Flooding is increasingly exacerbated by cascading and compounding risks, necessitating a systematic understanding of natural hazard interrelationships and the influence of anthropogenic processes on these dynamics. Integrating multi-hazard scenarios into flood hazard, impact, and risk models provides a more comprehensive understanding of flood risks by considering interactions such as earthquake-triggered landslides and rainfall-induced flooding, alongside the impacts of urbanisation, land-use change, and climate change. This approach enhances decision-making frameworks, enabling more effective preparation and response. Here, we first illustrate hazard interaction matrices as a methodology to systematically identify potential triggers and secondary hazards by synthesising evidence from local and global literature. We then highlight multi-hazard scenarios through examples from Nairobi (Kenya), İstanbul (Türkiye), the Kathmandu Valley (Nepal), and the Philippines, where frequent and high-magnitude hazards underscore the critical importance of preparedness and mitigation. Effective flood risk management also requires interdisciplinary collaboration among researchers, policymakers, and practitioners, leveraging disaster science methodologies to assess hazard relationships, enhance response strategies, and build resilient communities.
How to cite: Malamud, B. D.: Integrating Multi-Hazard Scenarios for Enhanced Flood Risk Management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21109, https://doi.org/10.5194/egusphere-egu25-21109, 2025.
Flood forecasting and reservoir flood control operation are important technical to realize the "four pre" of basin flood control. Taking Shanxi reservoir, Baizhangji reservoir and Zhaoshandu drinking water project in Feiyun River Basin in Zhejiang Province as examples, this paper analyzes the spatial relationship and hydraulic connection of various water conservancy projects in the basin, and establishes a distributed Xin'anjiang model for flood forecasting at important nodes in the basin, which is used as the input of the flood control joint optimization operation model of reservoir groups and solves the model. The results show that during the period of defending against the typhoon Megi, the flood control joint optimization operation of reservoir group considering flood forecast information can further play the potential of reservoir flood control, and the peak shaving effect of Zhaoshandu section in the downstream is obvious.
How to cite: Ren, M., Zhang, Q., and Zhao, L.: Research and application of joint optimal operation of reservoir group flood control considering flood forecast information, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15522, https://doi.org/10.5194/egusphere-egu25-15522, 2025.
Flooding ranks among the most devastating natural disasters, often triggered by excessive rainfall, river overflow and storm surges. The consequences can be catastrophic, leading to loss of lives, displacement of communities, demolition of infrastructure, and prolonged economic setbacks. Accurate real-time flood forecasting is essential for providing early warnings, optimizing resource allocation, and implementing mitigation measures. Flood mapping and modelled water levels strongly depend on the river network's connectivity, channel geometry, and interactions with the floodplains. The Mahanadi Delta region in the Odisha state of India is such a dynamic area where the main river splits into several distributaries as it flows downstream, eventually merging with the Bay of Bengal. In this study, a 2D hydraulic model, HEC-RAS, has been utilized to simulate water levels, velocities, and water surface elevations over time for the purpose of identifying overflowing banks and floodplains. To enhance connectivity and improve floodplain representation, surveyed cross-sections of the river network have been merged with an improved Carto-set digital elevation model (DEM). The model has been calibrated and validated using data from various flood years. The flood extent from the model was compared with the satellite-derived flood extent obtained from Sentinel-1. Different performance matrices are used to investigate the model accuracy. The years 2011, 2014, and 2022 witnessed major flood events in recent times. These events are then simulated, major flood-prone stretches and floodplains are identified, and mitigation measures have been suggested. Developing a modelling framework capable of capturing the movement of water in river networks and floodplains of the Mahanadi River enables disaster management authorities to improve their preparedness and response strategies, significantly reducing potential damage and loss of life.
Keywords: Flood, Mahanadi delta, HEC-RAS, Sentinel-1, Flood mitigation.
How to cite: Singh, V. P. and Kuiry, S. N.: Assessing Flood Risk and Mitigation Strategies in the Mahanadi Delta Using 2D Hydraulic Modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17641, https://doi.org/10.5194/egusphere-egu25-17641, 2025.
Efficient evacuation during storm floods remains a critical challenge for coastal cities, primarily due to its dynamic and complex nature involving flood progression, human behavioral uncertainties, and emergency resource constraints. Current evacuation models inadequately capture these multifaceted dynamics, limiting effective emergency planning. This study introduces an Agent-based Dynamic Coastal Flood Evacuation (DCFE) model that comprehensively simulates the interactions among flood dynamics, human behavioral responses, GIS-based transportation networks, and shelter systems. Using Shanghai as a case study, we evaluate city-scale evacuations during a 1,000-year return period storm event. Our analysis shows that issuing warnings 12 hours before flood peak reduces casualty rates by over 25% compared to scenarios without early warning, while optimized decision-making can double evacuation efficiency. The results further reveal critical spatial disparities in evacuation performance due to inequitable shelter distribution. This integrated approach provides practical guidelines for enhancing evacuation strategies in coastal megacities worldwide.
How to cite: Yang, Y.: Dynamic Flood Evacuation Modelling for Coastal Cities: A Case Study of Shanghai, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1829, https://doi.org/10.5194/egusphere-egu25-1829, 2025.
Fast flood modelling for pluvial flood management in Innsbruck, Austria
The increasing frequency and intensity of precipitation events leads to urban flooding, often causing significant damage to infrastructure and property. This phenomenon, known as pluvial flooding, arises when heavy precipitation exceeds the capacity of urban drainage systems, leading to surface water accumulation. Climate change is expected to exacerbate this issue, emphasizing the urgent need for efficient predictive models to mitigate the associated risks and impacts.
Traditional hydrodynamic models, such as coupled 1D-2D simulations, offer highly detailed flood assessments by simulating both surface runoff and sewer network interactions. However, these models are computationally demanding, requiring significant resources and time, making them unsuitable for real-time flood forecasting and decision-making during extreme weather events.
To address these limitations, fast flood models like the dynamic CA-ffé model, based on Jamali et al. (2019) and further developed by Gholami Korzani and Deletic (2023), provide a practical alternative. These models efficiently integrate surface flow and sewer network dynamics, enabling accurate flood forecasting at a much lower computational cost. Previously validated in smaller Australian catchments, the dynamic CA-ffé model has demonstrated its ability to provide timely and accurate urban flood simulations, significantly improving flood forecasting and risk management.
To address flooding challenges in Innsbruck, a larger, mountainous catchment area (~50 km²), the dynamic CA-ffé model was adapted based on the model approach of Gholami Korzani and Deletic (2023). This model approach combines a cellular automata-based 2D simulation with a 1D sewer network model using SWMM (Stormwater Management Model). By synchronizing data exchanges between surface runoff and sewer discharge at regular intervals, the model achieves faster and more accurate flood predictions, enabling high-resolution urban flood forecasting.
Adapting the model to Innsbruck required adjustments to account for the city's complex mountainous terrain and boundary conditions. Additional case-specific modifications were implemented to ensure compatibility with the larger and more challenging catchment area. The model was tested using historical flood events and validated against fire brigade records and photo documentations, as no prior citywide flood models were available for comparison.
The model's fast computation times allow the simulation of different flood scenarios, including assessments of the effects of climate change. These simulations will help to identify flood risks and inform heavy rainfall management strategies. Initial results confirm the model's ability to simulate urban-scale flooding, while highlighting challenges in adapting the approach to larger and more topographically complex study areas, such as land-use based runoff coefficients and the use of multiple rain gauges for precipitation data.
Funding:
BlueGreenCities (project No. KR21KB0K00001), funded by the Austrian Climate and Energy Fund from October 2022 until September 2025
Early Stage Funding (project: FFMFF) funded by the Vice-Rectorate for Research of the University of Innsbruck from November 2023 until October 2024.
References:
Gholami Korzani, M., Deletic, A., 2023. Dynamic CA-ffé: a hybrid 1D/2D fast flood evaluation model for urban floods. Sydney.
Jamali, B., Bach, P.M., Cunningham, L., Deletic, A., 2019. A Cellular Automata Fast Flood Evaluation (CA-ffé) Model. Water Resources Research 55, 4936–4953. https://doi.org/10.1029/2018WR023679
How to cite: Hauser, M., Rauch, N., Gholami Korzani, M., Deletic, A., and Kleidorfer, M.: Fast flood modelling for pluvial flood management in Innsbruck, Austria, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6551, https://doi.org/10.5194/egusphere-egu25-6551, 2025.
This study investigates flood-prone areas in the Loukous Basin, Northern Morocco, utilizing machine learning and remote sensing techniques to analyze and map zones at risk. Renowned for its agricultural significance, the region features low-lying, flat terrain, an active river system, and oceanic influences, all of which exacerbate flooding risks. Additionally, climate change poses increasing challenges, intensifying extreme weather events and altering precipitation patterns that further threaten this vulnerable region. The research aims to enhance flood prevention strategies and mitigate economic and human losses by identifying and prioritizing highly vulnerable zones. Results consistently highlight significant flood susceptibility along the Loukous River and its adjacent plains, areas characterized by lowland topography, high drainage density, proximity to canals, and intensive agricultural activity. While spatial variations exist among the models, a strong consensus emerges regarding zones of low and very high vulnerability, emphasizing the need for tailored interventions. These findings provide critical insights for integrating agricultural development planning with flood risk management in the Basin of Loukous. They underscore the importance of adaptive strategies that consider the compounded effects of climate change, such as improved land-use practices, enhanced drainage systems, and sustainable water management. This study establishes a robust scientific foundation for implementing targeted measures to reduce flood impacts, safeguard livelihoods, and build resilience in this economically vital and environmentally sensitive region, with broader implications for similar flood-prone areas worldwide.
How to cite: Mekkaoui, O., Morarech, M., En-negady, I., Bouramtane, T., and Akka, H.: Evaluating Flood Vulnerability in the Loukous Basin, Northern Morocco: Integrating Machine Learning, Remote Sensing, and Climate Change Impacts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12481, https://doi.org/10.5194/egusphere-egu25-12481, 2025.
