate forecasting models by exploiting precipitation satellite data powered by ML techniques with different complexity levels. Second, the referential forecasting models reached efficiencies (Nash-Sutcliffe efficiency, NSE) varying from 0.9 (1-hour lead time) to 0.5 (11-hours), which were comparable for the RF, ANN, and LSTM techniques. Whereas for the specialized models, we found an improvement of 5–20 % in NSE-values for all lead times. The proposed methodology and the insights of this study provide hydrologists with new tools for developing short-term runoff forecasting systems in complex basins otherwise limited by data scarcity and model complexity issues.

How to cite: Merizalde, M. J., Muñoz, P., Corzo, G., and Célleri, R.: Feature engineering strategies based on GIS and the SCS-CN method for improving hydrological forecasting in a complex mountain basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10937, https://doi.org/10.5194/egusphere-egu23-10937, 2023.

EGU23-11217 | ECS | Posters on site | HS3.8

International Natural Disasters Research and Analytics (INDRA) Reporter: A multi-platform Citizen Science Framework and Tools for Disaster Risk Reduction 

Manabendra Saharia, Dhiraj Saharia, Shreya Gupta, and Satyakam Singhal

With pervasive access to mobile phones with powerful sensors and processors, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from traditional sensors. But there is a lack of a comprehensive application programming interface (API)-based framework that can collect data from multiple sources through user-friendly workflows. INDRA Reporter has been designed with a mobile-first approach geared towards real-time applications and an emphasis on user-interface/user-experience (UI/UX) to maximize collection of higher fidelity data. This paper details a comprehensive suite of tools for active and passive crowdsensing of natural hazards such as floods, storm, lightning, rain etc. Currently the framework includes mobile applications, telegram chatbots, and a publicly available dashboard. Most citizen science applications in flooding are quantitative, which makes it difficult for non-specialists to provide accurate scientific information along with providing user insight into prevailing situation within a single coherent workflow. It is imperative that workflows targeting dangerous situations emphasize on speed and visual acuity while collecting critical data.  The main objective of INDRA is to provide a simple and intuitive way of collecting qualitative and quantitative data from people. Since traditional data collection through ground-based sensors and satellites suffer from various limitations, measurements collected using INDRA will supplement these sources and form the basis of developing multi-sensor data products. We are reporting the development and release of four components of the framework – a) open INDRA API b) INDRA Reporter mobile application, c) Telegram Chat bot, and d) web dashboard. The API has been kept extensible in order to expand the data collection to other hydrologic and meteorological phenomenon.

How to cite: Saharia, M., Saharia, D., Gupta, S., and Singhal, S.: International Natural Disasters Research and Analytics (INDRA) Reporter: A multi-platform Citizen Science Framework and Tools for Disaster Risk Reduction, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11217, https://doi.org/10.5194/egusphere-egu23-11217, 2023.

EGU23-11419 | ECS | Posters on site | HS3.8

Spatio-temporal analysis of storm surge in the Korean Peninsula 

Jung-A Yang

The Korean Peninsula (KP) located in the Northwest Pacific have different topographic features. West coast of the KP has large tidal variations. If storm surge occurred at high tide, the west coast is vulnerable to flooding. The south coast has a complex coastline with hundreds of islands. Its complex topography can amplify storm surge height (SSH) and it also makes it difficult to conduct numerical modeling for storm surge. Moreover, as the KP is located in the pathways of typhoons, it has been affected by an average of three typhoons every year. The KP has actually suffered from storm surge-induced disaster several times in the past. In order to plan efficient and effective countermeasures against storm surge disasters, it is required to identify the vulnerability of coastal regions in the KP. Therefore, this study quantitatively analyzed the frequency and cause of occurrence of storm surges that occurred along the Korean coast in the past.

First, this study collected observed tidal data at 48 tide stations which are installed along the coast of the KP and performed a hormonic analysis on the observed tidal data to build a database of SSH information that occurred along the coast of the KP from 1979 to 2021. Next, the cause of the storm surge was evaluated based on the occurrence time of the high-level SSH. If the storm surge occurred in winter season, it was treated as being caused by an extra-tropical cyclone, and if in summer season, by and tropical cyclone. Lastly, storm surge vulnerable areas were assessed based on frequency and level of the SSH. To this end, the coast of the KP was divided into five zones: the northwest coast, the southwest coast, Jeju island, the southeast coast and northeast coast. The frequency of the high-level SSH generated in those zones was calculated, and areas where storm surge occurred a lot were selected as vulnerable areas.

As a result of the study, it was found that the high-level SSH with more than 1 m in the KP are caused by tropical cyclone in summer, and the area most vulnerable to storm surge is the southeast coast.

However, the observed tidal data used in this study has a limitation in that the collection period differs depending on the zone: the observation period is longer for the southeast coast than for the southwest coast. Looking at the paths of past typhoons, many typhoons passed through the west coast, so the possibility that the southwest coast would have been judged to be more vulnerable than the southeast coast cannot be ignored if the observed tidal data for the southwest coast were more abundant. In addition, since storm surge is phenomenon that is affected not only by meteorological conditions but also by topographic conditions (e.g., complexity of coastline, water depth, etc.), spatio-temporal analysis of storm surge by topographic conditions is going to be conducted through future research.

 

Acknowledgement

This work was supported by the National Research Foundation of Korea grant funded by the Korea government(MSIT) (No. 2022R1C1C2009205).

 

How to cite: Yang, J.-A.: Spatio-temporal analysis of storm surge in the Korean Peninsula, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11419, https://doi.org/10.5194/egusphere-egu23-11419, 2023.

EGU23-12475 | Posters on site | HS3.8

Comparing different radar-raingauge precipitation merging methods for Tuscany region 

Rossano Ciampalini, Andrea Antonini, Alessandro Mazza, Samantha Melani, Alberto Ortolani, Ascanio Rosi, Samuele Segoni, and Sandro Moretti

Radar-based rainfall estimation represents an effective tool for hydrological modelling. Nevertheless, this data type is subject to systemic and natural perturbations that need to be considered before to use it. Because of that and to improve data quality, corrections based on raingauge observations are frequently adopted. Here, we compared the efficacy of different radar-raingauge merging procedures over a regional raingauge-radar network focusing on a selected number of rainfalls events.
We adopted the methods: 1) Kriging with External Drift (KED) interpolation (Wackernagel 1998), 2) Probability-Matching-Method (PMM, Rosenfeld et al., 1994), and 3) a kriging mixed method exploiting the Conditional Merging (CM) process (Sinclair-Pegram, 2005) based on elaborations available at DPCN (Italian National Civil Protection Department). These methods have been applied on the Tuscany Regional Territory using raingauge recorded rainfalls at 15’ time-step and CAPPI (Constant altitude plan position indicator) reflectivity data at 2000/3000/5000 m at 5’ and 10’.
Relationships describing precipitation VS radar reflectivity were integrated and elaborated as part of the development of the merging procedures, while the comparison involved the analysis of variance and diversity coefficients. Kriging-based elaborations showed different spatial patterns depending on the applied procedure, with patterns closer to radar variability when using DPCN, and more reflecting the gauge data structure when adopting KED. The probabilistic method (PMM), instead, had the advantage of integrating the gauge data while preserving the spatial radar patterns, confirming interesting perspectives.

How to cite: Ciampalini, R., Antonini, A., Mazza, A., Melani, S., Ortolani, A., Rosi, A., Segoni, S., and Moretti, S.: Comparing different radar-raingauge precipitation merging methods for Tuscany region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12475, https://doi.org/10.5194/egusphere-egu23-12475, 2023.

EGU23-12857 | Orals | HS3.8

Surge-tide interaction along the Italian coastline 

Alessandro Antonini, Elisa Ragno, and Davide Pasquali

Storm surge events are probably one of the most studied phenomena in coastal regions since they can lead to coastal flooding, environmental damage, and sometimes loss of human life. In regions of shallow water, among other localized processes, surges occurring at high astronomical tides tend to be damped while surges occurring at rising tides are amplified affecting water level extremes. This requires accounting for tide-surge interaction when defining the coastal hazards due to extreme water levels.

Cities along the Italian coast, such as Venice, Ravenna, Bari (Adriatic sea), Genova, Livorno, Napoli, and Palermo (Tyrrhenian sea), are vulnerable to coastal flooding. Hence, a thorough understanding of the interaction between water level components, i.e., storm surge and astronomical tides, is required to define a proper framework for coastal risk assessment.

Here, we analyze water level observations in several Italian coastal locations to investigate possible correlation and interaction between astronomical tide and the storm surge. Then we study the effect that such interaction has on extreme water level statistics to support the development of flood-resilient adaptation strategies.

How to cite: Antonini, A., Ragno, E., and Pasquali, D.: Surge-tide interaction along the Italian coastline, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12857, https://doi.org/10.5194/egusphere-egu23-12857, 2023.

EGU23-12909 | Posters on site | HS3.8

Smart Groundwater Monitoring System for Managed Aquifer Recharge Based on Enabled Real-Time Internet of Things 

Khan Zaib Jadoon, Muhammad Zeeshan Ali, Hammad Ullah Khan Yousafzai, Khalil Ur Rehman, Jawad Ali Shah, and Nadeem Ahmed Shiekh

Groundwater has provided a reliable source of high-quality water for human use. After India, USA and China, Pakistan is the fourth largest groundwater user in the world and around 60x109 m3 of groundwater is extracted annually. The situation in Pakistan has further exacerbated when government subsidized electricity for agricultural purposes – paving the way for installation of myriad tube wells across the country which resulted in excessive withdrawal of groundwater. The major challenges in sustainable groundwater management system are twofold. First, increasing withdrawals to meet growing human needs have led to significant groundwater depletion, which is usually not monitored due to high cost of monitoring system. Second, data limitations and the application of regional groundwater models for future prediction.

In this research, Internet of Things (IoT) enabled smart groundwater monitoring system has been developed and tested to monitor in-situ real-time dynamics of groundwater depletion. Each groundwater monitoring sensor is connected to an embedded module that consists of a microcontroller and a wireless transceiver based on Long Range Radio (LoRa) technology. The readings from each LoRa enabled module is aggregated at one (or more) gateways which is then connected to a central server typically through an IP connection. Sensors of the smart groundwater monitoring system were calibrated in the lab by fluctuation water levels in a 3-meter water column. A network of the low-cost groundwater sensors was installed in managed aquifer recharge wells to provide real-time assessment of groundwater level measurement remotely. The smart and resource efficient groundwater monitoring system help to reduce number of physical visits to the field and also enhance stakeholders participation to get social benefits (promote equity among groundwater users), economic benefit (optimize pumping, which reduces energy cost) and technical benefit (better estimates of groundwater abstraction) for sustainable groundwater management.

How to cite: Jadoon, K. Z., Ali, M. Z., Yousafzai, H. U. K., Rehman, K. U., Shah, J. A., and Shiekh, N. A.: Smart Groundwater Monitoring System for Managed Aquifer Recharge Based on Enabled Real-Time Internet of Things, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12909, https://doi.org/10.5194/egusphere-egu23-12909, 2023.

EGU23-13505 | Posters on site | HS3.8

Water observations by the public- experiences from the CrowdWater project 

Ilja van Meerveld, Franziska Schwarzenbach, Rieke Goebel, Mirjam Scheller, Sara Blanco Ramirez, and Jan Seibert

Hydrology is a data limited science, especially spatially distributed observations are lacking. Citizen science observations can complement existing monitoring networks and provide useful data. Engaging the public in data collection can also increase people’s interest and awareness about water-related topics. In this PICO, we will present the CrowdWater project, in which citizen scientists share, with the help of a smartphone app, hydrological observations on stream water levels, the presence of water in temporary streams, soil moisture conditions, plastic pollution, and general information on water quality. We will highlight the type of data that are collected, our quality control procedures, and the value of the data for hydrological model calibration. We will also discuss the motivations of the citizen scientists to join the project and to continue to contribute to the project. Although the majority of our frequent contributors are adults, we try to engage the youth in the project by giving presentations in schools and at science fairs. Therefore, we will end the PICO presentation with some examples of our outreach work and lessons learned.

How to cite: van Meerveld, I., Schwarzenbach, F., Goebel, R., Scheller, M., Blanco Ramirez, S., and Seibert, J.: Water observations by the public- experiences from the CrowdWater project, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13505, https://doi.org/10.5194/egusphere-egu23-13505, 2023.

EGU23-15389 | ECS | Posters on site | HS3.8

An innovative data driven approach improves drought impact analysis using earth observation data 

Ye Tuo, Xiaoxiang Zhu, and Markus Disse

Drought is a devastating natural hazard that can be of diverse magnitude, duration and intensity. It leads to economic and social losses and ecological imbalances. Ascribing to climate change, drought has occurred more frequently with high intensity worldwide in recent decades, such as the striking droughts in the summer of year 2022. In water resource aspect, one direct consequence of drought is the decrease of water amount in the rivers, which could further develop into water shortage for irrigation and drinking water supply, and cargo shipping disruption. Therefore, in order to make management decisions that help mitigate the drought damage, it is important to monitor river water anomalies and identify the vulnerable shrinking sections along the river network. Traditional river gauging stations only provide us limited observations of particular spots. A proper utilization of spatially distributed remote sensing data is necessary and effective. In this work, we develop a novel framework to monitor river water shrinking anomaly by including image processing and machine learning approaches, based on earth observation data. The Rhine, a major cargo-route river, is selected as the pilot case, because it had huge water decrease and caused shipping disruption during the 2022 summer’s drought in Germany. The Modified Normalized Difference Water Index (MNDWI) is calculated from the green and Shortwave-Infrared bands of Sentinel-2 satellite images.  MNDWI images of a specific non-drought period is defined as the reference datasets representing normal conditions. Afterwards, a new water shrinking index is introduced to quantify the river water anomaly during drought periods.  Specifically, a python based algorithm which includes image processing and machine learning clustering methods is developed to scan along the MNDWI images to compute the water shrinking index with adjustable river section size. With the index datasets, river sections are further grouped into categories with drought vulnerable levels. By parameterizing the section size, the algorithm is able to quantify and identify the vulnerable shrinking river sections with varying scales. It provides classified references of drought affected hotspots for the regional water management plans in case of drought mitigation. Such a scalable framework can offer a timely distributed monitoring of the drought impacts on the water resource along the river network. As a long term benefit, numerical connections can be identified between the river shrinking status and the economic losses of cargo shipping disruption due to drought.  This is of great value to facilitate the drought impact analysis and forecasts.

How to cite: Tuo, Y., Zhu, X., and Disse, M.: An innovative data driven approach improves drought impact analysis using earth observation data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15389, https://doi.org/10.5194/egusphere-egu23-15389, 2023.

EGU23-16292 | ECS | Posters on site | HS3.8

Hydrological decision-making systems using high-resolution weather radar observations –  a case study from Hungary 

Zsolt Zoltán Fehér, Erika Budayné-Bódi, Attila Nagy, Tamás Magyar, and Tamás János

According to past observations and long-term forecasts, the Carpathian Basin is distinguished by two precipitation trends. The frequency, length, and severity of periods of precipitation deficit and drought are increasing. Furthermore, as small-scale convective updrafts intensify, heavy thunderstorms become more intense. Both trends pose significant risks from an anthropogenic perspective. The former increases food insecurity due to intensifying droughts, which damages agricultural yields, while the latter mainly increases property damage via heavy hailstorms.

The 2022 drought year demonstrated that effective use of available water is the foundation for sustainable growth, which may be supported by well-designed infrastructure investments and smart water management technologies. A rainfall radar system with a high spatial and temporal resolution that contributes to near real-time machine decision-making is one conceivable component of such a complex system.

The Furuno WR-2100 precipitation radar, which was deployed on the outskirts of Debrecen in 2020 for benchmarking purposes, is the first component of such an intelligent decision-making system in Hungary. The radar's range comprises both urban and rural areas, allowing it to continually gather high-resolution test data for both urban hydrology and agricultural irrigation system developments.

The research presented in the article was carried out within the framework of the Széchenyi Plan Plus program with the support of the RRF 2.3.1 21 2022 00008 project.

How to cite: Fehér, Z. Z., Budayné-Bódi, E., Nagy, A., Magyar, T., and János, T.: Hydrological decision-making systems using high-resolution weather radar observations –  a case study from Hungary, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16292, https://doi.org/10.5194/egusphere-egu23-16292, 2023.

The quality of mosaic QPE directly determines the accuracy of QPF products from nowcasting models. However, there is a common spatial discontinuity phenomenon caused by the biases of multiple radars in mosaic QPE. Consistency correction, a type of multi-radar quality control method, can be used to mitigate the spatial discontinuity of mosaic QPE, but its improving effect on QPF products should be analyzed.

For this consideration, a consistency correction method based on GPM KuPR proposed by Chu et al (2018a) was applied to the three S-band operational radars of China, and the improvement on QPE by Z-R relationship, deterministic QPF by S-SPROG (Spectral Prognosis), and ensemble QPF by STEPS (Short-Term Ensemble Prediction System) were analyzed. The results showed: 1) the raw reflectivity factors by the three operational radars over the same equidistance area were significantly different. After the consistency correction, the differences decreased to be less than 0.5 dB and the spatial discontinuity of mosaic products disappeared. 2) The precision of mosaic QPE was significantly improved after the correction, and the average RMSE of QPE decreased by 19.5%, and the TS of heavy rainfall and above rose by 44.8%. 3) The 0-1h deterministic QPF by S-SPROG, and ensemble QPF by STEPS were significantly improved after the correction. The deterministic (ensemble) TS of moderate rain and above rose by 11.9% (10.2%), and that of heavy rain and above increased by 34.2% (38.7%). 4) Furthermore, the consistency correction method contributed to precipitation velocity estimation, and decreased its RMSE by 25.0%. Clearly, the consistency correction method is significantly contributive to multi-radar mosaic QPE and precipitation nowcasting.

How to cite: Chu, Z.: Improvement of Multi-Radar Quantitative Precipitation Nowcasting with Consistency Correction Method, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16647, https://doi.org/10.5194/egusphere-egu23-16647, 2023.

EGU23-766 | ECS | PICO | HS4.1

Bridges influence large wood trapping efficiency during large floods: insights from the Francolí River flood in 2019 

Llanos Valera-Prieto, Virginia Ruiz-Villanueva, and Glòria Furdada

Recent large floods across Europe, including those in Belgium and Germany in 2021 or, more recently, in Italy in October 2022, showed that major obstructions of bridges due to the mobilized large wood (LW) significantly influenced the flood-related damages. However, in principle, none of the dangers posed by wood was inherent to the wood itself but to the obstacles and infrastructures that were not designed to allow the wood to pass. Understanding this legacy effect on wood in rivers due to the increased artificial trapping efficiency of river structures (bridges, dam reservoirs) still needs to be completed.

The Francolí River in Catalonia, NW Iberian Peninsula (853 Km2 area and 59 km length) underwent a major flash flood on October 22, 2019, that caused six fatalities. The rainfall recorded in the NW basin was 293 mm in 24 hours. Consequently, significant bio-geomorphological changes occurred; a large amount of sediment was eroded, transported and deposited, and many trees were damaged or uprooted with subsequent large wood (LW) supply and transport. In addition, infrastructures were severely damaged (e.g., three bridges collapsed).

The legacy effects on instream large wood related to the human infrastructures in river systems is an essential factor to consider when assessing the effects of floods and potential risks. Therefore, this study's main objective was to evaluate the influence of bridges on large wood accumulation during floods. 

We analyzed a reach of 30 km along the Francolí River in which there were 23 bridges. The reach was split into 52 sub-reaches based on their morphological characteristics (i.e., the width of the valley bottom, slope, and sinuosity), the presence of infrastructures, or lithologic and anthropic knickpoints, and the junction with tributaries. The 52 sub-reaches were grouped into four main typologies based on statistical segmentation and clustering.

Individual pieces of LW and accumulations were digitalized using post-flood high-resolution orthophotos (i.e., 0.10 m resolution). They were characterized using four attributes: orientation with respect to the channel (parallel, perpendicular, oblique), transported (yes or not), location (active channel or floodplain), and length. Average Nearest Neighbour, Spatial Autocorrelation (Global Moran's I test) and Density were computed and revealed the depositional pattern of LW along the study reach.

Preliminary results showed that morphological characteristics favoured LW trappings: wide valley bottoms and sinuous bends. In addition, the standing vegetation and other in-channel obstacles were crucial to trap wood. The most significant aspect, however, was the presence of bridges. A significantly more considerable amount of wood (i.e., the highest density observed, ranging between 33 and 101 pieces/ha) was trapped upstream from bridges, where wood was deposited at significantly higher elevations. Further analyses will explore the characteristics of the bridges and upstream sub-reaches.

This study will provide crucial information to understand large wood accumulation at bridges during floods and will inform flood-hazard assessments and river management.

How to cite: Valera-Prieto, L., Ruiz-Villanueva, V., and Furdada, G.: Bridges influence large wood trapping efficiency during large floods: insights from the Francolí River flood in 2019, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-766, https://doi.org/10.5194/egusphere-egu23-766, 2023.

EGU23-1241 | ECS | PICO | HS4.1

Downward counterfactual analysis of historical rainfall events in Germany 

Paul Voit and Maik Heistermann

In the last 20 years a variety of heavy precipitation events (HPEs) have caused severe floods and large damages in Germany. However, the impact of an HPE is not solely determined by the event itself, but also by the geomorphologic characteristics of the location where it occurs.

Previous studies have shown that HPEs can happen anywhere in Germany. To find out where in Germany historical HPEs could have caused a potential hazard, we extracted the 10 most extreme HPEs by using the cross-scale weather extremity index (xWEI) from the last 20 years of radar data (RADKLIM) and shifted these events to every mesoscale subbasin in Germany.

We use the geomorphological instantaneous unit hydrograph as a simple screening tool to investigate the runoff concentration at the mesoscale and the following flood wave propagation in these subbasins as response to historical HPEs. While this method might not be sufficient to model precise discharge, it can be used to spot rapid increase in direct runoff and shed light on the peak development further downstream, depending on the spatiotemporal characteristics of the HPE. 

By using historical HPEs as benchmarks, our method can help to identify areas in Germany which are prone to flood hazard and assist to adjust mitigation measures accordingly.

How to cite: Voit, P. and Heistermann, M.: Downward counterfactual analysis of historical rainfall events in Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1241, https://doi.org/10.5194/egusphere-egu23-1241, 2023.

EGU23-3147 | ECS | PICO | HS4.1

Seamless rainfall and discharge forecasting using a scale-dependent blending of ensemble rainfall nowcasts and NWP 

Ruben Imhoff, Athanasios Tsiokanos, Jerom Aerts, Lesley De Cruz, Claudia Brauer, Klaas-Jan van Heeringen, Albrecht Weerts, and Remko Uijlenhoet

Flash flood early warning requires accurate rainfall forecasts with a high spatial and temporal resolution. As the first few hours ahead are already not sufficiently well captured by the rainfall forecasts of numerical weather prediction (NWP) models, rainfall nowcasting can provide an alternative. This observation-based method, however, quickly loses skill after the first few hours of the forecast due to growth and dissipation processes that are not accounted for. In addition, providing an additional forecasting method can let users drown in the amount of available information. A promising way forward is a seamless forecasting system, which combines the aforementioned forecasting methods. By optimally combining (blending) rainfall nowcasts with NWP forecasts, we can extend the skillful lead time of short-term rainfall forecasts and provide users with more consistent, seamless forecasts.

We implemented an adaptive scale-dependent ensemble blending method in the open-source pysteps library. In this implementation, the blending of the extrapolation (ensemble) nowcast, (ensemble) NWP and noise components is performed level-by-level, which means that the blending weights vary per spatial cascade level. These scale-dependent blending weights are computed from the recent skill of the forecast components, and converge to a climatological value, which is computed from a multi-day rolling window and can be adjusted to the (operational) needs of the user. To constrain the (dis)appearance of rain in the ensemble members to regions around the rainy areas, we have developed a Lagrangian blended probability matching scheme and incremental masking strategy.

We evaluate the method using three heavy and extreme (July 2021) rainfall events in four Belgian and Dutch catchments, focusing on both the rainfall forecasts and the resulting discharge forecasts using the fully distributed wflow_sbm hydrological model. We benchmark the results of the 48-member blended forecasts against the deterministic Belgian NWP forecast, a 48-member nowcast and a simple 48-member linear blending approach. When focusing on the resulting rainfall forecasts, the introduced blending approach predominantly performs similarly or better than only nowcasting (in terms of event-averaged CRPS and CSI values) and adds value compared to NWP for the first hours of the forecast. This holds for both the radar domain and catchment scale, although the difference, particularly with the linear blending method, reduces when we focus on catchment-average cumulative rainfall sums instead of instantaneous rainfall rates. We find similar results for the resulting discharge forecasts, although the effect of the catchment size and corresponding lag times becomes influential and determines the added value of nowcasting over NWP. By properly combining observations and NWP forecasts, blending methods such as these are a crucial component of seamless hydrometeorological forecasting systems.

How to cite: Imhoff, R., Tsiokanos, A., Aerts, J., De Cruz, L., Brauer, C., van Heeringen, K.-J., Weerts, A., and Uijlenhoet, R.: Seamless rainfall and discharge forecasting using a scale-dependent blending of ensemble rainfall nowcasts and NWP, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3147, https://doi.org/10.5194/egusphere-egu23-3147, 2023.

EGU23-5831 | PICO | HS4.1

Nowcasting localized heavy precipitation using a multi-parameter phased array weather radar (MP-PAWR) and a 3D recurrent neural network. 

Philippe Baron, Kouhei Kawashima, Dong-Kyun Kim, Hiroshi Hanado, Takeshi Maesaka, Shinsuke Satoh, Seiji Kawamura, and Tomoo Ushio

Temporal extrapolation of radar observations of precipitation is a means of nowcasting sudden localized heavy rains, i.e., restricted convective rains on a spatial scale of less than 10 km and a lifetime of a few tens of minutes. Such nowcasts are necessary to set up warning systems to anticipate damage to infrastructure and reduce the fatalities these storms cause. It is a difficult task due to the storm suddenness, their restricted area, and nonlinear behavior that are not well captured by current operational systems, even for a lead time of only 10 minutes. Often, conventional approaches use radar observations with 5 min resolution and a Lagrangian advection based extrapolation model with a poor description of the vertical dimension. In this study, we use a new Multi-Parameter Phased-Array Weather Radar (MP-PAWR) with a temporal resolution of 30 sec and a 3D recurrent neural network to improve 10-minute nowcasts of sudden localized rains. The MP-PAWR has been operational in Japan (Saitama prefecture) since 2018. The nowcast model is a supervised neural network trained with adversarial technique. It considers the 3D volume surrounding the instrument up the height of 10 km and the polarimetric information of the measurement.  Improvements with conventional nowcasting techniques will be discussed with some typical examples.

How to cite: Baron, P., Kawashima, K., Kim, D.-K., Hanado, H., Maesaka, T., Satoh, S., Kawamura, S., and Ushio, T.: Nowcasting localized heavy precipitation using a multi-parameter phased array weather radar (MP-PAWR) and a 3D recurrent neural network., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5831, https://doi.org/10.5194/egusphere-egu23-5831, 2023.

EGU23-6096 | ECS | PICO | HS4.1

Representative Hillslope Approach for Modeling Flash Flood Generation in Ungauged Catchments 

Ashish Manoj J, Franziska Villinger, Mirko Mälicke, Ralf Loritz, and Erwin Zehe

Convective rainfall extremes usually trigger due to their highly localised and intense input of mass and momentum ‘hot moments’ in water and matter cycling. Terrestrial systems then respond with strong Hortonian overland flow and erosion up to the formation of flash floods. While heavy precipitation events are characterised by multi-decadal variability, it is noteworthy that the largest observed floods in many rivers of Europe have occurred in the last three decades. Similarly, flash floods have also intensified. The recent clustering of extremes likely reflects the ongoing acceleration of the hydrological cycle, with expected increasing frequencies of intense convective rainstorms and related flash flood and erosion events due to Clausius-Clapeyron scaling. This urgently calls for an improved understanding and models that allow the design of strategies to mitigate onsite and catchment-wide offsite damages of flash floods and erosion events.

Hortonian overland flow occurs when precipitation intensity exceeds the soil’s infiltration capacity. The latter depends on the soil water content, soil hydraulic properties and the density and connectivity of vertical preferential flow paths and are often biologically mediated, as in the case of worm borrow and root channels. Whether locally generated surface runoff reaches the stream depends on the generated spatial connectivity of overland flow paths to the river network.

Here we propose that land use management and soil surface preparation bear the key to reducing the formation of Hortonian overland flow and the connectivity of its flow path, e.g., through a locally elevated infiltration capacity and roughness, thereby reducing the overland flow velocity and favouring its re-infiltration. Moreover, we demonstrate that physically based hydrological models are key to quantifying how changes in landuse and surface preparation techniques (including buffer areas, vegetation barriers, and fascines) in combination with local flood defense reservoirs reduce the formation of flood runoff during convective extremes. Specifically, we use the model CATFLOW and the representative hillslope approach to investigate flash floods observed in four ungauged headwaters catchments in the Kraichgau, Baden-Württemberg (Germany) in 2016. While each catchment drains into a regulated flood defense reservoir, we inverted the flood hydrograph/ inflow into the flood reservoirs using water level measurements and reservoir geometry equations. LULC maps are derived from LANDSAT images using spectral profiles obtained from field surveys over the region. Since flash floods are often associated with localised short-duration, high-intensity rainfall of convective origin, the model is forced using commercial radar-based precipitation products. The CATFLOW model was set up separately for the four headwaters by transferring a completed hillslope setup (soil catena, soil hydraulic properties, plant roughness parameters) from a gauged Weiherbach experimental catchment in the same landscape while deriving the representative hillslope profiles from the digital elevation data. Our results indicate that physically based models perform well in capturing the dynamics of the reconstructed hydrographs, which speaks a) for the transferability of physically based model structures within the same hydrological landscape and b) the feasibility of representative hillslope approach and c) the usefulness of the radar product.

How to cite: Manoj J, A., Villinger, F., Mälicke, M., Loritz, R., and Zehe, E.: Representative Hillslope Approach for Modeling Flash Flood Generation in Ungauged Catchments, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6096, https://doi.org/10.5194/egusphere-egu23-6096, 2023.

EGU23-7151 | ECS | PICO | HS4.1 | Highlight

Assessing the ability of a seamless short-range ensemble rainfall product to detect flash floods on the French Mediterranean area 

Juliette Godet, François Bouttier, Pierre Javelle, and Olivier Payrastre

Flash floods have dramatic economic, natural and social consequences, and efficient adaptation policies are required to reduce these impacts, especially in a context of global warming. This is why it remains essential to develop more efficient flash flood forecasting systems. This study was carried out in order to assess the ability of a new seamless short range ensemble rainfall forecast product, called PIAF-EPS and recently developed by Meteo France, to predict flash floods when it is used as input in an operational hydrological forecasting chain.

