Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot A
Spatio-temporal variability of the terrestrial hydrological processes (land heat and water storage anomalies) has important implications in the climate predictability through their effects on surface energy and water fluxes. The changes in seasonal precipitation patterns associated with the Indian Summer Monsoon can alter the hydrological processes; for a given catchment, which in turn can influence the exchange of water and energy at the land surface-atmosphere interface. Hence the reliable prediction of the basin-scale water cycle components in a physically based high-resolution hydrological model equipped with sophisticated Land Surface Models (LSMs) is of prime requirement. The modern LSMs can provide detailed representations of important biophysical, biogeochemical and hydrological processes of varying spatial and temporal scales by incorporating the necessary feedbacks between the land and the atmosphere. When coupled to a physically based fully distributed hydrological model, it can affect the soil moisture patterns means of recycling the surface and sub-surface runoff (lateral terrestrial flow). However, despite the role of lateral terrestrial hydrological processes for the improved simulation of soil moistures, the sensitivity studies involving the land surface and sub-surface feedbacks are less pronounced especially for a tropical humid region with complex physiographic settings (presence of complex topography) under monsoon regimes (strong synoptic forcings). Therefore, in the present study, we examined a process based diagnosis regarding the role of the lateral flow on the terrestrial hydrological processes (Evapotranspiration, surface and sub-surface runoff, stream flow) and surface energy fluxes (latent heat, sensible heat) by using a multi-configured modeling framework of offline WRF-Hydro with Noah-Multi parameterizations (MP) LSM to enable systematic evaluation of the multiple physical parameterizations of hydrologic process representation; the validation has been done with the reanalysis dataset, a remotely sensed product and ground based observations.
How to cite: Sarkar, S. and Lakshmikanthan, P.: Modeling the impact of lateral flow on terrestrial water balance components and surface energy fluxes using WRF-Hydro with multi-configuration ensembles: a study over Krishna River Catchment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1783, https://doi.org/10.5194/egusphere-egu25-1783, 2025.
South America holds vast freshwater reserves, contributing to its global prominence across various sectors. Understanding streamflows at different levels—minimum flows for ecosystem maintenance, mean flows for hydropower and navigation, and high flows associated with floods—is critical for ensuring societal and ecological resilience. These streamflows are influenced by changes in catchment characteristics and climate change, yet the relationship between climate and catchment drivers with streamflows, particularly in tropical regions, remains poorly understood. Recent advances in explainable artificial intelligence (XAI) offer promising avenues for addressing these gaps by linking observational data to potential causal inference. Here, we investigated the climatic and catchment drivers influencing five streamflow types (Q1, Q5, Qmean, Q95 and Q99) across 735 Brazilian watersheds using XAI approaches. Random Forest models were trained with 16 most important attributes for each streamflow type. SHapley Additive exPlanations were applied to explain the directionality and magnitude of each driver's impact, while inflection points were delineated to capture critical thresholds for streamflow changes. Results showed the aridity index (potential evapotranspiration/precipitation) as the most impactful predictor globally, likely due to its role in long-term water balance. However, for Q99, soil sand content emerges as the dominant factor, showing that catchment characteristics rival climatic factors in importance for rare streamflow events. The analysis highlighted critical thresholds, such as reductions in streamflow when the aridity index exceeds 1.30 and potential declines in streamflow for soil carbon content below 30%, likely due to reduced water infiltration and storage capacity. Similarly, forest cover below 40% potentially increases streamflows, possibly due to reduced evapotranspiration and water retention in soils. Regional differences were also observed: in central Brazil, land cover and land use, and topography potential response for decreased the low streamflows, while in the south and northeast, climatic factors such as aridity and precipitation seasonality control the potential decreases. Rare high events (Q99) in the south this watershed scale attributes height above the nearest, permeability and porosity potential increases the magnitude of events. These findings highlight that, while climatic attributes dominate streamflow relationships at a national scale, regional variations underscore the importance of catchment characteristics. This study demonstrates how data-driven models have the potential to capture the complex interplay between climatic and catchment attributes, linking these factors to streamflow dynamics.
How to cite: Brandão, A., Husic, A., Almagro, A., Schwamback, D., and Oliveira, P.: Climate and catchment influences on streamflows in Brazilian watersheds, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10531, https://doi.org/10.5194/egusphere-egu25-10531, 2025.
