Human perception is strongly influenced by communication. In the context of natural hazards, perception plays a crucial role in the resilience of affected populations. This is particularly true for people's perceptions of phenomena related to climate change (CC). Given this, it is essential to effectively calibrate communication, especially digital communication, which has significantly transformed how information is shared. Moreover, digital communication is the primary channel for younger generations, often labeled “digital natives.” However, the preferences of young people regarding digital communication tools have not been sufficiently explored.

In this study, in the framework of the Italian NRRP Tech4You Project, we examine the digital communication tools preferred by students from an Italian scientific high school. A questionnaire was administered to 74 students, asking them to select from various digital communication tools related to CC topics. Additionally, an open-ended question encouraged the students to explain their choices briefly. The communication preferences were analyzed via SPSS statistical software, whereas the comments were analyzed via the qualitative data analysis software AtlasTi.

The results highlight a preference for communication that is concise, simple, and similar to the content young people usually engage in. With respect to the proposed content, videos and images are preferred over explicating texts. These findings, which shed light on students' preferences for internet digital tools related to CC, offer valuable insights for better calibrating digital communication in the field of climate change adaptation (CCA), which involves young citizens.

This study provides a good basis for enhancing young people's access to information through digital communication, which could significantly improve their social resilience to CC-related events. This improvement is crucial, as the information of today's youth contributes to building more resilient adult citizens in the future.

This work was funded by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of ‘Innovation Ecosystems’, building ‘Territorial R&D Leaders’ (Directorial Decree n. 2021/3277) – project Tech4You - Technologies for climate change adaptation and quality of life improvement, n. ECS0000009

How to cite: Carone, M. T. and Antronico, L.: Climate change and digital communication: teens’ preferences, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6859, https://doi.org/10.5194/egusphere-egu25-6859, 2025.

Universities are critical in addressing scientific, environmental, social, and political challenges of climate change. But solving the many problems associated with this grand challenge requires: (1) an interdisciplinary approach connecting university experts from various knowledge domains and organizations; (2) synergy with stakeholders for developing and deploying actionable solutions to adapting to the climate crisis; and (3) communicating research deliverables to the public to inform the adoption of climate-friendly behavior.

Here, we examine the Climate Transformation Programme (CTP) at Nanyang Technological University (NTU) as a case study in interdisciplinary research, evidence-based policymaking, and stakeholder engagement for climate action in Southeast Asia. The CTP framework integrates expertise from science, technology, social sciences, and the arts and translates it into actionable items for decision-makers through a three-fold stakeholder engagement approach. This strategy includes engagement with government agencies, industry partners, and community groups.

To highlight the importance of an interdisciplinary approach within CTP, Kim et al. (2023) combined whole-genome sequencing with reconstructions of landscape change of Southeast Asia[1]. We showed that rapid sea-level rise drove early settlers in Southeast Asia to migrate during the prehistoric period. Our work was the first reported instance to provide proof that sea-level rise changed the genetic makeup of human populations in Southeast Asia – a legacy that continues to impact current populations, affecting the genetic diversity of the region today.

Through the CTP corporate partners network, researchers establish mutually beneficial alliances with businesses committed to developing long-term resilience to the climate crisis. To support the adoption of context-appropriate and feedback-driven climate solutions, partnerships with governmental and international organizations should be fostered. For example, Yim et al. (2024) estimated the global health impacts of air pollution over the past 40 years and its association with climate variability[2]. We revealed that 135 million premature deaths were attributable to PM2.5 air pollution during this period, with climate variability exacerbating health risks. This research was recognized at the 2024 World Health Organization (WHO) annual meeting and is employed in partnership with Prudential Insurance Company to assess health impact on individuals of Southeast Asia.

Effective climate communication is key to mobilizing the public to adopt pro-climate behaviors. Using plastic waste as an example, Xiong et al. (2024) investigated if virtual reality (VR) is a viable tool that could overcome several challenges facing climate communication[3]. Our finding indicates policymakers could adopt VR technologies to increase public members’ interest in learning about climate issues. In designing pro-climate behavioral interventions, policymakers should focus on facilitating individuals’ autonomous motivation by giving them a sense of control.

How to cite: Tsyrulneva, I., Kim, H. L., Yim, S. H. L., Ho, S. S., and Horton, B. P.: Integrating disciplines and stakeholders to address climate change challenges, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7680, https://doi.org/10.5194/egusphere-egu25-7680, 2025.

Etna's eastern flank is crossed by numerous seismogenic faults, which cause surface faulting, resulting in the destruction of buildings and exposing the local population to risk. Rebuilding damaged buildings in earthquake-prone areas raises ethical and economic concerns. A seismic event measuring Mw4.9 occurred on 26 December 2018, causing significant damage to over 3,000 buildings within an area of 205 km² populated by approximately 140,000 individuals residing on the Etna's eastern flank. The earthquake resulted in a ground rupture exceeding ten kilometres, encompassing several urban areas. Consequently, it was imperative to conduct a preliminary geostructural study to ascertain the most vulnerable tectonic zones and upgrade targeted buildings. The study identified the homogeneous microzones in seismic prospection, namely the Zones of Attention (ZA_ACF), Susceptibility (ZS_ACF) and Respect (ZR_ACF) of the faults activated during the 2018 earthquake. Buildings in the ZR_ACF were not permitted to be repaired because they were at serious risk of future damage, and owners were offered financial compensation to rebuild in seismically safer areas. Initially, some people demonstrated reluctance to accept the proposed relocation. Empathy and clear explanations regarding the rationale for the relocation were provided, and the provision of comprehensive support to people facing significant psychological challenges was identified as being necessary. This approach is currently being implemented in the reconstruction of other seismic areas in Italy, and it has the potential to become a common and sustainable model for the reconstruction of areas affected by natural disasters.

How to cite: Neri, M., Neri, E., and Leonardi, A.: Geoethical principles applied to the reconstruction planning of natural disasters: the Etna 2018 earthquake case study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9411, https://doi.org/10.5194/egusphere-egu25-9411, 2025.

How to cite: Korhonen, N., Laapas, M., Kühn, T., Pirinen, P., Luomaranta, A., Kuntsi-Reunanen, E., Vajda, A., and Gregow, H.: Utilizing Urban Microclimate Data in Education and Research , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17453, https://doi.org/10.5194/egusphere-egu25-17453, 2025.

In order to effectively mitigate, adapt to and benefit from climate change, society needs climate expertise. To enhance professional climate action competencies and to educate students in climate-informed decision making a new master-level course, "Living with changing climate" was developed. This course was created by a multidisciplinary team of experts from the Institute for Atmospheric and Earth System Research (UH-INAR), the Finnish Meteorological Institute (FMI) and the Department of Educational Sciences at the University of Helsinki, as part of the ClimComp-project funded by the Research Council of Finland.

Designed for an online learning platform, the course is part of the Climate University curriculum. Climate University (www.climateuniversity.fi), a network of higher education institutions in Finland that provides climate and sustainability education in collaboration with schools and working life. The "Living with changing climate" course covers the causes and complexity of climate change, its impacts and adaptation needs, future scenarios and their links to mitigation efforts, and acquaints students with open-source weather and climate data, applications and their use, and the principles of climate services. Additionally, students apply the knowledge gained in project work on a real-life example, enabling them to collaborate with stakeholders. The course was piloted during spring 2023 among a group of students with diverse background. Based on the feedback received, the course material was improved and published in the curriculum in autumn 2023. The design and development process of the course, including the challenges encountered, and lessons learned are presented.

How to cite: Mäkelä, A., Gregow, H., Vajda, A., Korhonen, N., Lavonen, J., Lauri, K., Makkonen, R., Merikanto, J., Räisänen, P., Siponen, J., Vesterinen, V.-M., Ylivinkka, I., Riuttanen, L., Böhnisch, A., and Kuntsi-Reunanen, E.: Climate competencies for real-life action: developing a master level course "Living with changing climate", EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18727, https://doi.org/10.5194/egusphere-egu25-18727, 2025.

The West African countries share a myriad of challenges, including environmental degradation, desertification, enhanced rainfall variability, unprecedented heat waves, floodings and declining agricultural productivity. The accelerated climate change along with other global change stressors like population growth and rapid urbanization contributes to land degradation, chronic poverty, food insecurity, and malnutrition.

To address these challenges, the Climate Services for Risk Reduction in Africa (CS4RRA) was initiated by France and Germany through their ministries of higher education and research (MESR and BMBF respectively), with West African regional and national institutions such as ACMAD, AGRHYMET/CILSS, WASCAL, African Centres of Excellence, Universities, National Governmental Services in West Africa with the aim to enhance climate resilience through Knowledge, Innovation, and Capacity Building (KIC). This initiative is built on the achievements of previous EU and AU programmes (H2020, JPI Climate/SINCERE, Copernicus CCS, ERA4CS, Climate-KIC, etc.). Four hybrid webinars (in-person and online), rooted in West African countries, were held to identify gaps and critical issues in climate services for risk reduction in Africa.

To capitalize on such Webinars Forum, CS4RRA culminated in an international Stocktaking Conference for West Africa, on 5 - 6 November 2024. Building on conclusions and recommendations from the webinars and aiming to address gaps in knowledge, innovation, and capacity development, this conference convened policymakers and representatives of governments, academia, donors, international agencies, and various stakeholders of the climate service value chain together in Africa. The main objective was to agree on the identified necessary research and innovation efforts and to address the corresponding funding gaps. This conference examined potential areas for multilateral cooperation to support research and innovation on climate services for risk management, resilience, and adaptation in West Africa and beyond. 

How to cite: Monfray, P., Ogunjobi, K., Sanfo, S., Roehrig, J., Fink, A., Kane, C., Sama, M., Sultan, B., Agboka, K., Alabi, T. A., Cherif, M., Gaye, A. T., Amponsah, W., and Dindane, A.: Climate Services for Risk Reduction in Africa (CS4RRA): a multilateral initiative between Europe and Africa, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20205, https://doi.org/10.5194/egusphere-egu25-20205, 2025.

 Generative AI is radically affecting the teaching landscape in Earth Sciences, which includes evertyhting from essays to coding. Staff have a variety of approaches, ranging from enthusiastic early adopters to 'head-in-the-sand' 'if I don't look at it it won't exist' wishful thinkers. How can we best help everyone learn about the pitfalls and advantages, so they are informed enough to use it correctly if they wish to? This abstract will cover reflections from teaching staff along their journey to integrating generative AI into teaching practice and describe workshops held to integrate staff with different levels of experience. The goal was to give beginners a supported first taste with signposted development resources, and share ideas and methods and resources for the more advanced users.

How to cite: Petrie, E.: Reflecting on the journey towards integrating Generative AI into understanding and practice, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20328, https://doi.org/10.5194/egusphere-egu25-20328, 2025.

The city of Follonica is an energy community and the high school is involved in the "Pecora River Agreement", a local project whose aim is to redevelop the river ecosystem.

Outdoor education is a method that encourages students to be active participants and become citizens aware of the importance of environmental protection.

Students have projected a study (using IBSE method) of the polluted area around the river. The city's history is studied, using local library, showing its importance in climate, the changes in the water regime, and the shape of the river during the XX century.

The environmental situation is measured through chemical and physical parameters of the water, soil texture, quality indicators of soil (analysis of soil fauna), and water (Extended Biotic Index).

The digital products are: a website, some reports, and an interactive map of the river.

School has communicated the situation to the local authorities as a part of the Agreement, moreover, students make proposals: plants on the riverbank, activities to sensitize the local community, and monitoring through an ecological index for the future of the city

How to cite: Severi, A.: Outdoor education for rehabilitation of a river, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20776, https://doi.org/10.5194/egusphere-egu25-20776, 2025.

Persistent Dry Spells (PDS) during winter in the eastern Mediterranean are crucial to understanding the regional challenges of water resources and mitigating agricultural and economic impacts. Winter dry spells significantly affect ecosystem stability, public health, and socioeconomic conditions in a region susceptible to climate variability. Therefore, extending the forecast horizon of these extreme weather events to subseasonal time scales is a key challenge. With this aim, we examine the covariability of the sea surface temperature of the Indian Ocean and Persistent Dry Spells during winter over the eastern Mediterranean. The positive Indian Ocean Dipole (IOD) phase alters global circulation patterns, notably increasing the geopotential height at 500 hPa and the sea-level pressure over western Russia, eastern Europe, and the eastern Mediterranean during PDS events. Concurrently, the positive IOD phase enhances moisture fluxes and decreases sea level pressure and geopotential height at 500 hPa in the Western Mediterranean, suggesting increased cyclonic activity in that region. This type of activity probably influences the formation of PDS in the eastern Mediterranean through latent heating and the formation of ridges downstream of the cyclones. The baroclinic, subtropical, and polar regimes are large-scale synoptic regimes alternately prevailing during PDS events. Changes due to the DMI phase are not identical under these regimes and sometimes have opposite trends. The baroclinic regime is the most frequent regime during PDS events. Consequently, the average changes in pressure intensity during PDS events strongly resemble those during baroclinic days. Positive DMI case studies exemplify the effect of these large-scale regimes. We provide evidence for a link between the positive phase of IOD in December and the frequency of longer (> 15 days) PDS events. The normalized frequencies of persistent 15-20-day events under the positive dipole mode index (DMI) are ~ 2% higher than the frequency of negative DMI. The frequencies of 6-7 day events are ~20% lower. Finally, we emphasize the sensitivity of persistent dry spells during winter to event definition, the chosen precipitation data source, and threshold definitions for climate indices. These considerations are essential for improving the accuracy of regional weather and climate predictions, further enhancing our understanding of the climatic impacts of IOD and other teleconnection patterns in the eastern Mediterranean and worldwide.

How to cite: Berkovic, S. and Hochman, A.: Links between the Indian Ocean Dipole and Persistent Dry Spells in the Eastern Mediterranean Winter, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-56, https://doi.org/10.5194/egusphere-egu25-56, 2025.

Quantifying uncertainties is a key aspect of data assimilation systems since it has a large impact on the quality of the forecasts and analyses. Sequential data assimilation algorithms, such as the Ensemble Kalman Filter (EnKF), describe the model and observation errors as additive Gaussian noises and use both inflation and localization to avoid filter degeneracy and compensate for misspecifications. This introduces different stochastic parameters which need to be carefully estimated in order to get a reliable estimate of the latent state of the system. A classical approach to estimate unknown parameters in data assimilation consists in using state-augmentation, where the unknown parameters are included in the latent space and are updated at each iteration of the EnKF. However, it is well-known that this approach is not efficient to estimate stochastic parameters because of the complex (non-Gaussian and non-linear) relationship between the observations and the stochastic parameters which can not be handled by the EnKF. A natural alternative for non-Gaussian and non-linear state-space models is to use a particle filter (PF), but this algorithm fails to estimate high-dimensional systems due to the curse of dimensionality. The strengths of these two methods are gathered in the proposed algorithm, where the PF first generates the particles that estimate the stochastic parameters, then using the mean particle the EnKF generates the members that estimate the geophysical variables. This generic method is first detailed for the estimation of parameters related to the model or observation error and then for the joint estimation of inflation and localization parameters. Numerical experiments are performed using the Lorenz-96 model to compare our approach with state-of-the-art methods. The results show the ability of the new method to retrieve the geophysical state and to estimate online time-dependent stochastic parameters. The algorithm can be easily built from an existing EnKF with low additional cost and without further running the dynamical model. 

How to cite: Guillot, J.: State and Stochastic Parameters Estimation with Combined Ensemble Kalman and Particle Filters, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-125, https://doi.org/10.5194/egusphere-egu25-125, 2025.

Since the inception of physics-informed neural networks (PINNs) by Raissi et al. in 2019, it has been seen as a promising approach to outperform conventional algorithms in terms of computational efficiency, reduced costs, and improved prediction accuracy, especially in small data regimes.PINNs incorporate known physical governing equations in the form of partial differential equations (PDEs) or ordinary differential equations (ODEs) into neural networks, and occasionally the governing equations are derived from observational or simulated data, allowing PINNs to address specific atmospheric systems.Moreover, depending on the problem being solved, most work adds the physical constraints directly into the loss or cost function, while others enhance performance using modified architectures or preprocessing techniques.In the realm of atmospheric sciences, challenges remain, including a heavy reliance on simulated data and limited use of observational datasets, which does not show the real-world applicability of PINNs. A detailed review of available results shows critical gaps in scalability, hybrid data integration, and standardization in atmospheric science.We identified a hybrid methodology by combining simulated and observational data, which includes optimizing hybrid loss functions to balance physics-based and observational accuracy, applying adaptive training techniques, and standardizing preprocessing schemes to handle multi-scale atmospheric phenomena.Results demonstrate the ability of PINNs to deliver faster computation, enhanced prediction accuracy, and robustness in sparse data environments. This highlights the transformative advantages of PINNs over traditional methods and suggests future directions for leveraging their capabilities in atmospheric science applications.

How to cite: Ekue, J. A., Hammond, D., and Agyei-Yeboah, E.: Envisioning the Role of Physics-Informed Neural Networks in Atmospheric Science: Advancements, Challenges, and Future Prospects, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-243, https://doi.org/10.5194/egusphere-egu25-243, 2025.

Accurate evaluation of cloud microphysical variables is essential for improving cloud parameterization and weather forecasting. However, obtaining high-resolution, spatially and temporally extensive observation dataset remains a challenge due to the limitations of in situ measurements. Therefore, this study addresses this gap by assessing existing equations for estimating vertically integrated liquid water content (VIL, kg/m²) from liquid water content (LWC, g/m³) using C-band dual-polarised doppler weather radar (DWR) data from IMD Jaipur station over 78 deep convective summer monsoon days in the years 2020-2022. A long-term climatological study (2003-2023) of total column cloud liquid water (TCCLW, kg/m²) from ERA5, liquid water cloud water content (LWCP, kg/m²) from MODIS and rainfall data from IMD, IMERG, and GPCP datasets is also performed. VIL is computed as the vertical integral of LWC across atmospheric layers using four reflectivity-LWC (Z-LWC) relationships and one reflectivity-differential reflectivity (Z, ZDR-LWC) relationship from existing literature. The performance of each equation is evaluated by comparing radar-derived VIL with satellite-derived parameters like MODIS cloud liquid water path (LWP, kg/m²) and TCCLW. The results show that VIL values increase with rainfall intensity and cloud vertical height, leading to higher estimation errors. Among the equations tested, the hybrid ZDR-based equation consistently demonstrated superior performance, particularly during high-intensity rainfall events, with lower root mean square error (RMSE) and mean absolute error (MAE) values which also captured more detailed spatial patterns of liquid water distribution and reduced bias, making it the most reliable estimator. Despite some limitations, such as beam blockage and slight spatial shifts due to interpolation, the study highlights the advantages of incorporating polarimetric radar products for VIL estimation. These findings provide a foundation for improving real-time precipitation forecasts and understanding cloud microphysics, with future work aimed at refining the methodology by addressing data gaps and enhancing cloud-type-specific estimators.

How to cite: Sabu, A., Syed, H. A., Das, S., Panda, S. K., Sharma, D., and Pal, J.: Evaluation of Vertically Integrated Liquid Water Content in Indian Summer Monsoon Clouds Using Dual-Polarimetric Doppler Weather Radar, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-888, https://doi.org/10.5194/egusphere-egu25-888, 2025.

Air quality monitoring remains a significant challenge in urban areas, particularly where high-cost infrastructure is unavailable or difficult to maintain. Traditional monitoring systems are often limited in scope due to expense and logistical constraints, leading to data gaps, especially in resource-constrained environments. Low-cost air quality sensors have the potential to transform environmental monitoring by providing accessible, affordable tools for collecting air quality data, especially in urban settings. As part of the SCORE project, a low-cost sensor system was developed to support real-time air quality monitoring across European cities. These sensors provide a more granular understanding of air pollution trends, making air quality data collection both scalable and accessible to a wider range of stakeholders, including local communities. This presentation will highlight the deployment of these sensors in Dublin, Ireland, where they have been successfully integrated into citizen science initiatives, enabling communities to actively participate in environmental data collection and contribute to air quality management.

Ensuring data accuracy and reliability is a key challenge in the use of low-cost sensors. We will examine the technical challenges of deploying low-cost sensors, such as calibration, accuracy, and long-term reliability in small-scale urban environments. The presentation will also discuss strategies for integrating sensor data into authoritative air quality monitoring networks to enhance overall data quality and spatial coverage.

In Dublin, the citizen science air quality initiative has built strong connections between local communities, researchers and policymakers. This collaboration exemplifies how co-created initiatives, backed by accessible technology, can empower citizens and bridge the gap between public engagement and formal policy processes. The outcomes of the Dublin case study suggest broader applicability for the SCORE model in other cities facing similar air quality challenges. By offering a replicable and scalable solution, low-cost sensors provide an affordable alternative to high-end monitoring stations, enabling resource-limited municipalities to expand their air quality infrastructure. The project demonstrates how engaging local communities in the data collection process can foster long-term, sustainable environmental stewardship. These insights underscore the importance of equitable partnerships between citizens, researchers, and governments in tackling air pollution, particularly in cities where financial or technical constraints have traditionally limited comprehensive air quality monitoring.

How to cite: Daruari, H., Crowley, S., Cocco, C., and Barrón, J. P. G.: Building Equitable Air Quality Networks: Low-Cost Sensors and Community-Led Monitoring in Dublin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1073, https://doi.org/10.5194/egusphere-egu25-1073, 2025.

The forecast of monthly rainfall is a significant topic for water resource management and hydrological disaster prevention. A critical need for precise hydrological forecasts in water resource management is addressed in this study by analyzing machine learning (ML) models for precipitation forecasting in the Boudh district of Odisha, India. Although machine learning (ML) models have demonstrated significant promise in rainfall forecasting due to their high performance, often surpassing that of certain physical models, the intricate physical processes involved in rainfall creation mean that a single ML model is typically insufficient to provide reliable rainfall projections. A thorough set of meteorological parameters, including precipitation wind speed, temperature, and humidity, are utilized to create four distinct models: Support Vector Regression (SVR), long and short memory neural networks (LSTM), Bi-LSTM and Convolutional neural network with LSTM (CNN-LSTM). The performance of these models is thoroughly assessed utilizing a range of evaluation metrics. In this work, the correlations between precipitation and climate factors are assessed using the cross-correlation function (XCF). With maxima consistently reported during months across all four sites, the XCF analysis shows a number of significant trends, including a strong correlation amid precipitation and maximum temperature. Moreover, precipitation is significantly correlated with wind speed and relative humidity. The results demonstrate the effectiveness of hybridized ML techniques in raising the precision of precipitation forecasts. The CNN-LSTM models, which have R² values between 0.93 and 0.97, generally perform better. Their remarkable accuracy highlights their efficacy in precipitation forecasting, outperforming rival models during both training and testing. These findings have important ramifications for hydrological processes, particularly in Odisha's Boudh region, where sustainable water resources management depends on precise precipitation forecasting.

How to cite: Samantaray, S., Sahoo, A., and Satapathy, D. P.: Rainfall Prediction using Hybrid CNN-LSTM approach: A case study in the Boudh district, Odisha, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1542, https://doi.org/10.5194/egusphere-egu25-1542, 2025.

How to cite: Jalan, S., Sukhatme, J., and Seshadri, A.: Genesis, structure and propagation of synoptic systems over the Indian Ocean during the Northeast Monsoon , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2672, https://doi.org/10.5194/egusphere-egu25-2672, 2025.

The Indo-Gangetic plains (IGP) in India are frequently affected by fog during the winter months of December, January, and February, which manifests in severe consequences for air and road traffic, thereby leading to health as well as economic losses. This region, which includes highly populated cities like the National Capital Territory of Delhi, also experiences a high concentration of aerosols during this period. While studies have indicated the importance of the role of aerosols in fog processes in the region, the role of different aspects of aerosol-radiation interaction (ARI) has not been studied in detail for the formation of fog in the region. Current numerical weather prediction models (NWP) still struggle to predict fog accurately because of the uncertainties in the representation of processes leading to fog formation, sustenance, and dissipation. The present study aims to understand the influence of aerosols and ARI on the fog over IGP with a focus on dense fog conditions using the Delhi Model with Chemistry and aerosol framework (DM-Chem1.0), which is a high-resolution (330 m) model used for operational forecasting of wintertime visibility and air quality at the National Centre for Medium-Range Weather Forecasting (NCMRWF), India. Four experiments (along with a Control experiment) were designed to analyze how both the scattering and absorbing nature of ARI influence the evolution of dense fog from temporal and spatial perspectives. Two experiments isolated the absorbing and scattering effect of aerosols, while the third excluded both these effects. The fourth experiment analyzed pristine conditions with minimal aerosol presence. The study indicated that turning off absorption had the greatest impact, significantly increasing dense fog-impacted areas and fog-associated parameters like cloud liquid water mixing ratio and cloud droplet number concentration (CDNC). Satellite data for the absorbing aerosol index also corroborated the greater contribution of absorbing aerosols in the model domain. Further, the study also indicates the importance of a realistic representation of aerosol for better model performance during daytime. The study highlights the importance of correctly representing radiative interactions in the numerical models for fog prediction. The policy measures need to focus on regulating high aerosol concentrations over IGP to mitigate the adverse effects of fog.

How to cite: Bhati, S., Anurose, T. J., Jayakumar, A., Mohandas, S., and Prasad, V. S.: Aerosol-Radiation Interaction During Dense Fog in the Indo-Gangetic Plains Region , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3008, https://doi.org/10.5194/egusphere-egu25-3008, 2025.

This study investigated convective initiations (CIs) in the western Jiangnan region of China using radar data spanning April to September from 2018 to 2021. An integrated approach combining objective identification and subjective validation was applied to identify, track and validate CIs, resulting in a more accurate CIs dataset. Based on this dataset, this study delved into the spatiotemporal variations and key environmental conditions associated with CIs. The results indicated distinct seasonal and diurnal patterns in CIs events. Seasonally, the spatial variations of CIs were demarcated by the Nanling Mountains, exhibiting higher frequency to the south and lower to the north. Generally, the seasonal distribution of CIs followed a unimodal pattern, peaking during June to August and reaching minima in April and September. Notably, CIs exhibited a pronounced convection feature in the afternoon, particularly during June to August, when the majority of CIs occurred between 11:00 and 19:00. Furthermore, the spatial variations influenced by terrain were prominent. With the Nanling Mountains as the dividing line, CIs in the northern region were located near relatively higher mountains, while in the southern region, they were concentrated in smaller mountains and coastal areas. Utilizing the K-means clustering method, CIs that could develop into Mesoscale Convective Systems are classified into four circulation types: the Western Pacific Subtropical High (WPSH) Control type (Type I), the WPSH Edge type (Type II), the Southwest Airflow type (Type III), and the Low Trough Shear type (Type IV). CIs under Type I and II were primarily attributed to afternoon thermal convection occurring under conditions of strong moisture and thermal instability. The distribution of CIs triggers for these types tended to cluster in the vicinity of high-elevation terrain. In contrast, CIs belonging to Type III and IV were primarily driven by the synergy of abundant moisture conditions and systematic dynamic factors such as low-level jets, upper-level troughs, and shear lines. These exhibited a north-low and south-high frequency distribution, with high-frequency CIs trigger zones observed particularly in regions of strong moisture flux convergence and near complex terrain.

How to cite: Wu, Z., Han, Y., Song, N., Ye, C., and Xiang, G.: Exploring the spatiotemporal variations and key environmental conditions of convective initiations in the Western Jiangnan Region of China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3431, https://doi.org/10.5194/egusphere-egu25-3431, 2025.

In this talk, I will highlight our recent advances and findings in changes in climatic extremes over east Asia monsoon region. I will focus specifically on monsoon duration, intensity, rainfall extremes changes, and mechanism, with dynamic and thermodynamic factors controlling rainfall extremes over East Asia in late summer. Moreover, I will present our latest research on climatic extremes such as heatwaves based on dry conditions and stationary waves. Despite increasing future rainfall, rainfall extremes and rainfall variability in many areas, our recent studies suggest also an increase in drought risk over eastern Asia as a result of changes in evapotranspiration. However, the underlying mechanisms of heat waves and potential atmospheric and land feedbacks are still not fully understood. Through feedback attribution analysis, we found that there are dry and hot heat waves with very different underlying physical processes and feedbacks. The increasing global warming is expected to exacerbate atmospheric water demand, worsening future conditions of extreme droughts and heatwaves. Compound drought and heatwaves (DHW) events have much attention due to their notable impacts on socio-ecological systems. However, studies on the mechanisms of DHW related to land-atmosphere interaction are not still fully understood in regional aspects. Here, we investigate drastic increases in DHW from 1980 to 2019 over northern East Asia, one of the strong land-atmosphere interaction regions. Heatwaves occurring in severely dry conditions have increased after the late 1990s, suggesting that the heatwaves in northern East Asia are highly likely to be compound heatwaves closely related to drought. Moreover, the soil moisture–temperature coupling strength increased in regions with strong increases in DHW through phase transitions of both temperature and heat anomalies that determine the coupling strength. As the soil moisture decreases, the probability density of low evapotranspiration increases through evaporative heat absorption. This leads to increase evaporative stress and eventually amplify DHW since the late 1990s. Focusing on changes in stomatal conductance due to CO2 changes, our research results reveal an increase in surface resistance with CO2 elevation. Particularly under drought conditions, potential evapotranspiration tends to overestimate drought severity in the East Asian region by approximately 17% when scenarios considering vegetation are not taken into account. Additionally, intensified land-atmosphere interactions due to soil moisture deficiency lead to more frequent and amplified occurrences of compound heatwaves and droughts over northern East Asia. Understanding the relationship between soil moisture and vegetation can contribute to comprehending future severe droughts and heatwaves under diverse surface conditions with warming and moistening.

How to cite: Ha, K.-J., Yeo, J.-H., and Seo, Y.-W.: Dynamics and Characteristics of Climatic Extremes over East Asia Monsoon region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3896, https://doi.org/10.5194/egusphere-egu25-3896, 2025.

Based on the minute by minute precipitation observation data from 46 national weather stations in the Yangtze River Delta region of China and hourly ERA5 reanalysis data from June to August 2018 to 2021, the temporal and spatial characteristics and environmental parameters of short-term heavy precipitation were analyzed. The short-term heavy rainfall was classified and compared according to the 19 environmental parameters representing water vapor, dynamic and thermal conditions. The results showed that:(1) There were more short-term heavy rainfall in the Yangtze River Delta in summer, and 58.7% of the weather stations appeared more than 5 times a year on average; most of short-term heavy rainfall appeared in August, accounting for 40.7%; From 14:00 PM to 17:00 PM was the high incidence period of short-term heavy rainfall; The duration of short-term heavy rainfall was mostly within 60 minutes, accounting for 85.9%, and the longest process lasted 282 minutes.(2) At the beginning of short-term heavy rainfall, water vapor was sufficient, PWAT generally exceeded 63mm, and the relative humidity at 850 hPa and 700 hPa exceeded 80%; The energy condition was good, and the average value of cape was 1516.9 J/kg; The vertical wind shear of 0-6 km was mainly distributed in the range of 8.1~16.7 m/s, belonging to medium weak or weak intensity; The thickness of warm clouds was large, most of which were more than 4395.2 m, which was conducive to higher precipitation efficiency.(3) The environmental parameters of the three types of short-term heavy rainfall were quite different. The water vapor of the first type was mainly concentrated in the lower layer, with high cloud base height and large Cape value, 75% of which was more than 1700 J/kg. The thermal conditions were prominent, and the dynamic effect was weak. The water vapor of the whole layer of the second type was sufficient, and the Cape value was high, with an average value of 1401.1 J/kg, the uplift condition of the middle and low layers was the best of the three types. The water vapor, thermal and dynamic effects were relatively balanced; The third type was rich in water vapor, with prominent water vapor conditions, large vertical wind shear in the lower layer and weak thermal effect. 

How to cite: Zhang, C. and Peng, L.: Characteristics of environmental parameters of short-term heavy rainfall in the Yangtze River Delta region in summer, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3935, https://doi.org/10.5194/egusphere-egu25-3935, 2025.

A novel deep learning (DL) transformer model, named the 3D-Geoformer, has been developed for ENSO-related modeling studies in the tropical Pacific. Multivariate input predictors and output predictands are selected to adequately represent ocean-atmosphere interactions; so, this purely data-driven model is configured in such a way that key fields for the coupled ocean-atmosphere system are collectively and simultaneously utilized, including three-dimensional (3D) upper-ocean temperature and surface wind stress fields, which represents the coupled ocean-atmosphere interactions known as the Bjerknes feedback in the region. The 3D-Geoformer achieves high correlation skills for ENSO prediction at lead times of up to one and a half years. The reasons for the successful prediction with interpretability are explored comprehensively by performing perturbation experiments to predictors and quantifying input‐output relationships in predictions using the 3D-Geoformer. This is achieved by investigating how the thermal precursors contribute to ENSO prediction skills, with the dependence of the precursor representations on preconditioning multi-month input predictors elucidated. Results reveal the existence of ENSO‐related upper‐ocean temperature anomaly pathways and consistent phase propagations of thermal precursors around the tropical Pacific in the DL framework. The research demonstrates that 3D thermal fields and their basinwide evolution during multi-month time intervals act to enhance long‐lead prediction skills of ENSO. It is demonstrated that the 3D-Geoformer can not only have its ability to effectively improve prediction skills of sea surface temperature variability in the eastern equatorial Pacific, but also explain how and why it is so, thus enhancing model explainability.

How to cite: Zhou, L. and Zhang, R.: Deep learning-based ENSO modeling and its prediction and predictability study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4905, https://doi.org/10.5194/egusphere-egu25-4905, 2025.

Sudden stratospheric warming (SSW), a well-known phenomenon in the polar atmosphere, changes the distribution of various atmospheric parameters due to the enhanced activity of planetary waves. These processes produce zonal asymmetry in total ozone content (TOC) with a wave-1 pattern. However, regional characteristic properties of the Antarctic TOC anomalies that occur during the SSW life cycle have not been studied in detail. We aim to analyze the connection of zonally asymmetric variations of TOC with SSW events. The analysis is based on a time series of ten research stations in the Antarctic region and gridded fields from MSR-2 TOC data. Here, we compare the evolution of TOC and wave amplitudes in three Southern Hemisphere SSW events. The TOC time series over ten stations in the Antarctic region and superposed epoch analysis for ±60-day time lags relative to the SSW central date were used. A regional division according to the geographic location of the stations and TOC climatology was introduced. According to the TOC asymmetry pattern, a division between Eastern and Western Hemisphere stations is used. We observe zonally asymmetric ozone responses in the two hemispheres during the SSW life cycle, including distinct precursor properties before the SSW onset. This research clarifies the different SSW properties in local ozone observations under the zonally asymmetric TOC field. The previously unknown regional manifestations of Antarctic TOC anomalies in the early stage of the SSW are discussed. The role of wave-1 and the zonally asymmetric Brewer-Dobson circulation in the Eastern–Western Hemisphere difference in the Antarctic TOC variability is also discussed. We also characterize total ozone levels in the years immediately preceding and following the three most significant SSW events. We examine the influence of planetary wave activity and large-scale climate modes on the level of interannual ozone variability and its regional patterns. There is evidence that Antarctic total ozone in the years adjacent to these SSW events is reduced, which may serve as a precursor signal of these events and an indicator of their longer-lasting influence. We discuss the implications and importance of these ozone perturbations for the regional Antarctic climate.

How to cite: Milinevsky, G., Yu, R., Grytsai, A., Evtushevsky, O., Klekociuk, A., and Ivaniha, O.: Ozone anomalies over Eastern and Western Hemisphere Antarctic stations during sudden stratospheric warming life cycle, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5045, https://doi.org/10.5194/egusphere-egu25-5045, 2025.

An on-going campaign monitors the greenhouse gases emissions of biogas plants in the Grand Est region, in France, using airborne in situ CO₂ and CH₄ concentrations and wind measurements from Uncrewed Aerial System, associated with a mass balance method. During 16 days in 2024, we quantified the instantaneous emissions of 19 agricultural biogas plants, with installed methane productions ranging from 128 to 312 Nm³.h^-1_,producing biogas injected into the network mainly from manure, energy crops and agricultural wastes.

Observations obtained to date were used to quantify emissions either representative of the globality of a biogas plant or of specific targeted sources inside a site (inputs, effluents, digesters or biogas purification). Global plant methane emissions among all sites range from 1.5 to 26 kg.h^-1, with average emissions of 10 kg.h^-1_. Repeated measurements of emissions on the same site at different dates depict a significant temporal variability, however overwhelmed by the variability of emissions among all sites. We estimated instantaneous methane losses ranging from 1.7 to 10 %, comparing monitored emissions with the installed productions. Emissions of targeted sources among sites suggest that inputs and effluents might be the predominant methane sources on the sites, while biogenic CO₂ emissions might be mostly attributed to the biogas purification process.

This campaign highlighted several limits intrinsically linked with the mass balance method. One of them is the sensitivity to contamination by parasite sources, which has to be anticipated during the field campaign preparation. Another difficulty is the risk of measuring truncated plumes, as the mass balance method requires the monitoring of an entire plume cross-section to provide quantifications representative of the complete source emissions. These limitations could be overturned in the future by alternative quantification methods, such as inversion methods based on Large Eddy Simulation of the atmospheric transport, considering the highly variable nature of the turbulent plume. These new developments, associated with evolutions of the monitoring protocol, may improve the reliability and precision of the results.

How to cite: Bonne, J.-L., Dumelie, N., Lauvaux, T., Abdallah, C., Burgalat, J., Albora, G., Vincent, J., Cousin, J., Parent, F., Moncourtois, V., and Joly, L.: Lessons learned from a UAS survey of methane emissions from multiple biogas plants in France, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5789, https://doi.org/10.5194/egusphere-egu25-5789, 2025.

Based on the traditional satellite-based convective initiation (CI) detection method, an improved algorithm for the identification and tracking of CIs using satellite data has been proposed. This algorithm then undergoes spatio-temporal matching with ground-based observation data such as radar and precipitation data. Incorporating experts domain knowledge, the algorithm utilizes a subjective-objective interactive approach to complete the verification and calibration of the satellite-drived CI identification results. This process results in a high-resolution annotation dataset of convective initiation that can be used for detection and forecasting of CI and artificial intelligence models.

Firstly, within a spatial-temporal window of 30 minutes before and after the satellite CIs trigger time and a radius of 20km, the satellite-derived CIs are matched with radar-identified CIs. Additionally, within a spatial-temporal window of 60 minutes after the satellite CI trigger and extending 2km outside the CI cloud clusters movement zone, the satellite-derived CIs are also matched with precipitation data. The two matching results are combined to form a comprehensive identification of CIs. Furthermore, using a calibration system and a back-to-back verification method by forecasters, the CI annotation results are revised, resulting in a high-resolution and reliable CI annotation dataset.

Using this methodology, a high spatio-temporal resolution CI dataset was established for the years 2018-2023, which allowed for the statistical analysis of CI distributions across different precipitation levels in each month. The highest proportion of CI events occurred in August, followed by July. Among these, CI events with moderate precipitation accounted for 46.2%, weak precipitation accounted for 34.4%, and strong precipitation accounted for 19.3%.

It can be seen that there is a noticeable northward shift in the occurrence of CI events, especially those associated with heavy precipitation, from April to August. In April, these events are mainly concentrated in a few provinces in the central and southern parts of the country. Subsequently, they gradually expand from south to north, covering the entire central and eastern research area by August. In September, they retreat back to the central and southern regions. This spatial evolution pattern of CI events once again verifies that the occurrence of severe convection events is closely related to the position changes of the Intertropical Convergence Zone (ITCZ) and the monsoon.The frequency of CI occurrences has also been proven to peak between 11 a.m. and 3 p.m., regardless of precipitation intensity.

How to cite: Peng, L., Ye, C., and Ou, X.: Convection Initiation Identification and The Construction of A High-value Dataset Using the Fengyun-4A Satellite, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6767, https://doi.org/10.5194/egusphere-egu25-6767, 2025.

The tropical middle atmosphere is characterized by long-period oscillations such as the Quasi Biennial Oscillation and the Semiannual Oscillation which are primarily driven by the interaction of a broad spectrum of atmospheric waves with the background flow. Using reanalysis datasets and independent rocket soundings from a low latitude location, we identified a hitherto unreported variability in the tropical middle atmosphere that appears at a variable interval of 2-5 years in the late 20^th century and 7-9 years in the early 21^st century. The newly identified variability, Quasi-Periodic Easterly Bursts (QPEBs) as we call them, manifests as enhanced easterlies during the easterly phase of the Stratopause Semiannual Oscillation around May-July. QPEBs are found to have remote influences on the Southern Hemispheric polar vortex as well as residual circulation in the lower mesosphere. A momentum budget analysis reveals that QPEBs are found to be primarily caused by enhanced cross-equatorial advection as well as gravity wave drag. Even though a close association with the Quasi Biennial Oscillation winds is observed, the cause of the observed periodicity remains elusive.

How to cite: Koushik, N. and Kumar, K. K.: An Enigmatic Variability in the Tropical Middle Atmosphere, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7147, https://doi.org/10.5194/egusphere-egu25-7147, 2025.

In past few decades there has been a noticeable increase in the frequency and intensity of extreme rainfall events (EREs) globally, including India. The Clausius-Clapeyron relationship explains how the warmer air can significantly hold more moisture. Hence, in present climate change scenario increasing temperature along with other factors can lead to further increase in EREs. Effective management strategeis in various sectors like disaster preparedness, smart-city planning, water quality, public health, agriculture planning, etc. can get improved, through proper understanding on the distribution and frequency of EREs. Keeping in mind the socio-economic impacts of EREs; this study aimed to identify the hotspot regions for EREs in India.

India is a country with vast spatio-temporal variability in rainfall pattern. Hence, this study implemented objective criteria to identify the spatio-temporal rainfall variability of EREs over four rainfall homogeneous regions for pre-monsoon, monsoon and post-monsoon seasons. Based on frequency distribution of daily accumulated rainfall, suitable rainfall threshold values for defining EREs are identified for each homogeneous region and each season. These threshold values vary region-wise as well as season-wise. Distribution of EREs show interannual as well as seasonal variability.

Clustering algorithms, popular unsupervised Machine Learning (ML) techniques, are handy tools to identify hotspots of extreme rainfall regions with similar spatial variability. To understand the ERE distribution and to identify rainfall hotspots based on long term daily gridded rainfall data, this study implemented K-means clustering and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithms. Comparative area distribution study between K-means and DBSCAN clustering help to identify the EREs hotspots in India. Overall, the K-means method shows more scattered hotspots compared to DBSCAN method, which are further validated using Davies-Boulding Index (DBI), Silhouette score, Calinski-Harabasz (CH) score and Dunn's Index. These score analysis methods serve as potential tools to support the clustering validation method. In addition to the area distribution, this study has addressed the temporal variability of the EREs hotspots. ST-OPTICS ( Spatio-Temporal Ordering Points to Identify the Clustering Structure) algorithm results clustering of hotspots based on their spatial and temporal similarity. This study shows that ML algorithms prove to be promising techniques for detecting and analyzing spatial as well as temporal variability of EREs hotspots which is effective for future management practice in various sectors.

Keywords: Extreme Rainfall Events; DBSCAN Clustering; K-Means Clustering; ST-OPTICS.

How to cite: Putatunda, I. and Vasudevan, R.: Extreme rainfall hotspots in India based on spatio-temporal variability of rainfall using unsupervised clustering techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7852, https://doi.org/10.5194/egusphere-egu25-7852, 2025.

Agricultural yield prediction plays a crucial role in food security and economic planning, yet existing models often struggle with the complexity and high dimensionality of agricultural data. This study presents a framework that combines explainable artificial intelligence (XAI) with feature reduction methodology to enhance the accuracy and efficiency of rice yield prediction. Our approach addresses the dual challenges of model interpretability and computational efficiency while maintaining high prediction accuracy.

The framework begins with a systematic development of prediction models utilizing advanced machine learning (ML) and deep learning (DL) techniques. We implemented comprehensive pre-processing steps, including data normalization, feature engineering, and missing value handling, to ensure data quality. Our evaluation encompassed various models, including Random Forest, Gradient Boosting Machines, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks with attention mechanisms. To optimize model performance, we employed hyperparameter tuning through grid search, effectively mitigating issues of overfitting and underfitting.

A notable innovation of our framework is the incorporation of SHapley Additive exPlanations (SHAP), enabling transparent insights into the model's decision-making process. Leveraging this XAI approach, we introduced a novel feature reduction methodology that systematically identifies and removes negatively contributing features while maintaining model accuracy. Our analysis of a multivariate dataset which is a public dataset from rice fields in the an Giang province of the Mekong Delta, Vietnam, required the integration of diverse satellite datasets, including optical data from Landsat and radar data from Sentinel-1. This revealed distinct patterns of feature influence on yield prediction, facilitating the optimization of the feature set for maximum effectiveness. Key radar polarization bands, VV (Vertical-Vertical) and VH (Vertical-Horizontal), provided crucial surface backscatter data, capturing information on crop structure, growth stages, and post-harvest soil conditions. Notably, the feature min_vh consistently emerged as the most significant predictor.

The implementation of our feature reduction strategy resulted in significant improvements in both model performance and computational efficiency. By removing 15-20 number of identified negatively contributing features, we achieved approximately 3-5% improvement in prediction accuracy while substantially reducing the computational overhead and model training time. This enhancement in efficiency did not compromise the model's interpretability, demonstrating the robust nature of our framework.

Our methodology represents a significant advancement in agricultural modeling by successfully addressing the challenges of high-dimensional data while maintaining model interpretability. The framework's ability to identify and eliminate non-contributing features while improving prediction accuracy demonstrates its potential for wide-scale application in agricultural yield prediction. Furthermore, the reduced computational requirements make it a practical solution for real-world applications where computational resources may be limited.

These results validate the effectiveness of our integrated approach in handling complex agricultural data while providing actionable insights for yield prediction. The framework offers a scalable, interpretable, and computationally efficient solution that can be adapted for various agricultural prediction tasks, potentially transforming how we approach agricultural yield forecasting.

How to cite: Dey, A., Saha, A., Sarkar, S., Mondal, A., and Mitra, P.: An Explainable AI-Driven Feature Reduction Framework for Enhanced Agricultural Yield Prediction, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7965, https://doi.org/10.5194/egusphere-egu25-7965, 2025.

Black carbon (BC) is a short-lived atmospheric aerosol having light absorbing properties with climate-changing potential. In addition, BC aerosols are also responsible for several adverse health effects including cardiovascular and respiratory problems. Here, we examine the long-term changes in BC, using MERRA-2 (Modern-Era Retro spective analysis for Research and Applications) and Emissions Database for Global Atmospheric Research (EDGAR) data for the period 2000–2019, and the associated health burden in rural India. This study finds a decreasing trend in BC in the rural IGP (Indo-Gangetic Plain) and NWI (North West India) during 2007–2019, at about -0.004 and –0.005 μg/m³/yr, respectively. A significant reduction in BC (from 0.03 to 0.01 μg/m³/yr after 2006) is observed in the rural Peninsular India (PI), where the reduced wind speed limits the transport of BC aerosols from other regions and thus, limits the BC concentration there. Our assessment finds that government policies such as BS (Bharat Stage) emission norms, electrification of rail routes, use of electric and compressed natural gas-based vehicles, the transformation of brick kilns to zig-zag technology, mechanised farming for on- site handling of crop residues and recent changes in atmospheric drivers (e.g. winds in IGP) contributed to this reduction in BC. However, the health burden associated with BC causes the highest all-cause mortality to be around 5,17,651 and 34,082 inhabitants in winter (December-February) and post-monsoon (October-November) seasons, respectively, in the rural IGP in the latest year 2019. In brief, the reduction of BC in rural India indicates that it complements the government policies. However, an improvement in the policy implementation might prove to be conducive to reduce the BC-driven mortality and regional climate warming.

How to cite: Pathak, M., Kuttippurath, J., and Kumar, R.:  Long-term changes in black carbon aerosols and their health effects in rural India during the past two decades (2000–2019), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9946, https://doi.org/10.5194/egusphere-egu25-9946, 2025.

The rapid urbanization of modern cities presents significant challenges, with air pollution emerging as a critical concern for public health and environmental sustainability. In Greece, while the government collects extensive air quality data as mandated by the EU Directive 2881/2024 (recast of 2008/50, 2004/107), limited efforts are made to communicate this data to the public. The existing network of large monitoring stations is often inaccessible to the pubic and primarily serving scientists and policymakers.

Addressing this gap, the FAIRCITY (ATTP4-0360457) project—a collaboration between the National Observatory of Athens, the National and Kapodistrian University of Athens and the Greek Innovation Company Energy4Smart—introduces the “Smart Stations” an innovative solution incorporating public benches powered by photovoltaics, equipped with free charging sockets for people with electrical wheelchairs as well as other smart city sevices, with embedded low cost air quality sensors, designed to make air quality data accessible, timely, and engaging. This initiative not only aligns with global sustainability goals but also serves as a model for other cities seeking to improve urban liveability. The low-cost sensors embedded within the bench at a height of approximately 3 meteres above ground, were selected based on size, technology and price criteria to continuously monitor eight key pollutants: three fractions of Particulate Matters (PM₁, PM_2.5, PM₁₀), carbon monoxide (CO), carbon dioxide (CO₂), nitrogen dioxide (NO₂), ozone (O₃) and sulfur dioxide (SO₂).

The Smart Stations are deployed in open, public spaces (e.g., commercial areas, residential zones, parks), at a distance from major pollutant sources and in collaboration with interested municipalities of the Attica Region. Their aim is to record the local air quality and pollutant diurnal variations in order to highlight the sources responsible (i.e., Korydallos high NO₂, NO, PM concentrations from traffic) and estimate the population exposure. Citizens can walk up to these stations, sit down and instantly access critical information about their local air quality from digital displays that provide in near real-time the simplified Air Quality Index (AQI) along with health protection and other environmental infomation.

Preliminary results indicate that the diurnal variations of the monitored pollutants follow closely the local anthropogenic activities (traffic by passing the area, central heating, cooking). The pollutant levels are similar across the different municipalities, presenting peaks at different times depending on the type of area. The hourly AQI is mainly affected by larger scale events such as an extensive air pollution episode or dust intrusion event.  

How to cite: Assimakopoulos, V., Fameli, K.-M., Kladakis, A., Efthymiou, C., Charalampidou, C., Sotiropoulou, M., Antoniou, I. –. M., Kytrilaki, A., Massas, A., and Assimakopoulos, M.-N.: Bridging the Gap: Smart Benches for Accessible Urban Air Quality Monitoring and Public Engagement in the Region of Attica, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10268, https://doi.org/10.5194/egusphere-egu25-10268, 2025.

Air quality monitoring is crucial for assessing environmental health and supporting mitigation strategies. This research focuses on the co-location of various low-cost particulate matter (PM) sensors—uRADMonitor, AirGradient, PurpleAir, Clarity, and sensors from community initiatives—alongside a mobile laboratory equipped with a reference-grade GRIM EDM 180 analyzer. The primary goal is to identify and quantify bias among these low-cost sensors for PM2.5 and PM10 measurements at the same location.

By systematically analyzing the measurement discrepancies, a generalized correction formula is derived, enabling the harmonization of readings across different sensor types. The corrected data will form the basis of a hybrid air quality monitoring network, which standardizes PM2.5 and PM10 concentrations regardless of the sensor manufacturer. This approach leverages the affordability and scalability of low-cost sensors while ensuring data quality comparable to reference instruments.

The results aim to address limitations in the current low-cost sensor ecosystem, enhance interoperability, and provide communities and policymakers with reliable, high-resolution air quality data. Ultimately, this study supports the development of inclusive and sustainable monitoring frameworks that empower both urban and rural regions with actionable environmental insights, using all kinds of sensors.

How to cite: Luchiian, A.: Harmonizing Low-Cost Air Quality Sensors for a Hybrid Monitoring Network, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10631, https://doi.org/10.5194/egusphere-egu25-10631, 2025.