For this purpose, eight flash flood events that occurred in the French Mediterranean region between 2019 and 2021 were reproduced, using a similar forecasting chain as the one implemented in the French “Vigicrues-Flash” operational flash flood monitoring system. The hydrological forecasts obtained from PIAF-EPS were compared to the hydrological simulations obtained from the radar observations, and to three deterministic forecasts using varied scenarios (future constant rain, deterministic PIAF, and a numerical nowcasting system called AROME-NWC).

The verification method applied in this work uses rank diagrams and scores calculated on contingency tables, in an original way. The verification process has been conducted on each 1km² pixel of the territory.

The results illustrate the added value of the ensemble approach for flash flood forecasting, and the benefits of the use of a “seamless” product combining radar observations and numerical nowcasting.   

How to cite: Godet, J., Bouttier, F., Javelle, P., and Payrastre, O.: Assessing the ability of a seamless short-range ensemble rainfall product to detect flash floods on the French Mediterranean area, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7151, https://doi.org/10.5194/egusphere-egu23-7151, 2023.

EGU23-7798 | ECS | PICO | HS4.1

Towards 2D flood forecasting with the HPC-enabled shallow water solver SERGHEI-SWE 

Ilhan Özgen-Xian, Mario Morales-Hernández, Michael Nones, and Daniel Caviedes-Voullième

Great advancement has been achieved in the last decade in 2D shallow water solvers for flood modelling. However, their application to physically-based flood forecasting continues to be experimental and not widespread. One of the central challenges towards operational flood forecasting with 2D solvers is their computational cost, which needs to be reconciled with the required lead times for forecasts to be of use. Nonetheless, these solvers have great potential to improve flood forecasting predictions, especially when it comes to flash floods, for which the established 1D and conceptual models may be significantly less applicable.

The shallow water solver SERGHEI-SWE leverages on robust and efficient numerical techniques and is implemented for High Performance Computing (HPC), allowing its use in supercomputers and opening new opportunities in 2D flood forecasting. In this contribution, we present proof-of-concept simulations of several flood events in different catchment and river systems. We show that, with SERGHEI-SWE, it is possible to run very high resolution flood simulations for large hydrological systems with runtimes significantly lower than the event duration. This property is essential to enable operational forecasting with useful lead times.

We run simulations on three river reaches, in the Italian river Po (125 km reach between Boretto and Pontelagoscuro) and in one of its tributaries, the river Secchia (20 km reach), and a meandering reach of the Ebro river through the city of Zaragoza. We also perform flash flood simulations on a 5 km2 district of Nice (France), and in a 50 km2 agricultural catchment in Jaén (Spain). The focus of the exercise is on the computational performance aspect and not on the model performance. The results show that high resolution simulations can be done with runtimes in the order of 100 times faster than real time, potentially allowing a very good forecast lead time. We also explore different combinations of computational resources, model resolution and ensemble size to explore the flexibility of the modelling approach under different computational systems, which may be available for flood forecasting.

How to cite: Özgen-Xian, I., Morales-Hernández, M., Nones, M., and Caviedes-Voullième, D.: Towards 2D flood forecasting with the HPC-enabled shallow water solver SERGHEI-SWE, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7798, https://doi.org/10.5194/egusphere-egu23-7798, 2023.

Flash floods occur when heavy rain causes a fast and powerful flow of water in a drainage area. In the Eastern Mediterranean region, which contains arid and semi-arid areas, the location and timing of rainfall is the most significant factor in the formation of flash floods. Predicting when and where extreme weather events such as storms, heavy rainfall, and flooding are likely to happen is a key challenge in the effort to prevent natural disasters. Here, we present an improved version of a previous work by Ziskin and Reuveni, which investigated the use of precipitable water vapor (PWV) data from ground-based global navigation satellite system (GNSS) stations, along with surface pressure measurements to predict flash floods in an arid region of the eastern Mediterranean. The previous study involved training three machine learning models to perform a binary classification task, using multiple unique flash flood events and testing the models using a nested cross-validation technique. The results showed that the support vector machine (SVM) model had the highest mean area under the curve (AUC) and the lowest AUC variability compared to random forest (RF) and multi-layer perceptron (MLP) models.  When tested on an imbalanced dataset simulating a more realistic flash flood occurrence scenario, all models demonstrated a decrease in the false alarm rate (precision) with a high hit rate (recall) performance.

In this study, we extend the previous work by integrating nearby lightning data as a new feature in our studied dataset. The inclusion of this feature is motivated by the observation that heavy rainfall, which can lead to flood events, is often accompanied before by an increase in lightning activity. The experimental results show that the adding a 24-hour vector of nearby lightning activity improves the precision score significantly.

How to cite: Reuveni, Y., Asaly, S., and Gottlieb, L.-A.: Flash flood predictions over the Eastern Mediterranean using artificial intelligence techniques with precipitable water vapor, pressure, and lightning data., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9979, https://doi.org/10.5194/egusphere-egu23-9979, 2023.

EGU23-11757 | ECS | PICO | HS4.1 | Highlight

Relevance of using ensemble forecasts of flash-flood impacts for an emergency service: an evaluation for the October 2018 flood event in the Aude river basin, France 

Maryse Charpentier-Noyer, Pierre Nicolle, Olivier Payrastre, Eric Gaume, François Bouttier, and Hugo Marchal

Flash floods (FF) represent an important part of the flood damages and fatalities in the world. Today, operational FF nowcasting and warning systems are often based on the use of precipitation weather radars, and therefore still offer limited anticipation. They also generally rather represent the intensity of the flood events than their severity in terms of impacts, which may limit the capacity of emergency services to take relevant decisions.

This contribution aims at evaluating the value of a new ensemble FF impacts forecasting chain for the decision making of an emergency service.  The case study corresponds to the Aude River flash floods that occurred on October 15 and 16, 2018, and which are among the most important FF observed in southeastern France in the recent years. This event is responsible for the death of 15 people (99 people injured), as well as particularly large material damages.

The tested FF impacts forecasting chain combines three new rainfall ensemble forecast products (provided by CNRM), specifically designed for short-range forecasting (0-6h), and a highly distributed rainfall-runoff model (Charpentier-Noyer et al., 2022). A simple impacts model is built and applied for each river reach based on a catalog of 8 inundation scenarios corresponding to return periods of 2 to 1000 years. The impacts are represented in terms of a number of inundated buildings.

The value of the ensemble impacts forecasts is finally evaluated based on the implementation of a multi-agent model, for the simulation of the field decisions taken by an emergency service. This new evaluation approach, based on simple but realistic hypotheses, allows to illustrate and measure the gains associated with a better anticipation of impacts, and the costs associated with false alarms, which lead to the unnecessary mobilization of rescue teams, to the detriment of really impacted locations. In case of extremely limited means for safety operations (low number of rescue teams), the decisions based on a naive zero future rainfall scenario may sometimes appear better than those using ensemble rainfall forecasts. Nevertheless, in all the simulated cases, the decisions taken from the ensemble rainfall forecasts appear more efficient than those based only on field observations.

 

 

Charpentier-Noyer, M., Peredo, D., Fleury, A., Marchal, H., Bouttier, F., Gaume, E., Nicolle, P., Payrastre, O., and Ramos, M.-H.: A methodological framework for the evaluation of short-range flash-flood hydrometeorological forecasts at the event scale, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2022-182, in review, 2022.

How to cite: Charpentier-Noyer, M., Nicolle, P., Payrastre, O., Gaume, E., Bouttier, F., and Marchal, H.: Relevance of using ensemble forecasts of flash-flood impacts for an emergency service: an evaluation for the October 2018 flood event in the Aude river basin, France, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11757, https://doi.org/10.5194/egusphere-egu23-11757, 2023.

EGU23-13338 | ECS | PICO | HS4.1

Debris flows risk assessment for Central Asia by application of Global Ensemble Output and Post-processed Precipitation 

Gavkhar Mamadjanova, Maria Shahgedanova, and Fatima Pillosu

Accurate predictions of heavy and intense rainfall are vital for impact-based forecasting that can be essential for mitigating the significant damage and loss of life across the globe. However, producing reliable forecasts capable of capturing the rainfall values is challenging in complex mountain terrain due to the forecast uncertainty and computational cost especially in data-scarce regions. Central Asia is one of these regions, where extreme rainfall leads to flash floods, landslides and debris flows in the mountains and foothills. The risk of these events increases with global warming, and the early warning systems based on reliable forecasts are particularly important to manage the risk in the region and adapt to climate change.

In this study, we have evaluated and compared the skills of two probabilistic forecasts developed by the European Centre for Medium-Range Weather Forecasts (ECMWF): standard Ensemble Forecasts (ENS) which consists of an ensemble of 51 members and ecPoint Rainfall produced by statistical post-processing of the ENS and delivers probabilistic forecasts of rainfall totals for points within a model gridbox (18 km resolution) that can be particularly useful in the mountains. Skills of both forecasts were assessed in relation to the forecast of debris flows in Central Asia.

Both forecast products were verified against SYNOP (surface synoptic observations) data for stations over Central Asia, mainly for the debris flow season (March-October) in 2022. In this case, two popular verification methods were used: Brier Score and Receiver Operating Characteristics (ROC) diagram for the exceedance of precipitation thresholds of 1 mm, 10 mm and 25 mm.

Verification trials over the 2022 debris flow season in Central Asia show that the performance of ecPoint Rainfall depending on the forecast lead-time can be a good proxy for the range of point rainfall values to define the warning areas of debris flow risk over the study area. The ecPoint Rainfall is recommended for the operational application of heavy rainfall leading to debris flow formation which can support impact-orientated forecasting and early warning systems in Central Asia.

How to cite: Mamadjanova, G., Shahgedanova, M., and Pillosu, F.: Debris flows risk assessment for Central Asia by application of Global Ensemble Output and Post-processed Precipitation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13338, https://doi.org/10.5194/egusphere-egu23-13338, 2023.

EGU23-14123 | PICO | HS4.1

Wavelet-based post-processing of NWP precipitation forecasts 

Fiona Johnson and Ze Jiang

Reliable flood forecasts are dependent on accurate quantitative precipitation forecasts. Despite improvements in the resolution and schematisation of Numerical Weather Prediction (NWP) models, there are still substantial biases in their precipitation forecasts. Biases are present at a range of time scales and correctly representing the multi-temporal scale properties of precipitation including its persistence and variability is vital. In this presentation a new method for post-processing NWP model precipitation forecasts is developed. The new method is based on continuous wavelet transforms (CWT) which correct the statistical characteristics of the precipitation forecasts across a range of time scales. The precipitation amounts are corrected using a simple quantile mapping of the amplitude of each time scale of the wavelet decomposition. To account for uncertainty in precipitation timing, we also adjust the phase of the CWT randomly to create an ensemble of post-processed forecasts. Spatial correlations are preserved by maintaining the same phase adjustments at each different precipitation forecast location.  

The new method is demonstrated using hourly forecast data from the ACCESS model over the period March 2018 to September 2021  for a network of 158 gauges around Sydney, in eastern Australia. The new method improves the correlation of the forecasts and reduces the root mean square error. The spatial correlation structure of the post-processed forecasts is also improved. Correctly representing spatial patterns of precipitation is vital to ensure that catchment averaged precipitation and the resulting flood forecasts are correct.

How to cite: Johnson, F. and Jiang, Z.: Wavelet-based post-processing of NWP precipitation forecasts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14123, https://doi.org/10.5194/egusphere-egu23-14123, 2023.

EGU23-90 | Posters on site | HS7.8

Embracing Large-sample Data to Characterize Streamflow Extremes at a Global-scale 

Sai Kiran Kuntla and Manabendra Saharia

The recurrent and destructive nature of floods causes enormous economic damage and loss of human lives, leaving people in flood-prone areas fearful and insecure. It is essential to have a thorough knowledge of the factors that contribute to it. However, most catchment characterization studies are limited to case studies or regional domains. A detailed global characterization is currently unavailable due to the limitation in the holistic dataset that it demands. This study aims to fill this gap by utilizing multiple global datasets describing physiographic explanatory variables to characterize streamflow extremes. The role of catchment features such as landcover, geomorphology, climatology, lithology, etc., on spatial patterns and temporal changes of high streamflow extremes, was investigated in detail. Moreover, the multidimensional correlations between streamflow extremes and catchment features were modeled using a Random Forest approach and integrated with an interpretable machine learning framework to find the most dominating elements in different climate classes. The interpretation reveals that climatological variables are the most influential across all climates. However, the variables and their influences fluctuate between climates. Furthermore, distinct geomorphological variables dominate throughout climatic classes (such as basin relief in warm temperate and drainage texture in arid climates). Overall, the insights of this study would play a vital role in estimating the unit peak discharge at ungauged stations based on known watershed features. In addition, these findings can also help assess the nature of extremes in future climate scenarios, consequently implicating risk management methods.

How to cite: Kuntla, S. K. and Saharia, M.: Embracing Large-sample Data to Characterize Streamflow Extremes at a Global-scale, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-90, https://doi.org/10.5194/egusphere-egu23-90, 2023.

EGU23-230 | ECS | Orals | HS7.8

The role of spatial dependence in global-scale coastal flood risk assessment 

Huazhi Li, Toon Haer, Alejandra Enríquez, and Philip Ward

Coastal flooding is among the world’s deadliest and costliest natural hazards. The impacts caused by coastal flooding can be particularly high when an event affects a large spatial area, as witnessed during Hurricane Katrina and Cyclone Xaver. Current large-scale flood risk studies assume that the probabilities of water levels during such events do not vary in space. This failure to capture flood spatial dependence can lead to large misestimates of the hazard and risk at large spatial scales, and therefore potentially misinform the risk management community. In this contribution, we assess the effects of spatial dependence on coastal flood risk estimation at the global scale. To this end, we compare the assessments using two spatial dependence scenarios: i) complete dependence and ii) modelled dependence of water level return periods. For the complete dependence scenario, we use the existing risk information calculated by the GLOFRIS global risk modelling framework. To estimate the spatially-dependent risks, we use an event-based multivariate statistical approach and consider 10,000-year extreme coastal flood events derived from the global synthetic dataset of spatially-dependent extreme sea levels. The associated spatially coherent return periods of each event are then combined with the GLOFRIS spatially-constant inundation layers to create the spatially-dependent inundation map. These hazard maps, overlaid with exposure layers and vulnerability information, are further used to assess the coastal flood impacts. The flood risk is estimated using Weibull’s plotting formula and presented in terms of expected annual population and expected annual damage. This study will improve our understanding of flood spatial dependence and will provide improved risk estimation at the global scale. Such reliable estimates could lead to improved large-scale flood risk management through better wide-area planning decisions, more accurate insurance coverage, and better emergency response. 

How to cite: Li, H., Haer, T., Enríquez, A., and Ward, P.: The role of spatial dependence in global-scale coastal flood risk assessment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-230, https://doi.org/10.5194/egusphere-egu23-230, 2023.

EGU23-1384 | Orals | HS7.8

Estimating very rare floods at multiple sites in a large river basin with comprehensive hydrometeorological simulations 

Daniel Viviroli, Anna E. Sikorska-Senoner, Guillaume Evin, Maria Staudinger, Martina Kauzlaric, Jérémy Chardon, Anne-Catherine Favre, Benoit Hingray, Gilles Nicolet, Damien Raynaud, Jan Seibert, Rolf Weingartner, and Calvin Whealton

Rare to very rare floods (associated to return periods of 1'000–100'000 years) can cause extensive human and economic damage. Still, their estimation is limited by the comparatively short streamflow records available. Some of the limitations of commonly used estimation methods can be avoided by using continuous simulation (CS), which considers many simulated meteorological configurations and a conceptual representation of hydrological processes. CS also avoids assumptions about antecedent conditions and their spatial patterns.

We present an implementation of CS to estimate rare and very rare floods at multiple sites in a large river basin (19 locations in the Aare River basin, Switzerland; area: 17'700 km²), using exceedingly long simulations from a hydrometeorological model chain (Viviroli et al., 2022). The model chain consisted of three components: First, the multi-site stochastic weather generator GWEX provided 30 meteorological scenarios (precipitation and temperature) spanning 10'000 years each. Second, these weather generator simulations were used as input for the bucket-type hydrological model HBV, run at an hourly time step for 80 catchments covering the entire Aare River basin. Third, runoff simulations from the individual catchments were routed for a representation of the entire Aare River system using the routing system model RS Minerve, including a simplified representation of main river channels, major lakes and relevant floodplains. The final simulation outputs spanned about 300'000 years at hourly resolution and cover the Aare River outlet, critical points further upstream as well as the outlets of the hydrological catchments. The comprehensive evaluation over different temporal and spatial scales showed that the main features of the meteorological and hydrological observations were well represented. This implied that meaningful information on floods with low probability can be inferred. Although uncertainties were still considerable, the explicit consideration of important flood generating processes (snow accumulation, snowmelt, soil moisture storage) and routing (bank overflow, lake regulation, lake and floodplain retention) was a substantial advantage compared to common extrapolation of streamflow records.

The suggested approach allows for comprehensively exploring possible but unobserved spatial and temporal patterns of hydrometeorological behaviour. This is particularly valuable in a large river basin where the complex interaction of flows from individual tributaries and lake regulations are typically not well represented in the streamflow records. The framework is also suitable for estimating more common, i.e., more frequently occurring floods.

Reference

Viviroli D, Sikorska-Senoner AE, Evin G, Staudinger M, Kauzlaric M, Chardon J, Favre AC, Hingray B, Nicolet G, Raynaud D, Seibert J, Weingartner R, Whealton C, 2022. Comprehensive space-time hydrometeorological simulations for estimating very rare floods at multiple sites in a large river basin. Natural Hazards and Earth System Sciences, 22(9), 2891–2920, doi:10.5194/nhess-22-2891-2022

How to cite: Viviroli, D., Sikorska-Senoner, A. E., Evin, G., Staudinger, M., Kauzlaric, M., Chardon, J., Favre, A.-C., Hingray, B., Nicolet, G., Raynaud, D., Seibert, J., Weingartner, R., and Whealton, C.: Estimating very rare floods at multiple sites in a large river basin with comprehensive hydrometeorological simulations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1384, https://doi.org/10.5194/egusphere-egu23-1384, 2023.

EGU23-2129 | Posters on site | HS7.8

The Prevalence and Impact of Heavy Tails on Hydrologic Extremes and Other Statistics 

Richard Vogel, Jonathan Lamontagne, and Flannery Dolan

The prevalence of heavy tailed (HT) populations in hydrology is becoming increasingly commonplace due in part to the increasing need and use of high frequency and high-resolution data.   In addition to the impact of HT on extremes, HT populations can have a profound impact on a wide range of other hydrologic statistics and methods associated with planning,  management and design for  extremes.   We review the known impacts of HT populations on the instability and bias in a wide range of commonly used hydrologic statistics. Experiments reveal that HT distributions result in the degradation of many commonly used statistical methods including the bootstrap, probability plots, the central limit theorem, and the law of large numbers.     We document the gross instability of perhaps the best-behaved statistic of all, the sample mean (SM) when computed from HT distributions.  The SM is ubiquitous because it is a component of and related to a myriad of statistical methods, thus its unstable behavior provides a window into future challenges faced by the hydrologic community.  We outline many challenges associated with HT data, for example, upper product moments are often infinite for HT populations, yet upper L-moment always exist, so that the theory of L-moments is uniquely suited to HT distributions and data.  We introduce a magnification factor for evaluating the impact of HT distributions on the behavior of extreme quantiles

How to cite: Vogel, R., Lamontagne, J., and Dolan, F.: The Prevalence and Impact of Heavy Tails on Hydrologic Extremes and Other Statistics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2129, https://doi.org/10.5194/egusphere-egu23-2129, 2023.

Extreme wildfires continue to be a significant cause of human death and biodiversity destruction across the globe, with recent worrying trends in their activity (i.e., occurrence and spread) suggesting that wildfires are likely to be highly impacted by climate change. In order to facilitate appropriate risk mitigation for extreme wildfires, it is imperative to identify their main drivers and assess their spatio-temporal trends, with a view to understanding the impacts of global warming on fire activity. To this end, we analyse monthly burnt area due to wildfires using a hybrid statistical deep-learning framework that exploits extreme value theory and quantile regression. Three study regions are considered: the contiguous U.S., Mediterranean Europe and Australia.

How to cite: Richards, J. and Huser, R.: Insights into the drivers and spatio-temporal trends of extreme wildfires with statistical deep-learning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2332, https://doi.org/10.5194/egusphere-egu23-2332, 2023.

EGU23-2974 | ECS | Orals | HS7.8

Stochastic Generation of Snow Depth in Canada 

Hebatallah Abdelmoaty and Simon Papalexiou

Snow depth is a significant component in the hydrological cycle and global energy and water balances, contributing to climate change impacts. Weather stations with gauges for snow depth are scarce, especially in complex terrain regions, and require high accuracy for measurements. Advances in observational systems offer unconventional solutions yet are expensive. To bridge these gaps, stochastic generation methods offer a cost-effective solution to reproduce time series of hydrological variables, preserving their stochastic properties. Stochastic generation methods are well-established for total precipitation but lack snow depth generation. Here, we introduce a stochastic method to exclusively generate snow depth time series that preserve their distinct statistical properties on different time scales. We use 450 observed snow depth time series and 470 CMIP6 simulations to detect Canada's observed and physical statistical properties. The results indicate that snow depth has a light tail, and the distribution might change daily. The probability of zero snow depth shows a clear seasonal pattern. The synthetic snow depth time series can be an alternative to climate models’ outputs, offering a computationally effective solution to investigate the snow depth variability. This method advances the generation of stochastic time series of snow depth and can be applied to investigate catastrophes from snowmelt processes and avalanches that lead to severe damage and fatalities.

How to cite: Abdelmoaty, H. and Papalexiou, S.: Stochastic Generation of Snow Depth in Canada, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2974, https://doi.org/10.5194/egusphere-egu23-2974, 2023.

EGU23-3709 | Posters on site | HS7.8

Spatial and Temporal Evolution of Drought Events Using High-Resolution SPEI and Dynamic Drought Detection Algorithm 

Jiyoung Yoo, Jiyoung Kim, Hyun-Han Kwon, and Tae-Woong Kim

Drought is one of the world's major natural disasters. In order to monitor drought and reduce drought damage through preemptive response, it is important to understand the spatiotemporal evolutionary characteristics of drought. Droughts have a three-dimensional (3-D) space-time structure, typically spanning hundreds of kilometers and lasting months to years. In this study, a high-resolution(5 km) SPEI-HR(Standardized Precipitation Evaporation Index) dataset was used, considering climatic (typical temperate continental climate) and various geographic characteristics (mountainous terrain, lowland basin, desert, grassland, etc.). In addition, all large- and small-scale drought events that evolve spatiotemporally were extracted using the dynamic drought detection technique (DDDT) algorithm. These 3D-drought properties are important information to explain the spatiotemporal evolution of drought and are characterized by drought patches in dynamic drought maps. As a result, most of the trajectories of droughts in Central Asia during the period 1981 to 2018 tended to move laterally to the east and west (ENE, E, ESE, WNW, W, WSW). In addition, droughts in Central Asia are characterized by very strong correlations between indicators of duration, severity, area, and trajectory movement distance. These Central Asian drought characteristics are interpreted as meaning that there is consistency among various drought information in determining the most severe drought event. In addition, the dynamic drought map, which includes all 3D-drought properties, has the advantage of producing high-level drought information (temporal continuity of drought events and dynamic evolution characteristics, etc.) that are not found in general drought maps through various conditional drought monitoring.

Acknowledgements: This work was supported by the National Research Foundation of Korea (No. NRF-2020R1C1C1014636) and Korea Environment Industry & Technology Institute (KEITI) (No.2022003610001) funded by the Korean government (MSIT and MOE).

How to cite: Yoo, J., Kim, J., Kwon, H.-H., and Kim, T.-W.: Spatial and Temporal Evolution of Drought Events Using High-Resolution SPEI and Dynamic Drought Detection Algorithm, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3709, https://doi.org/10.5194/egusphere-egu23-3709, 2023.

EGU23-3851 | ECS | Posters on site | HS7.8

On the Projected Changes in the Seasonality and Magnitude of Precipitation Extremes 

Dario Treppiedi, Gabriele Villarini, Jens Bender, and Leonardo Noto

Heavy precipitation events are strongly affected by climate change and there is a high confidence that these extremes will become more frequent and more severe in the future. Moreover, potential changes in the seasonality of these events are important in terms of planning and preparation against these events. While efforts have been focused on changes in the magnitude and seasonality of extreme precipitation events, these studies have treated these two quantities separately.

In order to overcome to this limit, a different perspective is here used by modeling the seasonality and magnitude of extreme precipitation events together through circular-linear copulae. We perform analyses at the global scale and develop bivariate models for an historical dataset. The outputs provided from several global climate models from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and Shared Socioeconomic Pathways (SSPs) from 1-2.6 to 5-8.5 are then used to examine the joint projected changes in the seasonality and magnitude of extreme precipitation at the global scale.

How to cite: Treppiedi, D., Villarini, G., Bender, J., and Noto, L.: On the Projected Changes in the Seasonality and Magnitude of Precipitation Extremes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3851, https://doi.org/10.5194/egusphere-egu23-3851, 2023.

Spatially co-occurring floods pose a great threat to the resilience and the recovery potential of the communities. A timely forecasting of such events plays a crucial role for increasing the preparedness of public and private sectors and for limiting the associated losses. In this study we investigated the potential of dilated Convolutional Neural Networks (CNN) conditioned on a set of large-scale climatic indexes and antecedent precipitation for monthly forecast of widespread flooding severity in Germany using 63 years of streamflow observations. The severity of widespread flooding (i.e., spatially co-occurring floods) was estimated as simultaneous (within a given month) exceedance of an at-site two-year return period for streamflow peaks across 172 mesoscale catchments. The model was trained for the whole country and for the three diverse hydroclimatic regions individually to provide insights on spatial heterogeneity of model performance and drivers of flooding. Evaluation of the model skill for floods generated by different processes revealed the largest bias for events generated during dry conditions. The bias for rain-on-snow flood events was the lowest despite their higher severity indicating higher predictability of these events from large scale climatic indexes. Model-based feature attribution and independent wavelet coherence analyses both indicated considerable difference in the major drivers of widespread flooding in different regions. While the flooding in the North-Eastern region is strongly affected by the Baltic Sea (e.g., East Atlantic pattern), the North-Western region is affected more by global patterns associated with the El-Niño activity (e.g., Pacific North American pattern). In the Southern region in addition to the effect of the global patterns we also detect the effect of the Mediterranean Sea (Mediterranean Oscillation Index), while antecedent precipitation seems to play less important role in this region compared to the rest of the country. Our results indicate a considerable potential for forecasting widespread flood severity using dilated CNN especially as the length of the available time series for training increases.

How to cite: Tarasova, L., Ahrens, B., Hoff, A., and Lall, U.: Forecasting the monthly severity of widespread flooding in Germany using dilated convolutional neural networks conditioned by large-scale climatic indexes, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4419, https://doi.org/10.5194/egusphere-egu23-4419, 2023.

EGU23-5298 | ECS | Posters on site | HS7.8

A spatial covariates model for storm surge extremes in the German Bight 

Gabriel Ditzinger, Henning Rust, Jens Möller, Tim Kruschke, Laura Schaffer, and Claudia Hinrichs

Storm surges and accompanying extreme water levels pose a major threat to coastal structures, urban and industrial areas and human life in general. In order to develop effective risk mitigation strategies, it is crucial to improve the understanding of these extreme events as well as their occurrence probabilities and quantiles, respectively.

The standard procedure to estimate extreme quantiles (return-levels) is to fit a suitable distribution to the observed extreme values on a site-by-site basis. However, this approach exhibits some disadvantages: 1) Estimates of extreme quantiles are only available at gauged locations. 2) The small amount of extreme events in tide gauge records makes these estimates highly uncertain.

We tackle both issues by pooling all available tide gauge records together through a covariates model that allows for smoothly varying distribution parameters in space. Using this approach, the model is not only able to reduce the uncertainty in quantile estimates, but also enables the interpolation of the distribution parameters at arbitrary ungauged locations, e.g. in between tide gauge locations.

Deploying our model for the German North Sea coast, we generate a probabilistic reanalysis of extreme water levels as well as associated probabilities for the period 2000 – 2019.

How to cite: Ditzinger, G., Rust, H., Möller, J., Kruschke, T., Schaffer, L., and Hinrichs, C.: A spatial covariates model for storm surge extremes in the German Bight, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5298, https://doi.org/10.5194/egusphere-egu23-5298, 2023.

Statistical dependency measures such as Kendall’s Tau or Spearman’s Rho are frequently used to analyse the coherence between time series in environmental data analyses. Autocorrelation of the data can however result in spurious cross correlations if not accounted for. Here, we present the asymptotic distribution of the estimators of Spearman’s Rho and Kendall’s Tau, which can be used for statistical hypothesis testing of cross-correlations between autocorrelated observations. The results are derived using U-statistics under the assumption of absolutely regular (or β-mixing) processes. These comprise many short-range dependent processes, such as ARMA-, GARCH- and some copula-based models relevant in the environmental sciences. We show that while the assumption of absolute regularity is required, the specific type of model does not have to be specified for the hypothesis test. Simulations show the improved performance of the modified hypothesis test for some common stochastic models and small to moderate sample sizes under autocorrelation. The methodology is applied to observed time series of flood discharges and temperatures in Europe and yields results that are consistent with the literature on flood regime changes in Europe. While the standard test results in spurious correlations between floods and temperatures, this is not the case for the proposed test, which is more consistent with the literature on flood regime changes in Europe.

How to cite: Lun, D., Fischer, S., Viglione, A., and Blöschl, G.: Attribution of flood changes with time series in the presence of autocorrelation: Modifications for Spearman‘s Rho and Kendall‘s Tau, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7352, https://doi.org/10.5194/egusphere-egu23-7352, 2023.

EGU23-7564 | ECS | Posters on site | HS7.8

Areal extremes from a different perspective: rainfall as 2D and 3D connected objects. 

Abbas El Hachem, Jochen Seidel, and András Bárdossy

Using the German weather radar data for the last 20 years with a high spatial and temporal resolution, the occurrence of rainfall extremes was analysed. By extracting and examining connected rainfall areas, several research questions were investigated: (1) How many extremes occur in a given area independent of their location? (2) To what extent is their occurrence in space a random and to what extent a structured process? (3) How are the connected volumes behaving in space and time? (4) How does the areal extent relate to event duration, rainfall volume, and discharge volume? The first two research questions were investigated for all of Germany, the last two by analysing rainfall and run-off data in several small and medium size headwater catchments in southern and western Germany.

The results show that the occurrence of events in space is related to their areal extent; there are regions where the frequency of occurrence of large spatially distributed events is greater than that of smaller ones. Moreover, there are interesting relationships between the spatial extent of an event, the event duration, and the event rainfall volumes. For high discharge values, not only does the rainfall intensity matter but also the event duration and spatial distribution of rainfall within a catchment. Many discharge peaks are not necessarily caused by high-intensity events (hourly or daily maxima) but by the accumulation of rainfall cells in space and time.

