Floods are extreme events that cause huge loss of lives and properties. The flood events are expected to be more intensified and recurrent in the future due to climate change. It is required to develop robust flood mitigation strategies under climate change to mitigate the flood risk especially in the basins with limited data or ungauged basins. However, flood mitigation planning requires a huge amount of in-situ data of pre and post flood events which is not possible in data-scarce or ungauged river basins and almost inaccessible in the impassable and high-altitude complex terrain. The availability and accessibility of remote sensing data provides accurate and precise information regarding pre and post flood events in these regions.  The critical review of published literature reveals that the concept of model regionalization could be the scalability would provide the robust strategies for planning flood mitigation under climate change especially in these regions which involves transfer of knowledge from gauged to data-scarce or ungauged basins. However, the inefficiency of conventional process-based models in regionalization of model has motivated the researchers to think about the Artificial Intelligence (AI) data-driven approach. The present study combines remote sensing with AI approach to investigate the scope of regional flood susceptibility model development. The model development utilizes the remote sensing derived flood affecting parameters (or indicators) such as terrain, morphological, metrological. It has been first developed in data-rich basins and then transferred its knowledge to data-scarce or ungauged basins. The remote sensing derived historical flood records were used to generate the ground control points for training (70%) and testing (30%) of the model. To accomplish the objectives of enquiring the scope of regional flood susceptible model, the present study has chosen the two smaller sub-basins, one from the Krishna River basins, Maharashtra and the other from the Lower Ganga basin of Bihar. The chooses sub-basin from the Krishna River basins has been used for model development and the sub-basin from Lower Ganga basin of Bihar has been considered to investigate the scalability of the developed model for the regional AI-based model for flood susceptibility. The results of statistics F-1 score and Receiver Operating Characteristic (ROC)-Area Under Curve (AUC) have shown good performance of the model during training and testing. It also shows good performance during the model scalability check that advocates developed model for regional flood susceptibility. However, it suggested to apply fine tuning for future improvement of the model.  It has been concluded that the integration of remote sensing with AI-based could help in the development of good regional flood susceptible model which could be beneficial for policymakers in evolving enhanced strategies for mitigating futuristics floods especially in the data-scarce or ungauged basins.

Keywords: Regionalization, remote sensing, Artificial Intelligence, data-scarce or ungauged, flood mitigation planning.

How to cite: Ranjan, R. and Keshari, A. K.: Integrating Remote Sensing and Artificial Intelligence based Techniques for Investigating Regional Flood Susceptibility to Improve Flood Mitigation Planning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-794, https://doi.org/10.5194/egusphere-egu25-794, 2025.

The Koyna River basin, situated in the ecologically sensitive and biodiverse Western Ghats of India, exhibits heightened vulnerability to the dual pressures of climate variability and land use/land cover (LULC) changes. In this study, the hydrological dynamics of the basin were modeled using the advanced ArcSWAT tool, which is well-suited for simulating the influence of climatic and land use changes on streamflow. The analysis incorporated historical LULC data from 1996 and 2016 and climate change scenarios represented by RCP4.5 and RCP8.5 pathways. The model was meticulously calibrated and validated using observed hydrological data spanning 1978 to 2016. Performance metrics such as the coefficient of determination (R²), Nash-Sutcliffe Efficiency (NSE), and percent bias (PBIAS) indicated robust model with high reliability and accuracy. Future climate projections were developed using six Regional Climate Models (RCMs) which were refined through bias correction with the CMhyd tool to minimize discrepancies between simulated and observed climatic variables. The analysis integrates historical data from 1978 to 2016 and future projections derived from the CNRM-CM5 climate model under RCP4.5 and RCP8.5 scenarios for three timeframes: early (2025–2050), mid (2051–2075), and end century (2076–2100). Key parameters, including rainfall, temperature, and hydrological responses, were used to simulate streamflow variations and assess the basin's hydrological sensitivity to changing climatic conditions. Results reveal significant increases in streamflow under both RCP scenarios, with RCP8.5 indicating the most pronounced impacts by the end of the century. Monsoonal months (June–September) dominate streamflow contributions, with projections of heightened peak flows and prolonged discharge during these periods. Streamflow during the monsoon season is expected to nearly double under RCP8.5, increasing the risk of flooding. Monsoon rainfall, a pivotal driver of the basin's hydrology, accounts for over 85% of the annual runoff, with future projections pointing to intensified monsoonal discharges and an increase in extreme weather events. Conversely, drier months show marginal increases, signalling potential changes in seasonal water availability. The study also highlights the synergistic effect of land use and land cover (LULC) changes on hydrology. Analysis of LULC datasets from 1996 and 2016 indicates increased streamflow driven by urban expansion and reduced vegetation. These shifts amplify runoff, particularly under future precipitation increases. This evolving hydrological regime highlights the urgency for adaptive management strategies tailored to the region’s unique climatic and ecological context. Sustainable land use planning and proactive water resource management are essential to mitigate the risks associated with these changes. The insights from this research are vital for stakeholders, including policymakers, agronomists, and water resource managers, enabling them to formulate evidence-based strategies for climate adaptation and mitigation.

