EGU General Assembly 2023  

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.

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.

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.

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.

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.

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.

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.

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.

 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.

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 1^st January 1979 - 1^st 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.
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• 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 95^th percentiles). By considering low soil moisture (below regional 2001-2020 JJA 10^th 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.

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.

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.

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 (T_max) was higher than 90th percentile of T_max 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 T_max 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 11^th to August 29^th).

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 (> 90^th percentile) and non-high humid (<50^th percentile) conditions. The events of high severity, where humidity conditions were comparatively high (> 50^th 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.

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.

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.

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>30^oC, followed by a tropical night, Tmin>20^oC) 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.

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.

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.

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.

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.

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.

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 90^th, 95^th and 98^th 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.

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.

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.

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 6^th 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.

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.

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.

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.

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 M_w7.5 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 M_w7.5 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.

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.

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 6^th 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.

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.

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.

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.

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.

As part of a global distributed acoustic sensing (DAS) campaign, multiple DAS interrogators (from academia and industry) recorded simultaneously from 1^st till 28^th 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.

On 6 February 2023, M_w7.8 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 M_w7.8 earthquake was followed by a M_w7.6 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 M_w7.0 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 M_w7.8 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, M_w6.8 Sivrice earthquake, while to the southwest, it extends to the east of the city of Antakya. The M_w7.6 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.

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.

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.

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 20^th 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.

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.

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.

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.

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.

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, T_w, is above the 90^th 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 T_w 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.

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.

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.

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.

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.

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
• David García-León.et.al, Current and projected regional economic impacts of heatwaves in Europe, Nature Communications, https://doi.org/10.1038/s41467-021-26050-z
• Christophe Lavaysse.et.al, Towards a monitoring system of temperature extremes in Europe, Nat. Hazards Earth Syst. Sci,doi:10.5194/nhess-2017-181, 2017
• Chloé Prodhomme. et.al, Seasonal prediction of European summer heatwaves,https://doi.org/10.1007/s00382-021-05828-3
• S. E. Perkins and L.V.Alexander, On the Measurement of Heat Waves, DOI: https://doi.org/10.1175/JCLI-D-12-00383.1
• S. E. Perkins-Kirkpatrick.et.al, Changes in regional heatwave characteristics as a function increasing global temperature, DOI:10.1038/s41598-017-12520-2
• Agrawal, R. and Srikant, Fast Algorithms for Mining Association Rules in Large Databases. Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, Santiago de Chile, 12-15 September 1994, 487-499.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 (D_i) 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 Z_i^dist -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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 13^th 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.

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.

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.

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.

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.

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.

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.

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.

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.

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 (PFRI_i). 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 (RF_i) 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 PFRI_i by future scenarios such as climate change and urbanization.

PFRI_i = n_P,k * RF_i / (A_k * ∑P)                                                                                                                                                                                     

PFRI_i=Potential Flood Risk Index; n_P,k= number of Persons on a private ground k; A_k=total object area; =total number of persons in the catchment; Rf_i= 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 PFRI_GIS. 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 PFRI_i.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 15^th 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 15^th. 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 21^st 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.