Bridges worldwide face significant risks from flood-related hazards, with hydraulic actions being the primary cause of structural damage, service disruption, and catastrophic failure. Bridge owners and regulatory agencies must assess multiple hydraulic risks, including scour, uplift forces, drag effects, debris impact, and deck displacement, all of which can compromise the load-carrying capacity of a bridge. The assessment of hydraulic actions on highway bridges in many parts of the world still relies on simplified or qualitative methods, whereby hydraulic models are yet to be embedded within guidance.
In the United Kingdom, the CS469 standard governs the assessment of hydraulic actions on highway bridges, providing guidance for risk evaluation and management strategies. The CS469 methodology calculates hydraulic flow characteristics at critical cross-sections within channels and bridge crossings utilising Bernoulli theorem and specific energy. While computationally efficient, this simplified approach relies on non-physical approximations. This fundamental limitation introduces substantial uncertainty into risk and vulnerability assessments, potentially compromising the reliability of management decisions.
This study presents an alternative approach utilising 2D HEC-RAS hydraulic models with bridges modelled as 1D elements within flow areas. The proposed methodology crucially maintains compatibility with existing data requirements from CS469 while adhering to open-source principles, requiring only publicly available data or information from existing assessments. This approach ensures cost-effectiveness and accessibility for bridge management teams while providing significantly improved accuracy.
Comparative analyses between the 2D HEC-RAS model and traditional CS469 calculations for six case study bridges revealed substantial differences in hydraulic response. The 2D model showed water depths up to 138% higher and flow velocities 64% lower than CS469 estimates. These differences significantly impact scour risk assessments, with HEC-RAS models typically predicting scour depths up to 2.3m lower (averaging 1.5m reduction) compared to simplified equations, resulting in more realistic risk classifications.
While hydraulic vulnerability assessments showed limited variation, the CS469 approach only considers threshold values without quantifying effects. Our findings demonstrate that numerical hydraulic simulations provide more accurate risk estimations with comparable resource requirements, suggesting that future revisions of risk assessment guidelines should prioritise this methodology. This advancement would enhance the accuracy and reliability of bridge risk assessments, ultimately improving infrastructure resilience and safety management strategies.
How to cite: Panici, D., Kripakaran, P., and Brazier, R.: A comparative analysis for assessment of hydrodynamic actions at bridges of 2D hydraulic models and traditional highway standards, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13884, https://doi.org/10.5194/egusphere-egu25-13884, 2025.
The risk associated with river floods has escalated significantly due to the increasing frequency and intensity of extreme precipitation events, compounded by the complex interplay of flood-generating mechanisms under a changing climate. Accurately estimating flood risks poses a formidable challenge, as these mechanisms often interact and exhibit varying influences across spatial and temporal scales. An in-depth understanding of flood-generating processes is critical for improving hydrological modeling, flood frequency analysis, and risk management strategies in diverse climatic regions. Despite India being one of the most flood-prone countries globally, a systematic classification of hydrometeorological flood-generating processes remains largely absent. Understanding the role of catchment and climate attributes in flood generation is crucial for advancing our ability to predict and manage flood risks. This study proposes a robust framework to classify flood-generating processes into three primary categories: long rainfall floods, short rainfall floods, and excess rain floods. The analysis focuses on major river basins across India, offering insights into region-specific flood dynamics. To achieve this, we leverage the CAMELS-IND dataset, a comprehensive repository of hydrological and meteorological data, covering 471 catchments across India from 1980 to 2020. Using a peaks-over-threshold (POT) approach, we identify significant flood events and assess their characteristics. We then employ a Light Gradient Boosting Machine (LightGBM) model, an advanced machine learning algorithm known for its efficiency and accuracy, to evaluate the contribution of various climate and catchment attributes in triggering these floods. To enhance interpretability, we integrate Shapley additive explanations (SHAP), which provide a localized and global understanding of the model's predictions, highlighting the relative importance of each attribute. Our findings underscore the dominant role of climate attributes, such as precipitation intensity, antecedent soil moisture, and temperature, in determining the spatial distribution of flood-generating processes across diverse climatic zones. Catchment attributes, including soil type, slope, and land use, also contribute but to a lesser extent. These insights have significant implications for flood risk management, particularly in ungauged catchments, and can enhance the accuracy of hydrological and hydrodynamic models under changing climatic conditions.
How to cite: Tripathi, V., Deopa, R., and Mohanty, M.: Unraveling Hydrometeorological Drivers of Floods: A Data-Driven Analysis Across India's River Catchments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15096, https://doi.org/10.5194/egusphere-egu25-15096, 2025.
Floods are among the most impactful natural phenomena affecting society. The associated risks are influenced by factors such as urban expansion into high-risk areas, modifications to rivers and watersheds (e.g., artificial channels, flow redirection, and structures like dams and dikes), and the effects of climate change, including more frequent and severe events. In recent years, the application of Artificial Intelligence (AI) in climate and weather risk assessment has gained increased attention due to its ability to handle numerical and categorical variables, uncover nonlinear relationships, and achieve high performance.
In this study, we introduce FloodGuard, an AI-powered tool for flood vulnerability and risk mapping. FloodGuard employs the concept of regionalization in ungauged basins and leverages a flood inventory derived from satellite imagery (e.g., Copernicus Emergency Mapping Service) over extensive areas (e.g., national or continental scales). The methodology selects the most relevant historical flood events and transfers this information to train a Random Forest machine learning model for estimating flood extent and producing a flood exposure map. Inputs to the model include the Geomorphic Flood Index (GFI), the Elevation, the Horizontal Distance to the Nearest River, Precipitation, the NDVI, and information on Land Use and Lithology. Flood prediction map is evaluated using maps generated from hydrological and hydraulic models. To assess vulnerability, we apply a geomorphic approach proposed by Manfreda and Samela (2019). This approach estimates flood depth, which is useful for estimating fast vulnerability levels. Finally flood risk is estimates with a GIS-based model.
The primary objective of this study is to provide a preliminary simple tool to estimate a flood risk and provide risk maps. At the same time, this study evaluates evaluate the transferability of machine learning models from regions with flood records to ungauged areas using satellite observations. Limitations include uncertainties inherent to machine learning models and the lack of association with specific return periods. Preliminary results across Italy demonstrate that the Random Forest model achieves high performance (AUC > 0.9) and exhibits robust generalization capabilities (e.g., combined error (rfp + (1-rtp)) of 0.58).
Keywords: Artificial Intelligence, Machine Learning, Flood risk, Flood vulnerability, GFI.
This study was carried out within the RETURN Extended Partnership and received funding from the European Union Next-Generation EU (National Recovery and Resilience Plan -NRRP, Mission 4, Component 2, Investment 1.3 -D. D. 1243 2/8/2022, PE0000005).
How to cite: Saavedra Navarro, J., Zhuang, R., Albertini, C., Samela, C., and Manfreda, S.: FloodGuard: An AI-Powered Tool for Flood Risk and Vulnerability Mapping in Ungauged Basins., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19363, https://doi.org/10.5194/egusphere-egu25-19363, 2025.
In many countries, continental and global scale flood hazard modelling methodologies are employed to provide an understanding of flood hazard over large geographical areas and at multiple return periods, flood generating mechanisms, and future climate change scenarios. These models are commonly used for estimating flood hazard in areas where high resolution flood mapping is unavailable, and for estimating portfolio risk for insurers and the financial sector. However, these products are generally lower accuracy and precision than local (e.g. regulatory, engineering-level) maps, and therefore the limitations and appropriate use cases of continental and global scale mapping should be understood when using these products to understand flood hazard and flood risk.
Public Safety Canada (PS) has the mandate to keep Canadians safe from a range of risks and is working towards several soon-to-be launched flood resilience policy programs that depend upon a consistent, Canada-wide characterization of flood risk, and has accordingly procured multiple flood hazard models. PS bridges policy work to data science and engineering practices by conducting quantitative risk analysis, using Canada-wide flood hazard models, robust exposure data, and damage estimation methodologies.
PS has done extensive testing of Canada-wide flood hazard models, including quality control and evaluation, to better understand their limitations and uses, and to support quantitative risk analysis for PS and other federal departments and agencies. This presentation will describe the results of PS’s evaluation and use of global models, including performance assessment against a set of comparable regulatory-quality flood maps across Canada and recommendations for appropriate use cases. These findings will contribute to a future partnership between PS and an academic research consortium to develop a made-in-Canada, open source, Canada-wide flood hazard model that will leverage data and expertise developed across government and other sectors.
How to cite: Pellerin, J., Hayes, S., Chastko, K., Ballard, M., Bourke, R., and Van de Valk, J.: Canada-wide Modelling – Analysis of Model Accuracy to Drive Appropriate Use and Risk Reduction Program Development, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7327, https://doi.org/10.5194/egusphere-egu25-7327, 2025.
As climate change drives sea level rise and raises critical questions about the design and efficacy of flood defences, there is an increasing demand for high-resolution coastal flood data. As a commercial provider of flood risk data to the insurance and banking sector, we have users that emphasise accuracy. For instance, these users seek not only to determine if floodwaters might reach a property but also whether they will exceed the height of the doorstep. While advancements in flood modelling continue to meet these growing needs, a persistent challenge remains: how can we balance the need for high accuracy with the practical constraints of production costs and the need for timely data delivery?
Focussing on coastal flood mapping examples in the UK, our presentation will compare outputs from two ends of the modelling spectrum: complex full hydrodynamic modelling and a simplified projection approach. Simplified approaches often face criticism for their limitations, but we will argue that they can provide valuable – as well as timely and efficient – outputs, when applied appropriately and with a clear understanding of their constraints.
This work aims to explore the importance of balancing advanced and simplified techniques, offering insights into the factors that most significantly influence flood modelling outputs. For example, we examine whether the choice of input data (e.g., the terrain data, the extreme sea levels) has a greater impact on results than the modelling approach itself (hydrodynamic compared to projection modelling techniques). By highlighting the trade-offs and opportunities, we aim to contribute to a more nuanced understanding of how to optimise coastal flood risk data production to meet user needs.