How to cite: Landage, A. B. and Keshari, A. K.: Assessing Impact of Climate Change on Streamflow of Koyna River, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11851, https://doi.org/10.5194/egusphere-egu25-11851, 2025.

Coastlines are increasingly vulnerable to the compound effects of high sea levels, intense rainfall, and extreme river discharge from tropical cyclones. Accurate compound flood modelling is critical for assessing flood risks and informing forecasts under current and future climate scenarios. However, in data-sparse regions like southeastern Africa, such modelling faces significant challenges due to the lack of river bathymetry data, which cannot be obtained remotely, and the limited or absent in situ gauge data required for model calibration. The recently launched Surface Water and Ocean Topography (SWOT) satellite mission offers a transformative solution, as it can observe compound water surface profiles with centimetre-scale vertical accuracy. This study explores the potential of SWOT water elevations to estimate river bathymetry for the Pungwe and Buzi Rivers in Mozambique. This bathymetry data is then integrated with FABDEM for the simulation of compound flooding caused by Tropical Cyclone Idai near Beira, Mozambique, using the LISFLOOD-FP hydrodynamic model. This simulation incorporates coastal water levels from the ADCIRC model as downstream boundary conditions, river discharge data from the ERA5-driven ECLand model as upstream boundary conditions, and precipitation data from ERA5 to drive the LISFLOOD-FP model. A unique aspect of this study is the calibration of the LISFLOOD-FP model using SWOT surface water elevations. This integrated approach enables accurate compound flood simulation in data-sparse regions. By integrating diverse data sources, this research enhances understanding of flood risks from tropical cyclones and provides a framework for enhanced early warning systems and mitigation strategies in data-sparse coastal regions.

How to cite: Su, X., Neal, J., Dass, G., Hawker, L., Prudhomme, C., and Bingham, R.: Leveraging SWOT Surface Data for River Bathymetry Estimation and Compound Flood Model Calibration in Data-Sparse Regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12482, https://doi.org/10.5194/egusphere-egu25-12482, 2025.

Five major debris flow events occurred in the Central Italian Alps in 2012 (Val Rabbia), 2018 (Rio Rotiano), 2020 (Torrente Vallaro), 2021 (Torrente Blé) and 2022 (Torrenti Re di Niardo e Cobello) were monitored with a multi-sensor and multi-system approach to assess their probability of occurrence and the potential of early warning systems. The five events caused one victim and severe damages to a camping site, buildings, road and energy infrastructures, structural flood control systems  and the environment and the measured point rainfall intensity had a frequency between 1 over 10 to 200 years, with the 2022 event being an exceptional outlier. Monitoring systems included two C-band radars, raingauges, IR and MW satellite sensors, water level sensors, video cameras with geophysical sensors (geophones and infrasound). Operational results of MOLOCH, a non-hydrostatic high-resolution  0.0113 degrees (1.25 km) meteorological model were analysed to assess the predictability of the events. The conducted analyses indicate the reliability of radar reflectivity, processed by considering also the delay in the atmosphere to ground rainfall induced by the falling velocity of raindrops, in capturing the timing and the spatial pattern of rainfall, although the Z(R) transformation still needs event-based or event-type calibration. Satellite images processed through the MASHA algorithm were effective in the synoptic-scale event of 2018 but still not always for some convective events.  The same happens for the MOLOCH meteorological models. The results, although promising, indicate that  the predictability of such debris flow events in mountain areas, on average, is still problematic and merging the different sources of information is needed for an effective early warning.

How to cite: Colosio, P., Marmaglio, C., Bonomelli, R., and Ranzi, R. and the Team of debris flow monitoring and control in the Central Italian Alps: Multisensor monitoring and early warning of precipitation in mountain catchments prone to debris flow events , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16231, https://doi.org/10.5194/egusphere-egu25-16231, 2025.