How to cite: El Hachem, A., Seidel, J., and Bárdossy, A.: Areal extremes from a different perspective: rainfall as 2D and 3D connected objects., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7564, https://doi.org/10.5194/egusphere-egu23-7564, 2023.

Alaba Boluwade*

School of Climate Change & Adaptation, University of Prince Edward Island, Charlottetown, Canada; aboluwade@upei.ca; abolu2013@gmail.com

*Correspondence: aboluwade@upei.ca

Abstract

Hydrological risk assessment, such as flood protection, requires estimates of variables (e.g., precipitation) measured from several weather stations. The spatial modeling of average rainfall estimates differs from extreme precipitation analysis. This is because extremes are focused on the tail of the probability distribution and the assumption of Gaussianity may not be suitable. Extreme Value Theory (EVT) application to univariate weather variables measured at weather stations has been well documented; however, extreme precipitation at closer stations tend to show trends and dependencies (similar values). It is, therefore, crucial to quantify the dependency structure and trend surface of weather stations in space. The max-stable process has been well documented to model spatial extremes. The objective of this study is to quantify the spatial dependency and trend of an annual maxima precipitation (annual highest daily precipitation, from 1970-2020) across selected weather stations in the Northern Great Plains (i.e., Nelson Churchill River Basin (NCRB)) of North America. The annual maxima data were extracted from the Global Historical Climatology Network Daily (GHCNd) and Environment and Climate Change Canada (ECCC). NCRB covers four states and four provinces in the United States and Canada. A heterogenous rainfall pattern characterizes NCRB. This is due to enormous quantities of orographic rainfall in the west and the convective precipitation in the Prairies (which is dominated by short-duration, sporadic, extreme rain), causing millions of dollars in damages. This study uses max-stable processes to examine spatial extremes of annual maxima precipitation.

The results show that topography, time, and geographical coordinates were important covariates in reproducing the stochastic extreme precipitation field using the spatial generalized extreme value (SPEV). Takeuchi’s information criterion (TIC) shows that the SPEV model with all the covariates above superseded the one without the covariates.   The inclusion of time as a covariate further confirms the impacts of climate change on extreme precipitation in the NCRB. The fitted Extremal-t max-stable model captured the spatial dependency and equally predicted the 50-year return period levels. Furthermore, ten realizations (equal probable) were simulated from the max-stable model. The study is relevant in quantifying the spatial trend and dependency of extreme precipitation in the Northern Great Plains. The result will help as a decision-support system in climate adaptation strategies in the United States and Canada.

 

Keywords: extreme events; Max-Stable processes; flood protection; maxima annual rainfall; flash flood protection; Canada, United States

How to cite: Boluwade, A.: Application of Max-Stable Process Model in Estimating the Spatial Trend & Dependency of Extremes in the Northern Great Plains, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9758, https://doi.org/10.5194/egusphere-egu23-9758, 2023.

EGU23-10538 | ECS | Orals | HS7.8

Improved data assimilation in regional frequency analysis of rainfall extremes across large and morphologically complex geographical areas 

Andrea Magnini, Michele Lombardi, Taha B. M. J. Ouarda, and Attilio Castellarin

In locations where measured timeseries are not available or not sufficiently long, reliable predictions of the rainfall depth associated with a given duration and exceedance probability may be obtained through regional frequency analysis (RFA). The scientific literature reports on a large number of different approaches to RFA of rainfall extremes, each one characterized by specific advantages and disadvantages. One of the most common drawbacks is that regional models specifically refer to a single duration or a single exceedance probability. Second, several approaches require the definition of a homogeneous region where the model is trained; this leads to higher accuracy, but also the applicability of the model is limited to those locations that are hydrologically similar to the homogeneous group used in the training. Moreover, most models require filtering the available gauged stations based on the length of the measured timeseries to perform reliable frequency analysis. These aspects lead to discard a significant amount of data, which could turn out to be detrimental to the accuracy of the regional prediction in some cases.

We set up a few alternative models aiming to investigate and discuss a different and innovative approach for RFA of rainfall extremes. We want to address three main research questions: (1) Can a single model represent the frequency of extreme rainfall events over a large, climatically, and morphologically complex geographical area? (2) Can a single RFA model handle all sub-daily  durations (i.e., from 1 to 24h)? (3) Is it possible to exploit all available annual maximum series, regardless of their length (i.e., very short ones too)? We select a large study area that is located in north-central Italy. We make use of more than 2300 Annual Maximum Series of rainfall depth for different time-aggregation intervals between 1 and 24 hours, that have been collected between 1928 and 2011 in the Italian Rainfall Extreme Dataset (I2-RED). For each gauged station, several morpho-climatic descriptors are retrieved (e.g., minimum distance to the coast, elevation of orographic barriers, aspect, terrain slope, etc.) and used as covariates for the prediction models. Our models are based on ensembles of unsupervised artificial neural networks (ANNs) and are able to predict parameters of a Gumbel distribution for any location and any duration in the 1-24 hours range based on the morphoclimatic descriptors. Through the analysis of results over 100 gauged validation stations, a profitable discussion is enabled on the potential and drawbacks of ensembles of unsupervised ANNs for regional frequency analysis of sub-daily rainfall extremes.

How to cite: Magnini, A., Lombardi, M., Ouarda, T. B. M. J., and Castellarin, A.: Improved data assimilation in regional frequency analysis of rainfall extremes across large and morphologically complex geographical areas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10538, https://doi.org/10.5194/egusphere-egu23-10538, 2023.

EGU23-11332 | Posters on site | HS7.8

Interpolation of design rainfall at ungauged locations exploiting the potential of convection-permitting climate models. 

Giuseppe Formetta, Francesco Marra, Eleonora Dallan, and Marco Borga

Quantifying design rainfall events at varying durations is crucial for assessing flood risk and mitigating losses and damages. Yet, in a changing climate, they are fundamental tool for a reliable design of water related infrastructures, such as flood retention reservoirs, spillways, and urban drainage systems. Usually, design rainfall is quantified where rain gauges are located, and regionalization methods are used to provide estimates in ungauged locations. During the last years, convection-permitting climate models (CPM) are receiving increasing attention because, thanks to their high spatial resolution (~3km) and ability of explicitly resolving atmospheric convection, they allow for better estimating precipitation spatial patterns and extreme rainfall at multiple durations compared to coarser models.

In this work, we combine at-site rain gauge measurements with CPM simulations, within a non-asymptotic statistical framework for the analysis of extreme rainfall. We aim at quantifying the added value of the physics-based information provided by CPM simulations for the estimation of high quantiles of rainfall in ungauged locations.     

The performance of the new regionalization approach is compared with traditional interpolation methods (i.e. interpolation of distribution function parameters) using leave- one-out cross-validation as well as considering different rain gauge densities.

Preliminary results show that the proposed methodology based on CPM simulation provides: i) similar performances compared to traditional gauge-based regionalization methods for high station density scenarios and ii) improved performances for low station density scenarios.

How to cite: Formetta, G., Marra, F., Dallan, E., and Borga, M.: Interpolation of design rainfall at ungauged locations exploiting the potential of convection-permitting climate models., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11332, https://doi.org/10.5194/egusphere-egu23-11332, 2023.

EGU23-11828 | ECS | Posters on site | HS7.8

The Future of Extreme Event Risk Assessment: A Look at Multivariate Return Periods in More than Three Dimensions 

Diego Armando Urrea Méndez, Dina Vanesa Gómez Rave, and Manuel Del Jesus Peñil

The multivariate return period is a measure of the frequency with which simultaneous sets of variables are expected to occur in a given area. So far, most approaches to calculate the multivariate return period of various hydrological variables have used copulas in two and three dimensions. (Salvadori et al., 2011) proposed a methodology for calculating the return period based on Archimedean copulas and the Kendall measure in 2 and 3 dimensions. (Gräler et al., 2013) proposed the calculation of the trivariate return period based on Vine copulas and Kendall distribution functions to describe the characteristics of the design hydrogram, considering the annual maximum peak discharge, its volume and duration. (Tosunoglu et al., 2020) applied three-dimensional Archimedean, Elliptical and Vine copulas to study the characteristics of floods. These studies have shown that the use of copulas can improve the accuracy of the risk measure of extreme events compared to univariate approaches, that only consider one variable at a time.

One of the limitations in describing the occurrence of multivariate extreme involving more than three simultaneous variables is the complex mathematical model to be solved (highest probability density point of a hypersurface) and the high computational cost that this imposes. However, in some areas of hydrology, developing more robust analyses that consider more than three variables can further improve risk assessments. For example, considering multiple rainfall stations in a watershed may help to properly capture the spatial structure of extremes -instead of relying on other spatial distribution procedures-. This improvement can provide a more accurate measure of the return period in the design of critical infrastructure, flood prediction, risk plans, etc.

In this context, we present an application where an extreme characterization of 5 rain gauges is considered simultaneously, using vine copulas based on Kendall distribution functions. More specifically, we analyze which measures are suitable for explaining the spatial and temporal correlation of rain events in different locations within a network of stations; which families and structures of vine copulas can optimally capture the spatial dependence structure within a region; how to solve the complex mathematics that is imposed when expanding the dimensionality; what is a computationally reasonable alternative to improve the computational cost involved.; and how multivariate analysis can improve the precision of the extreme event risk measure compared to univariate approaches.

These questions are answered by applying the proposed methods to a pilot case, which is developed in a basin located in northern Spain. Multivariate modeling is becoming increasingly relevant in the field of hydrology due to its ability to model extreme stochastic events, which are key to mitigating the risk and damages caused by floods.

References

Gräler, B., Berg, M. J. van den, Vandenberghe, S., Petroselli, A., Grimaldi, S., De Baets, B. & Verhoest, N. E. C., 2013. Multivariate return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation. Hydrol. Earth Syst. Sci., 17(4), 1281–1296.

Salvadori, G., De Michele, C. & Durante, F., 2011. On the return period and design in a multivariate framework. Hydrol. Earth Syst. Sci., 15(11), 3293–3305.

How to cite: Urrea Méndez, D. A., Gómez Rave, D. V., and Del Jesus Peñil, M.: The Future of Extreme Event Risk Assessment: A Look at Multivariate Return Periods in More than Three Dimensions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11828, https://doi.org/10.5194/egusphere-egu23-11828, 2023.

EGU23-12249 | ECS | Posters on site | HS7.8

Space-time downscaling of extreme rainfall using stochastic simulations, intense runoff susceptibility modeling and remote sensing-based pluvial flood mapping 

Arnaud Cerbelaud, Etienne Leblois, Pascal Breil, Laure Roupioz, Raquel Rodriguez-Suquet, Gwendoline Blanchet, and Xavier Briottet

Accurate rainfall modeling is crucial to understand the way water is intercepted, infiltrates and flows through surfaces and rivers. In particular, it is paramount for the study of the influence of rainfall spatio-temporal distribution on basin hydrologic response and the structure of floods. Current weather radar products allow capturing the variability of rainfall extremes mainly at 1 km spatial resolution. In France, radar measurements are performed at a 5-minute time step, while gauge-based reanalysis are computed at hourly resolutions. During short-duration high-intensity precipitations, pluvial floods (PF, or flash floods) can occur outside the hydrographic network in runoff-prone areas, leading to various types of damages such as soil erosion, mud and debris flows, landslides, vegetation uprooting or sediment load deposits. Contrary to fluvial floods, PF are highly correlated to local rainfall. Depending on generic susceptibility linked to topography, soil texture and land use, specific precipitation patterns can trigger intense overland flow. Hence, after extreme weather events, precise reports on PF locations provide key information for rainfall reanalysis and downscaling at fine spatial resolution.

This work focuses on two extreme Mediterranean events (more than 300 mm of rainfall in 24 hours) that took place in the South of France between 2018 and 2020. Time series of hourly rainfall intensities from Comephore radar reanalysis data at 1 km resolution (Météo-France) are confronted to (i) maps of PF that occurred during the events and (ii) generic susceptibility maps to surface runoff. For (i), runoff-related impact maps of the events are produced using the remote sensing-based FuSVIPR algorithm (Cerbelaud et al., 2023) based on Sentinel-2 temporal change images and Pléiades satellite or airborne post event acquisitions. For (ii), the IRIP© method (Dehotin and Breil, 2011; Cerbelaud et al., 2022) is used to generate PF susceptibility maps. The model is run with the RGE Alti® 5 m DEM, the OSO French land cover dataset, and soil type susceptibility characteristics derived from both climatological information and the ESDAC database.

We primarily show that areas with higher IRIP levels are more likely to be impacted by PFs, and even more so where short-term precipitation was heavier. Additionally, rainfall intensities are negatively correlated with IRIP susceptibility scores in PF impacted areas. This corroborates that somewhat higher rainfall intensities are required for flash floods to occur in less susceptible areas. Similarly, comparatively smaller rainfall amounts can trigger PFs in locations where susceptibility is high. Then, the Comephore products are downscaled at 50 m resolution on both events using the SAMPO stochastic simulator (Leblois and Creutin, 2013). Among multiple scenarios, optimal ones are chosen based on the assumption that the negative correlation with the IRIP susceptibility levels in the affected areas should be equally or even more present in the downscaled rainfall time series. This study hence suggests an original way of selecting disaggregated extreme rainfall scenarios that are consistent with the observed consequences of intense runoff on the land surface using various tools such as a stochastic simulator, a hydrological risk mapping method and earth observation data.

How to cite: Cerbelaud, A., Leblois, E., Breil, P., Roupioz, L., Rodriguez-Suquet, R., Blanchet, G., and Briottet, X.: Space-time downscaling of extreme rainfall using stochastic simulations, intense runoff susceptibility modeling and remote sensing-based pluvial flood mapping, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12249, https://doi.org/10.5194/egusphere-egu23-12249, 2023.

EGU23-12736 | ECS | Posters on site | HS7.8

Multivariate Probability Analysis of Compound Flooding Dynamics. 

Dina Vanessa Gomez Rave, Diego Armando Urrea Méndez, and Manuel Del Jesus Peñil

Coastal cities are increasingly prone to compound flooding events. Particularly in estuaries, interactions between both freshwater fluxes (rainfall or discharge) and coastal water levels (tide, surge, waves, or combinations thereof) can strongly modulate flood hazard. These separate but physically connected processes can often occur simultaneously (but not necessarily in extreme conditions), resulting in compound events that may eventually have significant economic, environmental and social impacts. Conventional risk assessment mainly considers univariate-flooding drivers and does not include multivariate approaches; nevertheless, ignoring compound analysis may lead to a significant misestimation of flood risk.

In this respect, the complex interactions between coastal flooding drivers imply multidimensionality, nonlinearity and nonstationarity issues, and consequently, more relevant uncertainties. Copula-based frameworks are flexible alternatives to overcome limitations of traditional univariate approaches, and can incorporate the joint boundary conditions in riverine and coastal interactions in a statistically sound way (Harrison et al., 2021; Bevacqua et al., 2019; Couasnon et al., 2018, Moftakhari et al., 2017).  However, incorporations are often limited to the bivariate joint case. Trivariate (or higher dimensional) joint distribution are scarce, due to the convoluted and computationally expensive composition (Latif & Sinonovic, 2022). Notably, a need for robust and efficient approaches that help to characterize the nature of compound hazard remains (Moftakhari et al., 2021).

This study aims to improve copula-based methodologies that can adequately estimate the compound flood probability in estuarine regions, considering more than two variables, including more sources of uncertainty into the stochastic dependence analysis, raising the degree of accuracy to risk inference. This work develops a vine copula framework for the analysis of estuarine compound flooding risk, considering interactions and dependency structures between several oceanographic, hydrological, and meteorological processes and variables (rainfall, river discharge, waves, and storm tides). We show the potential of the framework in Santoña, a strategic estuarine ecosystem in Northern Spain. In order to yield proper design events, we focus here on estimating the multivariate joint and conditional joint return periods statistics, using the best-fitted model in the assessment of the extreme regime, based on Archimedean and Elliptical copula families. We also present the complexities of determining the ensemble of compound events corresponding to a given return period and compare these ensembles to the results of univariate extreme value analysis, to remark the importance of multivariate characterization of extremes.

References

Bevacqua, E., Maraun, D., Vousdoukas, M. I., Voukouvalas, E., Vrac, M., Mentaschi, L., & Widmann, M. (2019). Higher probability of compound flooding from precipitation and storm surge in Europe under anthropogenic climate change. Science advances, 5(9), eaaw5531.

Couasnon, A., Sebastian, A., & Morales-Nápoles, O. (2018). A copula-based Bayesian network for modeling compound flood hazard from riverine and coastal interactions at the catchment scale: An application to the Houston Ship Channel, Texas. Water, 10(9), 1190.

How to cite: Gomez Rave, D. V., Urrea Méndez, D. A., and Del Jesus Peñil, M.: Multivariate Probability Analysis of Compound Flooding Dynamics., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12736, https://doi.org/10.5194/egusphere-egu23-12736, 2023.

EGU23-13328 | ECS | Posters on site | HS7.8

Assessing daily precipitation tails over India under changing climate 

Neha Gupta and Sagar Chavan

Daily precipitation extremes are crucial in the hydrological design of major water control structures. The extremes are usually present in the upper part of the probability distribution of daily precipitation data, known as the tail. The distributions are bifurcated into heavy or light-tailed distributions depending on the tail. Heavy tails signify a higher frequency of occurrences of extreme precipitation events. Prediction of extreme precipitation depends on reliable modelling of the tail. Tail behaviour can be studied by graphical as well as threshold-based fitting approaches; however, each approach has associated shortcomings. In this work, we utilize a versatile and simple empirical index known as the “Obesity Index” (OB) to assess the tail of probability distributions of daily gridded precipitation data for India. This comprehensive regional analysis has been undertaken to quantify the tail heaviness of 4801 daily precipitation records over India for historical (1970–2019) and future (2020–2100) time periods. Future projections of daily precipitation are downscaled from the latest generation of climate models knowns as Coupled Model Intercomparison Project Phase 6 (CMIP6) under different emission scenarios. Finally, the application of the OB-based approach is extended to characterize daily precipitation in Indian Meteorological Subdivisions. Results indicate the applicability of heavy-tailed distributions in representing daily precipitation over India and establish the utility of the OB-based approach in diagnosing tail behaviour. Also, the spatial patterns of the tail heaviness are found to be matching with the Köppen–Geiger climate classification of India. The findings from this can be an input for the policymakers to develop adaptation strategies in response to the projected climate change impact.

Keywords: Extreme precipitation, Climate Change, India, Obesity index, Tail heaviness

 

How to cite: Gupta, N. and Chavan, S.: Assessing daily precipitation tails over India under changing climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13328, https://doi.org/10.5194/egusphere-egu23-13328, 2023.

EGU23-13386 | ECS | Posters on site | HS7.8

Use of high temporal resolution data to identify the key drivers and locations where walls of water occur in the UK 

Felipe Fileni, David Archer, Hayley Fowler, Fiona McLay, Elizabeth Lewis, and Longzhi Yang

Walls of water (WoW) are a subset of flash floods characterised by an extremely fast increase in the discharge rate of rivers. In the UK, WoWs, events where an almost instantaneous increase in river flow happens, are responsible for several deaths, even when the maximum peak flow of the said event is not as noticeable. Using a national 15-minute continuous dataset, this study identified WoWs for catchments in the UK. Next, the antecedent atmospheric conditions for these WoWs were extracted from gridded datasets. Furthermore, catchment descriptors such as catchment area, elevation, slope, land use, and permeability of every catchment were downloaded from the National River Flow Archive. Finally, with the use of machine learning algorithms, that is, tree regressions and neural networks, this study identified vulnerable catchments and key conditions for WoWs to occur. Early results indicate that WoWs are not solely driven by rainfall intensity and that larger catchments (>500km) with low permeability are the most vulnerable to these hazards. Further studies using additional atmospheric conditions, i.e., temperature and windspeed will allow a better understanding of the drivers of these events.

How to cite: Fileni, F., Archer, D., Fowler, H., McLay, F., Lewis, E., and Yang, L.: Use of high temporal resolution data to identify the key drivers and locations where walls of water occur in the UK, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13386, https://doi.org/10.5194/egusphere-egu23-13386, 2023.

EGU23-13732 | ECS | Posters on site | HS7.8

Simulation of extreme precipitation events over south-west France: the role of large-scale atmospheric circulation and atmospheric rivers 

Namendra Kumar Shahi, Olga Zolina, Sergey K. Gulev, Alexander Gavrikov, and Fatima Jomaa

South-western France has witnessed some of the most devastating extreme precipitation events that eventually lead to record-breaking severe flash flooding in the region and cause losses to human lives, urban transportation, agriculture, and infrastructure. In this study, two cases of deadly flash floods that occurred/reported in the Aude watershed in south-western France during 12-13 November 1999 and 14-15 October 2018 are studied using the WRF4.3.1 model simulations, with a particular emphasis on the model ability to capture these heavy precipitation events. We performed two simulations one with parameterized convection and one without the use of convection parameterizations for each case at gray-zone resolution (~9 km horizontal grid spacing) using the ERA5 reanalysis as the lateral boundary condition. In addition, attempts have been made to investigate the role of large-scale atmospheric circulation and atmospheric rivers in the production of these heavy precipitation events. The results from model simulations are compared quantitatively with available observations and reanalysis and found that the simulations at ~9 km gray-zone resolution capture the observed spatio-temporal distribution of precipitation characteristics during both extreme cases. The added value of gray-zone resolution simulations over driving coarse-scale ERA5 reanalysis datasets is observed in the representation of the precipitation characteristics. It has also been observed that the model simulation without the use of convection parameterization yields a reasonable and realistic representation of the precipitation characteristics during both extreme cases, and this suggests that at this “gray-zone” resolution the organized mesoscale convective systems/processes can be resolved explicitly by the model dynamics. The contribution of the large-scale atmospheric circulation and the atmospheric river (i.e., moisture transport) in the production of these flood events has also been observed.

How to cite: Shahi, N. K., Zolina, O., Gulev, S. K., Gavrikov, A., and Jomaa, F.: Simulation of extreme precipitation events over south-west France: the role of large-scale atmospheric circulation and atmospheric rivers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13732, https://doi.org/10.5194/egusphere-egu23-13732, 2023.

EGU23-14934 | ECS | Posters on site | HS7.8

Updating annual rainfall maxima statistics in a data-scarce region 

Angelo Avino, Luigi Cimorelli, Domenico Pianese, and Salvatore Manfreda

The growing number of extreme hydrological events observed has raised the level of attention toward the impact of climate change on rainfall process, which is difficult to quantify given its strong spatial and temporal heterogeneity. Therefore, the impact of the climate cannot be determined on the individual hydrological series but must be assessed on a regional and/or district scale. With this objective, the present work aims at identifying the trends and dynamics of extreme sub-daily rainfall in southern Italy in the period 1970-2020. The database of annual maxima was assembled using all available rainfall data (provided by the National Hydrographic and Mareographic Service - SIMN, and the Regional Civil Protection). However, due to the numerous changes (location, type of sensor, managing agencies) experienced by the national monitoring network, the time-series were found to be extremely uneven and fragmented. Since the spatio-temporal discontinuity could invalidate any statistical analysis, gap-filling techniques (deterministic and/or geostatistical [Teegavarapu, 2009]) were applied to reconstruct the missing data. In particular, the “Spatially-Constrained Ordinary Kriging” (SC-OK) method [Avino et al., 2021] was used, namely a mixed procedure that adopts the Ordinary Kriging (OK) approach with the spatial constraints of the Inverse Distance Weighting (IDW) method. The SC-OK method allows to reconstruct only missing data for stations selected by the IDW method (those with a sufficient number of functioning neighbouring rain gauges). Then, the reconstructed dataset has been used to explore trends and regional patterns in annual maxima highlighting, how rainfall are evolving in the most recent years.

REFERENCES

Avino, A., Manfreda, S., Cimorelli, L., and Pianese, D. (2021). Trend of annual maximum rainfall in Campania region (Southern Italy). Hydrological Processes, 35.

Teegavarapu, R.S.V. (2009). Estimation of Missing Precipitation Records Integrating Surface Interpolation Techniques and Spatio-temporal Association Rules. Journal of Hydroinformatics, 11(2).

How to cite: Avino, A., Cimorelli, L., Pianese, D., and Manfreda, S.: Updating annual rainfall maxima statistics in a data-scarce region, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14934, https://doi.org/10.5194/egusphere-egu23-14934, 2023.

EGU23-15475 | Posters on site | HS7.8

A non-stationary gridded weather generator conditioned on large-scale weather circulation patterns for Central Europe 

Viet Dung Nguyen, Sergiy Vorogushyn, Katrin Nissen, and Bruno Merz

For many flood risk assessments at large spatial scales, long-term meteorological data (e.g. precipitation, temperature) with spatially coherent representation are needed. This is where a regional weather generator comes into play. Meteorological fields for a specific region are strongly dependent on weather circulation patterns (CP) at larger scales. Additionally, there is evidence that these fields covariate with the average regional surface temperature (ART). With future climate change, such changes in both CP and ART should be included in weather generators.

This study presents the development of such a non-stationary gridded weather generator conditioned on large-scale weather circulation patterns for Central Europe. The reanalysis dataset ERA5 (1o x 1o) is used for weather type classification. The E-OBS gridded observational dataset (0.25ox 0.25o) is used to parameterize the meteorological fields, such as precipitation and temperature (minimum, maximum, average). The spatial and temporal dependence is represented by the multivariate auto-regressive model. Daily precipitation amount is modelled by the extended generalized Pareto distribution and daily temperature is modelled by the transformed normal distribution. Both fields are conditioned on CP and allow to covariate with ART. In this way, the regional weather generator is capable of capturing “between-type” and “within-type” climate variability and can be used to generate long synthetic data for flood risk assessment in present and future periods.

How to cite: Nguyen, V. D., Vorogushyn, S., Nissen, K., and Merz, B.: A non-stationary gridded weather generator conditioned on large-scale weather circulation patterns for Central Europe, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15475, https://doi.org/10.5194/egusphere-egu23-15475, 2023.

EGU23-16623 | ECS | Posters on site | HS7.8

Mapping Hazard to Extreme Temperature Events Over the Indian Subcontinent 

Anokha Shilin, Naveen Sudharsan, Arpita Mondal, Pradip Kalbar, and Subhankar Karmakar

The recent AR6 report of the Intergovernmental Panel on Climate Change (IPCC) explicitly shows that the observed change in hot extremes (including heatwaves) with high confidence in human contribution to the observed changes has highly increased in the South Asian (SAS) domain which comprises the Indian subcontinent. Extreme heat events are more frequent and intense across the globe since the 1950s and have adverse societal and economic impacts. Considering current warming trends and projections, heatwaves are becoming a serious problem in India. Exposure to extreme heat in the population is increasing due to climate change. Also, observed temperatures are increasing globally as well as regionally as an effect of global warming. As heat stress occurs when the human body cannot get rid of the excess heat, it can be considered a good proxy for the heatwave hazard. Heat stress results in heat stroke, exhaustion, cramps, or rashes. Exposure to extreme heat can result in occupational illnesses and injuries. An agrarian country like India will have large economic damage when climate-related heat stress increases the occurrence of droughts and exacerbate water scarcity for irrigation. Hence the impact of the heat stress hazard is spotted and largely discussed both in the academic and political domains. In this study, Universal Thermal Climate Index (UTCI) based hazard map is developed for India with a non-parametric multivariate approach. The prominent heat stress hazard areas are identified and mapped with reference to the UTCI assessment scale which is categorized based on thermal stress. The probability of occurrence is also mapped using the exceedance probability with the UTCI reference. Heat stress hazard map provides the basis for a wide range of applications in public and individual precautionary planning such as heatwave action plans, urban and regional planning, the tourism industry, and climate research. Hence a country-level extreme temperature hazard map is of dire necessity.

Keywords: Exceedance probability, hazard map, heat stress, multivariate approach, non-parametric method

How to cite: Shilin, A., Sudharsan, N., Mondal, A., Kalbar, P., and Karmakar, S.: Mapping Hazard to Extreme Temperature Events Over the Indian Subcontinent, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16623, https://doi.org/10.5194/egusphere-egu23-16623, 2023.

EGU23-417 | ECS | Orals | HS4.2

Parameter transferability of a distributed hydrological model to droughts 

Giulia Bruno, Doris Duethmann, Francesco Avanzi, Lorenzo Alfieri, Andrea Libertino, and Simone Gabellani

Hydrological models often do not simulate properly streamflow (Q) during droughts, because of a poor representation of the interactions among precipitation deficits, actual evapotranspiration (ET), and terrestrial water storage anomalies (TWSA) during these periods. However, there is little research comprehensively evaluating model skills during droughts of varying intensity in a spatially distributed way. To shed further light into these drops in model skills and step toward more robust models in an anthropogenic era and a changing climate, we evaluated Q, ET, and TWSA simulations during moderate and severe droughts, and we tested if calibrating during a moderate drought could enhance model performances during a severe one. We applied the distributed hydrological model Continuum over the heavily human-affected Po river basin in northern Italy and the period 2010 – 2022. Moreover, we exploited independent ground- and remote sensing-based datasets to evaluate the temporal and spatial variability of Q, ET, and TWSA monthly simulations across the whole basin and 38 sub-catchments. Model performances for Q across the study sub-catchments were comparable during both wet years (2014 and 2020, mean KGE = 0.59±0.32) and moderate droughts (2012 and 2017, mean KGE = 0.55±0.25). Further, Continuum simulated well Q for the basin outlet even during a severe drought (KGE = 0.82 in 2022), while its performances generally decreased among the sub-catchments (mean KGE = 0.18±0.69 in 2022). In general, the model well represented ET and TWSA seasonality over the study area, and a decline in TWSA over the more recent years. Yet, during the severe 2022 drought we detected an increased uncertainty in ET anomalies, especially in human-affected croplands, that could explain the Q performance drop along with an increased anthropogenic disturbance. Including a moderate drought (2017) in the calibration period did not lead to a significant improvement in model skills during the severe event (mean KGE = 0.18±0.63 for Q during 2022), meaning that the severe 2022 drought was fairly unique for the study area both in terms of hydrological processes and human disturbance on them. By unveiling an increase in model uncertainty during a severe drought and possible causes for it, our findings are relevant to assess and possibly enhance model robustness in a changing climate and the anthropogenic era for adequate water management, disaster risk reduction, and climate change adaptation.

How to cite: Bruno, G., Duethmann, D., Avanzi, F., Alfieri, L., Libertino, A., and Gabellani, S.: Parameter transferability of a distributed hydrological model to droughts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-417, https://doi.org/10.5194/egusphere-egu23-417, 2023.