How to cite: Raven, E., Charge, J., Liam, H., James, S., and Rhiannon, W.: Balancing complexity and efficiency in coastal flood risk modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8901, https://doi.org/10.5194/egusphere-egu25-8901, 2025.
Calibration and validation are necessary steps in the field application of physically-based models for floods and landslides. These processes validate the model assumptions and adjust the parameterization to align with historical observations. However, calibration remains a time-consuming task due to the lack of globally available datasets directly linked to the calibration schemes of physically-based models.
As part of the FastFlood.org and FastSlide.org rapid modeling platforms, we have developed a built-in calibration scheme that leverages global observational datasets and data-driven approaches. This approach links the results of global calibration datasets to a standardized calibration scheme for flood and landslide models, enabling automatic calibration globally based on these datasets.
In this paper, we outline the setup, global dataset processing workflows, and the initial outcomes of this research. The global datasets are derived from multiple observation types. For example, discharge data for return period events in rivers is based on GloFAS reanalysis data, bias-corrected using an extreme-value analysis performed globally on GRDC discharge station time series. Flood extents from the Global Surface Water Explorer are included but corrected for the underrepresentation of flash flood events in the observational data. For landslide processes, landslide inventory collections and data-driven global landslide susceptibility maps are utilized to provide built-in calibration functionality.
To streamline the calibration process, specific automated schemes have been developed to preprocess these global datasets, enabling direct comparison with model outputs without user intervention. Furthermore, we explore some initial results of this calibration scheme, comparing its accuracy and relative performance against other calibration methods.
How to cite: van den Bout, B., Kolaparambil, F., Katarya, S., van Westen, C., and Meijvogel, D.: Global automated calibration procedures for the FastFlood and FastSlide rapid hazard models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9325, https://doi.org/10.5194/egusphere-egu25-9325, 2025.
Floods are considered one of the most damaging natural disasters known to humankind, and their severity has increased significantly due to the impacts of climate change and global warming in recent decades. Floods have become more unpredictable and erratic due to the influence of extreme hydroclimatic events. Therefore, obtaining near-real-time and accurate flood inundation maps of such events is essential for effective flood emergency response. These can be achieved easily by leveraging multi-source satellite imagery and remotely sensed data. The freely accessible satellite imagery and remotely sensed products can provide essential information that can significantly reduce the resources needed to create flood inundation maps and improve the accuracy of mapping and monitoring systems. This study integrated high-resolution satellite imagery and multiple remote sensing data to improve the flood inundation mapping technique in a data-scare South Asian watershed. The study considered the 2008 Bihar flood caused by the embankment breach of the Koshi River as a case study. This study used Landsat satellite's surface reflectance data to map the flood inundation using the commonly used water index known as the Modified Normalized Difference Water Index (MNDWI). MNDWI is a commonly used water classification technique to detect open surface water features using surface reflectance data sensed by the satellite. Further, the Normalized Difference Vegetation Index (NDVI), permanent water bodies, and Height Above the Nearest Drainage (HAND) datasets are used to mask the MNDWI map (initial flood inundation map) and improve the accuracy of the inundation map. In addition, different thresholding values of the final masked MNDWI map are applied to obtain more accurate and robust flood inundation maps with fewer false positive and false negative pixels.
How to cite: Aryal, A., Sinha, R., and Lakshmi, V.: Improving the Flood Inundation Mapping Technique using Satellite Imagery and Remote Sensing Data: A Case Study of the Bihar Flood Caused by the Koshi Embankment Breach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12576, https://doi.org/10.5194/egusphere-egu25-12576, 2025.
Floods are the most frequent disasters, affecting the largest number of people. In 2023, 164 floods were recorded worldwide, killing 7763 people, affecting 32.4 million people, and resulting in 20.4 billion USD losses (CRED 2023 Disasters in Number report). To mitigate flood risk, decision makers and civil protection authorities need reliable information on flood risk assessment, covering all disaster management stages.
In the framework of a Programming Agreement with the Prefecture of Attica, NOA/IAASARS/BEYOND, in cooperation with NTUA/ITIA, developed a holistic multiparameter methodology that was implemented in five flood-stricken river basins at high spatial resolution. The research teams collected all available Earth Observation data, spatial data and technical studies; conducted detailed field visits; and modified the DEM and land cover accordingly. Following rainfall-runoff modeling and hydraulic modeling, the flood hazard was assessed for different scenarios. Vulnerability was considered a weighted estimation of population density, population age, and building characteristics on the basis of the population-housing census at the building block level. Exposure was based on the land value. Flood risk was eventually assessed on the basis of the combination of flood hazard, vulnerability, and exposure. Moreover, critical points, which were identified from the field visits, were also crosschecked with the flood inundation maps. Finally, refuge areas and escape routes were proposed for the worst-case flood scenario. This innovative methodology was applied, among other methods, in the Mandra river basin and was validated with the results of the urban flash flood, which took place in 2017, the deadliest flood in Greece in the last 40 years. BEYOND developed a user-friendly web GIS platform in which all the collected and produced data, including flood risk maps, critical points, refuge areas and escape routes, are made available.
This flood risk assessment methodology was applied, following adaptation, in the Garyllis river basin in Cyprus, within the framework of the EXCELSIOR project, as part of the collaborative activities between ECoE and BEYOND. Data were collected from multiple sources, including satellite missions, governmental portals, in situ measurements, and historical records, at different resolutions. The collected data were calibrated via onsite visits and discussions with relevant actors, harmonized in terms of spatial and temporal resolution and used as inputs to estimate the flood hazard for different return periods. The vulnerability levels of the study area were quantified via the weighted linear combination of population density, population age, and building characteristics at the road level. The exposure levels were quantified in terms of the land value. Flood risk levels were estimated as a product of hazard, vulnerability and exposure levels. The validity of the proposed methodology was evaluated by comparing the critical points identified during the field visits with the estimates of the flood risk levels. Consequently, escape routes and refuge regions are recommended for the most extreme scenario.
This work supports relevant authorities in improving disaster resilience and in implementing the EU Floods Directive 2007/60/EC, the Sendai Framework for Disaster Risk Reduction, the UN SDGs, and the UN Early Warnings for All initiative.
How to cite: Tsouni, A., Panagiotou, C., Sigourou, S., Kountouri, J., Pagana, V., Dimitriadis, P., Iliopoulou, T., Sargentis, G.-F., Mettas, C., Evagorou, E., Ioannidis, R., Chardavellas, E., Dimitrakopoulou, D., Alexopoulos, M. J., Mamasis, N., Koutsoyiannis, D., Hadjimitsis, D., and Kontoes, C. (.: A transferable multicriteria methodology for flood risk assessment: two case studies in Greece and Cyprus, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15732, https://doi.org/10.5194/egusphere-egu25-15732, 2025.
Understanding flood hazard at scale requires consistently generated flood maps. To build flood maps at scale, automated frameworks must be used that can take input data, transform this into hydrodynamic simulations and process it into output flood maps.
This transformation from inputs into simulations can be influenced by the design of the framework and the choices made by modellers as to how the transformations are made. These choices can be influenced by quality and availability of input data, availability of computational resources and desired outputs. There may not be clear objectively better options, so subjective choices have to be made. Understanding the sensitivity of these choices is key in both making them, and rating the uncertainty of the outputs.
Here we present the outputs from some of the sensitivity testing undertaken when developing a global model framework including both their influence on flood hazard and their influence on other deciding factors (such as computational cost) made when building a global flood map. This presentation includes results from tests that were shown to develop key or interesting results, such as:
- How rivers are split into separate simulations
- Minimum size of fluvial river assessed
- Length of pluvial storm assessed
How to cite: Cooper, A. and Savage, J.: Sensitivity of global flood modelling frameworks to input parameter choices, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17337, https://doi.org/10.5194/egusphere-egu25-17337, 2025.
Flooding is a common and devastating disaster that happens recurrently in Bangladesh. Climate change, rapid urbanization, and inadequate drainage systems have exacerbated this event in recent years. In 2023, Satkania upazila in Chattogram district was severely affected by flood. Additionally, communities weren’t prepared for this flood, as they hadn’t faced any kind of flood in recent years, as reported by field observation. That severity helped to select Satkania upazila as a study area for this research. This study focuses on developing a GIS-based evacuation route plan to mitigate the adverse impacts of flooding by ensuring safe and efficient evacuation during emergencies. To identify high-risk and low-risk zones, Sentinel-1 SAR imagery was used to create an inundation map. Frequent and severe flooding criteria were used to find the high-risk zones. This area is designated as an assembly point where people can gather before moving to safer places. Low-risk zones were identified as suitable locations for emergency shelters to ensure people's safety during disaster events. ArcGIS network analyst tools are used to perform closest facility analysis and service area analysis. For this analysis, road network, elevation data, and building density were integrated with ArcGIS network analysis. Closest facility analysis helped to optimize the evacuation routes based on minimum travel time and shortest distance. To determine zones to locate emergency shelters, a service area analysis was performed to improve accessibility for vulnerable populations. The inundated map showed that 11.78% area was inundated during the 2023 flood. According to the network analysis, the assembly point that is closest to the emergency shelter is more accessible in both time and distance cases. A total of 3 out of 14 suggested emergency shelters were within 30 min, 7 within 60 min, 3 within 90 min, and 1 within 90 min walk from the assembly point. In terms of distance from the assembly site, there are 3 emergency shelters within 1000 m, 7 within 2000m, 3 within 3000 m, and 1 within 4000 m distance. The result showed the effectiveness of GIS-based route planning to minimize flood causalities and enhance disaster preparedness. This study provides actionable insights to design targeted interventions and response strategies for local governments and disaster management authorities.
How to cite: Ahmed, M., Anny, N. S., Khadem, S., and Baten, T. A.: A GIS-Based Evacuation Route Planning in Flood Inundated Area of Satkania, Chattogram, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20503, https://doi.org/10.5194/egusphere-egu25-20503, 2025.