Agricultural burning in Tucumán, Argentina, has been a major contributor to air pollution, particularly during the dry season (April to September). This environmental issue is mainly due to the limited availability of modern machinery for sustainable harvesting, leading to heavy reliance on traditional biomass burning for crop residue management. The combustion process generates large amounts of fine particulate matter (PM_2.5), which severely affects air quality and public health. To address this challenge, an inter-institutional collaboration under the Networking Initiative Breathe2Change.org, supported by the Alexander von Humboldt Foundation, facilitated the creation of the first air quality monitoring network in Tucumán. This initiative aimed to raise awareness and provide actionable data to local communities and scientists.

A custom sensor module was designed, integrating an OPC Plantower PMS5003 sensor for real-time PM_2.5 detection, CO₂ sensors using NDIR technology, as well as humidity and temperature sensors. A forced ventilation system was also incorporated to ensure representative air circulation inside the module without affecting airflow into the OPC sensor. The network, consisting of 25 sensor modules deployed throughout the 22,500 square kilometers of Tucumán, provided continuous data collection for 12 months in 2023. The data were shared on a publicly accessible data platform, developed as part of the Breathe2Change Initiative, which facilitated both citizen consultation and analysis by the scientists involved in the project.

During an initial 3-week intercomparison phase, 10 sensor modules were assessed for consistency, yielding a high correlation (R² > 0.9), confirming the reliability of the modules. Afterward, 23 of the 25 sensors were deployed across urban, suburban, and rural areas, including regions directly affected by agricultural fires. High- and low-flow reference samplers were used to collect daily PM2.5 concentrations from August to December, coinciding with the peak biomass burning period. During this period, two of the sensor modules were co-located with the reference samplers to allow for direct comparison. This phase was essential for deriving a local correction factor for the sensors.

Results showed considerably high PM2.5 concentrations, with monthly averages exceeding 60 µg/m³ in fire-impacted areas, well above the daily limits set by the World Health Organization (WHO). Even urban areas recorded average levels of 30 µg/m³, surpassing WHO guidelines. The region’s mountainous terrain and climate further exacerbated the pollution, triggering thermal inversion phenomena that trapped pollutants near ground level. Using the corrected sensor network, spatial distribution maps of PM_2.5 were generated through Kriging interpolation, revealing a strong correlation between elevated pollutant levels and fire activity. Higher PM_2.5 concentrations were observed in the central-eastern part of the province, likely linked to sugarcane production areas, and possibly influenced by rural traffic and biomass burning. Kriging analysis confirmed this spatial trend, with a marked reduction in localized concentrations after September, likely due to rainfall events.

This study underscores the degradation of air quality during biomass burning events and the need for regulatory measures and sustainable agricultural practices to mitigate environmental and health impacts. It also highlights the potential of low-cost sensors as effective tools for monitoring air pollution in resource-limited regions.

How to cite: Gibilisco, R. G., Aguilera Sammaritano, M., Reynoso Posse, F., Huber, K., Elizondo, J., Torkar, S., Saez, M. M., Scaglioti, A., Tames, F., Puliafito, E., Castellano, M. J., Diaz, M., Parellada, N., Ciancaglini, G., Schillman, B., Kurtenbach, R., Wiesen, P., Caggiano, A., Ben Altabef, A., and Teruel, M.: Spatio-Temporal Distribution of PM 2.5 and its Association with Agricultural Fires in Northern Argentina., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11635, https://doi.org/10.5194/egusphere-egu25-11635, 2025.

Severe weather events often develop rapidly and cause extensive damage, resulting in billions of dollars in losses annually. This paper explores Large Language Models (LLMs) to effectively reason about the adversity of weather hazards. To tackle this issue, we gathered National Weather Service (NWS) flood reports covering the period from June 2005 to September 2024. Two pre-trained LLMs including Bidirectional and Auto-Regressive Transformer (BART) models (large) and Bidirectional Encoder Representations from Transformers (BERT) were employed to classify flood reports according to predefined labels. These models encompass a range of sizes with parameter counts of 406 million, and 110 million parameters, respectively. We employed the Low-Rank Adaptation (LoRA) fine-tuning technique to enhance performance and memory efficiency. The fine-tuning and few-shot learning capabilities of these models were evaluated to adapt pre-trained language models for specific tasks or domains. The methodology was applied in Charleston County, South Carolina, USA— a vulnerable region to compound flooding. Extreme events reported during the training periods were unevenly distributed across training period, resulting in imbalanced datasets. The “Cyclonic” category represents significantly fewer instances in the report text data, while the “Flood” and “Thunderstorm” categories appeared more frequent.  The findings revealed that while few-shot learning significantly reduced computational costs, fine-tuned models resulted in more stable and reliable performance. Among multiple LLMs applied in this research, the BART model achieved higher F1 scores in the “Flood,” “Thunderstorm,” and “Cyclonic” categories—requiring fewer training epochs to reach optimized performance levels. Furthermore, the BERT model demonstrated a shorter overall training time (12 hours 17 minutes) compared to other LLMs, demonstrating efficient cost of computing. This comprehensive evaluation of LLMs across diverse NWS flood reports enhanced our understanding of their capabilities in text classification and offered valuable insights into leveraging these advanced techniques for weather disaster assessment.

How to cite: Neupane, A., Zafarmomen, N., and Samadi, V.: Leveraging Large Language Models for Enhancing and Reasoning Adverse Weather Hazard Classification, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13882, https://doi.org/10.5194/egusphere-egu25-13882, 2025.

Air quality monitoring is an essential procedure to ensure that pollutant levels remain within safe limits and do not pose a threat to public health, particularly for vulnerable populations. The deployment and maintenance of stationary air quality monitoring stations can be expensive, especially when a large number is required to create a comprehensive network. As a result, there has been growing interest in utilizing small, low-cost sensors that are easier to deploy and provide a more flexible and cost-effective alternative. In addition to these sensors, satellite systems have become valuable tools for air quality monitoring, offering high temporal resolution data that facilitates the assessment of air pollution over larger areas. This study looks into data fusion techniques to combine data from both stationary and mobile low-cost sensors with satellite data to analyze the air quality at the University of the Philippines, Diliman campus. Seven small sensors were deployed across the university, a mixed-use area with both vegetation and buildings, to measure pollutant concentrations, such as particulate matter. Satellite data from MODIS, Sentinel-5P, and ERA5 reanalysis were used to monitor aerosol optical depth (AOD), sulfur dioxide (SO2), nitrogen dioxide (NO2), and meteorological conditions. The time-series analysis focused on a three-day period during which mobile air quality data from an e-trike were collected around the university. The data from these mobile sensors, along with the stationary sensor measurements, were used to estimate PM2.5 concentrations across the campus. Kriging interpolation, a geostatistical method that estimates unknown values based on the spatial correlation of known data points, was employed to generate smooth surfaces of PM2.5 concentration across the university.  Kriging interpolation was used on the stationary sensor dataset to predict the PM2.5 levels at the location of the mobile sensors at a given timeframe. Moreover, cokriging was also applied by incorporating multiple correlated variables, improving predictions by utilizing relationships between the primary variable (PM2.5) and secondary variables, such as aerosol optical depth or SO2 and NO2 concentrations. The results obtained from both Kriging and Cokriging methods were compared with data collected from mobile sensors to assess the air quality at the University of the Philippines, Diliman. The interpolated PM2.5 values were compared with the data from the mobile sensors (SEN55 and PMS7003) as ground truth, and a mean absolute percentage error (MAPE) of 43.00% to 57.23% was obtained. Initial results of cokriging with NO2 showed MAPE of 36.67% to 52.55%. Further work is expanding the dataset and refining the interpolation models to enhance the accuracy and reliability of air quality assessments across the university. By integrating more data and conducting additional tests, this approach can provide more comprehensive air quality monitoring at reduced costs and address data gaps.

How to cite: Hizon, J. R., Torres, R. A., Togonon, A. C. E., Recto, B. A., Apostol, F. A., Magpantay, P., Eslit, J. J., Ganhinhin, J., Rosales, M., Austria, I., de Guzman, J., de Leon, M. T., Cajote, R., Co, P. J., and Ramos, R.: Air Quality Assessment In The University Of The Philippines Diliman Campus Through The Integration Of Small Sensors, Satellite Data, And Kriging Interpolation Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14960, https://doi.org/10.5194/egusphere-egu25-14960, 2025.

Fog significantly reduces visibility, impacting transportation and safety, particularly in regions like the United Arab Emirates (UAE) where it is a regular
occurrence, in particular in the winter months. This study develops a machine learning-based approach for automated fog detection and masking from near real-time observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument onboard the Meteosat Second Generation spacecraft to enhance fog detection and forecast. We evaluated six basic machine learning (ML) models trained with four different methods: (1) supervised training using SEVIRI pixel data and fog observations over airport stations; (2) as approach (1) but incorporating infrared channel data; (3) training with labeled fog and no-fog regions identified in SEVIRI night microphysics Red-Green-Blue (RGB) images through k-means clustering; and, (4) a fusion approach combining station-labeled data (approach 1) and k-means clustered-labeled data (approach 3). Among the models, the eXtreme Gradient Boosting (XGBoost) demonstrated slightly higher performance. Models trained on station data (approach 1) achieved a Probability of Detection (POD) of 0.73 and a False Alarm Ratio (FAR) of 0.11. For spatial fog masking, models trained on a combination of station-labeled and k-means cluster-labeled data (approach 4) performed best. Overall, the XGBoost method and the fusion approach (4) are recommended for fog detection and masking in the hyper-arid UAE. These findings demonstrate the potential for trained ML models to deliver accurate, near real-time fog detection and masking, enhancing monitoring over broad areas.

How to cite: Nelli, N. R., Francis, D., Cherif, C., Fonseca, R., and Ghedira, H.: Automated Nighttime Fog Detection and Masking Using Machine Learning from Near Real-Time Satellite Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14973, https://doi.org/10.5194/egusphere-egu25-14973, 2025.

The Madden-Julian Oscillation (MJO) is a crucial atmospheric phenomenon characterized by large-scale, eastward-propagating disturbances in the tropical atmosphere. It profoundly influences global climate and weather patterns and serves as a key source of predictability for subseasonal forecasts. In particular, the propagation characteristics of the MJO are critical parameters that impacts the timing and intensity of its effects. Variability in these characteristics can alter the MJO’s interaction with other climate components, thereby affecting weather patterns. Therefore, it is essential to investigate the variability of MJO propagation characteristics.

In this study, we aim to examine the changes in propagation characteristics of the MJO, such as propagation speed, across three primary regions: Indian Ocean, Maritime Continent, western Pacific. These changes will be compared between two distinct period (P1: 1979-1998, P2: 2003-2022). Furthermore, we will investigate the mechanisms driving variations in MJO propagation speed within each tropical region and assess potential future changes using reanalysis data and model outputs. By addressing these questions, this study can contribute to improve the predictability and accuracy of climate models in representing the MJO.

How to cite: Kim, H.-R. and Ha, K.-J.: Changes in MJO Propagation Characteristics and Regional Variations under a Changing Climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15784, https://doi.org/10.5194/egusphere-egu25-15784, 2025.

 In order to solve the problem of quantity traceability of precipitation phenomenon instrument, a precipitation phenomenon checking device was developed. By simulating the precipitation particles of 4.3 mm and 9.5 mm, corresponding to the velocities of 2m/s, 7M/s and 12M/s respectively, the on-site verification of the precipitation phenomenometer and the test program of the upper computer software are carried out, the relevant particle channels are recorded and displayed in the map, and the performance of the precipitation phenomenometer is judged automatically. It has many advantages, such as complete function, reasonable design, easy to carry, friendly software interface, one-button detection, automatically judge whether the equipment is qualified, and according to the template to generate a verification report. The practical application proves that the device provides a strong support for the meteorological department's equipment support personnel to carry out the verification work of the precipitation phenomenometer, improves the working efficiency, and plays a role in supervising and inspecting the quality of the precipitation phenomenometer's equipment, it has a good application prospect in the field verification of precipitation phenomenometer.

How to cite: Han, Y.: Development and application of the calibration device of precipitation phenomenometer, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17395, https://doi.org/10.5194/egusphere-egu25-17395, 2025.

The Multi-sensor Advection Diffusion Weather Research and Forecast (MAD-WRF) model is a state-of-the-art addition to the WRF model that includes a fast cloud-initialization procedure, making it more suitable for hydrometeors analysis and clouds forecasts. The MAD-WRF cloud initialization combines a cloud parameterization that infers the presence of clouds based on relative humidity with observations of the cloud mask and cloud top/base height to provide a three-dimensional cloud analysis. During the forecasts, the hydrometeors can be advected and diffused with no microphysics, in what we refer to as the MAD-WRF passive mode. Alternatively, these passive hydrometeors can be integrated into the explicitly resolved hydrometeors during a nudging phase, designated the MAD-WRF active mode (Jiménez et al., 10.1016/j.solener.2022.04.055). As such, MAD-WRF has been extensively used for solar energy predictions.

Here we have investigated the feasibility of using MAD-WRF to improve the accuracy of intense precipitation forecasts. An extreme precipitation event over Israel that led to urban floods and two casualties in Tel-Aviv during January 4^th, 2020, has been chosen as a case study. The extreme accumulated precipitation responsible for noon and early afternoon floods was triggered by a persistent cloud train that developed over the area several hours before. MAD-WRF model has been configured with 3-nested domains with 9, 3 and 1 km grid-sizes. We have run MAD-WRF in active mode incorporating satellite-retrieved cloud-top heights provided by the European Space Agency EUMETSAT in all three domains. EUMETSAT data are available in near real-time making it suitable for operational forecasts.

Independent precipitation data measured by the Israel Meteorological Service radar at Bet-Dagan (about 10 km south-east of Tel-Aviv) has been used for forecasts verification. Comparison between radar data and MAD-WRF forecasts with and without incorporation of EUMETSAT cloud-tops retrievals reveal the advantage MAD-WRF cloud initialization. The significant improvement in the forecast of the location and rate of the precipitation is observed up to 12 hours ahead in time.

On-going work focuses on the evaluation of the precipitation distributions and improvement of the forecast of dry areas.

How to cite: Gelman, A., Morin, E., Jiménez, P., Sheu, R.-S., and Rostkier-Edelstein, D.: Improving forecasts of extreme precipitation with MAD-WRF mesoscale model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17568, https://doi.org/10.5194/egusphere-egu25-17568, 2025.

Dengue fever outbreaks impose a severe healthcare burden in Vietnam, therefore the development of an early Dengue warning system is key to improve public health planning and mitigate the future burden produced by this disease. This study assessed the ECMWF ensemble re-forecast skill for relative humidity, temperature and precipitation, which are key factors for vector-borne disease transmission in Vietnam between 1-4 weeks in advance. We focused the analysis on the rainy season (May-October) using ERA5 reanalysis as a reference dataset. Re-forecast data was pre-processed using a quantile mapping technique to reduce the bias between re-forecast and observations. Results showed that corrected re-forecasts of weekly mean temperature, relative humidity and accumulated precipitation are skilful up to 2-3 weeks in advance and rank histograms verified the forecast reliability. Nonetheless the model is less skillful for the region of South Vietnam and seems to struggle at predicting extremely high/low values of temperature, relative humidity and precipitation. Results from this study demonstrate that ECMWF ensemble forecasts are suitable to use as inputs for a dengue early warning system up to 14-21 days in advance

How to cite: Perez, I., Sparrow, S., Weisheimer, A., Wright, M., and Main, L.: Verification of weather variables linked to Dengue incidence inthe sub‐seasonal scale in Vietnam, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19687, https://doi.org/10.5194/egusphere-egu25-19687, 2025.

Background: The increasing availability and usage of low-cost air quality sensors (LCS) presents both opportunities and challenges in terms of data accuracy, reliability, precision and interpretation. Various low cost sensors types differ in the degree of accuracy reliability and precision They can also be influenced by environmental conditions like temperatures and humidity. This study assesses three LCS, E-Samplers, Modulair^TM and AirQO, deployed alongside a reference-grade Beta Attenuation Monitor (BAM-1022) in Nairobi, Kenya, to upraise their performance under varying conditions and explore the strategies for calibration and integration into the monitoring networks.

Methods: The study used BAM-1022 data to validate and calibrate the LCS installed at the University of Nairobi’s Parklands Campus (27 February 2024 to 26 December 2024).  We analyzed sensor accuracy, precision and response to pollution across wet and dry seasons and varying temperature and humidity levels. We aligned the LCS data with BAM-1022 measurements using tailored correction factors and multiple linear regression (MLR) models. We used the coefficient of determination, represented by R-squared (R²), a statistical measure of how close the data from the LCS are from the data from the BAM and the Pearson correlation, r to show the strength of the linear relationship between the sensor measurements and reference measurements. Additionally, we conducted paired t-tests to determine whether statistically significant differences existed between the BAM-1022 and each LCS, and one-sample t-tests to find out if there was a statistically significant difference in the values recorded by low-cost sensors themselves. The study also explored the potential of LCS to improve spatial coverage and resolution while addressing challenges like sensor drift and environmental interference.

Results: The Modulair^TM sensor showed closer measurements in reference to BAM-1022 measurements (R²= 0.82, r =0.9458) followed by AirQO (R²=0.54, r =0.8933) and E-Sampler (R²=0.36, r =0.7166). During wet season, Modulair^TM maintained the closer measurements (R²=0.73, r =0.9123) with AirQO (R²=0.36, r =0.7219) and E-Sampler (R²=0.21, r =0.7812) showing lower alignment. Similar trend was observed in dry season with Modulair^TM (R²=0.8, r=0.8124) followed by AirQO (R²=0.51, r=0.7001) and E-Sampler (R²=0.28, r=0.6996). During high PM_2.5 concentration periods (July to December), Modulair^TM reported higher values than the BAM on certain days. AirQO generally recorded lower values except during these high concentration periods while the E-Samplers fluctuated between higher or lower values across the collocation period. Consequently, correction factors of -12.5, 31.55 and 29.65 were derived for Modulair^TM,AirQO and E-Samplers respectively. Statistical analysis revealed a significant difference between the BAM measurements and LCS (p-value < 0.001). However, no significant differences were observed between the measurements of each of the low-cost sensors.

Conclusion: The LCS can enhance air quality monitoring networks when collocated appropriately and, consistently and carefully calibrated. The readings should be corrected against reference sensor for accurate and reliable data.  Collocation with reference monitors or among the LCS units for regions with limited access to high-end monitoring infrastructure such as Nairobi is key before deployment. Air quality modeling can create a comprehensive monitoring networks hence improved spatial resolution and public health insights. 

How to cite: Nyamondo, J., Oguge, N., Anyango, S., Afulloh, A., Adera, N., and Okoth, B.: Air Quality Monitoring in Nairobi City, Kenya: Role of Collocation in Low Cost Sensor Deployment , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20267, https://doi.org/10.5194/egusphere-egu25-20267, 2025.

Tropical deep convective clouds (DCCs) play a pivotal role in Earth's hydrological cycle, with their dynamics strongly influenced by aerosols. Depending on their properties, aerosols can either invigorate or suppress cloud formation and development. Previous observational studies and cloud-resolving model simulations have shown that aerosols such as black carbon (BC) and sulfates modify cloud microphysics, affecting droplet size distribution, latent heat release, and precipitation patterns. However, the use of global climate models (GCMs) to study these aerosol-cloud interactions remain limited, despite their ability to capture large-scale circulation patterns and associated non-linear feedback. This study investigates the sensitivity of aerosol-induced cloud invigoration and suppression (AIVe) to major aerosol species during the Indian summer monsoon (ISM) season using the Community Earth System Model, specifically its atmospheric component, the Community Atmosphere Model version 5 (CESM-CAM5). The analysis focuses on DCCs over central India during the monsoon months of June–September (JJAS) for the period 2005–2008. Aerosol and cloud parameters from CESM-CAM5 simulations, conducted at 0.5-degree horizontal resolution, are compared with satellite observations. Five Atmospheric Model Intercomparison Project (AMIP)-style simulations were performed: one with aerosols at pre-industrial level (PI) levels, another at present-day (PD) levels, and three additional simulations perturbing specific aerosol species (dust, BC, and sulfate) under PD conditions to isolate their individual effects on AIVe. The findings highlight that aerosol physico-chemical properties critically influence DCC behavior. Black carbon near the boundary layer increases cloud condensation nuclei (CCN) concentrations, delaying precipitation, enhancing warm-phase invigoration, and strengthening updrafts. In the upper troposphere, BC absorbs solar radiation, causing atmospheric warming that promotes cloud deepening and cold-phase processes. Additionally, BC intensifies both shortwave and longwave heating, prolonging cloud lifetimes and supporting deeper convection. Sulfate aerosols primarily enhance warm-phase invigoration through increased CCN concentrations at lower altitudes. However, their weaker vertical transport limits their impact on cold phase processes and deep convection compared to BC and dust. Dust aerosols with high concentrations in the mid-troposphere, act as efficient ice-nucleating particles (INPs), enhancing cold phase invigoration. However, suppressed updrafts in the upper troposphere reduce their overall effect on deep convective systems, emphasizing the importance of aerosol size, number concentration, and properties in shaping AIVe. This study underscores the complex interplay between aerosol characteristics and their vertical distribution in influencing cloud dynamics during the ISM. Detailed results and further implications will be presented.

How to cite: Sharma, P., Ganguly, D., and Kant, S.: Sensitivity of Cloud Invigoration and Suppression Effects to Major Aerosol species During the Indian Summer Monsoon in a Global Climate Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-886, https://doi.org/10.5194/egusphere-egu25-886, 2025.

Black carbon (BC) aerosols have been reported to influence the precipitation patterns over South-East Asia. In this study, we present surface measurements of BC carried out from a rural location in the Western Ghats and covering all the seasons. Despite being a remote location with negligible anthropogenic emissions, the total BC concentration is strongly modulated by particles originating from fossil fuel burning (~75%). Contrary to the prominent role played by boundary layer dynamics in the diurnal variations of BC in the tropics, our measurements reveal a disconnection between boundary layer dynamics and BC concentration mostly due to the advection from a distant urban location being the dominant source of BC. However, this influence is conspicuous on the concentration of particles originating from biomass burning. Seasonal variations in the wind fields, surface temperature, and rainfall are observed to influence the BC concentration, thereby leading to distinct diurnal variations seldom reported elsewhere. Reanalysis data sets fail to capture these changing patterns in BC, with daily mean concentrations exhibiting large differences with our observations (particularly in winter months) and diurnal patterns being different throughout the season. Under this backdrop, incorporation of these measurements could possibly improve the monsoon forecast in global climate models and provide deeper insights on the role of meteorology and boundary layer dynamics on aerosol fields in complex environments.

How to cite: Sunil S, D., Narayana Sarma, A., Kudilil, S., Sreedharan Krishnakumari, S., and Krishnaswamy, K.: Modulation of temporal evolution of black carbon aerosols at a rural location in the Western Ghats by meteorology and boundary layer dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1100, https://doi.org/10.5194/egusphere-egu25-1100, 2025.

During winter, dense fog occurrences in the Indo-Gangetic Plain pose severe risks to visibility, air quality, and public health, emphasizing the need for improved fog forecasting in India. This study employs a high-resolution WRF-Chem model (2 km × 2 km) to identify optimal configurations for simulating fog in the region and investigate the impact of urbanization-induced UHI/UDI (Urban Heat Island/Urban Dry Island) and elevated emissions on the fog life cycle in and around the megacity of Delhi.

A comprehensive sensitivity analysis explores model configurations across microphysics, planetary boundary layer (PBL), land surface models (LSM), radiation schemes, chemistry, and emission inputs. Simulations of surface and vertical meteorology are evaluated against data from weather stations and radiosonde profiles, while modeled chemistry is compared with ground-based measurements. Results demonstrate that specific combinations of microphysics, PBL, and LSM schemes coupled with chemistry effectively simulate Liquid Water Content (LWC), a critical fog proxy. Modeled relative humidity, particulate matter concentrations, and fog life cycles show strong agreement with observations. We then utilize this optimized model configuration to quantitatively analyze individual and combined effects of urbanization and aerosols on fog formation.

How to cite: K Lal, A., Kunchala, R. K., and Mohan, M.: Evaluating WRF-Chem for simulating fog episodes: A Case Study from The National Capital Region Delhi, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1370, https://doi.org/10.5194/egusphere-egu25-1370, 2025.

Satellite remote sensing has the advantage of wide spatial coverage and high data consistency, which is an important technology for global atmospheric environment monitoring. However, due to the influence of cloud cover, satellite remote sensing faces the problem of data missing; moreover, the direct object of hyperspectral satellite remote sensing is the total amount of pollution gases in the atmosphere, which is different from the near-ground concentration that directly affects human health. To solve these problems, this research developed a remote sensing technology combining satellite spectral analysis and artificial intelligence. We use artificial intelligence to increase the spatial coverage of satellite and ground-based remote sensing, and make future short term predictions and their applications. Preliminary results show that the reconstruction of satellite remote sensing data supported by artificial intelligence is of great significance for environmental pollution monitoring and control.

How to cite: Liu, C., Hu, Q., Li, Q., and Zhang, C.: Spatiotemporal reconstruction of gas pollutants with high resolution and coverage using hyperspectral remote sensing and artificial intelligence, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1923, https://doi.org/10.5194/egusphere-egu25-1923, 2025.

In Indian metropolitan cities, two-wheelers (2W) constitute 60–70% of traffic, making their emissions a significant contributor to urban air pollution. This study measured 2W exhaust emissions and driver exposure under real-world traffic conditions in the Chennai metropolitan area. Emission factors for CO, HC, and NO were 1.1, 0.02, and 0.03 g/km, respectively. However, limited studies on 2W are available due to the complexity of real-world measurements in Indian traffic conditions. The gaseous emissions from the measured vehicles are lower than their respective Bharat Stage (BS) standards except for CO. Personal exposure levels for PM₁₀, PM_2.5, and PM₁ were 212.5, 78.1, and 58.9 µg/m³, with the highest exposures occurring during idling and driving behind heavy-duty vehicles. The Multiple Particle Path Dosimetry (MPPD) model was used to estimate the deposition fractions in the human respiratory tract (HRT). Results indicated that PM_2.5 and PM₁ deposition fractions are higher in the pulmonary region, whereas PM₁₀ deposition is higher in the head region. 2W drivers are exposed to higher concentrations than any other motor vehicle driver. Since there is no substantiation of a tolerable limit of PM₁ exposure or a threshold beyond which no detrimental health implications occur, cautious planning is needed when developing the roads.

How to cite: Ranjan, S., K. Kuppili, S., and Nagendra SM, S.: Exploring the Nexus of Two-Wheeler Gaseous Contributions and Driver Exposure in a Million-Plus Population City, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2082, https://doi.org/10.5194/egusphere-egu25-2082, 2025.

Volcano eruption is one of the most destructive natural disasters, and its direct release of toxic gases and volcanic ash can lead to atmospheric pollution, posing significant threats to human health and ecological balance. To investigate the environmental impact of volcanic emissions, we retrieved the vertical column densities (VCDs) of sulfur dioxide (SO₂) and bromine monoxide (BrO) using the Chinese highest-resolution atmospheric trace gas remote sensing satellite payloads: the Environmental Trace Gas Monitoring Instrument (EMI) series on-board the GaoFen (GF5-02) and DaQi (DQ-1) satellites.

Here, we present our study on two significant volcanic emission events. On January 15, 2022, a violent eruption occurred near the South Pacific Island nation of Tonga, which is a typical submarine volcano. During this eruption, the volcanic plume ascended directly into the stratosphere (above 20 km), releasing a substantial amount of SO₂ and spreading rapidly westward (~30 m/s). In contrast, the majority of the BrO dispersed southeastward slowly (~10 m/s) within the altitude range of 8–15 km on January 16. The differences in eruption height and timing resulted in the transport of SO₂ and BrO in distinct directions in the Southern Hemisphere.

Another case is the Sundhnukagigar volcano on Iceland's Reykjanes Peninsula, which is a typical fissure volcano. A significant eruption began at 21:00 on August 22nd, following an earthquake swarm; this was the largest eruption in the region since December 2023. Satellite data indicated that the volcanic eruption released high concentrations of SO₂, with the maximum SO₂ VCD exceeding 15 Dobson Units (DU). By the morning of the 26th, part of the air mass had been transported northward to the Arctic Svalbard region. Simultaneously, ground observations from Ny-Ålesund revealed that an unprecedented Arctic haze event occurred, with the SO₂ VCD reaching approximately 40 times the usual level. It is also important to note that, in the context of global warming, the ongoing activity of Iceland's volcanoes will further exacerbate the melting of local glaciers and permafrost. This, in turn, disrupts the gravitational balance of the overlying crust, leading to an intensification of volcanic activity. Therefore, it is essential to employ multi-instrument, multi-scale, and high-resolution observations to monitor volcanic activity and assess its impact on both regional and global climate and the environment.

How to cite: Luo, Y., Li, Q., Wu, K., Qian, Y., Zhou, H., and Si, F.: Two Typical Case Studies of Volcanic Eruption Trace Gases Based on EMI Observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2443, https://doi.org/10.5194/egusphere-egu25-2443, 2025.

As a major air pollutant and precursor of ozone (O₃), anthropogenic nitrogen oxides (NO_X = NO + NO₂) have been effectively controlled in China since peaking around 2012. However, the evolving contrast of emissions across cities and its impacts on secondary pollutants such as O₃ remain poorly understood, primarily due to the limitations of existing emission inventories. Here we track the historical high-resolution (5 km) NO_X emissions based on POMINO-OMI and POMINO-TROPOMI NO₂ VCDs, adopting our previously developed inversion, PHLET. The results demonstrate significantly weaker NO_X emission declines in economically small cities where environmental pollution received much less attentions, leading to a shift of emission burdens toward western and non-capital cities. Moreover, simulations based on GEOS-Chem indicate that such disparities in NO_X emission trends have inhibited the mitigation of O₃ mainly in the western China, and even added up to the O₃ increase in some areas of the North China Plain. Our study points to the value of satellite-based inversion to access historical environmental regulations, and emphasizes the importance of collaborative pollution control across regions for comprehensive pollution control in China and other Global South countries undergoing rapid emission changes.

How to cite: Kong, H., Lin, J., Chen, L., Zhang, Y., and Wang, S.: City-level Disparities in NOX Emission Trends and Their Inhibitory Effects on O3 Mitigation in China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3222, https://doi.org/10.5194/egusphere-egu25-3222, 2025.

Atmospheric ice nucleating substances (INSs) play a crucial role in ice cloud formation above -35°C, impacting cloud radiative properties, cloud lifetime, and the hydrological cycle. Characterizing inorganic (e.g., mineral dusts, volcanic ash, metals) and organic (e.g., bacterial cells, fungal spores, pollen, and various biomacromolecules) INSs has typically involved: 1) single-particle analyses, which offer high resolution but require specialized equipment, and 2) bulk sample treatment (e.g., heat, H₂O₂, (NH₄)₂SO₄) analyses, which are more accessible but may overestimate or underestimate INS concentrations due to non-target effects. There is a need for additional methods to quantify inorganic and organic INSs concentrations in the atmosphere to test and improve climate models.

Here we show a new density gradient centrifugation method to differentiate and quantify inorganic (densities ≥ 2.1 g cm^-3) and organic INSs (densities ≤ 1.6 g cm^-3). Density gradient centrifugation was used to separate the INSs suspension into their respective density isolate. This was followed by a wash procedure consisting of sequential differential centrifugation and ultrafiltration. Lastly, the INSs were quantified using a droplet freezing assay.

Our method successfully recovered organic water-soluble INSs (lignin, birch pollen washing water and filtered Fusarium acuminatum) and organic water-insoluble INSs (Snomax and Pseudomonas syringae) in the low-density isolate. We recovered inorganic water-insoluble INSs (K-feldspar) in the high-density isolate. In an INS mixed suspension, we recovered K-feldspar in the high-density isolate and lignin in the low-density isolate both at concentrations similar to the isolated K-feldspar or lignin tests. 

This work demonstrates the broad applicability of density gradient centrifugation for characterizing a wide range of inorganic and organic atmospheric INSs.

How to cite: Uppal, G. K., Worthy, S. E., Chen, L., Yeung, C., McConville, O., and Bertram, A. K.: SeParation of Ice Nuclei via Density Layers (SPINDL): A new method for characterizing ice nuclei using density gradient centrifugation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3841, https://doi.org/10.5194/egusphere-egu25-3841, 2025.

Monitoring emissions of nitrogen dioxide is crucial for understanding the atmospheric composition and its impacts on air quality and climate. This study aims to evaluate the accuracy of retrievals of nitrogen dioxide tropospheric column by the Tropospheric Emissions: Monitoring of Pollution (TEMPO) by comparing them against retrievals of the ground-based Pandora instruments.

The TEMPO is a visible and ultraviolet spectrometer flying aboard of a commercial telecommunications satellite, Intelsat 40e, in geostationary orbit over 91˚ W longitude, thus maintaining a continuous view of North America. High resolution measurements of radiance reflected by the Earth's back to the instrument's detectors enable retrievals of columns of nitrogen dioxide involved in the chemical dynamics of Earth’s atmosphere. TEMPO V03 Level 1, 2, and 3 data were recently made available from the Atmospheric Science Data Center (ASDC) via NASA EarthData Search.

Direct-Sun Pandora spectrometer is used to retrieve columnar amounts of trace gases in the atmosphere by the means of differential optical absorption spectroscopy at numerous locations around the globe.

ASDC has developed a set of Jupyter notebooks dedicated to TEMPO vs. Pandora comparisons of the columns of individual trace gases including one dealing with NO₂ tropospheric column. The notebooks allow a user to select a specific Pandora station and a timeframe of interest. The code downloads all relevant TEMPO L2 granules as well as the Pandora dataset. The latter is sub-set to the selected timeframe. Time series of the gas column retrievals along with their uncertainties are then derived with accounting for the quality flags from both datasets. Since Pandora measurements are significantly more frequent, a procedure computing weighted averages of them at the times of TEMPO retrievals was incorporated to the notebooks allowing direct comparison of gaseous columns from two sensors against each other.

The results derived by the ASDC tool show only qualitative agreement between the TEMPO and Pandora retrievals of nitrogen dioxide tropospheric column. it was also found that the discrepancies between the two are site dependent which may point to a potential problem with Pandora quality flags. Two attempts were made to improve comparison. Since TEMPO algorithm allows for negative NO₂ tropospheric columns, such retrievals were removed from consideration. There are also multiple TEMPO retrievals accompanied by uncertainty greater that the retrieved column. Removal of such retrievals constitutes another approach to improve comparison.

The findings of this study will contribute to the understanding of the reliability and applicability of space-based trace gases monitoring for air quality applications. The results will enhance our understanding of atmospheric processes related to tropospheric NO₂.

How to cite: Radkevich, A., Mahmoud, H., and Kaufman, D.: Comparison of nitrogen dioxide tropospheric columns retrieved by TEMPO and Pandora, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4493, https://doi.org/10.5194/egusphere-egu25-4493, 2025.

Wildfires and dust storms significantly contribute to air pollution, causing adverse health impacts and intensifying various aerosol-meteorology feedbacks in the atmosphere through direct and indirect aerosol effects. These effects, however, are highly variable and depend on prevailing synoptic conditions. In April 2020, Ukraine experienced one of its most severe air pollution episodes, which had a profoundly negative impact on the Kyiv metropolitan area. This event was triggered by wildfires in the abandoned exclusion zone around the Chornobyl Nuclear Power Plant (northern Ukraine) and a dust storm that swept across the entire territory of Ukraine from the west to the east. Despite similar aerosol emissions – characterized by elevated levels of dust, organic carbon (OC), and black carbon (BC) – the atmospheric effects varied significantly under different synoptic processes during April 2020. This study presents seamless modeling results that analyze the meteorological response to direct (DAE) and indirect aerosol effects (IDAE) under varying synoptic conditions during this pollution episode in Ukraine.

Using the Environment – HIgh-Resolution Limited Area Model (Enviro-HIRLAM) at a 1.5 km horizontal resolution, four simulations/runs were conducted to investigate the role of aerosols: DAE run, IDAE run, combined aerosol effects (COMB run), and a reference (REF run) representing a standard Numerical Weather Prediction configuration without aerosol effects. The uniform and continuous effects of biomass burning and dust aerosols were primarily observed in radiation parameters, leading to a reduction in downwelling global and net short-wave radiation by 25-40 W/m². A clear correspondence between aerosol distribution and changes in the spatial patterns of other meteorological parameters was evident during the atmospheric fronts and the dust storm episode. Notably, the movement of a warm front caused near-surface air temperature to decrease and specific humidity to increase ahead of the front, with the opposite effects observed behind it. Compared to the REF run, these parameters exhibited local variations ranging from -2.6°C to +1.0°C for air temperature and from -1.5 g/kg to +1.0 g/kg for specific humidity. Aerosol effects during the stationary cold front led to an increase in air temperature and cloud liquid water content. However, transported sulfur aerosols significantly influenced these effects against the background of OC and BC emissions. In contrast, the subsequent dust storm and cold front had the opposite effect on air temperature, also impacting changes in turbulent kinetic energy. Most of these effects were associated with areas in model domain affected by elevated concentrations of dust, BC, and OC in their coarse and accumulation modes.

We acknowledge support through the grant HPC-Europa3 Transnational Access Programme for projects “Integrated modelling for assessment of potential pollution regional atmospheric transport as result of accidental wildfires”; projects Horizon Europe programme under Grant Agreement No 101137680 CERTAINTY (Cloud-aERosol inTeractions & their impActs IN The earth sYstem); project No 101036245 RI-URBANS (Research Infrastructures Services Reinforcing Air Quality Monitoring Capacities in European Urban & Industrial AreaS) and No 101056783 European Union via FOCI-project (Non-CO2 Forcers And Their Climate, Weather, Air Quality And Health Impacts).

How to cite: Savenets, M., Mahura, A., Nuterman, R., and Petäjä, T.: Direct and indirect effects of biomass burning and dust aerosols under various synoptic processes during the April 2020 pollution case in Ukraine, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4697, https://doi.org/10.5194/egusphere-egu25-4697, 2025.

Black carbon (BC), the primary light-absorbing aerosol, has significant implications for atmospheric heating and climate change, with far-reaching effects on regional air quality and public health. In Iran, BC concentrations, primarily resulting from combustion processes such as industrial emissions, vehicular exhaust, and biomass burning, constitute a significant air quality challenge, particularly in urban regions with high levels of anthropogenic activity. However, there is a lack of studies on the long-term trends of BC in Iran, particularly regarding the effects of urban growth and land use changes on air quality and human health. This study systematically analyzes trends in BC concentrations from 1980 to 2023, both on a national and regional scales, using high-resolution data from the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2).  The analysis includes temporal and spatial variations to evaluate the impact of anthropogenic and natural factors on BC levels over this period. A substantial increase in BC concentrations was observed from 1980 to 2023, followed by a decline after 2010. Regional analysis revealed higher BC levels in western Iran, driven by concentrated anthropogenic and industrial activities, compared to the sparsely populated, desert-dominated eastern regions, characterized by arid landscapes. Seasonal variations in BC concentrations were observed nationwide, with peak levels occurring in Tehran and Ahvaz during the winter. Trend analysis across various land use and land cover (LULC) types indicated that urban and agricultural expansion were the primarily drivers of increasing BC concentrations. Positive correlations were observed between the aforementioned factors and aerosol emissions, while water and grassland coverage were associated with reduced emissions in most regions. These findings underscore the necessity of expanding natural land use, such as forest coverage, and promoting sustainable urbanization as strategies to mitigate BC emissions.

How to cite: Shaheen, A., Yousefi, R., Wang, F., Kaveh-Firouz, A., and Ge, Q.: Assessing black carbon dynamics in Iran: the role of urban growth and land use changes in long-term trends (1980–2023), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5323, https://doi.org/10.5194/egusphere-egu25-5323, 2025.

Cloud droplet size distribution is essential for quantifying the role of clouds in earth system, including cloud albedo, precipitation formation, and cloud lifetime. The response of cloud droplet spectral relative dispersion (ε) to aerosol number concentration (N_a) is highly uncertain, and the role of turbulence in ε–N_a relationships is yet puzzling. This study uses large eddy simulation to examine the ε–N_a relationship and derives an expression for ε from a minimal model to elucidate this relationship. Our findings indicate that as N_a increases, ε initially decreases due to the aerosol’s effect on weakening the intensity of turbulence-induced broadening greater than its effect on weakening the intensity of condensational narrowing. However, as N_a continues to increase, ε increases due to the aerosol’s effect on weakening the intensity of condensational narrowing more significant than its effect on weakening the intensity of turbulence-induced broadening. These findings improve the understanding of the aerosol effects on cloud droplet size distribution and address the challenge of quantifying aerosol indirect effects considering turbulence, potentially leading to new cloud microphysics parameterizations.

How to cite: Chen, Y., Chen, J., and Lu, C.: Turbulence-induced Non-Monotonic Influence of Aerosols on Cloud Droplet Size Distribution, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5362, https://doi.org/10.5194/egusphere-egu25-5362, 2025.

Delhi is one of the most polluted cities in the world with a rapidly growing population. Huge amount of VOCs is released into the atmosphere from both anthropogenic and biogenic emissions. Various types of VOCs are released from anthropogenic sources such as disinfectants and cleansers, paints and varnishes, wood preservatives, aerosol sprays, room fresheners, dry cleaners and organic solvents. Another important anthropogenic source is burning of fossil fuels in motor vehicles, which also releases VOCs. Various plants species also release VOCs like isoprene (biogenic VOCs) which upon oxidation with atmospheric oxidants like ozone (O₃), nitrate (NO₃) and hydroxyl radicals (OH) forms less volatile products which on further reaction forms secondary organic aerosols (SOA). VOCs are also responsible for formation of tropospheric ozone which is one of the major criterion air pollutants and causes various health issues.

Around 32 samples of VOCs have been collected in the NCT of Delhi using charcoal tubes from the selected sites, VIZ., Okhla Phase 2 (OKHL, Industrial site), Sri Aurobindo Marg (SAM, traffic intervention site), Income tax office (ITO, traffic intervention site), Jawaharlal Nehru University (JNU, Institutional site). Sample preparation has been done following the protocol given by NIOSH 1501 method for xylene analysis, which is widely accepted as a “golden standard” for Industrial Hygiene sampling. Collected samples were run on GC-FID and concentration of VOCs is determined. The average concentration of Total VOCs at SAM is found to be 382.07µg/m³ while it is 200.14, 242.63 and 452.62 µg/m³ at JNU, OKHL and ITO, respectively. Out of all the VOCS, benzene and toluene represents the highest percentage with benzene representing a percentage of 17%  and 18% at SAM, JNU, Okhla and ITO, respectively and Toluene  contributing to a percentage concentration of 15% , 13%, 16% and 15% respectively at SAM , JNU, Okhla and  ITO thus owing to high vehicular emissions in Delhi. Individual average concentration at evening is higher than individual average concentration at morning at all chosen sites.  Also individual concentration of benzene and toluene is higher than other VOCs being 64.14 µg/m³ and 59.13 µg/m³ respectively at SAM, 35.64 µg/m³ and 25.83 µg/m³ at JNU, 79.6 µg/m³ and 69.9 µg/m³ at ITO and 41.92 µg/m³ and 37.80 µg/m³ at Okhla. It has planned to evaluate both the carcinogenic and non-carcinogenic risk associated with the chosen VOCs. This research will help us to get knowledge of sources of emission of VOCs. Further we will get a knowledge of the carcinogenic and non-carcinogenic impacts of VOCs and the percentage of population in Delhi which is getting directly or indirectly exposed to the carcinogenic VOCs. Hence it would help us in determining the health risk associated with VOC emission which would help in formulating effective strategies for controlling VOC emission. This would further aid us in reducing tropospheric ozone which is also a pollutant of concern. This study can also be used further in understanding atmospheric chemical reactions, photochemical smog pollution, assessment and forecast of possible change in atmospheric environment on the regional/global scale.

How to cite: Sharma, R.: Volatile organic compounds in ambient air of Delhi, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5764, https://doi.org/10.5194/egusphere-egu25-5764, 2025.

NASA’s Atmospheric Science Data Center (ASDC) at Langley Research Center will present an overview of the Tropospheric Emissions: Monitoring of Pollution (TEMPO) mission, focusing on the cutting-edge tools and services available to users for air quality research and environmental monitoring. TEMPO is a pioneering geostationary satellite that provides day light hourly observations of pollutants over North America, including measurements of ozone, nitrogen dioxide, and other critical pollutants.This presentation will highlight the ASDC’s role in archiving, distributing, and providing user support for TEMPO data. Attendees will be introduced to data access tools, visualization platforms, and analysis services designed to facilitate the use of TEMPO observations for scientific research and decision-making. Key resources, such as NASA Earthdata Search, Earth GIS, OPeNDAP, Worldview, Github Tutorials and Harmony services on the cloud, will be showcased, demonstrating how researchers can efficiently explore and download high-resolution data products.Additionally, the presentation will cover the application of TEMPO data in studying air quality trends, emission sources, and the impacts of pollution on public health and climate. Attendees will also gain insights into ASDC's open science initiatives, which encourage collaboration and data sharing to enhance the impact of TEMPO and NASA’s broader Earth science mission.Through this presentation, the ASDC aims to empower the scientific community with the tools and knowledge needed to harness the full potential of TEMPO data in addressing pressing environmental challenges.

How to cite: Mahmoud, H. and Radkevich, A.: Leveraging TEMPO Data: Tools and Services for Air Quality Monitoring and Research from the Atmospheric Science Data Center, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7314, https://doi.org/10.5194/egusphere-egu25-7314, 2025.

Microplastics, particularly microplastic fibers, are an emerging pollutant of growing concern, frequently detected in the atmosphere. However, recent studies emphasized the predominance of artificial and natural microfibers over microplastic fibers. Despite this, research focusing on all types of microfibers, commonly grouped as anthropogenic microfibers (MFs) remains limited, especially in residential indoor environments. Therefore, this study explored the indoor atmospheric deposition of microfibers, in the residential homes of Chennai, India, marking the first such study in the country. Additionally, workplaces, including offices, laboratories, and hostel rooms, were examined. Bedrooms (16,736±7,263 MFs/m²/day) and student hostels (5,572±2,898 MFs/m²/day) recorded the highest contamination in respective categories, and this could be attributed to the abundance of textile products, such as bedsheets, carpets, quilts, towels, and curtains in the indoors of both the rooms. MFs shorter than 500 µm dominated the samples, comprising 78.8 and 65.9 % of total MFs in residential and workplace categories, respectively. The diameter of MFs ranged from 2.02–23 µm in residential spaces and 2.04–36.4 µm in workplaces, indicating their potential to penetrate human lungs. µ-FTIR analysis revealed the predominance of semi-synthetic MFs (48.2 %), followed by natural (29.3%) and synthetic (22.5 %) MFs, underscoring the need to consider all categories of MFs. Further classification revealed rayon (94.5±6.40 %), cotton (68.1±6.12 %), and polyethylene terephthalate (PET) (48.1±11.5 %) as major MFs, indicating textiles as a significant contamination source. The detection of black rubber/latex MFs indicates additional contributions from road dust. Surface morphological analysis, correlations with environmental and meteorological factors, and backward trajectory analysis further highlighted the primary role of indoor/local sources in MFs contamination. Overall, the study emphasizes the need to monitor all categories of MFs and calls for comprehensive investigations into the impact of indoor textile products and road dust on indoor atmospheric contamination in future research.

How to cite: Jessieleena, A., Kambapalli Ezhilan, I., Chandel, A. S., D'sa, S. V., Mohamed, N., and Nambi, I.: Atmospheric deposition of anthropogenic microfibers in different indoor environments of Chennai, India , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7978, https://doi.org/10.5194/egusphere-egu25-7978, 2025.

Nitrogen dioxide (NO₂), an atmospheric pollutant produced by fossil fuel combustion in vehicles and industrial processes, is harmful to human health, worsening respiratory and cardiovascular diseases. The main effects of NO₂ pollution on human health are respiratory infections, airway inflammation, asthma, and low birth weight, among others. Vehicle traffic in cities is one of the main sources of NO₂, affecting the health of the population living near highways. The highest NO₂ concentration occurs at distances between 200 and 500 meters from high-traffic highways. The study area, the metropolitan region of Campinas (MRC), Brazil, is a technological, industrial and economic hub with 3.3 million inhabitants and busy transport corridors that connect the southeast and central-west regions of the country. It is composed of 20 municipalities and is located in São Paulo state, the most developed and populated Brazilian state. The aims of this work are to map atmospheric NO₂ pollution and estimate NO₂ concentrations near the highways in the MRC using average daily vehicle flow (DVF) and NO₂ concentrations estimated from satellite images. Data on the tropospheric vertical column of nitrogen dioxide (in mol/cm²) values from 32 daily images from the Sentinel 5P satellite TROPOMI spectrometer that were collected from April 15 to May 20, 2024, were used. During that period, there was no rain, and the sky remained clear and cloudless. The images were processed to produce NO₂ median images during the study period. The NO₂ pollution map was produced by the spline interpolation algorithm method. To estimate the concentration of NO₂ near the MRC highways, a road map was used, and a 500 m buffer was drawn around the highways. The NO₂ pollution map was combined with the buffer map, and the median NO₂ concentration within the 500 m buffer around the highways was estimated. Pearson regression analysis was performed between the average DVF and the NO₂ concentration. The results revealed a positive and significant correlation (r=0.692; p= 0.004) between the DVF and NO₂ concentration near the highways estimated from satellite data. The highest NO₂ concentrations were observed near highways SP-083 (1.5591 mol/cm²; 45,000 vehicles/day), SP-330 (1.521 mol/cm²; 38,815 vehicles/day), and SP-075 (1.485 mol/cm²; 37,813 vehicles/day). The results of this study can be used in epidemiological research to identify neighborhoods and populations that live near high NO₂ concentration highways and are exposed to respiratory and cardiovascular disease risks. In the next step of this research, the NO₂ concentration values ​​estimated from Sentinel 5P images in mol/cm² units will be converted to µg/m³ units using data from ground-based measurement stations located in the MRC. In the future, this methodology can be used to produce highway NO₂ pollution maps for areas in which ground measurement station data are unavailable.

How to cite: Ferreira, M.: Using Sentinel 5P satellite and vehicle flow data to map NO2 air pollution near highways in the Metropolitan Region of Campinas, Brazil, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9328, https://doi.org/10.5194/egusphere-egu25-9328, 2025.

Extreme weather events, such as extreme temperatures, water vapor transport, and the resulting extreme precipitation, have been occurring with increasing frequency and are projected to intensify further in a warming climate. Understanding how these events respond to climate change is critically important. Numerical models serve as essential tools for uncovering the mechanisms behind these phenomena, with spatial resolution being one of the key challenges. Leveraging advanced supercomputing resources, we have recently made significant advancements in developing high-resolution Earth system models based on the Community Earth System Model (CESM), featuring a 25 km atmospheric resolution and a 10 km oceanic resolution. Compared to the commonly used CMIP5 and CMIP6 models, the high-resolution Earth system model demonstrates substantial improvements in reproducing extreme weather events, thereby greatly enhancing the confidence in future projections.

How to cite: Gao, Y.: Enhancing the Simulation and Prediction of Extreme Temperature and Water Vapor in a Warming Climate Using a High-Resolution Earth System Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11612, https://doi.org/10.5194/egusphere-egu25-11612, 2025.