In a mountainous watershed, there are many confluences at which two or more streams join. Due to inaccessible terrain and associated costs, river discharge data is collected only at a few confluences. It is, therefore, important to assess which confluence is critical. By critical, we mean the junction which will create maximum fragmentation in a river network. In this study, we analysed river networks with uneven topography in the Alaknanda River basin, which is vulnerable and prone to geo-hydro hazards. We applied Unsupervised Machine Learning (UML) algorithms such as Isolation Forest, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Linear Integer Programming (LIP) to identify the critical confluence locations. We compare our results with the well-established graph-based centrality metrics (Degree centrality, Betweenness centrality, Closeness centrality, and Eigen Vector Centrality). Our results suggest that DBSCAN outperformed other approaches in terms of detecting crucial nodes. We obtained better results using LIP than other techniques except DBSCAN. The outcome of this study will help the Central Water Commission, in deciding which confluence to focus on, and in assessing the locations of new gauges.
Keywords: Critical nodes; Alaknanda Basin; Machine Learning; Hazards
How to cite: Rajouria, N., Parajapati, P., and Jha, S. K.: Application of Unsupervised Machine Learning Algorithms for identifying critical river confluence in a mountainous watershed., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19050, 2025.
Predicting streamflow in ungauged basins remains a significant challenge in hydrological studies. In recent years, data-driven models have been shown to outperform traditional physics-based models in streamflow prediction for ungauged catchments. However, few studies have examined the potential of such models for predicting streamflow in ungauged basins within India. This study aims to evaluate the performance of two machine learning models, namely Support Vector Regression (SVR) and Random Forest (RF), alongside two deep learning models, Long Short-Term Memory (LSTM) and Bi-LSTM, in the context of streamflow regionalization within the Krishna River Basin in India. Each prediction model is trained using meteorological variables as input features, with streamflow as the output variable. K-means clustering is employed to group selected catchments (based on data availability) into an optimum number of clusters based on spatial proximity and physical similarity. It is assumed that catchments within a cluster share homogeneous characteristics. Regionalization is achieved by sharing model parameters across catchments within the same cluster. For each cluster, one gauged catchment is designated as the donor catchment, while the others are treated as pseudo-ungauged. Each proposed model is trained and tested using the meteorological inputs and streamflow data available at the gauged donor catchment. The trained model for each cluster is then transferred to the remaining receptor catchments within the cluster, where the meteorological variables corresponding to each ungauged catchment are used as inputs. The performance of the models in ungauged catchments is rigorously evaluated by comparing the simulated streamflow against observed streamflow using metrics such as Nash-Sutcliffe Efficiency (NSE), Root Mean Squared Error (RMSE), Coefficient of Determination (R²), and Percentage Bias (PBIAS). This study highlights the advantages of utilizing data-driven methods for streamflow prediction in both gauged and ungauged basins, particularly due to their ability to capture complex, non-linear relationships between meteorological inputs and streamflow generation. The findings of this study are expected to be instrumental in water resources planning and management, flood assessment, and the design of hydraulic structures in the Krishna River Basin.
How to cite: Kaur, S. and Chavan, S.: Data-driven models for streamflow regionalization in Krishna River Basin, India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15731, https://doi.org/10.5194/egusphere-egu25-15731, 2025.
The prior literature on hydrologic model performance is dispersed, encompassing a small number of catchments, different methodology, and rarely linking the results to specific catchment characteristics. This study addresses these constraints by systematically attributing model performance to catchment variables in 671 US catchments, providing a formal framework for determining the best models for specific conditions. Daily streamflow estimation was performed using eight process-based (PB) models and three deep learning (DL) models, with performance measured using the Nash-Sutcliffe Efficiency (NSE). The PB models were tested with a variety of optimization techniques, and the most effective approach for each model was chosen based on the number of catchments that exceeded a predetermined performance threshold. Four models were selected as the top performers based on three performance metrics. Further analyses, such as Classification and Regression Tree (CART) and SHAPley, were used to correlate model performance with catchment variables across all models.
The results showed that PB models (GR4J, HBV, and SACSMA) performed well in catchments with low to medium aridity and a high Q/P ratio, indicating quick hydrologic responses. In contrast, the LSTM-based DL model performed well in medium to high aridity regions but had limits in catchments with rapid precipitation responses and low sand percentages. These findings provide a thorough understanding of the links between model performance and catchment descriptors.
Keywords: Process-based models, Deep learning model, CART analysis, SHAPley analysis, catchment characteristics.
How to cite: sourya, D. A., manikanta, V., and rathinasamy, M.: Can the catchment features influence the performance of the conceptual hydrological and deep learning models? A study using large sample hydrologic data , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18458, 2025.