Floods are globally catastrophic, with profound impacts on India’s environment, agriculture, and infrastructure. Between 1953 and 2010, floods annually affected 7.2 million hectares and 3.2 million people. Odisha, particularly the Mahanadi basin, ranks seventh in flood vulnerability, with over 90\% of its annual rainfall occurring during the monsoon. Synoptic systems from the Bay of Bengal exacerbate rainfall, causing frequent and severe floods. Despite existing forecasting systems, downstream regions remain inadequately protected, highlighting the need for more accurate predictive mechanisms.
Recent advancements in Machine Learning (ML) and Deep Learning (DL) offer new opportunities for flood forecasting. Long Short-Term Memory (LSTM) models excel at capturing intricate temporal patterns in rainfall and streamflow data, outperforming conventional methods. Innovations like hybrid LSTMs and Spatio-Temporal Attention (STA) mechanisms enhance their performance, while novel architectures such as Temporal Convolutional Networks (TCNs) and Bi-LSTMs improve long-term predictions.
This study introduces a Kolmogorov-Arnold Network (KAN)-enhanced LSTM model for five-day-ahead flood prediction in the Mahanadi basin. KAN leverages learnable activation functions and spline representations, improving accuracy, interpretability, and computational efficiency. Evaluated against traditional LSTMs using metrics such as Nash-Sutcliffe Efficiency (NSE), time-to-peak prediction, and convergence speed, the KAN-enhanced model consistently outperformed standard LSTMs. It demonstrated a 12\% improvement in NSE, superior peak timing, and 20\% faster convergence, offering crucial advancements for early warning systems.
By integrating KAN's ability to model non-linear relationships with LSTM’s strength in sequential data analysis, this framework addresses complex hydrological dynamics, providing reliable flood forecasts. These findings underscore the potential of KAN-enhanced architectures to revolutionize flood prediction, offering scalable and interpretable solutions for flood-prone regions.
How to cite: Sarkar, S., Dey, A., Chatterjee, C., and Mitra, P.: KAN-Enhanced LSTM for Accurate and Scalable Flood Forecasting: A Case Study of the Mahanadi Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8031, https://doi.org/10.5194/egusphere-egu25-8031, 2025.
Hydrodynamic models solving various forms of the shallow water equations are commonly used to assess flow depths and velocities during floods. Recent advances have extended these models to incorporate elements of the catchment such as sewer systems, culverts, bridges, and weirs. However, many flood models still represent buildings as raised elevations or with increased roughness coefficients, blocking water from entering building areas. This can lead to an overestimation of flood depths and extents, as these storage areas are not considered. Until now, the cumulative effects of flow into buildings on city-scale inundation have not been simulated using physically based models. This study develops and implements a building indoor-inundation model, comparing it with simulations that neglect this effect (storage and retention inside buildings) to quantify the differences in flood depth and extent.
A hydrodynamic model was coupled with the indoor-inundation model to estimate flow into and out of buildings (through windows, doors, etc.). This model incorporates building geometry, including walls, doors, and stairwells, to determine flow throughout the building. It also accounts for the transition between pressurized and non-pressurized flow, allowing water to move from lower to upper floors. The building indoor-inundation model was coupled with the 2-D diffusive wave model P-DWave, which simulates surface flood inundation outside the buildings. Water is exchanged between the two models at run-time in a bi-directional manner, with water flowing both into and out of the buildings.
Flood simulations were conducted for the city of Baiersdorf in northern Bavaria, which experienced flash floods in 2007 caused by heavy rainfall, resulting in over 86 million euros in damages. The event was recreated using the dual-drainage model PD-Wave/SWMM to simulate interactions between overland flow and the sewer system. Three test cases were modeled: buildings represented with increased roughness coefficients, buildings raised above the floodplain, and buildings modeled using the presented hydrodynamic approach. The results show that modeling the flow into and out of buildings has a moderate to significant impact on both flood depths and extents, highlighting the importance of including building inundation in urban flood modeling.
How to cite: Johnson, T. G. and Leandro, J.: Assessing the Impact of Building Indoor-Inundation on Flood Depth and Extent at City Scale: A Novel 2-D Coupled Hydrodynamic and Building Inundation Model for Urban Flood Modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10398, https://doi.org/10.5194/egusphere-egu25-10398, 2025.
Traditional flood modelling approaches, which rely on deterministic methods, often fail to account for the inherent uncertainties of flood events, such as discharge estimates or flood defence parameters. Probabilistic flood modelling addresses this gap by quantifying the likelihood of various scenarios, based on the probability of occurrence of its inputs. However, the large number of required numerical simulations makes this framework computationally expensive. In this study, we explore the use of a multi-scale graph neural networks inspired by finite volume methods to accelerate probabilistic flood simulations. This approach is applied to several dike rings in the Netherlands - regions enclosed by levees - considering uncertainties in dike breach locations and inflow discharges. To improve the reliability of the model, we select among the output simulations only the ones that approximately preserve the total flood volumes over time, as calculated from the inflow boundary conditions. Our model generates thousands of flood scenarios with orders-of-magnitude speedups compared to traditional methods. The resulting output maps provide the expected frequencies of inundation extents for specific water depths, offering a robust tool for efficient and comprehensive flood risk assessment.
How to cite: Bentivoglio, R., Isufi, E., Jonkman, S. N., and Taormina, R.: Probabilistic flood modelling with multi-scale hydraulic-based graph neural networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10951, https://doi.org/10.5194/egusphere-egu25-10951, 2025.
With the ongoing expansion of urban development into underground spaces and the increasing occurrence of extreme rainfall events, the risk of flooding in these areas has heightened. Stairs, as critical connections within underground spaces, markedly influence water inflow and evacuation during flooding events. Therefore, the impact of different stair types on the hydrodynamic characteristics of water flow in underground spaces is worth studying. This paper constructed physical models of stairs at various underground locations to investigate two types of stairs and their hydrodynamic characteristics and the risk. The findings revealed that at low flow velocities, both stair types lowered water flow in the upper section; at medium flow velocities, the main impact was observed in the lower section; and at high flow velocities, the reduction effects were weaker. For the stair types allowing lateral outflow, increasing the lateral slit height enhanced outflow volume, thereby decreasing water flow on the stairs. However, excessively high slit gaps did not substantially increase lateral outflow and may introduce safety hazards, such as the risk of children falling through. Additionally, a comprehensive analysis of flow velocity and water depth reveals that, in addition to the higher risk in the lower section, particular attention must be given to the turning point between the stair and the rest platform. The intricate vortex flow pattern at this location, characterized by elevated flow velocities and water depths, results in a risk level surpassing that of the jet flow.
How to cite: Dai, Z. and Huang, G.: An experimental investigation into the effects of underground stairs types on hydrodynamics characteristics and risk., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-402, https://doi.org/10.5194/egusphere-egu25-402, 2025.
This study utilized AR6 daily rainfall data for Miaoli County, Taiwan, sourced from the National Science and Technology Center for Disaster Reduction (NCDR) through the "Taiwan Climate Change Projection Information and Adaptation Knowledge Platform" (TCCIP). Flood hazard maps were generated for the baseline period (1995–2014) and future projections (2081–2100), considering four greenhouse gas emission pathways: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Social vulnerability composite index data for Miaoli County, developed by NCDR under the "Disaster Reduction Dynamic Data" initiative, were utilized to construct vulnerability maps. Village-level population data were retrieved from the Miaoli County Household Registration Service Platform to create exposure maps. By integrating hazard, vulnerability, and exposure data, flood disaster risk levels for each village were assessed. The results reveal that flood disaster risk in Miaoli County escalates with increasing greenhouse gas emissions under future scenarios. These findings highlight a growing vulnerability to flooding in the county, emphasizing the need for proactive measures. The outcomes of this study provide critical insights for the Miaoli County Government, supporting the identification of high-risk villages and the prioritization of resources for flood mitigation infrastructure. This strategic approach aims to effectively reduce the threat of flood disasters under changing climate conditions.
How to cite: Liu, W.-C., Liu, H.-M., and Huang, W.-C.: Utilizing AR6 Daily Rainfall Data to Assess Climate Change Impacts on Flood Risk in Miaoli County, Taiwan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2360, https://doi.org/10.5194/egusphere-egu25-2360, 2025.
The increasing frequency and severity of climatic-driven extreme events such as heavy precipitation, floods, and droughts have raised global concerns due to their substantial social, economic, and environmental impacts. Among these, floods are considered the most devastating, causing extensive damage to life, property, and infrastructure.
This study focuses on assessing flood risks in Afghanistan, a country highly vulnerable to climatic disasters due to decades of conflict, environmental degradation, and limited mitigation capacities. Using remote sensing (RS) data and geographic information systems (GIS) techniques, the study evaluates flood hazard and vulnerability as key components of flood risk at the sub-basin and provincial levels. Principal component analysis (PCA) is employed to identify governing environmental, climatic and social indicators of flood risk. Additionally, the Analytical Hierarchy Process (AHP) is used to rank and prioritize the relative importance of various indicators in the hazard and vulnerability index, ensuring logical consistency through a systematic evaluation and minimizing bias by reducing subjective influence in decision-making.
The findings reveal that the eastern and northeastern regions of Afghanistan, mainly overlying the Amu and Kabul River basins, are severely exposed to very high flood hazards. This is primarily due to the combined effects of precipitation, topography, and drainage characteristics, all of which contribute to rapid runoff and increased flooding potential. The vulnerability assessment indicates that the densely populated rural areas in the northern and eastern regions are more susceptible to flood risk. Significant land use changes further intensify vulnerability, increasing the exposure of communities to flooding. Overall, the study identifies key flood-prone areas, providing essential guidance for policymakers. These findings offer a roadmap for resource allocation with an aim of developing targeted mitigation strategies, ultimately enhancing community preparedness and building a sustainable adaptive capacity to manage future flood risks effectively.
How to cite: Ishanch, Q., Mishra, K., Zarfl, C., and Fitzsimmons, K.: Flood Risk Assessment Using Morphometric and Hydrological Analysis in Afghanistan: An Integrated RS and GIS Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3596, https://doi.org/10.5194/egusphere-egu25-3596, 2025.