The Indo-Gangetic Plain (IGP) is a globally recognized hotspot for high aerosol loading, necessitating precise modelling to understand its spatial and temporal dynamics. This study evaluates the performance of differently parameterized Seasonal Autoregressive Integrated Moving Average (SARIMA) models in forecasting the Aerosol Optical Depth (AOD) at 550 nm retrieved from the CERES (Clouds and the Earth's Radiant Energy System) satellite platform across eight  locations: Delhi, Dhaka, Jaipur, Kanpur, Karachi, Kolkata, Lahore, and Varanasi in the IGP. Using long-term AOD datasets from CERES during the period of 2005 to 2020, we tested various SARIMA configurations to capture seasonal trends and irregular variations specific to urban environments. The SARIMA configurations tested include configure_1: (1,0,1)(1,0,1)₁₂, configure_2: (1,1,1)(1,1,1)₁₂, configure_3: (2,0,1)(2,0,1)₁₂, and configure_4: (2,1,1)(2,1,1)₁₂ These configure models were compared with CERES-derived observations for AOD at the study sites for the next two years, that is, Jan, 2021 to Dec, 2022. Each configuration was assessed for data stationarity using the Augmented Dickey-Fuller (ADF) test and if not follows, then the differentiation method has been used to stationaries the series. The Model performance was evaluated using multiple statistical metrics, including normalized Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Squared Error (RMSE), Mean Bias Error (MBE), and Mean Absolute Percentage Error (MAPE) for every configuration showed the low metric values. The result indicates high correlation coefficients, ranging from 0.54 to 0.91, and R-squared values, varying between 0.31 and 0.81 for all configurations that significantly determined the best-suited models for each location. Every modelled configuration has been checked with 95% and 99% confidence interval (with alpha=0.05 and 0.01, respectively) showing the p-value <0.001. These results emphasize the models' ability to replicate observed AOD patterns effectively. It reveal that parameter sensitivity plays a critical role in predictive accuracy, with optimal configurations varying across locations due to heterogeneity in aerosol sources and meteorological conditions. The present study underlines the importance of site-specific model tuning for reliable aerosol forecasting in densely populated and pollution-prone regions. These insights provide a foundation to enhance air quality prediction studies and address health, and climate impacts associated with aerosols in the IGP.

How to cite: Mall, A. and Singh, S.: Comparison of Differently Parameterized SARIMA Models using CERES-Derived Aerosol Optical Depth over Indo-Gangetic Plain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12277, https://doi.org/10.5194/egusphere-egu25-12277, 2025.

The aim of this study is to investigate Particulate Matter (PM) sources and mechanisms of formations over the city of Naples (Italy) and their seasonal and day-to-day variations.

We have sampled fine particles with diameter < 2.5 μm (PM_2.5) and < 10 μm (PM₁₀) daily on pre-cleaned (700 °C for 2 h) quartz filters, during the months of May and November 2016-January 2017, on top of the historical building complex in Largo San Marcellino, Naples. We have measured the concentrations of total N/C together with their isotopic composition (δ¹⁵N and δ¹³C). We have also measured the concentration of major ions and interpreted the results with data of gaseous compounds, as well as consideration of the meteorology, using data and state of the art models of atmospheric circulation (Hysplit). Our point was to show that the uncertainty associated with quantification of sources contribution with an apportionment model decreases when the model is constrained with information derived from different methods.

Seasonal differences: the results show that the concentrations of total PM₁₀/PM_2.5, N/C measured in autumn are more variable than those measured in spring. This is related to a different wind regime, whereby in spring air masses mostly originated from West and South (the “clean” Mediterranean sea), whereas in autumn the wind blew air from North (over the highly urbanized and “dirty” European continent). This interpretation is supported by the concentration of major ions showing more scattered values in autumn for species typically originating from land (K⁺, NH₄⁺, NO₃^-), with high values on the 9^th and the 26-27^th of December and the 2^nd of January 2017. However, neither the monthly mean δ¹⁵N and δ¹³C, nor the daily values corresponding to the spikes show significant changes, suggesting that the isotopic composition of total N/C has limited power in identifying changes of mean monthly sources or for the spikes. 

Day-to-day variations: a significant change of the main species measured is found around the middle of May. This event is associated with a change in weather pattern going from a typical land-sea breeze wind regime (typically causing poor air circulation and stagnation of air masses) to an intense synoptic with winds originating mostly from South/South-West (the sea). Correspondingly, there is a peak in the concentration of major ions originating mostly from land (NO₃^-, SO₄^2-, Ca₂⁺, C₂O₄^2-, K⁺) towards the end of the land-sea breeze regime (9-11^th May), followed (10-15^th May) by an increase of the concentration of major ions originating mostly from the sea (Na⁺, Mg²⁺, Cl^-). The entire period (9-14^th) is characterized by a concurrent variation of total N, C, δ¹⁵N and δ¹³C. While the changes of δ¹⁵N are caused mainly by isotope fractionations, associated with the dissociation of NH₄Cl producing NH₃ and HCl, the changes of δ¹³C are caused mostly by a change of the source of total C, associated with carbonate (CO₃^2-) apportion.

We conclude that the concentrations and isotopic compositions of N/C in PM are useful tools only when coupled with other tools like the analysis of the meteorology and the concentration of major ions.

How to cite: Rubino, M., Sirignano, C., Chianese, E., Hernández-Ceballos, M. Á., Angyal, A., Marzaioli, F., Di Rosa, D., Caso, G., and Riccio, A.: Multiple lines of evidence help identify the sources and formation mechanisms of Nitrogen and Carbon in Particulate Matter sampled in the historical center of Naples (Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13295, https://doi.org/10.5194/egusphere-egu25-13295, 2025.

Active and passive sensors onboard satellites and suborbital measurements have shown frequent aerosol-cloud overlapping situations over several regions worldwide on a monthly to seasonal scale. However, retrieving the optical properties of aerosols lofted over clouds poses challenges. Primarily, the assumption of aerosol single-scattering albedo (SSA) in the satellite-based algorithms is known to be one of the largest sources of uncertainty in quantifying the above-cloud aerosol optical depth (ACAOD). On the radiative forcing aspect, the sign and magnitude of the aerosol radiative forcing over clouds are determined mainly by the aerosol loading, the absorption capacity of aerosols (SSA), and the brightness of the underlying cloud cover.

We contribute to addressing the uncertainties surrounding the absorbing aerosols-cloud radiative interactions by offering a novel, NASA’s A-train-centric, one-and-half decade long (2006-2022) global retrieval product of aerosols above cloud that delivers 1) spectral ACAOD, 2) spectral SSA of light-absorbing aerosols lofted over the clouds, and 3) aerosol-corrected cloud optical depth (COD). The synergy algorithm combines lidar retrievals of ACAOD derived from the ‘De-polarization Ratio’ method applied to CALIOP and the top-of-atmosphere (TOA) spectral reflectance from OMI (354-388 nm) and MODIS (470-860 nm) sensors to deduce the joint aerosol-cloud product. The availability of accurate ACAOD accompanied by a marked sensitivity of the TOA measurements to ACAOD, SSA, and COD allow retrieval of SSA for above-cloud aerosols scenes using the ‘color ratio’ algorithm applied to UV and VIS sensors.

We will present multiyear (2006-2022), regional retrievals of UV-VIS spectral aerosol SSA above clouds, and it’s a comparison against ORACLES airborne in situ and remote sensing measurements and ground-based AERONET inversions. A preliminary uncertainty analysis suggests that an uncertainty of 20% in ACAOD can result in an error of ~0.02 at 388 nm and ~0.01 at 470 nm in the retrieved SSA from OMI and MODIS, respectively. Furthermore, the presented aerosol-cloud remote sensing algorithm assumes implications for the recently launched EarthCARE and PACE missions with potential synergy of ATLID lidar and OCI imager. The availability of the global aerosol-cloud joint product will reenergize the community by offering 1) an improved representation of aerosol extinction and absorption properties over clouds and 2) much-needed observational estimates of the radiative effects of aerosols in cloudy regions for constraining the climate models.

How to cite: Jethva, H., Torres, O., Kayetha, V., and Hu, Y.: One-and-half Decade Long Global Retrieval Dataset of UV-VIS Spectral Optical Depth and Single-scattering Albedo of Absorbing Aerosols above Clouds from A-train Active-Passive Synergy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14363, https://doi.org/10.5194/egusphere-egu25-14363, 2025.

We present the status of the Level 0-1 processor for the Tropospheric Emissions: Monitoring of Pollution (TEMPO), with a primary focus on radiometric calibration. Multiple version updates have significantly improved the TEMPO Level 1 products, enhancing the quality of Level 2 products and enabling the detection of city lights, nightglow, and aurora signals during twilight hours. However, assessments of TEMPO Level 1 data (versions 1 to 3) indicated overestimations of Sun-normalized radiances when compared to radiative transfer calculations. To investigate these biases, we compared TEMPO solar irradiance measurements to those from multiple independent instruments and a high-resolution reference solar spectrum. For Earth radiance assessments, we conducted intercomparisons with spaceborne measurements from the Advanced Baseline Imager (ABI) instruments onboard the Geostationary Operational Environmental Satellite (GOES)-16 and -19. Located at the checkout position of 89.5°W for post-launch testing, GOES-19 ABI has provided comparable viewing geometries with TEMPO (at 91.0°W) over North America. On the other hand, comparisons with GOES-16 ABI (located at 75.2°W) may require corrections for viewing angles and bidirectional reflectance distribution function (BRDF) effects due to larger differences in geometries. Additionally, we compared TEMPO Sun-normalized radiances with radiative transfer simulations over Railroad Valley, which use ground-based surface reflectance measurements as input. In this work, we present the intercomparison results and propose potential approaches to mitigate the radiometric biases.

How to cite: Chong, H., Liu, X., Houck, J., Flittner, D. E., Carr, J., and Hou, W. and the TEMPO instrument calibration team: The Tropospheric Emissions: Monitoring of Pollution (TEMPO) Level 0-1 Processor – Radiometric calibration and intercomparison, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14596, https://doi.org/10.5194/egusphere-egu25-14596, 2025.

Monoterpenes, the second most abundant biogenic volatile organic compounds globally, are crucial in forming secondary organic aerosols, making their oxidation mechanisms vital for addressing climate change and air pollution. This study utilized cyclohexene as a surrogate to explore first-generation products from its ozonolysis through laboratory experiments and mechanistic modeling. We employed proton transfer reaction mass spectrometry with NH₄⁺ ion sources (NH₄⁺-CIMS) and a custom-built OH calibration source to quantify organic peroxy radicals (RO₂) and closed-shell species. Under near-real atmospheric conditions in a Potential Aerosol Mass-Oxidation Flow Reactor, we identified 30 ozonolysis products, expanding previous data sets of low-oxygen compounds. Combined with simulations based on the Generator for Explicit Chemistry and Kinetics of Organics in the Atmosphere and relevant literature, our results revealed that OH dominates over ozone in cyclohexene oxidation at typical atmospheric oxidant levels with H-abstraction contributing 30% of initial RO₂ radicals. Highly oxidized molecules primarily arise from RO₂ autoxidation initiated by ozone, and at least 15% of ozone oxidation products follow the overlooked nonvinyl hydroperoxides pathway. Gaps remain especially in understanding RO₂ cross-reactions, and the structural complexity of monoterpenes further complicates research. As emissions decrease and afforestation increases, understanding these mechanisms becomes increasingly critical.

How to cite: Li, Y., Ma, X., Lu, K., and Zhang, Y.: Investigation of the Cyclohexene Oxidation Mechanism Through the Direct Measurement of Organic Peroxy Radical, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14804, https://doi.org/10.5194/egusphere-egu25-14804, 2025.

Anthropogenic aerosols significantly deteriorate the urban air quality and climate of the western Indian region, nevertheless, the contributions from different sources (power, residential, transport and industries) to ambient particulate pollution has been uncertain. In this regard, high-resolution simulations have been conducted employing the WRF-Chem (v3.9.1) model to comprehensively assess contribution from major anthropogenic sources in post-monsoon (November 2019), when air quality is typically poor in the region. Model evaluation is conducted by comparing simulated near-surface aerosol concentrations (PM_2.5 and PM₁₀) and aerosol optical depth (AOD) against ground-based measurements (CPCB), satellite data (MODIS), and the reanalysis dataset (MERRA-2). The results show that the model captures the spatial distribution of AOD satisfactorily, with WRF-Chem simulated AOD (0.38 ± 0.10) aligning well with MERRA-2 AOD (0.54 ± 0.10) and MODIS AOD (0.50 ± 0.20). Surface PM_2.5 and PM₁₀ concentrations also meet performance metrics of Fractional Bias ≤ 60% and Fractional Error ≤ 75%, with FAC2 values of 0.9 and 0.7, respectively. Sensitivity analysis reveals spatial heterogeneity in dominant sector that contributes to PM_2.5 concentration over western India. The power sector dominates in most areas with an average contribution of ~14% from regional power sources, followed by regional industries (~12%), regional residential emissions (~9%), and regional transport (~5%). In the trans-regional emissions from the Indo-Gangetic Plain (IGP) and central India also, the power sector remains the largest contributor (~15%), followed by industry (10.5%). Our findings underscore the need for targeted emission reductions in high-impact sectors to improve air quality over western India.

How to cite: Shekhar, S., Dhaka, S., Vaishya, A., Ojha, N., Pozzer, A., and Sharma, A.: Quantifying the sources of anthropogenic aerosols over western India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14890, https://doi.org/10.5194/egusphere-egu25-14890, 2025.

Air quality and climate over the western Indian region have been shown to be strongly influenced by trans-regional anthropogenic emissions originated from the Indo-Gangetic Plain (IGP) and central India, besides the local and regional processes. Nevertheless, the relative roles of local versus remote anthropogenic processes in changing precipitation over western India have remained unclear. In this regard, numerical simulations have been conducted using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to quantify regional versus trans-regional anthropogenic effects on cloud droplet number concentration (CDNC) and precipitation during monsoon (August 2019). WRF-Chem simulations show a good agreement with the ERA5 reanalysis for cloud fraction (CF) (r = 0.88, MB = 0.08 mm/day) and accumulated monthly precipitation (AMP) (r = 0.84, MB = -0.14 mm/day). Sensitivity simulations reveal that regional plus trans-regional anthropogenic emissions enhance CDNC by up to 5.1×10⁶ number/cm² (~121% of the average CDNC over WI) but significantly reduce the precipitation by up to 45 mm (~15% of the average precipitation). The findings also revealed that the impact of trans-regional emissions in perturbing CDNC and precipitation is higher than that of regional emissions. Our results suggest that anthropogenic emissions can substantially lower water resources in this already stressed arid region in India. The study also highlights that policies need to aim emission reductions ubiquitously and not only over western India for mitigating pollution impacts on regional precipitation.

How to cite: Dhaka, S., Lakshmi, S., Vaishya, A., Ojha, N., Pozzer, A., Ansari, T., and Sharma, A.: Impacts of anthropogenic emissions on monsoon precipitation over western India: Insights from high-resolution regional modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14891, https://doi.org/10.5194/egusphere-egu25-14891, 2025.

Dhaka, the capital of Bangladesh, is currently experiencing critically alarming levels of air pollution, with its Air Quality Index (AQI) exceeding 200, indicating hazardous conditions. This study investigates the factors contributing to Dhaka's deteriorating air quality over the past two decades by integrating AQI data with Land Use and Land Cover (LULC) analyses. Particular attention is given to the impacts of major development projects, including the Metro Rail, Elevated Expressway, and International Airport Terminal 3, on the city’s air quality. Comparative assessments of AQI before and after the completion of these projects reveal a significant worsening of air quality, attributed to increased construction activity and subsequent urbanization. The rapid expansion of impervious surfaces is identified as another critical factor exacerbating the AQI. The findings emphasize the urgent need for sustainable urban planning and air quality management strategies to mitigate the adverse effects of development on public health and the environment in Dhaka.

How to cite: Akhter, J. and Rayhan, M.: Assessing the Impact of Urban Development and Land Use Changes on Dhaka's Hazardous Air Quality, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14892, https://doi.org/10.5194/egusphere-egu25-14892, 2025.

Over the past 15 years, imaging spectrometers developed at the NASA Jet Propulsion Laboratory have significantly advanced the field of remote sensing of methane (CH4) and carbon dioxide (CO2) point source emissions. This began in 2008 with airborne observations from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), 2013 with the next generation AVIRIS-NG instrument, and has culminated with the launch of NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) in 2022.

These instruments have identified thousands of CH4 and CO2 point source emissions across the oil and gas, waste, and energy sectors, contributing in some cases to emission mitigation efforts. As part of an extended mission, EMIT coverage will expand beyond the arid regions of Earth to cover terrestrial surfaces between +51.6° and −51.6° latitude, enabling direct attribution of anthropogenic emissions on a global scale. EMIT's measurements and greenhouse gas data products are accessible through NASA’s Land Processing DAAC and the U.S. GHG Center, with all associated code available as open source. These data are already being utilized by public, private, and non-profit organizations, including UNEP IMEO and the Carbon Mapper Coalition. Additionally, new airborne instruments, such as AVIRIS-3 (2023) and the planned AVIRIS-5, promise enhanced sensitivity to CH4 and CO2 point sources, offering the potential for direct comparisons with satellite-based EMIT observations.

The Carbon Investigation (Carbon-I), a proposed mission for the NASA Earth System Explorer Program, reflects a dramatic advancement in greenhouse gas mapping capability. It provides a unique combination of coverage, high spatial sampling, and very high sensitivity, to permit quantification of emissions that cannot be observed with current technology. With contiguous global observations of CH4, CO2, and CO at 300 m sampling every 28 days with targeted observations at 30 m sampling, Carbon-I will permit emission quantification at the global to regional scales as well as for localized point sources. Consistent with NASA’s Open Source Science Initiative, all Carbon-I data and code will be publicly accessible, empowering Earth Action initiatives worldwide.

How to cite: Thorpe, A., Green, R., Frankenberg, C., Michalak, A., Thompson, D., Brodrick, P., Chadwick, D., Eastwood, M., Scott, V., Frazier, W., Fahlen, J., Coleman, R. W., Xiang, C., Jensen, D., Villanueva-Weeks, C., Lopez, A., Vinckier, Q., Bender, H., Chlus, A., and Chapman, J.: Advancing Greenhouse Gas Mapping with JPL Imaging Spectrometers: AVIRIS, EMIT, and Carbon-I, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14903, https://doi.org/10.5194/egusphere-egu25-14903, 2025.

The Southern Hemisphere (SH) stratosphere circulation can be organized around the development of the low-latitude jet (LLJ) in the upper stratosphere during winter months. The LLJ is associated with weak planetary wave activity, reduced residual circulation, and connections to westerly anomalies of the middle and upper stratosphere during early and mid-winter. The 2022 Hunga eruption coinciding with an anomalously strong LLJ year. Additionally, the LLJ is linked to a persistent, strong polar vortex in the lower stratosphere during October–December. This strong vortex, primarily driven by dynamical processes in winter, is further associated with enhanced ozone losses in spring, with ozone feedback reinforcing the vortex as sunlight returns in October.

How to cite: Wang, X., Yu, W., Randel, W., and Garcia, R.: Stratospheric Circulation in the Southern Hemisphere: links to tropical winds, ozone and Hunga Eruption, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15068, https://doi.org/10.5194/egusphere-egu25-15068, 2025.

Shipping is a high-energy-consuming sector and a significant source of climate-related and harmful pollutant emissions. In response to growing environmental concerns, the maritime sector has been subject to stringent regulations aimed at reducing emissions, achieved through the adoption of alternative fuels and emission control technologies. Accurate and diverse emission factors (EFs) are critical for quantifying shipping’s contribution to current emission inventories and projecting future trends under various policy scenarios. This study presents advancements in the development of emission factors for ships, incorporating alternative fuels, biofuels and emission control technologies. The methodology integrates statistical analysis of emission data from an extensive literature review with newly acquired on-board emission measurements. To ensure high resolution and applicability across diverse operational conditions, the emission factors are formulated as functions of engine load and categorized by engine type and fuel used. The results provide insights into the emission performance of ships and intend to support the development of robust, up-to-date emission models and inventories, contributing to the broader goal of sustainable maritime transport.

How to cite: Grigoriadis, A., Chountalas, T., Fragkou, E., Chountalas, D., and Ntziachristos, L.: Advancing load-dependent emission factors for ships: Integrating alternative fuels, biofuels, and control technologies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15097, https://doi.org/10.5194/egusphere-egu25-15097, 2025.

Nitrous acid (HONO) is known to be the significant source of hydroxyl radicals (OH), impacting air quality and climate as a major oxidant in the atmosphere. Many studies have highlighted that the photolysis of HONO can produce substantial amounts of OH throughout the day. Despite the crucial role of HONO in tropospheric chemistry, more research is needed to improve understanding of global HONO budgets. To address this, we developed a prototype HONO retrieval algorithm from the Geostationary Environment Monitoring Spectrometer (GEMS). The retrieval algorithm comprises two major processes, commencing with the spectral fitting of UV spectral range (343-371 nm) using the direct fitting method to obtain the slant columns. Subsequently, the conversion of slant columns into vertical columns is achieved by applying the air mass factor. The last step involves background correction, wherein the slant column amounts of HONO included in the radiance reference spectrum are added to the differential slant columns. Enhancements of HONO resulting from wildfire events in Asia were detected using GEMS. Refining the GEMS HONO retrieval algorithm is expected to enhance our understanding of the diurnal cycle of HONO, along with tropospheric chemistry in Asia.

How to cite: Cha, H., Kim, J., Chong, H., González Abad, G., Park, S. S., and Lee, W.: Nitrous Acid (HONO) Retrievals from wildfire events by using Geostationary Environment Monitoring Spectrometer (GEMS) ultraviolet spectra, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15191, https://doi.org/10.5194/egusphere-egu25-15191, 2025.

Increasing concentrations of greenhouse gases (GHGs) in the atmosphere are the primary driver of the observed rise in global surface temperatures, meanwhile exceeding 1°C above pre-industrial levels. Addressing this challenge requires linking GHG concentrations to specific anthropogenic and natural sources as part of the global carbon budget. This study investigates the relationship between GHG concentrations measured in Thessaloniki, Greece, and potential long-range transport sources using a clustering approach.

The GHG data were obtained from the EM27/SUN Fourier Transform Infrared (FTIR) spectrometer, a ground-based low-resolution infrared spectrometer operated in the framework of the Collaborative Carbon Column Observing Network (COCCON) at a mid-latitude urban site. The instrument provides column-averaged dry air molar fractions of CH₄, CO₂, CO, and H₂O. Meteorological data for trajectory simulations were derived from the Global Data Assimilation System (GDAS) with a spatial resolution of 1° × 1°.

Clustering analysis was performed using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Seven-day kinematic back trajectories were calculated for the period 2019–2024 at two arrival heights, 1500 m and 3000 m above mean sea level. The findings aim to specify the influence of long-range transport on GHG concentrations over Thessaloniki, contributing to a more complete understanding of regional GHG source-receptor relationships and transport patterns.

How to cite: Panou, T., Mermigkas, M., Topaloglou, C., Balis, D., Dubravica, D., and Hase, F.: "Investigating Regional and Long-Range Transport Contributions to GHG Concentrations of a Mid-Latitude Urban Site" , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15708, https://doi.org/10.5194/egusphere-egu25-15708, 2025.

Black carbon (BC) aerosols, strong absorbers of solar radiation, induce atmospheric heating, altering vertical profiles of temperature, water vapor, and clouds. These impacts can lead to localized precipitation changes, and may also initiate changes to atmospheric circulation, with potentially far-reaching impacts on precipitation patterns.

While prior studies suggest BC's significant influence on precipitation, its role in both local and remote precipitation change remains insufficiently quantified. To address this gap, we explore the extent to which historical BC emissions have shaped regional precipitation. Specifically, we ask: how much could future BC changes influence regional precipitation, based on insights from the historical period?

Using the Community Earth System Model version 2 (CESM2), we have generated a 20-member ensemble of simulations of 1950–2014 with anthropogenic BC emissions fixed at 1950 levels. By comparing these to standard historical simulations with evolving emissions, we isolate the impacts of BC emission trends from 1950 to 2014 on global and regional climates.

Our results reveal that BC emissions have caused localized drying in regions of high emissions, notably over Europe during the 1980s–1990s and Eastern China in the early 21st century. Furthermore, we find indications that BC exerts a dampening effect on the most extreme precipitation events, highlighting its historical role in modulating climate extremes.

How to cite: Stjern, C. W., Samset, B. H., Alterskjaer, K., and Johansen, A. N.: Tracing Black Carbon's Historical Impact on Regional Precipitation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15895, https://doi.org/10.5194/egusphere-egu25-15895, 2025.

In 2018, international shipping accounted for significant anthropogenic greenhouse gas emissions, contributing approximately 740 million tons of CO₂ according to the voyage-based method of the Fourth International Maritime Organization (IMO) Greenhouse Gases Study [1] and 880 million tons based on the CEDS and EDGAR inventories [2]. Recognizing this impact, the IMO adopted a long-term strategy in 2023 to achieve decarbonisation of global shipping by mid-century. However, concrete measures remain under development. A recent assessment of the 2018–2022 period suggests emissions are once again approaching 2008 levels, attributed to stagnation in improving energy efficiency [3]. This highlights the urgency of evaluating the potential of operational measures to mitigate emissions.

Voyage optimization, or ship weather routing, is an operational strategy leveraging meteo-oceanographic data to minimize energy consumption. This reduction can be achieved through spatial diversions, speed variations, or a combination of both. VISIR-2 [4], an open-source Python-based model, computes least-CO₂ routes by optimizing spatial diversions. Using a validated graph-search algorithm, the model integrates ocean currents and avoids adverse sea conditions [5].

In this study, we apply VISIR-2 to an ocean-going vessel operating on the Shanghai–Los Angeles/Long Beach route, identified as one of the first green corridors of shipping [6]. Simulations are conducted for both eastbound and westbound voyages over an entire calendar year, with and without the influence of ocean currents. We evaluate the resulting CO₂ savings, analysing their dependence on engine load and environmental conditions.

These results demonstrate the potential of operational measures like voyage optimization to contribute to shipping decarbonisation. The VISIR-2 model is currently employed within the EDITO-Model Lab project [7], contributing to developing a digital twin of the ocean. This work underscores the importance of open-source tools in fostering sustainable maritime practices and achieving the IMO's decarbonisation goals.

References
[1] https://www.imo.org/en/ourwork/Environment/Pages/Fourth-IMO-Greenhouse-Gas-Study-2020.aspx
[2] Deng, S., Mi, Z. A review on carbon emissions of global shipping. Mar Dev 1, 4 (2023). https://doi.org/10.1007/s44312-023-00001-2
[3] https://www.shippingandoceans.com/post/international-shipping-emissions-return-to-peak-2008-levels-due-to-insufficient-energy-efficiency-im
[4] https://doi.org/10.5281/zenodo.8305526
[5] Mannarini, G., Salinas, M. L., Carelli, L., Petacco, N., and Orović, J.: VISIR-2: ship weather routing in Python, Geosci. Model Dev., 17, 4355–4382, https://doi.org/10.5194/gmd-17-4355-2024, 2024
[6] https://www.c40.org/news/la-shanghai-implementation-plan-outline-green-shipping-corridor/
[7] https://www.edito-modellab.eu/

How to cite: Salinas, M. L. and Mannarini, G.: Voyage Optimization with the VISIR-2 Model on the Shanghai–Los Angeles Green Corridor of shipping, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16374, https://doi.org/10.5194/egusphere-egu25-16374, 2025.

The atmosphere is a critical reservoir for and transporter of microplastics (MPs) but little is known about the nature and drivers of their regional and climatic variability. In this study, dry deposition of MPs is quantified simultaneously over a seven-day period in nine Iranian cities encompassing different populations and climates and relationships with meteorological conditions and gaseous and particulate air quality parameters investigated. Overall, deposition ranged from < 5 to > 100 MP m^-2 h^-1 and was dominated by fibres of various sizes and constructed of different polymers (mainly polyethylene, polyethylene terephthalate, polypropylene, polystyrene and nylon), and there were clear and significant differences in mean values between the different cities that were not a simple function of climate or population. On a local scale, both positive and negative relationships between MP deposition and various meteorological and air quality parameters were observed among the cities. However, the pooled depositional data for MPs and various shapes and sizes thereof exhibited significant inverse relationships with wind speed and specific measures of airborne particulate matter (e.g., dust, PM-2.5, PM-10). The results suggest that there is a broadly consistent, fibre-dominated regional population of MPs whose deposition (and presumably resuspension) is influenced by variations in wind speed, but additional location-specific factors and sources contribute to temporal variations within the different cities. Despite the relationships between deposition and some gaseous and particulate air quality parameters identified at specific locations, it may be difficult to introduce a sharp parameter that can be used as a regional proxy for MP deposition.

Acknowledgements

We thank Shiraz University and Klaipeda University for technical support. This project has received funding from the Research Council of Lithuania (LMTLT), agreement No. S-PD-24-51.

How to cite: Abbasi, S., Dzingelevičienė, R., and Turner, A.: Regional and climatic variations in atmospheric microplastic deposition, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16376, https://doi.org/10.5194/egusphere-egu25-16376, 2025.

Aerosol-cloud interactions contribute to 75–80% of the total radiative effect of aerosols and remain a major source of uncertainty in predicting future climate. Aerosols significantly influence the warm cloud properties by serving as cloud condensation nuclei (CCN). An increase in CCN leads to the formation of more numerous and smaller cloud droplets, suppresses warm rain by reducing the efficiency of collision and coalescence processes, and extends the cloud lifetime, and liquid water path (LWP) and/or cloud fraction (CF). The activation of a CCN into a cloud droplet is strongly influenced by its size and chemical composition, which subsequently affects the size distribution of cloud droplets and other cloud properties. Although the physical processes of nucleation are well documented for individual particles, the impact of aerosol size on cloud properties is often underestimated because both fine and coarse aerosols co-exist together. To bridge this gap, this study aims to address the impact of size-differentiated aerosols on warm cloud properties over the Northern Indian Ocean (NIO) by utilizing ~20 years of multi-satellite observation data.

The Arabian Sea (AS) and the Bay of Bengal (BoB) in the NIO were chosen in this study as these regions experience a continuous load of aerosols from natural and anthropogenic sources with high seasonal variations. Comparative analysis of size-segregated aerosol optical depth (AOD) revealed the dominance of coarse mode particles (c-AOD) over AS, and fine mode (f-AOD) over BoB. However, a significant increasing trend in the mean f-AOD, particularly during the post-monsoon (ON) and winter (DJF) seasons, is observed over both the AS (0.05/decade) and BoB (0.045/decade) from 2000 to 2021, primarily driven by rising anthropogenic emissions. Further, a climatological analysis of warm cloud CF during these seasons reveals a corresponding increasing trend over the AS (0.07/decade) and BoB (0.05/decade). A correlation analysis of c-AOD and f-AOD with warm CF was conducted, which revealed a stronger annual positive correlation of warm CF with c-AOD (AS: r = 0.56, BOB: r = 0.41) compared to f-AOD (AS: r = 0.37, BOB: r = 0.27). To further investigate the impact of f-AOD and c-AOD on cloud effective radius (CER) for a fixed LWP, an additional correlation analysis was performed. For low LWP (up to 70 gm^-2), an increase in CER was observed with both c-AOD and f-AOD, with a more pronounced increase in CER associated with c-AOD over both the AS and BoB regions. However, as LWP increased, f-AOD exhibited a faster decrease in CER over the BoB compared to the AS. In contrast, c-AOD consistently showed an increasing CER with rising LWP, indicating a contrasting effect relative to f-AOD. These results indicate the dominant radiative effect of fine mode aerosols on cloud formation against the classical microphysical effect of coarse mode aerosols.  Further analysis, incorporating meteorological parameters such as relative humidity and atmospheric stability, is essential to better understand these relationships and enhance the robustness of this study.

How to cite: Bangar, V. and Mishra, A. K.: Satellite-Based Analysis of Size-Segregated Aerosols and Their Effects on Warm Cloud Properties over the Northern Indian Ocean, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16951, https://doi.org/10.5194/egusphere-egu25-16951, 2025.

How to cite: Vaittinen, A., Sarnela, N., Sipilä, M., Brasseur, Z., Boyer, M., Righi, C., Thakur, R., Mazzola, M., and Quéléver, L.: New Particle Formation and Condensable Vapours in an Arctic Site: Ny-Ålesund, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18095, https://doi.org/10.5194/egusphere-egu25-18095, 2025.

The EM27/SUN instrument is a FTIR spectrometer allowing to retrieve total atmospheric column abundance of CO2, CH4, CO and H2O. LSCE is currently developing a tropical network in the framework of the OBS4CLIM French project.

OBS4CLIM aims at deploying five EM27 at observatories located in tropical (Bolivia, French Guiana, Morocco, Ivory Coast) and background regions (Amsterdam Island, Indian Ocean) for long-term observations and satellite validation purposes (TROPOMI, OCO-2/3, GOSAT, MicroCarb). The chosen stations are also part of French National Observation Service and benefit from in situ greenhouse gas measurements.

The rapid growth of this EM27/SUN network requires developing tools to ensure data quality and availability. Therefore, LSCE has developed:

- An automatic data treatment chain based on PROFFAST (developed and maintained at KIT). Two models are used as a priori profiles (GGG2020, and CAMS) allowing to retrieve daily data in near real-time (NUBICOS project).

- Automatic enclosure systems to protect the instrument from a rough environment. This system allows increasing drastically the daily observations and data availability.

Four of the five stations are fully operational. We will present in details the network construction and the first measurement results.

How to cite: Lopez, M., Cassagne, M., Leuridan, H., Ticona, L., Burban, B., Mellouki, W., Hazan, L., and Ramonet, M.: A tropical EM27/SUN network for satellite validation and long term observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18600, https://doi.org/10.5194/egusphere-egu25-18600, 2025.

Hydrogen could play a crucial role in achieving climate neutrality by serving as an energy carrier for renewable sources, offering an alternative to traditional fossil fuels. However, researchers are investigating the impact of hydrogen emissions, as its leakage into the atmosphere poses a concern due to its potential to indirectly influence methane’s atmospheric lifetime and thereby extending its greenhouse effect. Therefore, minimising hydrogen emissions would reduce any potential environmental impact while enhancing safety and efficiency throughout the hydrogen value chain. Thus far, the literature lacks a verified data inventory on the amount of hydrogen emitted from the value chain. Little to no standardized data are present for many elements of the value chain. Otherwise, when present, efforts are still needed for their collection and validation in a unique inventory. The research community needs to address this by improving the capability to quantify small and large emissions and delivering validated methodologies and techniques for measuring or calculating them. An open-access and comprehensible user-friendly tool is urgently needed to better quantify the emissions from the whole hydrogen value chain. The pre-normative research on hydrogen release assessment (NHyRA) project is specifically designed to address these urgent needs. As a first step in this process, the project defined the hydrogen value chain, identifying its main components’ typical operative conditions and recognizing the potential sources of hydrogen emissions.  The next step, the project is working to update an open-access first version of the hydrogen emissions inventory to serve as a reference for the scientific and industrial community. Therefore, by welcoming and validating any contribution of new data, including from outside the NHyRA Consortium, subsequent versions of the inventory will include a more significant amount of data for some of the archetypes (i.e. processes or equipment) in the hydrogen value chain section, to ensure consistent scenario analysis and provide mitigation action recommendations. Furthermore, the NHyRA Consortium experts have identified hydrogen detection and quantification techniques and instruments, covering those which are commercially available and emerging. In this regard, partners of the Consortium have identified three monitoring categories: Detection of emissions at the component level, Detection and quantification of emissions at the component level, and detection and quantification of emissions at the area/site level. Additionally, new or adequately adapted experimental, theoretical, and simulation methodologies will be validated to perform laboratory or in-field measurements to achieve the ambitious goal. Experimental tests will also be performed on the most critical elements of the hydrogen value chains by the partners of the Consortium. A complete picture of the hydrogen emission scenarios, applied on the middle (2030) and long (2050) term European hydrogen economy, will be developed to enable decision-makers to quantify the impact of hydrogen emissions in the energy system transition, identifying  and prioritizing effective risk mitigation actions. Finally, the project will formulate recommendations for Standards and Technical Specifications.

How to cite: Connor, A., Guzzini, A., Holewa-Rataj, J., Piras, P., Claveul, J., Robino, M., and Kostereva, A.: Pre-normative research on hydrogen release assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19010, https://doi.org/10.5194/egusphere-egu25-19010, 2025.

The effects of major greenhouse gas (GHG) emissions in West Africa remain insufficiently documented. Over two consecutive years, we monitored soil GHG emissions using a chamber-based experimental setup across four contrasting land management conditions in the Sudanian savanna. The environmental drivers of the emissions were assessed through stepwise linear regression and ANOVA statistical tests. Our results show that, regardless of land management conditions, N₂O release occurs at the highest rate in rice fields (4.29±2.9 µg N m^-2 h^-1). The soil acts as a sink for CH₄ in the forest reserve (-1.09±7.67 µg C m^-2 h^-1), whereas degraded lands, such as cropland and rainfed rice farms, exhibit CH₄ release at rates of 1.03±13.1 µg C m^-2 h^-1 and 5.93±12.28 µg C m^-2 h^-1, respectively. Livestock breeding contributes significantly to CH₄ emissions in grasslands, where the annual mean CH₄ flux is the highest (16.79±6.69 µg C m^-2 h^-1). The statistical analysis indicates that 53.8% and 50.2% variability in the CH₄ flux is explained by soil moisture and soil temperature respectively in the grassland and rice field. Soil moisture is negatively correlated with N₂O release, while the relationship with CH₄ is positive in grassland and rice fields, where higher CH₄ emissions are observed. N₂O flux shows a positive correlation with soil temperature. These findings suggest that land degradation exacerbates CH₄ emissions, and the effect of fertilizer use on biomass during the growing season increases CH₄ release in rice fields by approximately threefold. At the peak of the raining season, the forest CH₄ sink reaches the highest -6.08±14.7 µg C m^-2 h^-1 while the rainfed rice field releases 9.14±29.57 µg C m^-2 h^-1. Overall, there is intra annual variability of GHG fluxes with dry and wet years showing different magnitude of N₂O and CH₄ emissions. The patterns of GHG flux dynamics in this data-scarce region is better clarified through our investigation. We conclude that GHG emissions in response to land cover degradation and agricultural practices, such as fertilizer use, are significant in the Sudanian savanna and urgent decisions are needed to mitigate these effects.

How to cite: Oussou, F. E., Sy, S., Bliefernicht, J., Spangenberg, I., Guug, S. S., Steinbrecher, R., Schäffler-Schmidt, A., Kiese, R., Ayamba, M., Yalo, N., Asiwaju-Bello, A. Y., Sawadogo, W., Olusegun, C. F., Amekudzi, L. K., and Kunstmann, H.: Evaluation of Chamber-based soil greenhouse gas emissions in contrasted land use of the Sudanian savanna, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19307, https://doi.org/10.5194/egusphere-egu25-19307, 2025.

 Providing sufficient and nutritious food while reducing climate emissions footprints from food systems is a Grand Engineering Challenge for India. The increasing dietary emissions pose a serious threat to achieving the national net-zero goal by 2070, yet such emissions are not yet accounted for in India’s Climate Action Plans. Since the 1990s, India’s dietary transitions have been largely propelled by economic development and intensive urbanization, yet such transitions have occurred unequally between urban and rural regions across India.

The regional and temporal heterogeneity in dietary consumption patterns across different populations and the corresponding GHG emissions is not well known. Here, we apply a life-cycle approach to quantify the regional, demographical, and food commodity-specific GHG emissions (CO₂, N₂O, and CH₄) based on detailed household-expenditure data across three national-scale censuses (1999, 2011, 2022). We differentiate such emissions across twelve major food groups that are typically consumed in 88 distinct NSSO regions with demographics (rural and urban) differentiated by income. Our findings suggest that between 1999-2022, the per capita consumption of animal-based products has increased by ~20% respectively, and a ~15% decrease in wholegrain intake. Emissions from dairy (34%), wholegrain (31%), and meat (18%) food groups contributed more than 80% of total dietary emissions for 2011.

The demographical analysis suggested that household expenditure directly influences GHG emissions. For example, the highest expenditure decile of the population was 2.2 kgCO₂eq cap^-1 day^-1  with 0.7 kgCO₂eq cap^-1 day^-1  for the lowest decile in 2011Both rural and urban regions have per capita GHG emissions similarly, but the total emissions and share of food groups varied extremely with the household expenditure. The disparities in total emissions remain as high as 65% among poor and rich households, with poor houses having wholegrain-dominated emissions and rich households having dairy-dominated emissions. The spatial examination further showed the high heterogeneity in emissions among and within Indian states. Our findings highlight the opportunities and challenges in using food consumption as a lever for climate change while also reducing food inequality by shifting to healthier diets. Such findings can help strengthen State Climate Action Plans to help towards green agriculture and sustainable consumption.

How to cite: Yadav, S. and Balasubramanian, S.: Quantifying regional and temporal heterogeneity in greenhouse gas emissions from Indian diets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19539, https://doi.org/10.5194/egusphere-egu25-19539, 2025.

In this study, we conducted the estimation of shortwave aerosol radiative forcing using the aerosol optical depth (AOD) and supplementary information from the Geostationary Environment Monitoring Spectrometer (GEMS) dataset. We used the libRadtran package for the radiative transfer modeling (RTM), and used the radiative forcing values provided from the Aerosol Robotic Network (AERONET) system for the input value of RTM and the validation task. Total 6 sites in the Korean peninsula are target regions, such as Seoul (Yonsei University and Seoul National university), Anmyeon, Gwangju, Gangneung, and Ulsan. In detail, we used the climatological mean of surface albedo and asymmetry parameter at 4 shortwave channels (440, 675, 870, and 1020 nm), and used daily representative single scattering albedo provided from the GEMS dataset in order to consider the different aerosol type (dust, non-absorbing, and black carbon types). These set-up conditions were finally decided after a number of sensitivity tests. As a result, our estimation of direct aerosol radiative forcing (DARF) at the surface and top of the atmosphere (TOA) shows high correlations with the DARF from the AERONET (correlation coefficient is 0.65 to 0.85 in all 6 sites). Our estimated DARF is a little underestimated compared to the DARF of AERONET, and it seems natural due to the spatial resolution difference. With this high performance, we can provide the daytime hourly variation of DARF over the whole Korean peninsula, which can be useful information to a number of application in the future.

How to cite: Koo, J.-H., Lee, J., and Yoo, J.-A.: Estimation and validation of direct aerosol radiative forcing in the Korean peninsula using the GEMS dataset, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19628, https://doi.org/10.5194/egusphere-egu25-19628, 2025.

Background aims: Life cycle assessment (LCA) is widely used to evaluate the carbon footprint (CF) of milk production. Changes in soil organic carbon (SOC) stock play a vital role in agricultural greenhouse gas emissions. However, no consensus has been reached to incorporate SOC changes into agricultural LCA. This study aims to evaluate the CF of milk production using LCA methodology with integrating  SOC balance based on data from Viikki Research Farm at Helsinki. Methods: The CF of milk production was analyzed for 2022 and 2023 using the Solagro Carbon Calculator. Furthermore, the study explored the soil carbon and nitrogen balances using the DNDC model, for a comparison with IPCC Tier 1 & Tier 2 methods and the real measurements. Results and conclusions: Real measurements demonstrated substantial SOC loss from grassland and subsequent annual cropland, which was 607 and 3939 kg C ha^-1 in 2022 and 2023, respectively. Incorporation of those results increased the CF of milk production. Estimated based on DNDC modeling, the SOC loss exceeded the measured results in 2022 and was underestimated in 2023, while the IPCC method showed SOC sequestration in 2022. The observed emissions fluctuation between the two years was related to the rotation between perennial grass and annual crop, and harsh wintertime conditions affecting crop growth. This study underscores the importance of SOC change in agricultural LCAs. While direct measurements may have limitations, a more profound understanding of SOC dynamics and better calculation is crucial to minimize bias in CF estimations.

How to cite: Gao, Y., Hu, T., Roitto, M., Jokiniemi, T., Sandell, M., Pihlatie, M., and Tuomisto, H.: Life cycle assessment of milk production: integrating changes in soil carbon stock with eddy covariance and DNDC modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20229, https://doi.org/10.5194/egusphere-egu25-20229, 2025.

The Cerrado biome, a globally significant biodiversity hotspot, is undergoing rapid degradation primarily due to anthropogenic activities. Large-scale conversion of native vegetation for agriculture, particularly soybean and cattle ranching, and strong urbanization rates are the main drivers of the biome losses. Additionally, unsustainable water use, infrastructure development, and recurrent fires exacerbate ecosystem degradation, leading to significant biodiversity decline and ecosystem service impairment. A direct consequence of this change in land use is the generation of substantial quantities of air pollutants, mainly particulate matter of 2.5 and 10 𝜇m (PM2.5 and PM10, respectively). These particles, emitted from biomass burning, soil erosion, and dust storms, can penetrate the respiratory tract, leading to various health issues, including respiratory infections, cardiovascular disease, and increased mortality rates. Using measurements of meteorological variables and air pollutants from CETESB (Environmental Company of the State of São Paulo) from 2017 to 2023 in an important urbanized region of the Brazilian Cerrado, we characterized the seasonal distribution of PM2.5 and PM10, together with other pollutants, such as nitrogen oxides (NOx), carbon monoxide (CO) and ozone (O3). In addition, using different combinations of meteorological and air pollution variables, we trained machine learning models to predict the concentration of PM2.5 and PM10. We list Random Forest, XGBoost, and Artificial Neural Networks (ANN) among these models. Our results show that a lower concentration of air pollutants (PM10, PM2.5, CO, and NOx) is observed during summer, while, in contrast, the peak occurs during winter. This is directly related to the seasons with higher and lower precipitation rates. Curiously, O3 peaks in spring and is minimal in autumn, likely related to cloud occurrence. During the whole analyzed period, NOx, PM10, and PM2.5 exceeded the daily average limits of the World Health Organization by about 15, 22 and 35%, respectively. Regarding the predictive models, the random forest better predicted PM10 and PM2.5 concentrations. For PM10, the statistical results for the train (80% of the data)/test (20% of the data) set were R² = 0.79/ 0.92 (p-value < 0.05), with RMSE of 10.7 and 6.5 𝜇g m-3. For PM2.5, the model returned R² = 0.74/0.91, with RMSE of 4.3 and 2.6 𝜇g m-3 for the train/test set, respectively. Although not the best, the ANN also worked relatively well after proper tuning. Future investigations will extend and validate the predictions obtained in this study to other stations in the Cerrado biome with multiple models to spatialize the PM prediction and obtain the regions in which the most air pollutants are emitted. 

How to cite: Franco, M. A. and Teixeira, M.: Characterization and machine learning prediction of atmospheric pollutants in an urban region of the Cerrado biome, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20571, https://doi.org/10.5194/egusphere-egu25-20571, 2025.

Oxygenated volatile organic compounds (OVOCs) significantly contribute to the radical formation in the troposphere, enhancing atmospheric oxidation capacity and driving secondary pollutant production. However, uncertainties in OVOC emissions hinder accurate assessments of their regional impacts. This study updates OVOC emission profiles for the Yangtze River Delta (YRD) region and integrates them into the Community Multiscale Air Quality (CMAQ) model to refine OVOC estimations. The updated model effectively captures the diurnal variations of most OVOCs, significantly reducing biases compared to simulations based on previous inventories. OVOCs, particularly formaldehyde (HCHO), are key precursors of hydroperoxyl radicals (HO₂), which play a dominant role in ozone production across the YRD. Anthropogenic emissions, primarily from industrial activities and vehicular sources, account for 40−60% of total OVOCs. Sensitivity simulations reveal that reducing emissions of reactive OVOCs, such as HCHO and glyoxal, effectively lowers regional ozone levels. These findings underscore the pivotal role of OVOCs in radical chemistry and ozone formation, providing insights for mitigating ozone pollution in rapidly urbanizing regions like the YRD.

How to cite: Li, J.: Photooxidation of Oxygenated Volatile Organic Compounds as a Major Source of Hydroperoxyl Radicals Driving Ozone Formation in the Yangtze River Delta Region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21194, https://doi.org/10.5194/egusphere-egu25-21194, 2025.

Alpine treeline ecotones are extremely vulnerable to climate change, making them important early warning systems in climate research. Advanced remote sensing tools, such as Light Detection and Ranging (LiDAR), enable detailed mapping and monitoring of these high-altitude zones, offering critical baseline data for future change detection. This study combines ground-based Terrestrial Laser Scanning (TLS) and space borne Global Ecosystem Dynamics Investigation (GEDI)- LiDAR data to analyze the structural attributes and delineate the position of alpine treelines in the Tungnath Himalaya, India, located at elevations between 3252 and 3,590 meters above mean sea level (a.m.s.l).  TLS provided high-resolution three-dimensional data on alpine vegetation, including tree height, diameter at breast height (DBH), and canopy structure. Using an automated algorithm, 84.84% of individual trees were segmented from TLS data. TLS-derived tree height and DBH estimates achieved root mean square errors of 44.74 cm and 78.45 cm, respectively, compared to field-measured values. A semi-automated method using GEDI LiDAR identified trees taller than 3 meters to delineate the treeline, achieving a positional accuracy of ~ ±40 m a.m.s.l when validated against TLS-derived data.  The results show that combining TLS with GEDI provides a non-destructive and effective method for assessing treeline structure and location in the Indian Himalaya. Future research might use multi-temporal LiDAR datasets to track treeline movements and obtain a better understanding of the long-term effects of climate change on alpine ecosystems.

How to cite: Mathew, J., Singh, C. P., Solanki, H., and Sedha, D.: Mapping Alpine Treeline Ecotones in the Tungnath Himalaya Using Terrestrial Laser Scanning and GEDI LiDAR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-208, https://doi.org/10.5194/egusphere-egu25-208, 2025.

The accelerated pace of urbanization, population growth, and extensive land-use changes has significantly disrupted the ecological balance and functionality of riverine wetland ecosystems, leading to substantial degradation of wetland health. This study evaluates the health and resilience of the Upper Ganga Riverine Wetland (UGRW) in India, which has experienced significant land-use transformations over the past two decades. The analysis highlights the wetland's resilience to various natural and anthropogenic stresses and its ability to sustain critical ecosystem services, including provisioning, regulating, cultural, and supporting services. The findings reveal drastic land-use and land-cover (LULC) changes, with built-up areas increasing by 245%, while forest and wetland areas decreased by 41% and 8%, respectively, between 2000 and 2020. These transformations have led to a marked decline in ecosystem resilience (23%) and a substantial reduction in ecosystem service values (ESVs), which decreased from 2138.28 million USD in 2000 to 1769.16 million USD in 2020—an overall loss of 18%. Urban expansion, deforestation, and wetland fragmentation have further exacerbated the decline in wetland health, diminishing its ecological balance and capacity to deliver vital services. This study underscores the urgent need for integrated environmental management strategies to mitigate the impact of LULC changes, conserve wetland ecosystems, and enhance their resilience. By assessing ecosystem services and their dependence on sustainable land use, this research provides critical insights for policymakers and stakeholders. It emphasizes the necessity of balancing developmental priorities with ecological preservation, offering a strategic framework to foster sustainability and resilience in one of India’s most vital riverine landscapes.

How to cite: Yadav, A., Kansal, M. L., and Singh, A.: Wetland Health in Transition: Resilience and Ecosystem Services Amid Urbanization and Land-Use Change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-885, https://doi.org/10.5194/egusphere-egu25-885, 2025.

Nitrous oxide (N₂O) is the most important stratospheric ozone-depleting gas of the 21^st century. Most N₂O emissions occur in soils and are associated with agricultural activities. Ammonia (NH₃) is not a greenhouse gas, but it can indirectly contribute to greenhouse gas emissions. NH₃ volatilization is an important indirect N₂O emission pathway in agricultural systems. In addition, NH₃ can have significant effects on both human health and the natural environment, and its emissions negatively affect biodiversity. A meta-analysis was conducted to evaluate NH₃ and N₂O losses and the effectiveness of adding urease and nitrification inhibitors on direct N₂O emissions and NH₃ volatilization. Data were used from 14 separate studies that simultaneously investigated direct N₂O emissions and NH₃ volatilization from irrigated wheat. All studies were conducted on irrigated wheat in semi-arid climates and on calcareous soils with urea application. The average direct N₂O emission factor for irrigated wheat was 0.4%. Our results showed that, on average, nitrification inhibitors reduced direct N₂O emissions by 35% and increased NH₃ volatilization by 29%. The average NH₃ emission factor was 32% and urease inhibitors reduced NH₃ volatilization by 41%. The results showed that indirect N₂O emissions from NH₃ volatilization should be considered in these conditions.