Floods are among the most destructive natural disasters, causing significant economic, social, and environmental impacts, particularly in developing countries like India. Settlements in flood-prone areas and a lack of information and awareness exacerbate flood risks. This study proposes an integrated framework combining machine learning and a hydrodynamic model (HECRAS) to map flood susceptibility in the lower Narmada River basin, India. For this purpose, the study evaluates and applies Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) to develop flood susceptibility maps. The framework incorporates flood hazard factors such as elevation, topographical wetness index, slope, distance from the river network, drainage density, rainfall, and landuse landcover (LULC) characteristics, along with vulnerability factors like population density, agricultural production, and road–river intersections. The model will be trained using flood depth data from the hydrodynamic model. Moreover, the HECRAS model will be validated with historical flood events using Normalized Difference Water Index (NDWI) analysis from satellite imagery. The integrated approach is expected to achieve high predictive performance, with certain variables anticipated to be key contributors to flood risk. Results demonstrate the robustness of combining machine learning with hydrodynamic modeling for flood mapping, offering improved spatial and temporal accuracy. This study provides a reliable tool for policymakers and stakeholders to identify flood-prone areas, implement mitigation measures, and enhance flood disaster management strategies in the region.

How to cite: Tripathi, I. M., Limbore, P., and Mohapatra, P. K.: Integrated Machine Learning and Hydrodynamic Modeling for Flood Susceptibility Mapping in the Lower Narmada River Basin, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15156, https://doi.org/10.5194/egusphere-egu25-15156, 2025.

Flood risk assessment is critical for minimizing the economic loss resulting from flood damages, mitigating adverse socio-economic impacts and sustainable water resource management in the flood-prone regions. The flood is becoming a major world-wide concern due to recent events of disastrous floods in several countries, and it is gaining high significance because of climate change. The present study is aimed to present a methodological framework that combines hydro-economic evaluation with the hydrodynamic modelling for assessing flood risk and evolving structural and non-structural adaptative strategies for mitigating flood in riverine condition. This framework has been employed to the Burhi Gandak River basin in India, a region frequently affected by severe flooding leading to significant agriculture, infrastructural, and social disruptions.  Employing a hydro economic optimization framework, the research integrates hydrological modelling, economic evaluation, and optimization techniques to assess and manage flood risk. It also examines direct and indirect losses due to flooding and potential gains from mitigation strategies. The hydrological data, land use patterns, and socio-economic indicators were analysed to simulate flood scenarios. The approach combines flood inundation model with economic cost-benefit analysis, capturing both under varying rainfall intensities and catchment conditions. The results show that the optimization techniques can be applied to identify cost-effective strategies, including structural measures such as levees, and retention basins and non-structural measures such as early warning systems, land use policies for managing flood disaster effectively. Results. reveal that a balanced combination of structural and non-structural interventions can significantly reduce flood damage while optimizing resource allocation. This study provides a decision-support tool for policymakers to prioritize investments and implement adaptive strategies that enhance resilience against flooding in the Budhi Gandak basin. The integration of hydro economic evaluation not only improves the flood risk management but also contributes to the sustainable development of vulnerable regions.

Keywords: Flood risk, Flood mitigation, Optimization, Hydrodynamic modelling, Hydro economic framework.

How to cite: Babu, K. L. and Keshari, A. K.: Evolving Flood Risk Mitigation Strategies Using Hydrodynamic Modeling Linked with Hydroeconomic Optimization for Burhi Gandak River , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6067, https://doi.org/10.5194/egusphere-egu25-6067, 2025.

Recurrent flooding poses a significant threat to various sub-catchments of the Narmada River Basin, one of India's major river systems. This study focuses on the flood-prone sub-catchment area upstream of the Sardar Sarovar Dam, where impacts are particularly severe on tribal communities, forests, and the newly formed reservoir ecosystem. To enhance flood risk management, this research investigates the application of Artificial Intelligence and Machine Learning (AIML) for high-resolution flood inundation mapping. The primary objective is to generate high-resolution flood inundation maps that surpass hydrological modelling in accuracy and spatial detail, enabling precise identification of vulnerable areas within the sub-catchment. A comprehensive dataset, including historical rainfall data (1990-2024) from IMD gridded data and local rain gauges, river discharge records from various gauging stations and a 12.5m resolution Digital Elevation Model (DEM), is used to train and validate AIML models (Artificial Neural Network (ANN), Random Forests (RF), and K-Nearest Neighbor (KNN)). Beyond flood inundation, the models were employed to simulate the effects of various flood control measures, including optimized reservoir operation, embankment construction, and afforestation, to inform optimal implementation strategies. The results are expected to demonstrate the superior performance of AIML in capturing and predicting future flood inundations in the region. Based on error calculation, the performance of combined models is expected to be better than that of individual models. The findings will help develop targeted early warning systems, improved land-use planning, and evidence-based decision-making for sustainable flood risk management in the Narmada Basin and contribute to the broader application of AI for disaster risk reduction globally.