Fluvial floods occur when the water level in a watercourse rises and exceeds the capacity of the river banks, thus flooding the adjacent floodplain. The aim of this study is to map and assess fluvial flood hazard at catchment scale as well as municipal scale using the rainfall-runoff modeling, hydraulic modeling, and geographic information systems (GIS). As the study area, we selected the Gidra River Basin, which is located in western Slovakia. Moreover, we selected twelve municipalities from the studied basin based on the condition that urban area of the municipality is completely or partially located in the studied basin, i.e. can be significantly affected during a fluvial flood event. The Stochastic Rainfall Generator model was used to synthetically generate rainfall time series based on the observed annual maxima daily rainfall from the Častá and Cífer rainfall stations for the period 1990 – 2020. In order to calculate the design peak discharge with 100-year return period, we used the Continuous Simulation Model for Small and Ungauged Basins. The estimated design peak discharges were calculated for five cross-sections in the Gidra River Basin, which were considered independent for hydraulic modeling. Hydraulic modeling was performed with 1D steady-state flow conditions using the Hydrologic Engineering Center's River Analysis System. We modeled selected river sections of the main Gidra River and its tributary named Štefanovský potok. Both river sections were selected because they are listed as critical river sections for possible occurrence of fluvial floods in the last cycle of the Preliminary Flood Risk Assessment in Slovakia from 2018, which was elaborated under the Directive 2007/60/EC of the European Parliament and of the Council of 23 October 2007 on the assessment and management of flood risks. The obtained results from the catchment-scale hydraulic modeling, i.e. flood extent, flow depth, and flow velocity for Q100 flood scenario, created the basis for the subsequent municipal-scale assessment. In order to distinguish the flood hazard at municipal scale, we calculated the fluvial flood hazard index (FFHI) using the flood extent, average flow depth, and average flow velocity in each municipality as indicators. First, we normalized the values of these indicators using the maximum method and then we used equal weighting of indicators to combine them to the final FFHI. Based on the obtained results, the highest fluvial flood hazard was recorded in the municipalities of Cífer, Budmerice, and Jablonec, which are located in the central part of the studied basin, but also in the municipalities of Píla and Častá at the upper part of the basin. The resulting FFHI at municipal level was compared with the number of previous fluvial floods in the studied municipalities in the period 1996 – 2024, where a very good agreement was achieved. Acknowledgment: Funded by the EU NextGenerationEU through the Recovery and Resilience Plan for Slovakia under the project No. 09I03-03-V03-00085.
How to cite: Vojtek, M. and Vojteková, J.: Mapping fluvial flood hazard at catchment and municipal scale: a case study from Slovakia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3314, https://doi.org/10.5194/egusphere-egu25-3314, 2025.
Coastal flooding is caused by a complex interplay of various factors, including storm surges, wave overtopping, river flooding due to heavy rainfall, and inland water inundation. To predict and prepare for potential coastal flooding, coastal inundation predicton maps estimating flood depth and area under various hypothetical scenarios have been developed and utilized. However, most existing coastal inundation predicton maps have limitations in comprehensively considering the diverse factors contributing to coastal flooding. This study aims to overcome these limitations by incorporating multiple flood-inducing factors in coastal areas. Specifically, numerical modeling using ADCIRC and empirical formulas from EurOtop 2018 were applied to predict flooding caused by storm surges and wave overtopping. Additionally, 660 to 735 hypothetical typhoon scenarios were developed and applied for different coastal regions. To account for the impacts of future climate change, sea-level rise projections based on the SSP 5-8.5 climate scenario for the year 2100 were also included. The resulting coastal inundation predicton maps, which integrate multiple factors, were developed for four return periods: 50, 100, 150, and 200 years. These maps can serve as essential tools for developing disaster prevention policies and assessing coastal flood risks, contributing to minimizing flood damage in coastal regions.
How to cite: Lee, H.-Y., Jeong, K.-Y., Cho, W.-H., Park, J.-J., Seo, G.-H., and Hogan, P.: Development of Integrated Coastal Inundation Prediction Maps Considering Multiple Factors and Climate Change Impacts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5380, https://doi.org/10.5194/egusphere-egu25-5380, 2025.
There is currently no pre-existing map of embankments, dikes, levees and merlons in the Upper Seine River basin as existing data is limited to specific local studies. Developing a comprehensive map of these features is needed for effective flood risk management and planning. Embankments and merlons play a significant role in shaping the basin’s hydrological and geomorphological dynamics, influencing water flow patterns, floodplain connectivity, and sediment transport. Accurate mapping enables authorities to identify areas where these structures may exacerbate flood risks or disrupt natural floodplain functions, which are essential for mitigating flood impacts. Leveraging advancements made in GIS and LiDAR DEM data, an automated tool was developed to detect and map these features within the floodplain. Their detection was made possible by extracting morphometric parameters related to relative height, slope, and convexity. The mapped features were then characterized to evaluate their potential impact on flood dynamics. This characterization considers three parameters: the valley’s width, the embankment’s position within the valley and its elevation. The tool demonstrates encouraging outcomes, with good detection accuracy and a characterization protocol consistent with field observations. These findings establish a scalable methodology that can be applied to geomorphologically similar regions, providing a framework for improved floodplain management.
How to cite: Dereppe, J., Laurent, V., and Arnaud-Fassetta, G.: Mapping embankments and merlons in the Upper Seine River basin : A GIS-based approach for comprehensive floodplain dynamics., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8185, https://doi.org/10.5194/egusphere-egu25-8185, 2025.
In lowland areas that are susceptible to flooding, for example due to river levee breaches, the presence of linear elements such as levees of minor channels or road/railway embankments can significantly influence the inundation dynamics. Being more elevated than the surrounding land, these embankments can provide an obstacle to flood propagation, reducing the flood extent, but sometimes increasing the water depths. To obtain accurate flood simulations, and hence reliable flood hazard maps, the correct representation of embankments in the computational domain is therefore crucial.
Numerical simulations for flood hazard assessment usually rely on the unrealistic assumption that the minor embankments in the floodable area are able to withstand the interaction with floodwaters without collapsing. However, especially when overtopped, these elements can be eroded or collapse, no longer limiting the flood propagation. The assumption of “non-erodible” embankments can thus lead to misestimating the flood hazard, but studies in literature about this issue are lacking.
In this work, a preliminary analysis on how flood hazard mapping in lowland areas can be influenced by the possible failure of minor embankments is carried out, focusing on a case study in Italy. The results of two-dimensional numerical simulations of flood scenarios performed considering the minor embankments either as non-erodible or as erodible are compared. The most important problem in simulating the case of erodible embankments is the difficulty in including failure criteria to predict their collapse. While this is indeed a limitation for roads and railways, for which the large variability in building materials and structure prevents the definition of reliable failure criteria, it can be presumed that the available models developed to predict the breaching of earthen dams/levees can be applied to the levees of irrigation and drainage channels as well. For this reason, the study focuses on an area that is crossed only by the earthen levees of minor channels, and a simple erosion model that can automatically predict the breaching of these elements due to overtopping is adopted. The scenario considered for the analysis is the inundation induced by a levee breach in a nearby river.
Results show that, when assuming “non-erodible” embankments, the flood extent can be underestimated, while the maximum water depth and the flood hazard classification can be overestimated upstream of the erodible embankments and underestimated downstream. Moreover, the flood arrival time can be anticipated in the downstream areas. Overall, despite being case-specific, the analysis suggests that the unphysical assumption of “non-erodible” embankments in lowland areas can significantly influence the flood hazard assessment, possibly underestimating it, and this limitation should be kept in mind by flood risk management authorities.
This work is part of the project "MORFLOOD" (PNRR-M4C2 - I1.1-Avviso MUR n.104 del 02-02-2022 - PRIN2022 - Project code 2022SJ2NJ9 - CUP Code D53D23004860006 - Funded by the European Union-NextGenerationEU).
How to cite: Dazzi, S.: Failure of minor embankments in inundated areas: influence on the flood hazard estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9269, https://doi.org/10.5194/egusphere-egu25-9269, 2025.
The design of flood hydrographs for specific return periods is crucial in hydrological studies, especially in poorly-gauged watersheds. Exploiting the background of the distributed grid-based hydrological model DREAM, to estimate flood hydrographs for assigned return periods we propose the QT-DREAM, which incorporates high-resolution geomorphological data to reduce structural uncertainty compared to traditional empirical models. By combining Hortonian and Dunnian infiltration models, QT-DREAM allows a spatially-distributed assessment of runoff generation based on local climatic and geomorphological characteristics. QT-DREAM was applied on twenty gauged catchments located in Puglia and Basilicata regions (Southern Italy), characterized by climates ranging from semi-arid Mediterranean to humid continental. Flood hydrographs were generated by the model for nested sub-basins for return periods of 30, 200, and 500 years and compared with those calculated using runoff maps of the entire basin. Results demonstrate that the QT-DREAM model successfully reproduces the frequency distribution of observed peak floods, ensuring consistency between peak discharges estimated at sub-basin and entire basin scales. This study provides a significant contribution to the development of indirect methodologies for flood estimation in poorly-gauged watersheds, with potential implications for hydraulic risk assessment and flood mitigation planning. In particular, model outputs can be integrated with two-dimensional hydrodynamic models to provide detailed flood hazard mapping, enhancing its applicability for basin-scale flood risk assessment and mitigation.
How to cite: Mesto, F., Gioia, A., Bonelli, R., Ciccone, M., Dal Sasso, S., Giuzio, L., Lombardo, M., Manfreda, S., Margiotta, M. R., Sileo, B., Perrini, P., Totaro, V., Iacobellis, V., Fiorentino, M., and Corbelli, V.: On the Use of the Distributed Hydrological Model QT-DREAM to Reproduce the Frequency Distribution of Observed Flood Peaks in Puglia and Basilicata (Southern Italy), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18066, https://doi.org/10.5194/egusphere-egu25-18066, 2025.