How to cite: Mirkhani, R., Jabbari Malayeri, M., Naserian Khiabani, B., Mousavi, S. M., Ghafariyan, M. H., Ghavami, M. S., Dercon, G., Shorafa, M., and Kheng Heng, L.: Meta-analysis of direct nitrous oxide emissions and ammonia volatilization from irrigated wheat in calcareous soils under semi-arid conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1026, https://doi.org/10.5194/egusphere-egu25-1026, 2025.

Seasonally different precipitation infiltration under monsoon humid areas may drive changes of groundwater flow systems and possible nitrate transformation processes in groundwater. In this study, dissolved greenhouse gases, noble gases concentrations (N₂ and Ar) and isotopes of N₂O were used to quantitively identify nitrification and denitrification to reveal spatial and temporal characterization of nitrate transformation in typical groundwater flow profiles in the Qingyi River basin, east China. In dry and wet seasons, the recharge altitudes of groundwater were distinctive and dominant nitrate transformation processes differed spatially and temporally. According to the N₂-Ar estimation, the recharge altitudes of groundwater in dry season were higher than those in wet season, indicating obviously less proportion of precipitation from lower altitudes and relatively increased proportion of recharge from regional recharge areas in dry season, whereas local groundwater flow systems were preferentially developed in wet season. Denitrification is commonly observed in groundwater during the dry season, with positive Excess-N₂ concentrations and phenomena that N₂O concentrations initially accumulate with progress of denitrification but later decrease due to enhanced N₂O reduction. In the wet season, nitrification is the dominant process in groundwater, with only a small portion of groundwater exhibiting denitrification, resulting in positive Excess-N₂ concentrations. In this case, N₂O concentrations initially increase during nitrification but later decline due to incomplete denitrification. Quantitative results based on δSP-N₂O isotopes indicated that the maximum contribution of nitrification in groundwater during the wet season ranged from 52.8% to 100%, with an average of 77.3%. The contributions from denitrification and N₂O reduction in wet season are limited, which is consistent with results identified by nitrate and ammonium isotopes. Spatially, due to more reducing redox environment in regional groundwater flow systems, the denitrification progress (DP) in most groundwater in discharge zones exceeds 99%, with denitrified NO₃⁻ concentrations reaching up to 25.72 mg/L, significantly higher than the average DP values in recharge zones (27.7%) and transition zones (31.6%).

How to cite: Huang, X.: Identification of nitrification and denitrification along groundwater flow paths using dissolved N2, Ar, and N2O in typical groundwater flow systems in the Qingyi River basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1233, https://doi.org/10.5194/egusphere-egu25-1233, 2025.

Climate change is expected to alter the frequency and intensity of extreme events, modifying the natural disturbance regimes to which ecosystems are currently adapted. Here, we present a spatially explicit risk index for mangroves and their associated biodiversity and ecosystem services based on projected frequency changes of tropical cyclone wind speeds and rates of relative sea level rise under SSPs 245, 370 and 585 by 2100.

To compute the risk index, we calculate the relative change of tropical cyclone frequency across different wind speed intensity categories based on probabilistic tropical cyclone tracks downscaled from 3 different CMIP6 models of varying climate sensitivity. This data is then combined with thresholds of sea level rise which are estimated to exceed mangrove adaptive capacity and mapped onto global mangrove extents.

Globally, approximately half of the total mangrove area (40-56% depending on the SSP) will be at high to severe levels of risk due to climate-modified tropical cyclone disturbance regimes. Further, we find mangrove areas with high levels of biodiversity and ecosystem services provision, including coastal protection for people and assets, carbon sequestration, and fishery benefits, are at proportionally higher levels of risk than mangrove forests generally. We also identify mangrove areas which are projected to experience non-analog tropical cyclone disturbances in the future. Our findings emphasize the need to anticipate changes in natural disturbance regimes to adapt ecosystem management, sustain ecosystem services in the future, and fully realize mangroves’ potential as nature-based solutions (NBS).

How to cite: Hülsen, S., Dee, L., Kropf, C., Meiler, S., and Bresch, D.: Mangroves and their services are at risk from climate-modified tropical cyclones and sea level rise , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1419, https://doi.org/10.5194/egusphere-egu25-1419, 2025.

   Abstract

This study explores advanced remote sensing, geophysical, and geospatial methodologies applied to the geologically diverse Aït Semgane region in Morocco. A multi-disciplinary approach was adopted, combining (1) automated lineament extraction using Digital Elevation Models (DEMs) and various topographic indices, (2) lithological classification leveraging machine learning algorithms on multispectral data, and (3) the integration of magnetic data to enhance geological interpretation.

For lineament analysis, approaches such as the Topographic Position Index (TPI), Hillshade, and shading models were applied to datasets including SRTM, ALOS PALSAR, and Sentinel-1 InSAR. Results highlighted the TPI method’s high sensitivity in detecting tectonic features, especially in NE-SW and E-W orientations, aligning with established geological knowledge. Cartographic analysis revealed fault density concentrations in the NW and southern sectors, confirming the tectonic complexity of the region.

Lithological classification was conducted using Support Vector Machines (SVM), Random Trees (RT), and Artificial Neural Networks (ANN) applied to Landsat 9 and Sentinel-2 data. SVM, particularly with Minimum Noise Fraction (MNF) transformation, consistently outperformed other algorithms, achieving high classification accuracies and well-defined lithological boundaries. The integration of dimensionality reduction techniques like MNF proved crucial for enhancing classification quality, while PCA showed limited efficacy.

Magnetic data were incorporated to validate and refine the tectonic and lithological interpretations, offering additional insights into subsurface structures and enhancing the understanding of fault systems and mineralized zones.

This research demonstrates the synergy between automated lineament extraction, machine learning-based lithological mapping, and magnetic data for improving geological analysis. The methodologies applied here have practical implications for mineral exploration and tectonic studies, offering robust tools for mapping complex terrains. Future research will aim to refine dimensionality reduction techniques, explore hyperspectral datasets, and further integrate geophysical data to enhance geological mapping accuracy.

How to cite: El-Omairi, M. A. and El Garouani, A.: Integrating Automated Lineament Extraction, Magnetic Data, and Machine Learning-Based Lithological Mapping in the Anti Atlas, Morocco, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2041, https://doi.org/10.5194/egusphere-egu25-2041, 2025.

Tidal wetland reclamation could profoundly alter ecological function and ecosystem service provision, but its impacts on sediment microbial communities and functions remain poorly understood. We investigated spatial and seasonal patterns of greenhouse gases (GHGs) production response to land-use changes in mangrove wetlands and unraveled the underlying mechanisms by integrating environmental parameters and microbial communities. Land-use changes substantially reduced microbial community richness and diversity and shaped their composition. Converting mangrove to drier orchard and vegetable field reduced sediment organic matter, carbon GHGs production rates, and microbial network complexity and stability, while increased N₂O production rates. Converting mangrove to chronically flooded aquaculture pond increased sediment CH₄ production rates, but reduced N₂O and CO₂ production rates. Although increasing anthropogenic disturbance in aquaculture pond have reduced microbial community richness and diversity compared to native mangrove wetland, they have increased complexity of species associations resulting in a more complex and stable network. Microbial community richness and network complexity and stability were strongly related to CH₄ and N₂O production rates, but not significantly associated with CO₂ production rates, suggesting microbial community richness, network complexity and stability are better predictors of the specialized soil/sediment functions CH₄ and N₂O production). Therefore, preserving microbial “interaction” could be important to mitigate the negative effects of microbial community richness and diversity loss caused by human activities. Furthermore, as the residual bait accumulation is a severe issue in aquaculture activities, we especially focused on the influence of bait input at time scale through a 90-day incubation experiment, aiming to observe temporal variations of physicochemical properties, sediment microbial community, and GHGs production in response to different amounts of bait input. The results showed that dissolved oxygen of overlying water was profoundly decreased owing to bait input, while dissolved organic carbon of overlying water and several sediment properties (e.g., organic matter, sulfide, and ammonium) varied in reverse patterns. Meanwhile, bait input strongly altered microbial compositions from aerobic, slow-growing, and oligotrophic to anaerobic, fast-growing, and copiotrophic. Moreover, both GHGs production and global warming potential were enhanced by bait input, implying that aquaculture ecosystem is an important hotspot for global GHGs emission. Overall, bait input triggered quick responses of physicochemical properties, sediment microbial community, and GHGs production, followed by long-term resilience of the ecosystem. Future research should comprehensively consider microbial diversity, species composition and interaction strength, functions, and environmental conditions to accurately predict soil/sediment functioning and emphasize the necessity of sustainable assessment and effective management.

How to cite: Lin, G. and Lin, X.: Responses of greenhouse gases production to land-use change and the underlying microbial mechanisms in mangrove wetlands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2079, https://doi.org/10.5194/egusphere-egu25-2079, 2025.

Forested upstream watersheds support clean freshwater and maintaining stable hydrological conditions of ecosystem services. The associations between vegetation growth and climatic variations play a vital role on hydrological regimes that are region-dependent, but the associations of climate-phenology-hydrology have rarely been investigated in tropical/subtropical regions particularly. In this analysis, the hydroclimate records (1991-2020) at two long-term studied forest watersheds, Fushan (FS) and Leinhuachi (LHC) experimental forest, Taiwan were used, and showed that the incidences of meteorological and hydrological droughts are becoming prominent after 2001. We further investigated the effects of monthly climate variables (temperature and precipitation) on vegetation growth using monthly PV (photosynthetic vegetation fraction) of a watershed derived from MODIS (Moderate Resolution Imaging Spectroradiometer), and examined the effects of spring and summer rainfall on the variations of vegetation phenological patterns and subsequent watershed streamflow during 2001–2020. The PV and temperature showed a linear relationship without time-lag effect (R² = 0.51-0.57, p < 0.001), whereas PV and precipitation exhibited no time-lag in FS but a log-linear relationship with 2-month lag (R² = 0.15-0.59, p < 0.001) existed in LHC, indicating the accumulation of rainfall during relatively dry season (winter-spring) was critical for vegetation growth. Structural equation modeling (SEM) revealed that earlier start of growing season (SOS) caused by relatively high spring rainfall (February-March) led to longer growing season (LOS) and higher P-Q deficit (precipitation minus runoff) during the growing season in LHC. Nevertheless, the large amount of precipitation during growing season has no effect on the end of growing season (EOS), LOS and P-Q deficit. Neither EOS has influence on LOS and P-Q deficit. However, these patterns were not found in FS. Understanding the vegetation responses to climatic variations is required for future hydrologic regime projections, especially under changing climate.

How to cite: Chang, C.-T., Lee, J.-Y., Chiang, J.-M., Wang, H.-C., Huang, C., and Huang, J.-C.: Hydrological responses to vegetation-climate interactions at two subtropical forested watersheds of Taiwan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2383, https://doi.org/10.5194/egusphere-egu25-2383, 2025.

Surfactants are extensively utilized across agriculture, pharmaceuticals, and environmental remediation due to their ability to modify surface and interfacial properties. In horticulture, wetting agents and synthetic surfactants are commonly employed to mitigate water repellency in organic growing media, particularly peat-based substrates. These agents are known to aid the substrate’s wettability and improve physical and hydraulic properties, optimizing plant growth and productivity. However, environmental persistence and the potential ecotoxicity of synthetic surfactants have raised significant concerns, highlighting the need for sustainable alternatives. Biosurfactants, particularly rhamnolipids, have gained considerable attention for their biodegradability and surface-active properties both at the scientific and commercial levels. Despite their potential, a comprehensive understanding of the interaction between rhamnolipid and peat essential for assessing its environmental fate and behavior is inadequate. In this regard, the main objective of this study is to quantify the sorption and desorption dynamics of rhamnolipid in peat using batch equilibrium and kinetic experiments to evaluate its suitability as a surfactant for horticultural systems, optimize application strategies, and assess the transport behavior and environmental implications of residual surfactants. Kinetic analysis revealed rapid initial adsorption followed by a gradual approach to equilibrium, with the adsorption and desorption kinetics being well described by the Elovich equation, indicating a chemisorption-dominated process. Furthermore, desorption followed both the Elovich and pseudo-first-order models, illustrating a complex and rate-dependent release process likely influenced by heterogeneous retention of rhamnolipid on the peat surface. Equilibrium analysis demonstrated that the adsorption data were best fitted by the Freundlich model, reflecting the heterogeneous nature of the peat surface and the complexity of its adsorption sites. Sequential desorption experiments exhibited notable hysteresis with reduced desorption efficiency, suggesting strong retention of rhamnolipid on the peat particles. These findings highlight the potential of rhamnolipid as a sustainable alternative to synthetic surfactants for mitigating water repellency in peat-based growing media. Equilibrium and kinetic modeling results will be presented with a comprehensive discussion of their practical implications, providing critical insights into their environmental significance and potential applications in horticultural systems.

Keywords: Water repellant peat, sorption, rhamnolipid biosurfactant

How to cite: Duggireddy, R. and Arye, G.: Sorption Behavior of Rhamnolipid Biosurfactant on Peat, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2449, https://doi.org/10.5194/egusphere-egu25-2449, 2025.

Human activities have increased nitrogen (N) and phosphorus (P) deposition, disrupting microbial activity and altering N-P cycling. Understanding how nutrient limitations and additions affect soil microbes is critical for predicting ecosystem succession and mitigating greenhouse gas emissions. Leveraging long-term N-P addition experiments in a subtropical forest, we developed an enhanced Microbial-ENzyme Decomposition (MEND) model by incorporating an enzyme-mediated P module. Following rigorous calibration and validation with multi-source data, we found that N-P addition has antagonistic effects on main fluxes, with P application mitigating N stimulation of fluxes and partially reducing N₂O emissions. On this basis, we refined the nitrogen saturation hypothesis (NSH) for subtropical ecosystems by attributing divergent nitrification patterns to ammonia inhibition, and we expanded the hypothesis to encompass denitrification and N fixation. By integrating microbiome data, we demonstrated the intrinsic effects of N addition on N cycle through differential expression of genes due to community change, while P addition can counteract effects of N increase by alleviating microbial P limitations. Additionally, we highlight the significance of microbial-enzyme activities feedback in regulating P cycle to maintain ecological balance. Integrating microbially-enabled C-N-P model with diverse experimental data, particularly microbiome information, enhances interpretability and reveals ecosystem mechanisms beyond direct experimental observation.

How to cite: Lv, Z.: Refining the nitrogen saturation hypothesis by accounting for microbial roles in nitrogen and phosphorus cycling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2779, https://doi.org/10.5194/egusphere-egu25-2779, 2025.

Inland waters are an important source of greenhouse gas methane (CH₄). The production of CH₄ is influenced by various factors, including the concentration of dissolved organic matter (DOM), redox conditions, and the composition of microbial communities, with clear spatiotemporal heterogeneity in inland waters. Refractory DOM (RDOM) can resist rapid biodegradation and preserve up to thousands of years; therefore, it is important for assessing the natural carbon sequestration potential of aquatic ecosystems. As a critical part of carbon biogeochemical processes in inland waters, the production of CH₄ and RDOM depends on the microbial successive processing of organic carbon. However, it is unclear yet the link of these two processes and the underlying microbial regulation mechanisms. Therefore, a large-scale survey was conducted in China’s inland waters, with the measurement of CH₄ concentrations, DOM chemical composition, microbial community composition, and relative environmental parameters mainly by chromatographic, optical, mass spectrometric, and high-throughput sequencing analyses, to clarify the abovementioned questions. Here, we found a synchronous production of CH₄ and RDOM linked by microbial consortia in inland waters. The increasing microbial cooperation driven by the keystone taxa (mainly Fluviicola and Polynucleobacter) could promote the transformation of labile DOM into RDOM and meanwhile benefit methanogenic microbial communities to produce CH₄. This process was also influenced by environmental factors such as total nitrogen and dissolved oxygen concentrations. Future studies need to combine more field investigations and laboratory control experiments to fully understand these complex processes. This study deepened the understanding of microbial-driven carbon transformation and highlighted the role of microbial keystone taxa in these processes, providing some useful references for the future laboratory control experiments (e.g., the selection of microbial species). Considering that CH₄ emission and RDOM production are closely related to the carbon source-sink relationship, this finding will help to more accurately evaluate the budget in inland aquatic ecosystems.

How to cite: Shi, X., Li, W., Wang, B., Yang, M., and Liu, C.-Q.: Keystone taxa drive the synchronous production of methane and refractory dissolved organic matter in inland waters, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4681, https://doi.org/10.5194/egusphere-egu25-4681, 2025.

Pollution places an 'everlasting' burden on brownfields, with a lot of money going towards the management of sites where nothing happens. Action is administratively unattractive, and managers and area developers find it difficult to connect. The development of the surrounding area is also halted. This limitation is becoming increasingly urgent with the growing spatial pressure due to the energy transition, climate adaptation, and housing needs. However, much more is possible than has been achieved so far; redevelopment is often indeed possible.

Public and private parties can work on upgrading brownfields. This can also generate money to better manage risks. In many places, developing parks to make surrounding residential areas more attractive is popular. Parks also play a role in climate adaptation and increasing biodiversity. Solar panels can be installed along the edges of the park in such a way that greenery is also possible underneath.

Altogether, there are twelve known functions that can upgrade brownfields. The value increases if two or more functions enable each other, such as greenery and solar panels. Upgrading brownfields can be done singly, but can also be multiple by stacking functions. What does this yield, and how do you do that, especially how do you finance a multiple project? The paper discusses the multiple redevelopment of former landfills and particularly the financing thereof.

How to cite: van der Heijden, J.: Multiple Redevelopment of Brownfields, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4724, https://doi.org/10.5194/egusphere-egu25-4724, 2025.

Abstract
Peatlands are important global terrestrial carbon (C) sink. Most of Irish peatlands have been 
influenced in past by anthropogenic management, primarily through drainage for forestry, 
agriculture, or energy and horticultural extraction. Given the recent Irish peatland restoration 
activities, it is essential to deepen our understanding of the key drivers of peatland C-dynamics 
and to improve methodologies for reporting and verifying terrestrial CO₂ removals/emissions 
from drained and restored peatlands. The dependency of CO₂ fluxes on water-table (WT) levels 
in peatland ecosystems, under different land-use (LU), has been recognised in existing literature 
[1], indicating on the importance of accounting for WT variable in predictive models. This study 
focuses on assessing the application of random forest (RF) to predict WT in total eight Irish 
peatland sites under different LU (natural, rewetted, forest, grassland), which were monitored - 
i.e. low-level Irish blanket-bog sites from Co. Mayo and raised-bog sites from Co. Offaly [2]. The 
RF was chosen due to its ability to effectively manage mixed-data (numerical and categorical) and 
to provide robust predictions without the need for extensive data-preprocessing. Used were the 
data from ca. 2017 to 2020 on-site measurements [2], as well as the selected geospatial data 
derived from E-OBS daily grided-meteorological dataset [4]. The RF was applied to a number of 
numerical and categorical variables, by splitting the data into training- and testing-datasets. 
Hyperparameter tuning was done using ‘caret’ R-package [5]. Model evaluation (using 
performance metrics) was conducted on WT-predictions from testing-dataset. While findings 
from this study on selected eight Irish peatland sites indicate a relatively good potential of RF to 
predict WT (R² = 0.78), the work highlights the importance of assessing the ‘variable importance’ 
to reduce the number of variables in the model for practical applicability purposes, as well as to 
include more sites.

Acknowledgements
The authors are grateful to the Irish Environmental Protection Agency (EPA) for funding projects 
CO2PEAT (2022-CE-1100) and AUGER (2015-CCRP-MS.30) [EPA Research Programmes 2021-
2030 and 2014–2020], and to University of Limerick funding.

References
[1] Tiemeyer, B., et al., 2020. A new methodology for organic soils in national greenhouse gas inventories: Data synthesis, derivation and application,
Ecological Indicators, Vol. 109, 105838,  https://doi.org/10.1016/j.ecolind.2019.105838.
[2] Renou-Wilson, F., et. al, 2022. Peatland Properties Influencing Greenhouse Gas  Emissions and Removal (AUGER Project) (2015-CCRP-MS.30), EPA Research Report, Irish Environmental Protection Agency (EPA) https://www.epa.ie/publications/research/climate-change/Research_Report_401.pdf.
[3] Premrov, A., et.al, 2023. Insights into the CO2PEAT project: Improving methodologies for reporting and verifying terrestrial CO₂ removals and emissions from Irish peatlands. IGRM2023, Belfast, UK. https://www.researchgate.net/publication/369061601_Insights_into_the_CO2PEAT_project_Im
proving_methodologies_for_reporting_and_verifying_terrestrial_CO2_removals_and_emissions
_from_Irish_peatlands.
[4] Copernicus Climate Change Service, Climate Data Store, (2020): E-OBS daily gridded meteorological data for Europe from 1950 to present derived from in-situ observations. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.151d3ec6.
[5] Kuhn, M. 2008. Building Predictive Models in R Using the caret Package. Journal of Statistical Software, 28(5), 1–26. https://doi.org/10.18637/jss.v028.i05.

How to cite: Premrov, A., Yeluripati, J., Renou-Wilson, F., Walz, K., Byrne, K. A., Wilson, D., Hyde, B., and Saunders, M.: Assessing the application of random forest (RF) to predict water-table (WT) in selected Irish peatlands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5122, https://doi.org/10.5194/egusphere-egu25-5122, 2025.

Gap dynamics is one of the key drivers of forest succession in temperate forests. The primary successional trajectory involves the transition from early to late successional species, each with distinct trait characteristics. I will present a modeling approach to forest successional dynamics based on scaling plant traits from individual to community levels. In this work, the shade tolerance index is statistically linked with plant traits that characterize early and late successional species using the U.S. Forest Inventory dataset. Discrete and continuous mathematical models, represented by Markov chains and autoregressive models, are employed to predict forest dynamics. An individual-based model is also used to assess the robustness of this scaling approach under different disturbance regimes. Overall, modeling forest successional dynamics based on the scaling of shade tolerance-related functional traits from the individual to the ecosystem level addresses major limitations of models based on the traditional stand age metric.

How to cite: Strigul, N.: Plant Trait-Based Modeling of Forest Succession, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6451, https://doi.org/10.5194/egusphere-egu25-6451, 2025.

Global Ecosystem Dynamics Investigation (GEDI) mission, operating from International Space Station, is a full-waveform LiDAR measuring vertical 3-dimensional structure of terrestrial ecosystems. The vertical canopy cover (CC) available from the GEDI L2B product has applications in forest ecosystem, forest health and climate change studies, and management practices. Some studies have assessed the accuracy and uncertainty of the GEDI vertical canopy cover profile product using aerial LiDAR scans and in-situ measurements. However, in-situ measurements taken using angle-of-view effectively produces canopy closure whereas GEDI measures vertical CC. Cajanus tube, regarded as ideal canopy cover measurement technique, is time consuming and impractical for larger areas. In this study, suitable in-situ canopy cover measurement methodologies were assessed alongside GEDI vertical CC. Canopy cover measurements were taken under GEDI footprints in the Indian Western Himalayan region using spherical densiometer, hemispherical photographs and digital canopy photographs with narrow angle-of-view. The plot dimensions were adjusted to accommodate horizontal geolocation uncertainty of GEDI version 2 data products. Following data collection, measurement techniques were assessed based on R-squared, RMSE and MAE.

How to cite: Paygude, A., Pande, H., and Tiwari, P. S.: Assessment of In-situ Canopy Cover Measurement Techniques and GEDI Vertical Canopy Cover in the Indian Western Himalayan Region, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7236, https://doi.org/10.5194/egusphere-egu25-7236, 2025.

Bound biomarkers, which are covalently linked to kerogen or asphaltene macrostructures, exhibit enhanced stability against mixing effects, contamination, and biodegradation. Although previous studies have noted that the results of bound and free biomarkers in assessing sedimentary environment and maturity are not exactly consistent, specific criteria for assessing bound biomarkers have not been proposed. In this study, microscale sealed vessel catalytic hydrogenation (MSSV-Hy) was used to extract bound biomarkers from shale and compare them with free biomarkers. The study demonstrates the reliability of bound biomarkers indices in evaluating depositional environments and maturity, and it systematically compares the differences between bound and free biomarkers. The results revealed that the maturity assessment of bound biomarkers is lower than that of free biomarkers. Additionally, C29 regular steranes are selectively consumed during rapid heating, resulting in a decrease in the input parameters from terrigenous sources. Adjusted criteria for bound biomarkers can more accurately evaluate the sedimentary environment and maturity of shale.

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How to cite: Yuan, L.: Application of Bound Biomarkers in the Evaluating the Deposition Environment and Maturity of Shale, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7582, https://doi.org/10.5194/egusphere-egu25-7582, 2025.

The seagrass meadows are critical for organic carbon storage and play a significant role in mitigating climate change. However, the ongoing degradation of the seagrass meadows in Thailand reduces their ability to sequester carbon effectively, potentially contributing to greenhouse gas (GHG) emissions. This study examines variations in carbon storage, carbon metabolism, and GHG emissions across degraded, healthy seagrass and bare sand areas along Andaman Sea, Thailand. The average carbon storage within the surface sediment (top 10 cm) varies across seagrass conditions, with the highest carbon storage in heavy degraded (365.2 ± 206 g C m^-2), followed by bare sand (289.5 ± 236 g C m^-2) and healthy seagrass (86.47 ± 5.8 g C m^-2). Furthermore, degraded seagrass and bare sand exhibited heterotrophic ecosystem functions with an average NCP value of 0.44 ± 0.49 and -0.13 ± 0.79 mmol C m⁻² d⁻¹, respectively. Conversely, healthy seagrass maintained autotrophic ecosystem functions with NCP 1.30 ± 0.508 mmol C m⁻² d⁻¹. The average total carbon sink varied among seagrass conditions, with the highest in degraded seagrass (4328 ± 2395 CO₂-eq m⁻² d⁻¹), compared to bare sand (3981 ± 4120 CO₂-eq m⁻² d⁻¹) and healthy seagrass (1630 ± 0 CO₂-eq m⁻² d⁻¹). The study also revealed that CH₄ emissions dominated GHG fluxes in all seagrass conditions, with the highest mean CH₄ fluxes recorded in degraded seagrass (1.16 ± 0.51 µmol m⁻² h⁻¹), followed by bare sand (1.02 ± 0.41 µmol m⁻² h⁻¹) and healthy seagrass (0.48 ± 0.07 µmol m⁻² h⁻¹). On the other hand, the CO₂ emissions remained consistently low in both seagrass meadows (healthy and degraded) and bare sand areas. These findings are important to indicate and provide the baseline of GHG emissions for healthy and degraded tropical seagrass meadows.

Keywords: Blue carbon, Climate Change, Emission, Greenhouse gas, Seagrass meadows

How to cite: Halim, M., Stankovic, M., and Prathep, A.: Estimating the Potential Greenhouse Gas Emission from Degraded Seagrass Meadows: A Case Study from Thailand's Seagrass Ecosystems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7982, https://doi.org/10.5194/egusphere-egu25-7982, 2025.

Open-cast lignite mining significantly disrupts cultivated soils. Restoration and re-cultivation processes enable the conversion of these disturbed areas back into productive land. These processes involve mixing original topsoil (~20%) with parent material loess (~80%), diluting the organic carbon (C) and nitrogen (N) pools, as well as the soil's biological parameters. To restore soil fertility and physical structure, Phase I includes the cultivation of alfalfa to replenish C and N, re-establish biological functions, and the addition of mineral fertiliser (N:P:K, 15:15:15 kg ha^-1). Following two to three years of Phase I, the restoration transitions to Phase II for three to five years, with an initial application of green waste compost (30 t ha^-1) and annual basal mineral fertiliser (N:P:K, 200:80:60 kg ha^-1). Phase III then involves returning the land to farmers with a typical rotation including sugar beet-winter wheat and a mix of organic and mineral fertilisation.

Previous studies have shown that soil C recovery and several key biological functions have only partially recovered, even after more than 50 years since re-cultivation. However, the evolution of P cycling, especially microbial-mediated P cycling, along this gradient remains unknown. This study aims to investigate interactions between soil P, soil microorganisms, and soil properties that affect microbial P cycling and P availability to plants following mining activity.

Hedley fractionation was employed to estimate various P pool sizes, while ion-exchange kinetics (IEK) assessed P exchangeability and reactivity in eight soils (soils restored from 2022 – year 0 – Phase I, 2020 – year 2 – Phase I, 2018 – year 5 – Phase II, 2014 – year 9 – Phase III, 2006– year 17 – Phase III, 1979 – year 44 – Phase III and 1964 – year 59 – Phase III and an original soil undisturbed). In three key soils (year 0 - Phase I; year 59 - Phase III; original undisturbed soil), ¹⁸O-labeled water was used in incubation to determine the degree of ¹⁸O integration within microbial biomass and in various P fractions.

In Phase I, a decrease in the relative size of the most labile-P pool was observed. In Phases II and III, this proportion increased, notably with a larger NaOH-extractable-P increase. P exchangeability decreased during Phase I, then significantly increased in older soils, surpassing that of the original undisturbed soil. Preliminary results indicate microbial P processing is highly correlated with total soil organic C. For instance, microbial P processing was nearly non-existent in newly formed soil (organic C: 0.54 g kg^-1) and was found to be twice as low in 59-year-old soil (organic C: 1.24 g kg^-1) compared to the original soil (organic C: 1.62 g kg^-1).

The current findings demonstrate that despite measured P levels surpassing those of the original soil in the oldest soils, biologically-driven P cycling has not fully recovered more than 50 years after soil re-cultivation.

How to cite: Raymond, N. S., Tamburini, F., Oberson, A., Reichel, R., and Mueller, C. W.: Microbial phosphorus processing in a gradient of agricultural soil development following mining activity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8511, https://doi.org/10.5194/egusphere-egu25-8511, 2025.

The increasing frequency and intensity of drought events make them a growing threat for plants that are sensitive to water scarcity. It is therefore important to understand how plants react to drought stress. The willow trees from the short-rotation coppices (SRC) on the DTU-Risø Campus in Denmark (DK-RCW) are particularly sensitive to water shortage as they are rainfed. We address the following question: how do extremely dry conditions affect the willows growth? We study the plants response mechanisms to periods of water scarcity and examine how these responses impact their gross primary productivity (GPP). There is a particular relevance to this in the current context of global warming, where the SRC are used to produce bioenergy and can store carbon to mitigate climate change.

Field measurements were carried out at the DK-RCW site to gather information on canopy structure (leaf area index). These results were integrated into a modelled relationship with carbon flux data from an eddy covariance flux tower located onsite and providing continuous CO₂ and H₂O flux data in more than 10 years. The simple empirical model was used to contrast the GPP’s sensitivity to stomatal and non-stomatal processes by comparison of extreme drought conditions (summer 2018 in Denmark) and wetter conditions (summers 2015 and 2021). These years represent the same stage of the rotational cycle.

This new model enables us to highlight two complementary responses to drought: the trees immediately react by adapting their physiology (stomatal resistance, increased sensitivity to vapour pressure deficit under drought), but also by changing the canopy structure as the drought increases (reduction of the leaf area index) and other responses on canopy photosynthetic capacity. High vapour pressure deficit and the reduction of the leaf area index both reduced the photosynthesis of willow trees under dry conditions. The simulated data imply limited drought recovery after the dry period had ended. For these reasons, the carbon uptake by the willow SRC is lower during droughts and thus limits the SRC productivity and carbon sink strength. We conclude from the very clear results from this case study that different drought response mechanisms must be considered when trying to understand and predict plant responses to extreme drought.

How to cite: Jaujay, M. and Ibrom, A.: Drought sensitivity of gross primary productivity in willow: effects from physiological versus structural responses, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10635, https://doi.org/10.5194/egusphere-egu25-10635, 2025.

Many dioecious plants are dominant foundational species (e.g., grasses, poplars, ginkgoes) that structure ecosystems and provide essential resources for diverse ecological communities. Due to their higher nutrient demands and reproductive costs, female plants generally appear more sensitive to environmental changes, such as increased temperatures and drought conditions. The soil ecosystem is critical for providing the substrate, nutrients, and habitat for terrestrial plant communities to exist. Male and female plants are likely to interact with the soil environment differently, with implications for ecosystem functioning. Recent research has shown that female and male plants differ in their soil microbial diversity and community composition. However, how plant sex affects soil communities is still unknown. This study investigated how female and male plants of Ilex vomitoria differ in fungal diversity and composition and subsequent cascading effects on soil arthropods. Fungal operational taxonomic units (OTUs) were identified from DNA sequencing data, and arthropods were extracted and identified from 91 soil samples collected under the canopies of female and male Ilex vomitoria individuals across three locations in southeastern Texas, USA. We found that male plants of I. vomitoria exhibit higher fungal diversity compared to female plants, with both sexes associating with distinct fungal communities. Conversely, soil arthropod diversity and community composition were affected by location but not plant sex. Our results provide valuable insights into the ecological interactions of dioecious plants, emphasizing the role of plant sex as a key trait that influences soil biodiversity and the associated functioning of ecosystems.

How to cite: Bradley Jimenez, R.: Ecological interactions in dioecious plants: implications for soil fungi and arthropods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13963, https://doi.org/10.5194/egusphere-egu25-13963, 2025.

Tidal Marshes are wetland ecosystems at the marine-terrestrial interface which serve as strong sinks for atmospheric carbon dioxide and large reservoirs of soil organic carbon (SOC). However, tidal marsh soils also produce and emit the potent greenhouse gas methane (CH₄). Previous work has demonstrated that CH₄ flux is inversely related to salinity, and that methane flux is negligible compared to carbon dioxide (CO₂) uptake in marshes with salinities of >18 parts per thousand (ppt). However, in lower salinity tidal marshes, CH₄ flux is highly variable, and can spike sharply following the depletion of sulfate supply. In order to better understand drivers of methane flux across a range of salinities, we established three transects along estuarine gradients in Rhode Island and Connecticut, USA. At landward and seaward sites along each transect, we measured methane flux, salinity, and conducted various porewater and soil chemical analyses. We found that methane flux was significantly higher and more variable in marshes where salinity is < 18 ppt. The highest magnitude methane fluxes occurred when sulfate was nearly depleted in marsh porewater, indicating that sulfate abundance dampens methane production, but demonstrating the need for further investigation into processes governing sulfate depletion and replenishment in salt marshes, and the degree to which salinity is a reliable proxy for sulfate concentration. Additionally, the lack of spatial data products which delineate tidal marshes according to salinity complicates efforts to estimate methane budgets in tidal estuaries. Our results indicate that spatial differences in salinity should inform wetland mapping in order to facilitate estimations of greenhouse gas budgets, but more high-resolution monitoring of salinity is needed to accurately delineate map units.

How to cite: Norton, M., Moseman-Valtierra, S., and Stolt, M.: Greenhouse Gas Flux in Coastal Salt Marshes: Field Measurements along Estuarine Gradients in Northeastern USA, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14217, https://doi.org/10.5194/egusphere-egu25-14217, 2025.

         Blue carbon ecosystems (mangroves, seagrass beds, and salt marshes) are one of the most effective carbon sinks on Earth and are critical to climate change mitigation and adaptation. Hainan Province in China accounts for 82% of the country's mangrove area and 64% of the country's seagrass bed area. Hainan's blue carbon plays an important role in local and national carbon sink enhancement efforts. From the perspective of economics, Hainan's blue carbon system plays a major supporting role in the local economy. Existing research on the protection of China's blue carbon ecosystems focuses on carbon sink accounting and economic valuation, and rarely involves microeconomic impact analysis of blue carbon protection actions. In particular, there are few studies specifically conducted on the impact on residents' livelihoods and well-being in Hainan.

        In this context, we are attempting to conduct research in Hainan Province to answer the following questions: What impact does the protection and restoration of Hainan's blue carbon ecosystem have on the livelihoods of its coastal communities? We refined this question into three points: First, what are the livelihood sources and livelihood structures of Hainan's coastal and non-coastal communities; what changes have occurred around 2020? Second, has Hainan's special action on the protection and restoration of blue carbon ecosystems had an impact on the livelihoods of coastal communities? Third, through what channels does Hainan's special action on the protection and restoration of blue carbon ecosystems affect the livelihoods of coastal communities?

        According to preliminary research, Hainan Province's special action for the protection and restoration of blue carbon ecosystems has a two-way impact on the livelihoods of coastal communities. On the one hand, blue carbon protection can maintain and promote the local fishery economy and tourism; on the other hand, due to restrictive regulations on the relevant use of marine resources at the policy level, the protection and restoration of mangroves may have a negative impact on fisheries. Maintaining a balance between fishermen's livelihoods and blue carbon protection may be one of the difficulties in blue carbon conservation. Treating the special action for the protection and restoration of blue carbon ecosystems as a quasi-natural experiment, we are going to conduct policy evaluation in our study. We will conduct a community questionnaire survey and introduce the propensity matching difference-in-difference (PSM-DID) model to reveal the net effect of Hainan's blue carbon ecosystem protection on the livelihoods of coastal communities.

How to cite: Chen, Y.: Study on the Impact of Blue Carbon Ecosystem Protection on the Livelihoods of Coastal Communities in Hainan Province, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14443, https://doi.org/10.5194/egusphere-egu25-14443, 2025.

Alkalinity enhancement in rivers is a proposed carbon dioxide removal strategy which leverages physical and biogeochemical properties of rivers to promote uptake of atmospheric carbon dioxide. Robust monitoring, reporting and verification of carbon dioxide removal is necessary to instill trust in carbon credits and market activity stemming from river alkalinity enhancement. Rivers have the unique characteristic of reflecting integrated watershed characteristics along a one-dimensional trajectory. Depending on the size of the river, alkalinity dosing location and quantity, transit distance to the ocean and availability of monitoring locations, carbon dioxide uptake can be quantified through a hybrid approach leveraging direct measurements and models. In this poster, we propose a flexible, multiscale quantification framework which can be adapted to a wide range of rivers and deployment scenarios. 

How to cite: Yin, J., He, J., Sutherland, K., and Gill, S.: A flexible, multiscale quantification framework for river alkalinity enhancement, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14511, https://doi.org/10.5194/egusphere-egu25-14511, 2025.

Seagrass ecosystems are vital for coastal resilience, biodiversity, and as critical carbon sinks. With global seagrass declines, restoration has emerged as a key strategy for ecological and carbon recovery. Although through seagrass restoration, various ecosystem services return, there is a lack of information on the return of the carbon sequestration and accumulation. This study aims to assess the potential recovery of blue carbon benefits through seagrass restoration across various sites in Thailand. We analyzed carbon stocks and accumulation rates in restored Enhalus acoroides meadows at four sites, evaluating spatial variability in carbon recovery in restored versus natural meadows and unvegetated sediment. Despite successful seagrass establishment, the organic carbon (OC) content (%) within the surface sediment (top 20 cm) was not significantly different among restored, natural seagrass meadows, and bare sand, averaging 0.8 ± 0.1%, 0.9 ± 0.2%, and 0.9 ± 0.2% respectively. Although significant differences in OC content (%) were observed between sites, no differences were noted between the habitat types within each site. Predominantly sandy sediment (over 90%) with minimal mud content (1% or less) were found at all sites. The highest organic carbon stock in surface sediment was in unvegetated sediment, averaging 16.8 ± 3.4 Mg C ha^-1. Significant differences in OC stocks were also observed across all site comparisons, with higher stocks generally found in bare sand compared to restored and natural seagrass meadows. Sediment accumulation profiles, indicated by the absence of excess ²¹⁰Pb, suggest a lack of net fine sediment accumulation over the past decade or mixing of the upper sediment, precluding reliable sedimentation rate estimation. These findings suggest that these restored meadows are not forming depositional environments contributing to significant additional carbon sequestration, as evidenced by the minimal increase in OC stocks across the sites. Additionally, the low OC content (%) and minimal mud presence suggest overall low sedimentation rates, even in natural seagrass meadows. These results highlight the complexity of achieving carbon sequestration goals through seagrass restoration, emphasizing the need for site-specific restoration strategies that consider local sediment dynamics and ecological conditions to enhance carbon storage capabilities.

Keywords: organic carbon, carbon additionality, carbon accumulation, seagrass, restoration

How to cite: Stankovic, M., Kaewsrikhaw, R., Masqué, P., Vanderklift, M. A., Upanoi, T., and Prathep, A.: Lack of blue carbon recovery in restored tropical seagrass ecosystems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14608, https://doi.org/10.5194/egusphere-egu25-14608, 2025.

Disturbances resulting from anthropogenic global change pose ongoing threats to plant biodiversity. Functional trait-based approaches enable ecologists to observe species-level stress responses with implications for community-level adaptations to disturbances. DRAGNet (Disturbance and Resources Across Global Grasslands) leverages grassland restoration to explore the mechanisms driving disturbance recovery and community assembly. In this single-site study, we examine how plant composition and traits vary across disturbance (tillage) and soil resource (NPK+) gradients. Plant composition will be surveyed in 28 plots, with root and soil samples extracted for trait analysis and soil nutrient testing. We predict that plants in disturbed, nutrient-enriched plots will exhibit divergent functional traits, including reduced root biomass and specific root length, alongside changes in above-ground traits. Preliminary data illustrates the impact disturbance can have on community composition, particularly by promoting invasive species (PERMANOVA, p = 0.0576). This finding underscores the influence of disturbance on plant community assembly and highlights the potential vulnerability of restored grasslands to invasive species proliferation under human-induced disturbances. This study aims to uncover the root functional traits driving the recovery of a restored grassland across both soil disturbance and resource gradients.

How to cite: Campbell, A., Sullivan, L., Celestin, M., and McCary, M.: Getting Out from Under: The Belowground Response of a Restored Grassland to Soil Disturbance and Resource Addition, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14964, https://doi.org/10.5194/egusphere-egu25-14964, 2025.

Oceanic islands have high biodiversity due to high rates of endemicity, which is now severely threatened by global change, including biological invasions. Invasive plants are predicted to displace native plants via vigorous resource use associated with fast growth rates and population expansion. The corresponding dynamics associated with invasive plant litter offer important insights to bridge live foliage traits associated with competition with invasive plant effects on ecosystem function via litter decomposition. Evidence has accumulated to support the prediction that invasive species produce higher quality litter than native species, which decomposes more rapidly, in turn providing positive feedback that facilitates their expansion. However, litter quality can vary among and within species across climate gradients, which is likely to contribute to spatial variation in native-invasive plant interactions. In this study, we synthesize a large body of litter trait data using systematic review methods and quantitative analyses, to investigate litter trait differences between native island plants and non-native plants established in natural habitats across steep elevation (7.5 – 2660 m) and mean annual rainfall (272 – 6362 mm) gradients of the Hawaiian Islands. We found that litter traits are highly variable in both native and invasive species, with considerable overlap in multivariate trait space. Intraspecific and interspecific differences were the main sources of litter trait variation, which explained 40% and 41% of the total variance, respectively. Nonetheless, as predicted, invasive plants had litter that tended to be of higher nutritional quality and lower toughness than native plants, although this difference explained only 8% of the total variance across all traits. Interestingly, litter traits varied significantly with respect to temperature and rainfall, and the patterns differed between native and invasive plants. These results corroborate previous studies on live foliage traits that climate mediates invasive-native plant interactions across the heterogeneous environment of Hawaii. These patterns emphasize the importance of considering litter as part of the functional syndrome of plants and for a better understanding of how invasive plants may alter their novel ecosystems.

How to cite: Satdichanh, M., Harrigan, W., Ostertag, R., and Barton, K.: Plant litter trait variation between native and nonnative species across steep climate gradient in Hawaiian Islands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14991, https://doi.org/10.5194/egusphere-egu25-14991, 2025.

Plant-insect herbivore interactions are essential in shaping forest ecosystem health. The resource availability hypothesis (RAH) and the leaf economics spectrum (LES) theory predict that species in high-resource environments tend to adopt a "fast" strategy but are more susceptible to herbivory. However, this contradicts reports of increased insect herbivory in the context of global drought intensification, and hinders accurate prediction about how different plant species respond to herbivorous insect feeding.

To fill this knowledge gap, we conducted an observational study in two temperate forests dominated by Quercus mongolica and Betula platyphylla in eastern China to compare their leaf herbivory patterns and explore possible mechanisms. We measured three leaf herbivory proxies (consumed leaf area, percent consumed, and herbivory frequency), some leaf traits (leaf area, specific leaf area, leaf water content, leaf nitrogen, phosphorus and non-structural carbohydrate contents), and soil properties (pH, soil water content, soil organic carbon content, soil nitrogen and phosphorus contents).

We found that Q. mongolica, growing in poorer soil environments with lower water and nutrient contents, experienced higher leaf herbivory than B. platyphylla. Regarding leaf traits, Q. mongolica had a higher leaf area and non-structural carbohydrate content, but lower specific leaf area, leaf nutrient and water contents than B. platyphylla. At the leaf level, leaf area, rather than specific leaf area, of both tree species was positively correlated with leaf herbivory. At the tree level, species-specific patterns emerged, i.e., leaf herbivory of B. platyphylla was positively related to leaf area and negatively related to leaf nitrogen and water contents and soil phosphorus content, whereas that of Q. mongolica was only positively affected by soil phosphorus content.

These findings challenge the predictions of RAH and LES theory, as Q. mongolica that grows in resource-poorer soil environments with a conservative strategy suffers higher leaf herbivory than B. platyphylla, shedding some light on the proverb that trouble follows the needy. Moreover, water-related factors (i.e., leaf and soil water contents) and leaf area showed an important effect on driving interspecific and intraspecific leaf herbivory variations here, implying that climate-induced droughts may exacerbate herbivore pressure in temperate forests.

How to cite: Zhao, C. and Tian, D.: Trouble follows the needy: more severe leaf herbivory in the resource-poorer temperate oak forest than in the birch forest, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17774, https://doi.org/10.5194/egusphere-egu25-17774, 2025.

Optimizing carbon use efficiency (CUE) and water use efficiency (WUE) is a critical challenge for temperate forests worldwide, particularly under changing climatic conditions. CUE refers to the proportion of carbon assimilated during photosynthesis that contributes to biomass, while WUE quantifies the carbon gained per unit of water lost through transpiration. The region of Wallonia, Belgium, with temperate forests covering 33% of its land, serves as an exemplary case for analyzing the relationship between CUE and WUE under varying ecological and climatic conditions. Globally, the coupling of CUE and WUE remains insufficiently understood, especially at the species level. This study investigates the dynamics of CUE and WUE across several dominant tree species in Wallonia. It utilizes outputs from the CARAIB dynamic vegetation model to evaluate species-specific responses to thinning practices and climate scenarios (RCP 8.5 and RCP 2.6) over the period 1980 to 2070.

Our analysis distinguishes between the isohydric and anisohydric behaviors of tree species, emphasizing their contrasting long-term responses to climatic changes and their influence on ecosystem efficiency. Trees such as Abies and Picea tend to be isohydric. They conserve water by closing their stomata early during drought. They benefit from thinning practices initiated at 40 years, with intervals of 3–9 years designed to manage competition as they mature. Conversely, trees like Quercus and Populus tend to be anisohydric. They maintain photosynthesis under stress by keeping their stomata open. Populus requires earlier thinning interventions, typically starting at 30 years, with shorter regrowth periods of 15 years to optimize light penetration and nutrient availability. In contrast, Quercus thinning is initiated at 40 years, with regrowth periods of 30 years, to support their growth and optimize resource utilization. Thinning reduces competition and reallocates resources, modulating trade-offs between WUE and CUE while supporting species-specific growth under varying climatic stressors. Tailored thinning practices enhance resource availability for both isohydric and anisohydric species. Isohydric species gain from improved water availability, complementing their inherent drought resilience, while anisohydric species benefit from increased carbon assimilation through enhanced access to light and nutrients.

These findings underscore the importance of aligning species composition and management strategies with localized environmental conditions to bolster forest resilience. With this study, we investigate species-specific management strategies to support sustainable forestry, identifying species that are better adapted to changing climatic conditions and capable of maintaining vital ecosystem services.

How to cite: Verma, A., Lanssens, B., Tölle, M., Chaudhari, T., Hambuckers, A., and Francois, L.: Quantifying Carbon and Water Use Efficiencies of Forest Ecosystems in Wallonia, Belgium: Insights from Species-Specific Responses to Thinning and Climate Change , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18429, https://doi.org/10.5194/egusphere-egu25-18429, 2025.

An accurate representation of biomass burning aerosol emissions is essential in climate and Earth System Models to capture aerosol properties and their interactions. The sources of regional smoke plumes include the widespread prevalence of numerous small fires, which are  common across Savanahs, and larger more episodic wildfires, such as the extreme Californian wildfire event of September 2020. Capturing emissions from such a diverse range of fire activity is a major challenge and some atmospheric models, including the UK Earth System Model (UKESM) have scaled up aerosol emissions to ensure modelled AOD match observations. Past evaluations have struggled to provide a clear answer as to how to reconcile emissions and modelled aerosols, with contrasting outcomes for different regions and/or assessments of seasonal means versus individual smoke plumes. Our modelling study leverages observational data from the unprecedented wildfires in September 2020 to identify potential issues in capturing the aerosol from large / extreme wildfires in the global modelling system of UKESM. Running in nudged mode and with daily emissions from GFED4.1s emissions enables a realistic simulation of the thick smoke plumes that ensued across the continent and out into the Pacific, with little overall bias in AODs between UKESM and co-located observations (AERONET, VIRS, MAIAC). However, scaling emissions by a factor of 2 provides better agreement globally and across regions dominated by smaller fires. We therefore develop a means of differentiating between small and large fires based on the daily dry matter (fuel) consumption and apply this to enable scaling of emissions from small fires that seem to otherwise be underestimated in the model, whilst avoiding scaling those from large fires. Our results indicate a way forward to ensure a global simulation of biomass burning aerosol and fidelity in modelling extreme events.

How to cite: Johnson, B., Quaze, L., and Haywood, J.: Evaluating aerosol emissions from wildfires in the UK Earth System Model: What we have learnt from modelling the extreme wildfires in California during September 2020 , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18758, https://doi.org/10.5194/egusphere-egu25-18758, 2025.

The field of Blue Carbon research has traditionally focused on the carbon sequestration capacity of coastal vegetated habitats, despite these habitats comprising only a small fraction of oceanic sediment. However, continental shelf sediments also play a significant role in carbon sequestration and represent a significantly larger surface area. While the majority of organic carbon deposited in the shelf sediment is initially fixated by phytoplankton, and then potentially cycled through other marine organisms, some of it is originated from coastal and terrestrial producers, such as marine macroalgae, then transported, deposited and sequestered into shelf sedimentary basins. In this study, we investigated the sedimentary organic carbon (OC) stocks and sequestration rates at two sites of the continental shelf of Portugal, each adjacent to major wetland systems dominated by saltmash and seagrasses: the northern site is located off the Sado estuary at the Arrábida coast where macroalgae forests are also present, and the southern site off the Ria Formosa coastal lagoon. We also assessed the contributions of marine and terrestrial primary producers to sedimentary OC using various proxies such as C/N ratios, carbon isotopic signature (δ13 C), magnetic susceptibility, lipid contents and sedimentary DNA metabarcoding. Our findings revealed similar OC sequestration rates at both sites (23.3 ± 7.1 g OC m⁻² yr⁻¹ and 20.9 ± 5.7 g OC m⁻² yr⁻¹ for the northern and southern sites, respectively) and comparable OC stocks in the top 25 cm of sediment (29.5 ± 2.33 g OC cm⁻² and 21.1 ± 3.01 g OC cm⁻², respectively). Clear differences were observed on the contributions of terrestrial versus marine sources to the sediment organic matter, with the northern site showing lower terrestrial contribution as opposed to the southern site. This conclusion is supported by the different proxies used. For example, the northern site consistently exhibited higher OC contents at comparable particle sizes, indicative of a greater deposition rate of organic carbon not adhered to sediment particles, typical of oceanic primary productivity. Sedimentary DNA metabarcoding detected seagrass and saltmarsh genetic material in sedimentary organic matter from both sites, indicating that detritus from the two wetlands are being exported to the continental shelf. Further investigation is needed to quantify the relative magnitude of this export. Understanding this process is essential to accurately assess the role of coastal vegetated habitats in the global carbon cycle, as current estimates focus solely on in-situ sequestration and often overlook the potential contribution of exported organic matter. Our study highlights the need to expand our perspective on the interconnectedness of coastal and oceanic carbon dynamics.