The purpose of this study was to evaluate the applicability of medium and long-term satellite rainfall estimation (SRE) precipitation products for drought monitoring over mainland China. Four medium and long-term (19 a) SREs, i.e., the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42V7, the Integrated Multi-satellite Retrievals for Global Precipitation Measurement V06 post-real time Final Run precipitation products (IMF6), Global Rainfall Map in Near-real-time Gauge-calibrated Rainfall Product (GSMaP_Gauge_NRT) for product version 6 (GNRT6) and gauge-adjusted Global Satellite Mapping of Precipitation V6 (GGA6) were considered. The accuracy of the four SREs was first evaluated against ground observation precipitation data. The Standardized Precipitation Evapotranspiration Index (SPEI) based on four SREs was then compared at multiple temporal and spatial scales. Finally, four typical drought influenced regions, i.e., the Northeast China Plain (NEC), Huang-Huai-Hai Plain (3HP), Yunnan– Guizhou Plateau (YGP) and South China (SC) were chosen as examples to analyze the ability of four SREs to capture the temporal and spatial changes of typical drought events. The results show that compared with GNRT6, the precipitation estimated by GGA6, IMF6 and 3B42V7 are in better agreement with the ground observation results. In the evaluation using SPEI, the four SREs performed well in eastern China but have large uncertainty in western China. GGA6 and IMF6 perform superior to GNRT6 and 3B42V7 in estimating SPEI and identifying typical drought events and behave almost the same. In general, GPM precipitation products have great potential to substitute TRMM precipitation products for drought monitoring. Both GGA6 and IMF6 are suitable for historical drought analysis. Due to the shorter time latency of data release and good performance in the eastern part of mainland China, GNRT6 and GGA6 might play a role for near real-time drought monitoring in the area. The results of this research will provide reference for the application of the SREs for drought monitoring in the GPM era.

How to cite: Cheng, S.: Evaluating the Drought-Monitoring Utility of GPM and TRMM Precipitation Products over Mainland China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-571, https://doi.org/10.5194/egusphere-egu23-571, 2023.

EGU23-621 | ECS | Orals | HS4.2

Agricultural Drought Monitoring using Satellite based Surface Soil Moisture Data 

Hussain Palagiri and Manali Pal

Agricultural drought refers to a period with declining Soil Moisture (SM) content and consequent crop failure from water stress. SM plays an important role in indicating water stress and thereby identifying agricultural drought. Due to the lack of large scale, fine resolution, and accurate/quality SM many agricultural drought studies are mostly based on ground-based SM observations having limited spatiotemporal variability and cannot be applied for large scale studies. Microwave remote sensing showed capability in estimating geophysical properties like SM and paved the way for a continuous agricultural drought monitoring. European Space Agency (ESA) under Climate Change Initiative (CCI) developed an active-passive multi-satellite merged ESA CCI SM dataset. In this study, ESA CCI SM’s potential in agricultural drought monitoring is explored, by deriving Empirical Standardized Soil Moisture Index (ESSMI) to identify agricultural drought in Indian state of Telangana from 2001 to 2020. Telangana is a severely drought-prone state of India heavily impacted by significant water stress and water shortages due to frequent droughts. This increases the need for accurate agricultural drought characterization in the state. Keeping in mind the necessity of drought monitoring system for Telangana and availability of large-scale satellite soil moisture data from ESA CCI, this present study employs the ESSMI using the non-parametric distribution of ESA CCI SM data, to characterize the agricultural drought in drought prone Telangana. The efficiency of ESSMI in drought monitoring is evaluated by comparing it to the Standardised Precipitation Index (SPI) and Rainfall Anomalies (RFA) calculated from India Meteorogical Department (IMD) daily gridded rainfall data. Both the indices along with the RFA identified 2009 as dry year and 2020 as wet year. Temporal evolution of monthly drought identified by ESSMI showed monthly delayed response when compared with SPI, whereas yearly ESSMI showed consistency with SPI and RFA. Different classes of drought areas identified by ESSMI are compared with SPI which showed near normal and mild dry regions for most of the study period. ESSMI is able to effectively capture near normal to moderate drought events and shows a consistent association with the SPI and RFA both in short and long term (monthly and annual) temporal scale. The study showed the overall performance of ESSMI is reliable for agricultural drought monitoring and can be used to develop effective drought warning and risk management.

How to cite: Palagiri, H. and Pal, M.: Agricultural Drought Monitoring using Satellite based Surface Soil Moisture Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-621, https://doi.org/10.5194/egusphere-egu23-621, 2023.

EGU23-737 | ECS | Posters on site | HS4.2

Green Water Scarcity Index Mapping for India Using Geospatial Data Products 

Saicharan Vasala and Shwetha Hassan Rangaswamy

Green water assessment is evolving as a significant aspect of hydrological science since its existence is critical for crop production in rain-fed areas. The green water scarcity index (GWSI), which is based on evapotranspiration and effective rainfall, can assist researchers in understanding the water requirements of agriculture and the current water stress condition. To generate a GWSI map of India from 2017 to 2019 at monthly and yearly scales, this study employed Indian Meteorological Department (IMD) gridded rainfall and TerraClimate-based actual evapotranspiration data products. The results showed that India experienced low GWSI throughout the monsoon season, as was to be expected, but interestingly, there were no high GWSI values (> 0.9) during the summer months, as seen in the winter. India experienced average GWSI values of 0.87, 0.86, and 0.83 in 2017, 2018, and 2019, respectively. In comparison to other years, 2019 has a lower GWSI, and rest years have similar GWSI values in the July and December months. In contrast to how almost all months in all years have similar GWSI values, the substantial discrepancy is only seen in September 2019. Due to the high frequency of rainfall events in September 2019, the ER rate has increased, which has led to a decrease in the GWSI in India's month of September 2019. According to the findings of this study, the monsoon has less of an impact on GWSI scarcity. India experiences green water scarcity all year round, necessitating extensive irrigation for agriculture. The lack of gree water resources enabled the transition away from rainfed agriculture cultivation. This research will aid in determining the precise condition of water stress in the targeted region, as well as the zoning of water-scarce regions, so that future irrigation planning can be done appropriately.

How to cite: Vasala, S. and Hassan Rangaswamy, S.: Green Water Scarcity Index Mapping for India Using Geospatial Data Products, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-737, https://doi.org/10.5194/egusphere-egu23-737, 2023.

EGU23-1431 | Orals | HS4.2

Exhaustive Searching and LASSO for Reliable Drought Forecasting over South Korea 

Taesam Lee, Yejin Kong, Taekyun Kim, and Saejung Lee

The spring drought over South Korea has been extensive damage recent years and its forecasting can be important in water management and agricultural industries. However, the drought forecasting is not an easy task because of the difficulty to find predictors to the precipitation predictand. Also, limited hydrological records for applying to complex models such as nonlinear or deep learning models do not produce reliable forecasting results. In the current study, we proposed the drought forecasting approach by exhaustive searching for explanatory variables and a regression model for limited record lengths. At first, the target drought index was set with the accumulated spring precipitation (ASP) obtained by the median of the 93 available weather stations over South Korea. Then, exhaustive searching for predictors was performed with association between the ASP and the differences of two pair combination of the global winter MSLP, say Df4m, for the time lag of the spring seasonal drought. The 37 Df4m predictors were found with high correlation over 0.55. The detected 37 variables were categorized into three subregions. The predictors in the same region contain highly similar to each other. Subsequently, the multicollinearity problem cannot be avoidable. To solve the multicollinearity problem, the Least Absolute Shrinkage and Selection Operator (LASSO) model was applied resulting five Df4m predictors and the good agreement of the forecasting value with the observed value as R2=0.72. Therefore, we concluded that the proposed LASSO model with the exhaustive searching of the global MSLP can be a good alternative to forecast the spring drought over South Korea. The spring drought forecasting with the LASSO model and the Df4m predictors can be extensively used for water managers and water industry.  

How to cite: Lee, T., Kong, Y., Kim, T., and Lee, S.: Exhaustive Searching and LASSO for Reliable Drought Forecasting over South Korea, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1431, https://doi.org/10.5194/egusphere-egu23-1431, 2023.

EGU23-1645 | ECS | Orals | HS4.2

Sectoral water use responses to droughts and heatwaves: analyses from local to global scales from 1990-2019 

Gabriel Antonio Cárdenas Belleza, Marc F.P. Bierkens, and Michelle T.H. van Vliet

Water security is threatened by a growing global population and the associated increase in sectoral water demand. This condition is worsened by the occurrence of droughts and heatwaves, which mainly lead to a reduction in the available water, increasing water scarcity. The resulting threats to water security are expected to become more pertinent when considering that such extreme events are expected to increase both in frequency and severity. Nonetheless, little is known about the responses in sectoral water use during extreme hydroclimatic events.


This research therefore quantifies responses in water use for different sectors (i.e. irrigation, livestock, domestic, energy and manufacturing) during droughts, heatwaves and compound events at global, regional and local scales. To achieve this, the spatial extent, times of occurrence and durations of these hydroclimatic extremes were identified worldwide for the period 1990-2019. Next, sectoral water use responses were evaluated during these extreme events and compared to normal (non-extreme) periods for sectoral water withdrawal or consumption.


Our results show that extreme events affect water use responses differently per sector and region. At a global scale, the overall use of water for domestic and irrigation sectors increased while it decreased for thermoelectric and manufacture sectors during heatwaves. Also, water use response patterns show that irrigation and domestic sectors are prioritized over livestock, thermoelectric and manufacturing on a global level. Furthermore, stronger impacts are found for heatwaves and compound events compared with impacts during droughts. Finally, our analyses show that water use drivers -such as income level, use of alternative water sources, and regulatory water policies- impact the magnitude of change in sectoral water use under these extreme events.


These results set the foundation for the development of a new global sectoral water use model which will allow more accurate quantifications of sectoral water use responses and water scarcity during present and future projected droughts and heatwaves.

How to cite: Cárdenas Belleza, G. A., Bierkens, M. F. P., and van Vliet, M. T. H.: Sectoral water use responses to droughts and heatwaves: analyses from local to global scales from 1990-2019, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1645, https://doi.org/10.5194/egusphere-egu23-1645, 2023.

Streamflow drought is addressed as below-normal water availability in large rivers and tributaries. Streamflow drought impacts several sectors, including irrigation, river ecology, hydroelectric potential, financial, and drinking water supply. Analyzing variability in streamflow drought timing and the nonlinear interactions between drought onset and severity is necessary not only for better understanding of drought predictability but also of its temporal change, which aids in developing climate adaptation strategies. Very few studies have assessed the seasonality of streamflow droughts, although a few analyses have been performed focusing on other hydroclimatic extremes, such as extreme precipitation and floods. However, little is known about understanding the shifting behaviour of streamflow drought onset patterns at a local or regional scale. Further, a few studies have assessed the severity of low flows at a global and local scales. However, most of these studies have either considered a constant threshold approach to delineate low-flow episodes or employed sub-seasonal (monthly) temporal scales to access streamflow droughts using standardized indices of precipitation or runoff. However, none of the studies have investigated the non-linear interactions between streamflow drought onset and deficit volume and how these bivariate interactions evolve over time across large river basins. Here we investigate the timing of the streamflow drought onset and its severity (i.e., deficit volume) over 472 catchments that are spatially distributed across 21 Intergovernmental Panel on Climate Change (IPCC) Special Report on Managing the Risks of Extreme events and Disasters to Advance Climate Adaptation (SREX) reference regions in the global Tropics. We identified those catchments with little or no potential anthropogenic influences and were selected based on a detailed quality assessment of continuous streamflow records and their proximity to dam locations. We implemented a daily variable threshold approach with an 80% exceedance probability of the flow record to identify streamflow drought episodes. Moreover, based on large streamflow records, we compare the potential shifts in the seasonality of streamflow droughts in the recent (1994-2018) versus the pre-1990s (1969-1993). We show a strong persistency in the timing of streamflow drought onset in the core monsoon-dominated regions. In the northern hemisphere, the mean onset is observed primarily during August and September, whereas in the southern hemisphere, the onset timing is temporally clustered around November to March. Our proof of concept analysis suggests that North-East South-America is the most vulnerable region, in which an earlier occurrence of drought is compounded by an increasing deficit volume, indicating a drying trend throughout. Furthering this, we investigate the non-linear interactions between drought characteristics, onset time, and severity to decipher the pattern of associations across disparate climate regimes, especially in regions with pronounced seasonal cycles. The obtained insights has important implications for water resources management in tropics, where seasonal climates dominates. The findings can inform drought monitoring, planning and improve drought resilience to multiple climate stressors.

How to cite: Raut, A. and Ganguli, P.: Examining Changes in Nonlinear Interactions of Streamflow Drought Seasonality versus its Severity across Global Tropics, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2182, https://doi.org/10.5194/egusphere-egu23-2182, 2023.

EGU23-2252 | Orals | HS4.2

Assessing the impacts of future climate change scenarios on water systems supplied by karst aquifers 

David J. Peres, Nunziarita Palazzolo, Claudio Mineo, Stefania Passaretti, Eleonora Boscariol, Anna Varriale, and Antonino Cancelliere

Water resources management is becoming increasingly challenging under current climate change. Water utilities need to assess planning adaptation strategies aimed at sustainable water resource exploitation. In this study, we estimate the potential impacts of climate change on hydrological variables and future spring discharge availability. Specifically, we exploit an empirical regressive model based on the statistical relationship between Standardized Precipitation-Evapotranspiration Index (SPEI) and minimum annual spring discharge, in combination with Regional Climate Models (RCMs) provided by the EURO-CORDEX initiative. In this regard, two Representative Concentration Pathways (RCPs) are considered, RCP4.5 (intermediate emissions scenario) and RCP8.5 (high emissions scenario), as well as two future time horizons, namely the near future 2021-2050 and the far future 2041-2070. Then, after bias correction of the so estimated minimum spring discharge values, the curves relating spring discharge and reliability in satisfying water demand are assessed. We carried out our investigation for karst aquifers located in the Italian Apennines, which are used for the water supply system of the city of Rome (Italy) and the surrounding areas, managed by ACEA Ato2, serving over 4 million users. Overall, the results indicate a general decrease in the demand that can be satisfied with high reliability, pointing out significant potential impacts of climate change on water availability on both near and far future. The proposed methodology could be a useful tool for water managers, since it provides a support for planning adaptation measures aimed at minimizing future socio-economic impacts of climate change.

How to cite: Peres, D. J., Palazzolo, N., Mineo, C., Passaretti, S., Boscariol, E., Varriale, A., and Cancelliere, A.: Assessing the impacts of future climate change scenarios on water systems supplied by karst aquifers, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2252, https://doi.org/10.5194/egusphere-egu23-2252, 2023.

EGU23-3043 | Posters on site | HS4.2

Monthly vegetation drought forecasting using copula functions, numerical weather prediction and artificial intelligence models 

Jeongeun Won, Jiyu Seo, Chaelim Lee, and Sangdan Kim

Drought inhibits vegetation growth, triggers wildfires, reduces agricultural production and has a significant impact on the health of terrestrial ecosystems. Continuously monitoring and forecasting the effects of drought on vegetation health can provide effective information for ecosystem management. The purpose of this study is to forecast the effect of meteorological drought on vegetation, that is, the ecological drought of vegetation. Because vegetation drought is a complex phenomenon, it should be approached based on the probabilistic relationship between meteorological drought and vegetation. Accordingly, a probabilistic approach was constructed to model the bivariate joint probability distribution between meteorological drought and vegetation using the copula function. In order to predict ecological drought based on the joint probability distribution, predictive information on meteorological drought and vegetation health is required. To this end, a meteorological drought was predicted using numerical weather prediction, and a short-term vegetation prediction model considering the meteorological drought prediction results was developed. The vegetation prediction model combining Convolutional Long Short-Term Memory and Random Forest was able to improve the prediction performance of vegetation by considering spatial and temporal patterns. The vegetation drought was forecast by linking the prediction information of vegetation and meteorological drought with the joint probability distribution. The approach of this study will be able to provide useful information to respond to the drought risk in terms of ecology.

 

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1A2B5B01001750).

How to cite: Won, J., Seo, J., Lee, C., and Kim, S.: Monthly vegetation drought forecasting using copula functions, numerical weather prediction and artificial intelligence models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3043, https://doi.org/10.5194/egusphere-egu23-3043, 2023.

EGU23-3295 | ECS | Orals | HS4.2

Drivers of sustained drought over the Arabian Peninsula in recent decades 

Md Saquib Saharwardi, Hari Prasad Dasari, Karumuri Ashok, and Ibrahim Hoteit

The predominantly desert region of the Arabian Peninsula (AP), comprising seven nations, is characterized by high temperatures and meager rainfall. Temperature, and dust activity, are exacerbating over the AP. In the current study, we found that drought frequency and severity have increased in the AP over the last two decades compared to the previous five decades. This recent drought intensification is characterized by dominant decadal variability in addition to what appears to be a long-term trend. The current droughts intensification appears to be driven by increased warming over the AP than by a decrease in local precipitation. The Atlantic Multidecadal Oscillation (AMO) cycle is strongly related to decadal drought variability, and the current unprecedented multiyear drought is associated with current positive phase of AMO. We developed a statistical model for future projections that indicates that the frequency and intensity of droughts over the AP are expected to decrease significantly in the coming year.

How to cite: Saharwardi, M. S., Dasari, H. P., Ashok, K., and Hoteit, I.: Drivers of sustained drought over the Arabian Peninsula in recent decades, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3295, https://doi.org/10.5194/egusphere-egu23-3295, 2023.

EGU23-3415 | ECS | Posters on site | HS4.2

Mapping of large-scale low water situations using satellite-based water-land boundaries 

Bastian Gessler, Silke Mechernich, Robert Weiß, and Björn Baschek

In the summers of 2018 and 2022, low water levels of German waterways massively restricted the transport performance of freight ships. Furthermore, oxygen and temperatures were critically high for the ecosystem. In such hydrological extreme situations, information on the location and shifting of the boundaries between water and terrain (water-land boundary) is relevant, e.g. for improved forecasting and monitoring of sediment displacements.

Satellite-based methods are an effective way to monitor such situations and can be used to observe large areas in a short time. Due to their independence from solar illumination and weather conditions, radar data offer considerable advantages compared to optical data. Particularly the radar satellite Sentinel-1 (ESA, Copernicus) is of great relevance, since the data are available free of charge and a continuous future supply is assured. For this reason, we use Sentinel-1 data as basic information in the project "Sat-Land-Fluss".

Here, we will present an example of S-1 water-land-boundary detection for the low water event in 2018 at the Middle Rhine. Comprehensive validation data are available, as an imagery flight was assigned by BfG on behalf of the Freiburg Waterways and Shipping Authority (WSA) at the lowest water level in November 2018. The water-land boundaries were derived from the 10-cm-resolution aerial photographs by the Federal Institute of Hydrology.

The water surfaces from S-1 data is obtained by a thresholding method of backscatter intensity. Various ancillary data were integrated and their potential for improving the result was analyzed, e.g.:

  • the location of the shipping channel (©WSA Rhein) led to a significant reduction of misclassifications, since e. g. overlay effects from ships or bridges can be removed.
  • The land cover information (©ESA World Cover 2020) allowed the correct classification of areas with low backscatter effects (e.g. agriculture) as non-water.
  • The HAND (Height Above Nearest Drainage) index from the high-resolution terrain information (DTM-5 of the Federal Agency for Cartography and Geodesy) helped to exclude areas that could be classified as not covered by water due to their topographic location.

The algorithm based only on S1-data yields about 85-92 % of correct water-classification, and together with the additional data in a)-c) we gain up to approximately 94-98 % of correct classification depending on the S-1 scene. We highlight that particularly the usage of landcover data and high resolution DTMs highly improves the reliability of the water-land boundary from S-1 data. The main remaining weaknesses are located near the water-land-boundary within approximately 50 m. Since the spatial resolution of S-1 data is rather low with about 5 x 20 m, the resulting spatial accuracy of the water-land-boundary is less than about 10 m. To improve this, the integration of a 1-m-digital terrain model of the water course (DGM-W) together with measured or predicted water level information is ongoing. This will provide water level information in areas where Sentinel-1 is not able to record information (e.g. areas of radar shadow due to vegetation, buildings, bridges or topography).

How to cite: Gessler, B., Mechernich, S., Weiß, R., and Baschek, B.: Mapping of large-scale low water situations using satellite-based water-land boundaries, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3415, https://doi.org/10.5194/egusphere-egu23-3415, 2023.

Agro-climatological droughts have been a dominant driver of various socio-economical losses. However, the association between drought hazards & their socio-economic impacts is still less explored on a global scale. The objective of this study is to understand this linkage by globally analyzing drought hazards and their socio-economic impacts during 2001-2021.

To monitor the agro-climatological drought hazard, we have developed a new combined drought indicator (CDI) integrating satellite and reanalysis model-based four input variables (i.e., precipitation- CHIRPS data, temperature, and soil moisture – ERA5-Land data, normalized difference vegetation index – MODIS data). In CDI, the Principal Component Analysis was applied to combine all the variables. To examine the socio-economic impacts of drought hazard, we used the Geocoded Disaster (GDIS) dataset, which provided the location information of subnational-level drought events. Since GDIS shows the actual impact of drought events on socio-economic conditions, the drought vulnerability at a sub-national level can be quantified by performing a comparative analysis between CDI and GDIS.

Based on CDI, the maximum frequency of severe drought events (> 7) is observed over sub-Saharan Africa, followed by parts of south Asia. During these events, the CDI values ranged between -1.5 to -3, signifying the critical hydrometeorological conditions in the respective region. The comparative analysis shows that the CDI-based drought clusters can represent the GDIS drought events at a statistically significant level. Both CDI and GDIS methods noticed that the parts of Argentina, Brazil, the horn of Africa, western India, and north China are continuously under the grips of severe droughts. In these regions, even less severe agro-climatological (CDI) droughts have caused substantial socio-economical (GDIS) losses making these areas highly vulnerable to drought. In contrast, the outcomes of CDI also indicated extreme drought cases over parts of North America and Europe, but these events were inconsistent with GDIS, meaning that developed countries are less vulnerable to drought.

This study highlighted the importance of GDIS data for accurate drought impact assessment at the subnational level and in validating CDI. The proven subnational level association between CDI and GDIS from this study could help to identify the socio-economically vulnerable areas to drought on a finer scale and priorities the regions that demand more concern. 

How to cite: Kulkarni, S. and Sawada, Y.: Monitoring and Assessment of Global Patterns of Subnational droughts using Combined Drought Indicator and Geocoded Disaster Dataset, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3914, https://doi.org/10.5194/egusphere-egu23-3914, 2023.

EGU23-3951 | ECS | Posters on site | HS4.2

Drought behaviour in Barcelona from its instrumental precipitation series (1786-2022) 

Josep Barriendos, María Hernández, Salvador Gil-Guirado, Mariano Barriendos, and Jorge Olcina-Cantos

The current climate change scenario increases the concern for water resource management and for the increase in the frequency of droughts in the Mediterranean region. This work proposes the analysis of the instrumental precipitation series of the city of Barcelona (1786-2022), which extends from the end of the Little Ice Age to the current climatic period. This series, due to its temporal length, constitutes a continuous scenario of pluviometric information that allows the identification and analysis of the periods in which the most severe droughts occur.

This work is organized following two main objectives. The first objective consists on the analysis of the values of this precipitation series using different statistical techniques, including drought indices. The second objective is the evaluation of the severity of the most significant drought events that appear in the instrumental precipitation series of Barcelona.

To achieve these objectives, the methodologies used in this work consist on the application of some statistical techniques on the instrumental precipitation series, such as the detection of its breaking points. At the same time, this work proposes the application of different drought indices as the SPI index and the SPEI index on the entire instrumental precipitation series of Barcelona (1786-2022). The use of these methodologies allows the comparison between the different droughts included in the instrumental series. These also allow distinguishing the most relevant droughts according to their severity. Two significant examples of the most severe droughts are the ones of the first third of 19th century (1812-1825) and the droughts of the 21st century (1998-2008). We also want to determine the relevance of the current drought (2021-2022) in contrast to the overall instrumental series of precipitation of Barcelona.

Additionally to these methodologies and results, for the most significant droughts detected in the precipitation series, it is also proposed to use monthly barometric indices to characterise the general atmospheric circulation of those periods. It would have the aim to contrast the results on the instrumental precipitation series with the synoptic conditions that produce these droughts. This comparison also would help to determine if these conditions have changed over time, especially considering recent decades in the context of current climate change.

How to cite: Barriendos, J., Hernández, M., Gil-Guirado, S., Barriendos, M., and Olcina-Cantos, J.: Drought behaviour in Barcelona from its instrumental precipitation series (1786-2022), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3951, https://doi.org/10.5194/egusphere-egu23-3951, 2023.

EGU23-4708 | ECS | Orals | HS4.2

Is solar-induced chlorophyll fluorescence derived index much useful in agricultural drought monitoring 

Vaibhav Kumar, Hone-Jay Chu, and Mohammad Adil Aman

Drought is multifaceted, more frequent hydrometeorological phenomena occurring worldwide. The intensity and frequency of droughts are increased with rising trend of global warming. These events significantly impact society which directly linked to agricultural productivity and economy. India witnessed these extreme drought events and have faced serious economic loses. Therefore, more effective, and reliable drought monitoring is essential for its mitigation and to enhance early warning systems. In addition, there are limited studies looking at the sensitivity of solar-induced chlorophyll fluorescence (SIF) to response of meteorological parameters during drought event.   

Therefore, a maiden attempt is taken to understand how terrestrial vegetation response under severe drought event which experienced in 2009 summer monsoon period (June to September) over Indo-Gangetic plain regions in India. We studied the productivity of vegetation over IGP region using solar-induced fluorescence as a proxy. Moreover, we have derived drought indices herein Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Soil Moisture Index (SSI), and SIF Health Index (SHI). These indices were utilized gridded monthly precipitation, evapotranspiration, soil-moisture, land surface temperature (LST) and solar-induced fluorescence (SIF) datasets from 2001 to 2020 over IGP region. In addition, statistical relationships and trends among these indices are evaluated through the Pearson correlation coefficient and Mann-Kendall test.

Our findings provide promising results by addressing the major drought events over Indo-Gangetic plains in India in terms of intensity and spatial coverage. There is great significance to further understand the application of SIF in agriculture drought detection. The spatio-temporal patterns and trends of standardized precipitation evapotranspiration index (SPEI), and standardized soil-moisture index (SSI), have compared against solar-induced chlorophyll fluorescence health index (SHI) anomaly for short, and mid-term (herein 01, 03 and 06 month time scales) for seasonal drought monitoring. Furthermore, the spatial extent of SPEI, SSI and SHI anomaly well agreed for the 2009 drought event across region.

Overall, SIF can be reliable tool for agricultural drought monitoring in a timely and accurate manner. The resultant water stress leads to reduction in vegetation which reflected changes in SHI anomaly. This showcasing the ability of SIF to provide insight the link between carbon and water during droughts. Furthermore, it will enhance information for stakeholders, interested into future carbon-water cycle studies.

 Keywords: SPEI, SSI, SHI, and agricultural drought.

How to cite: Kumar, V., Chu, H.-J., and Aman, M. A.: Is solar-induced chlorophyll fluorescence derived index much useful in agricultural drought monitoring, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4708, https://doi.org/10.5194/egusphere-egu23-4708, 2023.

Extreme value statistics are well established for floods and are also receiving increasing attention in drought hydrology. They allow the user to characterize the severity of an event by a statistical probability or return period, a concept that is well understood in the scientific, policy, and public arenas. Frequency analysis is usually carried out on the basis of annual extreme event series, which  is straightforward in its application and interpretation. However, in seasonal climates with a warm and a cold season, the low-flows can be generated by different processes, which violates the basic assumptions of extreme value statistics and can lead  to inaccurate conclusions.

Here we assess the value of a mixed distribution approach for low-flows to perform frequency analysis in catchments with a mixed summer/winter regime. We first present the theoretical concept of the mixed probability estimator for low-flows. We then illustrate the characteristics of the model for archetypal low-flow regimes, from pronounced summer and winter regimes to flow mixtures with weak seasonality. We successively evaluate the gain in performance from the mixed distribution model for a range of low flow regimes, based on a comprehensive Austrian dataset. We finally scrutinize the assumption of the mixed probability estimator and review the added value of using an extended, Copula-based  framework. The results show large differences of event return periods, and suggest that the mixed estimator is relevant not only for mountain forelands, but for a much wider range of catchment typologies across Europe. These even include typical summer regimes when only single winter low-flows are mixed in. We conclude that the mixed distribution approaches outperform the conventional frequency estimator and should be used by default in seasonal climates where summer and winter low flows occur.

How to cite: Laaha, G.: The value of mixed distribution approaches for low-flow frequency analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4914, https://doi.org/10.5194/egusphere-egu23-4914, 2023.

The Standardized Precipitation Index (SPI) is applied worldwide for drought assessment. In general, in many studies, SPI was estimated from a two-parameter gamma distribution. However, in other climatic regions, there are also studies that suggest that distributions other than the Gamma distribution are more suitable. In addition, as the frequency of drought events increases, the need for daily SPI calculated with relatively short time-scales for immediate drought response is increasing. In this study, the optimal probability distribution for estimating SPI using daily precipitation in the southern part of the Korean Peninsula was explored. Gumbel, Gamma, GEV, Loglogistic, Lognormal, and Weibull are applied as candidate distributions, and optimal distributions for each season, region, and time-scale are investigated. The Chi-square test was applied to investigate the probability distribution function appropriate to the cumulative daily precipitation time series for various time-scales. In the process of calculating the SPI, when the cumulative daily precipitation has a value of 0, the cumulative probability value was calculated by reflecting the probability of having a value of 0. Then, by applying the candidate distribution, it was verified whether the estimated SPI conformed to the standard normal distribution. Finally, a more precise drought assessment could be performed by determining the optimal probability distribution for each region, season, and time-scale. It is also expected to increase the applicability of daily SPI by reducing problems that occur in a short time-scale.

 

Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1A2B5B01001750).

How to cite: Lee, C., Seo, J., Won, J., and Kim, S.: Investigation of optimal probability distribution of Standard Precipitation Index for daily precipitation time series in Southern Korean Peninsula, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4931, https://doi.org/10.5194/egusphere-egu23-4931, 2023.

EGU23-5680 | Orals | HS4.2

Vegetation dynamic and drought: South African savanna case study. 

María P. González-Dugo, María J. Muñoz-Gómez, Hector Nieto, María José Polo, Timothy Dube, and Ana Andreu

Semiarid rangelands are one of Africa’s most complex and variable biomes. They are a mosaic of land uses, where extensive livestock is the main economic activity, and agriculture is also crucial. They are highly controlled by the availability of water, e.g., pasture and rainfed crop production. Although the vegetation is adapted to variable climatic conditions and dry periods, the increase in drought intensity, duration, and frequency precipitate their degradation. By integrating Earth Observation data into models, we can evaluate, on the one hand, the vegetation water stress and, on the other, its primary production. This allows us to assess the interaction of both processes, improving our knowledge about the vegetation's behavior in the face of drought.

 

In this work, we set up an open-source cloud framework to monitor water consumption and primary production interaction over this semiarid mosaic in the long term, to analyze system tipping points. This information can help reduce the uncertainty associated with the public administration and farmers’ decision-making processes. A surface energy balance model, previously validated in the area, was applied to estimate evapotranspiration (ET) from 2000-2020 (monthly, at a 1 km spatial resolution, using MODIS data and global atmospheric reanalysis dataset). The anomalies of evapotranspiration (ET) to reference ET were used as an indicator of drought for the period. The biomass production was estimated by applying an adaptation of the Monteith LUE (light use efficiency) model based on the relationship between plant growth and incident solar radiation. The parameterization of the model corresponded to semi-natural grasslands and crops, and it was applied at a daily scale with 250 m of spatial resolution. The model’s estimation presented an acceptable agreement over the area.