The rising likelihood and severity of flooding caused by climate change heightens the demand for detailed and precise hydrodynamic models. Flood modeling commonly involves analyses done either in a simplified watershed-scale 1D model, a detailed local-scale 2D model, or a combination of both approaches. However, the laborious setup and high data requirements hinder detailed watershed-scale modeling, particularly in urban areas like Hong Kong, where intricate drainage systems pose additional challenges. Alternative methods have been developed to consider underground drainage capacity such as subtracting the water volume held by pipes from the surface runoff and representing it with an equivalent infiltration rate. However, these methods do not give sufficient information on the flow movement underground. Enabled by Hong Kong’s extensive datasets, this study attempts the integration of the digitized urban drainage system into a watershed-scale hydrodynamic model. Hong Kong Island, with a land area of 99.5 square kilometers, is set as the study area. This domain covers 35,259 conduits, 32,622 junctions, and 31 rain gages. A case study is adopted using the September 2023 black rainstorm event to demonstrate the model’s capability to map surface flood inundations and describe the dynamics of the underground drainage system at once. Observed flood depths during the event are then used for results validation. Large-scale urban drainage models like this may aid decision makers in flood risk assessment and emergency action planning.
How to cite: Canlas, A., Zhang, L. M., and He, J.: Large-scale Urban Drainage Modelling for Hong Kong Island, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15323, https://doi.org/10.5194/egusphere-egu25-15323, 2025.
Flood events are among the most devastating natural hazards, presenting multi-risks to infrastructure, ecosystems, and communities. Beyond the immediate impact of inundation, the entrainment and transport of large wood (LW) during these events amplify their destructive potential. Mobilized LW can obstruct critical infrastructure such as bridges, leading to increased backwater effects, exacerbating flooding, and causing structural damage. However, LW also plays a vital ecological role, contributing to habitat formation, nutrient cycling, and riverine biodiversity. As such, it cannot simply be removed without ecological consequences. Understanding the dynamics of LW entrainment, transport, and deposition is crucial for balancing flood risk reduction with the preservation of ecosystem functions.
This study addresses the challenge of modeling LW dynamics in rivers, with a specific focus on the Allier River in France. For this purpose, the study utilizes the ORSA2D_WT model, an Eulerian-Lagrangian two-way coupled approach, which integrates the two-dimensional Shallow Water Equations (SWE) with the Discrete Element Method (DEM). This hybrid model allows for a representation of entrainment thresholds, transport pathways, and inelastic collisions between LW elements and obstacles such as riverbanks and infrastructure.
This research integrates extensive field data, numerical simulations, and experimental findings to enhance predictions of wood mobilization during flood events. Field data collected from the Allier River, France (2020–2024), provides a robust basis for model improvement. This dataset includes Radio Frequency Identification (RFID)-tracked LW positions over multiple years, high-resolution Digital Terrain Models, granulometric sediment analyses and LW characteristics such as size, shape, density and burial conditions.
By combining numerical simulations with extensive field data, this study aims to refine the model’s ability to predict LW mobilization and transport across different flood scenarios, from moderate flows to extreme flood events. Furthermore, the study seeks to enhance the understanding of how environmental factors, such as LW properties and sediment dynamics, influence LW behavior during floods. The outcomes of this research will contribute to the development of a more accurate and reliable hydrodynamic model coupled with a LW transport model, offering insights into how the dynamics of LW affect riverine systems during flood events.
How to cite: ennouini, W., Persi, E., Ravazzolo, D., Petaccia, G., Sibilla, S., Hortobágyi, B., and Piégay, H.: Floods and Large Wood in Rivers: Exploring Their Dynamic Interactions Through Numerical Modelling and Field Data on the Allier River, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-535, https://doi.org/10.5194/egusphere-egu25-535, 2025.
Floods are the most common natural disaster, and recent studies suggests that their frequency and magnitude will increase due to climate change. Factors such as demographic growth, urbanization, and land consumption contribute to heightened vulnerability for structures, infrastructure, and populations, elevating the risk of cascading incidents. In this context, flood events can lead to multiple simultaneous releases of hazardous materials, causing severe harm to both the environment and human health. In these cases, the term Natech accident is used, referring to industrial accidents triggered by natural events, for which a multi-risk approach is required. Natech accidents caused by floods require particular attention, as the high velocities of water can rapidly transport pollutants to areas far from the point of emission. The need to focus on this issue is further justified by the fact that many chemical and petrochemical plants are in flood-prone areas, making them particularly vulnerable to the risk of failure following a flood. In the context of emergency management, having access to rapid-response models for assessing the fate and transport of spills is crucial for evaluating their trajectory and for planning recovery interventions. Additionally, these models are key for generating risk maps for various spill scenarios. Within Natech risk management, particular attention is given to oil spills in water, as they introduce additional complexity due to the unique behaviour of this substance in water and their potential toxicity, as well as the risk of cascading events (i.e. environmental contamination, fires, explosions). The need to develop specific models for simulating oil spills in floodwaters is particularly important, as the existing literature provides numerous models for offshore spills, but knowledge regarding fluvial systems is still limited.
This study presents the initial results from the implementation of oil spill routing within the CAESAR-LISFLOOD flood inundation model, which addresses the challenge of solving a simplified shallow water equations using a straightforward numerical approach. This results in a model that is computationally efficient while still being grounded in a solid physical framework. The model is enhanced with a module that simulates the dispersion of oil in floodwaters, accounting for the key processes that influence oil movement in a river system. This implementation allows the model to track the behaviour of an oil slick after a spill in areas with complex topography. It provides valuable insights into the dynamics of the spill, the changes in the slick’s thickness over time, and the extent of the affected area. The model was tested on a case study in Italy, where several simulations were performed for multiple spill scenarios, demonstrating the model’s effectiveness, its ability to accurately simulate the oil spill propagation, as well as its computational efficiency.
How to cite: Di Fluri, P., Wilson, M., and Domeneghetti, A.: A new physically-based numerical model to simulate flood triggered oil spills, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-697, https://doi.org/10.5194/egusphere-egu25-697, 2025.
The Saint-Venant equations are extensively employed to model water flow in channels, particularly when a comprehensive analysis is necessary. This study presents a mesh-free approach utilizing Physics-Informed Neural Networks (PINNs) to address the 1D Saint-Venant equations under diverse initial and boundary conditions. PINNs provide substantial benefits compared to conventional hydrodynamic models by enabling predictions at any location within the computational domain without the necessity for predefined computational points or cross-sections. This versatility is especially advantageous for applications necessitating elevated spatial resolution or dynamic adaptability. In contrast to traditional machine learning (ML) methods, Physics-Informed Neural Networks (PINNs) do not necessitate labelled data. Their loss function integrates the residual error of the governing partial differential equations (PDEs) with initial and boundary conditions, thereby ensuring predictions that are physically consistent. This addresses the interpretability deficit frequently linked to machine learning models. The constructed PINNs architecture was evaluated on four test cases that exemplify various channel geometries and flow conditions. The first scenario pertains to a horizontal bed exhibiting a constant upstream velocity. The second case analyses a rectangular channel with a constant slope and dynamic inflow, whereas the third and fourth cases comprise channels with changing slopes and widths. These scenarios reflect real-world water transport channels. In cases 1 and 2, the maximum depth error was ±0.08 m relative to numerical solutions, with the most significant errors occurring at the points of initial water arrival. In cases 3 and 4, the maximum depth errors were ±0.2 m and ±0.4 m, respectively. These findings indicate that PINNs can accurately reproduce numerical solutions without the necessity of a computational mesh. The adaptability of PINNs in sampling collocation points eliminates the necessity for re-simulating the model when results are needed at new locations. This study underscores the efficacy of PINNs for real-time water resource management and flood forecasting, particularly where conventional methods may be computationally expensive or inflexible. Future research will investigate the expansion of the PINNs framework to encompass higher-dimensional Saint-Venant equations and the incorporation of stochastic inputs to address uncertainties in flow conditions.
How to cite: Sasanapuri, S. K., Chadrika Thulaseedharan, D., and Ashwini Kumar, G.: Physics-Informed Neural Networks: A Novel Framework for Solving 1D Saint-Venant Equations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-767, https://doi.org/10.5194/egusphere-egu25-767, 2025.
This study investigates the seepage behavior of homogeneous earthen and rockfill dams during transient conditions, focusing on the temporal variation in seepage discharge as influenced by material composition and structural characteristics. Using experimental modeling, seepage rates were analyzed over 200 minutes, revealing distinct patterns between the two dam types. For the earthen dam, the initial seepage rate was approximately 1 cm³/sec. This discharge decreased steadily over time, stabilizing at around 0.5 cm³/sec by the end of the observation period. The consistent decline reflects the lower permeability of earthen materials, which results in a controlled redistribution of water as the hydraulic gradient diminishes and seepage pathways stabilize. In contrast, the rockfill dam exhibited significantly higher initial seepage rates, starting at 2.5 cm³/sec due to its highly porous structure and larger void spaces that facilitate rapid water movement. Over the 200-minute observation period, the seepage rate decreased gradually, stabilizing at approximately 1.2 cm³/sec. This slower decline and higher stabilization point highlight the greater permeability of rockfill materials, allowing prolonged seepage flow before reaching equilibrium. The comparison of seepage dynamics underscores the impact of dam material properties on hydraulic performance under transient conditions. Earthen dams, with their steady reduction in seepage, are wellsuited for scenarios requiring controlled seepage management. However, the need for proper drainage systems to handle pore pressure buildup remains critical. Conversely, rockfill dams are effective at managing high initial seepage rates but may require additional seepage control measures, such as enhanced drainage systems or impermeable barriers, to ensure long-term stability and prevent structural compromise. These findings emphasize the importance of designing tailored seepage management strategies that account for the unique material properties and structural behaviors of each dam type. For earthen dams, measures to manage gradual seepage and stabilize pore pressures are essential, while for rockfill dams, addressing prolonged seepage flow and high initial rates is critical. This study provides valuable insights into the optimization of dam designs to enhance safety and efficiency, particularly under dynamic hydraulic conditions such as fluctuating reservoir levels or rapid drawdown scenarios. By highlighting the contrasting seepage behaviors of earthen and rockfill dams, this research contributes to the development of resilient and efficient water-retaining structures capable of withstanding diverse environmental and operational challenges.