How to cite: Martins, M., Magalhães, V., Salgueiro, E., Cordeiro, L. G., Abrantes, F., Masqué, P., de los Santos, C. B., and Santos, R.: Carbon accumulation, storage and provenance in the Portuguese continental shelf , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19205, https://doi.org/10.5194/egusphere-egu25-19205, 2025.

Dredging of the fairway in the Ems estuary was driven by the need to accommodate the increasing draft of ships. This modification has had negative effects on the sediment balance and ecology of the estuary. The fairway deepening results in strong alterations of the tidal dynamics, such as tidal amplitude and duration, as well as hydrodynamics such as current velocity and turbulence. This results in increased fine sediment input, which, at high suspended sediment concentrations, contributes to the formation of fluid mud—a mixture of silt, clay, and organic matter. The dynamics of fluid mud, particularly the differences between spring and neap tides, are not yet fully understood.

We investigated the formation, dispersion, and entrainment of fluid mud during the semi-diurnal tidal cycle. Therefore, the influence of flow velocity and salinity at different water depths, and the differences between spring and neap tides based on two dedicated measurement campaigns in 2023 was analyzed using high-resolution spatiotemporal monitoring data. Salinity data were used as an indicator of stable stratification. Additionally, sediment samples have been collected using a sediment corer to analyze the composition and properties of the fluid mud layer.

Our Spring-neap tide analysis showed a reduction of the flow cross-section during neap tide leading to differences in hydrodynamics between spring and neap tide driven by high sediment concentrations and fluid mud occurrence. During neap tide fluid mud was found to cover a larger fraction of the water column than during spring tide. This further highlights the strong influence of flow velocity on the dynamics of fluid mud and the need to include spring-neap considerations for future sediment management plans for the Ems.

How to cite: Lehn, J., Slabon, A., Holthusen, D., Rovelli, L., Fiskal, A., Hoffmann, T., and Borgsmüller, C.: Spring-neap tidal variation of fluid mud occurrence in the hyper-turbid Ems estuary , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19333, https://doi.org/10.5194/egusphere-egu25-19333, 2025.

The use of substrates combined with biochar has been highlighted in agricultural and horticultural production, including the cultivation of Raphanus sativus L. (radish), due to the benefits in water retention, nutrient supply and stimulation of root development. This study evaluated the growth and development of radishes under different combinations of substrates with biochar, with or without microbial inoculation. The experiment was carried out between November 2024 and January 2025, in a greenhouse with automatic temperature control (18-42 °C) at the Federal University of São Paulo, São José dos Campos Campus. The treatments included: A) Substrate with biochar (SB-control); B) Substrate (S-control); C) Substrate with biochar inoculated with MELRC (SBI-MELRC); D) Substrate inoculated with MELRC (SI-MELRC); E) Substrate with biochar inoculated with MEU (SBI-MEU); F) Substrate inoculated with MEU (SI-MEU); G) Substrate with biochar inoculated with TSB (SBI-TSB); and H) Substrate inoculated with TSB (SITSB). The variables analyzed were number of leaves (NF), leaf area (AF), total length (CT), tuber weight (PT), tuber diameter (DT), root length (CR), fresh root mass (MFR), tuber height (HT) and root dry mass (MSR). Data were submitted to analysis of variance (ANOVA) and regression, and significant differences were evaluated by the F test at probability levels of 0.01 and 0.05. The results indicated that treatment D (SI-MELRC) had the greatest positive impact on all variables evaluated, standing out as the best combination for the development of Raphanus sativus L. The use of substrates combined with biochar and microbial inoculation showed promise in the cultivation of Raphanus sativus L. (radish), promoting significant improvements in the growth and development variables evaluated. Among the treatments tested, the substrate inoculated with MELRC (SI-MELRC) stood out, presenting the best results in all variables analyzed. These findings reinforce the potential of biochar as a substrate conditioner and highlight the importance of microbial inoculation to maximize the benefits of this system. Future studies can explore the replicability of the results under field conditions and with other agricultural crops.

Keywords: biochar, Raphanus sativus L., radish cultivation, microbial inoculation, substrate conditioner, agricultural production.

How to cite: da Paixão Oliveira, L. and Dos Anjos Verzutti Fonseca, J. and the OLIVEIRA, Lorena da Paixão1; FONSECA, Dos Anjos Verzutti; CORTEZ, Christian Zenichi de Oliveira Ueji 1, Alexandre UEZU2 , SANTOS, Erika³; ARÁN, Diego³ e ESPOSITO, Elisa1 .: Effects of Biochar Substrate and Microbial Inoculation on the Development of Raphanus sativus L. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20449, https://doi.org/10.5194/egusphere-egu25-20449, 2025.

Organic inputs in grasslands are known to enhance soil carbon sequestration. However, it remains unclear whether long-term organic inputs lead to greenhouse gas (GHG) emissions, specifically methane (CH₄) from livestock and nitrous oxide (N₂O) from soils, that outweigh the benefits of carbon sequestration. Addressing this issue is crucial, as it directly impacts the evaluation of organic farming practices for sustainable land management and climate change mitigation. In this study, we employed the process-based Denitrification-Decomposition (DNDC) model to estimate the fluxes of major greenhouse gases (GHGs) in a long-term grassland silage experiment established in 1969. The model was validated against measured data, effectively capturing the dynamics of N₂O emissions, soil temperature, biomass, and soil organic carbon (SOC). Simulations under different IPCC Shared Socioeconomic Pathway (SSP) scenarios of altered temperature, CO₂ concentrations, and radiative forcing were conducted. Treatments with high levels of cattle manure and pig manure under the SSP1-2.6 scenario exhibited a net GHG sink, whereas conventional fertilization resulted in a net GHG source under both SSP1-2.6 and SSP2-4.5. Grass yields decreased under conventional fertilization in both SSP2-4.5 and SSP5-8.5 scenarios. However, the application of organic matter inputs resulted in yield increases across all scenarios. These findings highlight the potential of organic farming practices, especially with high organic inputs, to mitigate GHG emissions and enhance productivity in grassland ecosystems. Therefore, adopting organic farming practices with adequate organic inputs could serve as a sustainable strategy for balancing food production and environmental conservation.

How to cite: Meng, X., Khalil, I., and Osborne, B.: Enhancing crop yield, carbon sequestration, and greenhouse gas mitigation through organic matter inputs: long-term grassland farming observations and DNDC model predictions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20890, https://doi.org/10.5194/egusphere-egu25-20890, 2025.

There is interest in biogeochemical cycling and ecosystem functioning in the Clarion Clipperton Zone (CCZ, equatorial Pacific) due to the possibility in the near future of deep sea mining of polymetallic nodules. A set of important processes and ecosystem services relate to the fixation, export and deposition in sediment of organic carbon (C). This is important to understand both as a mechanism for C sequestration, and also as a set of processes which feeds deep sea biological communities. However, due to the remote nature of the CCZ, and the considerable resource required, it has rarely been possible to directly observe the coupling between processes from the sea surface all the way through the water column to the fate of organic C in sediments, nor how those linked processes vary over time or in response to mining disturbance.

Here we present data on C fixation, export, sediment composition and relationships with benthic community biomass. We show clear coupling between surface productivity, export and sinking flux, but that sediment organic C concentrations are not always closely coupled to water column processes. Repeated measurements over a period of ~18 months show inter-annual variability at the seafloor, rather than a stable seasonal pattern. Organic C delivery to the sediment is reflected in the biomass of faunal groups, with different temporal responses in the different groups (macrofauna, metazoan meiofauna and foraminifera) linked to factors such as competition, predation pressure and life cycle differences. Changes in sediment total organic carbon following a mining vehicle test will also be considered.

How to cite: Woulds, C., Lough, A., and Homoky, W. and the Carbon fixation, export, sedimentation and utilisation Team: Temporal variability in organic carbon fixation, export, sedimentation and utilisation in the Clarion Clipperton Zone, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21501, https://doi.org/10.5194/egusphere-egu25-21501, 2025.

Tomato production is vital to Central India's agricultural output and plays a significant role in the region's economy. However, the escalating impacts of climate change pose a serious threat to the sustainability and productivity of tomato farming in this region. This study assesses the effects of variations in solar radiation and temperature on tomato yields utilizing a calibrated process-based crop simulation model (CSM). Climate forecasts utilizing SSP4.5 and SSP8.5 pathways were applied to model yields in near (2010-2039) and mid-future (2040-2069) scenarios. Significant findings indicate a large reduction in yield potential, particularly under mid-future high-emission scenarios (SSP8.5), accompanied by considerable geographical variability. Regions such as Damoh and Western Nimar demonstrate enhanced resilience owing to advantageous local climatic circumstances, whilst areas like the Kymore Plateau and Bundelkhand Agro-Climatic zone display the most significant decreases. Key developmental phases, including flowering and fruit set, are especially susceptible to elevated temperatures and diminished solar radiation. This research highlights the need for region-specific adaptation techniques to alleviate climate impacts, including modifying planting schedules and adopting heat-tolerant varieties. These insights offer a crucial basis for policymakers and farmers to guarantee the sustainability of tomato production in Central India under changing climate circumstances.

Keywords: Crop Simulation Model (CSM), Tomato Yields, GCMs, Central India, policymakers

How to cite: Singh, P. N. and Srivastava, P. K.: Quantifying Climate Impact on Tomato Production in Central India: A Process-Based Yield Simulation for Near and Mid-Future Scenarios, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-544, https://doi.org/10.5194/egusphere-egu25-544, 2025.

Near-surface wind speed (NSWS) plays a critical role in water evaporation, air quality, and energy production. Despite its importance, NSWS changes in South Asia, a densely populated region, remain underexplored. This study aims to understand and quantify the uncertainties in projections of NSWS over South Asia, particularly in relation to internal variability. Utilizing a 100-member large ensemble simulation from the Max Planck Institute Earth System Model, we identified the Interdecadal Pacific Oscillation (IPO) as the leading mode of internal variability influencing South Asian NSWS in the near future. Our findings reveal that the IPO could significantly impact future NSWS, with its positive phase being linked to strengthened westerly flows and increased NSWS across South Asia. Notably, the study shows that accounting for the IPO's impact could reduce NSWS projection uncertainty by up to 8% in the near future and 15% in the far future. This underscores the key role of internal variability, particularly the IPO, in shaping regional NSWS projections. By reducing uncertainties in these projections, our findings can inform climate adaptation strategies for South Asia, helping optimize wind resource assessments in the context of changing wind patterns.

How to cite: Yuan, H.-S. and Shen, C.: Quanatifying the contributions of internal varibility in South Asian near-surface wind speed, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1831, https://doi.org/10.5194/egusphere-egu25-1831, 2025.

Accurate rainfall forecasting is crucial for effective urban planning and disaster management in Dhaka, the capital of Bangladesh, a city highly vulnerable to urban flooding and extreme weather events. Traditional forecasting methods often struggle to capture the region's complex rainfall patterns, resulting in inaccurate rainfall forecasts. This study evaluates the performance of two traditional machine learning algorithms, Random Forest Regression and Multi-layer Perceptron (MLP), alongside one deep learning algorithm, the Long Short-Term Memory (LSTM) network. These models are trained and tested to forecast rainfall over 1 to 5-day lead times, emphasizing their ability to handle temporal dependencies in time series data. Atmospheric and hydrologic variables, including temperature, surface pressure, evaporation, solar surface radiation, total column rainwater, large-scale precipitation, and total cloud cover, from the ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis v5 (ERA5) dataset, were used as model inputs. Model forecasts were validated against ERA5 rainfall data and compared with the forecasts from the Global Forecast System (GFS) model. Results indicate that the Random Forest model outperforms all others, achieving an RMSE of 6.11 mm and Pearson’s correlation coefficient (R) of 0.74 for a 1-day lead time. The LSTM model achieved an RMSE of 7.46 mm, while the MLP performed less effectively than both RF and LSTM, with an RMSE of 7.61 mm. In comparison, the GFS forecasts displayed an RMSE of 9.16 mm. The RF model outperformed the other models at all lead times; however, its accuracy decreased as the lead time increased. This study highlights the potential of machine learning to improve short to medium range rainfall forecasts, contributing to timely decision-making for urban resilience and resource management.

How to cite: Sarker, M. M., Zobeyer, A. M., Rouf, T., and Rahman, S. M. M.: A Comparative Analysis of Data-Driven Machine Learning Models for Rainfall Forecasting in Bangladesh, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1959, https://doi.org/10.5194/egusphere-egu25-1959, 2025.

Forecasting extreme rainfall events (EREs) locally is a major difficulty for meteorological organizations in India's diverse topography, including Assam, Uttarakhand, and Himachal Pradesh. Flash floods cause major socioeconomic damage in certain areas. These extremes are increasingly commonplace during the southwest monsoon season in the country and one of the most destructive EREs occurred in June 2022 and 2023 over Assam. This work explores deep learning (DL) models, specifically spatial attention-based U-Net, in conjunction with simulated daily collected rainfall outputs from different parametrization schemes rainfall output from the Weather Research and Forecasting (WRF) model, considering the limitations of deterministic numerical weather models in accurately forecasting these events. The model trained over the districts of Assam for all days (days 1-4) except the districts where the EREs occurred. The suggested model exhibited a greater ability to predict rainfall at the district scale with a mean absolute error of less than 10 mm over four days in June 2022, outperforming both individual and ensemble outputs of WRF. Furthermore, the suggested model had a high prediction accuracy of 91.9% in categorical rainfall prediction, outperforming WRF models by 51.3%. Furthermore, by accurately forecasting EREs at the district level, including Barpeta, Kamrup, Kokrajhar, and Nalbari, the suggested model has shown improved spatial variation when compared to the WRF model. The suggested DL model is tested for real-time ERE events over Assam in June 2023. In the second part, the model has trained for ERE occurred in 2022 and tested for 2023 over Assam at the district level. The district-level performance of the DL and WRF models is compared, and the DL model performs better than the WRF model in capturing EREs, with a noteworthy accuracy of 54.4% compared to only 22.8% for the WRF model. Notably, the DL model accurately represents the amount and severity of rainfall in Assam's western and southern regions. In summary, the study's conclusions directly affect the development of effective strategies for increased preparedness, mitigation, and adaptation measures over complex hilly regions to lessen the loss of life and property, as well as the improvement of early warning systems and related follow-up action.

How to cite: Trivedi, D., Pattnaik, S., and Sharma, O.: Enhancing Heavy Rainfall Predictions Over Vulnerable Regions in Assam Using a Spatial Attention-Based Deep Learning Network, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2071, https://doi.org/10.5194/egusphere-egu25-2071, 2025.

The Southern Great Plains are subject to fluctuating precipitation extremes that pose significant challenges to agriculture and water management. Despite advancements in forecasting, the mechanisms driving these climatic variations remain incompletely understood. This study investigates the relative contributions of the tropical Pacific and Atlantic Oceans to April-May-June precipitation variability in this region. Using partial ocean assimilation experiments within the Community Earth System Model, we identify a substantial influence of inter-basin interactions, with the Pacific and Atlantic contributing approximately 70% and 30%, respectively, to these variations. Our statistical analysis suggests that these tropical inter-basin contrasts offer a more reliable indicator for late-spring precipitation anomalies than the El Niño-Southern Oscillation. This finding is corroborated by analyses from seven climate forecasting systems in the North American Multi-Model Ensemble, providing a promising outlook for improving real-time forecasting in the Southern Plains. However, the predictive skill of these inter-basin contrasts is currently limited by the lower predictability of the tropical Atlantic, underscoring the need for future research to enhance climate prediction models.

How to cite: Chikamoto, Y., Wang, S., Chang, H.-I., and Castro, C.: Seasonal Predictability of Late-Spring Precipitation in the Southern Great Plains , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3766, https://doi.org/10.5194/egusphere-egu25-3766, 2025.

The Eddy-Diffusivity Mass-Flux (EDMF) parameterization (Giordani et al., 2020) offers a new, coherent way to simultaneously parameterize local (diffusivity) and non-local (convective thermal) vertical mixing. This second component parametrizes sub-grid-scale convective plumes propagating through the water column which, through energy conservation, can propagate counter to the stratification gradient. The EDMF scheme is assessed in a 13-year global ¼° coupled NEMO4.2-SI3 simulation, forced by ERA5 atmospheric reanalysis. Its performance in representing observed ocean temperatures is compared to that of a twin simulation using the commonly applied Enhanced Vertical Diffusivity (EVD) parameterization.

The EDMF simulation shows globally reduced temperature biases relative to in-situ observations (0–700 m) compared to the EVD simulation, with similar RMSD (Root Mean Square Deviation) values between the two. By better representing tropical night-time shallow convection, EDMF reduces the cold bias typically observed in EVD simulations within the tropical ocean. We show that the horizontal scales (convective areas), penetration depths and vertical velocities of the simulated plumes agree with measurements of deep convective plumes in the Labrador Sea, and with diurnal convection in the equatorial Pacific Ocean. Additionally, first estimates of convection's contribution to Ocean Heat Content are proposed.

How to cite: Piton, V., Bourdallé-Badie, R., and Giordani, H.: The Eddy-Diffusivity Mass-Flux parameterization: improved representation of convective mixing, global evaluations and implications for Ocean Heat Content, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4069, https://doi.org/10.5194/egusphere-egu25-4069, 2025.

The TCRE relationship underlies the necessity of net zero emissions for climate stabilization and the utility of carbon budgets as a policy tool. TCRE emerges near universally from Earth system models, and is consistent with observations. However, recent work has systematically dismantled the leading hypothesis explaining the phenomenon, concluding “that this proportionality is not amenable to a simple physical explanation, but rather arises because of the complex interplay of multiple physical and biogeochemical processes.'' (Gillett, 2023). Here we set two intermediate complexity Earth system models (EMICs) to abiotic states, then turn on broad components of Earth's biogeochemical cycles one at a time to see which combination of processes cause TCRE to emerge.

We find that TCRE emerges when ocean alkalinity is set to observed values, without life on land. TCRE likewise emerges independently when the terrestrial biosphere is turned on, with the ocean in an abiotic low alkalinity state. Idealized experiments with the EMICs show that TCRE occurs for configurations of the Earth system where characteristic timescales of carbon absorption and heat absorption are nearly the same. Our results suggest that the emergence of TCRE does in-fact rely on a simple physical mechanism, but why the living components of Earth system are matching the characteristic timescale of carbon absorption to that of heat remains mysterious.

Gillett, N.P.: Warming proportional to cumulative carbon emissions not explained by heat and carbon sharing mixing processes. Nature Communications 14(1), 6466 (2023)

How to cite: MacDougall, A. and MacIsaac, A.: The double emergence of TCRE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6364, https://doi.org/10.5194/egusphere-egu25-6364, 2025.

Water scarcity is a growing challenge exacerbated by climate change, particularly in regions like Lombok Island, Indonesia, where water resources are crucial for sustainable development. This research aims to identify suitable locations for Rainwater Harvesting (RWH) by integrating geospatial analysis, the Analytic Hierarchy Process (AHP), and climate projections using CMIP6 data. The study utilizes multiple parameters, including rainfall, land use/land cover (LULC), slope, drainage density, soil texture, and runoff depth, to develop a comprehensive suitability map for RWH.

Historical rainfall data from CHIRPS (1981–2010) and future rainfall projections for mid-century (2031–2060) and end-century (2071–2100) under SSP2-4.5 and SSP5-8.5 scenarios were analyzed to account for climatic variations. Each parameter was processed using geospatial tools, with weights assigned through AHP based on expert input, ensuring a robust multi-criteria decision-making framework. Suitability maps were generated for each temporal scenario, highlighting areas with high to very high potential for RWH, particularly in North and East Lombok.

The results reveal dynamic shifts in RWH site suitability over time, with increasing precipitation under SSP5-8.5 scenarios expanding high-suitability areas. These findings highlight the potential for RWH to manage water resources adaptively in response to projected climate variability. By aligning the outputs with existing water management infrastructure, such as dams, the study provides actionable insights for regional planners and policymakers.

How to cite: Ulfah, A., Renwick, J., and Patria Megantara, R.: Integrating Climate Projections and Geospatial Analysis to Identify Rainwater Harvesting Suitability in Lombok Island, Indonesia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7421, https://doi.org/10.5194/egusphere-egu25-7421, 2025.

Precisely predicting weather parameters is crucial for precision horticulture, especially in horticultural lands where timely environmental insights significantly impact crop yield and quality. This study presents a novel hybrid modeling approach employing 1D Transformer networks integrated with traditional machine learning techniques to predict hourly temperature variations. Utilizing the ERA5 reanalysis dataset spanning from 1940 to December 2024, the hybrid model efficiently captures location-specific spatiotemporal dependencies and nonlinear trends in historical weather data.

The predicted weather data generated by the hybrid model is used in FarmD, a web-based user interface developed for farmer-centric applications. FarmD provides real-time visualization of critical weather parameters, including temperature, relative humidity, wind patterns, rainfall, and soil temperature, specifically tailored to horticultural regions. Through its intuitive interface, users can query predicted and historical data by selecting attributes, dates, and times, with an option for location-specific searches to support targeted agricultural decision-making.

This integration of predicted data with an accessible web platform highlights significant advancements in delivering actionable insights to end users. By combining advanced computational methods with user-focused design, FarmD enables horticulturists to adopt data-driven practices, contributing to sustainable and efficient agricultural management.

How to cite: Ramalingam, S.: FarmD: A Web Interface for Visualization of Predicted Weather Parameters Using 1D Transformer Hybrid Models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10613, https://doi.org/10.5194/egusphere-egu25-10613, 2025.

Estimates of remaining carbon emissions budgets to limit global warming to 1.5°C or 2°C rely on the near-linear relationship between the change in global mean temperature and total CO₂ emitted since the pre-industrial era. This relationship is known as the Transient Climate Response to cumulative Emissions (TCRE). Previous estimates of TCRE are derived from Earth System Models (ESMs) which are known to lack key processes that affect warming and therefore diagnosed CO₂ emissions. Here we use the UK Earth System Model to quantify, for the first time, the impact on TCRE of including six Earth system processes in isolation (results in parenthesis): fire-vegetation interactions (TCRE increased 14.6%); nitrogen limitation of vegetation (+9.7%); diffuse radiation effects on vegetation (+8.5%); changes in vegetation distribution (-1.5%); climate impacts from wetland methane emissions (+5.1%) and from biogenic volatile organic compounds (-1.4%). From these results we recalculate the TCRE of 11 ESMs of the 6th Coupled Model Intercomparison Project (CMIP6) as though each included all six processes. Averaged over the 11 models, TCRE increased by 23.7%, reducing by 19% the associated remaining carbon budget to both 1.5°C and 2°C.

How to cite: Liddicoat, S., Jones, C., Mercado, L., Robertson, E., Sitch, S., and Wiltshire, A.: Role of Earth system processes in the Transient Climate Response to cumulative Emissions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11584, https://doi.org/10.5194/egusphere-egu25-11584, 2025.

Evaporation and potential evapotranspiration are components of hydrological cycle that represent the loss of water from the surface and vegetation to the atmosphere. Potential evapotranspiration is a theoretical index that demonstrates the maximum evaporation and transpiration rates assuming sufficient water availability in soil and canopy. Six identical Regional Climate Models (RCMs) of Euro-CORDEX project were selected in order to obtain a unified ensemble for both characteristics for estimation under RCP2.6, RCP4.5 and RCP8.5 scenarios for the middle (2021-2050) and the end of the 21^st century (2071-2100) for Ukraine. ERA5 observational dataset is used as a baseline climate normal (1991-2020) for tracking the future changes. In this study we applied a quantile mapping approach for bias correction for smoothing systematic errors between observational and simulated datasets. For the baseline period, the sums of evaporation varied mainly between 20-30 mm in winter to 250-290 mm in summer, with the exception of the Carpathians and southern regions near marine coastal areas (more than 300 mm). Climate normals of evapotranspiration were zonally distributed with the exception of mountainous region and varies from 20-50 mm in winter to 290-550 mm in summer. The most tremendous changes of evaporation are expected to occur in winter. In general, during the following 30-year period of 2021-2050, the most significant increase by 8-18% (compared to 1991-2020 baseline) would be expected for RCP4.5 with more pronounced increase during 2071-2100, reaching its highest values up to 40% under RCP8.5 The maximum rates are observed in the Carpathians and the northeast of Ukraine. In contrast, evapotranspiration in winter is expected to increase only by 1-6% during 2021-2050 for all RCPs and 12-22% by the end of the century. The Carpathians will face even a decrease by -4%. Changes in evaporation will be lower for the spring season, with changes by 2-4% in 2021-2050 and 6-12% by the end of the century. The highest spring changes up to 28% also will occur in the Carpathians. The same rates are estimated for evapotranspiration, for which the sharpest changes are 10-16% under RCP 8.5 for 2081-2100 In comparison to winter and spring, summer and autumn seasons will face much slower changes. Moreover, summer season will be characterized by a decrease in evaporation at a rate up to -2..-4% under RCP2.6 and varying within ±1% for other scenarios by the mid-century, showing the typical tendencies for so called “evaporation paradox”. In 2071-2100, the decrease can reach by up to-6% for RCP4.5 and RCP8.5. It must be noted the different tendency for evapotranspiration with an increase by 1-6% in general for all RCPs in 2021-2050, and maximum up to 14% by the end of the century. For autumn the most typical increase in both parameters is within 2-6% for all RCPs, with the highest rates of evaporation in the Carpathians up to 15%.  The obtained results show the importance of considering evaporation in future water management, agriculture and food security in Ukraine, highlighting the seasons and regions with it significant changes.  

How to cite: Rybchynska, V., Pysarenko, L., Pushkar, H., Savenets, M., and Osadchyi, V.: Seasonal changes in evaporation and potential evapotranspiration under different scenarios of climate change on the territory of Ukraine, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12181, https://doi.org/10.5194/egusphere-egu25-12181, 2025.

Deep learning, a prominent artificial intelligence method, is increasingly applied in research addressing the impacts of global warming in the future. However, it is widely acknowledged that deep learning exhibits limitations in extrapolation, as it typically predicts accurately only within the range of the training data. When future scenarios extend beyond this range, the reliability of predictions can diminish significantly. In Japan, for example, the annual maximum precipitation is reported to be increasing, according to the Japan Meteorological Agency, indicating a potential for future values to exceed historical records. Despite this, limited studies have explored the extent to which deep learning methods can reliably extrapolate beyond the training data range. This study quantitatively evaluates the extrapolation capability of deep learning in hydrology, specifically focusing on rainfall-runoff modeling at the watershed scale. Meteorological data, including precipitation and temperature, are utilized as inputs, while river flow serves as the output. The Long Short-Term Memory (LSTM) model, which is well-suited for time-series data, was employed as the deep learning framework. Data were partitioned into training, validation, and test datasets, with river flow values categorized using threshold percentiles of 90, 95, 97, 98, and 99, rather than conventional time-based splits. This approach allows for a focused investigation into the range of accurate extrapolation beyond the training dataset. Preliminary findings reveal that the LSTM model successfully captured peak river flows up to 250.1% higher than the maximum values of the observed river flow discharge in the training-validation dataset. These results demonstrate the potential for deep learning to extrapolate in hydrological modeling, though further research is necessary to assess the performance of alternative deep learning methods and additional case studies. 

How to cite: Junsei, S.: Evaluating the extrapolation capability of deep learning in rainfall-runoff, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14457, https://doi.org/10.5194/egusphere-egu25-14457, 2025.

In the context of climate change, a global, widespread shift to increased water limitation is expected over approximately 73% of terrestrial ecosystems, with important implications for food and water security, CO2 uptake, and evaporative cooling. Water-limited regions, exposed to climate-change-related increasing droughts and intense anthropogenic water use, are extremely vulnerable to transitions towards drier eco-hydro-climatological regimes. In the longer term, the ongoing drought conditions may intensify the decline of groundwater levels, threatening groundwater-dependent ecosystems and exacerbating the risk of desertification, thereby amplifying a positive feedback on regional climate change. In some Mediterranean climate-type regions, such as SouthWestern Australia, a dry and warm transition has already been observed. Recent findings are a clear warning that also over the Euro-Mediterranean sector groundwater level may have a negative trend resulting from a decrease in precipitation and/or increasing withdrawal. 

Soil water storage  and groundwater dynamics represent important hydrological processes related to these transitions but they are greatly simplified in state-of-the-art Earth System Models (ESMs). Therefore, it is  essential to improve the representation of hydrological processes and their coupling with the atmosphere and the land surface in ESMs. In this respect, the land surface model included in EC-Earth (ECLAND) still lacks a representation of groundwater and instead implements a free drainage condition at the bottom of the unsaturated soil column. 

In this work, we intend to implement a more realistic groundwater representation in EC-Earth by including a global-scale water table to replace the free drainage bottom boundary condition. As a preliminary measure, the impact of groundwater on the shallow, unsaturated zone is evaluated by constraining the vertical water fluxes with a static water table depth (WTD) derived from a global estimate simulation based on observations. We evaluated the effects of this implementation on water and energy fluxes against a network of stations in land-only simulations from 1979 to the present, with boundary forcing taken from ERA5 reanalysis. First findings suggest that including a WTD has an impact on water exchanges between saturated and unsaturated soil in water-limited regions, particularly in semi-arid and transitional climates, which can not be neglected in Earth system models.

How to cite: Senigalliesi, V., Alessandri, A., Kollet, S., Gelsinari, S., Cherchi, A., and Di Carlo, E.: Enhancing Hydrological Processes in Earth System Models: Implementing Groundwater Dynamics for Improved Climate Representations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18989, https://doi.org/10.5194/egusphere-egu25-18989, 2025.

The construction and analysis of daily temperature data series in long enough a time period is important to understand decadal to multi-decadal variability and changing trends in extreme temperature events. This paper reports a new analysis of extreme temperature indices over the last 140 yr in Wuhan, China, with an emphasis on changes in extreme high temperature changes. The daily temperature data from 9 stations from 1881 to 1950 and 1 modern station from 1951 to 2020 were used for the analysis. Based on the data, and the commonly used extreme temperature indices, the variations and long-term trends of extreme high temperature events in Wuhan since 1881 were analyzed. The results show that there was no clear warming trend in maximum temperature, but a quite large inter-annual and inter-decadal fluctuation. In contrast, there was a very significant increase in minimum temperature, with a large upward trend overall. The extreme temperature indices exhibit a periodic variability, and frequent extreme heat events have been experienced over the last 140 yr in Wuhan. Most extreme temperature indices did not exhibit remarkable changes during the first half of the period analyzed. However, the majority of the extreme temperature indices showed significant upward trends over the latter half of the 140 yr period. The possible causes of the observed changes in the extreme high temperature events in the different time periods are also discussed.

How to cite: Zheng, X., Ren, G., He, J., Zhao, Y., Ren, Y., and Yang, G.: Temporal characteristics of extreme high temperatures in Wuhan since 1881, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-456, https://doi.org/10.5194/egusphere-egu25-456, 2025.

Climate change is expected to exacerbate heat stress, particularly in urban areas where the urban heat island (UHI) effect tends to amplify warming compared to surrounding rural regions. Due to the heterogeneity of urban environments, heat stress can vary significantly within cities. Heat vulnerability maps, which combine data on heat sensitivity, heat exposure and adaptive capacity, are valuable tools for identifying areas that should be prioritized for heat stress mitigation measures. One important component of such heat vulnerability maps is data on the spatial distribution of heat. The present study explores the use of satellite data to generate high-resolution temperature maps, addressing two key challenges in the process.

The first challenge arises from the fact that satellites measure land surface temperature (LST) rather than air temperature (AT), whereas the latter is needed as input for most heat stress indicators. While linear models calibrated with weather station data are frequently used to estimate AT from LST, there are cities where the availability of weather stations is insufficient for calibrating models with multiple control variables. Additionally, the LST-AT relationship depends on the prevailing atmospheric conditions. The second challenge of using satellite data is that satellite images are usually not available on an hourly or daily basis due to factors such as satellite scheduling or excessive cloud cover.

To address the first challenge, we adopt a technique introduced by the ECOSTRESS mission, which leverages reanalysis data (GEOS-5) to estimate AT using LST, the normalized difference vegetation index (NDVI), and albedo. We apply this method to spatially downscaled LST data (100m) from the VIIRS instrument aboard the Suomi NPP satellite, AT reanalysis data from ERA5-Land (9km), as well as NDVI and albedo derived from Harmonized Landsat Sentinel (HLS) data (aggregated to 100m). Applying the method to individual satellite images enables day-specific adjustments for varying atmospheric conditions. To overcome the second challenge, we utilize high-resolution AT maps derived from LST images to calculate spatial patterns of air temperature distribution, which are then used to downscale ERA5-Land AT data for those times without satellite images available.

To evaluate the approach described, it is exemplary applied to various cities, whereby the downscaled temperature estimates are validated against (i) temperature estimates based on alternative methods than the ECOSTRESS technique to derive AT from LST, (ii) weather station data, and (iii) existing results from urban climate models.

How to cite: Kortschak, D., Gallaun, H., Kernitzkyi, M., Köberl, J., Miletich, P., and Strohmaier, M.: Spatial downscaling of urban temperatures: evaluation of an approach using satellite and reanalysis data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1411, https://doi.org/10.5194/egusphere-egu25-1411, 2025.

Underwater cultural heritage, such as ancient shipwrecks and submerged archaeological sites, faces increasing risks from climate-driven environmental changes. Salinity shifts, temperature anomalies, and biofouling contribute to the degradation of these resources [1]. This study explores deploying two IoT-enabled devices with a crowdsourcing strategy to monitor and address these challenges effectively.

The first device, designed for divers, measures pressure, temperature, and salinity during underwater campaigns and can be placed on the seabed for long-term data collection [2]. The second device, used by local communities like fishers and diving centers, is deployable from boats to 2-3 meters, capturing salinity, temperature, and chlorophyll concentration. Each device incorporates a data logger built on a microcontroller, connected to sensors via robust serial interfaces such as RS485. This configuration ensures reliable communication and minimizes signal degradation in challenging underwater conditions. The microcontroller interfaces with sensors to record measurements, storing data locally until retrieval. Both devices feature a power management system with custom-designed PCBs for efficient energy use.

Data gathered by the devices is stored locally and transferred to a cloud platform via an intuitive mobile app. Communication between the devices and the smartphone uses Bluetooth Low Energy (BLE), while data uploads to the cloud via LTE. This simplifies retrieval and reduces the need for complex equipment or infrastructure.

Community participation plays a central role in this system. Local communities deploy and retrieve boat-based sensors, improving the coverage and frequency of monitoring activities. By pooling data from various contributors, detailed information of environmental conditions near cultural heritage sites is acquired.

The devices undergo rigorous calibration to ensure reliable data collection. Conductivity sensors are standardized with salinity benchmarks, temperature sensors tested with laboratory-grade instruments, pressure sensors calibrated in controlled chambers, and chlorophyll sensors validated using fluorescence references.

Field trials at two underwater sites tested the system under diverse conditions, providing a robust environment to assess device performance and crowdsourcing effectiveness. Feedback from divers, local participants, and heritage professionals refined functionality. Adjustments included stronger enclosures, improved BLE connection stability, and an enhanced mobile app interface.

This study demonstrates the potential of combining smart sensor technology with community engagement to protect underwater heritage. Leveraging IoT devices and collaboration expands monitoring, reduces costs, and fosters local stewardship, offering a scalable, sustainable solution to mitigate environmental impacts on submerged cultural treasures.

References:

[1] P. Michalis, C. Mazzoli, V. Karathanassi, D. I. Kaya, F. Martins; M. Cocco, A. Guy and A. Amditis, "THETIDA: Enhanced Resilience and Sustainable Preservation of Underwater and Coastal Cultural Heritage," IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 2208-2211, doi: 10.1109/IGARSS53475.2024.10642229.

[2] L. Pavlopoulos, P. Michalis, M. Vlachos, A. Georgakopoulos, C. Tsiakos and A. Amditis, "Integrated Sensing Solutions for Monitoring Heritage Risks," IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024, pp. 3352-3355, doi: 10.1109/IGARSS53475.2024.10641101.

Acknowledgement:

This research has been funded by European Union’s Horizon Europe research and innovation programme under THETIDA project (Grant Agreement No. 101095253).

How to cite: Vlachos, M., Michalis, P., Mourounas, I., Koukio, P., Gkatzogias, A., Georgakopoulos, A., and Amditis, A.: IoT-Enabled Underwater Devices and Crowdsourcing for Monitoring Climate Risks at Submerged Heritage Sites, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3194, https://doi.org/10.5194/egusphere-egu25-3194, 2025.

Assessing the risks posed by climate change to cultural heritage (CH) is crucial for developing effective strategies to preserve this non-renewable heritage. This study provides a comprehensive approach to assess climate change-related risks to cultural heritage across five selected sites in Europe: Choirokoitia, Aegina, Epidaurus, Kalapodi, and Ventotene. By applying the Heritage Outdoor Microclimate (HMR_out) and Predicted Risk of Damage (PRD) indices, the study quantifies potential damage to inorganic materials due to long-term changes in temperature and relative humidity (RH). Climate projections are based on high-resolution EURO-CORDEX Regional Climate Model (RCM) simulations under three Representative Concentration Pathways (RCP2.6, RCP4.5, and RCP8.5) for the periods 2021–2050, and 2071–2100. Results reveal a significant increase in temperature and the related indices under all emission scenarios highlighting a warming trend and intensified heat stress across the CH sites. The projected rise in temperature leads to an increase in the HMR_out index across all the CH sites, with the rate of change differing between time periods and scenarios. This rise in the HMR_out index suggests an increase in the predicted risk of damage (PRD) to monuments made of inorganic materials due to heat stress. In contrast, RH and the associated PRD index are expected to decrease. Overall, the projected changes in the HMR_out and PRD indices provide a deeper insight into how climate change may influence preservation of cultural heritage sites constructed from stone and marble.

This work is based on procedures and tasks implemented within the project “Toolbox for assessing and mitigating Climate Change risks and natural hazards threatening cultural heritage - TRIQUETRA”, which is a Project funded by the EU HE research and innovation program under GA No. 101094818.

How to cite: Tringa, E., Georgoulias, A. K., Akritidis, D., Feidas, H., and Zanis, P.: Climate Change and Cultural Heritage: Assessing Future Risks of Damage at Selected European Cultural Heritage Sites, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3882, https://doi.org/10.5194/egusphere-egu25-3882, 2025.

Crop yield is important for agricultural productivity and country’s economy. Accurate crop yield estimation is critical for policymakers, farmers, and governments because it allows better management techniques, decision making and the implementation of practicable agricultural policies. While crop yield estimation is an essential aspect of modern agriculture, it continues to be one of the most challenging tasks to manage effectively. In this study, we used the Food and Agriculture Organization (FAO) of the United Nations developed AquaCrop model to estimate the crop yields of corn and soybean crops in Illinois, United States (US). Data of various meteorological parameters as precipitation, maximum and minimum temperature, relative humidity, wind speed, solar radiation datasets were collected from NASA Prediction of Worldwide Energy Resources (POWER), for a period of 25-years from 2000 to 2024. Whereas, reference evapotranspiration was calculated by using the modified Hargreaves method. The objective of this study is to assess the accuracy of yield estimation of corn and soybean by using the AquaCrop model. The AquaCrop model was simulated for the growing period of corn and soybean from May to September. Using the AquaCrop model, the maximum and minimum corn yields were found to be 14.49 tons/ha in the year 2022 and 7.60 tons/ha in the year 2005, respectively. Similarly, the maximum yield of soybean was found to be 4.33 tons/ha in the year 2022, while the minimum yield was 2.26 tons/ha in the year 2012. The coefficient of determination (R²) values of 0.72 for maize and 0.76 for soybean, gives a satisfactory level of model accuracy. The model's performance can be improved further by incorporating more ground-truth data and appropriate parameters. This study demonstrates the AquaCrop model's ability to estimate crop production with few input parameters, as well as suggest opportunities for improvement. To improve prediction accuracy and promote informed agricultural planning and food security, future study might use sophisticated methodologies, localized farming practices, crop phenology, and specific soil data. 

Keywords:  AquaCrop, Crop yield, Illinois, Yield Predictions.

How to cite: Gautam, V. and Pathak, S.: Optimizing Corn and Soybean Yield Predictions in Illinois Using the AquaCrop Model , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3943, https://doi.org/10.5194/egusphere-egu25-3943, 2025.

Climate change is reshaping the species composition, distribution and extent of forests worldwide. Across vast areas in Central Europe widespread Norway spruce (Picea abies) has exhibited large-scale decline, primarily due to its vulnerability to drought events. Forest management is thus facing important questions related to the replacement of Norway spruce, especially in areas where it was introduced due to its high economic value.

This study investigates the potential of Douglas fir (Pseudotsuga menziesii), a drought- and pest-tolerant non-native species, as a more resilient alternative for use in production forests. At the experimental plot in Jable, central Slovenia where both species coexist, we monitored the xylogenesis of five Douglas firs and five Norway Spruces from March to October 2024 by sampling phloem, cambium and xylem tissue every two weeks using the Trephor tool. Additionally, we collected tree cores from 20 trees of each species to perform dendrochronological analyses. These analyses aim to assess climate-growth correlations and growth-based resilience indicators (resilience, resistance, recovery and recovery period).

 The main objective of this study is to determine whether Douglas fir is to compare 1) interannual growth dynamics, 2) intra-annual growth dynamics of xylem and phloem, 3) climate-growth relationships, and 4) resilience components of both species. We hypothesize that non-native Douglas fir will exhibit greater growth rates and better resilience indicators and could thus be considered as a replacement for Norway Spruce at similar forest sites in central Slovenia and beyond. By addressing critical knowledge gaps regarding the responses of these species to climate variability, this research can provide important insights to support the strategic adaptation of forestry practices and improve the resilience of ecosystems in the face of environmental change.

How to cite: Balzano, A., Partemi, R., Jevšenak, J., Krže, L., and Merela, M.: Douglas Fir (Pseudotsuga menziesii) as an alternative species for the declining Norway spruce (Picea abies) in central Europe: Dendrochronological and xylogenetic insights from Slovenia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4269, https://doi.org/10.5194/egusphere-egu25-4269, 2025.

Coastal sea level changes have profound impacts on coastal ecosystems, infrastructure, and communities. Interannual sea level variations along the U.S. East Coast are influenced by a combination of dynamic and thermodynamic processes, including local wind forcing, Gulf Stream variability, regional ocean circulation changes, and thermosteric contributions. These processes are interconnected and strongly modulated by large-scale climate modes such as the North Atlantic Oscillation (NAO), El Niño-Southern Oscillation (ENSO), and Atlantic Multi-decadal Oscillation (AMO). This study leverages machine-learning-based predictive models to quantify and forecast interannual sea level variability by integrating diverse climate indicators. By incorporating indices of large-scale climate modes alongside local and regional oceanographic parameters, the model quantifies the relative contributions of each factor and identifies the dominant processes driving observed variability. The results demonstrate the potential of machine-learning approaches to capture complex nonlinear relationships between climate modes and regional sea level changes. NAO-driven atmospheric forcing and ENSO-related ocean-atmosphere interactions emerge as key predictors, with the models successfully replicating observed variability along different sections of the U.S. East Coast. The findings highlight the importance of integrating large-scale climate dynamics into regional sea level prediction frameworks and suggest new opportunities for improving forecast accuracy at interannual timescales.

How to cite: Ye, Z., Ye, Z., and Zhao, J.: Predicting Interannual Sea Level Variations Along the U.S. East Coast Using Machine Learning and Climate Indicators, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7561, https://doi.org/10.5194/egusphere-egu25-7561, 2025.

Environmental and socioeconomic factors increase small islands' exposure and vulnerability to climate change. This study reviews issues related to current and future climatic change and its impacts on the small island environments in the Canary Islands. Convection-permitting regionalized projections driven by data from three global climate models included in the Coupled Model Intercomparison Project (CMIP5) have been performed, covering the recent past (1980–2009) and future (2070–2099) periods, under two Representative Concentration Pathways, 4.5 and 8.5. The impact analysis includes water resources, energy, ecosystems and biodiversity, natural hazards, and health issues. We provide a succinct review of sectors that warrant particular attention, due to their weight in the gross domestic product, agriculture and tourism. The concluding section discusses adaptation and response strategies, and the portfolio of research that needs to be addressed. 

How to cite: Carrillo, J., Barrancos, J., Tondreau, P. S., Pérez, J. C., González, A., Expósito, F. J., and Díaz, J. P.: A review of climate change impacts in the Canary Islands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8577, https://doi.org/10.5194/egusphere-egu25-8577, 2025.

The aim of this study was to find the connection between the large-scale atmospheric circulation in the winter season and the occurrence of extreme precipitation in the spring months at the regional scale. For the large-scale circulation, climate indices (GBOI and NAOI) associated with the Greenland-Balkan Oscillation and the well-known North Atlantic Oscillation were considered, and for the regional scale, certain representative stations for the middle and lower Danube basins were considered. The tests were carried out for a 120-year interval (1901-2020), by applying the extreme value theory (EVT). The modelling of maximum precipitation was carried out through the generalized extreme value (GEV) distribution. In order to see the impact of the large-scale circulation, the results obtained by incorporating NAOI as covariate into the location parameter of GEV distribution, were compared with the results obtained considering GBOI as covariate. For extreme precipitation in the lower basin area, the influence of GBOI is much more significant than that of NAOI, while for the middle basin area, the differences between the two indices are not so significant.

How to cite: Mares, I., Dobrica, V., Mares, C., and Demetrescu, C.: On the links between large-scale atmospheric circulation and extreme precipitation in the middle and lower Danube basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9608, https://doi.org/10.5194/egusphere-egu25-9608, 2025.

Since Shi et al. proposed that the climate in the drylands of Northwest China experienced a significant transition from a “warming and drying” trend to a “warming and wetting” trend in the 1980s, researchers have conducted numerous studies on the variations in precipitation and humidity in the region and even in arid Central Asia. In particular, the process of the “warming and wetting” trend by using obtained measurement data received much attention. However, there remain uncertainties about whether the “warming and wetting” trend has paused and what its future variations may be. In this study, we examined the spatiotemporal variations in temperature, precipitation, the aridity index (AI), vegetation, and runoff during 1950–2019. The results showed that the climate in the drylands of Northwest China and the northern Tibetan Plateau is persistently warming and wetting since the 1980s, with an acceleration since the 1990s. The precipitation/humidity variations in North China, which are mainly influenced by summer monsoon, are generally opposite to those in the drylands of Northwest China. This reverse change is mainly controlled by an anomalous anticyclone over Mongolia, which leads to an anomalous easterly wind, reduced water vapor output, and increased precipitation in the drylands of Northwest China. While it also causes an anomalous descending motion, increased water vapor divergence, and decreased precipitation in North China. Precipitation is the primary controlling factor of humidity, which ultimately forms the spatiotemporal pattern of the “westerlies-dominated climatic regime” of antiphase precipitation/humidity variations between the drylands of Northwest China and monsoonal region of North China. The primary reasons behind the debate of the “warming and wetting” trend in Northwest China were due to the use of different time series lengths, regional ranges, and humidity indices in previous analyses. Since the EC-Earth3 has a good performance for simulating precipitation and humidity in Northwest and North China. By using its simulated results, we found a wetting trend in the drylands of Northwest China under low emission scenarios, but the climate will gradually transition to a “warming and drying” trend as emissions increase. This study suggests that moderate warming can be beneficial for improving the ecological environment in the drylands of Northwest China, while precipitation and humidity in monsoon-dominated North China will persistently increase under scenarios of increased emissions.

How to cite: Xie, T.: Discussion of the “warming and wetting” trend and its future variation in the drylands of Northwest China under global warming, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10025, https://doi.org/10.5194/egusphere-egu25-10025, 2025.

The Himalayan region of India is experiencing warmer winters and hotter summers, which are causing reduced yields and putting the production of traditional fruit species in danger. In order to gain an understanding of the thermal growing conditions, it is essential to have chill and heat accumulation monitored. In the current investigation, the Dynamic model is utilized to compute the chill accumulation, while the Growing Degree Days (GDD) method is utilized to compute the heat accumulation. In order to calculate these indices, gridded hourly temperature data from the European Centre for Medium-Range Weather Forecasts (ERA)5 dataset was utilized. The time period covered by this dataset is from 1940 to 2023. The study's findings revealed the best elevation ranges for several of the region's most significant fruits, such as citrus fruits, almond trees, and fresh fruits. Furthermore, places with elevations ranging from 1000 to 2000 are good for growing fresh fruits. This is due to the fact that 70 percent of the Chilling Portion (CP) values are high enough to be greater than 60.

How to cite: Shukla, Y. and Gupta, V.: Assessing Climate Change Effects on Fruit Growing Conditions in the Northwestern Himalayan Region of India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14951, https://doi.org/10.5194/egusphere-egu25-14951, 2025.

Fires can alter the properties of soil and other material via heat transfer. The identification of soil heating effects in hearths, for example, has long been a cornerstone in archaeological investigations. However, wildfires can also alter soils, and there is a surprising level of uncertainty into what degree soils are heated and to which depth this occurs in wildfires. This can lead to erroneous assumptions regarding the potential impact of wildfires when attributing heat induced changes in the soil, especially when laboratory heating results are extrapolated to field conditions.

To address this research gap, we compiled and examined new and published field data on maximum temperatures and heating durations for mineral soils during wildfires and prescribed burns in forests, shrublands and grasslands around the globe; and compared these to data obtained from laboratory heating experiments.

Most fires heated only the uppermost centimetres of the mineral soil, rarely exceeding 300 °C below 1 cm depth. Their heat pulses were shorter (<500 s) than those often applied in laboratory studies (1800-3600 s). The highest near-surface temperatures occurred in shrubland wildfires, whereas the longest heating durations in forests with deep organic layers and high fuel loads.

While it is clear that smouldering logs, tree trunks and root systems, or slash pile burns can impart intense heating to substantial depths akin to that under hearths, most landscape-scale fires generate short and shallow heat pulses that are unlikely to lead to detectable lasting changes in the mineral soil. 

How to cite: Doerr, S., Badia-Villas, D., Bryant, R., Matthew, D., Antonio, G.-G., Jorge, M.-S., Jessica, M., Carmen, S.-G., Cristina, S., Cathelijne, S., and Pete, R.: Soil heating under wildfires and prescribed burns and their relevance to archaeological investigations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15465, https://doi.org/10.5194/egusphere-egu25-15465, 2025.

Fires are among the most significant causes leading to significant alterations, both at the level of the natural and built landscape. These in fact induce significant alterations not only on the vegetation cover, but also on fauna, soil, atmosphere, artifacts and, inevitably, economic losses as well. In the context of the archaeological heritage, fires are a cause of extensive damage especially at the territorial scale, on sites and fragments not yet subject to either excavation or reconnaissance campaigns, but also on known sites that suffer from insufficient protection actions.

Traditional methods of assessing fire severity and property damage incur costs in terms of money and time because of the necessary field survey activities. A combination of geodata science and remote sensing, on the other hand, turns out to be an inexpensive and effective tool for modeling fires, understanding their causes and fire evolution.