 

Close links between grassland/crop production and drought events were found and evaluated. 2016 was the worst year regarding the state of the vegetation, followed by 2015, 2003, and 2002, all coincident with drier events (as measured by ET/ETo anomalies). The different production patterns of each patch of vegetation were visible. Although crops were mainly rainfed (probably being irrigated if necessary) and followed the precipitation rates, they were less dependent on rain than grassland. Croplands had higher production peaks during February/March than natural grasslands, although trends were similar. Production rates were much higher than usual during 2004, 2009, and 2017. These vegetation blooms came after a drought where biomass production rates were minimal. A thorough analysis of these results can provide insights to better cope with future droughts.

Acknowledgment: This work has been carried out through the project "DroughT impACt on the vegeTation of South African semIarid mosaiC landscapes: Implications on grass-crop-lands primary production" funded by the European Space Agency in the framework of the "EO AFRICA R&D Facility".

How to cite: González-Dugo, M. P., Muñoz-Gómez, M. J., Nieto, H., Polo, M. J., Dube, T., and Andreu, A.: Vegetation dynamic and drought: South African savanna case study., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5680, https://doi.org/10.5194/egusphere-egu23-5680, 2023.

EGU23-5990 | Orals | HS4.2

Meteo-hydrological precursors of water crisis in the Turin area: a first forecasting and management chain 

Elisa Brussolo, Christian Ronchi, Alessio Salandin, Roberto Cremonini, and Secondo Barbero

The Piedmont region (north-western Italy) is located between the Alps and the Mediterranean area, two territories that are recognized as climate hotspot regions, showing amplified climate change signals and associated with environmental, social and economic impacts.
A number of water crisis that affected the Italian territory in the last twenty years exacerbated conflicts in different territories with regard to the priority use of water resource. The recent drought events (2017, 2021, and 2022) have seen areas not normally characterized by this type of phenomenon, such as the Piedmont region, go into crisis, involving all water users and human activities.
In this framework, there is a renewed urgency for improved drought monitoring, forecasting and assessment methods, that will allow for better anticipation and preparation and will lead to better management practices, in order to reduce the vulnerability of society to drought and its subsequent impacts.
As drought can be defined in a number of ways and the determination of drought magnitude and impacts can be quite complex, the top scientific priority and social challenge are the identification of meteo-hydrological precursors of water crises. This will lead from meteo-hydrological drought to socio-economic drought and drive water management and decision-making with a strong scientific basis.
In this work we  focused on the Turin area and after identifying the events that have sent in crisis the drinking water supply sources, the meteorological data and appropriate drought indexes have been analyzed. Critical thresholds and parameters have been identified and a first combined index, for developing an operational chain that can alert water utilities, stakeholders and mayors reasonably in advance, is proposed.

How to cite: Brussolo, E., Ronchi, C., Salandin, A., Cremonini, R., and Barbero, S.: Meteo-hydrological precursors of water crisis in the Turin area: a first forecasting and management chain, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5990, https://doi.org/10.5194/egusphere-egu23-5990, 2023.

EGU23-6177 | ECS | Posters virtual | HS4.2

Influence of reservoir on propagation from meteorological to hydrological drought for Tapi river basin 

Akshay Pachore, Nirav Agrawal, Komiljon Rakhmonov, Sanskriti Mujumdar, Gulomjon Umirzakov, and Renji Remesan

Meteorological drought generally gets propagated into agricultural and hydrological drought. Hydrological drought is characterized by reduced streamflow in the river regime. Due to the interconnection between different drought types, it is important to analyze the drought propagation time. Propagation from meteorological to hydrological drought is of prime concern, as hydrological drought is having immediate consequences on industry, agriculture, and the water supply system. In the present study propagation time from meteorological to hydrological drought was studied using the spearman rank correlation coefficient for the Tapi river basin of India having semi-arid climatic conditions.  Spearman rank correlation was calculated between lagged values of the standardized precipitation index (SPI-1,2,3,4,5,6,7,8,9,10,11,12), and monthly standardized streamflow index (SSI-1). Drought propagation under the influence of the Ukai reservoir was analyzed for Sarangkheda and Ghala gauging stations. Sarangkheda station is in the upstream of the Ukai reservoir whereas, Ghala station is in the downstream. Results indicated that there is a clear influence of reservoir on propagation time from meteorological to hydrological drought. The highest correlation for the Sarangkheda station was observed for SPI-5 and SSI-1, whereas, for the Ghala station, it is for SPI-12 and SSI-1. Propagation time has significantly increased for reservoir-influenced gauging station as compared to gauging station in the natural catchment. The present study is important as information on propagation time under the influence of a reservoir can be useful to the water resource manager, stakeholders, and policymakers for doing the required preparation and taking necessary measures.

How to cite: Pachore, A., Agrawal, N., Rakhmonov, K., Mujumdar, S., Umirzakov, G., and Remesan, R.: Influence of reservoir on propagation from meteorological to hydrological drought for Tapi river basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6177, https://doi.org/10.5194/egusphere-egu23-6177, 2023.

The SUDOE AQUIFER project (http://www.igme.es/aquifer/) aims at capitalizing, testing, diffusing and transferring innovative practices for groundwater monitoring and integrated management.

BRGM has developped the « MétéEAU Nappes » web platform (https://meteeaunappes.brgm.fr/fr) for several years. It enables to visualize the current and future behavior of groundwater bodies in France and to forecast groundwater availability in many monitoring wells which have been modeled using a lumped hydrological model [1].

Although more than 500 wells are monitoring groundwater level in real time in unconfined aquifers in the Adour-Garonne basin (France) (https://ades.eaufrance.fr/), none of these monitoring points have been modeled to enable 6 months groundwater levels forecast. The SUDOE AQUIFER project enables to model ten monitoring points in 2022 and 2023 to forecast groundwater levels using different climatic scenarios. These forecasts are updated on a monthly basis and can be compared to groundwater levels thresholds (piezometric drought thresholds from local authority use-restriction orders [2]).

These groundwater level forecasts are further used to predict groundwater withdrawable volume using a three-dimensional groundwater flow model in the Garonne, Tarn and Aveyron alluvial plain [3]. The main activity of this region is agriculture and the main groundwater use is crop’s irrigation. Groundwater withdrawal is especially important in the summer, and can impact the volume of groundwater reaching the rivers and sustaining their baseflow. This competition in use creates the need to accurately define potential withdrawable volumes.

Combining the lumped hydrological models with a three-dimensional groundwater flow model enables to define the potential withdrawable volume based on (1) the summer climatic scenario chosen by the decision maker, (2) the forecasted groundwater level at the end of the low-water season and (3) the status of the groundwater body (critical, balanced, conservative) to achieve at the end of the low-water season. This decision support tool is developed as a web platform and will be accessible to groundwater managers and decision makers. After choosing the groundwater level forecasted at the start of the irrigation period within 6 scenarios based on different climatic conditions, three potential withdrawable volumes will be defined depending on the status of the groundwater body considered acceptable to obtain at the end of the low-water season. This information can then be communicated to groundwater users.

These innovative practices will be extended to other regions where increase groundwater pressure forces local authority to develop methods and tools to sustainably manage groundwater bodies.

Références bibliographiques :

 [1] Mougin B., Nicolas J., Vigier Y., Bessière H., Loigerot S. (2020). « MétéEAU Nappes » : un site Internet contenant des services utiles à la gestion des étiages. La Houille Blanche, numéro 5, p. 28-36. https://doi.org/10.1051/lhb/2020045

[2] Surdyk N., Thiéry D., Nicolas J., Gutierrez A., Vigier Y., Mougin B. (2022). MétéEAU Nappes: a real-time water-resource-management tool and its application to a sandy aquifer in a high-demand irrigation context. Hydrogeology Journal. https://doi.org/10.1007/s10040-022-02509-1

[3] Le Cointe, P., Nuttinck, V., Rinaudo, JD. (2020). A Tool to Determine Annual Ground-Water Allocations in the Tarn-et-Garonne Alluvial Aquifer (France). In: Rinaudo, JD., Holley, C., Barnett, S., Montginoul, M. (eds) Sustainable Groundwater Management. Global Issues in Water Policy, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-030-32766-8_13

How to cite: Beranger, S., Le Cointe, P., and Mougin, B.: Groundwater level and withdrawable volume forecasts in the Adour-Garonne basin (France) to enable sustainable groundwater management, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6607, https://doi.org/10.5194/egusphere-egu23-6607, 2023.

EGU23-6835 | ECS | Orals | HS4.2

Multiscaling behavior of vegetation, precipitation and aridity time series in semiarid grasslands. Persistence and multifractal sources. 

Ernesto Sanz, Andrés Almeida-Ñauñay, Carlos G. Díaz-Ambrona, Antonio Saa-Requejo, Margarita Ruíz-Ramos, Alfredo Rodriguez, and Ana M. Tarquis

Grazing is an important ecosystem process affecting more than a third of the global land surface. However, it is challenging to predict responses of rangelands to changing grazing regimes due to complex interactions between grazers, vegetation and climate. Understanding the multiscaling behavior of vegetation and climate time series can be key to improving grazing and vegetation management in semiarid areas where climate change is heavily affecting vegetation-climate complex systems. 

A grassland plot in central Spain (Madrid) was selected to study this system. This plot was selected based on proximity to a meteorological station and maximum surface covered by grasses. For this plot, reflectance data were collected from MODIS (MOD09A1.006) to study the Normalized Difference Vegetation Index (NDVI). These series, from 2002 to 2020, have a 250 m spatial resolution and 8-days temporal resolution. Daily meteorological precipitation and evapotranspiration were obtained from the closest station from AEMET (Spanish Meteorological Agency). Precipitation was accumulated over 8-days and the aridity index was calculated (accumulated precipitation over accumulated potential evapotranspiration) for every 8-days to match the temporal resolution of NDVI. With these three series (NDVI, precipitation and aridity), multifractal detrended fluctuation analysis was performed, to calculate the persistence (H2) and multifractality. Furthermore, this was also done to these series after shuffling and surrogating them. 

The aridity index showed a high persistent character, while precipitation had a light persistence and NDVI showed no persistence or antipersistence, instead, it had a random character. The aridity index and NDVI displayed a decrease in H2, progressively, when surrogate and shuffle series were used. On the other hand, precipitation showed a higher H2 when the surrogate series was used compared to the original series. The shuffle precipitation series had a lesser value of H2 than the original and surrogate precipitation series. The increase in persistence on the precipitation surrogate series, have been reported in other precipitation series and it may indicate that the year that cause a decrease in persistence in the original series are separated along the original series. 

The most multifractal series was found to be NDVI followed by aridity index and finally precipitation. The multifractality always declined when the surrogate series was used in all series. Moreover, when shuffle series were used multifractality was almost eliminated in NDVI shuffle series, but some was retained for precipitation and aridity index, showing a larger source of multifractality due to the probability density function in these two series, mixing with a long-range correlation source of multifractality (mostly dominant for NDVI). 

Acknowledgements: The authors acknowledge the support of Clasificación de Pastizales Mediante Métodos Supervisados - SANTO, from Universidad Politécnica de Madrid (project number: RP220220C024).

Bibliography:

Baranowski, Piotr, et al. "Multifractal analysis of meteorological time series to assess climate impacts." Climate Research 65 (2015): 39-52.

Sanz, Ernesto, et al. "Generalized structure functions and multifractal detrended fluctuation analysis applied to vegetation index time series: An arid rangeland study." Entropy 23.5 (2021): 576.

Sanz, Ernesto, et al. "Clustering Arid Rangelands Based on NDVI Annual Patterns and Their Persistence." Remote Sensing 14.19 (2022): 4949.

 

How to cite: Sanz, E., Almeida-Ñauñay, A., Díaz-Ambrona, C. G., Saa-Requejo, A., Ruíz-Ramos, M., Rodriguez, A., and Tarquis, A. M.: Multiscaling behavior of vegetation, precipitation and aridity time series in semiarid grasslands. Persistence and multifractal sources., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6835, https://doi.org/10.5194/egusphere-egu23-6835, 2023.

EGU23-6869 | ECS | Orals | HS4.2

Vegetation response to extreme drought events in northern Italy 

Alice Baronetti, Matia Menichini, and Antonello Provenzale

The increase in drought conditions is one of the main consequences of climatic change, that affects both natural and socioeconomic systems. Northern Italy is historically rich in water resources, and one of the most fertile areas in Italy. However, in the last decades drought events increased also here, affecting the hydrological behaviour of the Po River and vegetation growth.

This study aims to quantify the spatial distributions of drought events and identify their effects on vegetation greenness in northern Italy during the 2000-2020 period using MODIS images at 1 km spatial resolution. For this purpose, correlation maps between fields of bi-weekly vegetation indices (NDVI and EVI) and drought indices (SPI and SPEI) were estimated.

The NDVI and EVI indices were extracted from the atmospherically corrected MODIS images and vegetation trends were investigated by mean on the Mann-Kendall test. To analyze drought events, 150 daily precipitation ground station series were collected, aggregated at bi-weekly scale, reconstructed, homogenised and spatialised at 1km resolution by mean of the Universal Kriging with auxiliary variables. Land Surface Temperature (LST), assumed as air temperature, was collected from MODIS images. Pixels with clouds were removed, and the accuracy was determined against the high resolution gridded temperature dataset available for northern Italy. The NDVI-LST space was investigated at yearly scale exploring the link between NDVI and LST for 6000 random points in the study area. Evapotranspiration was estimated by means of the Hargreaves equation and severe and extreme drought episodes were detected by means of drought indices (SPI and SPEI) calculated at 12-, 24- and 36-months aggregation time. Trends were analysed and the main drought events were characterised, identifying the percentage of area under drought, and the magnitude, duration and frequency of droughts. Each pixel was analysed to investigate the impacts of severe and extreme drought events on vegetation properties, and the Pearson’s correlation between NDVI/EVI and SPEI/SPI at different time scales was estimated. Finally, on the basis of the correlation maps and on the CORINE Land Cover 2020, drought impacts on different vegetation communities at medium (12 months) and long (24 and 36 months) time scales were detected as the percentage of vegetation under drought stress.

The study highlights the importance of applying multiple indices to study droughts, since even though positive temperature trends were recorded in northern Italy, in the last two decades the main trigger of droughts is the lack of precipitation. Moreover the western portion of northern Italy was mostly interested by drought intensification. The investigation on drought duration revealed that the longest extreme drought events were detected in the Po Valley, where the strongest negative impacts on vegetation were detected. The results also indicated that first droughts interested herbaceous vegetation, while subsequently affecting also sparse and open forests.

How to cite: Baronetti, A., Menichini, M., and Provenzale, A.: Vegetation response to extreme drought events in northern Italy, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6869, https://doi.org/10.5194/egusphere-egu23-6869, 2023.

EGU23-6903 | Orals | HS4.2

Winter Warm Spells and snowpack ablation in western North America 

Lucia Scaff, Sebastian Krogh, Keith Musselman, Adrian Harpold, Mario Lillo-Saavedra, Ricardo Oyarzún, Yanping Li, and Roy Rassmusen

Winter warm spells (WWS) are extreme temperature anomalies that might impact the snowpack. WWS amplify snowmelt and sublimation in mountain regions with uncertain consequences to timing and volume of water resources. Most studies focus on the spring season when snowmelt rates and streamflow response are high. However, winter snowmelt events are important in places where the snowpack and air temperatures are closer to the freezing point during winter, and thus it will become important in other regions in a warmer climate.

This study aims to understand the effect of WWS on snowpack ablation patterns in the mountainous western North America and how they might change under a warmer climate. For this, we use two convection permitting regional climate model simulations to represent historical (2001-2013) and future atmospheric and the surface conditions. The future simulation is performed with a Pseudo Global Warming approach for a high emission scenario (RCP8.5). We verify WWS using gridded maximum daily temperature observation, and winter ablation using snow pillows. Then we characterize WWS and relate them to snowpack ablation.

Although days with ablation during WWS represent a small fraction (8.3%, 0.6 days on average), 55% of total ablation occurs during WWS over regions with significant snowpack (mean peak snow water equivalent over 150 mm). Consistently, a larger ablation rate (53%) is found during WWS than non-WWS events. Total ablation during WWS increases about 157% in a warmer climate; however, the extreme ablation (99th percentile) rates show slight decrease (5%). Classifying the domain based on its humidity and temperature, we found that ablation rates during WWS in humid regions are larger in a warmer climate than those of the dry regions, which is explained by the differences in the energy balance and the snowpack cold content. WWS predominantly drive snowmelt (93.8%) rather than sublimation (6.2%), which has relevance to water resources such as flood risk, soil moisture, and streamflow response. Furthermore, the median snowmelt rate during WWS found to increase in response to warming by 179% compared to the median sublimation rate (125%). This study provides a comprehensive description of the impact of extreme temperature events and a warmer climate over our changing snowpack. We acknowledge financial support by Centro CRHIAM Project ANID/FONDAP/15130015, and the Anillo project ACT-210080.

How to cite: Scaff, L., Krogh, S., Musselman, K., Harpold, A., Lillo-Saavedra, M., Oyarzún, R., Li, Y., and Rassmusen, R.: Winter Warm Spells and snowpack ablation in western North America, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6903, https://doi.org/10.5194/egusphere-egu23-6903, 2023.

EGU23-7180 | ECS | Orals | HS4.2

Human interventions impacts: the role of reservoir operations on drought propagation 

Tesfaye B. Senbeta, Emilia Karamuz, Krzystof Kochanek, Jaroslaw J. Napiorkowski, and Ewa Bogdanowicz

The reservoir is a hydroengineering structure to regulate discharge in rivers and store water. It can be used for flood control, water supply, irrigation, power generation, etc. It is also used for physical water management to cope with droughts at the catchment scale. The reservoir operations can have a mitigating and/or enhancing impact on droughts and their propagation from meteorological to agricultural and hydrological drought.

The aim of the study is to assess the role of reservoir operation on drought propagation using the Sulejow and Wiory reservoirs as case studies in the catchments of the Pilica and Kamienna rivers (central Poland), respectively. Two approaches, namely hydrological modelling and the observation-based approaches, were used for the study. In the hydrological modelling method, the naturalised hydrological variables in the post-dam period simulated using the Soil and Water Assessment Tool (SWAT) were compared with the observed values in the same period, while in the observation-based approach, the upstream and downstream hydrological variables such as soil moisture (remote sensing data) and observed river discharge were used. In addition, the SWAT with reservoir was considered by applying the target reservoir release method for simulating the downstream hydrological variables and comparing it with the method without reservoir. The threshold method, based on the parameter transfer method, was applied in the analysis of drought conditions to account for the non-stationarity of the hydro-climatic variables.

Preliminary results suggest that the two approaches are consistent in showing the impact of reservoir operations on the propagation and characteristics of droughts. In addition, the comparative analysis between the reservoirs shows differences based on their purpose. The results of the study can be used to understand the propagation of drought in human-altered watersheds and to appropriately manage water resources for drought mitigation.

Acknowledgements

This work was supported by the HUMDROUGHT (https://humdrought.igf.edu.pl) project carried out at the Institute of Geophysics of the Polish Academy of Sciences and funded by the National Science Centre (contract 2018/30/Q/ST10/00654).

How to cite: Senbeta, T. B., Karamuz, E., Kochanek, K., Napiorkowski, J. J., and Bogdanowicz, E.: Human interventions impacts: the role of reservoir operations on drought propagation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7180, https://doi.org/10.5194/egusphere-egu23-7180, 2023.

EGU23-7193 | ECS | Orals | HS4.2

Developing a national-scale hydrological model for drought monitoring in Ireland 

Sri Vengana and Fiachra O'Loughlin

Ireland’s climate is changing with the same pattern as global trends. This has the potential to have significant impacts on precipitation and water availability throughout the country. It is vital to be able to quantify the size of these impacts. One way to do this is by hydrological models tuned for the extremes of interest. This study focuses on the development of a national scale hydrological model calibrated for droughts and low flows across Ireland. A total of 332 catchments have been used to calibrate and validate the national scale model hydrological model using the Modular Assessment of Rainfall-Runoff Models toolbox (MARRMoT) over the chosen 332 catchments. These catchments range in sizes (50km2 to 10,800 km2) and all chosen catchments have a minimum of 30 years of data available so that the model calibration and validation can be performed adequately. A few different objective functions focusing on droughts were used in calibration and validation including Kling and Gupta efficiency of discharge KGE(Q) function and logarithmic transformation based KGE. Initial results show that the simulated discharges can reproduce the observed discharges across the majority of catchments and that catchment size and the amount of baseflow are the important factors that influence the accuracy of the simulations.

How to cite: Vengana, S. and O'Loughlin, F.: Developing a national-scale hydrological model for drought monitoring in Ireland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7193, https://doi.org/10.5194/egusphere-egu23-7193, 2023.

EGU23-7487 | Orals | HS4.2

Monitoring of drought in the Netherlands in an online portal 

Marjolein van Huijgevoort, Esther Brakkee, Gé van den Eertwegh, Erwin Vonk, Dion van Deijl, and Ruud Bartholomeus

In 2018-2020 water managers in the Netherlands were confronted with extreme drought. This event had a large impact on nature, agriculture, shipping and drinking water supply. To better anticipate dry conditions and improve water management during a drought, up-to-date and accurate information about the meteorological and hydrological situation is crucial. During the 2018 drought it became clear that current information about groundwater levels was scattered across many different organisations. In addition, each organisation had different methods to compare current groundwater levels with historical data to indicate the severity of the drought event. There was a clear need for an uniform indication of drought severity.

We developed an online information portal with up-to-date measurements for precipitation and groundwater levels. To quantify the drought severity, the Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiraton Index (SPEI) and Standardized Groundwater Index (SGI) are determined. The availability of long-term records (30> years) of groundwater observations is limited for most regions in the Netherlands. Therefore, the SGI is based on simulations with a time series model for all locations for the same period (27 years). Time series models are developed for 5818 wells with observations. Several criteria have been applied to evaluate the time series model, for example, a minimum value of the explained variance, resulting in 1931 wells for which SGI values are calculated. We have also compared SGI values directly derived from observations with the SGI values from simulated groundwater levels for locations with longer time periods. This comparison indicated that due to errors or missing values in observations, the SGI values from simulations are more reliable to gain a global overview of the drought situation.

By combining the information on meteorological and hydrological drought in one decision-support system (www.droogteportaal.nl), water managers and stakeholders can now get an up-to-date overview of the current situation. Due to the uniform determination of drought severity, regions within the Netherlands can be compared. This can help to implement targeted water management decisions for adaptation measures for mitigating drought impacts. Part of the information of the portal is also included in the national drought monitor of Rijkswaterstaat (Dutch Ministry of Infrastructure and Water Management). At the moment, the portal gives forecasted information for 7 days, but the data provides an excellent opportunity to include forecasts on longer timescales ((sub-)seasonal) to improve water management.

How to cite: van Huijgevoort, M., Brakkee, E., van den Eertwegh, G., Vonk, E., van Deijl, D., and Bartholomeus, R.: Monitoring of drought in the Netherlands in an online portal, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7487, https://doi.org/10.5194/egusphere-egu23-7487, 2023.

EGU23-7644 | Orals | HS4.2

Response of vegetation indices to drought in western Spain 

Elia Quirós and Laura Fragoso-Campón

Drought is a transitory anomaly, prolonged, characterised by a period with precipitation values lower than normal in a specific area. The initial cause of any drought is a shortage of precipitation (meteorological drought) which leads to a shortage of water resources (hydrological drought) necessary to supply the existing demand. Flash drought is a critical sub-seasonal phenomenon that can be devasted for the ecosystems and, consequently, for general economy and health. There are areas where droughts are more devastating, and Spain is in a medium risk zone. In addition to water supplies, one of the first elements where the effects of droughts are first felt is on vegetation. Recent studies have addressed the relationship between NDVI and drought events. They concluded that, although vegetation activity over large parts of Spain is closely related to the interannual variability of drought, there are clear seasonal differences in the response of the NDVI to drought.

The World Meteorological Organization (WMO) categorises various drought indices into different groups such as (a) meteorology, (b) soil moisture, (c) hydrology, (d) remote sensing and (e) composite or modelled. Within the group of indices that can be defined by remote sensing, it points out some indices as possible predictors or evaluators of drought periods such us the Enhanced Vegetation Index (EVI), Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI). However, WMO leaves open the use of other possible VIs for drought prediction or assessment. In the current scenario, where there are multiple vegetation indices that can be used from satellite imagery, the initial objective of the study is to establish, from a set of VIs proposed or not by the WMO, which ones have the highest correlation with drought events in the study area. This correlation will be analysed according to vegetation type  (using the categorisation of the recently published ESA World cover map), in order to attempt to determine the behaviour of the vegetation index under meteorology according to each type. The study area is located in the Extremadura region of western Spain. The mean annual precipitation of the zone ranges from 446 to 1323 mm. The precipitations occur mainly from October to April while June, July and August suffer a significant drought with none or close to zero precipitation amount. The land cover types are mainly forests, agricultural and impervious cover. Regarding the temporal extent, two episodes of severe drought (2005-2006 and 2021-2022) will be studied.

Firstly, the vegetation indices available in open collections like OpenEO or Copernicus Global Land Service will be used. All available indices will be used to create time series to be compared with meteorological time series. Once the correlation is established, it will be analysed according to the type of coverage of the World cover map, in order to establish which index correlates better with drought episodes and thus try to establish the best predictor/evaluator.

How to cite: Quirós, E. and Fragoso-Campón, L.: Response of vegetation indices to drought in western Spain, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7644, https://doi.org/10.5194/egusphere-egu23-7644, 2023.

EGU23-8074 | ECS | Orals | HS4.2

Human and natural drought impacts on groundwater fluxes of non-Amazonian South America 

Jorge Vega Briones, Steven M. de Jong, Edwin Sutanudjaja, and Niko Wanders

The consistent impact of droughts and the progressive use of groundwater for the superficial allocation of crops has extremely increased groundwater withdrawal. The rapid economic expansion is increasing water usage and is likely to exacerbate hydrological drought. While global drought intensities are increased by 10–500\% due to human water consumption, the consequences at a regional and global scale are aggravated by changing precipitation patterns, resulting in multi-year droughts and decreased groundwater recharge. 

An essential factor to better understand how human activities affect drought characteristics and development is to quantitatively distinguish natural and human components. At the same time, we see that the recovery from a severe drought is also impacted by catchment characteristics and regional climatology. In this study, we focus on the south American non-Amazon region which has frequently experienced multi-drought periods with severe impacts on surface and groundwater.

We estimate the drought impact on groundwater with the model PCR-GLOBWB2 at a 5 arcmin resolution under natural and human influence. Aggregations of the model output at a catchment level of the groundwater and subsurface partitioned run-off was performed. To determine the influence with and without lateral water flux at high resolution, the flux differences of groundwater components such as baseflow and groundwater recharge were quantified. Finally, the drought termination (DT) framework was applied to understand the recovery response of simulated surface runoff, interflow, and groundwater recharge.

The PCR-GLOBWB2 identifies regions influenced by human impact in the non-Amazon basins, supported by the drought duration, deficit, and groundwater fluxes. The differences in fluxes show an increasing groundwater withdrawal due to irrigated zones, affecting hydrological processes at a catchment and regional scale. The recovery of fluxes during these events consists of a relevant indicator for groundwater behavior due to drought and/or human consumption. We quantified the impact on groundwater resources by addressing the land-use component to understand the variability in water volumes. This study is beneficial to identify groundwater drought vulnerability in regions where observations are lacking and help to predict drought recovery periods, lateral-flux impacts, and characteristics.

How to cite: Vega Briones, J., de Jong, S. M., Sutanudjaja, E., and Wanders, N.: Human and natural drought impacts on groundwater fluxes of non-Amazonian South America, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8074, https://doi.org/10.5194/egusphere-egu23-8074, 2023.

EGU23-8726 | ECS | Posters on site | HS4.2

Drought Risk Assessment for an Agricultural Basin in Turkey using SPEI and SPI 

Mohammadreza Khandandel, Onur Cem Yoloğlu, Daniele Secci, Valeria Todaro, Irem Daloğlu Çetinkaya, Nadim Kamel Copty, and Ali Kerem Saysel

The Konya province in the Central Anatolia Region of Turkey features a semi-arid climate with cold winters and hot, dry summers. Although the annual precipitation of the Konya Closed Basin is about 350 mm, the basin is considered one of the main agricultural regions of Turkey. Given the effects of drought on crop yields and food security, evaluation of drought risks is crucial. This study aims to describe historical as well as future drought characteristics of the Konya basin by means of two widely used meteorological drought indices: the standardized precipitation index (SPI) and the standardized precipitation-evapotranspiration index (SPEI). The indices were calculated for different timescales (6–24-month timescale) to better assess agricultural drought conditions. For the SPEI index, the potential evapotranspiration (PET) was calculated using the Hargreaves and Samani method, commonly used in arid and semi-arid weather conditions. The analysis was performed over the period 1980-2020 using precipitation and temperature data from 18 weather stations located within Konya Closed Basin. Based on drought classification by SPI and SPEI, values equal to or lower than -2 are considered extreme droughts. The results show that the number of extreme climatic drought periods at the considered stations within the Konya basin based on SPI is higher than that based on SPEI. The findings also reveal that both SPEI and SPI characterize a general increase in drought severity, areal extent, and frequency over 2000-2010 compared to those during 1980-1990, mostly because of the decreasing precipitation and to a lesser extent rising potential evapotranspiration. To assess future drought frequencies, the drought indices were calculated using precipitation and temperature data provided by 17 regional climate models from the EUROCORDEX project. The results for both RCP 4.5 and RCP 8.5 scenarios show significantly more frequent extreme and severe droughts, particularly for the second half of the 21st century. Overall, this study implies that SPEI may be more appropriate than SPI to monitor drought periods under climate change since potential evapotranspiration increases in a warmer climate.

This work was developed under the scope of the InTheMED project. InTheMED is part of the PRIMA program supported by the European Union’s Horizon 2020 research and innovation program under grant agreement No 1923.

How to cite: Khandandel, M., Yoloğlu, O. C., Secci, D., Todaro, V., Daloğlu Çetinkaya, I., Copty, N. K., and Saysel, A. K.: Drought Risk Assessment for an Agricultural Basin in Turkey using SPEI and SPI, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8726, https://doi.org/10.5194/egusphere-egu23-8726, 2023.