Knowing evapotranspiration (ET) accurately at fine spatial scales is very important. This would improve understanding hydrological processes and contribute to the advancement of water resource management. In this study, we set a framework based on deep learning to downscale Terra Net Evapotranspiration Gap-Filled 8-Day Global 500m dataset, developed and managed by NASA's Earth Observing System. This approach resulted in a scale enhancement of 20 times. The U-Net architecture was used for this purpose. It incorporated MODIS Land Cover Type 1 (LC Type 1) as an auxiliary variable. This was done to account for land cover changes. The study covered a diverse region that encompasses latitudes 28° to 32°N and longitudes 74° to 78°E. A synthetic design of experiments was utilized to systematically generate and evaluate training data, this ensures robust model performance and reliable downscaling outcomes across the heterogeneous terrain of the study area. Model training, validation, and testing were conducted using the 2001–2014 dataset, 2015–2018, and 2019–2023 dataset, respectively. The model showed excellent performance on the testing dataset. The average PSNR was 34.35 dB and the mean SSIM was 0.8517. The U-Net module effectively downscale and enhance the spatial resolution of ET data. The results show ET's spatial and structural features are well preserved. This study shows how deep learning improves climate data spatial resolution. It provides reliable local hydrological and agricultural resources.
How to cite: Jha, S. K. and Gupta, V.: Downscaling MODIS ET using deep learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18599, 2025.
Accurate and timely representation of spatiotemporal precipitation patterns is critical for monitoring and predicting hydrological extremes, particularly in operational hydrology and early warning systems. In regions with limited in-situ precipitation data, satellite precipitation products (SPPs) offer an accessible solution. However, the latency of these datasets—the delay between data collection and availability—remains a key challenge for real-time applications. This study developed a machine learning model based on the Random Forest (RF) algorithm to predict precipitation using low-latency data from GOES-16 Advanced Baseline Imager (ABI) bands. The model was applied to the Jubones River basin (3,391 km²) in southern Ecuador, a region characterized by complex terrain and hosting a key hydropower project. Leveraging hourly data over a five-year period, the RF model addressed the five-hour latency of traditional SPPs by generating near-real-time precipitation maps with a latency of only 10 minutes. The model’s performance was evaluated using quantitative and qualitative metrics across temporal scales, demonstrating progressive accuracy improvements with larger temporal aggregations. Root Mean Square Error (RMSE) values decreased from 0.48 to 0.05 mm/h, while Pearson’s Cross-Correlation (PCC) improved from 0.59 to 0.87 for scales ranging from hourly to monthly. Qualitative metrics, including Probability of Detection (POD), False Alarm Ratio (FAR), and Critical Success Index (CSI), further validated the approach. These findings highlight the potential of integrating advanced hydroinformatics techniques with remote sensing for managing hydrological extremes in diverse basins. The study underscores the importance of leveraging low-latency satellite data and machine learning to enhance real-time forecasting and operational hydrology. Future work will focus on refining the model for improved detection of extreme precipitation events and exploring its integration into stakeholder-driven decision-making frameworks.
How to cite: Muñoz, J., Muñoz, P., Muñoz, D. F., and Célleri, R.: Leveraging machine learning and satellite precipitation data to overcome latency challenges in operational hydrology, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19086, 2025.
Landslides are a widespread geohazard with significant impacts on lives and economies worldwide. While past research has primarily emphasized creating inventories, and analysing spatial and temporal patterns, the objective of this study is to explore the relationship between landslides events taken place in different catchments using only topographical and physical attributes from the disasters’ areas. The aim is to improve the understanding of the occurrence and susceptibility of such events, as well as the possible similarities between the events and the catchments. To this end, multicollinearity and mutual information analysis were performed to identify both linear and nonlinear relationships between the variables, assisting on the identification of the most relevant driving factors to historical landslides in the study area. Furthermore, the events were grouped using 5 different unsupervised clustering techniques, KMeans, Mean Shift, DBSCAN, Hierarchical and Spectral Custering, to analyse the relationship between landslides taken place in different catchments and their underlying driving forces. Clustering evaluation metrics, i.e. Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index, were used assess the performance of these algorithms. The results show that, for a preliminary study and providing insights on the relevance of driving factors and similarities between events, unsupervised learning proves to be an important tool. Nevertheless, to find more applicable and in-depth associations between extreme disasters and its driving factors, more robust machine learning techniques can and should be used.
How to cite: Antunes Meira, M., Xuan, Y., and Wang, H.: Using Unsupervised Learning to Explore Landslides Driving Factors from Topographic and Hydrological Catchment Features, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6708, https://doi.org/10.5194/egusphere-egu25-6708, 2025.
Runoff forecasting remains a critical challenge in many basins worldwide, particularly those featuring a complex topography, where the scarcity of hydrometeorological data is a prevalent challenge. Data fusion offers a promising alternative to conventional single-source data modelling, which often fails to capture the full spatial and temporal variability of precipitation. By integrating multiple sources, data fusion seeks to generate enhanced satellite precipitation datasets, essential for data-driven runoff forecasting models. This study aims to evaluate the effectiveness of the Three-Cornered Hat (TCH) method for fusing satellite precipitation products (SPPs) and its influence on the performance of a Random Forest-based runoff forecasting model.