How to cite: Kumar, S., Bhat, A. M., and Mohapatra, P. K.: Flood Inundation Management in the Narmada Basin: An AIML Application for the Upstream Area of Sardar Sarovar Dam, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16497, https://doi.org/10.5194/egusphere-egu25-16497, 2025.

Flood risks in river basins are increasingly exacerbated by rapid Land Use and Land Cover (LULC) changes driven by urbanization, deforestation, and agricultural expansion. The Narmada basin, particularly its lower reaches, serves as a critical case study due to its hydrological importance, diverse landscapes, and susceptibility to monsoonal flooding. This study explores the interplay between evolving LULC patterns and flood dynamics in the lower Narmada basin through advanced machine learning and hydrological modelling techniques. The analysis starts by classifying historical and current LULC patterns using remote sensing data from Landsat and Sentinel-2, leveraging Support Vector Machine algorithms for accurate mapping. Future LULC scenarios are predicted using a Cellular Automata-Markov Chain model under various development trajectories. Rainfall data, combined with projected LULC maps, is processed through HEC-HMS to simulate rainfall-runoff relationships and estimate discharge. These discharge values are then used as inputs in HEC-RAS for detailed flood simulations, providing insights into flood extents and inundation depths under extreme rainfall events. Additionally, Long Short-Term Memory (LSTM) networks are employed to analyse and predict flood-prone areas by understanding the complex relationships between LULC changes, rainfall, and runoff. Preliminary findings reveal significant urban expansion and vegetation loss, intensifying flood risks in downstream regions, particularly near Bharuch city. Simulated inundation maps indicate substantial increases in flood extents in urbanized zones, emphasizing the need for adaptive land management strategies and optimized barrage operations. By combining AI-driven methodologies, hydrological modelling (HEC-HMS), and hydrodynamic simulations (HEC-RAS), this study offers a comprehensive framework for addressing flood risks in rapidly transforming landscapes. The results provide actionable recommendations for urban planning, flood mitigation policies, and sustainable water resource management in the Narmada basin.

How to cite: Rathod, K., Parmar, B., Mohapatra, P. K., and Patel, D.: Predicting Flood Dynamics in the Narmada Basin: Integrating LULC Projections with Hydrodynamic Modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21407, https://doi.org/10.5194/egusphere-egu25-21407, 2025.

In streams and rivers, elevated level of microbial pollution is a major concern because it can impact public and animal health negatively, and has potential to transport infectious diseases and outbreaks from upstream to downstream. During storm and extreme precipitation events, flood water containing runoff, overflowing septic tanks,  untreated water, sediment particles, and particle attached pathogens and fecal coliforms, and consequential microbial contamination poses substantial risks to human health, and mitigating these risks requires understanding of pathogen fate and transport at catchment and subbasins scales. The use of catchment hydrology driven model can be particularly useful for predicting microbial pollution in ambient water during flood events. In this study, a FORTRAN based program was developed to determine the particle attached and water borne pathogen transport in river and streams, and the model was integrated with the soil and water assessment tool (SWAT) tool to determine the microbial pathogen levels in rivers and streams to evaluate microbial water risks and microbial loads in water column and bed sediments during storm and flood events

How to cite: pandey, P.: Harnessing catchment hydrology and soil and water assessment tools for predicting microbial pollution in rivers and streams during flood events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14799, https://doi.org/10.5194/egusphere-egu25-14799, 2025.

The Assi River, once a vital cultural and ecological lifeline in the middle Ganga plain, has undergone significant degradation due to siltation, urban encroachment and channel disappearance. Historically an alluvial rivulet originating near Durvasha Rishi Ashram in Allahabad, took the shape of River Assi and after traversing around 120 km merges with the Ganga in Varanasi city. Currently, only the last 8 km retain any semblance of a river, with more than 90% of the upstream channel buried under silt. The degradation of River Assi catchment has led to the emergence of a new stream of the Morwa River, which drains the flows from the Assi's buried sections and join to River Varuna as a tributary. Using Landsat 5 imagery, SRTM DEM, NDVSI, and PCA of NDVI, this study identified the paleochannels of River Assi and reconstructed the its historical course. Additionally, hydrological modelling was done using Arc SWAT to delineate the sub-basins.