EGU23-8730 | ECS | Orals | HS4.2

Global Drought Hazard Monitoring in Rainfed Areas 

Neda Abbasi, Stefan Siebert, Petra Döll, Harald Kunstmann, Christof Lorenz, and Ehsan Eyshi Rezaei

Droughts are a significant threat to the agricultural sector in general, and rainfed farming in particular. Therefore, effective and timely responses to manage droughts and their impacts are required so that farming systems can limit the negative effects of droughts on food production. We developed a crop drought index (CDI) by integrating drought hazard and exposure and applied this index at the global scale to evaluate the influence of drought on the exposed rainfed areas for different crops. In an attempt to develop an operational, multisectoral global drought hazard forecasting system, we computed and analyzed CDI for historical periods. We further used bias-corrected seasonal climate forecasts to project the drought development in a 7-month period. The CDI was calculated by using the Global Crop Water Model (GCWM) at a global extent (5 arc-minute resolution) from 1980 to 2020. We compared the drought conditions in specific years to the CDI in the 30-year reference period 1986 to 2015. The CDI was computed for 25 specific crops or crop groups based on the relative deviation of the ratio between actual evapotranspiration (ETa) and potential evapotranspiration (ETp) in a specific year from the long-term mean ratio of ETa/ETp during the crop growing season. To test the skill of the seasonal drought forecasts, CDI computed with bias-corrected ensemble forecasts was compared to simulations with standard ERA5-reanalysis data for the year 2018 when severe drought conditions were observed across Europe and other regions. The skill of the CDI to detect drought impacts was tested for historical years by comparing the time series of the harvested area weighted CDI to detrended yield anomalies for crops and countries with predominantly rainfed production. The results of the comparison with historical yield anomalies showed that the CDI is a good indicator for negative yield anomalies, in particular in regions known to be affected regularly by droughts. The model simulations employing the bias-corrected ensemble forecasts reproduced well the reference drought condition in the year 2018 in countries such as Argentina, Australia, Italy, and Spain but showed little skill to reproduce the severe drought in Western Europe. Data availability constraints also had an impact on the accuracy of historical reconstructions and forecasts. For instance, the hazard and exposure analysis rely on static input data for crop shares and crop calendars, which can impact the results (i.e., as cropping patterns are dynamic and often can change over time). The findings suggest that bias-corrected seasonal ensemble forecasts have a significant potential to enhance seasonal drought forecasts, although the skill of the forecasts varies considerably for specific regions. Further research is needed to analyze this potential across different periods and geographies systematically to increase forecasting system efficiency and minimize processing time before this system can be run operationally. In our study, we hence want to demonstrate the current status of the CDI-based forecasting system and discuss the potential, limitations, and uncertainties of such CDI forecasts for agricultural applications.

 

How to cite: Abbasi, N., Siebert, S., Döll, P., Kunstmann, H., Lorenz, C., and Eyshi Rezaei, E.: Global Drought Hazard Monitoring in Rainfed Areas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8730, https://doi.org/10.5194/egusphere-egu23-8730, 2023.

EGU23-8917 | ECS | Orals | HS4.2

Comparison of Meteorological Drought Indices in Georgia (1931-2020) 

Mariam Tsitsagi, Zaza Gulashvili, Nana Bolashvili, and Michael Leuchner

Recently, the severity of droughts has been increasing due to climate change. Due to the multifaceted nature of droughts (meteorological, hydrological, economic, ecological, etc.), it affects almost all aspects of community life directly or indirectly, both short and long term. Georgia is characterized by diverse terrain and, accordingly, climatic conditions. Most types of climates are present in Georgia except savanna and tropical forests (from humid subtropical to dry subtropical, and climate of eternal snows and glaciers). Therefore, droughts are expressed differently in this small area (67,900 km²). The complexity of different indices used in drought studies depends on the availability of the used data. The purpose of the study was to analyze the intensity of droughts in the short and long term in the territory of Georgia and their distribution for 1931-2020. In this study, we focused on the widespread Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI).  Both indices were calculated based on the data of more than 100 rain gauges located in the study area for several time-scales including 3, 6, 12 and 24 months covering the period from 1931 to 2020. As SPI uses only precipitation data, evapotranspiration is also taken into account in SPEI, which offers a more complete picture of the background of the diversity of Georgia's climate. Daily temperature (for calculation of ET) and precipitation data are used in the research. We calculated the Pearson correlation, R² and RMSE. The correlation of SPI and SPEI allowed us to determine climate type with decisive role of temperature in assessing droughts. The frequency of severe droughts has increased throughout the country, especially in recent decades. This trend is especially striking in the case of the eastern Georgian lowland. In the example of Eastern Georgia‘s precipitation data, another trend was revealed. Here the correlation of SPI and SPEI was relatively low and decreased as the period increases; for example, the correlation for 12- and 24-month periods was lower than for 3- and 6-month periods. This shows that when assessing droughts in East Georgia, it is crucial to take into account the change in temperature along with the change in precipitation. Therefore, in western Georgia, where there is a humid subtropical climate, it is possible to create an idea about the nature of droughts only by using SPI. In the lowland of Eastern Georgia, where it is unlikely to see the accurate picture with only one index, and it is better to use multivariable indices, where along with precipitation, temperature and other data will be taken into account.

How to cite: Tsitsagi, M., Gulashvili, Z., Bolashvili, N., and Leuchner, M.: Comparison of Meteorological Drought Indices in Georgia (1931-2020), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8917, https://doi.org/10.5194/egusphere-egu23-8917, 2023.

Effective drought characterization and monitoring are urgent challenges especially in arid and semi-arid regions. The Tensift basin is in the center-west of Morocco and is exposed to recurrent droughts. The effects of climate change, which has already led to several economic, agricultural, hydrological and social losses over the past decades, exacerbate the situation. The objective of this study is to characterize the drought in the Tensift basin and to assess its impact on water resources by using the potential of satellite products. For this purpose, satellite products and reanalysis data were selected for the evaluation of observed data in the study area. These datasets were used due to the availability of long-term data, near real-time data series, relatively high spatial and temporal resolutions and open access data. In particular, precipitation and temperature retrieved by ERA5-Land (https://cds.climate.copernicus.eu) and CHIRPS (https://www.chc.ucsb.edu/data) datasets, as well as the corresponding data observed by in-situ stations, were used and statistically analyzed and evaluated by common metrics (R, R², BIAS, RMSE, and the Nash and Sutcliffe Efficiency) to compare their performance and accuracy. The obtained results showed that most meteorological stations agree with satellite and reanalysis products, with some slight errors. Based on these results, several drought indices during the period 1982-2021 have been calculated at several spatio-temporal scales to determine the impacts of drought on water supply. The results show that the Tensift Basin suffered from multiple droughts over the past 40 years. The years 2000 and 2015, 2017, 2019, 2020, 2021 were common drought periods by either the Standardized Precipitation Index (SPI) and the Standardized Precipitation and Evapotranspiration Index (SPEI); however, the Vegetation Condition Index (VCI), which was provided by NOAA-AVHRR data (https://www.star.nesdis.noaa.gov), indicate more dry years than the other indices. The drought indices provide a powerful tool to monitor drought and its impacts on water resources. These tools could potentially allow decision makers to better manage water resources as to minimize drought impacts. Furthermore, the considered drought indices could be used separately or in combination within a drought early-warning system in the study area for drought monitoring and forecasting.

Keywords: Drought, Water supply, Satellite products, Tensift basin, Remote sensing, Reanalysis data

How to cite: Naim, M. and Bonaccorso, B.: Evaluation of satellite products for drought characterization and impact assessment on water resources in the Tensift Basin (Morocco), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8975, https://doi.org/10.5194/egusphere-egu23-8975, 2023.

EGU23-9279 | ECS | Orals | HS4.2

Current and future drought hazards in the Flemish pear sector 

Brecht Bamps, Anne Gobin, Ben Somers, and Jos Van Orshoven

Recurring episodes of drought have become a hot topic in recent years in the Flemish pear sector. Damages associated with these episodes increasingly cause economic losses and create uncertainty for fruit growers. This trend is expected to continue in the future, as episodes of drought are likely to increase in frequency, intensity and duration as a result of climate change.

This problem calls for the development of efficient risk management methods, which rely on accurate estimates of the hazard imposed by extreme weather. Therefore, our study aims to quantify the location-specific hazard and impact of past and projected drought episodes on pear orchard vigour and productivity in the region of Flanders (Belgium). The hazard under the recent past climate is characterised based on daily historical meteorological observations (1961-2022) with 5x5 km spatial resolution (Gridded Observational Dataset of the Royal Meteorological Institute of Belgium). The future hazard is determined based on daily regional climate model projections from the CORDEX ensemble (12.5x12.5 km spatial resolution). Climate projections are bias-corrected using Multivariate Quantile Mapping based on a N‐dimensional probability density function transform.

Regional AquaCrop, a spatially distributed modelling system of the field-scale crop growth model AquaCrop1, is used to calculate the soil water balance on a daily timestep, covering the region of Flanders at a spatial resolution of 12.5x12.5 km. Phenology-dependant thresholds of critical values of the soil water potential are used to analyse the frequency, intensity, duration and timing of drought-related stress episodes for rainfed pear orchards (cv. Conférence). Moreover, changes in the characteristics of potentially damaging episodes of drought under future climates are analysed.

Preliminary findings show an increase in projected frequencies of stress-inducing occurrences under Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 for the period 2022-2072 compared to the reference period 1972-2022. Moreover, spatial variation in drought hazards for pear orchards across Flanders points to local environmental factors such as soil type and groundwater depth.

The spatially explicit hazard maps associated with the future climatic conditions resulting from this analysis are useful for decision-making by fruit growers, governments and insurance companies.

 

1Raes, D., Steduto, P., Hsiao, T. C., and Fereres, E.: AquaCrop – the FAO crop model to simulate yield response to water: II. Main algorithms and software description, Agron. J., 101, 438–447, https://doi.org/10.2134/agronj2008.0140s, 2009.

How to cite: Bamps, B., Gobin, A., Somers, B., and Van Orshoven, J.: Current and future drought hazards in the Flemish pear sector, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9279, https://doi.org/10.5194/egusphere-egu23-9279, 2023.

EGU23-10679 | Orals | HS4.2

Quantifying the climatic drivers of drought using a standardized aridity index 

Song Feng and Miroslav Trnka

Drought is one of the costly natural disasters that affect water resources, agriculture and ecosystems. This study developed a standardized aridity index (SAI) to quantify the short- and long-term drought, and then decipher the climate drivers of the drought on local, regional and continental scales.  The ratio of total precipitation (P) to total potential evapotranspiration (PET) for a given month or multiple months was firstly calculated, and then normalized to calculate the SAI. The contribution of P, PET as well as temperature, solar radiation, wind speed and relative humidity on SAI can be decomposed by apply partial derivation of SAI and PET algorithm (i.e., Penman-Monteith model). The SAI is highly correlated to several frequently used drought indexes.  We also examined the temporal variations and spatial extent of different droughts across the global. The contributions of different climate variables on these droughts were also examined. The spatial distribution of individual droughts and their intensity revealed by SAI are comparable to those calculated using existing drought indexes and drought monitors. For example, the 12-month SAI and other drought indexes all suggested a several drought condition in the central Europe during 2015-2020, which is unprecedented in the past 2,000 years. We found that this drought was firstly initiated by precipitation deficit, but the PET became important in the late years of this drought. On average, the precipitation contributed to 70%, while the PET contributed to another 30% to this multi-year drought. The temperature warming alone contributed to about 20% of the drought intensity.

How to cite: Feng, S. and Trnka, M.: Quantifying the climatic drivers of drought using a standardized aridity index, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10679, https://doi.org/10.5194/egusphere-egu23-10679, 2023.

EGU23-11208 | ECS | Posters on site | HS4.2

Future drought prediction using time-series of drought factors and the US drought monitor data based on deep learning over CONUS 

Bokyung Son, Jaese Lee, Jungho Im, and Sumin Park

Predicting future drought conditions is crucial for preventing massive agricultural and/or hydrological resource damage caused by drought. This study predicts future (in this case, 3-month forecast lead time) drought conditions in the contiguous United States, especially focusing on five different dry and drought severity classes indicated by the United States Drought Monitor (USDM) during 2000-2020. A deep learning model was trained using the time-series of USDM and four different types of drought-related variables (i.e., hydro-meteorological variables) such as precipitation and temperature from Phase 2 of the North American Land Data Assimilation System. UNet, one of the image-to-image translation techniques, was used as a basic deep learning architecture to consider the spatial characteristics (extents of each drought severity class) of drought across the continent. As drought classes in USDM are ordinal, the loss function of the deep learning model was set to be able to consider ordinal problems utilizing the cross-entropy loss function. The results of the proposed model were compared to the existing seasonal drought outlooks provided by the National Oceanic and Atmospheric Administration Climate Prediction Center. The performance for the validation period (2 years) showed an overall accuracy of about 65%. When compared to the seasonal outlooks, it demonstrated about a 6% improvement in terms of overall accuracy for changing drought conditions. Future research will further discuss the performance of the proposed model with other comparable reference data and the impact of each input variable to predict future drought conditions.

How to cite: Son, B., Lee, J., Im, J., and Park, S.: Future drought prediction using time-series of drought factors and the US drought monitor data based on deep learning over CONUS, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11208, https://doi.org/10.5194/egusphere-egu23-11208, 2023.

EGU23-11265 | Orals | HS4.2

Near-real Time Daily Drought Monitoring Using an Ensemble of Gridded Precipitation Datasets 

Olivier Prat, David Coates, Scott Wilkins, Denis Willett, Ronald Leeper, Brian Nelson, Michael Shaw, Steve Ansari, and George Huffman

We present a near-real time drought monitoring framework that uses precipitation estimates from a selection of satellite (CMORPH-CDR, IMERG) and in-situ (NClimGrid) gridded precipitation products datasets. The near-real time availability of precipitation datasets allows for the computation of the standardized precipitation index (SPI) over various time scales (30-, 90-, 180-, 270-, 365-, 730-day) and daily update of drought conditions. The three drought products generated: CMORPH-SPI (Global; 1998-present; 0.25°x0.25°degree spatial resolution), NClimGrid-SPI (CONUS; 1951-present; 0.05°x0.05°), and IMERG-SPI (Global; 2000-present; 0.1°x0.1°) are being evaluated focusing on the influence of the sensors characteristics and resolutions, differing period of record, and various SPI formulations. The remotely sensed and in-situ SPIs are also compared against existing droughts monitoring resources and in particular the US Drought Monitor (USDM).

The use of cloud-scale computing resources (Microsoft Azure, Amazon Web Services) reduces considerably the computation time. Gain in computational time and process optimization allow for the implementation of a drought amelioration module that is run conjointly with the daily SPI. The drought conditions derived from the precipitation datasets enable us to estimate the amount of deficit precipitation needed to alleviate drought conditions as a function of drought severity and accumulation periods. The process flexibility also allows for the addition of other variables (i.e. temperature, ET) to develop more complex drought indices.  For instance, daily temperature information available from NClimGrid, is used to compute the Standardized Precipitation-Evapotranspiration Index (SPEI) that is evaluated against NClimGrid-SPI over CONUS.

Finally, we present the effort to transfer the SPI from research to operation (R2O). The global daily SPI derived from CMORPH-CDR is publicly available via the Global Drought Information System (GDIS) dashboard (https://gdis-noaa.hub.arcgis.com/pages/drought-monitoring). The other products developed (NClimGrid-SPI, IMERG-SPI) are expected to be added to the existing portfolio of near-real time drought monitoring capabilities.

How to cite: Prat, O., Coates, D., Wilkins, S., Willett, D., Leeper, R., Nelson, B., Shaw, M., Ansari, S., and Huffman, G.: Near-real Time Daily Drought Monitoring Using an Ensemble of Gridded Precipitation Datasets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11265, https://doi.org/10.5194/egusphere-egu23-11265, 2023.

The Vietnamese Mekong Delta (VMD) is the most productive region in Vietnam in terms of agriculture and aquaculture. Unsurprisingly, droughts have been a prevalent concern for stakeholders across the VMD over the past decades. However, the VMD precipitation moisture sources and their dominant factors during drought conditions were not well understood. By using the ERA5 reanalysis data as inputs, the Water Accounting Model-2layers (WAM-2layers), a moisture tracking tool that traces moisture sources using collective information of evaporation, atmospheric moisture, and circulation, was applied to identify the VMD precipitation moisture sources from 1980 to 2020. The modelling simulation indicates that the moisture sources transported from the upwind regions dominate the VMD precipitation by 60.4% to 93.3%, and the moisture source areas vary seasonally with different monsoon types. The VMD precipitation moisture sources mainly come from the northeast area (e.g. the South China Sea) in dry seasons due to the northeast monsoon, while the southwest region (e.g. the Bay of Bengal) provides the primary precipitation moisture in wet seasons. Based on the causal inference algorithm, the driving factors in the process of moisture transport were also investigated. The results show that the specific humidity and wind speed are the dominant factors for driving moisture transport and determining the amount of VMD precipitation in dry and wet seasons, respectively. During the drought events in 2015-2016 and 2019-2020, the reduced moisture transport in the 2015 and 2016 dry seasons was mainly caused by the anomaly of both specific humidity and wind speed, while the negative anomaly of moisture sources in the 2020 dry season was dominant by the specific humidity. In the 2019 wet season, the wind speed anomaly led to the reduction of tracked moisture. These findings are important to understand the VMD precipitation moisture sources and their dominant factors during recent drought events.

How to cite: Zhou, K., Shi, X., and Renaud, F.: Understanding precipitation moisture sources of the Vietnamese Mekong Delta and their dominant factors during recent drought events, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11377, https://doi.org/10.5194/egusphere-egu23-11377, 2023.

In the past two decades, Europe has been hit by major summer heat waves and droughts, with heavy impacts on ecology, economy and civil society.

In addition to increased risk of crop failure, forest fires and danger to human health, extensive dry conditions may lead to riverine low flows and general water scarcity. Low flow conditions can restrict river navigation, hydropower production, and limit water use for power plant cooling and irrigation agriculture. Furthermore, the ecological state of the river is impaired.

To address these challenges, setting up a hydrological model based on a large ensemble climate simulation provides the required data to evaluate the water availability under future heat and drought conditions. Therefore, we create a hydrological large ensemble with 50 realizations for the periods 1990 – 2099 featuring the Water balance Simulation Model (WaSiM). The single-model initial condition large ensemble (SMILE) CRCM5-LE (CRCM5-Large Ensemble) used consists of 50 transient simulations (50 members) of a regional climate model of 150 years each (1950-2099, 7500 model years, hourly time step, 0. 11° spatial resolution) and provides the meteorological forcing data, after bias correction and statistical downscaling to the hydrologic model application scale, for 98 gauges simulated with the WaSiM-ETH water balance model in hydrological Bavaria. Due to the high number of model years, this model chain on the one hand provides a novel way to transfer and assess the non-linear relationships of the natural variability of the climate system within the hydrological system, and on the other hand results in a sufficiently large number of extreme events to conduct a robust statistical analysis.

Based on the modeling results, the dynamics of the low flow situation in Bavaria is mapped for the reference period (1981-2010), spatial patterns of drought are highlighted, and regional correlations are identified. To allow for seasonal comparisons of the negative anomalies of the runoff event, the variable-threshold approach is used. Here, the threshold is defined as the 15th percentile for the 30-day moving average of the discharge value for each day of the year, averaged over the reference period. An undershoot of this threshold for at least 20 days is considered a drought event. The use of climate simulation data allows for an analysis of how these characteristics (intensity, duration, spatial occurrence of the drought event) will change in the future due to climate change. Emphasis is placed on the potential change in the seasonal regime and the associated impacts on river system usage. By accounting for the natural variability of the climate system through the ensemble approach, the results become more robust, particularly with respect to extremes, and strengthen confidence in the change signals that are observed.

Results of these analyses are presented using a representative sample of watersheds for the entire study area, highlighting common features as well as unique characteristics. The evaluations provide important evidence for the basic definition of low-flow events and a robust estimate of how their intensity, frequency, and seasonality changes in the future as a result of climate change impacts.

How to cite: Sasse, A., Böhnisch, A., and Ludwig, R.: Low flow in Bavaria: derivation of drought characteristics and their future development in a hydrological single-model large ensemble., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11800, https://doi.org/10.5194/egusphere-egu23-11800, 2023.

EGU23-12010 | ECS | Posters on site | HS4.2

Hydrological drought monitoring in the Ebro basin: Standardized Soil Moisture Index 

Guillem Sánchez Alcalde, Maria José Escorihuela, and Giovanni Paolini

Recent studies manifest that the frequency and severity of droughts are increasing due to climate change. Drought stands as a major climate risk; thus, its understanding and study are of utter importance. Such phenomenon results from complex interactions between the atmosphere, the continental surface and water resources management, and it can lead to large socioeconomic impacts.

Following the work of Wilhite and Glantz, droughts can be categorized based on their severity as: meteorological, agricultural, hydrological, and socioeconomic (Wilhite, D.A.; and M.H. Glantz, 1985). The first three approaches are described by the physical impact of the drought, while the latter deals with drought in terms of supply and demand (e.g., the lack of energy, food or drinking water).

Meteorological drought is associated with a precipitation deficiency period, which can also be accompanied by high temperatures or low relative humidity. If such a period persisted in time, we would start observing a deficiency in soil moisture, and a reduction in crop population and yield. Such circumstances would indicate that we are under the influence of an agricultural drought, with the potential to evolve into a hydrological drought with time. The frequency and severity of hydrological drought are defined typically on a river basin scale, with an impact on the surface and subsurface water supply (i.e., reduced streamflow or inflow to reservoirs, lakes and ponds).

Due to the effects and frequency of droughts, monitoring them is of sheer importance. Different indices have been developed for the study of droughts, based on variables such as precipitation or vegetation status. One of the most used indices is the standardized precipitation index (SPI), which shows the deviation from average precipitation. Hence, it is related to drought hazards. Each index provides different information about the drought; therefore, a combination of indices is required to identify and assess them.

Drought indices can also be obtained from L-band (21 cm, 1.4 GHz) radiometers, which provide soil moisture data, among other variables. Soil moisture plays a key role in agricultural monitoring and drought forecasting. While vegetation-based drought indices can only be applied once the drought is already causing vegetation damage, soil moisture observations can forewarn of impending drought conditions.

The main drawback of precipitation-based drought indices is that they require in-situ data, providing a discrete image of the drought. Despite precipitation indices based on theoretical models providing a continuum picture of the drought, their performance and reliability should be taken with a grain of salt. On the other side, soil moisture data not only does not depend on any model but also displays a continuum image of the drought.

In this presentation, we will study the performance of a variety of drought indicators based on precipitation and soil moisture data in the Ebro basin region and show how they manifest hydrological drought. Namely, we have developed the standardized soil moisture index (SSI). The SSI is based on the SPI method, and we have tested this index for different integration times.

How to cite: Sánchez Alcalde, G., Escorihuela, M. J., and Paolini, G.: Hydrological drought monitoring in the Ebro basin: Standardized Soil Moisture Index, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12010, https://doi.org/10.5194/egusphere-egu23-12010, 2023.

EGU23-12437 | ECS | Orals | HS4.2

Forest Drought Impact Prediction based on Spatio-temporal Satellite Imagery and Weather Forecasts -- A Spatio-Temporal Approach using Convolutional LSTM Models 

Emine Didem Durukan, Selene Ledain, Thomas Brunschwiler, Devis Tuia, Manuel Günther, and Benjamin Stocker

Recent hot and dry summers in Europe have had a significant impact on forest functioning and structure. In 2018 and 2019, Central Europe experienced two extremely dry and hot summers. These extremes resulted in widespread canopy defoliation and tree mortality. The objective in this study is to create a predictive model for predicting the density of vegetation, as measured by the NDVI index. We predict NDVI at a horizon of a month utilising data from the previous months as input to determine where and when drought impacts are triggered. Such predictive models should take into account both spatial and temporal dependencies between environmental variables and impacts. We hereon focus on Switzerland's forests as a region of interest to leverage high-quality model input layers and applications to typical stakeholder needs. Widely used vegetation indices and mechanistic land surface models are not effectively informed by the full information contained in Earth Observation data and the observed spatial heterogeneity of land surface greenness responses at hillslope-scale resolution. Effective learning from the simultaneous evolution of climate and remotely sensed land surface properties is challenging. Modern deep learning and machine learning techniques, however, have the capacity to generate accurate predictions while also explaining the relationship between climate and its recent history, the position in the landscape, and influences on vegetation. The task is to predict the future NDVI over forest areas, given past and future weather and surface reflectance. Giving future weather predictions as an input to the model, we are going for a 'guided-prediction' approach where the aim is to exploit weather information from forecasting models in order to increase the predictive power of the model - similar to the EarthNet2021 Challenge. Models are fully data-driven, without feature engineering and trained on spatio-temporal datacubes which can be seen as stacked satellite imagery for a specific geo-location and a timestep of past Sentinel 2 surface reflectance, past (observed) and future (forecasted) climate reanalysis, time-invariant information from a digital elevation model, and land cover map. The data pre-processing step includes implementing a customized dataset for drought impact prediction task, and a customized data sampler in order to be able to sample data (scenes) both spatially and temporally. Additional data operations include  aggregation of the weather data, normalization, and data imputation both on the image-level and missing-day level. For the prediction task, we used Convolutional Long-Short Term Memory models. In the temporal domain, models are trained on the period between 2015-2018, and be validated between 05-2019 and 09-2019. For the test period summer months of 2020 and 2021 will be used. However, in the spatial domain, for the sake of testing the generalizability of the model, different regions were used for train, validate and test processes. In order to asses the models performance on the temporal domain, tests with different training and testing window sizes are used. As for evaluating the performance of the model, Mean Squared Error was used. The project will lay the basis for an early warning platform to enable periodically updated near-term drought-impact forecasts.

How to cite: Durukan, E. D., Ledain, S., Brunschwiler, T., Tuia, D., Günther, M., and Stocker, B.: Forest Drought Impact Prediction based on Spatio-temporal Satellite Imagery and Weather Forecasts -- A Spatio-Temporal Approach using Convolutional LSTM Models, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12437, https://doi.org/10.5194/egusphere-egu23-12437, 2023.

EGU23-12672 | ECS | Orals | HS4.2

Forest drought impact prediction based on satellite imagery and weather forecasts - a spatially distributed approach using a recurrent deep neural network 

Sélène Ledain, Emine Didem Durukan, Thomas Brunschwiler, Manuel Günther, Devis Tuia, and Benjamin Stocker

The increased frequency of temperature anomalies and drought events in Switzerland has major ecological implications, with impacts over whole ecosystems. In Swiss forests, the 2018 drought, which was the most severe drought event recorded led to widespread leaf discoloration, premature leaf-shedding, and tree mortality. While work has been carried out to analyse droughts a posteriori, the prediction of potential drought impacts would make it possible to anticipate ecological responses, manage resources and mitigate damage. Current approaches to drought prediction include mechanistic models. However, such models are often limited by data accessibility and resolution to effectively describe local effects. Deep learning models trained on remote sensing and atmospheric data have been applied to drought fore- casting, but face the “black box” issue and often discard domain knowledge on drought mechanisms.

In this work, we propose a spatio-temporal deep learning method for drought forecasting in forests based on Sentinel-2 satellite imagery and weather variables, with the inclusion of topographic and environmental information. Drought is monitored by a proxy of early leaf wilting, using the normalized difference vegetation index (NDVI) that can be derived from Sentinel-2 bands. By predicting future NDVI values of pixels, we predict the potential occurrence of droughts in the short term.

Hand-crafted features based on environmental data are used as input for the model, such as high-resolution topographic features which can capture micro-climatic effects, as well as soil- vegetation-climate relationships. Environmental information is provided to the model through data on soil and forest properties. This explicit modelling with topographic and environmental features increases the model interpretability, compared to models performing feature extraction and based only on image bands.

A sequence model with long short-term memory (LSTM) cells was selected for its capacity to learn long-term dependencies as required in our application. We implement a pipeline to process spatiotemporal data, including data aggregation, normalization, missing data impu- tation and sample pixel timeseries for the prediction task. The model is trained and tested on data between 2015 and 2021, using the mean squared error to evaluate performances. A month (3 timesteps at Sentinel-2 acquisition rate) is forecasted given the past 3 months (9 timesteps) at a specific location. We opt for a “guided prediction” approach where the model has also access to weather forecasts for the future timesteps. The model is trained and tested in different regions in Switzerland to assess its generalization in space. A feature importance study was performed to identify key factors for drought forecasting and further improve the model.

This research combines drought predictors known to have an impact in ecology and hydrology with a guided deep learning model. We offer a method for dealing with heterogeneous spatiotemporal data and train an interpretable model for forecasting potential forest drought.

How to cite: Ledain, S., Durukan, E. D., Brunschwiler, T., Günther, M., Tuia, D., and Stocker, B.: Forest drought impact prediction based on satellite imagery and weather forecasts - a spatially distributed approach using a recurrent deep neural network, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12672, https://doi.org/10.5194/egusphere-egu23-12672, 2023.

EGU23-12961 | ECS | Posters virtual | HS4.2

A pan-European analysis of drought events and impacts 

Martina Merlo, Matteo Giuliani, Yiheng Du, Ilias Pechlivanidis, and Andrea Castelletti

A drought is a slowly developing natural phenomenon that can occur in all climatic zones, and propagates through the entire hydrological cycle with long-term economic and environmental impacts. Climate change has made drought one of the greatest natural hazards in Europe, affecting large areas and populations. Different definitions of drought exist, i.e. meteorological, hydrological, and agricultural droughts, which vary according to the time horizon considered and differ in the variable used to define them. Just as there is no single definition of drought, there is no single index that accounts for all the types of droughts. As a consequence, capturing the evolution of drought dynamics and associated impacts across different temporal and spatial scales remains a critical challenge.

In this work, we analyze existing standardized drought indexes in terms of their ability in detecting drought events at the pan-European scale using data from HydroGFD2.0 reanalysis and E-HYPE hydrological model simulations over the time period 1993-2018. We firstly compare the frequency and mean duration of drought events detected by different indexes to identify the river basins mostly affected by droughts and to assess similarities and differences in the information provided by different indexes. We then compare them with the drought impacts recorded in the Geocoded Disasters (GDIS) dataset to examine agreements and discrepancies between index-detected droughts and impact data.

Preliminary results show that different indexes generally agree in pointing out that Southern England, Northern France, and Northern Italy are the regions that experienced the highest number of drought events, whereas other regions, such as Southern Spain, experienced intense droughts events, which are not consistently indicated by all indexes. In terms of drought duration, the areas affected by the longest droughts are instead the Baltic Sea region and Normandy. Clustering the 35408 European basins according to dominant hydrologic processes reveals that the variables mainly controlling the drought process vary across clusters and depends on the characteristics of each cluster. While substantial agreement exists between observed impact and detected drought, several areas without GDIS records show critical index values. Such asymmetry can be explained by incomplete reporting in GDIS but also due to some non-physical hydrometeorological factors influencing drought dynamics, such as controlled water infrastructure, that are not adequately captured by standardized indexes. These findings suggest the need of adjusting the formulation of drought indexes to the specific characteristics of different river basins in order to improve drought detection and management.

How to cite: Merlo, M., Giuliani, M., Du, Y., Pechlivanidis, I., and Castelletti, A.: A pan-European analysis of drought events and impacts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12961, https://doi.org/10.5194/egusphere-egu23-12961, 2023.