Three scenarios were evaluated: (i) a TCH-fused dataset combining three SPPs: Integrated Multi-satellitE Retrievals for GPM – Early Run (IMERG-ER), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks – Cloud Classification System (PERSIANN-CCS) and the Global Satellite Mapping of Precipitation – Near Real Time (GSMaP-NRT); (ii) an individual SPP (IMERG-ER); and (iii) an already fused benchmark product, the Multi-Source Weighted-Ensemble Precipitation (MSWEP). All scenarios performed comparably for lead times of 3, 6, 12, and 24 hours, with MSWEP slightly outperforming across Nash-Sutcliffe Efficiency, Kling-Gupta Efficiency, and Root Mean Square Error metrics. However, TCH demonstrated better bias reduction as reflected by the Percent Bias metric.
A key limitation of the fusion method was identified at hourly scales, where statistical dependence arises during periods with no precipitation over the basin, hindering the effectiveness of TCH. The introduction of a matrix regularization step addressed this issue. This study provides valuable insights for enhancing SPP fusion methods and offers a replicable framework for improving runoff forecasting, particularly in data-scarce regions and other hydrological contexts.
How to cite: Luna-Abril, P., Muñoz, P., Samaniego, E., Muñoz, D. F., Merizalde, M. J., and Célleri, R.: Evaluating the Three-Cornered Hat Method for Satellite Precipitation Data Fusion and its Influence on Runoff Forecasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16242, 2025.
Understanding and modeling surface and groundwater resources are critical due to the effects of droughts and climate change, especially in semi-arid, arid, or hyper-arid regions. GeoLinkage, developed by Troncoso (2021), facilitates the creation of linkage files for integrated models. These linkage shapefiles act as a communication interface between a surface hydrological domain (D1) and an aquifer domain (D2). The surface domain (D1) comprises nodes and arcs that represent hydrological elements and their relationships, while the aquifer domain (D2) contains geometric elements such as grids or Quadtree diagrams. D1 defines a surface topology (τ1), D2 defines a groundwater topology (τ2), and the linkage file establishes a surface-groundwater topology (τ1-2). This new topology, τ1-2 ,imposes constraints that influence the relationship between τ1 and τ2. For instance, the superposition of elements in τ1-2 should be considered a spatial relationship. Depending on the type of superposed elements, this relationship must be reflected in τ1 or τ2. To enforce these τ1-2 specific restrictions, GeoLinkage has been enhanced with a post-processing module called GeoChecker. This module evaluates the quality of the resulting linkage files. GeoChecker currently performs a superposition check to ensure that overlaps between cells in the linkage file—whether between groundwater and catchments or groundwater and demand sites—are accurately represented as connections in the surface model (WEAP). The aquifer is represented by a MODFLOW model fully linked to the WEAP model. GeoLinkage2.0 and GeoChecker were developed using the tutorial WEAP-MODFLOW model, considered a small model, and were tested in large integrated models, such as the Azapa Valley (3,000 km²) and the Limarí River Basin (12,000 km²), Chile.
How to cite: Sanzana, P., Torga, A., Hitschfeld, N., and Lobos, C.: GeoLinkage2.0 and GeoChecker: Hydroinformatics tools for large and complex hydrological-hydrogeological models using WEAP-MODFLOW. Case Study: Severe drought in the Limarí River Basin, Chile, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20678, 2025.
The release of low-temperature water from a reservoir can have negative impacts on downstream fish spawning and crop growth in irrigation areas. Therefore, predicting the discharged water temperature accurately and swiftly is crucial. This study focused on the Pubugou Hydropower Station, a major project situated on the Dadu River in the upper reaches of the Yangtze River, and evaluated the impacts of meteorological factors and reservoir operational parameters on the released water temperature using Spearman correlation coefficients (R). To predict the discharged water temperature of Pubugou Reservoir, five models were optimized by genetic algorithms including random forests, support vector regression, convolutional neural network, long short-term memory network, and the lightweight gradient boosting machine respectively. The results showed that: (1)The dew point temperature exhibited the highest correlation with discharged water temperature (R = 0.89), However, the correlation coefficient between wind speed, cloud cover, solar radiation, dam front water level, and discharge water temperature was not found to be 0.4. (2) All the five models optimized by genetic algorithms performed well on the training set, especially the random forest model (R2 = 0.997). The worst performing model is the long short-term memory network model (R2 = 0.985). (3) In the prediction of discharge water temperature, all models have good fitting effects, with r2 greater than 0.93, average absolute error not greater than 0.662 ℃, and mean square error not greater than 0.852 ℃. Random forest models and lightweight gradient boosting machine models have shown good performance on the most of sample data, with a small residual range, while support vector regression models and convolutional neural network models have smaller maximum residuals. This research indicated that machine learning methods can effectively predict water temperature released from reservoirs, providing more reliable decision support for formulating relevant measures to alleviate the impact of reservoir discharge water temperature.