The altered hydrological dynamics of the River Assi have cascading impacts on downstream ecosystems, including the Varuna River basin, which has experienced increased flooding frequency and severity. Due to the disruption of natural drainage networks in River Assi catchment, In 2022, over 10,000 households were affected by flooding in the Varuna basin. Flood mapping using Sentinel-1 SAR data and Google Earth Engine (GEE) revealed that altered flow regimes in River Assi exacerbate water accumulation in River Varuna during monsoons. The study highlights the importance of restoring paleochannels to mitigate flooding and improved hydrological stability. The integration of high-resolution DEMs, land use data, and GEE tools provides a cost-effective approach in flood risk management and underscores the necessity of addressing upstream river concerns to safeguard flood-prone downstream basins.

How to cite: Singh, P. K., Mishra, A., and Ohri, A.: Impact of degradation of River Assi Catchment on Flood Dynamics of Varuna Basin in the Middle Ganga Plain (India), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20068, https://doi.org/10.5194/egusphere-egu25-20068, 2025.

Ernakulam district in Kerala, India, has experienced frequent flooding in recent years due to a combination of natural and human-induced factors. Heavy monsoon rainfall often overwhelms the district’s drainage systems, resulting in widespread flooding. The low-lying terrain, with many areas below sea level, further exacerbates the issue. The district’s coastal location exposes it to storm surges, tidal flooding, and sea-level rise. High sea levels and storm surges can physically block rivers and streams from discharging water into the ocean, compounding the flooding problem. Rapid urbanization and infrastructure development have significantly altered the district’s landscape. The construction of buildings, roads, and other structures has obstructed natural drainage channels, while deforestation and land-use changes, such as converting wetlands and paddy fields into residential or commercial areas, have diminished natural flood buffers. Additionally, poorly maintained or clogged drainage systems hinder efficient water flow. Climate change is projected to increase the frequency and intensity of extreme weather events, including heavy rainfall, making the district even more vulnerable to future flooding.

The 2018 Kerala floods severely affected Ernakulam district, triggered by heavy rainfall, dam releases, and other factors. To analyze the flood inundation dynamics, a hydrodynamic simulation was conducted using the HEC-RAS software developed by the US Army Corps of Engineers’ Hydrologic Engineering Center (HEC). The study focused on a segment of the Periyar River Basin between Kalady and Mangalapuzha. The simulation incorporated the basin’s physical, hydrological, and operational attributes, such as inflow sources, tributaries, seasonal flow patterns influenced by monsoon rainfall, and the generation of a Digital Elevation Model (DEM) for delineating the watershed and river network. Hydrodynamic models are based on the numerical integration of momentum and mass conservation equations, describing the physical processes in the basin (World Meteorological Organization, 2009). These models, such as HEC-RAS, are powerful tools for predicting water levels, current velocities, waves, and sediment transport, particularly in regions with sparse field measurements. Using the Saint-Venant equations, the HEC-RAS model accounts for factors like travel time between two points along the river, slope, cross-section, water flow, and dynamic velocity. The equations are solved using the four-point implicit box finite difference scheme to estimate discharge and water surface elevation at specific points. Observed rainfall and discharge data from peak flood events during the 2018 monsoon were used for the simulations. On July 16, 2018, the peak discharge at the Kalady station (upstream) was recorded at 5107.89 m³/s. The downstream station at Mangalapuzha, located approximately 22 km away, also observed significant discharge levels. A key finding from the flood simulation was the complete inundation of the Cochin International Airport (CIAL), situated on the outer banks of the river. The airport’s runway, aligned roughly parallel to the river, was submerged during the flooding. The recurrence of similar rainfall events, coupled with flood-induced river discharges, poses a persistent threat to critical infrastructure such as CIAL. Hence, the Government of Kerala must develop and implement effective flood mitigation strategies to minimize future risks and damages.

How to cite: Rajamanickam, M. G., Moothedan, A. J., and Kochukrishnan, M.: Hydrodynamic Simulation and Flood Inundation Analysis for Framing Robust Flood Management Strategies: Insights from the 2018 Kerala Floods , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15064, https://doi.org/10.5194/egusphere-egu25-15064, 2025.