In Flanders, a cumulative precipitation deficit of no less than 330 mm was calculated during the growing season of 2022 (April - September) (Soil Service of Belgium). This high precipitation deficit reflects the importance and need of additional water supply to meet the water demand and the yield potential of the crop. However, this additional water must be administered as efficiently as possible to avoid water waste, while maximizing yields. For decades, the Soil Service of Belgium already offers paid irrigation advice based on simulations with a soil water balance model calibrated with manual soil samples, and weather data, while considering weather predictions separately. With the rise of affordable, autonomous sensors and IoT (Internet-of-Things) technology, it is possible to monitor the soil moisture in a field online and in real time. The use of these sensors offers opportunities such as data accessibility, model calibration, and optimization of irrigation advice.

Soil moisture model simulations and forecasts alone may be less accurate than in situ soil moisture measurements. However, soil moisture forecasts make it possible to anticipate drought or precipitation forecasts, which makes it easier to plan irrigation in advance. Sensor data alone fall short in this respect, as sensors only provide data on the previous and current soil moisture content, but do not provide information on future soil moisture development. Both approaches can be combined by calibrating the model with sensor data via inverse modelling. In this study, DREAM is used as inverse modelling approach to estimate model parameters, including soil and crop growth parameters, as well as their uncertainty. These parameter distributions result in soil moisture simulations, and, when inserting weather forecasts, predictions, along with their uncertainty. The uncertainty of the calibrated model simulations can be used to determine the probability of the soil moisture dropping below the critical water stress threshold.

When this combined approach is compared to the irrigation advice based on a model alone, the soil moisture is simulated and predicted more accurately, resulting in a more efficient water application, while the crop experiences less stress. In the dry growing season of 2022, for example, a celery trial in Flanders (Research Station for Vegetable Production) saved about 45 mm (21%) of water without sacrificing crop quality and yield. In addition to irrigation yield responses, the approach is also validated in light of parameter estimation, and soil moisture simulations, comparing simulated and measured soil moisture content.

How to cite: Hendrickx, M., Diels, J., Vanderborght, J., and Janssens, P.: Simulating and predicting soil water content by combining soil water balance calculations, weather forecasts and soil sensors with inverse modelling for optimal irrigation advice: A case study in Flanders, 2022, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13028, https://doi.org/10.5194/egusphere-egu23-13028, 2023.

EGU23-13266 | ECS | Orals | HS4.2

Water scarcity and climate change impacts in the Eastern Italian Alps: A case study of the Adige river basin 

Susen Shrestha, Mattia Zaramella, Giacomo Bertoldi, Marco Borga, Stefano Terzi, and Pittore Massimiliano

Over the past decade, the Adige river basin in the Eastern Italian Alps has experienced water scarcity during early spring and late summer, due to a combination of decreased snowmelt, less precipitation, and increasing water demand. This condition has caused tension and disputes between upstream and downstream water users, particularly between hydropower companies in the upstream region (Trentino/South Tyrol) and agricultural users in the downstream areas (Veneto region). The potential for water scarcity impacts to intensify and expand in the future remains a major concern with climate change leading to more frequent warm and snow droughts in the region. Informing the region's administration, institutions, communities, and businesses to manage water scarcity conditions, is essential to prepare and mitigate the potential future impacts. This work aims to explore decision-making options in drought conditions in the Adige river basin, along with the potential impacts of climate change, by exploiting hydrological models for the river basins and for the major reclamation consortium in the area. The study will focus on years with severe drought, such as the 2022 drought period, using simplified decision options and examining how the decisions to meet the water needs of hydropower agencies in the upstream part of the Adige river basin could impact agricultural water use in the downstream part. The analysis will then be repeated in similar conditions, but with the added element of climate change forcing and reduced glacier volumes in the Alps. This study will identify those needs that might not be fulfilled in certain drought scenarios providing valuable insights for decision-makers and supporting the development of effective strategies to prepare and better manage future water scarcity conditions in the region.

How to cite: Shrestha, S., Zaramella, M., Bertoldi, G., Borga, M., Terzi, S., and Massimiliano, P.: Water scarcity and climate change impacts in the Eastern Italian Alps: A case study of the Adige river basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13266, https://doi.org/10.5194/egusphere-egu23-13266, 2023.

EGU23-13769 | Orals | HS4.2

Understanding drought indicator-to-impact relationships to improve drought monitoring and early warning: Thailand as a case study 

Maliko Tanguy, Lucy Barker, Michael Eastman, Chaiwat Ekkawatpanit, Daniel Goodwin, Jamie Hannaford, Ian Holman, Eugene Magee, Liwa Pardthaisong, Simon Parry, Dolores Rey, and Supattra Visessri

Thailand has already been experiencing an increase in severity and duration of its droughts as a consequence of the changing climate. Developing a reliable drought monitoring and early warning system (DMEWS) is an integral part of strengthening a country’s resilience to droughts. However, for DMEWS to be useful for stakeholders, the indicators they monitor should be translatable to potential drought impacts on the ground and, ideally, inform mitigating actions. Here, we analyse these drought indicator-to-impact relationships in Thailand, using a novel combination of correlation analysis and random forest modelling. In the correlation analysis, we study the link between meteorological drought indicators and high-resolution remote sensing vegetation indices used as proxies for general crop health and forest growth. Our analysis shows that these links vary greatly depending on land use (cropland vs. forest), season (wet vs. dry) and region (north vs. south). The random forest models built to estimate regional crop productivity provided a more in-depth analysis of the crop- and region-specific value of different drought indicators. The results highlighted seasonal patterns of drought vulnerability for individual crops, usually linked to their growing season, although the effect was somewhat masked in irrigated regions (North). This new high-resolution knowledge of crop- and region-specific indicator-to-impact links can be used as the basis of targeted mitigation actions in an improved DMEWS in Thailand. In addition, the framework developed here can be applied elsewhere in the Southeast Asia region, as well as other drought-vulnerable areas internationally, in particular those that are data sparse.  

How to cite: Tanguy, M., Barker, L., Eastman, M., Ekkawatpanit, C., Goodwin, D., Hannaford, J., Holman, I., Magee, E., Pardthaisong, L., Parry, S., Rey, D., and Visessri, S.: Understanding drought indicator-to-impact relationships to improve drought monitoring and early warning: Thailand as a case study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13769, https://doi.org/10.5194/egusphere-egu23-13769, 2023.

EGU23-13796 | ECS | Orals | HS4.2

Innovative system for monitoring and forecasting hydrological dynamics in semi-arid Ceará, NE-Brazil 

Klaus Vormoor, Erwin Rottler, Martin Schüttig, Axel Bronstert, Ályson Estácio, Renan Rocha, Valdenor Nilo de Carvalho Junior, Clecia Guimarães, and Eduardo Martins

The state of Ceará is located in the semi-arid northeast of Brazil and is characterized by strong inter- and intra-annual variability in precipitation. Thus, droughts and an uncertain water supply threaten the people in one of the most densely populated dryland regions in the world. To store and supply water during dry periods, tens of thousands of dams of various sizes have been built, especially since the end of the 19th century. Only 155 of these reservoirs are systematically monitored and managed. For the remaining reservoirs, there is no systematic monitoring and coordinated water resource management so far. In addition to a comprehensive monitoring, it requires an adapted hydrological modeling and forecasting tool to best manage water resources in Ceará and to reduce the impact of future droughts.

In this project, an innovative system for monitoring and forecasting hydrological dynamics in Ceará was developed in collaboration with the Federal Agency of Hydrology and Meteorology (FUNCEME). This system is based on an integrated use of climate modeling, process-based hydrological modeling, remote sensing, and existing databases. Specifically, the following three complementary products have been developed:

  • Satellite-based monitoring of stored water volume in reservoirs: Weekly monitoring of water masks of > 30,000 reservoirs is performed by evaluating and classifying Sentinel-1 scenes. The stored water volume can then be inferred from the area-volume relationship derived using high-resolution TanDEM-X CoSSC DEMs for these reservoirs during explicit drought years (i.e. when reservoirs were empty).
  • Modeling and seasonal forecasting of hydrological dynamics using WASA-SED: The process-based hydrological model WASA-SED, developed for semi-arid areas, was adapted and calibrated for the state area of Ceará. Information from satellite-based reservoir monitoring is dynamically assimilated in the simulations. Based on an ensemble of ECHAM4.6 climate simulations (updated monthly), the adapted hydrological model is used to generate seasonal forecasts with six months lead time on streamflow and reservoir filling conditions.
  • Web-based visualization of monitoring and forecast results: The results of satellite-based monitoring and dynamic hydrological modeling and forecasting are centrally managed in a database and can be retrieved from there by a web application. The corresponding information is visualized online as maps and graphics and made available to different user groups and decision makers.

How to cite: Vormoor, K., Rottler, E., Schüttig, M., Bronstert, A., Estácio, Á., Rocha, R., de Carvalho Junior, V. N., Guimarães, C., and Martins, E.: Innovative system for monitoring and forecasting hydrological dynamics in semi-arid Ceará, NE-Brazil, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13796, https://doi.org/10.5194/egusphere-egu23-13796, 2023.

EGU23-14106 | ECS | Orals | HS4.2

Reinvestigating Groundwater Drought Using In Situ and GRACE Data 

Jinyuan Wang, Kaniska Mallick, Natascha Kuhlmann, Patrick Matgen, Stéphane Bordas, and Laurent Pfister

Groundwater plays a unique role in the terrestrial water cycle. It is one of the prime sources of water during periods of severe drought. Depletion of groundwater reaching certain thresholds substantially lead to the degradation of water quality. Among all the hydrological variables, it has a characteristics behavior due to its lagged response to precipitation, evapotranspiration, soil water content variations, and surface water variation due to anthropogenic activities. Groundwater drought has been studied in various regions in the world, which revealed significant correlation among hydrological factors, including precipitation, soil water content, and various terrestrial water storage. Terrestrial water storage variables used for monitoring groundwater drought are total water storage change (TWSC) and groundwater storage change (GWSC). While the TWSC can be estimated from the Gravity Recovery and Climate Experiment (GRACE), GWSC can be estimated from in situ groundwater level within the network of well records using relevant hydrogeological information. Previous studies showed the ability and reliability of GRACE data in groundwater monitoring in the regions under extreme drought. Hydrological model outputs, e.g., the Global Land Data Assimilation System (GLDAS), have been used to derive groundwater drought indicators that reached certain reliability. The present study conducts a systematic investigation on the ability of the GRACE data to reflect the groundwater drought conditions, by comparing in situ groundwater data, TWSC estimated from GRACE (TWSCGRACE), GWSC estimated from the conjuncture of GRACE and GLDAS (GWSCGLDAS), Standardized Precipitation Index (SPI), and satellite land surface temperature. Further, by estimating the vadose zone water storage change (VZWC) using TWSC and in situ groundwater data (VZWCin situ), as well as using TWSC and GLDAS (VZWCGLDAS), we investigate the ability of GRACE and in situ data to monitor the vadose zone water content. Our results show that TWSCGRACE correlates better with in situ groundwater data as compared to GWSCGLDAS in all three study areas located in India, Australia, and Belgium, which are some of the hotspots suffering from intensive flash drought in the recent decade. TWSCGRACE shows stronger correlation and better consistency with SPI and land surface temperature as compared to in situ groundwater data. VZWCin situ correlates well with VZWCGLDAS but is limited to data availability from the well network. Results from GWSCGLDAS and VZWCGLDAS show that hydrological model outputs can serve as replacement or supplement to estimate GWSC and VZWC when in situ groundwater data is significantly missing.

How to cite: Wang, J., Mallick, K., Kuhlmann, N., Matgen, P., Bordas, S., and Pfister, L.: Reinvestigating Groundwater Drought Using In Situ and GRACE Data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14106, https://doi.org/10.5194/egusphere-egu23-14106, 2023.

EGU23-14395 | ECS | Posters on site | HS4.2

Deciphering the declining runoff in the Thaya river basin 

Petr Pavlik, Milan Fischer, Adam Vizina, Juraj Parajka, Martha Anderson, Petr Štěpánek, Martin Hanel, Petr Janál, Song Feng, Evžen Zeman, and Miroslav Trnka

This study aims at understanding the changes in the water balance in the Thaya river basin over the past 40 years. The Thaya River is one of the tributaries to the Danube basin with a catchment area of more than 13 000 km2. A number of hydroclimatic variables related to runoff were examined by a trend analysis based on Theil-Sen regression and Mann-Kendall tests for the two periods 1981–2020 and 2001–2020. The latter period was selected because it allows analysis of several relevant variables derived from the Moderate Resolution Imaging Spectroradiometer (MODIS). These variables ecompass snow cover, leaf area index and land surface temperature based actual evapotranspiration.

With our analyses we confirm previously found increasing trends in air temperature, ETo, and no trends in precipitation. We also found a consistent increase of ET during spring months and indication of summer decrease (not statistically significant). This change was associated with a significant increase of spring vegetation development followed by summer stagnation. We identified a significant trend decline in runoff, mainly in the upland sourcing areas. The correlation analysis reveals a different behavior along the elevation gradient, with evapotranspiration in the uplands being limited by energy and in the lowlands by water, especially in spring. During summer, however, the entire basin is often water-limited, with a more pronounced limitation in the lowlands. According to attribution analysis for the past 20 years, the significantly decreasing runoff is driven primarily by non-significantly decreasing precipitation, significantly increasing air temperature and vapor pressure deficit. Global radiation and wind speed affect the runoff only to a very limited extent. We conclude that complex adaptation measures reflecting the site specificity and elevation gradient are needed to sustain the water dependent sectors operating in the region facing increasing aridity. 




How to cite: Pavlik, P., Fischer, M., Vizina, A., Parajka, J., Anderson, M., Štěpánek, P., Hanel, M., Janál, P., Feng, S., Zeman, E., and Trnka, M.: Deciphering the declining runoff in the Thaya river basin, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14395, https://doi.org/10.5194/egusphere-egu23-14395, 2023.

EGU23-14822 | Orals | HS4.2

Spatio-temporal assessment of groundwater drought risk in the Souss-Massa aquifer: Impacts of climate variability and anthropogenic activity 

Soumia Gouahi, Mohammed Hssaisoune, Mohammed Nehmadou, Brahim Bouaakkaz, Hicham Boudhair, and Lhoussaine Bouchaou

Although the several studies carried out in the Souss Massa region, in terms of water resources, the assessment of the drought is still understudied, particularly groundwater drought which remains a gap in the previous studies. In this work, meteorological drought is investigated by using the standardized precipitation index (SPI) to shed light on its impact on groundwater drought occurrence. Thereafter, a combination of reliability analysis and standardized water level index (SWI) is used for groundwater risk modeling. Reliability analysis accounts for the safety and the failure of a system regarding loads, which take into account the external effects (withdrawals and recharge), and resistance which accounts for the system's capacity, thereafter values of Groundwater Drought Risk (GDR) and Environmental Hazard Index (EHI) are generated and then spatially distributed to assess groundwater risk for mild, moderate, severe, and extreme droughts for the whole region of Souss-Massa. Results showed a wavering between short dry and wet periods based on SPI, and demonstrated a weak correlation between the SPI and the SWI, hence the upward trend in the SWI is explained by the anthropogenic overexploitation of the aquifer. Furthermore, groundwater drought risk (GDR) values are low in the upper Souss and increase in the middle part and in the Massa basin, where significant effects are potentially expected. Based on the EHI results, it is confirmed that the Massa basin and the middle Souss are susceptible to groundwater drought and its environmental impact and need immediate intervention to properly manage the groundwater resources. This model could be helpful for the policymakers for better planning of water supply by providing useful information about the expected frequency and severity of water shortage in the studied area.

Keywords:
Groundwater drought, Reliability analysis, meteorological drought, anthropogenic activities, Souss Massa basin.

 

How to cite: Gouahi, S., Hssaisoune, M., Nehmadou, M., Bouaakkaz, B., Boudhair, H., and Bouchaou, L.: Spatio-temporal assessment of groundwater drought risk in the Souss-Massa aquifer: Impacts of climate variability and anthropogenic activity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14822, https://doi.org/10.5194/egusphere-egu23-14822, 2023.

EGU23-14961 | Orals | HS4.2

Multi-temporal drought rarity curves - a yearly classification of meteorological drought severity in France 

Juliette Blanchet, Baptiste Ainési, and Jean-Dominique Creutin

Droughts are recurrent phenomena, impacting eco- and socio-systems at varied temporal and spatial scales. Their impact depends on both the severity of the antecedent meteorological conditions and the recovery dynamics of the impacted systems. The drought severity analysis proposed in this study accounts for the ”memory effect” of rainfall accumulation by considering across time the rarity of antecedent precipitation at multi-temporal scales. It applies to rainfall accumulation over a single area. In this presentation, we define the yearly curve of multi-temporal drought rarity by the non exceedance probability of the smallest rainfall accumulations observed that year over a range of accumulation durations. Each rarity curve is thus defined by as many values as the number of durations considered. We apply this concept to droughts in France from 1950 to 2022, with accumulation durations varying from 4 weeks to 260 weeks. We show that the rarity curves are easy tools to summarize how droughts build and persists across time and temporal scales. We use an automatic classification of the curves to discriminate years associated to short- to long-term droughts (basically from half a year to five years). Although the concept is here used for rainfall over a single area, France, it could be applied as well to a set of areas and/or to other drought variables such as discharge or soil moisture. 

How to cite: Blanchet, J., Ainési, B., and Creutin, J.-D.: Multi-temporal drought rarity curves - a yearly classification of meteorological drought severity in France, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14961, https://doi.org/10.5194/egusphere-egu23-14961, 2023.

EGU23-15473 | ECS | Posters on site | HS4.2

The implementation of the GEOframe system in the Po River District for the hydrological modelling and water budget quantification 

Gaia Roati, Giuseppe Formetta, Marco Brian, Silvano Pecora, Silvia Franceschi, Riccardo Rigon, and Herve Stevenin

As observed in the last years, flood and drought events are getting more likely to happen due to climate change and can cause significant environmental, social and economic damages.

For this reason, already in 2021, the Po River District Authority (AdbPo) undertook the implementation of the GEOframe modelling system on the whole territory of the district in accordance with the GCU-M (Gruppo di Coordinamento Unificato-Magre) to update the existing numerical modelling for water resource management and with the objective of producing a better quantification and forecast of the spatial and temporal water availability across the entire river basin and, finally, to improve the planning activity of the

Authority.

The GEOframe system was developed by a scientific international community, led by the University of Trento, and is a semi-distributed conceptual model, with high modularity and flexibility, completely open-source.

The implementation of GEOframe in the Po River District has begun in the Valle d’Aosta Region, the most upstream part of the district.

After an initial part of meteorological data collection, validation, spatial interpolation, and geomorphological analysis, a first running of the model to assess all the components of the hydrological balance (evapotranspiration, snow accumulation, water storage and discharge) was carried out.

Consequently, the calibration phase started, consisting of the research of the values of the characteristic parameters of the model which fit the discharge evolution recorded in the hydrometers of the region in the best possible way, comparing the modelled discharge trend with the measured one.

The calibration, based on KGE method, has been executed in 10 hydrometers in Valle d’Aosta across a 4 years period. The results were encouraging, with an objective function of 0.76 at the closure point of the region.

The same process is now in progress in Piemonte, one of the biggest regions of Italy, which contains more than 100 hydrometers. The resulting objective functions are in general rather high and will be presented in this work.

At the same time, thanks to the geomorphological analysis, most part of Po River District (up to Pontelagoscuro (FE)), which totally occupies more than 42,000 km2, has been divided into subbasins, the hydrological reference units where the simulation process takes place, and this dataset will be open-source and shared with the scientific community.

On the other hand, the interpolation and spatialization of the meteorological data will be carried out according to the 1 km2 European Environmental Agency reference grid.

In conclusion, in this initial stage of implementation of the model and calibration of its parameters, it was possible to assess the capacity of GEOframe to simulate not only the water discharge but also the other components of the water cycle, namely the evapotranspiration, the water storage and the snow accumulation. Furtheremore, implementing GEOframe in a mountainous area underlines the importance and the influence that snow and glaciers, especially in a higher temperature scenario due to climate change, can have on water availability and, therefore, a better modelling component of these elements will be implemented in the future developments of GEOframe.

How to cite: Roati, G., Formetta, G., Brian, M., Pecora, S., Franceschi, S., Rigon, R., and Stevenin, H.: The implementation of the GEOframe system in the Po River District for the hydrological modelling and water budget quantification, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15473, https://doi.org/10.5194/egusphere-egu23-15473, 2023.

EGU23-16413 | ECS | Posters on site | HS4.2

Comparing the performance of process-based models for drought simulation in Scotland 

Shaini Naha, Kit Macleod, Zisis Gagkas, and Miriam Glendell

Scotland is increasingly vulnerable to periods of dry weather, impacting water users and the natural environment. In 2022, large parts of Scotland have experienced water scarcity, resulting in Scotland Environmental Protection Act (SEPA) suspending water abstractions for abstraction licence holders in some Scottish catchments. To understand and manage these water scarcity events in Scotland, we need to monitor and model the drought processes. This research is a part of a Scottish Government funded project ‘Understanding the vulnerabilities of Scotland’s water resources to drought’ which has been co-constructed with a range of national level stakeholders and aims to understand what the specific impacts of droughts are and what are the vulnerabilities that may apply to Scotland under future change. This includes the understanding of the spatial variability and characteristics of future hydrological drought events and short-term forecasting of drought duration to inform adaptive catchment management, while considering water resources requirements of different user sectors. As a first step towards constructing a national short-term drought forecasting framework, we have reviewed the state-of-the art hydrological modelling approaches currently applied in the UK. Our review suggests a lumped conceptual model, GR6J and a distributed hydrological response unit-based model, HYPE, are the most appropriate hydrological models for both simulating and short-term forecasting of droughts, based on the following criteria: openly available model code, proven ability at simulating and forecasting low flows, and widely used and supported model. In next steps, we will design a common modelling framework for drought simulation and forecasting in Scotland. Using both HYPE and GR6J, we will set up and test both models in a medium size long-term monitoring test catchment in Tarland in northeast Scotland (~70km2) where we have good process understanding and recent hydro climatological datasets. Comparison of the model performances of HYPE and GR6J will guide us to take a decision on which model to move forward with for upscaling in Scotland. Machine learning approaches for low-flow forecasting using long-short-memory networks will also be explored in developing a multi-model drought forecasting ensemble.  

Keywords: Drought, water scarcity, modelling, HYPE, GR6J, forecasting 

How to cite: Naha, S., Macleod, K., Gagkas, Z., and Glendell, M.: Comparing the performance of process-based models for drought simulation in Scotland, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16413, https://doi.org/10.5194/egusphere-egu23-16413, 2023.

EGU23-16778 | Orals | HS4.2

Understanding the hydrological drought processes in the Paraná River Basin. 

Luz Adriana Cuartas, Thais Fujita, Juliana Andrade Campos, Ana Paula Cunha, Cintia Berttachi Uvo, Elisangela Broedel, and José Antonio Marengo

Brazil has endured the worst droughts in recorded history over the last decade, resulting in severe socioeconomic and environmental impacts. The country relies heavily on water resources, with 77.7% of water consumed for agriculture (irrigation and livestock), 9.7% for industry, and 11.4% for human supply. Hydropower plants generate about 64% of all electricity consumed. One of the most impacted basins was the Paraná River basin It concentrates a third of the Brazilian population in urban centres such as São Paulo, the largest city in Latin America, thus it is the river basin with the greatest demand in the country. This basin is also the most important in hydropower generation, by the highest install capacity for hydropower; 57 reservoirs in the main steam and its tributaries (Grande, Paranaíba, Tietê, Paranapanema and Iguaçu Rivers), with Itaipu having the largest installed capacity (14,000 MW). This study aimed to advance the state of knowledge regarding hydrological drought patterns in the Paraná River Basin for improved monitoring and forecasting.

Droughts, like all hydrometeorological processes, are multivariate processes, that is, they are the result of the interaction of multiple hydrometeorological, climatic, and anthropogenic variables, among others. Therefore, several studies have shown the need to consider a multivariate approach to analysis and modelling drought events, which allows a better evaluation of the characteristics and conditions of its.

In this study we applied: i) well know univariate drought index: SPI, SPEI and SSFI; ii) a multivariate index, obtained through the Copulas Theory and; iii) potential soil moisture conditions obtained through the Normalized Terrain Model HAND, to understand and characterized hydrological droughts in the Paraná River Basin and Subbasins. We used rainfall data from CHIRPS, streamflow data obtained from the Brazilian National Electrical System Operator (ONS) and the National Water and Sanitation Agency (ANA), the SPEI global drought monitor dataset and HAND MERIT dataset (90 m spatial resolution).

The results show that the hydrological droughts in the last decade of 1981–2021, were the most severe and intense. Among the indices, SPEI, SSFI and the multivariate index, presented the strongest evidence, at time scales of 12, 24, 36 and 48 months. The multivariate index together with HAND information allow us to understand better the process of developing, duration, and recovery of drought events.

How to cite: Cuartas, L. A., Fujita, T., Andrade Campos, J., Cunha, A. P., Berttachi Uvo, C., Broedel, E., and Marengo, J. A.: Understanding the hydrological drought processes in the Paraná River Basin., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16778, https://doi.org/10.5194/egusphere-egu23-16778, 2023.

EGU23-16949 | Orals | HS4.2

Effective hydrological drought monitoring depending on the catchment's hydrological regime 

Oscar Manuel Baez Villanueva and Mauricio Zambrano-Bigiarini

There is an expected increase in the occurrence and severity of hydrometeorological extremes in many regions worldwide. Current research indicates that despite a positive trend in reducing drought impacts, most regions still need to adapt their monitoring practices to cope with projected drought events effectively. On the other hand, we still need a clear understanding of how a changing climate can modify the hydrological regime of catchments in the future. Therefore, it is essential to understand which drought indicators are relevant to monitoring catchments with different hydrological regimes.

Therefore, this study aims to elucidate which drought indices are required to effectively monitor hydrological drought depending on the catchment’s hydrological regime, using  100 near-natural Chilean catchments with contrasting climatic conditions and hydrological regimes as a case study. For this purpose, different drought indices were computed at different temporal scales: SPI and SPEI at 3, 6, 9, 12, and 24 months; the Empirical Standardised Soil Moisture Index (ESSMI) at 3, 6, and 12 months; and a standardised snow water equivalent index (SSWEI) at 3 and 6 months. State-of-the-art gridded datasets used for computing the drought indices were: CR2MET v2.5 (a Chilean dataset based on ERA5) for precipitation and potential evapotranspiration; ERA5, ERA5-Land, and SMAP (L3 and L4) for soil moisture; and ERA5 and ERA5-Land for snow water equivalent. These indices were evaluated against the Standardised Streamflow Index (SSI) to select indices that are able to effectively monitor hydrological droughts, considering different hydrological regimes. A cross-correlation analysis and an event coincidence were used to assess which index had the highest correlation with SSI. Results showed that the indices and temporal scales used to effectively monitor hydrological droughts changed according to the catchment's hydrological regime. The results of the present work are pivotal for water managers as they provide insights on how the hydrological regime of the catchments should be considered in drought monitoring.

How to cite: Baez Villanueva, O. M. and Zambrano-Bigiarini, M.: Effective hydrological drought monitoring depending on the catchment's hydrological regime, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16949, https://doi.org/10.5194/egusphere-egu23-16949, 2023.

EGU23-17222 | Posters on site | HS4.2

Risks of Future Droughts and their Impacts on Scottish Private Water Supplies 

Sayali Pawar, Sarah Halliday, Paola Ovando, and Miriam Glendell

In recent years, Scotland has been experiencing lower-than-average rainfall in the spring and summer seasons leading to water scarcity in many parts of the country, especially during the summer months. Climate change is likely to exacerbate these dry conditions even more in the future, presenting significant risks to water resources management. Businesses and households, especially those relying on Private Water Supplies (PWS) in rural areas, such as boreholes and springs, have already observed noticeable changes in the quantity and quality of water during the dry periods. Around 3.5% of the Scottish population relies on PWS which includes households, industries, agriculture, and the tourism industry. This study aims to project future drier periods from 2041-2080 across Scotland on a 1-km grid, using the Standardised Precipitation and Evapotranspiration Index and the observed meteorological data from 1981-2020 as the baseline. Results suggest low to extreme drought conditions in all 1-km cells , with increases in dry conditions likely to be highest in the eastern parts of Scotland, showing a distinct spatial variability in drought characteristics across Scotland. In future work, past and future drought occurrences will be linked with the water quality characteristics of PWS to understand the likely impact of future droughts on Scotland’s water security. The water quality dataset has been made available from the Drinking Water Quality Regulator for Scotland for the period 2006-2020 for nearly 6000 PWS locations. These PWS have been monitored twice a year on an average for their water quality. They span across 25 administrative areas in Scotland and represent roughly 27% of the total PWS in Scotland.  Water quality variables such as faecal coliforms, E.coli, iron, turbidity, lead, pH, colour, nitrate and phosphate will be included in the analysis to facilitate planning for effective, resilient water resources management and ensure access to clean water to maintain health and livelihoods. 

How to cite: Pawar, S., Halliday, S., Ovando, P., and Glendell, M.: Risks of Future Droughts and their Impacts on Scottish Private Water Supplies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17222, https://doi.org/10.5194/egusphere-egu23-17222, 2023.

EGU23-17360 | ECS | Orals | HS4.2

Drought characterization across Peru and Ecuador and its relationship with ocean-atmospheric indices 

Fiorella Vega-Jácome, Axel Bronstert, Carlos Antonio Fernandez-Palomino, and Waldo Lavado-Casimiro

Peru and Ecuador have suffered high economic losses because of extreme events (Floods and Droughts). The analysis of the meteorological droughts and their drivers is of paramount importance for water resources management and risk assessment in these countries. This study aims to characterize the spatiotemporal variability of droughts across Peru and Ecuador over the last four decades (1981-2020) and evaluate the relationship with the ocean-atmospheric circulation patterns. The Rain for Peru and Ecuador (RAIN4PE) gridded precipitation dataset was used to estimate the Standardized Precipitation Index (SPI) at time scales of 3 and 12 months to assess short and long-term droughts, respectively. Droughts were characterized by the number of events, duration, intensity, and severity, and the relationship was evaluated by computing the Pearson correlation to identify the leading oceanic-atmospheric indices: E (Eastern Pacific SST anomalies), C (Central Pacific SST anomalies), PDO (Pacific Decadal Oscillation), SOI (Southern Oscillation Index), MEI2 (Multivariate Enso Index), TPI (Tripole Index for the Interdecadal Pacific Oscillation), TNA (Tropical North Atlantic index), and TSA (Tropical Southern Atlantic Index).

The results show high spatiotemporal variability of drought characteristics with the high frequency of extreme droughts over the southern Peruvian Andes in Peru and the eastward of the Andes in Ecuador. The ranking of the extremeness of drought events based on the areal extent, duration, and intensity identified that three of the four more extreme events match ENSO conditions in Peru (1992/02, 1988/08, 1990/01) and Ecuador (1985/04, 1990/01, 1995/04). Finally, strong relationships between ocean-atmospheric indices and droughts in Peru and Ecuador were identified. Droughts in Peru evidence significant correlations with E, C, and TNA indices. Similarly, droughts in Ecuador show high correlations with E, C, PDO, TPI, and SOI indices. These results provide more insights into the characteristics of droughts and the possible drivers, information that is useful for water resource management decisions and can help as the basis for developing drought forecasts.