How to cite: Junguang, C.: Application of Machine Learning in Predicting the Water Temperature Released from Reservoirs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18342, 2025.
The accurate estimation of crop evapotranspiration (ETc), root zone soil moisture depletion, and irrigation demands is critical for optimizing water resource management and enhancing sustainability in precision agriculture. The FAO-56 model serves as a foundational tool for these predictions; however, its conventional workflow necessitates the manual acquisition of essential inputs such as climatic data and soil moisture from disparate external sources. This process can be time-intensive, cost-prohibitive, and susceptible to human error. Furthermore, the deterministic nature of FAO56 can lead to inaccuracies if reference evapotranspiration and crop coefficients are not meticulously estimated. This study introduces NeuralFAO56, a Python package that integrates advanced machine learning models and real-time data acquisition with the FAO-56 framework to automate and improve the estimation of ETc and irrigation demands. By leveraging application programming interfaces (APIs) to automatically collect real-time climatic data from meteorological stations and NASA’s Soil Moisture Active Passive (SMAP), NeuralFAO56 dynamically updates model inputs. The package incorporates a range of machine learning models, including Long Short-Term Memory (LSTM) and transformer architectures, to generate data-driven ETc estimations, thereby enhancing the accuracy and adaptability of irrigation predictions. NeuralFAO56 is designed with a modular architecture, enabling users to customize its functionalities for diverse agro-hydrological contexts. This tool provides a robust, user-friendly platform for researchers, water resource managers, and agricultural professionals, facilitating intelligent irrigation decision-making, improving water-use efficiency, and contributing to sustainable agricultural practices.
How to cite: Neupane, A. and Samadi, V.: A NeuralFAO56 Python Package for data-driven Irrigation Demand Calculation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13200, https://doi.org/10.5194/egusphere-egu25-13200, 2025.
Drought is one of the most severe climate-induced phenomena; with significant impacts on agriculture, water resources, and ecosystems. Drought monitoring under climate change scenarios becomes crucial, particularly in regions vulnerable to water scarcity, such as semi-arid areas in Iran. Although Global Climate Models (GCMs) contain coarse spatial resolutions, they provide valuable insights in better assessing the variability of drought characteristics—such as duration, severity, and intensity in the future. To achieve this aim, downscaling of climate variables as triggers of droughts is required to monitor drought in local scale. Latyan region in Iran, as an important area to supply water, is a critical place based on its climate, drought event occurrences, and water demand and supply stress. This study tried to accurately downscale and bias-correct the climate variables utilizing the latest CMIP6 models (ACCESS-CM2, BCC-ESM1, CanESM5, HadGEM3-GC31-LL, and MIROC6) and AI techniques in the case study. This research employs a predictor selection technique in conjunction with a stack generalization model to improve the accuracy of the downscaling process. After careful examination of predictors, surface temperature, precipitation, and surface air pressure have been used along with annual cycles for training four machine learning models including Multilayer Perceptron (MLP), Support Vector Regression (SVR), Random Forest and Stack Generalization (SG) models for the sake of downscaling. Results showed that MIROC6 model is the best model according to all downscaling methods. In addition, among MLs, stacked generalization model improved the statistical metrics considerably with a Nash-Sutcliffe Efficiency (NSE) of 0.64, Mean Squared Error (MSE) of 1051.3, and Kling-Gupta Efficiency (KGE) of 0.68 for MIROC6 model. Selection of the proper GCM and downscaling method can help decision-makers take proper measures against drought to reduce drought impacts.
How to cite: Mirdarsoltany, A., Rahimi, L., Anderson, C., and Graf, T.: Fusion of Stacked Generalization and Predictor Selection Technique for Downscaling in Drought Monitoring: A Case Study in a Semi-Arid Area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7150, https://doi.org/10.5194/egusphere-egu25-7150, 2025.
Accurately predicting Snow Water Equivalent (SWE) has become increasingly crucial. It holds particular significance for managing water resources in regions heavily reliant on snowmelt. The present study introduces an integrated Long Short-Term Memory (LSTM) model that incorporates extreme heat events and diverse climate change projections to generate detailed SWE distribution maps and long-term trend analyses. By including lagged SWE observations and climate indicators, the model captures the intricate temporal dynamics of snowfall accumulation and melt processes, thereby improving forecast accuracy and stability.
Previous studies indicate that areas dependent on seasonal snowpack face accelerated snowmelt timing and reduced water availability under rising temperatures. These shifts can exert critical impacts on agricultural irrigation, ecosystem habitats, and water allocation strategies, highlighting the importance of robust forecasting tools for proactive resource management. Furthermore, the development of comprehensive risk maps pinpoints high-risk hotspots where anticipated temperature increases coincide with substantial changes in SWE and snowmelt patterns. These zones are prime candidates for early adaptation measures, including infrastructure upgrades and policy interventions aimed at mitigating potential water shortages.