How to cite: Shrivastava, S., N Khatri, V., and Pasupuleti, S.: Seepage Dynamics in Homogeneous Earthen and Rockfill Dams: Insights from Experimental Modeling Under Transient Conditions., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1056, https://doi.org/10.5194/egusphere-egu25-1056, 2025.
Over the last twenty years, floods have represented the most predominant natural disasters occurred worldwide. Just in 2024, more than 15 European countries from Italy to Poland and from Spain to the Czech Republic experienced severe floods leading to catastrophic impacts. Between September 17 and 20, the Lamone River basin in the Emilia-Romagna Region in Northern Italy was hit by extreme precipitations and a levee-breach-induced inundation caused the flooding of urban settlements and crops near the Traversara village, an area already affected by huge floods no later than May 2023.
In the present work, the hydrological model Rhyme (River HYdrological ModEl) and the hydrodynamic model PARFLOOD are adopted to reconstruct the hydrological processes that occurred over the watershed and the dynamic of the flooding event. The spatially explicit Rhyme model enabled the description of the rainfall-runoff processes at the catchment scale by using as meteorological forcing hourly rainfall, daily cumulative potential evapotranspiration, and daily average temperatures. Due to the availability of a 16 year-series of water levels recorded at a gauging station located at the basin outlet and stage-discharge relationships, the model was calibrated from 2008 until 2024 using a Markov Chain Monte Carlo algorithm.
The flow hydrographs estimated by the hydrological model for the September 2024 event were then provided as inflow conditions to the hydrodynamic model PARFLOOD, which is a 2D parallel finite volume scheme. The breach opening on the left levee of the Lamone River was modelled by adopting a geometric approach and information about the breach characteristics (e.g. opening time and length) was provided through direct observations. The resulting flooding maps showed that after a few hours of overflowing, the levee-breach-induced flood affected the village of Traversara, urban settlements, crops, and vineyards in less than 10 hours. Moreover, the numerical results highlighted how minor channel embankments spread in the domain confined the flood propagation to the west, thus avoiding the flooding of a highly densely populated area.
Over the last two years, the Lamone River basin was affected by extreme precipitations that in many gauge stations exceeded the 500-year return period and broke historical records. Focusing on the September 2024 event, the close match between the resulting flooded areas and the observed ones, and the fair agreement between the water levels recorded at three gauge stations along the river and the resulting ones, highlighted the capability of the numerical models here adopted to support the assessment of extreme events and increase the preparedness for at-risk populations.
How to cite: Ferrari, A., Passadore, G., Vacondio, R., Carniello, L., Pivato, M., Crestani, E., Carraro, F., Aureli, F., Carta, S., and Mignosa, P.: Reconstruction of the September 2024 extreme flood on the Lamone River in Northern Italy, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13123, https://doi.org/10.5194/egusphere-egu25-13123, 2025.
Flood hazard map is necessary to develop strategies for mitigation of flood damages and sustainable management of catchments especially in drylands with flash flood and megafloods. Here, we applied multiplicative long short-term memory (mLSTM) deep learning model to map flood hazard in an arid catchment – Shamil-Minab plain – in southern Iran. In order to, variables controlling flood hazard consisting of variables extracted from digital elevation model (DEM) (e.g., curvature, plan curvature, profile curvature, slope, stream power index (SPI), topographic position index (TPI)), normalized difference vegetation index (NDVI), hydrological variables (e.g., river density, distance from river), land use, lithology and soil types were mapped spatially. An inventory map for flood was generated according to field survey and historical data. Inventory map provides training and test datasets for building the predictive flood models. Finally, mLSTM model used to map flood hazard in the study area, and its performance was assessed by accuracy measures. The results shown that 27%, 19.7% and 26% of total area were belonged to very low, low and moderate hazard classes, whereas high and very high hazard classes were occupied 15.9% and 11.4% of total study area, respectively. The combination of our suggested methodology with MCDM models can be useful to map flood risk, and to mitigate destructive consequences of floods in drylands.
How to cite: Gholami, H., Firouzy, S., Mohammadifar, A., and Golzari, S.: mLSTM deep learning model to map flood hazard in an arid catchment , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1924, https://doi.org/10.5194/egusphere-egu25-1924, 2025.
Faced with climate change and the increasing pressure exerted by marine dynamics on the coastline (notably coastal erosion and marine submersion), adapting impacted territories has become a major challenge. Several solutions exist to protect coastal communities. In Quebec, numerous beach nourishments have been completed or are currently underway. This type of solution is increasingly being implemented (Hinkel et al., 2013). However, follow-up studies are necessary to better quantify their impact on the coast, particularly through multidisciplinary approaches (socio-economic, ecological, geomorphological, etc.).
In September 2022, an extratropical storm (Fiona) significantly affected Quebec's coastline. A heritage site of importance (Havre-Aubert, Îles-de-la-Madeleine) experienced significant marine submersion. A beach nourishment had been carried out shortly before this event. In-situ measurements were taken a few days before and after the storm, allowing for the creation of an exceptional dataset on the storm and its impacts on the site.
This dataset was used to perform various numerical simulations with the open-source morphodynamic model XBeach (Roelvink et al., 2009). This model allows for different computation modes: phase-averaged or phase-resolved, as well as a specific mode for gravel beaches (XBeach-G, McCall et al., 2014). All these configurations were used to simulate this storm event with and without the beach nourishment. The results of these simulations are compared and discussed.
Our results show that the nourishment played a protective role by significantly reducing marine submersion and damage to infrastructure. Under the impact of the storm, the nourishment rapidly adjusted towards a Dean-type equilibrium profile. A reprofiling of the nourishment was observed without significant sediment loss offshore.
Hinkel, J., Nicholls, R. J., Tol, R. S., Wang, Z. B., Hamilton, J. M., Boot, G., ... & Klein, R. J. (2013). A global analysis of erosion of sandy beaches and sea-level rise: An application of DIVA. Global and Planetary change, 111, 150-158.
McCall, R. T., Masselink, G., Poate, T. G., Roelvink, J. A., Almeida, L. P., Davidson, M., & Russell, P. E. (2014). Modelling storm hydrodynamics on gravel beaches with XBeach-G. Coastal Engineering, 91, 231-250.
Roelvink, D., Reniers, A., Van Dongeren, A. P., De Vries, J. V. T., McCall, R., & Lescinski, J. (2009). Modelling storm impacts on beaches, dunes and barrier islands. Coastal engineering, 56(11-12), 1133-1152.
How to cite: Caulet, C., Bernatchez, P., Savoie-Ferron, F., Sauvé, P., St-Onge, S., and McKinnon, R.: Protective role of a gravel-beach nourishment on built-up area during an extra tropical storm (Fiona, September 2022), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16109, https://doi.org/10.5194/egusphere-egu25-16109, 2025.
Posters virtual: Mon, 28 Apr, 14:00–15:45 | vPoster spot 3
EGU25-17185 | ECS | Posters virtual | VPS12
Geomorphological transformation and prediction of urban meander loop: A case study of Barak River, IndiaMon, 28 Apr, 14:00–15:45 (CEST) | vP3.17
Barak River is one of the highly meandering rivers in India causing several problems to society during flooding events. In this study geomorphological changes of an urban meander loop, situated at the main city of Silchar Assam, India was carried out. Based on the adopted analysis, it was found that meander length, meander width, meander ratio, wavelength showed an increasing trend while sinuosity and radius of curvature shows a decreasing trend. The land use and land cover were also analyzed of this urban meander loop and found that settlement increased gradually by 16.1798 % and waterbodies, dense vegetation and agricultural land decreased by 0.5732 %, 2.5832 % and 13.1558%, respectively. Autoregressive integrated moving average (ARIMA) model was employed for the prediction and the results recommended that shifting of channel in the urban meander loop fluctuated unexpectedly either to rightwards or leftwards. Observed and predicted values of showed a determination coefficient (R2 = 0.8). The final step of the research was to generate the predicted values of channel shifting up to 2030.
How to cite: Annayat, W., Samantaray, S., and Yaseen, Z. M.: Geomorphological transformation and prediction of urban meander loop: A case study of Barak River, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17185, https://doi.org/10.5194/egusphere-egu25-17185, 2025.
EGU25-12404 | ECS | Posters virtual | VPS12
Assessing Flood Susceptibility using Geospatial Techniques and Analytical Hierarchy Process in an Indian CatchmentMon, 28 Apr, 14:00–15:45 (CEST) | vP3.18
Being a natural calamity, flood poses serious threat to the livelihood of all living beings. Due to the adverse effects of climate change and anthropogenic activities, significant changes in the occurrence of extreme floods are happening day-by-day. An accurate flood susceptibility map plays a crucial role to adopt proper adaptation and mitigation strategies in protecting the vulnerable communities. This study performs a flood susceptibility mapping of the Jagatsinghpur district lying in the delta region of the Mahanadi River basin in the eastern part of India. This river basin has suffered from numerous recurring floods of variable extremities since the 1960s. A major concern arose when the frequency of extreme floods in this delta increased drastically post the 2000s. This study considered several key factors affecting flood occurrence like rainfall, topographic wetness index, land use/land cover, distance from river, elevation, slope and drainage density. The map layers of all these factors are integrated in the Geographic Information System (GIS) platform, wherein the Analytical Hierarchy Process (AHP) is used to develop and evaluate the flood susceptibility maps. The findings suggest that more than one-third of the study area falls into the low to high flood susceptibility zone. Nearly 40% of the area is under very low to low zone, and a small portion fell under the high to very high flood prone zone. The study serves as a preliminary study towards flood risk management and provides critical insights for the decision makers to develop appropriate disaster risk reduction strategies and strengthen the flood management policies.