How to cite: Vega-Jácome, F., Bronstert, A., Fernandez-Palomino, C. A., and Lavado-Casimiro, W.: Drought characterization across Peru and Ecuador and its relationship with ocean-atmospheric indices, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17360, https://doi.org/10.5194/egusphere-egu23-17360, 2023.

EGU23-17410 | ECS | Orals | HS4.2

Environmental Vulnerability Assessment of Anthropogenic Droughts in Regulated Basins 

Ali Mehran and Amir AghaKouchak

Man-made local water supply infrastructure (in particular reservoirs) affects future water availability because it is built specifically to cope with climatic extremes. A system with multiple reservoirs, and therefore more local resilience, will be less vulnerable to climatic change and variability compared to a system with limited local capacity to cope with extremes. Therefore, different regions will see different water availability changes depending on their local infrastructure and capacity to cope with variability or adapt to change. The key questions that are studied in this proposal is the extent and intensity of environmental impact of the water stress. To address the question, this study proposes a multidisciplinary framework that integrates top-down (local inflows) and bottom-up (historical water use categories) factors to quantify the human induced water stress in each reservoir and the overall impact on the system’s resilience (water availability). The human induced water stress in regulated basins (with multiple reservoirs) is tracked by assessing the historical water use categories, which are later used to develop hypothetical water demand scenarios for near-future water stress assessment. Recent studies have shown that by changing water use policies, the system builds up resilience to cope with water stress. Our study explores reservoirs with multiple basins and tracks the policy changes impact on the system regarding the reservoirs orientation in the basin. Furthermore, this study tracks the environmental impact of the socioeconomic drought condition in regulated basins and highlights the changes due to water use policies.

How to cite: Mehran, A. and AghaKouchak, A.: Environmental Vulnerability Assessment of Anthropogenic Droughts in Regulated Basins, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17410, https://doi.org/10.5194/egusphere-egu23-17410, 2023.

EGU23-803 | ECS | PICO | HS4.5

Impact-based seasonal rainfall forecasting to trigger early action for droughts 

Tim Busker, Hans de Moel, Bart van den Hurk, and Jeroen C.J.H. Aerts

The Horn of Africa faces an ongoing multi-year drought due to five consecutive failed rainy seasons, a novel climatic event with unpreceded impacts. Over 50 million individuals in the region are expected to be highly food insecure by the end of 2022 and early 2023. The severity of these drought impacts call for the urgent upscaling and optimisation of early warning systems that trigger early actions. However, drought research focuses mainly on meteorological and hydrological forecasting, while early action is seldom addressed specifically. This leads to a gap between early warning and early action, which heavily reduces the effectiveness of these systems.

To address this gap, this study investigates the effectiveness of early action for droughts by using seasonal ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 system, predicting rainfall for the March-April-May (MAM) and October-November-December (OND) rainy seasons. We show that these seasonal rainfall forecasts reflect major on-the-ground impacts, which we identify from 9 years of monthly drought surveillance data from 21 counties in Kenya. Subsequently, we show that the SEAS5 drought forecasts with short lead times have substantial potential economic value (PEV) when used to trigger action before the OND season across the region (PEV max = 0.43). Increasing lead time to one or two months ahead of the season decreases PEV, but the benefits of early action still persist (PEV max = 0.2). Highest value for early action is found for the OND season in Kenya and Somalia, with excellent PEV max  of around 0.8 in Somalia. This indicates exceptional potential for early action to reduce impacts in this drought-prone country. The potential for early action is relatively low for the MAM season across the region, due to the season’s lower predictability. To illustrate the practical value of this research, we showcase how our methodology can be used by a pastoralist in the Kenyan drylands to effectively trigger livestock destocking ahead of a drought using SEAS5 forecasts.

These results are making headway to the development of concrete early action triggers for drought-prone regions, which are urgently needed to translate early warning to early action for droughts. It also emphasizes the need to expand historical datasets of drought impacts and early actions to support future research and policy development. Therefore, this work supports early decision-making and the development of early action protocols across the different countries in the Horn of Africa.

How to cite: Busker, T., de Moel, H., van den Hurk, B., and C.J.H. Aerts, J.: Impact-based seasonal rainfall forecasting to trigger early action for droughts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-803, https://doi.org/10.5194/egusphere-egu23-803, 2023.

EGU23-1569 | ECS | PICO | HS4.5

Improved flood-related decision-making in case of urban flash floods in a metropolitan city of India 

Akshay Singhal, Nibedita Samal, Sanjeev Jha, Louise Crochemore, and Isabelle Ruin

Occurrences of short-duration extreme rainfall have significantly increased over India, leading to frequent flash floods. Growing incidences of urban floods pose a challenge to rainfall forecasting agencies and disaster mitigation authorities. Advancement in the numerical weather prediction (NWP) models has resulted in improved skills of rainfall forecast for longer lead times. However, in recent years, there is a growing emphasis on developing an impact-based approach to communicate the probable impacts of the forecast and reduce the socio-economic losses. In this study, we aim to generate Impact-Based Forecasts (IBFs) in response to the growing incidences of urban flash floods in metropolitan cities of India such as Mumbai. IBFs will provide warnings about the potential impacts as well as communicate protective responses based on the category of impact, i.e., high, moderate, and low. To this end, an inventory of several urban floods over the city of Mumbai during the past decades is prepared, and the relationship between past extreme hazards and related impacts is investigated. Various available Quantitative Precipitation Forecasts (QPFs) from the European Centre for Medium-range Weather Forecasts (ECMWF), Japan Meteorological Agency (JMA), UK Met Office (UKMO), and National Centre for Medium-Range Weather Forecasting (NCMRWF) will be used in the study. Moreover, several observation datasets, such as from the Indian Meteorological Department (IMD), and from Integrated Multi-satellitE Retrievals for GPM (IMERG), will be used to validate the forecast information. The raw precipitation forecasts will be post-processed using a Bayesian joint probability (BJP) model-based rainfall post-processing approach to improve reliability and accuracy. With this study, decision-makers are expected to gain crucial insights regarding the probable impacts arising due to multiple realistic flash floods in Mumbai scenarios. The analysis is underway, and the results will be presented at the conference.

How to cite: Singhal, A., Samal, N., Jha, S., Crochemore, L., and Ruin, I.: Improved flood-related decision-making in case of urban flash floods in a metropolitan city of India, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1569, https://doi.org/10.5194/egusphere-egu23-1569, 2023.

EGU23-1894 | ECS | PICO | HS4.5

Using hydrodynamic flood modelling to support impact-based forecasting: a case study for Super-Typhoon Haiyan in the Philippines 

Asha Barendregt, Irene Benito Lazaro, Sanne Muis, Marc van den Homberg, and Aklilu Teklesadik

The Philippines is one of the countries most at risk to natural disasters. Amongst these disasters, typhoons and its associated landslides, storm surges and floods have caused the largest impact. Due to increased typhoon intensity, the country’s high population density in coastal areas and rising mean sea levels, the coastal flood risk in the Philippines is only expected to increase. The 510 initiative of the Netherlands Red Cross uses an Impact Based Forecasting (IBF) model based on machine learning to anticipate the impact of an incoming typhoon to set early action into motion. The IBF model underperformed in regions that are susceptible to storm surges. Most notably, it showed a poor performance for Super-Typhoon Haiyan (2013), which caused storm surges to reach up to over five meters high. The goal of this research is to evaluate how the IBF model can be improved by applying a fast hydrodynamic modelling approach that can forecast storm surges and coastal flooding associated with typhoons. First, the accuracy of the Global Tide and Surge Model (GTSM) in simulating Haiyan’s coastal water levels was examined. GTSM was forced with two different meteorological datasets: a gridded climate reanalysis dataset, ERA5, and observed track data combined with Holland’s parametric windfield model. Second, GTSM’s water levels were used as input for a hydrodynamic inundation model to simulate the flood depth and extent in San Pedro Bay, which was subjected to a widespread coastal flood during Haiyan. This was explored both with and without the inclusion of wave setup. Our results show that Haiyan’s flood cannot adequately be indicated using the ERA5 reanalysis dataset as meteorological forcing, as it underestimated Haiyan’s extreme wind speeds with ~60 m/s. By applying the Holland parametric wind field model, more accurate flood predictions and storm surge simulations can be made. Additionally, GTSM’s temporal resolution influences the models performance substantially. By increasing the 1 hour resolution to a 30 minute resolution the prediction of the overall flood extent improved by 16%. In future research we recommend examining the applicability of the Global Tide and Surge Model when using a higher spatial resolution to help better represent local processes. Additionally, exploring the accuracy for other typhoons that struck the Philippines and the applicability in operational setting using forecasted track data can contribute to further improving forecast-based early action systems in anticipating coastal flood occurrences.

 

 

How to cite: Barendregt, A., Benito Lazaro, I., Muis, S., van den Homberg, M., and Teklesadik, A.: Using hydrodynamic flood modelling to support impact-based forecasting: a case study for Super-Typhoon Haiyan in the Philippines, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1894, https://doi.org/10.5194/egusphere-egu23-1894, 2023.

EGU23-3876 | PICO | HS4.5

Rapid global hazard forecasting to support early action in data poor regions 

fredrik huthoff, kris van den berg, and carolien wegman

In March 2022, the United Nations set as a five year target that every place on Earth should be served by Early Warning Systems (EWS) for natural hazards. Such an EWS provides emergency alerts when a natural disaster is imminent and can support local or international (aid) organizations to take effective action early on. Places most vulnerable to natural disasters are often those where little local data and capacity is available to locally develop and operate such a system. As local EWS are not yet available everywhere, robust and reliable global approaches and collaboration initiatives are needed as initial and possible fallback solution.

We propose an innovative flood hazard mapping method based on globally available data that can spatially indicate oncoming floods and thereby inform on preparatory actions to take, such as required emergency stocks, needed shelter capacity, clearing of evacuation routes, and strategic protection of vulnerable people and assets. It instantaneously calculates forecasted flood extents based on global precipitation forecasts and the terrain’s natural drainage network. Its functioning is demonstrated for a selection of historical flood events and shows to good agreement with satellite-observed inundated areas, even where flood extents have gone beyond catchment boundaries. The method can easily be scaled-up to other areas around the world and can be expanded to issue automated warnings and provide impact estimates.

 

How to cite: huthoff, F., van den berg, K., and wegman, C.: Rapid global hazard forecasting to support early action in data poor regions, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3876, https://doi.org/10.5194/egusphere-egu23-3876, 2023.

EGU23-7070 | ECS | PICO | HS4.5

A Bayesian decision framework to support flood anticipatory actions in the urban data scarce city of Alexandria, Egypt 

Adele Young, Biswa Bhattacharya, and Chris Zevenbergen

Ensemble prediction systems (EPS) have been proposed to quantify uncertainty in forecasts, but to what extent they are useful for supporting flood anticipatory actions in an urban data-scarce city has not been fully explored. This research uses a Bayesian decision theory framework to support sequential decisions for reducing flood impacts. The predictive information is derived from probability distributions of flood depth simulated from a coupled ensemble Weather Research and Forecasting (WRF) and hydrodynamic MIKE urban inundation model. A damage function is used to value user actions and expected damages. Posterior probabilities are computed using prior probabilities and expected damages to select an action which minimises the expected losses.

The analysis is done for the Egyptian coastal city of Alexandria, which experiences extreme rainfall and pluvial flooding from winter storms resulting in disruptions, damages and loss of lives. The decision framework supports anticipatory actions which can be taken 12-72 hours before an event. These include cleaning drains, dispatching pump trucks to critical flood locations before events, and proactive pumping to increase storage.

Results suggest the use of a probabilistic decision framework can help support mitigating actions and reduce the occurrence of false and missed alarms. However, it depends on the combination of event intensity and probability (e.g. high intensity, low probability) the specific action and the loss function used. This approach helps decision-makers understand the value of probabilistic forecasts and models to trigger actions for improved decision support.

How to cite: Young, A., Bhattacharya, B., and Zevenbergen, C.: A Bayesian decision framework to support flood anticipatory actions in the urban data scarce city of Alexandria, Egypt, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7070, https://doi.org/10.5194/egusphere-egu23-7070, 2023.

EGU23-8724 | ECS | PICO | HS4.5

Flood Early Warning and Hazard Mapping for Railway and Dam Management 

Heather J. Murdock, Antje Otto, Anna Heidenreich, and Annegret H. Thieken

Floods in Europe regularly cause damage and disruption to communities and infrastructure. The extreme flood of July 2021 which affected Germany, Belgium, Luxemburg and the Netherlands provides an example of a flood event with a rapid onset time with corresponding short warning times and high uncertainty. This was a flood event with high velocities and volumes of debris. In addition to casualties there was extensive damage and disruption to infrastructure including roads, rail, water supply, and power transmission. Some negative impacts can be mitigated through the use of flood early warning systems (FEWS) and spatial planning using hazard maps. For such risk reducing measures, it is important to understand what challenges remain towards implementation. For example, challenges may differ between actors with different mandates and capacities.   

Infrastructure operators have an important role in flood risk management as the functioning of critical infrastructure (CI) is of high importance for society. CI in this context includes infrastructure, such as dams and railroad which we focus on, whose failure or impairment results in lasting disruptions to the overall system. Is it therefore possible that the prevention of damage and disruption to CI can reduce risk for society as a whole? Flood early warning information can support early action including moving mobile assets to higher ground, preventative closures, or protecting critical parts of a network with mobile flood barriers. Little empirical data exists, however, to address this question. It is therefore unclear to what extent flood risk management measures have become integrated into CI management by infrastructure operators.   

In this study we conduct expert interviews with CI operators in Germany and Belgium to investigate: (1) what FEWS information CI operators use, (2) how has it been applied during past flood events, particularly in 2021, (3) what information is shared with other stakeholders in an emergency context, (4) what flood hazard maps do operators currently use, and (5) how are flood hazard maps integrated into infrastructure planning. Our focus on dam and railway operators is due to the important role they play in water management and regional transportation, respectively. The interviews are transcribed and coded using MaxQDA to address the five points mentioned above. The empirical basis of this research can help to shed light on the effectiveness of available information to reduce risk in an emergency management context as well as for infrastructure planning. 

How to cite: Murdock, H. J., Otto, A., Heidenreich, A., and Thieken, A. H.: Flood Early Warning and Hazard Mapping for Railway and Dam Management, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8724, https://doi.org/10.5194/egusphere-egu23-8724, 2023.

EGU23-9345 | ECS | PICO | HS4.5

Lessons from Red Cross Red Crescent Anticipatory Action 

Arielle Tozier, Eduardo Castro Jr., Hafizur Rahaman, Dorothy Heinrich, Yolanda Clatworthy, and Luis Mundorega

The Red Cross Red Cresent is among the organizations with the longest and most extensive experience with forecast-based action. We present the findings of recently-published research based on interviews with 139 stakeholders involved in Red Cross Red Crescent (RCRC) AA programs in 18 countries. We find that the organizaitonal benefits of forecast-based ation include capacity building, more proactive operations, and expedited humanitarian response. Forecast-based action can also help to overcome common challenges in climate services by providing a framework and decision-making and resources for early action. Despite these benefits, AA practitioners struggle with challenges common to climate services, development, and humanitarian aid, including local project ownership, capacity and infrastructure, integration with existing systems, data availability, forecast uncertainty, and monitoring and evaluation. We conclude that forecast-based action systems can only be sustainble if they address these perennial challenges and focus on building capacity and ownership. Furthermore, donors can play a major role in facilitating these shifts by providing funding designed to support long-term multi-stakeholder processes.

How to cite: Tozier, A., Castro Jr., E., Rahaman, H., Heinrich, D., Clatworthy, Y., and Mundorega, L.: Lessons from Red Cross Red Crescent Anticipatory Action, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9345, https://doi.org/10.5194/egusphere-egu23-9345, 2023.

EGU23-9862 | ECS | PICO | HS4.5

Assessing ensemble flood forecasts with action-relevant scores to support flood preparedness actions: An application to the Global Flood Awareness System in Uganda. 

Douglas Mulangwa, Andrea Ficchi, Philip Nyenje, Jotham Sempewo, Linda Speight, Hannah Cloke, Shaun Harrigan, Benon Zaake, and Liz Stephens

This study investigates the importance of assessing ensemble flood forecasts with action-relevant scores to support flood preparedness actions by analyzing the discrepancies between traditional general scores that focus on the overall accuracy with other more specific flood event-based scores. Popular general scores such as the Kling-Gupta Efficiency (KGE) or the Continuous Ranked Probability Score (CRPS) are widely used in hydrological modeling and forecasting, but they aggregate different aspects of model quality into a single overall score. On the other hand, flood event-based scores, such as Flood Timing Error (FTE), False Alarm Ratios (FAR) and Probability of Detection (POD), provide more specific verification measures of forecast quality that should be more informative to decision-makers. Both classes of overall accuracy and event-based scores include either deterministic or probabilistic scores, focusing on either the ensemble mean (or quantiles) or on probabilities. 

Results are presented for ten catchments in Uganda with different morphological and hydrological characteristics. An evaluation of extended-range re-forecasts from the Copernicus-Emergency Management Service Global Flood Awareness System (GloFAS) has been carried out against observed streamflow data, contrasting overall performance scores, including the KGE and the CRPS, and event-based scores, including the FTE, FAR and POD for forecasts at different lead times (< 45 days). The relative performance of two different versions of GloFAS (2.1 and 3.1) is assessed by this multi-criteria verification setting. Results show that the relative ranking of forecast performance across model versions and catchments may vary based on the scores considered, suggesting that a multi-criteria and event-based evaluation is needed to inform flood preparedness actions.

How to cite: Mulangwa, D., Ficchi, A., Nyenje, P., Sempewo, J., Speight, L., Cloke, H., Harrigan, S., Zaake, B., and Stephens, L.: Assessing ensemble flood forecasts with action-relevant scores to support flood preparedness actions: An application to the Global Flood Awareness System in Uganda., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9862, https://doi.org/10.5194/egusphere-egu23-9862, 2023.

EGU23-10940 | ECS | PICO | HS4.5

Automatic generation of impact-based drought forecast, implications for early warning and action in East Africa 

Nishadh Kalladath Abdul Rasheed, Viola Otieno, Herbert Misiani, Jully Ouma, Erick Otenyo, Jason Kinuya, and Ahmed Amdihun

The regions of east Africa are facing unprecedented drought impacts at present and it is expected to intensify with climate change. Impact based forecast can give critical information for disaster preparedness, adaptation, and anticipatory action thereby increasing communities’ resilience. Probabilistic forecasts with uncertainty metrics have in the past provided early warning information for early actions. However, the complexity of drought as a disaster, encompassing and effecting wide range of socio-economic activities with interlinked compounding and cascading effect often makes drought impact forecasting bound to be less effective and robust (Boult et al. 2022). Moreover, drought impacts which are subjected to the influence of other high-impact weather related events, increases the difficulty to ascertain the extent of the impact. Therefore, drought impact forecasting should be viewed as a dynamic process that involves multi-stakeholders to realize its full potential of triggering early action (de Brito 2021). In such a scenario, the availability of an open, and widely accessible information portal can be effective in ensuring early waning information is disseminated widely across all stakeholders to trigger timely action.   

This study demonstrates an automatic impact-based drought forecast system to be integrated with existing East Africa Drought Watch (EADW) web portal. For the last two-to-three years, EADW has proven to be single window portal for major hazard related information dissemination for disaster early warning and action. The proposed automatic impact-based drought forecast system is based on TMAST ALERT probabilistic soil moisture and Water Requirement Satisfaction Index (WRSI) forecast using their data Application Programming Interface (API). TAMSAT ALERT is region specific validated, calibrated data source and its effectiveness assessed in impact-based forecast for the region (Boult et al. 2020, Busker et. al 2022). CLIMADA, an open-source software for climate risk assessment was used for integrating the soil moisture hazard data with exposure, and vulnerability to forecast socio-economic impact of drought. The current version of the system, directed for agriculture drought IBF, uses Spatially-Disaggregated Crop Production Statistics Data in Africa and WRSI maize crop unimodal relationship as impact function. The probabilistic forecast of WRSI is used to generate the Impact Based Forecasting (IBF), impact versus probability matrix for region specific map generation.  Finally, implications for early warning and early action on agricultural practices in the Eastern Africa region are discussed.  

1. Boult, Victoria L., et al. "Towards drought impact-based forecasting in a multi-hazard context." Climate Risk Management 35 (2022): 100402. 

2. de Brito, Mariana Madruga. "Compound and cascading drought impacts do not happen by chance: A proposal to quantify their relationships." Science of the Total Environment 778 (2021): 146236.​ 

3. Boult, Victoria L., et al. "Evaluation and validation of TAMSAT‐ALERT soil moisture and WRSI for use in drought anticipatory action." Meteorological Applications 27.5 (2020): e1959. 

4. Busker, T., de Moel, H., van den Hurk, B., Asfaw, D., Boult, V., and Aerts, J.: Impact-based drought forecasting for agro-pastoralists in the Horn of Africa drylands, IAHS-AISH Scientific Assembly 2022, Montpellier, France, 29 May–3 Jun 2022, IAHS2022-255, https://doi.org/10.5194/iahs2022-255, 2022. 

How to cite: Kalladath Abdul Rasheed, N., Otieno, V., Misiani, H., Ouma, J., Otenyo, E., Kinuya, J., and Amdihun, A.: Automatic generation of impact-based drought forecast, implications for early warning and action in East Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10940, https://doi.org/10.5194/egusphere-egu23-10940, 2023.

EGU23-11434 | ECS | PICO | HS4.5

Evaluating the explainability and performance of an elementary versus a statistical impact-based forecasting model 

Sahara Sedhain, Marc van den Homberg, Aklilu Teklesadik, Maarten van Aalst, and Norman Kerle

The disaster risk community has notably shifted from a response-driven approach to making informed anticipatory action choices through impact-based forecasting (IBF). Algorithms are being developed and improved to increase impact prediction abilities, and to allow automatic triggers to reduce the reliance on human judgement. However, as complexities in modelling algorithms increase, it becomes more difficult for decision makers to interpret and explain the results. This reduces the accountability and transparency, and can lead to lower adoption of the models. Therefore, humanitarian decision-makers can benefit from a mechanism to evaluate different IBF approaches, which has not yet been developed. Through a case study of anticipatory action for tropical cyclones in the Philippines, we evaluated two very different approaches to IBF: (1) a statistical trigger model that uses a machine learning algorithm with several predictor variables, and (2) an elementary trigger model that combines damage curves and weighted overlay of vulnerability indicators, to predict the impact and prioritize areas for intervention. The models were evaluated based on their performance for damage prediction and their sensitivity to different risk indicators for Typhoon Kammuri (2019) in the Philippines. The study also proposed a way of characterising the explainability specific to an IBF model, and that gives clarity on which elements, and why, influence the results, done via a model card. To facilitate this process a prototype interactive decision portal was built, which shows decision makers the sensitivity of the results to variations in input parameters. The results show that in relative terms the elementary model performed better and would have allowed to maximise impact reduction through early action, suggesting that, for this particular case, complex was not necessarily a better choice. However, the uncertainty in both models due to limitations in the initial hazard forecast indicates that multiple models need to be evaluated for practical cases that cover different characteristics of the hazard and socio-vulnerable situations. For this, the evaluation framework we developed can be expanded across operational IBF projects.

How to cite: Sedhain, S., van den Homberg, M., Teklesadik, A., van Aalst, M., and Kerle, N.: Evaluating the explainability and performance of an elementary versus a statistical impact-based forecasting model, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11434, https://doi.org/10.5194/egusphere-egu23-11434, 2023.

EGU23-14435 | PICO | HS4.5

Towards a global machine learning based impact model for tropical cyclones 

Mersedeh Kooshki, Marc van den Homberg, Kyriaki Kalimeri, Andreas Kaltenbrunner, Yelena Mejova, Leonardo Milano, Pauline Ndirangu, Daniela Paolotti, Aklilu Teklesadik, and Monica Turner

Due to its geographical location, the Philippines is prone to tropical cyclones (TC) which produce strong winds, accompanied by heavy rains and flooding of large areas, resulting in heavy casualties to human life and destruction to livelihoods and properties. To reduce the humanitarian impact of TC, the Philippine Red Cross with the German Red Cross and 510, an initiative of The Netherlands Red Cross, designed and implemented a machine learning impact-based forecasting model based on XGBoost, which is used operationally to release funding and to trigger early action. The model predicts the percentage of houses that will be completely damaged due to a TC using predictive features for the hazard (wind speed, rainfall, storm surge and landslides), exposure (such as ruggedness and population density) and vulnerability (such as housing material and poverty) . However, this model is not easily transferable to other countries, due to its use of country specific data from the Philippines.

Here, we develop upon this line of research around the XGBoost model, in three ways. First, we evaluate multiple ML algorithms for classification and regression of impact data of tropical storms. Secondly, we perform a sensitivity analysis on the predictive features, replacing where possible those features for which only Philippines-specific data sources can be used with features for which data from global open data sources are available. Thirdly, the XGBoost model provides predictions at the aggregated geographical level of a municipality. Our research centres on transforming it to a grid based model with a resolution of 0.1 x 0.1 latitude-longitude degrees. For all experiments, due to the scarcity and skewness of the training data (algorithms are trained on only 40 historical typhoon events), specific attention is paid to data stratification, sampling and validation techniques. 

We find that XGBoost slightly outperforms random forest and that regression is more suitable to detect outliers than classification. Furthermore, we show that we can limit the predictive features from the original model to a subset of 20 features. The transformation to a grid-based model was possible by de-aggregating the impact data using OpenStreetMap housing data obtained from Humanitarian Data Exchange. Preliminary results show that the ML model performance worsens when going from municipality to grid-based level. This is likely caused by a larger error variance between the individual grid cells of a municipality which get averaged when aggregated. To conclude, relying on globally available data sources and working at grid level holds potential to render a machine learning based impact model generalisable and transferable to locations outside of the Philippines impacted by TCs. Future research will focus on validation with data for other countries. Ultimately, a transferable model will facilitate the scaling up of anticipatory action for tropical cyclones. 

How to cite: Kooshki, M., van den Homberg, M., Kalimeri, K., Kaltenbrunner, A., Mejova, Y., Milano, L., Ndirangu, P., Paolotti, D., Teklesadik, A., and Turner, M.: Towards a global machine learning based impact model for tropical cyclones, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14435, https://doi.org/10.5194/egusphere-egu23-14435, 2023.

EGU23-15188 | ECS | PICO | HS4.5

Machine-learning enhanced forecast of tropical cyclone rainfall for anticipatory humanitarian action 

Andrea Ficchì, Guido Ascenso, Matteo Giuliani, Enrico Scoccimarro, Linus Magnusson, Rebecca Emerton, Elisabeth Stephens, and Andrea Castelletti

Tropical Cyclones (TCs) have the potential to cause extreme rainfall and storm surge, which in turn can lead to riverine and coastal flooding with huge damage to property and loss of lives.

The use of precipitation forecasts in the context of decision-making and anticipatory action is currently hampered by the limited skill of numerical weather prediction models in forecasting the characteristics of such extreme rainfall events (especially their severity and location) with a sufficiently long lead time.

In this study, we present a post-processing scheme for precipitation forecasts based on a popular deep-learning algorithm (U-Net). We design our Machine Learning (ML) model to reduce the local biases of precipitation forecasts from TCs and adjust the spatial distribution of extreme rainfall. For this, we use a composite loss function to train the model, based on the combination of the Mean Absolute Error (MAE) and the Fractions Skill Score (FSS). We first demonstrate the potential of our ML-based approach working on ERA5 reanalysis data and subsequently apply it to the ensemble mean of ECMWF sub-seasonal forecasts with a lead time up to 10-days. As for the ensemble spread, we investigate possible post-processing adjustments based on the improvement of the spread-error relationship and of action-relevant scores of interest for humanitarian agencies, namely False Alarm Ratios (FAR) and Hit Rates (HR). We train and validate the model on a historical dataset of global TC precipitation events, using ECMWF re-forecasts over 20 years and a multi-source observational dataset (MSWEP) as reference. The results are evaluated with a multi-criteria approach including MAE, FSS, FAR, and HR, to assess the capacity of improving the predicted severity and spatial patterns of TC precipitation, as well as their potential for triggering anticipatory actions. Finally, we discuss how the outputs of our model can be used and further improved to support humanitarian actions aimed at saving lives in vulnerable communities in Mozambique.

How to cite: Ficchì, A., Ascenso, G., Giuliani, M., Scoccimarro, E., Magnusson, L., Emerton, R., Stephens, E., and Castelletti, A.: Machine-learning enhanced forecast of tropical cyclone rainfall for anticipatory humanitarian action, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15188, https://doi.org/10.5194/egusphere-egu23-15188, 2023.

EGU23-15732 | PICO | HS4.5

How a flood forecasting system saved lives and property in West Africa 

Jafet C.M. Andersson, Aishatu T. Ibrahim, Ahmed Lamine Soumahoro, Vakaba Fofana, Abdou Ali, and Berit Arheimer

Floods pose an increasing challenge for societies in West Africa; causing loss of lives, damaged infrastructure, and food insecurity. Improving flood management is hence paramount for the region, which several initiatives aim to contribute to. Hydrological forecasting systems can help, but only if they lead to appropriate action.

This presentation focusses on how a flood forecasting system has been used to save lives and property in West Africa within the FANFAR project (www.fanfar.eu). The system was co-designed and co-developed together with hydrological services, emergency management agencies, river basin organisations, and regional expert centres in 17 countries. The pilot system was launched early in the project, producing new forecasts every day. This enabled operational staff at national and regional agencies to utilize the system during the current rainy season, for every season since 2019.

During 2020, Nigeria experienced severe flooding. The Nigeria Hydrological Services Agency (NIHSA) hence decided to utilize FANFAR to warn the population of forthcoming flood risks, which resulted in 2 500 lives saved on one occasion, and minimisation of property damage on another. In the presentation we describe these events, and how NIHSA acted together with other institutions to entice action.

FANFAR was also used in Ivory Coast during the 2022 rainy season. Operational staff at SODEXAM – the meteorological services of Ivory Coast – utilized the system to inform two flood-prone communities of forthcoming flood risks. This resulted in on-the-fly construction of a drainage ditch, which reduced impacts on the nearby community. In the presentation we describe the event and also the approach SODEXAM took to build trust and communicate with the communities.

We also briefly describe the FANFAR system that employs a daily forecasting chain including meteorological reanalysis and forecasting based on HydroGFD, data assimilation of gauge observations, hydrological initialisation and forecasting with the HYPE model, flood severity assessment, and distribution through e.g. web visualisation. 

How to cite: Andersson, J. C. M., Ibrahim, A. T., Soumahoro, A. L., Fofana, V., Ali, A., and Arheimer, B.: How a flood forecasting system saved lives and property in West Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15732, https://doi.org/10.5194/egusphere-egu23-15732, 2023.