As global warming persists, this modeling framework provides stakeholders, policymakers, and local communities with valuable insights into emerging water resource risks. The integration of climate change scenarios into the LSTM model underscores the necessity of forward-looking research that can inform both short-term operations and long-term planning. Ultimately, this approach lays the groundwork for crafting sustainable adaptation strategies, preserving agricultural output, protecting ecosystems, and ensuring water security in regions where snowmelt is pivotal to resource availability.
How to cite: Tsang, P. J. and Tsai, W. P.: Risk Mapping and Adaptation Strategies: Enhancing SWE Predictions with an LSTM Model for Snowmelt-Dependent Regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7201, https://doi.org/10.5194/egusphere-egu25-7201, 2025.
The São Francisco River Basin is crucial for Brazil’s agriculture, hydropower, and water security. However, climate change has intensified challenges like reduced water flow and frequent extreme events, threatening its socio-economic sustainability. This study aims to forecast flow in the São Francisco River Basin, enabling proactive decision-making to mitigate risks associated with both droughts and floods. To address these challenges, this study propose a novel methodology based on Artificial Intelligence (AI), combining Recurrent Neural Networks (RNN) and complex network techniques. The method creates new features and assigns importance weights to enhance the algorithm’s ability to generate probabilistic flow forecast. The results are promising, demonstrating the method’s ability to deliver accurate probabilistic forecasts. This research can support risk mitigation strategies and improve water resource management in the São Francisco Basin. Additionally, the proposed framework is scalable, offering potential applications to other critical watersheds facing similar challenges
How to cite: Caseri, A., Aparecido Rodrigues, F., and Victal Cerqueira, M.: Ensemble Approach for Hydrological Forecasting Based on Recurrent Neural Networks and Complex Networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13991, https://doi.org/10.5194/egusphere-egu25-13991, 2025.
Flash floods are one of the most severe natural hazards worldwide; they can occur within a few minutes or hours, and can move at high flow velocities, striking with violence and little warning. Early warning of flash floods is extremely important for vital risk mitigation and requires the knowledge of the critical rainfall producing flooding that is typically considered as “warning index”. The small spatial and temporal scales at which flash floods occur make the prediction of critical rainfall challenging, particularly in data-poor environments, where high-resolution weather models and advanced monitoring networks may not be available.
In this research, we present a methodology to estimate the critical rainfall for flash flooding based on an integrated hydrologic-hydrodynamic model. The model is applied in the Lilantas River catchment in Evia, Greece, considering a relatively large number of rainfall and soil moisture conditions scenario combinations in order to (1) determine inflow hydrographs used as boundary conditions for the hydrodynamic model and (2) calculate the distribution of “critical hazard” across the cells of the two-dimensional (2D) computational domain. In the present work, we define critical hazard combining the main hydrodynamic characteristics that are water depth and flow velocity, and we import all calculated “critical hazard” values into a GIS-based database.
Key findings include maximum peak discharges from all simulated scenarios, allowing a sensitivity analysis of varying Curve Number and soil moisture conditions, as well as the effects of rainfall duration and intensity combinations on flood responses. Furthermore, based on the calculated critical hazard, estimates of critical rainfall values for the selected study area are provided, along with an example of the flood warning system’s operation.
How to cite: Papoulakos, K., Mitsopoulos, G., Baltas, E., and Stamou, A. I.: Estimating critical rainfall for flash flood warning systems using integrated hydrologic-hydrodynamic modelling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3970, https://doi.org/10.5194/egusphere-egu25-3970, 2025.
Flood forecasting is essential for hydrological assessment and catastrophe mitigation, particularly in flood-prone areas such as Bangladesh. Nonetheless, the direct measurement of water levels (WL) and discharge frequently encounters obstacles related to time, technological limits, and economical constraints. This study posits that flood levels can be accurately predicted utilising accessible data during flood events, employing a trained Artificial Neural Network (ANN) model. The complexity of hydrological systems, exacerbated by transboundary contributions from significant rivers like the Brahmaputra-Jamuna, hinders accurate forecasting. To tackle these problems, the study employed Artificial Neural Networks (ANN), a flexible and data-driven methodology adept at modelling non-linear relationships, to predict flood water levels with a lead time of up to seven days in Sirajganj, a district particularly susceptible to river flooding and bank erosion. Daily Data on water levels and rainfall were collected from the Bangladesh Water Development Board (2002–2015) for the monsoon season (May–October) were analysed, utilising information from four rainfall stations and six water level stations located 62–237 km upstream. The ANN model, employing a Sigmoid activation function with one to three hidden layers, indicated that augmenting the number of hidden layers provided only negligible enhancements in performance. Performance metrics, such as the goodness-of-fit (R²: 0.985–0.554), Root Mean Square Error (RMSE: 0.024–0.617), and Mean Absolute Error (MAE: 0.087–0.604), demonstrated a marginal improvement when rainfall and water level data were combined. This study highlights the efficacy of Artificial Neural Networks (ANN) in tackling hydrological prediction issues, confirming its ability to utilise readily accessible datasets to provide reliable and effective flood forecasts, thus aiding disaster preparedness and mitigation efforts in resource-limited areas such as Bangladesh.