In this work we use the potential of geodata science methods applied to spatial and satellite data, to analyse past trends and its correlation with environmental and anthropic factors and to forecast fire risk in the context of climate change, considering the evolution of environmental parameters stated from the Intergovernmental Panel on Climate Change (IPCC, 2022). These findings can be the starting point for the development of forecasting models also with a view to proposing prevention and protection strategies for the archaeological heritage of the Basilicata Region.

Reference

IPCC, 2022: Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, S. Löschke, V. Möller, A. Okem, B. Rama (eds.)]. Cambridge University Press. Cambridge University Press, Cambridge, UK and New York, NY, USA, 3056 pp., doi:10.1017/9781009325844.

How to cite: Danese, M., Florio, V., Masini, N., and Lasaponara, R.: Impact of fire risk on archaeological heritage in the Age of climate change. Geodata science for prediction and development of strategies for protection., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16429, https://doi.org/10.5194/egusphere-egu25-16429, 2025.

When discussing climate change and cultural heritage, the focus often lies exclusively of the vulnerability aspects of the latter. However, cultural heritage can also play an active role in activating strategies and actions to increase its sustainability and mitigate environmental impacts.

Energy rehabilitation and reuse of existing buildings hold the potential to contribute to sustainable heritage conservation while embracing new energy efficiency principles.

According to literature, energy rehabilitation and retrofitting of the building envelope need to be carried out with respect to historic and cultural features and the protection of cultural heritage. This applies as much to listed buildings as to those that, although not formally protected, are part of the historical heritage and define the identity and the skyline of the place (Magrini, Franco, 2016).

In this work, starting from the spatial modeling of the territory and use of satellite data thank to the free-cloud application Google Earth Engine (GEE), it is possible to perform some preliminary analysis. These ones are useful to derive some formal characteristics that directly influence both the energy requirements and the choice of some technological solutions for integrating renewable energy sources (Forster et al.,2025).

References

Forster et al.,2025: Forster, J., S. Bindreiter, B. Uhlhorn, V. Radinger‐peer, and A. Jiricka‐pürrer. 2025. 'A Machine Learning Approach to Adapt Local Land Use Planning to Climate Change', Urban Planning, 10.

Magrini, Franco, 2016: Magrini, A., and G. Franco. 2016. 'The energy performance improvement of historic buildings and their environmental sustainability assessment', Journal of Cultural Heritage, 21: 834-41.

How to cite: Florio, V., Danese, M., and Biscione, M.: Preliminary analysis for energy efficiency assessment. Deriving technical parameters with spatial analysis and GEE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16568, https://doi.org/10.5194/egusphere-egu25-16568, 2025.

Monitoring key parameters that drive land-surface processes, such as surface soil moisture (SSM)), is essential for understanding global biogeochemical cycles, including those of water, energy, and carbon. While Earth Observation (EO)-based SSM products have demonstrated significant potential, their practical application is often limited by coarse spatio-temporal resolution. Therefore, downscaling these operational products is a critical scientific challenge for enabling their effective use in regional and local-scale applications.

This study’s aims at presenting an innovative approach for downscaling operational soil moisture products using a variant of the so-called “triangle” method, named the “simplified” triangle. The use of the proposed technique is demonstrated herein using the European Space Agency's (ESA) operational soil moisture product from the Soil Moisture and Ocean Salinity (SMOS) and optical data from ESA’s Sentinel-3 platform. The enhanced spatial SSM estimates are compared against near collocated reference ground data from multiple validated experimental sites across Europe. The results obtained indicate a satisfactory agreement, confirming the proposed approach's promising potential to accurately estimate key land-surface interaction parameters. Conceptually the proposed herein methodological framework is applicable to any operational product, a topic of further investigation.

How to cite: Detsikas, S. E., Petropoulos, G. P., Saviolakis, P., Lekka, C., Karymbalis, E., Katsafados, P., and Georgaki, F.: Downscaling Earth Observation Operational Soil Moisture Products Using multi-sensor Satellite Data: “A Triangle Inversion Approach", EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19591, https://doi.org/10.5194/egusphere-egu25-19591, 2025.

Advances in geo-information technologies, including Earth Observation (EO), GIS, cloud computing and software tool development, have shown great potential towards addressing key societal challenges faced today associated with the study of land-atmosphere interactions. Accurate information on spatially explicit, distributed estimates of land-atmosphere fluxes and soil surface moisture is essential in a wide range of disciplines, including meteorology, hydrology, agriculture and ecology.

Use of simulation process models has played a key role in extending our abilities to study Earth system processes and enhancing our understanding on how different components of it interplay. A special category of such models includes the so-called Soil Vegetation Atmosphere Transfer (SVAT) models. Those are deterministic simulation models that describe the physical processes controlling energy and mass transport in the soil/vegetation/atmosphere system.

SimSphere is such a software toolkit written in Java for simulating the interactions of soil, vegetation and atmosphere layers of the Earth’s land surface. Its use is at present continually expanding worldwide both as an educational and as a research tool for scientific investigations. It is being used either as a stand-alone application or synergistically with EO data and important advancements particularly in the recent years have been implemented to the model.

Herein, we present state of the art advancements introduced recently to SimSphere SVAT model aiming at making its use more robust when integrated with EO data via the so-called “triangle” method. Use of the recently developed add-on to SimSphere is illustrated herein using a variety of examples that involve both satellite and UAV data. The presented work  is of key significance to the users' community of the model and very timely, given that variants of the so-called “triangle” method being considered for deriving operationally regional estimates of energy fluxes and soil moisture from EO data provided by non-commercial vendors.

KEYWORDS: land surface interactions, geoinformation, earth observation, triangle, SimSphere   Acknowledgements The research presented herein has been conducted in the framework of the project LISTEN-EO (DeveLoping new awareness and Innovative toolS to support efficient waTer rEsources man- agement Exploiting geoinformatiOn technologies), funded by the Hellenic Foundation for Research and Innovation programme (ID 15898). 

How to cite: Gkatzios, G., Petropoulos, G. P., Detsikas, S. E., Lekka, C., Karymbalis, E., and Katsafados, P.: Advancing our understanding of land surface interactions via the development of innovative geoinformation tools , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21175, https://doi.org/10.5194/egusphere-egu25-21175, 2025.

Powerful volcanic eruptions inject into the stratosphere sulphur and tephra that may be spread globally and affect the Earth’s climate. Over the last 2500 years, Sigl et al. (2015) made a synthesis of the polar ice core atmospheric sulphur record and climate anomalies from dendrochronological records. Aside from a few historical events, most large eruptions with a bipolar imprint and a significant climate anomaly are from the tropical latitudes, but their sources are unknown.

We analysed the micron-size crytotephra composition accompanying the (stratospheric) sulphate of the 1458 CE and 426 BCE volcanic events recorded in three Antarctic ice cores. The 1458 CE event occurred within a cool climate and was initially attributed to the Kuwae (Vanuatu) eruption. This link is however questioned by Hartman et al. (2019) from their study of a South Pole ice core. The 426 BCE event appears concomitant with a significant global climate cooling, but its source is unknown.

Within the sulphate peak, the particle size distribution, when available, helps documenting the dynamics of the arrival of the stratospheric plume. Cryptotephra are collected by filtration and after carbon-coating, analysed by an EPMA microprobe. We applied the analytical procedure of Narcisi et al. (2019) (who identified the 1257 CE Samalas eruption), adapted to the micron-size of the crytotephra.

For the 1458 CE event, a medium-K dacite to rhyolite composition is consistently observed from Vostok and Dome C ice core samples (218 values). The dacite patch (SiO2~68%) fits well the composition of proximal Kuwae deposits as well as that of an ash layer (~140 values) on Efate Island (Standberg et al, 2023). The rhyolite composition patch (SiO2~72%) is unlikely from a South American source, but appears discretely represented in proximal Kuwae deposits as well as in sediments in the nearby Epi Submarine zone. We suggest that rhyolite is a daughter product from dacite by evolving in the upper layers of the magmatic chamber, and it was spread out first and far away by the eruption.  

 For the 426 BCE event, the cryptotephra composition (220 values) is consistently found within the three ice cores (Vostok, Dome C, Talos Dome) and belongs to high-K rhyodacite. Coincidentally such composition is very close to Kuwae’s (except for higher K) suggesting it was issued from a very similar magmatic chamber. The 10 km wide Ambrym caldera located 50 km north of Kuwae, collapsed ~2000 years ago appears the best candidate. 

References

Hartman et al. (2019). Nature Sci. Rep. 9. https://doi.org/10.1038/s41598-019-50939-x.

Narcisi, B. et al., 2019. Quat. Sci. Rev. 210, 164-174 https://doi.org/10.1016/j.quascirev.2019.03.005.

Sigl, M., et al. Nature 523, 543–549 (2015). https://doi.org/10.1038/nature14565

Strandberg NA et al., (2023). Front. Ecol. Evol. 11: 1087577.doi: 10.3389/fevo.2023.1087577

How to cite: Petit, J.-R., Savarino, J., Delmonte, B., Gautier, E., Ginot, P., and Batanova, V.: Cryptotephra fingerprinting of 1458 CE and 426 BCE volcanic events in East Antarctic ice cores, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2329, https://doi.org/10.5194/egusphere-egu25-2329, 2025.

The unique mass-occurrence of tiny heteromorph ammonites found in a single layer in the Mt. Cipollara locality of Cerreto d'Esi (Maiolica Formation, Umbro-Marchean Basin, Italy) provides critical insights into the depositional environments of the Cretaceous upper Maiolica Formation. The mechanisms behind the formation and preservation of these ammonite assemblages within black shales remain poorly understood. To solve this knowledge gap, we constrained by means of biostratigraphy and stable Carbon isotopes the whole sections hosting the ammonites-rich layer. The latter was then subjected a high-resolution paleoecological analyses. Samples were collected systematically across multiple stratigraphic levels to ensure comprehensive coverage. The ammonite assemblages were documented, focusing on their morphology, abundance, and associated sedimentary structures. Additionally, sedimentological petrographic examinations were conducted to elucidate depositional processes. Our results reveal a rich assemblage dominated by the family Leptoceratoididae, exhibiting relatively good preservation within a predominantly dysoxic low-energy environment at the bottom. Calcareous nannofossils data suggest the presence of a well-stratified water column, with a low salinity water cap. The multidisciplinary analyses indicates that these black shales served not only as a repository for ammonite remains but also reflected localized paleoecological conditions characterized by reduced turbulence and increased organic deposition. This unique sedimentary context suggests that the deposition of these assemblages could have been influenced by both regional sea-level fluctuations and local hydrographic conditions. In conclusion, the study of the Mt. Cipollara heteromorph ammonites underscores the complexity of Cretaceous paleoenvironments and provides an enhanced understanding of the occurrence of black shales within the Maiolica Formation.

How to cite: Conti, C., Faraoni, P., Mancini, A. M., Luca, M., and Negri, A.: Unusual mass-occurrence of small, uncoiled ammonites in a black shale of the Maiolica Formation in the Umbria-Marche Basin (Central Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3477, https://doi.org/10.5194/egusphere-egu25-3477, 2025.

Aeolian mineral dust and diatom influxes at the summit of Roosevelt Island (79.364°S, 161.706°W, 550 m a.s.l.) were investigated over the last 2 kyrs from the RICE ice core (Bertler et al., 2018). Mineral dust at the site is mainly related to large-scale atmospheric circulation patterns within the Eastern Ross and Amundsen Seas, while aeolian diatoms, mainly consisting of Fragilariopsis spp. (F. nana , F. cylindrus, , F. curta), depend on the local oceanic influence of air masses from the marine boundary layer. Thus, the complementarity of these proxies allows appreciating climatic and atmospheric changes experienced at Roosevelt Island over the last 2000 years, in response to some major forcing factors such as ENSO. During the 550-1470 CE period, when higher/less depleted stable water isotope values are observed, the increased importance of blocking ridges in the Amundsen Sea and a weakened Amundsen Sea Low promoted dust-rich air mass advection to RICE. This pattern was accompanied by an increasing trend in snow accumulation and reduced sea ice in the Eastern Ross and Amundsen Seas. At about 1300 CE, the maximum expression of the Ross Sea dipole is reached, with enhanced katabatic outflow in the Western Ross Sea and reactivation of the Ross Sea polynya. At the same time,  the Eastern part of the Ross Sea was still under the influence of blocking ridges promoting maritime air mass advection to RICE and southward shift of the South Westerly Winds. After 1470 CE, unprecedented peaks of aeolian diatom concentration suggest a rapid reorganization of local atmospheric circulation, that probably occurred in relation to the eastward enlargement of the Ross Sea polynya culminating with the opening of the  Roosevelt Island polynya.
For the RICE site, we suggest that several drivers contribute to the long-term dust, sea-ice and polynya variability, but ENSO-driven teleconnections are particularly prominent. On a longer (multidecadal) timescale it seems that El Niño-dominating conditions promoted the establishment of the Ross Sea dipole, while La Niña conditions favored a deeper Amundsen Sea Low and an eastward expansion of the polynya. 

How to cite: Delmonte, B., Lagorio, S., Tetzner, D., Malinverno, E., and Bertler, N.: Aeolian dust and diatoms at Roosevelt Island (Ross Sea, Antarctica) over the last two millennia reveal the local expression of climate changes and the history of the Ross Sea polynya., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8574, https://doi.org/10.5194/egusphere-egu25-8574, 2025.

Drought significantly affects the growth and physiological responses of Zagros forests, one of the most important natural habitats in Iran. The Brants oak (Quercus brantii Lindl), a widely distributed and dominant tree species in the Central Zagros Mountains of western Iran, serves as a valuable natural archive for studying historical climate variability and ecological changes. For climate-growth analysis, 30 Q. brantii trees cored from Lordegan area (1820 to 2280 m a.s.l.) in the southwest of Zagros forests of Iran. After preparing the samples, measuring the tree ring widths and cross-dating developed the tree ring chronology (1710-2023) using dplR. The relationships between tree-ring widths (TRW) and monthly mean temperature and precipitation values and the Standardized Precipitation Evapotranspiration Index (SPEI) were analyzed. The strongest climate signal of SPEI was found from previous September until April, representing the pre-growing and early-growing seasons. Among these reconstructions were acknowledged extremely narrow rings in 1870, 1923, 1960, 1964, and 2018, while extremely large rings were found in 1784, 1852, 1957, and 1976. Based on preliminary calculations showing a strong winter SPEI signal, this chronology could be used for climate reconstruction, but further analysis is required. These studies indicate the vulnerability of oak forests in the Zagros Mountains to ongoing climate change and a pressing need for sustainable forest management strategies to preserve these vital ecosystems.

 Keywords: Zagros forests, Iran, Quercus brantii, reconstruction, tree-ring widths

How to cite: Hatami, F., Klesse, S., Treydte, K., Verstege, A., Etemad, V., Pourtahmasi, K., Gessler, A., and Iranmanesh, Y.: Potential for a 300-year drought reconstruction in the Zagros Mountains, Iran based on the tree-ring width of Quercus brantii Lindl. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10067, https://doi.org/10.5194/egusphere-egu25-10067, 2025.

Oases are critical locations for human survival in desert areas. With heavily reliant on runoff from the surrounding mountains, oases in the hyperarid Tarim Basin are especially fragile and sensitive to both climatic–environmental changes and human activity. However, the local evolution process of oases in desert area remains unclear due to strong erosion and contemporaneous heterogeneity, which restricts our understanding of the coupling relationship among climate change, oasis evolution, and human activity. Here, we reconstruct the evolution of oases since the last deglacial period (~15 ka) in the Tarim Basin. The results indicate that oases advanced during 15–11.5 ka, 9–4.5 ka, and 2–1 ka. Through the integration of multiple records of palaeoclimate, palaeoenvironment, archaeology and history, we found that human activity dominated and decoupled oasis evolution from climate change since ~2 ka in the Tarim Basin. Oasis were artificially expanded to sustain the flourishing society during the Han–Tang period, but they declined synchronously afterward. More attention should be paid to the proper management of land and water resources to achieve sustainable development in hyperarid areas.

How to cite: Sun, A.: Human activity has decoupled oasis evolution from climate change since ~2 ka in the Tarim Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10215, https://doi.org/10.5194/egusphere-egu25-10215, 2025.

While the effects of OAEs are well known for the pelagic successions of the Tethys Ocean, little is known about their impact on the Peri-Tethyan shallow water carbonate systems. Here we present the preliminary results of a study related to the geological mapping of the sheet 390 – Frosinone of the Geological Map of Italy (CARG Project), focussed on the identification and description of the perturbation induced in the Lower Cretaceous shallow water carbonate succession of the Latium-Abruzzi Carbonate Platform by the well-known Early Cretaceous Oceanic Anoxic Events (OAEs).

In the Ernici Mts. (central Apennines, Italy), an Upper Triassic to Upper Cretaceous shallow-water carbonate succession is exposed (Cosentino et al., 2010; Fabbi et al., 2023). This study specifically examines the Lower Cretaceous "calcari ciclotemici a gasteropodi" fm. (CCG - Berriasian p.p. - lower Aptian p.p.), which mainly consists of whitish limestones with intercalations of light grey dolostones. Within this succession, a layer of black dolostone, about ten centimetres thick, has been observed in several outcrops of the dolomitic lithofacies (CCGa) of CCG, at the same stratigraphic position.

Two stratigraphic sections were measured to characterise the microfacies and compositional variations observed between the light-coloured (whitish to light grey) and black layers. SEM images, along with Energy-Dispersive X-ray Spectroscopy (EDS) and Wavelength-Dispersive X-ray Spectroscopy (WDS) analysis indicated the presence of siderite and pyrite aggregates (Meng et al. 2024). These aggregates appear in high concentration starting from the basal part of the blackish dolostone layer and gently decrease towards the upper part of the study interval. TOC and sulphates show similar trends.

Changes in chemical composition between the whitish and blackish dolostones (CCGa) were investigated in situ using the laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) facility at Roma Tre University. The results show a significant increase in elemental concentration of P, Fe, Zn, As, Ba, Pb, and U, as well as in the Fe/Al ratio in the blackish dolostones. These elements are generally considered as redox-sensitive proxies associated with anoxic paleoenvironments (Bodin et al., 2007; Craigie, 2018).

Biostratigraphic calibration performed on the collected samples has established a Hauterivian p.p. age for the investigated CCGa levels. A preliminary attempt for U-Pb dating of the CCGa black dolostone was carried out through LA-ICP-MS investigations at Roma Tre and ETH facilities. In the Tera-Wasserburg diagram, the U-Pb measurements on CCGa black dolostone yielded a lower intercept age of 125.7± 1.8 Ma (MSWD=1.6; N=19). These promising results suggest that the changes in the elemental concentration of the redox-sensitive proxies observed in the CCGa black dolostone were induced by the late Hauterivian Faraoni Oceanic Anoxic Event.

How to cite: Artegiani, F., Cipollari, P., Cosentino, D., Rabiee, A., Guillong, M., Rossetti, F., Cipriani, A., and Fabbi, S.: Early Cretaceous Oceanic Anoxic Events (OAEs) in Peri-Tethyan shallow-water carbonate systems: Evidence from the Latium-Abruzzi Carbonate Platform (Ernici Mts, Central Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11624, https://doi.org/10.5194/egusphere-egu25-11624, 2025.

Located in the arid inland of Asia, the eastern part of the Silk Road is marked by certain routes being close to or even crossing large deserts, such as the Taklimakan Desert, one of the largest deserts worldwide. As a result, sand and dust activities have a considerable impact on the transport routes, the desert-oasis ecosystem, and human society along the Silk Road. However, the evolution of dust activity over the past two millennia and its relation to the changes of the Silk Road civilization remains ambiguous. Here, we present a high-resolution (~3 yr) stalagmite record from Xinjiang (northwest China) spanning the past 2,500 years, dated with 19 U/Th ages. Although the stable isotopes and trace elemental ratios of the stalagmite reveal remarkable decadal- to centennial-scale variability of the regional hydroclimate, the Mg/Ca ratio shows a quite different variation pattern compared with other geochemical proxies. Considering various factors that might influence the Mg/Ca ratio of stalagmites, our analysis reached the conclusion that the geographical location close to the desert made the imported dust likely to predominate the increase of Mg/Ca in stalagmites during many characteristic periods. For instance, we found significant increases in the Mg/Ca ratios lasting for more than two centuries during approximately 650-850 CE and 1650-1950 CE (i.e., the Little Ice Age). This generally demonstrates a pattern of reduced dust activities during the Medieval Warm Period and enhanced dust activities throughout the Little Ice Age, which is supported by evidence from the eolian sedimentary section in the southern margin of the Taklimakan Desert that directly reflects dust activity. We further found that the enhanced dust activity during the 650-850 CE might have caused the route shift of the Silk Road from south to north in the Tarim Basin. In addition, the rapid drying of Lop Nur in recent decades could also be attributed to abnormally increased dust activity, as this period was characterized by the most intense dust activity in our records over the last 2,000 years. Our findings further substantiate the argument regarding the association between societal and climatic change along the Silk Road, where the dust production from large deserts poses challenges to sustainable development in the present and the future.

How to cite: Liu, X., Chen, S., Chen, J., Wang, H., Shen, C.-C., Wang, X., and Chen, F.: Reconstruction of dust activity using geochemical proxy from cave stalagmite in the northern Taklimakan Desert, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12114, https://doi.org/10.5194/egusphere-egu25-12114, 2025.

The Canary Islands, located in the central North Atlantic, provide an exceptional setting for investigating long-term climate dynamics within the Macaronesian region. This study presents sedimentary records from volcanic lacustrine basins across Tenerife, La Gomera and La Palma, analyzed using a multi-proxy approach including magnetic susceptibility, XRF geochemistry, elemental composition (TOC, TN, TS), mineralogy, lipid biomarkers, and updated age models. Preliminary age models suggest that the sequences of La Vega Lagunera (northern Tenerife) extend back up to 400,000 years, and El Malpaís de La Rasca (southern Tenerife), Garajonay (La Gomera), and Playa de Taburiente (La Palma) span the Holocene.
Preliminary results from La Vega Lagunera, a Pleistocene clastic lake, indicate colder conditions during MIS 2 and MIS 4, warmer conditions during the Holocene, MIS 3, and MIS 5, and millennial-scale cycles during MIS 3 and MIS 4. Climate during the Last Glacial Maximum (MIS 2) was notably drier, resembling mid-latitude records. 
Holocene records from paleolacustrine deposits of two closed-drainage basins located in two volcanic craters (La Gomera and Malpaís de La Rasca) and the lacustrine-marsh system of Playa de Taburiente showed coherent patterns of Holocene regional climate variability, with increased fluvial and alluvial activity during the Greenlandian (11.7 to 8.2 ka), a decline during the Northgrippian (8.2 to 4.2 ka), and reduced clastic input during the Meghalayan (last ~4.2 ka). These trends suggest increasing aridity throughout the Holocene. 
These new sedimentary sequences from Tenerife, La Gomera, and La Palma provide further evidence of rapid climate dynamics during glacial and interglacial intervals. Improved age models (OSL, 14C) are still being developed to characterize the cyclic patterns better, while multi-proxy analyses are enhancing our understanding of past climate dynamics. Further research is needed to clarify the roles of regional climate and local factors.
This work is supported by TED2021-129695A-I00 project funded by MICIU/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR; PALEOMOL (2915/2022) and IVRIPARC (2779/2021), both funded by the Spanish National Parks Organism, and IMPACT (2022CLISA04, Fundación CajaCanarias and Fundación La Caixa).

How to cite: Ocón-Bermúdez, C., Galofre-Penacho, M., Valero-Garcés, B., Armenteros-Armenteros, I., Herrera-Herrera, A., Égüez, N., Martín-Luis, M. C., Casillas Ruiz, R., Vegas, J., Castellano-Rotger, L., Diez-Herrero, A., Casado-Vara, R., and Jambrina-Enríquez, M.: A Multi-Proxy Approach to Reconstructing Long-Term Climate and Environmental Dynamics in the Canary Islands: Inter-Island Comparisons, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12407, https://doi.org/10.5194/egusphere-egu25-12407, 2025.

Human-induced carbon emissions are altering the modern climate, with severe repercussions on ecosystems. Among others, anthropogenic pressure is causing deoxygenation of the bottom water, with the widespread establishment of hypoxic zones in several Mediterranean areas. The geological archives allow the investigation of past deoxygenation dynamics (sapropel events) and their impact on marine ecosystems. Here, we compare the causes and the evolution of deoxygenation dynamics that occurred during two different time periods (Messinian and Holocene) in different paleoceanographic settings based on their micropaleontological content. The Messinian sapropel events are the result of increased export productivity during a relatively cold and arid context, triggering bottom anoxic conditions. The Holocene sapropel formed in response to weakening/stopping of the thermohaline circulation due to increasing temperature and freshwater input. Our results suggest that the deoxygenation dynamics in the Mediterranean in the near future will not follow the trend characteristic of the Holocene deep-sea sapropel because of the predicted drying trend. Differently, the paleoceanographic setting triggering the Messinian shallow-sea sapropels is comparable with the modern situation in different Mediterranean areas, where human-induced eutrophication is promoting deoxygenation. Based on these results, we suggest that the patchy deoxygenation trend in the Mediterranean Sea caused by climate warming may lead to a drastic change in the ecosystem services which would likely impact human activities.

How to cite: Lozar, F., Mancini, A. M., Morigi, C., Gennari, R., and Negri, A.: Past analogues of deoxygenation events in the Mediterranean Sea: Comparison between shallow and deep settings , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15403, https://doi.org/10.5194/egusphere-egu25-15403, 2025.

The Pliensbachian/Toarcian event (P/T-E) and the Toarcian Oceanic Anoxic Event (T-OAE) are two intervals of carbon cycle perturbations linked to massive ¹²C-enriched carbon emissions, causing severe biotic and environmental changes. Here organic carbon isotope, mineralogical composition and sedimentology have been analyzed across the Pliensbachian-Toarcian transition from the Monte Serrone section (Umbria-Marche Basin), which was deposited in a pelagic setting in the western Tethys. A marked negative carbon-isotope excursion occurred across the Pliensbachian-Toarcian boundary and lower Toarcian, respectively, which can be used to identify PTE and T-OAE in the study area. The P/T-E and T-OAE intervals witnessed carbonate production crisis revealed by reduced carbonate contents. We hold that the 0.5 m-thick laminated black shales indicated that the T-OAE was a highly condensed succession because it included the full duration of the T-OAE. Therefore, the T-OAE interval at Monte Serrone coincided not only with diminished carbonate production but also with reduced siliciclastic input, forming quite thin black shale deposition. Abundant marine organisms were present preceding the T-OAE. Nevertheless, none of them survived during the most negative carbon-isotope excursion of the T-OAE, revealing a biotic crisis at this time. Elevated seawater temperature could induce this crisis in the study area. The recovery of benthic foraminifera was delayed at Monte Serrone.

How to cite: Nie, Y., Fu, X., and Rigo, M.: Carbon cycle perturbations during the Pliensbachian-Toarcian transition in the Monte Serrone section (Northern Apennines, Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16338, https://doi.org/10.5194/egusphere-egu25-16338, 2025.

The climatic signals recorded by loess sequences vary between different regions, which makes it important to study loess sequences worldwide. The loess deposits in northern Iran are situated in the transitional zone between the European loess and Central Asian loess. However, the depositional dynamics and paleoenvironmental significance of the loess deposits in this region are not well understood, making it difficult to establish detailed correlations with loess deposits elsewhere, partly due to the lack of systematic and high-resolution chronological control. We used K-feldspar pIR₅₀IR₂₉₀ and MET-pIRIR₂₅₀ luminescence dating protocols to date fifty-two K-feldspar samples from the Toshan-19 section in the northern foothills of the Alborz Mountains, northern Iran. These chronological data, along with the climate proxies of magnetic susceptibility and redness, combined with a comparison with published loess records from various regions, indicate the following: (1) K-feldspar luminescence ages obtained using pIRIR and MET-pIRIR protocols are consistent, and their luminescence ages up to ~200 ka are deemed dependable. The loess at Toshan was primarily deposited during 78–24 ka, corresponding to MIS 4–2, and the paleosols developed during 139–78, and 24–1.7 ka, corresponding respectively to MIS 5, and late MIS 2–MIS 1. (2) Drier conditions prevailed during the last glacial and wetter conditions dominated during the last interglacial. Moisture variations during the substages of MIS 5 in this region indicate cold-dry and warm-wet climatic characteristics. The reasons for increased moisture from late MIS 2 onwards in this region still require further investigation. (3) The loess-paleosol records indicate a consistent pattern of climate change over Eurasia on the scale of the last interglacial-glacial cycle. During the substages of MIS 5, warm-wet and cold-dry conditions in northern Iran were in-phase with those on the Chinese Loess Plateau, Europe, and southern Tajikistan; however, they were anti-phased or out-of-phase with those in Xinjiang.  

How to cite: Li, D., zhao, H., and Xie, H.: Loess-paleosol sedimentological characteristics in northern Iran since the last interglacial and their paleoenvironmental significance, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16630, https://doi.org/10.5194/egusphere-egu25-16630, 2025.

The Lagoa Real uranium province (LRUP) is the main Brazilian target for uranium. Their geochronological studies began in the 80s and provided controversial ages for mineralization. Since then, advances in geochronological methods, increased local petrological data, and knowledge of the uranium cycle have helped geosciences understand crust and mantle behavior over time. As a result, recent geochronological studies developed by CDTN researchers have now begun to reinterpret the evolution of the LRUP.

These studies dated metasomatic  U-ore bodies providing ages between 545 Ma to 520 Ma (in situ U–Pb dating of andradite and titanite, Santos et al., 2023; Journal of South America Earth Science) coeval with the late Pan-African Cycle. Geochronological studies were also carried out on the host rocks (A-Type granites) of the mineralized bodies, providing ages between 1762 Ma to 1741 Ma (U–Pb dating of magmatic Zircon, Amorim et al., 2022; Journal of South America Earth Science), coeval to bimodal magmatism well documented in Brazil and Africa.

Although some of the data obtained suggest that granites might not be the source of uranium, their volcanic expression (metaryolites located to the NW of the LRUP) could be a good candidate. Thus, the uranium mobilization began before the metasomatism, through magmatic processes, coeval with the Post-Archean Uranium Recycling, a global event that incorporates U in the crust from the mantle. Furthermore, preliminary macroscopic and microscopic data from gneisses show evidence of partial melting related to regional metamorphism that may have occurred before metasomatism. This process generated Neoproterozoic uranium deposits in Namibia, at the Southern of the African counterpart of Brazil. Therefore, LRUP could result from overlapping processes in central Brazil accompanied by crustal differentiation episodes leading to a polycyclic evolution.

How to cite: Azevedo, R. A. and Rios, F. J.: The Lagoa Real Uranium Province: polycyclic evolution in the Brazilian geochronological record, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19922, https://doi.org/10.5194/egusphere-egu25-19922, 2025.

Groundwater is a vital resource for domestic, agricultural, and industrial purposes in many regions. However, the increasing demand and unsustainable extraction practices have raised concerns about the long-term viability and sustainability of groundwater storage (GWS), especially in areas where groundwater is the primary source of meeting various demands. Here, we focus on GWS changes in India’s Northwestern states, including Gujarat, Rajasthan, Punjab, Haryana, Uttara Pradesh, and Delhi over two decades (2002-2023). These states encompass 875,249 km² area within the Indus and Ganges river basins, constitute approximately 59% cultivated land, and sustain 525.52 million people. Leveraging GRACE-based TWS data and GLDAS model data, our analysis reveals significant (P<0.05) declining GWS trends with a slope of −20.88 ± 0.53 mm/year, which is more acute than previously reported estimates. Some trend change points in February 2008 and June 2016 are detected that lead to segmented trends with slopes of −18.97 ± 2.45 mm/year (Jan-2002 to Feb-2008), −9.16 ± 1.96 mm/year (Feb-2008 to Jun-2016), −11.80 ± 2.51 mm/year (Jun-2016 to Dec-2023). Spatially divergent trends are found with high decreasing trends of more than 40 mm/year in Punjab, Haryana, Delhi, and some parts of Rajasthan and Uttara Pradesh. This is primarily due to anthropogenic activities like groundwater extraction for domestic and agricultural purposes. In contrast, Gujrat shows subtle positive trends, less than 10 mm/year, due to improved water management, irrigation practices, artificial recharge efforts, monsoonal rainfall, and efficient water extraction management​. Multi-decadal variability and the recent depletion across these six states may foster discussions on policy actions and enhanced multilateral cooperation for a sustainable future, especially in the face of escalating groundwater extraction and a warming climate. This highlights the critical need for immediate attention to water resource challenges in the Northwestern states of India.

Keywords: Groundwater storage (GWS); GRACE; GLDAS; Anthropogenic activities; Policy interventions.

How to cite: Anjaneyulu, R. and Abhishek, A.: A more acute and continuous decline in Groundwater in Northwest India , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-543, https://doi.org/10.5194/egusphere-egu25-543, 2025.

Urbanization has exacerbated challenges faced by urban watersheds, including increased impervious surfaces, deteriorating water quality, and heightened flood risks. Previous research has extensively employed the Genetic Algorithm (GA)  to optimize urban grey-green infrastructure (GGI), primarily focusing on preventing system-wide overflow during design storm events. However, the high costs associated with these solutions have often hindered their implementation. This study proposes a practical approach to enhance urban stormwater management by prioritizing interventions at critical locations within watersheds. A multi-index fuzzy comprehensive evaluation (MFCE) model was developed to identify critical nodes in the drainage network based on hazard (overflow volume and duration), topological characteristics (degree and Katz centrality), and vulnerability (peak hour traffic flow). Problematic segments within the drainage network, including those with adverse slopes, mismatched pipe diameters, and ground depressions, were identified using a combination of SWMM simulations and graph-based analyses. Subsequently, the Genetic Algorithm (GA) was employed to optimize the design and placement of grey-green infrastructure solutions, subject to the constraint of preventing overflow at these critical nodes during design storm events. A case study in Guangzhou, China, demonstrated the efficacy of this approach. The optimized grey-green infrastructure system significantly reduced budgetary costs and peak flow compared to traditional grey infrastructure systems, while enhancing flood control and improving the overall resilience of the urban watershed.

How to cite: Yang, G., Huang, G., and Zeng, B.: Developing Practical Green-Grey Solutions Based on Critical Node Identification for Stormwater Management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2656, https://doi.org/10.5194/egusphere-egu25-2656, 2025.

The sustainable management of soil moisture and salinity is a critical challenge for semi-arid regions like the Mahabad Plain in northwestern Iran. This study applies the HYDRUS-1D model, calibrated using sensor-based data, to simulate water and salt dynamics in a 4 HA sugar beet field. The Mahabad Plain, covering 249 km², experiences annual precipitation of 402 mm and evaporation rates of 1,560 mm. Despite its fertile soils, the region faces persistent challenges such as waterlogging, salinity, and unsustainable irrigation practices, exacerbated by agricultural expansion and climate variability. Sensor data were collected every other day from four soil depths (0–25 cm, 25–50 cm, 50–75 cm, and 75–100 cm) in a single sugar beet field between late June 2024 and late July 2024. These measurements were used to calibrate the HYDRUS-1D model, optimizing parameters such as residual and saturated water content, hydraulic conductivity, and dispersion coefficients. Calibration metrics, including RMSE and Nash-Sutcliffe efficiency, confirmed the reliability of the simulations in replicating observed conditions. The results revealed critical inefficiencies in irrigation practices. Over-irrigation was observed, particularly in deeper soil layers, where moisture levels exceeded the optimal range of 18–25% for sugar beet cultivation. Surface layers (0–25 cm) also exhibited frequent waterlogging after irrigation events, with moisture levels surpassing 25%. Electrical conductivity (EC) levels, however, remained within the safe range of 0.6–1.3 dS/m, indicating effective salt leaching and no immediate risk to crop health. Simulations demonstrated that increasing irrigation intervals by 1–2 days could reduce water consumption by 15–30%, prevent excessive soil saturation, and promote healthier root growth. This approach ensures that soil moisture remains within the optimal range while maintaining crop yield and quality. This study is the first of its kind for the Mahabad Plain, offering a novel application of sensor-calibrated HYDRUS-1D modeling. It provides actionable recommendations for addressing water scarcity and improving agricultural sustainability. By integrating field observations with advanced modeling, the research bridges gaps in water resource management and offers replicable solutions for semi-arid agricultural systems worldwide. The findings are especially relevant as the region faces increasing agricultural demands and environmental challenges, including efforts to restore Lake Urmia. By improving irrigation efficiency and reducing agricultural water consumption, more water can be directed toward Lake Urmia, contributing to its restoration and the broader ecological balance of the region.

How to cite: Hossaini Baheri, M. and Tajrishy, M.: Simulation of Salt and Moisture Dynamics in Agricultural Fields Using HYDRUS: Insights from a Sensor-Based Calibration, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2901, https://doi.org/10.5194/egusphere-egu25-2901, 2025.

Fractured aquifer parameters are expected to have complex non-Gaussian spatial distributions. Gaussian Mixture Models, known for their effectiveness in representing non-Gaussian distributions, present a promising alternative for capturing the complex heterogeneity of fractured geologic settings however their usage in the fractured geologic settings is unexplored. In this study we extended the application of Gaussian mixtures to transient hydraulic tomography on laboratory-based fractured geologic settings using sequential Gaussian Mixture Model (GMM). We further examined the impact of the number of Gaussian components, sampling strategies and the amount of pumping data on the performance of the sequential GMM. Results demonstrate that GMM with an optimal number of Gaussian components effectively identifies high and low conductivity regions, fracture connectivity, and reasonably predicts drawdowns (R² = 0.61) pumping from validation ports. Stratified sampling of GMM parameters (R² = 0.74, average RMSE_median= 9.89 mm) outperforms other sampling strategies like random (R² = 0.61, average RMSE_median= 20.64 mm ), uniform (R² = 0.64, average RMSE_median= 11.70 mm) and quasi-random sampling (R² = 0.67, average RMSE_median= 11.40 mm) techniques in mapping the fracture connectivity and parameter distribution. Stratified sampling with reduced and information-based pumping data maintains commensurable accuracy (R² = 0.75, average RMSE_median= 11.34 mm). Overall, our findings suggest that the sequential GMM combined with stratified sampling technique effectively captures the spatial variability of aquifer parameters in fractured media.

How to cite: Muraharirao, P. C. and Kbvn, P.: Sequential Gaussian Mixtures for Transient Hydraulic Tomography Inversion in Fractured Aquifers, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4107, https://doi.org/10.5194/egusphere-egu25-4107, 2025.

Recent water demands have created immense stress on groundwater, especially in the region facing water scarcity. Hence, optimizing groundwater pumping and developing sustainable water management strategies becomes important for such areas. The traditional mesh-based methods, such as finite difference (FDM) and finite element methods (FEM) for groundwater modelling requires high-quality mesh generation. In these methods, generating a high-quality mesh for complex aquifers is a time-consuming task. Therefore, meshless methods that work with scattered field nodes and avoid mesh generation are more suitable for complex groundwater problems. The present study uses the meshless generalized finite difference method (GFDM) for modelling the groundwater flow and integrating it with particle swarm optimization (PSO) to determine the optimal pumping rates for a hypothetical aquifer system. In this work, optimal pumping rates for different groundwater withdrawal scenarios are obtained through the proposed meshless simulation-based optimization model (GFDM-PSO), indicating its application to real-world problems.

How to cite: Singh, K. G. and Pathania, T.: Optimization of groundwater pumping rates using meshless simulation-based optimization model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4251, https://doi.org/10.5194/egusphere-egu25-4251, 2025.

Title: Hydrogeochemical Dynamics of Middle Andaman: Unraveling the Impact of Seawater Intrusion and Limestone Caves on Groundwater Chemistry

Pardeep Kumar^1,2#, Saumitra Mukherjee^1*

^*Corresponding author- saumitramukherjee3@gmail.com

^#Presenting Author: Pardeepranga001@gmail.com

¹School of Environmental Sciences, Jawaharlal Nehru University, New Delhi

²Quality Council of India, New Delhi

Abstract: Groundwater resources in coastal and island aquifers are increasingly threatened by seawater intrusion, exacerbated by climate change, sea level rise, erratic rainfall patterns, and over-extraction of groundwater. These challenges are particularly pronounced in Middle Andaman, where the interaction of groundwater, surface water, and seawater occurs within a complex hydrogeological framework. To assess the groundwater chemistry and its suitability for drinking and irrigation, a comprehensive study was conducted using geochemical, geospatial, and statistical methods.

Groundwater samples (n=24) and a reference seawater sample were analyzed for major ionic compositions using ICP, spectrophotometry, and flame photometry. Hydrogeochemical indices, including Chloro-Alkaline Indices (CAI), Water Quality Index (WQI), and agricultural suitability indices such as total hardness (TH), residual sodium carbonate (RSC), and magnesium adsorption ratio (MAR), were evaluated. A combination of ionic ratios—Cl/HCO₃, Ca/(HCO₃ + SO₄), (Ca + Mg)/Cl, Ca/Mg, and others—was used to characterize the influence of seawater intrusion and the dissolution of limestone minerals in the aquifers.

The results revealed that 24% of groundwater samples were unsuitable for drinking based on WQI, while 80% and 12% of samples were unsuitable for irrigation based on TH and MAR, respectively. The Durov plot and Schoeller's diagram indicated a dominance of Ca-HCO₃ and Na-HCO₃ water types in 48% and 24% of the samples, respectively, with enrichment of alkali and alkaline earth metal salts due to seawater intrusion. Chloride ion relationships suggested a reverse ion exchange process in 64% of samples, while X-ray diffraction analysis confirmed the presence of limestone minerals such as aragonite, calcite, dolomite, and magnetite.

Geospatial integration of hydrochemical data showed that 44% of the region was moderately affected, and 54% was slightly affected by salinity. Active tectonic lineaments and interconnected faults were found to facilitate seawater intrusion into the deep aquifer, highlighting the role of structural geology in the region's hydrogeochemical dynamics. This study underscores the urgent need for sustainable water resource management strategies to mitigate the adverse impacts of seawater intrusion on groundwater quality in Middle Andaman.

Keywords: Middle Andaman; Groundwater; Seawater intrusion; Water quality Index; Limestone caves

How to cite: Kumar, P. and Mukherjee, S.: Hydrogeochemical Dynamics of Middle Andaman: Unraveling the Impact of Seawater Intrusion and Limestone Caves on Groundwater Chemistry, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4752, https://doi.org/10.5194/egusphere-egu25-4752, 2025.

The relationship between groundwater and seismicity has been documented in various regions worldwide. Mexico is no exception to this phenomenon. On September 19, 2017, a magnitude 7.1 earthquake struck between the states of Puebla and Morelos, as reported by the National Seismological Service.

Approximately 50 km from the epicenter, the Agua Hedionda spring exhibited significant physical and chemical changes as a result of the earthquake. These changes highlight the dynamic interactions within the critical zone—the near-surface environment where rock, soil, water, air, and living organisms interact to shape the Earth's surface. The spring's discharge showed notable alterations, including a decrease in flow rate, reductions in major ion concentrations, and shifts in its isotopic composition, providing clear evidence of the connection between regional seismicity and the quality and availability of groundwater.

The analysis of changes in the spring's groundwater over time revealed its vulnerability to losing essential properties, either temporarily or permanently. Hydrochemical and volumetric flow rate data indicated that the spring underwent noticeable changes even before the earthquake. While the water chemistry showed gradual recovery by 2022, the flow rate only returned to approximately 25% of its pre-earthquake level.

In a country like Mexico, where groundwater is essential for numerous activities and where the interaction of five tectonic plates creates a dynamic seismic environment, studying the interplay between seismicity, groundwater, and processes within the critical zone is crucial for understanding and managing water resources sustainably.

How to cite: Sierra Garcia, B. A., Escolero, O., Olea Olea, S., and Medina Ortega, P.: Seismicity and Groundwater Dynamics: Impacts on the Critical Zone in spring of center Mexico, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5314, https://doi.org/10.5194/egusphere-egu25-5314, 2025.

Hydropower development in river basins has significantly promoted economic growth while greatly changing the river ecosystems. Effective reservoir management is crucial to maintaining economic benefits while minimizing impacts on fish species. This study focuses on Reservoir X, which has annual regulation capacity, and proposes an ecological scheduling model for the fish spawning period using the NSGA-II algorithm combined with water temperature and TDG (Total Dissolved Gas) predictions. The model framework is as follows: first, hydrological analysis is conducted based on natural flow data at the dam site to determine the flow requirements for target fish species during their spawning period, providing constraints for optimization. Second, multiple regression methods are used to predict the discharge water temperature and TDG saturation of Station X. Finally, multi-objective optimization is performed considering hydropower generation, fish spawning period water temperature requirements, and TDG risks as objectives, with flow requirements during the spawning period, flood control, and water balance as constraints. The proposed model provides practical parameters for reservoir operation and guidance for different optimization objectives across various reservoirs.

How to cite: He, C. and Liang, R.: Study on Reservoir Ecological Scheduling Based on Multi-Objective Optimization, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9851, https://doi.org/10.5194/egusphere-egu25-9851, 2025.

Anthropogenic impact on aquifers leads to variations of groundwaters chemical content. This study is determined to describe current geochemical characteristics of springs in Shelkovo district in order to assess the quality of the water that is used for drinking purposes by residents.

The geological structure of the territory includes Devonian, Upper Carboniferous, Upper Jurassic and Lower Cretaceous terrigenous-carbonate rocks, overlapped by thin Quaternary sandy deposits. Surface sediments are permeable to polluted runoff waters, which can increase the vulnerability of groundwater and reduce its quality.

This research presents the obtained results of water parameters (COD, pH, electrical conductivity), the content of major ions (Ca²⁺, Mg²⁺, Na⁺, K⁺, NH₄⁺, HCO₃^-, Cl^-, SO₄^2-, NO₃^-)  for 12 springs. The spring waters are slightly mineralized (M=0.1-0.5 g/l), pH values vary from 5.5 to 7.5.  The total hardness is 0.63-5.7 mg-eq/l. The composition of the water is variable. Springs could be divided by the content of major anions: the dominance of HCO₃^- which is due to natural causes. In some cases the presence of Cl^- and SO₄^2- because of the use of fertilizers and deicing reagents in urban territories. 

The concentration of major ions was compared to maximum permissible concentrations in drinking water (by WHO standards). It was noted to slightly exceed the limit for nitrate ion as well as for chemical oxygen demand.  Some waters had a pH indicator lower than the standard range.

Comparison of the ratios Cl^-/(Cl^-+Na⁺) and Na⁺/(Na⁺+Cl^-) to total dissolved salt was applied in order to figure out the mechanism of spring waters forming (Gibbs, 1970). The results showed that chemical composition is primarily controlled by rock weathering. The ratio relationships between equivalent content Cl^-/Na⁺, HCO₃^-/Na⁺, Ca²⁺/Na⁺ indicate the type of rocks as a silicate (Gaillardet, 1999). The effect of human impact on groundwaters used to be assessed by comparing the equivalent ratios Cl^-/Na⁺ and NO₃^-/Na⁺ (Zhang et al, 2024). The calculations performed summarised anthropogenic impact, including agronomic activities. Significantly connections between various major ions were pointed out due to correlation analysis: as well as fertilizer components and pesticides, anti-icing reagents for roads in winter season and household chemicals from sewers were detected. 

The studied waters were formed by dissolving silicate rocks by atmospheric precipitation. As it was figured out by a significant role of chloride and sulfur ions, and presence of nitrogen-ions, the area of springs' feeding is located in permeable contaminated quaternary sediments. But despite anthropogenic impact, the chemical composition of springs correspond to WHO standards for drinking waters.

References

Gaillardet J., Dupre B., Louvat P., Allegre C.J. Global silicate weathering and CO₂ consumption rates deduced from the chemistry of large rivers // Chemical geology – 1999. – Т. 159. – №. 1-4. – С. 3-30.

Gibbs R. J. Mechanisms controlling world water chemistry //Science. – 1970. – Т. 170. – №. 3962. – С. 1088-1090. 

Zhang, H., Wang, Z., Wang, X. et al. Hydrochemical characterization and health risk assessment of different types of water bodies in Fenghuang Mountain Area, Northeast China. Environ Geochem Health 46, 292 (2024)

How to cite: Gusarova, D. and Yablonskaya, D.: Ecohydrogeological characteristics of spring waters in rural areas (northeast of Moscow region), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11817, https://doi.org/10.5194/egusphere-egu25-11817, 2025.

Accurate estimation of the soil hydraulic conductivity function (SHCF), which describes the relationship between hydraulic conductivity and matric suction in soil, is essential for modeling flow and transport processes in the vadose zone. Traditional steady-state methods for directly determining SHCF are often laborious, time-consuming, and sometimes inadequate for capturing transient-state flow conditions. This study aims to propose a simple, quick, and accurate method for estimating SHCF that facilitates transient-state flow analysis during vadose zone modeling. The proposed method involves inverse numerical modeling using cumulative infiltration and final moisture content data from surface infiltration tests conducted with a handy mini disc infiltrometer (MDI). To validate this approach, the MDI-inverse modeling results were compared with SHCF results from another transient-state method, the instantaneous profile method (IPM), under similar initial soil conditions. The MDI infiltration tests were performed in homogeneously packed soil columns for two soils (identified as loam and silty clay loam textures) collected from nearby field sites. For each soil, separate IPM tests were conducted in soil columns equipped with soil moisture and matric suction sensors at various depths to facilitate calculation of reference SHCF. A comparison between the MDI and reference IPM results revealed a good agreement, with a low normalized RMSE (under 15%) for the estimated SHCFs and a low relative error (under 35%) for the optimized van Genuchten parameters α and n. The findings indicate that MDI-based cumulative infiltration measurements can reliably estimate SHCF via inverse simulation, providing a practical solution for field applications where traditional sensor deployment is challenging. Moreover, the results also establish MDI as a rapid, convenient, and non-invasive tool for determining SHCF for transient-state flow scenarios.

How to cite: Naik, A. P. and Pekkat, S.: Evaluating a rapid approach for estimating soil hydraulic conductivity function from near-surface infiltration measurements , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15178, https://doi.org/10.5194/egusphere-egu25-15178, 2025.

Sociohydrology, an interdisciplinary field exploring the dynamic interactions between human and water systems, has emerged as a critical area of study to address the growing complexity of water management challenges in the Anthropocene. Transdisciplinary practices in sociohydrology extend beyond traditional academic boundaries, integrating diverse knowledge systems, stakeholder perspectives, and real-world practices. These approaches bridge the gap between science and society, enabling the co-creation of solutions that are socially equitable, environmentally sustainable, and contextually relevant. This study explores the transformative potential of transdisciplinary approaches in sociohydrology, emphasizing collaborative governance, stakeholder engagement, and sustainable water management. Drawing on an extensive review of literature and following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses), the research highlights diverse applications of transdisciplinary methodologies in water management, ranging from integrating citizen science frameworks to fostering adaptive strategies for climate resilience. Case studies spanning the Katari River Basin in Bolivia to community-led monitoring in Australia's Great Barrier Reef illustrate how integrating ecological, social, and economic dimensions can address complex hydrological challenges. These practices underscore the importance of co-producing knowledge among researchers, policymakers, and communities, thus bridging gaps between scientific inquiry and real-world implementation. By synthesizing insights from multi-scalar analyses, the paper offers a framework for designing adaptive, equitable, and sustainable water management strategies. The findings advocate for institutional reforms and capacity-building initiatives to strengthen collaborative governance and propose a roadmap for applying transdisciplinary methodologies to global water crises. This research contributes to the evolving discourse on sociohydrology, emphasizing the need for integrated systems thinking and participatory processes to achieve long-term water security.

How to cite: Kabir, Md. H.: Advancing Collaborative Governance and Sustainable Water Management: Transdisciplinary Practices in Sociohydrology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15437, https://doi.org/10.5194/egusphere-egu25-15437, 2025.