How to cite: Khatun, A., Baruah, S., and Chatterjee, C.: Assessing Flood Susceptibility using Geospatial Techniques and Analytical Hierarchy Process in an Indian Catchment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12404, https://doi.org/10.5194/egusphere-egu25-12404, 2025.
EGU25-14551 | ECS | Posters virtual | VPS12
Estimation approach for T-year hydrological events using non-stationary dataMon, 28 Apr, 14:00–15:45 (CEST) | vP3.19
As stated in The IPCC Sixth Assessment Report, heavy rainfall events of unprecedented scale have occurred in recent years increasingly in terms of both frequency and intensity because of global climate change. As a matter of course, greater attention must be devoted to flooding caused by heavier-than-ever rainfall events. This flooding includes both levee breach and inland water rise effects.
In Japan, T-year hydrological events, such as 100-year-rainfall events with a return period of 100 years as estimated from frequency analysis, have been used conventionally as targets of river improvement plans. In fact, "Guidelines for Small and Medium-Sized River Planning” have been consulted when hydrological quantities are estimated. Nevertheless, the flow chart in the guideline drawn by the MLIT (*) has been discounted completely in work by Kuzuha et al. (2021, 2022a,b,c). In fact, it is most inappropriate to use the SLSC as the criterion for validating stochastic models; it is also inappropriate for usage of the Jack-knife or bootstrap method. Mizuki and Kuzuha (2023) present related supporting details.
As described in this paper, we intend to present other issues which must be urgently resolved: The fact that the precipitation population has not been stationary. It must be regarded as non-stationary because of global climate change.
Explanations of frequency analysis based on the non-stationarity of the precipitation population have been presented in the literature by Hayashi et al. (2015) and by Shimizu et al. (2018). We have considered different approaches than theirs. Ours predict future T-year hydrological events under the condition of non-stationary precipitation population, as presented below. In other words, those approaches can be adapted to recent quite heavier rainfall data.
- We use d4PDF data (2015) data. In fact, d4PDF data were calculated using climate simulations of 50 ensemble members. Each ensemble member has climate data obtained during 1951–2010: we can use annual maximum rainfall of 3,000 years. We specifically examined the area around Kumano city, Mie prefecture and analyzed the annual maximum around Kumano.
- First, we calculated the annual maximum 1-hour precipitation at Kumano described above.
- For example, there are 50 annual maximum 1-hour precipitation events in 1951, because there are 50 ensemble members. Therefore, we can estimate 100-year rainfall in 1951 using 50 data and the Gumbel distribution. We can estimate time-variational 100-year rainfall during 1951 and 2010.
- The blue line in the figure shows the time variational 100-year rainfall between 1951 and 2010.
- The orange line represents future 100-year rainfall calculated using the triple exponential smoothing method.
At the presentation, we intend to show other approaches which can be useful to predict future 100-year precipitation.
* MLIT: The Ministry of Land, Infrastructure, Transport and Tourism, Japan
How to cite: Ohashi, R., Mizuki, C., and Kuzuha, Y.: Estimation approach for T-year hydrological events using non-stationary data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14551, https://doi.org/10.5194/egusphere-egu25-14551, 2025.
EGU25-4693 | Posters virtual | VPS12
Three-Dimensional Numerical Modeling of a River Section under Extreme Discharge Conditions from a Tropical Storm: The Santa Catarina River Case Study, MexicoMon, 28 Apr, 14:00–15:45 (CEST) | vP3.20
In this study, the hydrodynamic behavior of a section of the Santa Catarina River in Nuevo León, Mexico, during Tropical Storm Alberto was investigated. A three-dimensional numerical simulation of river flow was performed using unsteady Reynolds-Averaged Navier-Stokes (RANS) equations coupled with the Volume of Fluid (VOF) method to model the water-air interface. The computational domain was constructed based on the specific area Digital Elevation Model (DEM), accurately capturing the river's morphology, with a structured mesh refined near the riverbed to resolve localized velocity gradients. The simulations focused on high-density water flows induced by extreme precipitation, analyzing key parameters, including velocity distribution, turbulence intensity, and effective viscosity, to evaluate the performance of turbulence models in replicating fluvial dynamics. Validation was achieved using velocity data derived from video footage of the storm, tracked via motion analysis techniques and compared against simulation outputs to ensure accuracy.
The comparative study included the Spalart-Allmaras, standard k-ε, realizable k-ε, and standard k-ω turbulence models. A sensitivity analysis and mesh independence verification ensured robust numerical predictions validated against field data obtained from video-derived velocity measurements.
Findings reveal distinct model performance under varying turbulence conditions. The realizable k-ε model captured peak effective viscosity (μeff) values of up to 820 kg/m·s at low turbulence intensities, demonstrating its suitability for flows with strong energy gradients and lower dissipation rates. Conversely, the standard k-ω model excelled under high turbulence intensity, effectively resolving dissipation dynamics and exhibiting μeff values between 150–500 kg/m·s. These results highlight the capacity of these models to represent different aspects of riverine hydrodynamics, although neither achieved full optimization across all conditions.
Velocity profiles showed significant gradients near the riverbed, where high shear stress and energy dissipation dominated, reinforcing the importance of mesh refinement in capturing localized effects. Turbulence intensity exhibited a sharp decrease in shallow areas and near structural boundaries, directly influencing μeff distributions.
While the evaluated turbulence models provided reliable frameworks for simulating complex fluvial flows, further refinements are needed. Incorporating advanced turbulence models, such as Reynolds Stress Models (RSM) or Large Eddy Simulations (LES), could enhance predictions, particularly for cases involving sediment transport and fluid-structure interactions.
This study contributes to the development of robust methodologies for river modeling under extreme conditions, with practical implications for flood management, hydraulic structure design, and sediment transport assessments. Future research should explore the performance of these models in simulating freshwater flows, assess their application under varying sediment concentrations, and investigate their capability to account for fluid-structure interactions related to bridge columns and other critical infrastructure.
How to cite: Bonasia, R., De la Cruz-Ávila, M., Barrios Piña, H. A., and Castillo Guerrero, F. J.: Three-Dimensional Numerical Modeling of a River Section under Extreme Discharge Conditions from a Tropical Storm: The Santa Catarina River Case Study, Mexico, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4693, https://doi.org/10.5194/egusphere-egu25-4693, 2025.
EGU25-664 | ECS | Posters virtual | VPS12
Dynamic Flood and Erosion Modeling for the Sabarmati River Using RUSLE and GEEMon, 28 Apr, 14:00–15:45 (CEST) | vP3.26
Dynamic Flood and Erosion Modeling for the Sabarmati River Using RUSLE and GEE
Nensi A. Sachapara a(0009-0000-9510-6198), Manan Patel a(0009-0004-4712-3531) , Hasti Dhameliya b(0009-0003-8908-7906)
Keval H Jodhani c (0000-0002-3800-2402), Nitesh Gupta c(0000-0003-0471-0133) , Dhruvesh P. Patel d (0000-0002-2074-7158) Sudhir Kumar Singh e (0000-0001-8465-0649)
aUnder Graduate Student, Civil Engineering Department, Nirma University, Ahmedabad, 382481, Gujarat, India. (nensisachapara16@gmail.com; mananrp07@gmail.com )
bUnder Graduate Student, Biomedical Engineering Department, LD College of Engineering, Ahmedabad, 382481, Gujarat, India. (dhameliyahasti8@gmail.com)
cAssistant Professor, Department of Civil Engineering, Institute of Technology, Nirma University, Ahmedabad, 382481, Gujarat, India. (jodhanikeval@gmail.com, niteshraz@gmail.com)
dDepartment of Civil Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar, 382007, Gujarat, India (dhruvesh1301@gmail.com)
6 K. Banerjee Centre of Atmospheric and Ocean Studies, IIDS, Nehru Science Centre, University of Allahabad, Prayagraj-211002, Uttar Pradesh, India (sudhirinjnu@gmail.com)
Abstract: Flooding and soil erosion are major environmental challenges impacting the Sabarmati River Basin (SRB), adversely affecting its ecology, agriculture, and infrastructure. This study employs the Google Earth Engine (GEE) platform to comprehensively analyze flood-prone areas and soil erosion using the Revised Universal Soil Loss Equation (RUSLE) model. High-resolution datasets from USGS Earth Explorer and GEE are integrated with remote sensing and geospatial technologies to assess the basin's vulnerabilities. Flood-prone regions were identified by analyzing historical rainfall (maximum annual rainfall of 1,667.15 mm in 2017), hydrological patterns, and topographic features. The RUSLE model quantified soil erosion, incorporating factors such as rainfall erosivity (R factor: 11,202.65–29,243.64 MJ mm ha⁻¹ h⁻¹ yr⁻¹), soil erodibility (K factor: 0.20–0.20004 t ha h ha⁻¹ MJ⁻¹ mm⁻¹), slope length and steepness (LS factor: 0–0.499), land cover (C factor: 0.327–1.078), and conservation practices (P factor: 1). Results indicate critical hotspots of soil erosion, with losses peaking at 1,232.33 t/ha/year in the northern SRB. Flood hazard mapping revealed that low-lying areas with recurrent flood events align with regions experiencing high rainfall and sediment transport. The overlap between high soil erosion and flood-prone zones highlights the need for integrated management strategies. These risks have significant socio-economic implications, including diminished agricultural productivity, infrastructure damage, and community displacement. This dual analysis underscores the efficacy of GEE for rapid environmental assessments, providing actionable insights for policymakers to prioritize interventions. The findings align with Sustainable Development Goals 13 (Climate Action) and 15 (Life on Land), suggesting for adaptive strategies to mitigate flood and erosion risks and promoting sustainable resource management in vulnerable regions.
Keyword: RUSLE, GEE, Flood Hazard, SDG 13 & 15, Sabarmati Basin
How to cite: Sachapara, N., Patel, M., Dhameliya, H., Jodhani, K., Gupta, N., Patel, D., and Singh, S. K.: Dynamic Flood and Erosion Modeling for the Sabarmati River Using RUSLE and GEE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-664, https://doi.org/10.5194/egusphere-egu25-664, 2025.