How to cite: Das, P. and Mohib, K. M.: Data-Driven Flood Forecasting Using ANN: A Resource-Efficient Approach for High-Risk Regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14431, https://doi.org/10.5194/egusphere-egu25-14431, 2025.
The increasing frequency and intensity of climate hazards, as emphasized by the IPCC’s Sixth Assessment Report, underscore the urgent need for effective disaster risk reduction strategies. Using the devastating floods of September 2024 in Nepal’s Kathmandu Valley, and the April 2024 floods in Kenya’s Nairobi, this study examines the persisting gaps in flood resilience despite early warnings using disaster forensics techniques. The Kathmandu floods, which were triggered by an extreme rainfall event resulting from the convergence of a low-pressure system from the Bay of Bengal and a cyclonic circulation from the Arabian Sea, caused extensive loss of life, property damage, and economic disruption in the Nakhu Khola watershed, despite timely early warnings issued by the government. In Kenya, a notable gap exists in the warning systems, whether in their issuance, dissemination, or uptake, despite the presence of advanced operational forecasting systems. Encroachment on floodplains, unplanned urbanization, and land-use changes have exacerbated vulnerability, while weak governance and poor enforcement of disaster risk management legislation has left populations and assets exposed. Additionally, risk assessment efforts are scarcely integrated into government plans or those of other stakeholders, highlighting a critical area for improvement in disaster preparedness and management.
Using the UNDRR’s forensic disaster analysis framework, this research investigates the underlying causes, risk drivers, and lessons from these events. The populations most affected are identified, including those living in floodplains, including marginalized communities, and critical infrastructure. Local investments in disaster preparedness are also critically examined for efficacy. Results highlight that while early warnings were disseminated through various channels, gaps in risk communication and community-level preparedness persisted. The findings emphasize the need for education and awareness and integrated approaches to disaster risk management that address root causes such as unplanned urban growth and environmental degradation. Empowering youth and fostering leadership in disaster risk reduction are critical to ensure climate resilient societies of tomorrow. This research contributes actionable insights to reduce vulnerability, enhance preparedness, and minimize losses in future climate hazard events in the Kathmandu Valley and Kenya, as well as similar rapidly urbanizing regions.
How to cite: Kettner, A., Gupta, A., Singh Shrestha, M., Trigg, M., Cohen, S., Hawker, L., Prades, L., Rudari, R., Salamon, P., Tellman, B., Sperna Weiland, F., and Wu, H.: Devastating Flooding Despite Early Warning: Lessons Learned from the Nepal and Kenya Floods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21199, 2025.
Flooding has posed a significant challenge to urban infrastructure, necessitating effective and real-time risk management strategies [1]. One of the most devastating impacts is on urban transportation, where disruption can lead to significant economic losses or even human casualties [2-3]. This study has focused on the key financial and commercial areas in downtown London, where an innovative system has been developed to integrate real-time flood risk forecasting with traffic data visualisation and dynamic decision support for emergency response and resource allocation. First, with access to the Google Maps API, real-time and forecast traffic data have been collected for local streets. Then, these datasets can facilitate a 15-minute resolution forecast for the next 8 hours, enabling an in-depth understanding of traffic flow patterns during flood events. Furthermore, by employing flood forecasting measures on these real-time datasets, streets at risk of inundation can be identified faster, with their traffic conditions assessed accordingly.
A key aspect of this study is to consider different factors dynamically for weighting and prioritising streets. On one hand, pre-existing factors such as road hierarchy, connectivity, access to critical facilities, land use, infrastructure vulnerability, and proximity to evacuation zones are converted into dynamic factors by attaching a temporal variable to these pre-existing factors. On the other hand, real-time dynamic ones include flood depth, traffic congestion, accessibility for emergency services, and community needs reported. The integration of all these factors leads to the development of a transportation-based decision support system (TBDSS) tailored to urban flood management. The TBDSS has facilitated the allocation of emergency resources, prioritisation of street reopening, and planning for evacuation or relief operations. For instance, streets connecting to hospitals or shelters have been given higher priority, while those serving industrial or low-density areas have been weighted lower. As such, our proposed system can dynamically adjust priorities based on evolving flood and traffic conditions, ensuring optimal response strategies.