Currently, climate change and increasing water demand pose a growing threat to the future availability of water for human societies and ecosystems that depend on it. At the same time, growing evidence suggests that groundwater is playing an increasingly active role in the global water cycle, particularly in sustaining river flows worldwide (Xie et al., 2024). In this context, quantifying the water exchange between these two components of the hydrological cycle becomes essential for an integrated assessment of water availability. For this purpose, baseflow separation methods are valuable tools, though their limitations remain a subject of debate.

Several authors have suggested that commonly used baseflow separation methods should be applied with caution, since these methods often produce large estimation errors, when they are compared with results obtained using three-dimensional flow numerical models (hereafter referred to as 3D models), thereby limiting their applicability. Nevertheless, these methods remain a widely used alternative due to their lower data and resource requirements compared to 3D models. To address these limitations, we proposed a novel methodology based on baseflow separation methods for analysing the interactions between a shallow alluvial aquifer system and the overlying river network. Subsequently, we tested its performance against a 3D model.

The study area is the alluvial aquifer system located at the confluence of the Tarn, Aveyron and Garonne rivers. A 3D model was developed using the BRGM’s MARTHE software. The study area was divided into sub-zones that meet the same isolation conditions for the river network delimited for the analysis of the results to ensure a more robust validation. Time series of flow and cumulative volume for components of the water balance in the river network, as well as flow at gauging points, were analysed. Additionally, different integration periods (quarterly, half-yearly, annual, and biannual) were examined. Several baseflow separation methods were tested, including both digital filtering and graphical methods.

The results showed that the methods proposed by Chapman (1991) and Chapman and Maxwell (1996) consistently outperformed all others across the entire study area and for all integration periods. R² coefficients of determination greater than 0.8 were obtained in both cases for integration periods exceeding six months. Notably, shorter integration periods better captured the temporal variation of water exchange between the aquifer and the river network. However, longer integration periods produced more accurate overall results, likely because the filters struggled to capture flow reversals between the aquifer and river network during flood events.

Acknowledgments: Authors acknowledge the funding provided by project WaMA-WaDiT (PCI2024-153483) funded by MICIU /AEI /10.13039/501100011033/ UE

References

Chapman, T. G. (1991). Evaluation of automated techniques for base flow and recession analyses. Water Resources Research, 27(7), 1783–1784. https://doi.org/10.1029/91WR01007

Chapman, T. G., & Maxwell, A. I. (1996). Baseflow separation: Comparison of numerical methods with tracer experiments. Paper presented at the Hydrology and Water Resources Symposium: Water and the Environment, Institution of Engineers, Australia.

Xie, J., Liu, X., Jasechko, S., et al. (2024). Majority of global river flow sustained by groundwater. Nature Geoscience, 17, 770–777. https://doi.org/10.1038/s41561-024-01483-5

How to cite: García Montealegre, J. P., Caballero, Y., and Del Jesus Peñil, M.: Are baseflow separation methods suitable for assessing shallow alluvial aquifers’ contribution to streamflow?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15495, https://doi.org/10.5194/egusphere-egu25-15495, 2025.

The Indo-Gangetic plain, well-known for its Alluvial landscape for human settlement, is currently facing unprecedented industrialization, and urbanization population, leading to high stress on its aquifer. On the other hand, co-contamination of arsenic (As) and chromium (Cr) in shallow aquifers has been showing an alarming global presence that varies with redox conditions, geochemical signatures, and human activities. We aim to address the influence of the suburban and urban land use on the co-contamination of As and Cr, using various geostatistical tools, models, and indices. Among twenty-six (n=26) groundwater samples, the majority of water types were found to be Mg²⁺-HCO₃^- and Na⁺-K⁺ exhibiting carbonate weathering and evaporation enrichments with saturation indices depicting the supersaturation of calcite and dolomite. The aquifer conditions in both suburban and urban settings were identified as reducing, facilitating the desorption of arsenic. Probability exceedance implied inverse correlation between contaminant concentrations and the probability of their likelihood of surpassing regulatory thresholds. Factor analysis indicates that the natural alignment of contaminants, particularly As and Cr, is maintained under suburban land use but significantly altered in urban settings. The influences of oxidation-reduction potential (ORP), dissolved oxygen (DO), pH, and iron (Fe) concentration on As and Cr co-contamination are effective in suburban environments, while urban aquifers face additional confounding factors, including artificial sources from industries and subsurface leaching. An integrated cluster heatmap has identified a trifecta of As, Cr, and lead (Pb), closely linked to pH, DO, and K⁺, highlighting the effects of increased anthropogenic activities in alluvial floodplains. Finally, a conceptual model was developed to clarify the common processes in these environments, facilitating the creation of universal management strategies for aquifers impacted by As and Cr co-contamination.

Keywords: arsenic; chromium; redox; mid-Gangetic plains; co-contamination

How to cite: Saxena, A., Kumar, M., and Bahukhandi, K. D.: Land use alters the alignment of Arsenic and Chromium co-contamination in the unconsolidated aquifer under reducing environments of the Mid-Gangetic Plains, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16583, https://doi.org/10.5194/egusphere-egu25-16583, 2025.

Groundwater assessment in the Deccan basalt region of India is challenging due to its heterogeneous geology and complex aquifer dynamics. This study integrates hydrogeophysical methods, including DC resistivity and time domain Induced Polarization (DCIP) and slug tests, to evaluate aquifer potential near Bhopal, Madhya Pradesh. The research focuses on both shallow unconfined and deeper semi-confined aquifers within weathered and fractured basalt formations.

Electrical resistivity surveys included more than 25 DCIP profiles targeting weathered and fractured zones. Resistivity values ranged from 15–70 Ωm in weathered/fractured basalts and varied based on the degree of water saturation and fracturing, reflecting lithological heterogeneity. ERT profiles revealed low-resistivity and moderate-to-high chargeability zones, indicative of fracture porosity and groundwater retention. Fracture anisotropy and resistivity contrasts provided critical insights into aquifer connectivity and dynamics.

Slug tests conducted at a borehole with a drilled depth of 61 m validated geophysical findings. Hydraulic parameters, including hydraulic conductivity (3.9E-7 m/s), transmissivity (1.9E-5 m²/s), storativity (0.001), and specific storage (2.1E-5 m^-1), were estimated using Bouwer-Rice and Cooper-Bredehoeft-Papadopulos solutions. These localized parameters complement the spatially extensive data from geophysical surveys. Seasonal water-level fluctuations emphasize the significance of monsoonal recharge in sustaining aquifers.

This integrated approach highlights the role of fractures, weathered zones, and advanced geophysical techniques in delineating groundwater zones and assessing recharge potential. The findings contribute to effective groundwater exploration and sustainable management strategies, addressing water scarcity challenges in basaltic terrains of the Deccan Traps.

Keywords: Aquifer potential, Bouwer-Rice and Cooper-Bredehoeft-Papadopulos solution, ERT, slug test, weathered/fractured basalts, hydrogeology.

How to cite: Khalique, A., Singh, A., and Gaurav, K.: Integrating Geophysical and Hydrogeological Methods for Groundwater Assessment in the Deccan Basalt Region of India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17885, https://doi.org/10.5194/egusphere-egu25-17885, 2025.

This abstract investigates the evolution of urban stormwater management, contrasting traditional methods with emerging approaches, emphasizing the integration of Low Impact Development (LID) strategies and Building Information Modeling (BIM). A comprehensive review of Scopus and Web of Science articles synthesizes existing research to identify trends, challenges, and opportunities in this interdisciplinary domain. Key insights include the effectiveness of LID practices such as permeable pavements, rain barrels, and the application of simulation tools like SWMM and HEC-RAS in reducing runoff and enhancing urban hydraulic modeling. The findings highlight the critical role of green infrastructure in mitigating rainfall impacts and the importance of cost-benefit analyses for evaluating LID implementation. Despite proven benefits, gaps persist in integrating LID into land-use planning, particularly in addressing future climate risks and accommodating urban growth. The study underscores the potential of 3D digital technologies to enhance stormwater management strategies, especially under extreme rainfall conditions. Additionally, the review identifies the lack of high-resolution data as a barrier to informed decision-making. It advocates for stronger collaboration between researchers and policymakers to foster sustainable urban development, improve water conservation, and minimize flooding risks. LID practices, integrated with Building Information Modeling offer a cost-effective solution to urban stormwater challenges, paving the way for resilient and sustainable cities.

How to cite: Fahimi, F. and Ostad Mirza Tehrani, M. J.: Enhancing Urban Stormwater Management: Traditional Measures versus Future Perspectives, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18797, https://doi.org/10.5194/egusphere-egu25-18797, 2025.

The resilience of a large-scale water infrastructure system to cascading effects is
fundamentally dependent on the interdependencies of its components within the
infrastructure network. These interdependencies—which means that the states of
two or more infrastructure components are tightly interrelated through mechanisms
such as physical connection, geographical proximity, and information relay—can
cause a localized event to spread into a system-wide event. Of these, logical
interdependencies remain poorly understood. Little is known about how two
infrastructures affect the state of each other through human decisions and how such
logical connections can be detected and measured. In this study, we tackled this
gap by conducting an applied case study on the Lake Mendocino Reservoir in
California, USA. Crucially, our approach focuses on reservoir institutions (rules)
that structure human decisions around reservoir systems. Reservoir management
relies heavily on operational rules and regulations, but climate change demands
more adaptive and discretionary decision-making by operators. This may further
introduce logical interdependencies in a reservoir system. We develop a novel
framework that integrates Institutional Analysis using Large Language Models to
advance Natural Language Processing (NLP) techniques and Bayesian Network
Modeling to systematically analyze and quantify risk associated with logical
interdependencies. We aim to improve decision-making and risk management in
reservoir operations. This research provides essential insights into enhancing the
resilience of water management infrastructures, particularly in the face of climate
change.

How to cite: Gautam, S., Yu, D. J., and Hoon Cheol, S.: Enhancing Resilience in Human-Reservoir Systems with NLP and AI Frameworks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20066, https://doi.org/10.5194/egusphere-egu25-20066, 2025.

Moisture content available in soil, is a critical parameter for understanding the health of ecosystems, agricultural productivity, and the management of water resources. Soil moisture is an essential component in the growth of vegetation, climate regulation, and the hydrological cycle. The correct estimation of soil moisture is very crucial for optimizing irrigation, enhancing crop yields, and managing water resources. Spatial coverage limits traditional in-situ measurements, while remote sensing-based approaches, especially using SAR imagery, provide scalability to large-scale spatial coverage for soil moisture estimation. This study compares five machine learning-based models- Long Short-Term Memory (LSTM), Random Forest (RF), Multiple Linear Regression (MLR), Multi-layer Perceptron (MLP), and Support Vector Machines (SVM)-for deriving estimates of soil moisture using features based on VV and VH polarizations and incidence angle from SAR imagery. Model performance was also evaluated using in-situ measurements from Vaira Ranch in California's Central Valley, which comprises grasslands and wetlands. Meteorological data, which include precipitation and antecedent rainfall from the ERA5, were used to improve prediction. Each model was hyperparameter tuned, with LSTM adjusting layers, units, and learning rate; RF optimizing tree number, depth, and feature selection; MLR modifying regularization strength; MLP refining layers, neurons, and activation function; and SVM fine-tuning kernel type, C, and gamma. Performance metrics used for evaluation included R² and Root Mean Square Error (RMSE). The results indicated that LSTM outperformed other models with a R² of 0.89, followed by SVM at a value of 0.81 and RF at a value of 0.78. MLP and MLR values were lower at 0.67. This research focuses on the advantages of the integration of remote sensing data and meteorological information for better soil moisture estimation using machine learning and show that the advanced models such as LSTM and RF can effectively predict soil moisture, with important implications for improving agricultural management and resource planning.

How to cite: Sharma, V., Barbhuiya, S., and Gupta, V.: Enhancing Soil Moisture Estimation through Machine Learning Models and Remote Sensing Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20070, https://doi.org/10.5194/egusphere-egu25-20070, 2025.

New global challenges, such as the intense desire for development and climate change, can exacerbate water conflicts among stakeholders (at local, regional, national, and international levels). Material interests are often regarded as effective tools for resolving conflicts and fostering a spirit of cooperation among riparian countries in transboundary river basins. However, discourses and narratives produced by the media, alongside material factors, play a decisive role in shaping water interactions, a topic that has received less attention. The primary objective of this study is to examine the impact of media on transboundary water interactions. To achieve this goal, a systematic review of existing research across various databases was conducted, alongside an analysis of library resources on the influence of media on water interactions. The findings indicate that conflict is an inherent and natural feature of water systems, particularly in shared river basins. However, most media articles and reports tend to intensify water conflicts in shared basins, with limited coverage dedicated to pathways for cooperation that could bring riparian stakeholders together. The use of media to advance the interests of states within a basin often strengthens the potential for water conflicts. Therefore, constructive changes in water diplomacy and conflict transformation require an understanding of the media's role in water cooperation and disputes. This understanding is essential for shaping the water policies of riparian countries.

How to cite: farzaneh, F., Mianabadi, H., Andik, B., and Ghadimi, S.: The role of media in shaping the hydropolitical interactions in the transboundary basins, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20284, https://doi.org/10.5194/egusphere-egu25-20284, 2025.

Contaminant co-occurrence in water resources poses significant threats to public health and ecosystem stability, necessitating comprehensive monitoring and analysis. This study investigates the spatiotemporal distribution of arsenic, fluoride, and perfluorooctane sulfonate (PFOS) contamination in groundwater and surface water across Yorkshire from 2000 to 2023. Data for this assessment were obtained from the Environment Agency, ensuring reliable and standardised measurements across the study period. The results reveal a concerning trend of increasing arsenic and fluoride concentrations, particularly in the eastern and southern regions, with arsenic levels exceeding 10 µg/L and fluoride concentrations surpassing 1.5 mg/L in several areas by 2023. The PFOS contamination, assessed in both groundwater and surface water for 2023, highlights significant contamination in the southern regions, with concentrations exceeding 0.001 µg/L in some hotspots. The co-contamination maps indicate overlapping regions of high contaminant concentrations, suggesting potential sources of industrial pollution and agricultural runoff. This study emphasises the need for targeted mitigation strategies and continuous monitoring to protect public health and ensure water quality standards across the region.

How to cite: Agarwal, V., Kumar, M., and Saxena, A.: Spatiotemporal Assessment of Arsenic, Fluoride, and PFOS Co-Contamination in Yorkshire's Water Resources, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20494, https://doi.org/10.5194/egusphere-egu25-20494, 2025.

The prediction of soil moisture movement remains challenging due to the complexity of underground flow processes and the availability of accurate soil parameters. There have been attempts to overcome this issue with parametric models and inverse modeling, but it remains challenging because it requires knowledge of initial and boundary conditions. While deep learning offers a solution, the one significant constraint remains not to violate the physical constraints. I present a novel physics-informed neural network (PINN) framework that integrates the soil moisture movement governing equation constraints with deep learning to predict soil moisture dynamics. The new approach follows mass conservation principles and soil hydraulic properties into the neural network's loss function. The model ensures physically consistent predictions. The framework simultaneously learns soil hydraulic parameters and water content distributions, adapting to heterogeneous soil conditions through a hybrid optimization strategy. The model incorporates the Van Genuchten parameterization within the physics-informed architecture to ensure consistency and accuracy. This methodology bridges the gap between computationally intensive traditional numerical solutions and pure data-driven approaches, offering a new paradigm for modeling soil water dynamics.

How to cite: Pathak, V. S.: Physics-Informed Deep Learning for Soil Water Dynamics, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20587, https://doi.org/10.5194/egusphere-egu25-20587, 2025.

The adoption of community-based water purification technology in rural communities is strongly influenced by psychological factors, yet these factors often suffer from endogeneity, leading to biased estimations of their true impact. Our study investigates this critical issue, revealing that traditional estimation methods significantly underestimate the effects of key psychological determinants. Specifically, we found that perceived ease of access and descriptive norms, when treated as exogenous, were underestimated by 175% and 76%, respectively. This oversight highlights the importance of addressing endogeneity to accurately capture the relationship between psychological factors and adoption behavior. The endogenous nature of perceived benefits and descriptive norms highlights a crucial bidirectional relationship: as adoption increases, so do positive social norms and perceived benefits, creating a reinforcing cycle that further drives adoption within the community. Interventions that fail to consider this mutual reinforcement risk undervaluing key psychological factors, potentially undermining their effectiveness. We propose that cultural factors serve as instrumental variables (IVs) to mitigate endogeneity and offer a clearer pathway through which psychological factors influence behavior. For instance, cultural traits such as "work-luck" dynamics shape individuals' proactive or passive approaches to overcoming barriers to technology access. Similarly, generalized morality, which prioritizes communal welfare over individual gain, strengthens descriptive norms that promote widespread adoption. In collectivist societies, these norms hold significant influence, compelling individuals to adopt technologies to maintain social cohesion and uphold communal values.

Our study introduces a robust theoretical framework that integrates cultural factors into the analysis of technology adoption. By leveraging cultural traits, interventions can align more closely with community values, enhancing the likelihood of sustainable adoption. This approach not only provides deeper insights into the dynamics of technology adoption but also offers practical strategies for designing culturally sensitive interventions.

In conclusion, addressing the endogeneity of psychological factors through the lens of cultural influences provides a more accurate and comprehensive understanding of the adoption process. This study advocates for the incorporation of cultural contexts in intervention strategies, ensuring they resonate with the community’s intrinsic values and beliefs. Future research could expand on this dynamic by employing system dynamic models to further explore the bidirectional feedback between psychological factors and behavior, ultimately contributing to more effective and sustainable adoption of community-based water purification technologies.

How to cite: Raj, M., Pande, S., and Ramesh, M.: Exploring Endogeneity in Psychological Determinants of Community-Based Water Purification Technology Adoption, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20733, https://doi.org/10.5194/egusphere-egu25-20733, 2025.

The Coastal Unconfined Shallow Sandy Aquifer of the Keta Basin, made up of Quaternary gravel, sand and clay and Neogenic Limonic deposits is the most economically accessible aquifer in Southern Volta, Ghana, West Africa. Water from the aquifer supports domestic supply and vegetable farming, which is the main stay of the area’s economy (Nerquaye-Tetteh 1993; Helstrup et al. 2007; Yidana et al. 2007). Despite the importance of this shallow aquifer, it is vulnerable to contamination from saline intrusion and agricultural activity in the area (Gill 1969; Nerquaye-Tetteh 1993; Kortatsi 1994; Kortatsi et al. 2005; Helstrup 2006). Protecting and managing this aquifer effectively will require the appreciation of the flow regime and dynamics via the collection of hydrogeological information such as geochemical properties and their variation over the years, hydraulic gradient, flow direction, well density, and their abstraction rates. However, this data is non-existent. This work set out to collect these basic hydrogeological information. This work was done via: geochemical sampling and analyses of thirty-five (35) wells for facies discrimination; the hydraulic head mapping of forty-five (45) wells for flow direction mapping and hydraulic gradient distribution, and particle size distribution testing of sampled aquifer material for the hydraulic conductivity distribution. The geochemical datasets showed: neutral and well buffered water groundwater; nitrates occurring in all the samples, with [NO₃^-] ranging between 0.35 – 25.3 mg/L, indicative of possible human influence on groundwater in the area; four (4)  main water types from the analyses as: Na-Cl, Ca-(HCO₃)₂, Na-HCO₃,  and Ca-Cl₂ with percentage dominances of 47, 41, 9 and 3% respectively. Na-Cl and Na-HCO₃ waters characterised by very high SECs and  found in farm wells located near the coast and lagoons suggest saline intrusion (due to heavy pumping on farms) from the sea and lagoon. The central part of the area, has fresh water which with the Ca-(HCO₃)₂ water type, indicative of natural rock weathering processes and flow dynamics. Analysing the irrigation water use parameters from the geochemical showed that the water in the area was suitable with respect to residual sodium carbonate (RSC) and magnesium absorption ratio (MAR), whereas waters mostly affected by saline intrusion did not meet the  sodium percentage (Na%), sodium absorption ratio (SAR) and Kelly’s ratio (KR). Heavy  groundwater abstraction without regulation is fingered for causing saline intrusion in the area because of reduction of groundwater levels. The hydraulic gradient in the area mimics that of the natural ground level, with relatively gentle slope of 0.002, with the dominant groundwater flow direction of north to south. This work is novel as it sets the tone for the first-ever initial hydrogeological characterisation of the aquifer, whose state can be continuously monitored for advising the Government, and the Water, Agricultural and Health Directorates of the Municipal Assembly for the regulation of agriculture and abstraction in the area, so as to protect the aquifer and human health.

How to cite: Agbotui, P. Y., Amissah, M. B., Ewusi, A., and Woode, A.: A step towards the protection and management of the Shallow Aquifer of the Keta Basin, in Ghana West Africa: an initial physico-chemical characterisation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21834, https://doi.org/10.5194/egusphere-egu25-21834, 2025.

We investigate the potential of using Long Short-Term Memory (LSTM) neural networks for estimating streamflow in (sub)tropical catchments under contrasting hydroclimatic regimes (semi-arid and humid). We have used 176 Brazilian catchments with at least 30 years of streamflow data and LSTM models with 16 static catchment attributes as input features. We tested different LSTM model configurations to assess their sensitivity to varying input sequence lengths (lookbacks). The primary objective was to explore the hydrological insights offered by LSTM-based streamflow models and compare their performance with the traditional GR4J hydrological model. With this design, we aim to address two research questions: (i) Does the performance of LSTM models depend on catchments' hydroclimatic characteristics? (ii) How effective are LSTM-based models for streamflow simulation in tropical and subtropical catchments under semi-arid and humid conditions? We adopt two modeling approaches: (1) regionalized models trained on catchments within the same hydroclimatic regime and (2) a composite model trained on a heterogeneous sample combining both arid and humid catchments. The findings reveal distinct sensitivities of LSTM models to hydroclimatic conditions. LSTM models exhibit higher sensitivity to the length of input sequences (lookbacks) in humid catchments, with longer sequences yielding better performance. This is attributed to the dominant hydrological processes in humid regions, which are influenced by long-term memory effects such as soil moisture and groundwater storage. Conversely, this sensitivity is not observed in semi-arid catchments, where streamflow dynamics are primarily driven by short-term precipitation events and exhibit less dependence on long-term hydrological processes. Furthermore, the composite model, which combines semi-arid and humid catchments, demonstrates a decrease in performance for semi-arid catchments. This suggests that adding catchments with contrasting hydroclimatic characteristics introduces heterogeneity in the dataset, potentially reducing the model's ability to capture the specific dynamics of semi-arid catchments. Overall, the regionalized LSTM models outperformed the GR4J model in both semi-arid and humid regimes, particularly in humid catchments. Approximately 87% of humid catchments and 50% of semi-arid catchments achieved Kling-Gupta Efficiency (KGE) values above 0.60 during the testing phase of the regionalized LSTM models. These results highlight the potential of LSTM networks for streamflow regionalization, especially in humid regions where long-term hydrological memory plays a critical role. The study underscores the strengths and limitations of LSTM models in tropical and subtropical catchments with contrasting hydroclimatic regimes. The findings suggest that LSTM models could serve as valuable tools for regional hydrological applications, aiding local and regional decision-making processes. Additionally, the results emphasize the importance of tailoring LSTM model configurations to the specific hydrological characteristics of catchments, particularly the choice of input sequence length, to maximize model performance. 

How to cite: Maria de Andrade, J., Nóbrega, R., Ribeiro Neto, A., Rico-Ramirez, M., Coxon, G., and Montenegro, S.: Streamflow simulations using regionalized Long Short-Term Memory (LSTM) neural network models in contrasting climatic conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-580, https://doi.org/10.5194/egusphere-egu25-580, 2025.

Spatio-temporal variability of the terrestrial hydrological processes (land heat and water storage anomalies) has important implications in the climate predictability through their effects on surface energy and water fluxes. The changes in seasonal precipitation patterns associated with the Indian Summer Monsoon can alter the hydrological processes; for a given catchment, which in turn can influence the exchange of water and energy at the land surface-atmosphere interface. Hence the reliable prediction of the basin-scale water cycle components in a physically based high-resolution hydrological model equipped with sophisticated Land Surface Models (LSMs) is of prime requirement. The modern LSMs can provide detailed representations of important biophysical, biogeochemical and hydrological processes of varying spatial and temporal scales by incorporating the necessary feedbacks between the land and the atmosphere. When coupled to a physically based fully distributed hydrological model, it can affect the soil moisture patterns means of recycling the surface and sub-surface runoff (lateral terrestrial flow). However, despite the role of lateral terrestrial hydrological processes for the improved simulation of soil moistures, the sensitivity studies involving the land surface and sub-surface feedbacks are less pronounced especially for a tropical humid region with complex physiographic settings (presence of complex topography) under monsoon regimes (strong synoptic forcings). Therefore, in the present study, we examined a process based diagnosis regarding the role of the lateral flow on the terrestrial hydrological processes (Evapotranspiration, surface and sub-surface runoff, stream flow) and surface energy fluxes (latent heat, sensible heat) by using a multi-configured modeling framework of offline WRF-Hydro with Noah-Multi parameterizations (MP) LSM to enable systematic evaluation of the multiple physical parameterizations of hydrologic process representation; the validation has been done with the reanalysis dataset, a remotely sensed product and ground based observations.

How to cite: Sarkar, S. and Lakshmikanthan, P.: Modeling the impact of lateral flow on terrestrial water balance components and surface energy fluxes using WRF-Hydro with multi-configuration ensembles: a study over Krishna River Catchment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1783, https://doi.org/10.5194/egusphere-egu25-1783, 2025.

Flash floods are one of the most devastating natural disasters, posing significant risks to both human life and infrastructure. The classification of their underlying drivers—such as high precipitation, dam breaches, landslides, and melting snow—remains a critical yet challenging task, especially in regions like China, where diverse geographical and climatic factors exacerbate disaster complexity. In this study, we propose a Transformer-based Graph Network (TGN) designed to tackle these challenges by leveraging a dataset of over 53,000 flash flood events, each characterized by non-uniform geographical attributes and varying levels of data completeness. Unlike traditional graph neural networks (GNNs) that depend on predefined graph structures, TGN dynamically learns and refines edge weights during training, enabling it to uncover asymmetric dependencies. This adaptability is particularly valuable when explicit relationships between nodes are unavailable or incomplete.

Integrating multi-head self-attention mechanisms from Transformer architectures, TGN captures complex interdependencies across watershed features while maintaining interpretability through sparsity and diversity constraints. A distinguishing feature of this framework is its ability to identify meaningful graph structures without prior knowledge, offering insights into critical connections and interactions within disaster-prone regions. For instance, our experiments demonstrate how TGN emphasizes high-risk upstream-downstream relationships, providing actionable knowledge for localized flood management. The model significantly outperforms traditional GNNs and machine learning methods in accuracy and robustness, achieving superior classification performance across all four disaster categories. Furthermore, the TGN framework is supported by rigorous evaluation metrics, including Precision, Recall, F1-score, and Overall Accuracy, ensuring its reliability in real-world applications.

By combining innovative graph-based modeling with interpretable mechanisms, this study bridges the gap between theoretical advancements and practical disaster management. The proposed approach not only enhances prediction capabilities but also provides an analytical lens for understanding the intricate relationships among flash flood drivers, paving the way for more effective mitigation strategies and informed decision-making. This work underscores the transformative potential of adaptive graph neural networks in addressing complex environmental challenges and advancing the state of flood risk assessment.

How to cite: Wang, H., Xuan, Y., Zhang, Z., Antunes Meira, M., Li, Q., and Liu, C.: A Transformer-based Graph Network for Flash Flood Disaster Classification, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2646, https://doi.org/10.5194/egusphere-egu25-2646, 2025.

This study proposes a novel approach for analyzing block minima data within the Generalized Extreme Value Distribution (GEVD) framework by incorporating the Negative Power Transformation (NPT). The NPT method, which includes a hyper-parameter to adjust data bounds (effectively reducing to the Reciprocal Transformation (RT) when the hyper-parameter is 1), aims to improve the accuracy and robustness of long-term return level (RL) estimations. Traditional transformation methods often exhibit limitations in accurately predicting RLs for extended return periods. Through extensive Monte Carlo simulations, we demonstrate that the NPT-GEVD method outperforms conventional approaches in terms of bias, standard error (SE), and root mean square error (RMSE) for return periods of 25, 50, and 100 years. Notably, the NPT-GEVD consistently provides reliable RL estimates across various parameterizations and sample sizes, particularly when using L-moments for estimation with smaller datasets. The efficacy of the NPT-GEVD method is further validated through its application to inter-arrival time (IAT) rainfall data from South Korea. The analysis revealed that RLs for detecting the time to exceed hourly cumulative rainfall thresholds of 60 mm, 90 mm, and 110 mm varied significantly across locations, ranging from 30 minutes to over 4 hours. This research underscores the significance of advanced transformation techniques in enhancing the accuracy and reliability of environmental risk assessments. The NPT-GEVD method offers valuable insights for improving flood prediction and mitigation strategies in the face of climate change.

How to cite: Yoon, S. and Prahadchai, T.:  Transformed Technique for Applying the Generalized Extreme Value Distribution to Block Minima, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2660, https://doi.org/10.5194/egusphere-egu25-2660, 2025.

Climate change and urbanization intensify urban pluvial flooding, posing significant threats to human lives and infrastructure. This situation underscores the critical need for efficient and accurate predictive systems for disaster prevention and mitigation. Traditional flood simulation models, while precise, are often limited by their data-intensive requirements and substantial computational complexity. In contrast, deep learning (DL) models show their advantages by high efficiency and powerful capability in processing large-scale non-linear data, making them highly appropriate for modeling complex flood dynamics. Consequently, integrating DL with conventional urban flood models has emerged as a promising strategy to enhance the accuracy and efficiency of flood prediction systems. However, existing research predominantly focuses on inland flooding, with limited attention to the role of tidal levels in coastal cities, which can significantly impact the accuracy of urban flood simulations.
To bridge the GAP, this study proposes an innovative hybrid DL approach that explores spatial and temporal data to improve the accuracy and efficiency of urban flood simulations, particularly in coastal areas. Simulation results from physics-based urban flood models are utilized to construct a comprehensive database for the DL model. Afterwards, patch-size and random sampling methods are employed to construct the sample dataset for training DL models. The convolutional neural network (CNN)-based data-driven urban pluvial flood model can simulate floods using topographic, rainfall, and tidal data, enabling the simulation of large urban areas within seconds. Incorporating diverse input data and advanced network architectures enhances model robustness and generalization across various scales and rainfall events. Fusion models that combine the strengths of DL and traditional hydrological models demonstrate improved prediction accuracy and computational efficiency by integrating tidal data and other environmental factors. Consequently, these hybrid models hold significant potential for integration into early warning systems and supporting decision-making processes in urban flood risk management.

How to cite: Zeng, B., Huang, G., and Yang, G.:  A Diversity Driven Deep Convolutional Network for Enhanced Coastal Urban Flood Risk Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2662, https://doi.org/10.5194/egusphere-egu25-2662, 2025.

South Korea experiences significant regional variation in precipitation due to its unique topographical features. Over the years, the intensification of summer rainfall concentration has led to recurring damage from floods and torrential downpours. To mitigate such impacts, the Korea Meteorological Administration monitors precipitation using observed data from weather stations and estimated values for non-observed locations. The Generalized Extreme Value (GEV) distribution is commonly employed to model annual maximum precipitation, enabling the calculation of return levels that serve as foundational data for flood prevention. This study aims to estimate the spatially generalized additive GEV distribution of daily maximum precipitation using data from 54 Automatic Synoptic Observation System (ASOS) stations between 1972 and 2024. Spatial elements (latitude, longitude, altitude) and temporal elements (year) were incorporated into the model. The location, scale, and shape parameters of the GEV distribution were estimated using the maximum likelihood method, with smoothing functions accounting for spatial and temporal factors. The results indicate that the location and scale parameters, influenced by latitude and longitude, are relatively lower in central regions, while the shape parameter, influenced by altitude, shows similar trends. Furthermore, return levels for 50-year and 100-year return periods are notably higher in mountainous regions. Goodness-of-fit tests, such as the Anderson-Darling test, were performed on the GEV distributions of 53 ASOS stations, excluding one. However, 12 stations located in island regions, high-altitude areas, or regions affected by typhoons exhibited distributions that were difficult to explain spatially. These findings are expected to aid in the development of efficient water resource management strategies and regional flood prevention measures based on the distribution characteristics of precipitation.

How to cite: Lee, B., Hwang, Y., and Yoon, S.: Spatial and Temporal Extreme Modeling of Daily Maximum Precipitation Based on a Generalized Additive Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2663, https://doi.org/10.5194/egusphere-egu25-2663, 2025.

Asset-based Dynamic Flood Risk Assessment: Case Study of London Downtown

Flooding poses significant risks to urban centres, with particular challenges faced by business hubs where disruptions can have devastating consequences on national and global economies [1]. Business hubs are the lifeblood of national and global economies. During flood events, businesspeople encounter disruptions that not only obstruct daily operations but also ripple through supply chains and financial systems [2-3]. This study emphasises the importance of protecting critical assets in Downtown London, a vital business hub, to mitigate economic and social impacts during floods. Through a watershed-based approach, Downtown London, a vibrant business hub with numerous critical assets, has been selected as the case study area. The district contains key commercial buildings and infrastructure that are vital to economic and social continuity. Using Digimap and Verisk, essential commercial buildings and critical assets are pinpointed based on their usage and significance. These tools facilitate generating an accurate map of assets requiring priority attention during flood events.

The proposed decision support system (DSS) is developed to aid risk management authorities, including policy-makers, decision-makers, and technical staff. The system operates on two key bases. Real-time population density data for critical assets is obtained using Google API. This data helps evaluate the human vulnerability component during flood scenarios. A flood forecasting system is integrated to predict water levels at 15-minute intervals for the coming hours. This system provides granular and actionable insights into evolving flood conditions. For each critical asset, two risk values are computed: one based on population density and another on forecasted water levels. These values are combined to derive a dynamic risk level for each time step, enabling authorities to respond effectively. The integration of real-time data and predictive modeling in the DSS offers a comprehensive framework for flood risk assessment. By prioritising critical assets based on dynamic risk levels, authorities can implement targeted preparedness and response measures such as early warnings and evacuation plans. This approach ensures both human safety and economic resilience. The findings have demonstrated the feasibility of applying real-time data and cutting-edge modeling to enhance urban flood resilience. By combining flood risk maps, real-time population density, and a comprehensive prioritisation framework, this approach provides a promising tool for urban planners and emergency responders to protect critical business assets and ensure economic continuity during flood events.

References

[1] Bakhtiari, V., Piadeh, F., Behzadian, K. and Kapelan, Z. (2023). A critical review for the application of cutting-edge digital visualisation technologies for effective urban flood risk management. Sustainable Cities and Society, p.104958.

[2] Bakhtiari, V., Piadeh, F., Chen, A.S. and Behzadian, K. (2024). Stakeholder analysis in the application of cutting-edge digital visualisation technologies for urban flood risk management: A critical review. Expert Systems with Applications, 236, p.121426.

[3] Piadeh, F., Behzadian, K. and Alani, A.M. (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, p.127476.

How to cite: Bakhtiari, V. and Piadeh, F.: Asset-based Dynamic Flood Risk Assessment: Case Study of London Downtown, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3504, https://doi.org/10.5194/egusphere-egu25-3504, 2025.

Social media applications have emerged as reliable communication channels, especially when traditional methods falter [1]. Their integration into emergency management presents significant advantages, including enhanced situational awareness during unfolding events, rapid dissemination of news and alerts to broader audiences, and improved coordination among decision-makers and stakeholders [2]. Both remote sensing and social media data offer distinct advantages in large-scale flood monitoring and near-real-time flood monitoring [3]. To better understand these advantages and challenges, a comprehensive review and analysis of the literature on the application of social media in this field was conducted.  Social media facilitates participatory and collaborative structures, enabling collective knowledge-building in public information and warning systems. To realise this vision, the authors examined, 73 studies conducted from 2014 to 2024 to systematically evaluate the current literature surrounding communication on social media and the latest research in social media informatics related to disasters. This review identified key challenges within existing studies. The articles included 23 related to pluvial floods, 12 related to fluvial floods, 17 related to storm floods and 21 paper that were unspecified The majority of the studies were conducted in China, followed by the United States. Various software platforms, including Twitter, YouTube, and other social media networks, were analysed. Data extraction from these platforms was performed using Python programming. The study periods ranged from 1 to 3,650 days. These findings serve as guidance for researchers examining the relationship between social media and disaster management. They aim to develop the use of social networks during disasters, analyse patterns, and create programming to identify best practices for utilising social media in times of crisis. In the future, a mapping framework and tool can be developed to automatically extract information from social media through text and image analysis. By integrating this data with other available information sources, it will be possible to generate more accurate inundation maps in real-time. It is essential to recognise that information about floods obtained from social media may be incomplete during communication interruptions. To address this issue, future research should prioritise integrating big data from urban Internet of Things networks and improving communication infrastructure repairs. By adopting this strategy, we can collect more comprehensive disaster information to enhance flood emergency response effectiveness.

References

[1] Piadeh, F., Behzadian, K. and Alani, A.M. (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, p.127476.

[2] Piadeh, F., Ahmadi, M., Behzadian, K. (2020). A Novel Planning Policy Framework for the Recognition of Responsible Stakeholders in the of Industrial Wastewater Reuse Projects. Journal of Water Policy, 24 (9), pp. 1541–1558.

[3] Bakhtiari, V., Piadeh, F., Chen, A., Behzadian, K. (2024). Stakeholder Analysis in the Application of Cutting-Edge Digital Visualisation Technologies for Urban Flood Risk Management: A Critical Review. Expert Systems with Applications, p.121426.

How to cite: Panahi, A., Karkhaneh, N., and Piadeh, F.: Community-based flood early warning system: Current practice and Future directions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3526, https://doi.org/10.5194/egusphere-egu25-3526, 2025.

Flooding has posed a significant challenge to urban infrastructure, necessitating effective and real-time risk management strategies [1]. One of the most devastating impacts is on urban transportation, where disruption can lead to significant economic losses or even human casualties [2-3]. This study has focused on the key financial and commercial areas in downtown London, where an innovative system has been developed to integrate real-time flood risk forecasting with traffic data visualisation and dynamic decision support for emergency response and resource allocation. First, with access to the Google Maps API, real-time and forecast traffic data have been collected for local streets. Then, these datasets can facilitate a 15-minute resolution forecast for the next 8 hours, enabling an in-depth understanding of traffic flow patterns during flood events. Furthermore, by employing flood forecasting measures on these real-time datasets, streets at risk of inundation can be identified faster, with their traffic conditions assessed accordingly.

A key aspect of this study is to consider different factors dynamically for weighting and prioritising streets. On one hand, pre-existing factors such as road hierarchy, connectivity, access to critical facilities, land use, infrastructure vulnerability, and proximity to evacuation zones are converted into dynamic factors by attaching a temporal variable to these pre-existing factors. On the other hand, real-time dynamic ones include flood depth, traffic congestion, accessibility for emergency services, and community needs reported. The integration of all these factors leads to the development of a transportation-based decision support system (TBDSS) tailored to urban flood management. The TBDSS has facilitated the allocation of emergency resources, prioritisation of street reopening, and planning for evacuation or relief operations. For instance, streets connecting to hospitals or shelters have been given higher priority, while those serving industrial or low-density areas have been weighted lower. As such, our proposed system can dynamically adjust priorities based on evolving flood and traffic conditions, ensuring optimal response strategies.

The findings have demonstrated the feasibility of leveraging real-time data and advanced modeling to enhance urban flood resilience. By combining flood risk maps, traffic forecasts, and a comprehensive prioritisation framework, this approach has provided a promising tool for urban planners and emergency responders.

[1] Piadeh, F., Behzadian, K., Alani, A. (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, p.127476.

[2] Gao, G., Ye, X., Li, S., Huang, X., Ning, H., Retchless, D., Li, Z. (2024). Exploring flood mitigation governance by estimating first-floor elevation via deep learning and google street view in coastal Texas. Environment and Planning B: Urban Analytics and City Science, 51(2), 296-313.

[3] Naghedi, S. N., Piadeh, F., Behzadian, K., and Hemmati, M.: Unveiling the Interplay: Flood Impacts on Transportation, Vulnerable Communities, and Early Warning Systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13189, https://doi.org/10.5194/egusphere-egu24-13189, 2024.

How to cite: Naghedi, R., Piadeh, F., Huang, X., and Wu, M.: Real-time Transportation-Based Flood Warning System: A Case Study in Downtown London, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3543, https://doi.org/10.5194/egusphere-egu25-3543, 2025.

Flash floods are one of the most severe natural hazards worldwide; they can occur within a few minutes or hours, and can move at high flow velocities, striking with violence and little warning. Early warning of flash floods is extremely important for vital risk mitigation and requires the knowledge of the critical rainfall producing flooding that is typically considered as “warning index”. The small spatial and temporal scales at which flash floods occur make the prediction of critical rainfall challenging, particularly in data-poor environments, where high-resolution weather models and advanced monitoring networks may not be available.

In this research, we present a methodology to estimate the critical rainfall for flash flooding based on an integrated hydrologic-hydrodynamic model. The model is applied in the Lilantas River catchment in Evia, Greece, considering a relatively large number of rainfall and soil moisture conditions scenario combinations in order to (1) determine inflow hydrographs used as boundary conditions for the hydrodynamic model and (2) calculate the distribution of “critical hazard” across the cells of the two-dimensional (2D) computational domain. In the present work, we define critical hazard combining the main hydrodynamic characteristics that are water depth and flow velocity, and we import all calculated “critical hazard” values into a GIS-based database.

Key findings include maximum peak discharges from all simulated scenarios, allowing a sensitivity analysis of varying Curve Number and soil moisture conditions, as well as the effects of rainfall duration and intensity combinations on flood responses. Furthermore, based on the calculated critical hazard, estimates of critical rainfall values for the selected study area are provided, along with an example of the flood warning system’s operation.

How to cite: Papoulakos, K., Mitsopoulos, G., Baltas, E., and Stamou, A. I.: Estimating critical rainfall for flash flood warning systems using integrated hydrologic-hydrodynamic modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3970, https://doi.org/10.5194/egusphere-egu25-3970, 2025.

Landslides are a widespread geohazard with significant impacts on lives and economies worldwide. While past research has primarily emphasized creating inventories, and analysing spatial and temporal patterns, the objective of this study is to explore the relationship between landslides events taken place in different catchments using only topographical and physical attributes from the disasters’ areas. The aim is to improve the understanding of the occurrence and susceptibility of such events, as well as the possible similarities between the events and the catchments. To this end, multicollinearity and mutual information analysis were performed to identify both linear and nonlinear relationships between the variables, assisting on the identification of the most relevant driving factors to historical landslides in the study area. Furthermore, the events were grouped using 5 different unsupervised clustering techniques, KMeans, Mean Shift, DBSCAN, Hierarchical and Spectral Custering, to analyse the relationship between landslides taken place in different catchments and their underlying driving forces. Clustering evaluation metrics, i.e. Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index, were used assess the performance of these algorithms. The results show that, for a preliminary study and providing insights on the relevance of driving factors and similarities between events, unsupervised learning proves to be an important tool. Nevertheless, to find more applicable and in-depth associations between extreme disasters and its driving factors, more robust machine learning techniques can and should be used.

How to cite: Antunes Meira, M., Xuan, Y., and Wang, H.: Using Unsupervised Learning to Explore Landslides Driving Factors from Topographic and Hydrological Catchment Features, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6708, https://doi.org/10.5194/egusphere-egu25-6708, 2025.

Drought is one of the most severe climate-induced phenomena; with significant impacts on agriculture, water resources, and ecosystems. Drought monitoring under climate change scenarios becomes crucial, particularly in regions vulnerable to water scarcity, such as semi-arid areas in Iran. Although Global Climate Models (GCMs) contain coarse spatial resolutions, they provide valuable insights in better assessing the variability of drought characteristics—such as duration, severity, and intensity in the future. To achieve this aim, downscaling of climate variables as triggers of droughts is required to monitor drought in local scale. Latyan region in Iran, as an important area to supply water, is a critical place based on its climate, drought event occurrences, and water demand and supply stress. This study tried to accurately downscale and bias-correct the climate variables utilizing the latest CMIP6 models (ACCESS-CM2, BCC-ESM1, CanESM5, HadGEM3-GC31-LL, and MIROC6) and AI techniques in the case study. This research employs a predictor selection technique in conjunction with a stack generalization model to improve the accuracy of the downscaling process. After careful examination of predictors, surface temperature, precipitation, and surface air pressure have been used along with annual cycles for training four machine learning models including Multilayer Perceptron (MLP), Support Vector Regression (SVR), Random Forest and Stack Generalization (SG) models for the sake of downscaling. Results showed that MIROC6 model is the best model according to all downscaling methods. In addition, among MLs, stacked generalization model improved the statistical metrics considerably with a Nash-Sutcliffe Efficiency (NSE) of 0.64, Mean Squared Error (MSE) of 1051.3, and Kling-Gupta Efficiency (KGE) of 0.68 for MIROC6 model. Selection of the proper GCM and downscaling method can help decision-makers take proper measures against drought to reduce drought impacts.

How to cite: Mirdarsoltany, A., Rahimi, L., Anderson, C., and Graf, T.: Fusion of Stacked Generalization and Predictor Selection Technique for Downscaling in Drought Monitoring: A Case Study in a Semi-Arid Area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7150, https://doi.org/10.5194/egusphere-egu25-7150, 2025.

Soil moisture dynamics play a crucial role in hydrological processes, influencing runoff generation, drought stress, and water management. To better understand the complex drivers of soil moisture dynamics, we present a novel hybrid architecture integrating Vision Transformers (ViT), spatial attention mechanisms, and Long Short-Term Memory (LSTM) networks. This architecture enables investigation of controlling factors across diverse landscapes in the Continental United States (CONUS) by incorporating spatial awareness at two levels: through ViT's ability to capture spatial patterns and through explicit spatial attention between neighboring stations. We leverage a comprehensive set of environmental data sources, including in-situ measurements from the International Soil Moisture Network (ISMN), ERA5 climate reanalysis, USGS elevation products, MODIS land cover, and SoilGrids soil characteristics. Initial results from a one-year training period and three-month testing period (R² = 0.73, 0.72, 0.73 for 24h, 48h, and 72h predictions) reveal important insights about the hierarchical importance of different drivers across prediction windows. Our preliminary analysis shows that static physical properties (particularly slope and soil structure) and hydraulic characteristics maintain high importance across temporal scales, while the influence of dynamic weather features varies with prediction horizon. The model's dual spatial attention mechanisms and temporal components enable discovery of both local and regional controls on soil moisture dynamics. The identified feature importance hierarchies provide initial insights into the spatiotemporal controls on soil moisture dynamics across CONUS. Ongoing work extends the training to the full temporal extent of available data to develop a more comprehensive understanding of these driving factors. This approach advances our fundamental understanding of soil moisture processes at continental scales, with implications for a future tool for land characterization and ecological site classification.

How to cite: Rahman, M., Meles, M., Bradford, S., and Nearing, G.: Drivers of Soil Moisture Dynamics over Continental United States, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7192, https://doi.org/10.5194/egusphere-egu25-7192, 2025.

Accurately predicting Snow Water Equivalent (SWE) has become increasingly crucial. It holds particular significance for managing water resources in regions heavily reliant on snowmelt. The present study introduces an integrated Long Short-Term Memory (LSTM) model that incorporates extreme heat events and diverse climate change projections to generate detailed SWE distribution maps and long-term trend analyses. By including lagged SWE observations and climate indicators, the model captures the intricate temporal dynamics of snowfall accumulation and melt processes, thereby improving forecast accuracy and stability.

Previous studies indicate that areas dependent on seasonal snowpack face accelerated snowmelt timing and reduced water availability under rising temperatures. These shifts can exert critical impacts on agricultural irrigation, ecosystem habitats, and water allocation strategies, highlighting the importance of robust forecasting tools for proactive resource management. Furthermore, the development of comprehensive risk maps pinpoints high-risk hotspots where anticipated temperature increases coincide with substantial changes in SWE and snowmelt patterns. These zones are prime candidates for early adaptation measures, including infrastructure upgrades and policy interventions aimed at mitigating potential water shortages.

As global warming persists, this modeling framework provides stakeholders, policymakers, and local communities with valuable insights into emerging water resource risks. The integration of climate change scenarios into the LSTM model underscores the necessity of forward-looking research that can inform both short-term operations and long-term planning. Ultimately, this approach lays the groundwork for crafting sustainable adaptation strategies, preserving agricultural output, protecting ecosystems, and ensuring water security in regions where snowmelt is pivotal to resource availability.

How to cite: Tsang, P. J. and Tsai, W. P.: Risk Mapping and Adaptation Strategies: Enhancing SWE Predictions with an LSTM Model for Snowmelt-Dependent Regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7201, https://doi.org/10.5194/egusphere-egu25-7201, 2025.

South America holds vast freshwater reserves, contributing to its global prominence across various sectors. Understanding streamflows at different levels—minimum flows for ecosystem maintenance, mean flows for hydropower and navigation, and high flows associated with floods—is critical for ensuring societal and ecological resilience. These streamflows are influenced by changes in catchment characteristics and climate change, yet the relationship between climate and catchment drivers with streamflows, particularly in tropical regions, remains poorly understood. Recent advances in explainable artificial intelligence (XAI) offer promising avenues for addressing these gaps by linking observational data to potential causal inference. Here, we investigated the climatic and catchment drivers influencing five streamflow types (Q₁, Q₅, Q_mean, Q₉₅ and Q₉₉) across 735 Brazilian watersheds using XAI approaches. Random Forest models were trained with 16 most important attributes for each streamflow type. SHapley Additive exPlanations were applied to explain the directionality and magnitude of each driver's impact, while inflection points were delineated to capture critical thresholds for streamflow changes. Results showed the aridity index (potential evapotranspiration/precipitation) as the most impactful predictor globally, likely due to its role in long-term water balance. However, for Q₉₉, soil sand content emerges as the dominant factor, showing that catchment characteristics rival climatic factors in importance for rare streamflow events. The analysis highlighted critical thresholds, such as reductions in streamflow when the aridity index exceeds 1.30 and potential declines in streamflow for soil carbon content below 30%, likely due to reduced water infiltration and storage capacity. Similarly, forest cover below 40% potentially increases streamflows, possibly due to reduced evapotranspiration and water retention in soils. Regional differences were also observed: in central Brazil, land cover and land use, and topography potential response for decreased the low streamflows, while in the south and northeast, climatic factors such as aridity and precipitation seasonality control the potential decreases. Rare high events (Q₉₉) in the south this watershed scale attributes height above the nearest, permeability and porosity potential increases the magnitude of events. These findings highlight that, while climatic attributes dominate streamflow relationships at a national scale, regional variations underscore the importance of catchment characteristics. This study demonstrates how data-driven models have the potential to capture the complex interplay between climatic and catchment attributes, linking these factors to streamflow dynamics.

How to cite: Brandão, A., Husic, A., Almagro, A., Schwamback, D., and Oliveira, P.: Climate and catchment influences on streamflows in Brazilian watersheds, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10531, https://doi.org/10.5194/egusphere-egu25-10531, 2025.

The accurate estimation of crop evapotranspiration (ETc), root zone soil moisture depletion, and irrigation demands is critical for optimizing water resource management and enhancing sustainability in precision agriculture. The FAO-56 model serves as a foundational tool for these predictions; however, its conventional workflow necessitates the manual acquisition of essential inputs such as climatic data and soil moisture from disparate external sources. This process can be time-intensive, cost-prohibitive, and susceptible to human error. Furthermore, the deterministic nature of FAO56 can lead to inaccuracies if reference evapotranspiration and crop coefficients are not meticulously estimated. This study introduces NeuralFAO56, a Python package that integrates advanced machine learning models and real-time data acquisition with the FAO-56 framework to automate and improve the estimation of ETc and irrigation demands. By leveraging application programming interfaces (APIs) to automatically collect real-time climatic data from meteorological stations and NASA’s Soil Moisture Active Passive (SMAP), NeuralFAO56 dynamically updates model inputs. The package incorporates a range of machine learning models, including Long Short-Term Memory (LSTM) and transformer architectures, to generate data-driven ETc estimations, thereby enhancing the accuracy and adaptability of irrigation predictions. NeuralFAO56 is designed with a modular architecture, enabling users to customize its functionalities for diverse agro-hydrological contexts. This tool provides a robust, user-friendly platform for researchers, water resource managers, and agricultural professionals, facilitating intelligent irrigation decision-making, improving water-use efficiency, and contributing to sustainable agricultural practices.

How to cite: Neupane, A. and Samadi, V.: A NeuralFAO56 Python Package for data-driven Irrigation Demand Calculation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13200, https://doi.org/10.5194/egusphere-egu25-13200, 2025.

The São Francisco River Basin is crucial for Brazil’s agriculture, hydropower, and water security. However, climate change has intensified challenges like reduced water flow and frequent extreme events, threatening its socio-economic sustainability. This study aims to forecast flow in the São Francisco River Basin, enabling proactive decision-making to mitigate risks associated with both droughts and floods. To address these challenges, this study propose a novel methodology based on Artificial Intelligence (AI), combining Recurrent Neural Networks (RNN) and complex network techniques. The method creates new features and assigns importance weights to enhance the algorithm’s ability to generate probabilistic flow forecast. The results are promising, demonstrating the method’s ability to deliver accurate probabilistic forecasts. This research can support risk mitigation strategies and improve water resource management in the São Francisco Basin. Additionally, the proposed framework is scalable, offering potential applications to other critical watersheds facing similar challenges

How to cite: Caseri, A., Aparecido Rodrigues, F., and Victal Cerqueira, M.: Ensemble Approach for Hydrological Forecasting Based on Recurrent Neural Networks and Complex Networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13991, https://doi.org/10.5194/egusphere-egu25-13991, 2025.

Flood forecasting is essential for hydrological assessment and catastrophe mitigation, particularly in flood-prone areas such as Bangladesh. Nonetheless, the direct measurement of water levels (WL) and discharge frequently encounters obstacles related to time, technological limits, and economical constraints. This study posits that flood levels can be accurately predicted utilising accessible data during flood events, employing a trained Artificial Neural Network (ANN) model. The complexity of hydrological systems, exacerbated by transboundary contributions from significant rivers like the Brahmaputra-Jamuna, hinders accurate forecasting. To tackle these problems, the study employed Artificial Neural Networks (ANN), a flexible and data-driven methodology adept at modelling non-linear relationships, to predict flood water levels with a lead time of up to seven days in Sirajganj, a district particularly susceptible to river flooding and bank erosion. Daily Data on water levels and rainfall were collected from the Bangladesh Water Development Board (2002–2015) for the monsoon season (May–October) were analysed, utilising information from four rainfall stations and six water level stations located 62–237 km upstream. The ANN model, employing a Sigmoid activation function with one to three hidden layers, indicated that augmenting the number of hidden layers provided only negligible enhancements in performance. Performance metrics, such as the goodness-of-fit (R²: 0.985–0.554), Root Mean Square Error (RMSE: 0.024–0.617), and Mean Absolute Error (MAE: 0.087–0.604), demonstrated a marginal improvement when rainfall and water level data were combined. This study highlights the efficacy of Artificial Neural Networks (ANN) in tackling hydrological prediction issues, confirming its ability to utilise readily accessible datasets to provide reliable and effective flood forecasts, thus aiding disaster preparedness and mitigation efforts in resource-limited areas such as Bangladesh.

How to cite: Das, P. and Mohib, K. M.: Data-Driven Flood Forecasting Using ANN: A Resource-Efficient Approach for High-Risk Regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14431, https://doi.org/10.5194/egusphere-egu25-14431, 2025.

Predicting streamflow in ungauged basins remains a significant challenge in hydrological studies. In recent years, data-driven models have been shown to outperform traditional physics-based models in streamflow prediction for ungauged catchments. However, few studies have examined the potential of such models for predicting streamflow in ungauged basins within India. This study aims to evaluate the performance of two machine learning models, namely Support Vector Regression (SVR) and Random Forest (RF), alongside two deep learning models, Long Short-Term Memory (LSTM) and Bi-LSTM, in the context of streamflow regionalization within the Krishna River Basin in India. Each prediction model is trained using meteorological variables as input features, with streamflow as the output variable. K-means clustering is employed to group selected catchments (based on data availability) into an optimum number of clusters based on spatial proximity and physical similarity. It is assumed that catchments within a cluster share homogeneous characteristics. Regionalization is achieved by sharing model parameters across catchments within the same cluster. For each cluster, one gauged catchment is designated as the donor catchment, while the others are treated as pseudo-ungauged. Each proposed model is trained and tested using the meteorological inputs and streamflow data available at the gauged donor catchment. The trained model for each cluster is then transferred to the remaining receptor catchments within the cluster, where the meteorological variables corresponding to each ungauged catchment are used as inputs. The performance of the models in ungauged catchments is rigorously evaluated by comparing the simulated streamflow against observed streamflow using metrics such as Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), Coefficient of Determination (R²), and Percentage Bias (PBIAS). This study highlights the advantages of utilizing data-driven methods for streamflow prediction in both gauged and ungauged basins, particularly due to their ability to capture complex, non-linear relationships between meteorological inputs and streamflow generation. The findings of this study are expected to be instrumental in water resources planning and management, flood assessment, and the design of hydraulic structures in the Krishna River Basin.

How to cite: Kaur, S. and Chavan, S.: Data-driven models for streamflow regionalization in Krishna River Basin, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15731, https://doi.org/10.5194/egusphere-egu25-15731, 2025.

Runoff forecasting remains a critical challenge in many basins worldwide, particularly those featuring a complex topography, where the scarcity of hydrometeorological data is a prevalent challenge. Data fusion offers a promising alternative to conventional single-source data modelling, which often fails to capture the full spatial and temporal variability of precipitation. By integrating multiple sources, data fusion seeks to generate enhanced satellite precipitation datasets, essential for data-driven runoff forecasting models. This study aims to evaluate the effectiveness of the Three-Cornered Hat (TCH) method for fusing satellite precipitation products (SPPs) and its influence on the performance of a Random Forest-based runoff forecasting model.

Three scenarios were evaluated: (i) a TCH-fused dataset combining three SPPs: Integrated Multi-satellitE Retrievals for GPM – Early Run (IMERG-ER), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Cloud Classification System (PERSIANN-CCS) and the Global Satellite Mapping of Precipitation – Near Real Time (GSMaP-NRT); (ii) an individual SPP (IMERG-ER); and (iii) an already fused benchmark product, the Multi-Source Weighted-Ensemble Precipitation (MSWEP). All scenarios performed comparably for lead times of 3, 6, 12, and 24 hours, with MSWEP slightly outperforming across Nash-Sutcliffe Efficiency, Kling-Gupta Efficiency, and Root Mean Square Error metrics. However, TCH demonstrated better bias reduction as reflected by the Percent Bias metric.

A key limitation of the fusion method was identified at hourly scales, where statistical dependence arises during periods with no precipitation over the basin, hindering the effectiveness of TCH. The introduction of a matrix regularization step addressed this issue. This study provides valuable insights for enhancing SPP fusion methods and offers a replicable framework for improving runoff forecasting, particularly in data-scarce regions and other hydrological contexts.

How to cite: Luna-Abril, P., Muñoz, P., Samaniego, E., Muñoz, D. F., Merizalde, M. J., and Célleri, R.: Evaluating the Three-Cornered Hat Method for Satellite Precipitation Data Fusion and its Influence on Runoff Forecasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16242, https://doi.org/10.5194/egusphere-egu25-16242, 2025.

The release of low-temperature water from a reservoir can have negative impacts on downstream fish spawning and crop growth in irrigation areas. Therefore, predicting the discharged water temperature accurately and swiftly is crucial. This study focused on the Pubugou Hydropower Station, a major project situated on the Dadu River in the upper reaches of the Yangtze River, and evaluated the impacts of meteorological factors and reservoir operational parameters on the released water temperature using Spearman correlation coefficients (R). To predict the discharged water temperature of Pubugou Reservoir, five models were optimized by genetic algorithms including random forests, support vector regression, convolutional neural network, long short-term memory network, and the lightweight gradient boosting machine respectively. The results showed that: (1)The dew point temperature exhibited the highest correlation with discharged water temperature (R = 0.89), However, the correlation coefficient between wind speed, cloud cover, solar radiation, dam front water level, and discharge water temperature was not found to be 0.4. (2) All the five models optimized by genetic algorithms performed well on the training set, especially the random forest model (R² = 0.997). The worst performing model is the long short-term memory network model (R² = 0.985). (3) In the prediction of discharge water temperature, all models have good fitting effects, with r² greater than 0.93, average absolute error not greater than 0.662 ℃, and mean square error not greater than 0.852 ℃. Random forest models and lightweight gradient boosting machine models have shown good performance on the most of sample data, with a small residual range, while support vector regression models and convolutional neural network models have smaller maximum residuals. This research indicated that machine learning methods can effectively predict water temperature released from reservoirs, providing more reliable decision support for formulating relevant measures to alleviate the impact of reservoir discharge water temperature.

How to cite: Junguang, C.: Application of Machine Learning in Predicting the Water Temperature Released from Reservoirs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18342, https://doi.org/10.5194/egusphere-egu25-18342, 2025.

The prior literature on hydrologic model performance is dispersed, encompassing a small number of catchments, different methodology, and rarely linking the results to specific catchment characteristics. This study addresses these constraints by systematically attributing model performance to catchment variables in 671 US catchments, providing a formal framework for determining the best models for specific conditions. Daily streamflow estimation was performed using eight process-based (PB) models and three deep learning (DL) models, with performance measured using the Nash-Sutcliffe Efficiency (NSE). The PB models were tested with a variety of optimization techniques, and the most effective approach for each model was chosen based on the number of catchments that exceeded a predetermined performance threshold. Four models were selected as the top performers based on three performance metrics. Further analyses, such as Classification and Regression Tree (CART) and SHAPley, were used to correlate model performance with catchment variables across all models.
The results showed that PB models (GR4J, HBV, and SACSMA) performed well in catchments with low to medium aridity and a high Q/P ratio, indicating quick hydrologic responses. In contrast, the LSTM-based DL model performed well in medium to high aridity regions but had limits in catchments with rapid precipitation responses and low sand percentages. These findings provide a thorough understanding of the links between model performance and catchment descriptors.

Keywords: Process-based models, Deep learning model, CART analysis, SHAPley analysis, catchment characteristics.

How to cite: sourya, D. A., manikanta, V., and rathinasamy, M.: Can the catchment features influence the performance of the conceptual hydrological and deep learning models? A study using large sample hydrologic data , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18458, https://doi.org/10.5194/egusphere-egu25-18458, 2025.

Knowing evapotranspiration (ET) accurately at fine spatial scales is very important. This would improve understanding hydrological processes and contribute to the advancement of water resource management. In this study, we set a framework based on deep learning to downscale Terra Net Evapotranspiration Gap-Filled 8-Day Global 500m dataset, developed and managed by NASA's Earth Observing System. This approach resulted in a scale enhancement of 20 times. The U-Net architecture was used for this purpose. It incorporated MODIS Land Cover Type 1 (LC Type 1) as an auxiliary variable. This was done to account for land cover changes. The study covered a diverse region that encompasses latitudes 28° to 32°N and longitudes 74° to 78°E. A synthetic design of experiments was utilized to systematically generate and evaluate training data, this ensures robust model performance and reliable downscaling outcomes across the heterogeneous terrain of the study area. Model training, validation, and testing were conducted using the 2001–2014 dataset, 2015–2018, and 2019–2023 dataset, respectively. The model showed excellent performance on the testing dataset. The average PSNR was 34.35 dB and the mean SSIM was 0.8517. The U-Net module effectively downscale and enhance the spatial resolution of ET data. The results show ET's spatial and structural features are well preserved. This study shows how deep learning improves climate data spatial resolution. It provides reliable local hydrological and agricultural resources.

How to cite: Jha, S. K. and Gupta, V.: Downscaling MODIS ET using deep learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18599, https://doi.org/10.5194/egusphere-egu25-18599, 2025.

In a mountainous watershed, there are many confluences at which two or more streams join. Due to inaccessible terrain and associated costs, river discharge data is collected only at a few confluences. It is, therefore, important to assess which confluence is critical. By critical, we mean the junction which will create maximum fragmentation in a river network. In this study, we analysed river networks with uneven topography in the Alaknanda River basin, which is vulnerable and prone to geo-hydro hazards. We applied Unsupervised Machine Learning (UML) algorithms such as Isolation Forest, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Linear Integer Programming (LIP) to identify the critical confluence locations. We compare our results with the well-established graph-based centrality metrics (Degree centrality, Betweenness centrality, Closeness centrality, and Eigen Vector Centrality). Our results suggest that DBSCAN outperformed other approaches in terms of detecting crucial nodes. We obtained better results using LIP than other techniques except DBSCAN. The outcome of this study will help the Central Water Commission, in deciding which confluence to focus on, and in assessing the locations of new gauges.

Keywords: Critical nodes; Alaknanda Basin; Machine Learning; Hazards

How to cite: Rajouria, N., Parajapati, P., and Jha, S. K.: Application of Unsupervised Machine Learning Algorithms for identifying critical river confluence in a mountainous watershed., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19050, https://doi.org/10.5194/egusphere-egu25-19050, 2025.

Accurate and timely representation of spatiotemporal precipitation patterns is critical for monitoring and predicting hydrological extremes, particularly in operational hydrology and early warning systems. In regions with limited in-situ precipitation data, satellite precipitation products (SPPs) offer an accessible solution. However, the latency of these datasets—the delay between data collection and availability—remains a key challenge for real-time applications. This study developed a machine learning model based on the Random Forest (RF) algorithm to predict precipitation using low-latency data from GOES-16 Advanced Baseline Imager (ABI) bands. The model was applied to the Jubones River basin (3,391 km²) in southern Ecuador, a region characterized by complex terrain and hosting a key hydropower project. Leveraging hourly data over a five-year period, the RF model addressed the five-hour latency of traditional SPPs by generating near-real-time precipitation maps with a latency of only 10 minutes. The model’s performance was evaluated using quantitative and qualitative metrics across temporal scales, demonstrating progressive accuracy improvements with larger temporal aggregations. Root Mean Square Error (RMSE) values decreased from 0.48 to 0.05 mm/h, while Pearson’s Cross-Correlation (PCC) improved from 0.59 to 0.87 for scales ranging from hourly to monthly. Qualitative metrics, including Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI), further validated the approach. These findings highlight the potential of integrating advanced hydroinformatics techniques with remote sensing for managing hydrological extremes in diverse basins. The study underscores the importance of leveraging low-latency satellite data and machine learning to enhance real-time forecasting and operational hydrology. Future work will focus on refining the model for improved detection of extreme precipitation events and exploring its integration into stakeholder-driven decision-making frameworks.

How to cite: Muñoz, J., Muñoz, P., Muñoz, D. F., and Célleri, R.: Leveraging machine learning and satellite precipitation data to overcome latency challenges in operational hydrology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19086, https://doi.org/10.5194/egusphere-egu25-19086, 2025.

About 90% of extreme precipitation in the midlatitudes can be assoicated with front boundaries. Therefore, it is important to identifiy frontal locations for short term weather forecasting or long-term prediction of precipitation in climatology. Deep learning (DL) refers to machine learning alogrithms that use multiple layers of neural networks to derive features from input data. It is generaly useful to process 2-dimensional image data. One of the potential advantages of employing DL to detect surface weather fronts is that the developed DL based functions can be applied to automatic detection of surface weather fronts for climate models. However, justifications of its applicability on climate models are needed.

In this study, we developed deep learning based methodology to detect surface weather fronts. Specifically, a U-shape convolutional network (U-net) based deep learning model is developed to predict surface weather fronts over Japan and surrounding sea in summer (June, July, and August). We justify the applicability of the deep learning model in predicting surface fronts in summer on outputs from large-scale Global Climate Models (i.e. GCMs) from two aspects. First, the coarse resolution of GCMs (e.g., 1.25 degrees) can capture the general morphological features of surface fronts. Second, models trained in a colder climate are applied to predict fronts in a warmer climate with some decrease in predicted peak frequency of fronts, but the general features of the spatial distribution of fronts can be represented by the deep-learning model predictions. We also see that the locations of peak frequency tend to move slightly more southwesterly in a slant zone within the belt region between 25N to 40N as climate warms in the future.

How to cite: Mao, Y. and Yamada, T.: Applicability of deep learning based detection of surface weather fronts on large scale climate models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19530, https://doi.org/10.5194/egusphere-egu25-19530, 2025.

Understanding and modeling surface and groundwater resources are critical due to the effects of droughts and climate change, especially in semi-arid, arid, or hyper-arid regions. GeoLinkage, developed by Troncoso (2021), facilitates the creation of linkage files for integrated models. These linkage shapefiles act as a communication interface between a surface hydrological domain (D₁) and an aquifer domain (D₂). The surface domain (D₁) comprises nodes and arcs that represent hydrological elements and their relationships, while the aquifer domain (D₂) contains geometric elements such as grids or Quadtree diagrams. D₁ defines a surface topology (τ₁), D₂ defines a groundwater topology (τ₂), and the linkage file establishes a surface-groundwater topology (τ_1-2). This new topology, τ_1-2 ,imposes constraints that influence the relationship between τ₁ and τ₂. For instance, the superposition of elements in τ_1-2 should be considered a spatial relationship. Depending on the type of superposed elements, this relationship must be reflected in τ₁  or τ₂. To enforce these τ_1-2 specific restrictions, GeoLinkage has been enhanced with a post-processing module called GeoChecker. This module evaluates the quality of the resulting linkage files. GeoChecker currently performs a superposition check to ensure that overlaps between cells in the linkage file—whether between groundwater and catchments or groundwater and demand sites—are accurately represented as connections in the surface model (WEAP). The aquifer is represented by a MODFLOW model fully linked to the WEAP model. GeoLinkage2.0 and GeoChecker were developed using the tutorial WEAP-MODFLOW model, considered a small model, and were tested in large integrated models, such as the Azapa Valley (3,000 km²) and the Limarí River Basin (12,000 km²), Chile.

How to cite: Sanzana, P., Torga, A., Hitschfeld, N., and Lobos, C.: GeoLinkage2.0 and GeoChecker: Hydroinformatics tools for large and complex hydrological-hydrogeological models using WEAP-MODFLOW. Case Study: Severe drought in the Limarí River Basin, Chile, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20678, https://doi.org/10.5194/egusphere-egu25-20678, 2025.

Digital twins, virtual representations of physical systems, integrate sensor data and predictive models to enable real-time simulation and analysis. They are instrumental in monitoring weather, infrastructure health, and water levels, particularly in flood management. By modeling mitigation techniques, forecasting risks, and enhancing emergency responses, digital twins improve decision-making, reduce economic losses, and enhance public safety in flood-prone areas [1][2]. This study developed a digital twin system to monitor and forecast flood disasters in western Iran. The system combined multidimensional sensor data on temperature, flood flow, vegetation cover, and water levels using an offline databank. Time-series analysis tracked trends, while a linear regression-based predictive model estimated future flood conditions. Threshold values for flood warnings and high-risk alerts were defined using hydrological principles and environmental data [3]. Game theory concepts were employed to optimize flood management strategies by modeling interactions among stakeholders, including authorities, responders, and communities. A non-cooperative game theory approach simulated conflicting objectives, such as minimizing economic losses and optimizing resource allocation. Stable solutions were identified through the Nash equilibrium, ensuring no stakeholder could unilaterally improve outcomes. Visualization dashboards presented time-series data, risk levels, and stakeholder strategies, facilitating informed decision-making. Simulation results demonstrated the system's effectiveness in flood risk assessment. Water levels remained below the 2.5-meter warning threshold but rose significantly during simulated abnormal conditions. In later stages, some areas approached the 3.0-meter high-risk threshold, indicating zones vulnerable to flooding. Flood flow rates frequently exceeded the 40 m³/s threshold, with peaks above 60 m³/s, highlighting the need for continuous flow monitoring. Temperature fluctuations were minimal, consistently below the 25°C threshold, suggesting limited influence on flood risks during the study. However, vegetation cover often fell below the 30% threshold, correlating with increased flood risks and reinforcing its importance in mitigation. The system effectively categorized risk levels, with most instances classified as "Normal" or "Warning." High-risk alerts were concentrated during elevated water levels and flows. This research highlights the potential of digital twins for real-time flood monitoring and collaborative decision-making, providing a robust framework to enhance disaster resilience.

Keywords: Digital Twin; Flood Risk Assessment; Game Theory; Predictive Modeling; Multidimensional Data Analysis.

References

[1] Ghaith, M., Yosri, A., & El-Dakhakhni, W. (2021, May). Digital twin: a city-scale flood imitation framework. Canadian Society of Civil Engineering Annual Conference (pp. 577–588). Singapore: Springer Nature Singapore.

[2] Gheibi, M., & Moezzi, R. (2023). A Social-Based Decision Support System for Flood Damage Risk Reduction in European Smart Cities. Quanta Research, 1(2), 27–33.

[3] Kreps, D. M. (1989). Nash equilibrium. In Game Theory (pp. 167–177). London: Palgrave Macmillan UK.

How to cite: MohammadZadeh, F., Eghbalian, H., Gheibi, M. G., Yeganeh-Khaksar, R., Ghazikhani, A., and Behzadian, K.: A Digital Twin Framework for Real-Time Flood Monitoring and Multidimensional Prediction: A case study in Iran, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20713, https://doi.org/10.5194/egusphere-egu25-20713, 2025.

The increasing frequency and intensity of climate hazards, as emphasized by the IPCC’s Sixth Assessment Report, underscore the urgent need for effective disaster risk reduction strategies. Using the devastating floods of September 2024 in Nepal’s Kathmandu Valley, and the April 2024 floods in Kenya’s Nairobi, this study examines the persisting gaps in flood resilience despite early warnings using disaster forensics techniques. The Kathmandu floods, which were triggered by an extreme rainfall event resulting from the convergence of a low-pressure system from the Bay of Bengal and a cyclonic circulation from the Arabian Sea, caused extensive loss of life, property damage, and economic disruption in the Nakhu Khola watershed, despite timely early warnings issued by the government. In Kenya, a notable gap exists in the warning systems, whether in their issuance, dissemination, or uptake, despite the presence of advanced operational forecasting systems. Encroachment on floodplains, unplanned urbanization, and land-use changes have exacerbated vulnerability, while weak governance and poor enforcement of disaster risk management legislation has left populations and assets exposed. Additionally, risk assessment efforts are scarcely integrated into government plans or those of other stakeholders, highlighting a critical area for improvement in disaster preparedness and management.

Using the UNDRR’s forensic disaster analysis framework, this research investigates the underlying causes, risk drivers, and lessons from these events. The populations most affected are identified, including those living in floodplains, including marginalized communities, and critical infrastructure. Local investments in disaster preparedness are also critically examined for efficacy. Results highlight that while early warnings were disseminated through various channels, gaps in risk communication and community-level preparedness persisted. The findings emphasize the need for education and awareness and integrated approaches to disaster risk management that address root causes such as unplanned urban growth and environmental degradation. Empowering youth and fostering leadership in disaster risk reduction are critical to ensure climate resilient societies of tomorrow. This research contributes actionable insights to reduce vulnerability, enhance preparedness, and minimize losses in future climate hazard events in the Kathmandu Valley and Kenya, as well as similar rapidly urbanizing regions.

How to cite: Kettner, A., Gupta, A., Singh Shrestha, M., Trigg, M., Cohen, S., Hawker, L., Prades, L., Rudari, R., Salamon, P., Tellman, B., Sperna Weiland, F., and Wu, H.: Devastating Flooding Despite Early Warning: Lessons Learned from the Nepal and Kenya Floods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21199, https://doi.org/10.5194/egusphere-egu25-21199, 2025.

Analyzing deep soil water use (DSWU) response to precipitation change and its impact on tree physiology is necessary to disentangle tree mortality mechanisms, especially in drylands. In this study, a process-based model parameterized with in-situ measured fine root distribution data for 0-2000 cm and a root-cutting (below 200 cm) numerical experiment were used to explore DSWU strategies across different precipitation years and its contribution to total water consumption, as well as its relationship to tree gas exchange traits in mature apple (Malus pumila Mill) and black locust (Robinia pseudoacacia L.) plantations in both a wetter (Changwu, 583 mm) and a drier (Yan’an, 534 mm) sites on China’s Loess Plateau. Results showed that DSWU at 200-2000 cm depth in different precipitation years of both species mainly occurred during the early growing seasons. On average, DSWU contributed 22.9% and 25.1% to the total water consumption of apple trees and black locust, respectively, and its contribution increased to 26.0% and 36.7% in extremely dry years. Moreover, the lack of DSWU significantly decreased (p<0.05) stomatal conductance (by 16.9%, 16.9%, 47.4% and 11.4%, respectively) and photosynthetic rates (by 37.1%, 20.1%, 28.5% and 16.4%, respectively) of Changwu apple trees, Yan’an apple trees, Changwu black locust and Yan’an black locust in extremely dry years. Similar reductions occurred only in Yan’an for both tree species in normal years. In contrast, no significant differences were found in gas exchange traits in extremely wet years. Our results highlight that DSWU is an important strategy for plantations in deep vadose zone region to resist extreme drought.

How to cite: Zhao, X., Shao, X., and Gao, X.: Deep soil water use can compensate drought effect on gas exchange in dry years than in wet years for dryland tree plantations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1681, https://doi.org/10.5194/egusphere-egu25-1681, 2025.

Per- and polyfluoroalkyl substances (PFAS) represent a significant and persistent threat to water quality worldwide, posing major challenges due to their chemical stability, resistance to conventional treatment methods, and documented health risks. These contaminants, once released, persist in the environment for extended periods and have been detected in drinking water, surface water, groundwater, and even human blood. Conventional remediation techniques, such as granular activated carbon (GAC) adsorption, ion-exchange resins, and reverse osmosis, often struggle with shorter-chain PFAS compounds and merely shift contamination from one medium to another. As climate change intensifies rainfall and extreme weather, PFAS transport through runoff becomes increasingly likely, heightening the need for advanced treatment solutions.

In response to this pressing need, our work investigates an innovative remediation approach employing far-UVC radiation (222 nm) delivered by krypton chloride (KrCl*) excimer lamps. Unlike conventional low-pressure UV (LPUV) systems, which typically emit at 254 nm, or vacuum UV (VUV) systems at 185 nm, far-UVC at 222 nm offers a unique balance of high photon energy and minimal absorption by water. This balance enables deeper penetration into the water matrix and provides the potential for enhanced PFAS photolysis and subsequent defluorination. Preliminary findings indicate that certain PFAS, previously resistant to direct UV photolysis, may be more susceptible under far-UVC irradiation, thereby opening a promising new pathway for their degradation.

While direct photolysis at 222 nm shows considerable promise, integrating far-UVC treatment with electrochemical oxidation (EOP) and UV-advanced reduction processes (UV-ARP) can further enhance PFAS degradation. EOP effectively removes dissolved organic matter (DOM), which often competes with PFAS for reactive species, thus reducing the overall efficiency of PFAS degradation. Meanwhile, UV-ARP generates highly reactive hydrated electrons (e_aq⁻) capable of breaking down PFAS. Although adding sulfide ions is one way to produce e_aq⁻, applying a sufficiently negative potential at a GAC cathode can also generate e_aq⁻ without introducing sulfur species. This approach requires careful consideration of competing hydrogen evolution reactions (HER), which may be thermodynamically unfavorable at a GAC cathode. By combining EOP with in situ e_aq⁻ generation in UV-ARP, PFAS can be more effectively targeted and degraded without adding extra chemicals. This integrated treatment aims to meet or surpass stringent U.S. Environmental Protection Agency (EPA) standards, ultimately facilitating the development of a portable, cost-effective, chemical-free, point-of-use water treatment system. Such a system would be especially valuable for communities experiencing environmental vulnerabilities, such as those in Puerto Rico studied by the PROTECT Center at Northeastern University, where limited infrastructure, contaminated water sources, and heightened susceptibility to adverse health outcomes underscore the urgency for sustainable PFAS remediation solutions.

By advancing the understanding of PFAS photolysis under far-UVC radiation and harnessing the combined power of EOP and UV-ARP, this work endeavors to provide an innovative solution. In doing so, it seeks not only to bridge the gap between laboratory research and practical application but also to enhance the resilience of water treatment systems against emerging contaminants and the challenges posed by a changing climate.

How to cite: Arekhi, M., Ehsan, M. F., and Alshawabkeh, A.: Enhancing PFAS Degradation through Far-UVC Photolysis Coupled with Electrochemical Oxidation and UV-Advanced Reduction Processes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2175, https://doi.org/10.5194/egusphere-egu25-2175, 2025.

Accurately capturing rainfall patterns is crucial for hydrometeorological applications, particularly in regions like Burkina Faso, West Africa, where rainfall variability significantly impacts water resources and agricultural productivity. However, challenges remain in identifying the most reliable rainfall products for such purposes. This research evaluates the effectiveness of satellite precipitation products (SPPs) and soil moisture-derived rainfall products (SM2RPPs) in representing rainfall patterns in Burkina Faso. Results show that SPPs generally perform better than SM2RPPs across daily to annual timescales. An analysis of total bias components highlights that hit biases dominate but are more pronounced in SM2RPPs. Systematic errors contribute significantly to these hit biases, indicating the potential for improvement through bias correction. Wavelet analysis reveals that both SPPs and SM2RPPs capture seasonal and annual rainfall variability effectively. However, all products exhibit limitations in accurately representing extreme rainfall indices, although SPPs demonstrate superior performance compared to SM2RPPs. While SM2RPPs currently underperform relative to SPPs in Burkina Faso, they show promise for hydrometeorological applications and could achieve comparable or improved results with enhanced bias correction techniques.

How to cite: Yonaba, R., Belemtougri, A., Fowé, T., Mounirou, L. A., Nkiaka, E., Dembele, M., Akpoti, K., Coly, S. M., Koïta, M., and Karambiri, H.: Rainfall Estimation in West Africa: A Performance Comparison of Satellite and Soil Moisture-Derived Products, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2576, https://doi.org/10.5194/egusphere-egu25-2576, 2025.

Concerning water supply in mountainous regions where surface water plays an important role, the understanding of lake stratification or even hypolimnia can be important for water treatment actions.

The historical dam reservoirs were used for the continuous water supply to the ore mines in the Upper Harz Mountains. The first reservoirs were built in the 16th century. The dam heights reach up to 15 m and the stored water volumes are between 10,000 and 600,000 m³. There are about 70 of such lakes around Clausthal-Zellerfeld. Today only few of them are directly used for drinking water supply in the surrounding communities.

Hydrogeochemical data of the lakes have been investigated for about ten years. The specific electrical conductivity (SEC) of the lakes’ surface water ranges between 30 and 280 µS/cm (Bozau et al., 2015, Schäfer et al. 2024). Three lakes (Kiefhölzer, Langer and Oberer Grumbacher Teich) differing in chemical composition and morphometry (area, mean depth and maximum depth) were selected for the investigation of seasonal changes in the water columns. Samples were taken by boat with a Ruttner sampler. SEC and pH were measured on the boat. The titration for HCO₃ was done directly after sampling. The main ions were analyzed by ion chromatography and the trace elements by ICP-MS.

Stratification during summer could be clearly observed in all of the three lakes. The degradation of organic material and accompanying redox reactions are seen in the measured pH, SEC, HCO₃^-, Fe(II), NO₃^-, NH₄⁺ and SO₄^2- concentrations. Each lake showed a characteristic temporal and chemical behaviour. The development of an anoxic hypolimnion above the lake sediments was obvious in the two shallower lakes Langer Teich (max. depth ~ 5 m) and Kiefhölzer Teich (max. depth ~ 7 m) as being accompanied by H₂S-odor in the water column starting ~ 1 m above sediment.  This feature was absent in the deepest lake Oberer Grumbacher Teich (max. depth ~ 9 m), which also showed weaker increase of SEC and HCO₃^- in the profile. The aeration of the hypolimnion started in autumn leading to a well mixed, chemically uniform water column. 

Bozau, E., Licha, T., Stärk, H.-J., Strauch, G., Voss, I., Wiegand, B. (2015): Hydrogeochemische Studien im Harzer Einzugsgebiet der Innerste. Clausthaler Geowissenschaften, 10, 35-46.

Schäfer, T., Bozau, E., and Hutwalker, A.: Reservoir lakes in the Upper Harz Mountains (Germany): GIS Implementation and hydrochemical development, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5085, https://doi.org/10.5194/egusphere-egu24-5085, 2024.

How to cite: Schäfer, T., Bozau, E., and Hutwalker, A.: Seasonal Changes in Water Columns of Historical Reservoir Lakes in the Upper Harz Mountains (Germany), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2730, https://doi.org/10.5194/egusphere-egu25-2730, 2025.

Probable Maximum Precipitation (PMP) is a key input in the design and risk assessment of critical infrastructures such as large dams and nuclear power plants. Traditionally, PMP is computed as a fixed upper bound of the precipitation assuming a stationary climate. However, due to climate change, the stationarity assumption may not remain valid in the future. Limited attempts have been made in the past to develop methods for estimating PMP by accounting for non-stationarity in the related hydroclimatic variables. In view of shortcomings associated with those methods, three new nonstationary models are proposed and their potential in determining PMP in a changing climate is illustrated through application to three major flood-prone river basins in India. In this analysis, historical records of precipitation, surface temperature and relative humidity, and their future projections corresponding to eleven CMIP-6 SSPs (Coupled Model Intercomparison Project-6 Shared Socio-economic Pathways) were utilized. The results indicate that PMP estimates obtained using the proposed nonstationary models are significantly higher than those obtained from their underlying conventional stationary model, especially for high-emission scenarios in the near future. The results obtained from this study could be utilized to update historical PMP values and to determine the increase in risk associated with the corresponding probable maximum flood.

How to cite: Bhatt, J. and Srinivas, V. V.: Revising probable maximum precipitation (PMP) estimates under changing climate , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4786, https://doi.org/10.5194/egusphere-egu25-4786, 2025.

The growing need for environmentally friendly wastewater treatment technology has prompted researchers to look into natural alternatives. Among these, algal-bacterial systems have received attention for their capacity to combine biological treatment and biomass production. This study focuses on the use of algal-bacterial flocculent biomass for wastewater treatment in a 400 L pilot-scale raceway pond, with a focus on its potential as a sustainable option for lowering environmental impacts. The synergistic interactions between algae and bacteria in the consortia improve nutrient removal from wastewater, while also providing biomass for future use. The aim was to develop a high-concentration flocculent algal-bacteria biomass. The raceway system was placed in a greenhouse with water temperature 32±8^oC for about 230 days. The pilot-scale experiment evaluates treatment efficiency of domestic wastewater in a batch mode procedure.  The removal of chemical oxygen demand, ammonia, nitrate, and total phosphorus was over 95%.  The biomass concentration stabilized at about 4 g/L after 70 days of operation. The implementation of algal-bacteria flocculent processes for the treatment of domestic or source-separated domestic wastewater shows great promise as a low-cost, sustainable, and efficient solution.

How to cite: Kakavas, D., Biliani, S., and Manariotis, I.: Long-term behavior of syntrophic algal-bacterial biomass in a pilot-scale raceway pond treating domestic wastewater , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6521, https://doi.org/10.5194/egusphere-egu25-6521, 2025.

Cyanotoxins in agricultural waters pose a human and animal health risk. These chemical compounds can be transported to nearby crops and soil during irrigation practices; they can remain in the soils for extended periods and be adsorbed by root systems. Additionally, in livestock watering ponds cyanotoxins pose a direct ingestion risk. This work evaluated the performance of the randomForest algorithm in estimating microcystin concentrations from eight in situ water quality measurements at one active livestock water pond (Pond 1) and two working irrigation ponds (Pond 2 and 3) in Georgia, USA. Sampling was performed monthly from June of 2022 to October of 2023. Measurements of microcystin along with eight in situ sensed water quality parameters were used to train and test the machine learning model. The model performed better at Pond 1 (R² = 0.601, RMSE =3.854) and Pond 2 (R² = 0.710, RMSE = 2.310) compared to Pond 3 (R² = 0.436, RMSE = 0.336). Important variables for microcystin prediction differed among the three ponds, temperature and chlorophyll, phycocyanin and turbidity, and temperature and phycocyanin in Ponds 1, 2 and 3, respectively. Separating nearshore and interior samples in Ponds 1 and 2 lead to better predictive capacity of the model in nearshore samples compared with the interior samples. Overall, the random forest algorithm explained 50% to 70% of the microcystin concentration variation in three Georgia agricultural ponds with data from in situ sensing. In situ sensing showed a potential to aid in the water sampling design for microcystin to characterize the spatial variation of concentrations in studied ponds using readily available in situ sensing data.

How to cite: Smith, J., Stocker, M., Hill, R., and Pachepsky, Y.: Microcystin concentrations and water quality in three agricultural ponds: A machine learning application, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6696, https://doi.org/10.5194/egusphere-egu25-6696, 2025.

North China Plain is one of the agricultural region in the world with severe water shortage. Flood irrigation is still the most popular irrigation method in NCP, and have caused very low water use efficiency. Groundwater depletion becomes the most concerned issue for sustainable development. To determine the water & nitrate fluxes is important for better water resouces management. We built up a 48-m in depth of cassion and a 36 lysimeter group for this purpose to study the water budget and water/nitrate movement in the deep vadose zone. In this study, we will present the observation facts using these two facilities to reveal the differences between water transport velocity and celerity in the deep vadose zone of nearly 50 meters. This is the first time to observe the variations or responses of soil potential, moisture, temperature, and electricity conductivity to water inputs from land surface, such as extreme rainfall, directly in the deep vadose zone of 48 meters. We  will also present the fresh observation results from the 36 lysimeters about ET and drainage fluxes of different cropping patterns, with different watering and fertilizing treatments. The latter experiment could provide useful information for improving the water/nutrients management for different cropping systems in NCP, and will be beneficial to sustainable groundwater management at the aspects of quantity and quality. The results of the observatoins using these new facilities is presented at international conference at the first time. We hope it could be interested by the colleagues worldwide. 

How to cite: Shen, Y., Zhang, Y., Min, L., Wu, L., Li, H., and Li, H.: Water/nitrate fluxes and tranport in deep vadose zone of typical irrigated cropland in North China Plain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6894, https://doi.org/10.5194/egusphere-egu25-6894, 2025.

Climate change significantly impacts water quality and quantity, intensifying extreme weather events, such as floods, droughts, and heat waves. Rising temperatures can increase humidity and dryness, disrupt the water cycle, cause saltwater intrusion into upstream lakes due to sea-level rise, and reduce dissolved oxygen in rivers, thereby deteriorating freshwater quality. Thus, accurate prediction of key climate variables, such as precipitation and temperature, is essential for mitigating detrimental impacts. This study evaluates three modeling approaches, including Process-Based (PB) models, Deep Learning (DL) models, and Process-Based Deep Learning (PBDL) models, to highlight their strengths and limitations.

Our assessment shows that PB models, which are based on physical laws and account for complex interactions between the atmosphere, land, and water bodies, require high parameterization and computational simplifications, which can lead to inaccurate results. DL models can uncover complex relationships from large datasets. They are effective in co-predicting variables, simulating General Circulation Model (GCM) outputs, optimizing PB models, and filling spatiotemporal data gaps. However, their performance depends on the availability of extensive temporal-spatial data, particularly for extreme events. The other group, PBDL models, known as physics-informed or hybrid models, can integrate the strengths of PB and DL approaches. Indeed, these models consider physical laws, such as mass balance and energy conservation, while leveraging DL's pattern recognition capabilities. Even with limited data, these models achieve superior predictions by combining pre-trained PB model outputs, which reduces computational demands.

Although these methods are used to evaluate (actual) evapotranspiration, snowmelt rate, soil permeability, hydraulic conductivity, and the effect of a warming climate on water temperature and streamflow, the interconnected influences on water systems, especially water quality indicators such as dissolved oxygen, heavy metals, nutrients, and water clarity, remain underexplored, presenting a critical research gap. Findings confirm that incorporating simultaneous predictions from DL models with proper variable selection and hyperparameter tuning can further enhance model robustness. Advancing PBDL models through integrating well-calibrated hydrological models, expanding spatiotemporal data coverage, and improving measurement accuracy yields more reliable climate change predictions and bolsters sustainable water resource management strategies.

To identify promising solutions, researchers are encouraged to address the non-stationary behavior of natural systems, considering not only meteorological factors (e.g., wind speed and solar radiation) but also the compound impacts of anthropogenic climate change on water resources. Additionally, selecting appropriate models and coupling them can improve an overall understanding of climate and water system interactions.

How to cite: Karimnejad, A., khorashadi zadeh, F., and Moghim, S.: Assessment of climate change-water resources interaction by different models , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7025, https://doi.org/10.5194/egusphere-egu25-7025, 2025.

Escherichia coli (E. coli) is a key marker for monitoring microbial water quality, with significant consequences for both public health and agricultural practices. To address the challenges of traditional water quality assessments, remote sensing offers a promising alternative. In this research, we implemented the random forest (RF) algorithm to forecast E. coli levels in irrigation ponds using three distinct data sources: (1) conventional water quality measurements, (2) multispectral reflectance values from drones, and (3) remote sensing indices derived from these reflectance values. To enhance the model’s accuracy, a linear transformation was applied during postprocessing. The RF model achieved strong performance (R² = 0.74) with conventional water quality variables, while moderate results were obtained using multispectral reflectance values alone (R² = 0.56). The best outcomes were observed when remote sensing indices were used as inputs, yielding an R² of 0.76. Shapley additive explanations (SHAP) were employed to evaluate the importance of individual variables. Dissolved oxygen, pH, and Chlorophyll-a emerged as critical predictors among water quality parameters. Meanwhile, the visible atmospherically resistant index (VARI) and normalized difference turbidity index (NDTI) were the most significant remote sensing indices. Furthermore, location-based comparisons highlighted differences in the impact of VARI and NDTI between interior and nearshore sampling sites. These findings suggest that remote sensing indices effectively capture water quality features crucial for E. coli persistence. This study underscores the potential of using drone-derived multispectral data to enhance predictions of E. coli concentrations in irrigation ponds.

How to cite: Hong, S., Morgan, B., Stocker, M., Smith, J., Kim, M., Cho, K. H., and Pachepsky, Y.: Assessing the effectiveness of remote sensing indices for predicting E. coli concentrations in an irrigation pond, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7313, https://doi.org/10.5194/egusphere-egu25-7313, 2025.

Antimicrobial resistance (AMR) in irrigation waters is a major worldwide health issue. Crops irrigated with waters containing antibiotic resistant bacteria (ARB) or related genes (ARG) can serve as a vector for AMR throughout food supply systems. The current extent of AMR in irrigation waters is poorly understood and even less so for small lentic waters such as farm ponds. The objectives of this work were to characterize the variability of ARG concentrations in an actively used irrigation pond and to determine if stable spatial patterns in the concentration data exist which can be used to inform monitoring designs. Water sampling was conducted on 9 dates between June and September 2023 at 20 locations within an actively used irrigation pond in Maryland, USA. The ARG tetracycline gene tetA was enumerated using dQPCR in all collected samples. Due to the presence of non-detects, the robust regression on ordered statistics (ROS) method was applied to the dataset to impute non-detectable concentrations on each date. Spatial variation of tetA concentrations was date-dependent with coefficients of variation ranging from 97 % to 377 % with an average of 181 %. Concentrations steadily declined throughout the observation period which significantly correlated with increases in water temperature (r_s = - 0.738; p = 0.023). Rainfall events throughout the observation period did not result in higher concentrations of tetA in the pond. On a majority of dates, significant outliers in the data were identified according to the extreme studentized deviate test.  The mean relative difference analysis revealed that samples collected at the pond banks contained higher tetA concentrations than those collected in the pond interior. Elevated concentrations of the ARG at bank sites were attributed to on-land activities as well as hydrological conditions within the waterbody. Sampling sites were identified that best represented the spatiotemporal average of the concentration data which is useful if large sample sets cannot be collected. This work is the first to evaluate fine-scale spatial variation of ARG in lentic waters used for irrigation and the results show that the choice of where to sample for ARG enumeration in ponds or lakes should not be made arbitrarily.

How to cite: Stocker, M., Smith, J., Pachepsky, Y., Gabriel, E., Sharma, M., and Gutierrez, A.: Fine-scale spatial patterns of antibiotic resistance gene concentrations in irrigation pond water, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7320, https://doi.org/10.5194/egusphere-egu25-7320, 2025.

Modeling is an efficient approach for predicting microbial water quality and suggesting related management practices. Escherichia coli or enterococci concentrations are commonly used to indicate microbial contamination and characterize microbial water quality. Small watersheds provide drainage into first- or second-order creeks, exhibit significant variation in land use, management, and conservation practices. Modeling microbial water quality in the small watersheds can help account for and mitigate the heterogeneity within larger hydrologic response units. A model for microbial water quality should incorporate key hydrologic components such as runoff, in-stream water fluxes, and meteorological inputs such as precipitation, air temperature, and solar radiation. Additionally, animal waste management, including the quantity and application schedule, are also important for microbial water quality simulations. The Agricultural Policy Environmental eXtender is a useful tool for hydrological, meteorological, and management drivers of microbial water quality, as it has been developed for small watersheds. Major microbial fate and transport processes include animal waste deposition, degradation, erosion, survival on soil, release from waste and transport by rainfall or irrigation, and microbial survival and resuspension in water or sediment. These processes can be simplified, for instance, by modeling proportional release of the indicators and animal waste during erosion. We can also use a two-phase survival model for manure and temperature-dependent rate of microbial survival in surface waters. Animal waste aging should also be considered in the microbial model, as daily bacterial survival and erodibility are influenced by it. The microbial module in APEX was used to the headwater watershed of Conococheague Creek in Pennsylvania, USA. The total watershed area is 34321.6 ha, with 15 subareas and the dominant land use is deciduous forest. Three years of hourly stage observations with rating curves and weekly E. coli concentrations at the outlet were available. The primary source of E. coli was animal waste from white-tailed deer, with an average density of 19 deer per square kilometers. Deer population dynamics reflect seasonal changes including fawn births, predation, pre-hunting, and post-hunting population phases. E. coli concentrations at the watershed outlet varied seasonally, ranging from 5 to 500 CFU (100 mL)^-1. The model reasonably captured the temporal fluctuations in E. coli concentrations at the outlet. Ongoing improvements to the model include incorporating deer behavior patterns, animal waste preservation in snow, and runoff during snowmelt.

How to cite: Lee, J., Harriger, D., Hong, S., Jeong, J., Guber, A., Hill, R., and Pachepsky, Y.: Modeling fate and transport of indicator microorganisms in small rural watersheds, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7321, https://doi.org/10.5194/egusphere-egu25-7321, 2025.

Coffee-banana intercropping, widely practiced by smallholder farmers in South America and East Africa, is recognized for its potential to combine sustainability with resilience to climate change. This practice promotes crop diversification, but may also enhance water-use efficiency. However, its effectiveness may vary depending on the local conditions and agricultural practices. The lack of quantitative data on drought stress and the complexity of interactions within coffee-banana intercropping systems pose significant challenges in modelling and optimizing water use efficiency. This study aims to develop and refine innovative methods to assess drought stress in coffee-banana intercropping systems, with a focus on stable carbon isotope values (δ¹³C), leaf temperature, and mid-infrared spectroscopy (MIRS). While stable carbon isotope analysis is a promising tool, its application may face challenges due to factors such as crop size, canopy heterogeneity, banana-coffee canopy overlapping, leaf age, orientation, or position (leaf morphological aspects), leading to variable competition for water and light. These factors affect the way sampling for stable carbon isotope and leaf temperature analysis should be conducted, in addition to physiological differences between coffee genotypes, agronomic practices, and complexities in data interpretation. Sampling and analytical protocols must be adapted to address these factors and their effects, while accounting for leaf morphology and microenvironmental parameters. Initially, we evaluated the influence of these factors on δ¹³C variability in coffee leaf samples, in addition to their correlation with leaf temperature. Samples were collected from a 0.15 hectares experimental farm managed by the Agricultural Research Company of Minas Gerais (EPAMIG) in Brazil, an intercrop of Arabica coffee and Cavendish banana plants at 3.6 a distance apart. Coffee leaves were sampled using a metal puncher and leaf temperature was measured using an infrared thermometer, considering varying levels of sunlight exposure. Ten plants of the Catuaí Vermelho IAC 44 coffee cultivar were randomly selected: five under conventional management (chemical fertilizers) and five under organic management (cattle manure). For each plant, samples were taken at three different heights (Top, Middle and Bottom), three orientations (South, East and West), and two branch sides, including young and mature leaves, resulting in 36 leaves per plant. The poster presents key findings on the variability of δ¹³C isotopes in coffee leaves within a banana-coffee intercropping system and their relationship with leaf temperature under different management practices (organic and conventional). This presentation highlights the observed effects of leaf sampling parameters, such as age, position, and sunlight exposure, on δ¹³C values, as well as the implications for improving drought stress screening methodologies.

How to cite: Bernardo, T., Vezzone, M., Felizardo, J. P., Rodrigues, C., Moura, W., Gomes Soares, L., Sebastião Sant' Anna Andrade, H., Victor Vieira Queiroz, C., Nakamya, J., Vantyghem, M., Dercon, G., and Meigikos dos Anjos, R.: Unravelling Sampling Bias in δ¹³C Isotope Variability in Coffee-Banana Intercropping for Drought Stress Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9556, https://doi.org/10.5194/egusphere-egu25-9556, 2025.

Reducing the carbon footprint of residential buildings has become increasingly crucial for decarbonizing the construction sector globally. Implementing various sustainable practices is essential for attaining carbon neutrality and addressing climate change. Therefore, integrating Green Concrete Materials (GCMs) and Nature-Based Carbon Dioxide Removal (Nb-CDR) strategies represents sustainable solutions for reducing CO₂ emissions and achieving a circular economy (CE) in residential buildings. In this regard, the study aims to investigate the potential synergies of sustainable building materials and eco-friendly building systems by utilizing Recycled Aggregate Concrete (RAC), Fly Ash (FA), Green Roof system (GR), and a Green Façade system (GF) as an attempt for reducing CO₂ emissions for residential building sector significantly. The Design for Integration (DFI) approach is used to develop novel sustainable solutions for future residential buildings and investigate how integrating different strategies can substantially enhance the overall benefits of reducing the sector’s carbon footprint. The system dynamics are used to create a simulation model that can estimate the synergies between GCMs and Nb-CDR to reduce CO₂ emissions and clarify the inner variables’ relations using Vensim software. Thereby, a comparative analysis between the traditional and optimized building designs is applied to the new Egyptian residential buildings. The results indicated potential integration could significantly lower a building’s CO₂ emissions during the building life cycle compared to conventional solutions. Additionally, it promotes circularity performance and decarbonization for the construction sector. The study demonstrated that incorporating eco-friendly materials and green building systems requires more attention in the early design stage of residential buildings. Public awareness should be considered, and new policies should be implemented to promote incentives and influence the effectiveness of Nb-CDR with GCMs in the future.

 Keywords:  Residential Buildings; Green concrete; Nb-CDR; System Dynamics; Design and simulation; CO₂ emissions.

How to cite: Marey, H., Kozma, G., and Szabó, G.: The Potential Synergies Between the Integration of Green Concrete Materials and Natural-Based Carbon Dioxide Removal Strategies in Residential Buildings Sector, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9762, https://doi.org/10.5194/egusphere-egu25-9762, 2025.