The findings have demonstrated the feasibility of leveraging real-time data and advanced modeling to enhance urban flood resilience. By combining flood risk maps, traffic forecasts, and a comprehensive prioritisation framework, this approach has provided a promising tool for urban planners and emergency responders.
[1] Piadeh, F., Behzadian, K., Alani, A. (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, p.127476.
[2] Gao, G., Ye, X., Li, S., Huang, X., Ning, H., Retchless, D., Li, Z. (2024). Exploring flood mitigation governance by estimating first-floor elevation via deep learning and google street view in coastal Texas. Environment and Planning B: Urban Analytics and City Science, 51(2), 296-313.
[3] Naghedi, S. N., Piadeh, F., Behzadian, K., and Hemmati, M.: Unveiling the Interplay: Flood Impacts on Transportation, Vulnerable Communities, and Early Warning Systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13189, https://doi.org/10.5194/egusphere-egu24-13189, 2024.
How to cite: Naghedi, R., Piadeh, F., Huang, X., and Wu, M.: Real-time Transportation-Based Flood Warning System: A Case Study in Downtown London, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3543, https://doi.org/10.5194/egusphere-egu25-3543, 2025.
Digital twins, virtual representations of physical systems, integrate sensor data and predictive models to enable real-time simulation and analysis. They are instrumental in monitoring weather, infrastructure health, and water levels, particularly in flood management. By modeling mitigation techniques, forecasting risks, and enhancing emergency responses, digital twins improve decision-making, reduce economic losses, and enhance public safety in flood-prone areas [1][2]. This study developed a digital twin system to monitor and forecast flood disasters in western Iran. The system combined multidimensional sensor data on temperature, flood flow, vegetation cover, and water levels using an offline databank. Time-series analysis tracked trends, while a linear regression-based predictive model estimated future flood conditions. Threshold values for flood warnings and high-risk alerts were defined using hydrological principles and environmental data [3]. Game theory concepts were employed to optimize flood management strategies by modeling interactions among stakeholders, including authorities, responders, and communities. A non-cooperative game theory approach simulated conflicting objectives, such as minimizing economic losses and optimizing resource allocation. Stable solutions were identified through the Nash equilibrium, ensuring no stakeholder could unilaterally improve outcomes. Visualization dashboards presented time-series data, risk levels, and stakeholder strategies, facilitating informed decision-making. Simulation results demonstrated the system's effectiveness in flood risk assessment. Water levels remained below the 2.5-meter warning threshold but rose significantly during simulated abnormal conditions. In later stages, some areas approached the 3.0-meter high-risk threshold, indicating zones vulnerable to flooding. Flood flow rates frequently exceeded the 40 m³/s threshold, with peaks above 60 m³/s, highlighting the need for continuous flow monitoring. Temperature fluctuations were minimal, consistently below the 25°C threshold, suggesting limited influence on flood risks during the study. However, vegetation cover often fell below the 30% threshold, correlating with increased flood risks and reinforcing its importance in mitigation. The system effectively categorized risk levels, with most instances classified as "Normal" or "Warning." High-risk alerts were concentrated during elevated water levels and flows. This research highlights the potential of digital twins for real-time flood monitoring and collaborative decision-making, providing a robust framework to enhance disaster resilience.
Keywords: Digital Twin; Flood Risk Assessment; Game Theory; Predictive Modeling; Multidimensional Data Analysis.
References
[1] Ghaith, M., Yosri, A., & El-Dakhakhni, W. (2021, May). Digital twin: a city-scale flood imitation framework. Canadian Society of Civil Engineering Annual Conference (pp. 577–588). Singapore: Springer Nature Singapore.
[2] Gheibi, M., & Moezzi, R. (2023). A Social-Based Decision Support System for Flood Damage Risk Reduction in European Smart Cities. Quanta Research, 1(2), 27–33.
[3] Kreps, D. M. (1989). Nash equilibrium. In Game Theory (pp. 167–177). London: Palgrave Macmillan UK.
How to cite: MohammadZadeh, F., Eghbalian, H., Gheibi, M. G., Yeganeh-Khaksar, R., Ghazikhani, A., and Behzadian, K.: A Digital Twin Framework for Real-Time Flood Monitoring and Multidimensional Prediction: A case study in Iran, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20713, 2025.
Asset-based Dynamic Flood Risk Assessment: Case Study of London Downtown
Flooding poses significant risks to urban centres, with particular challenges faced by business hubs where disruptions can have devastating consequences on national and global economies [1]. Business hubs are the lifeblood of national and global economies. During flood events, businesspeople encounter disruptions that not only obstruct daily operations but also ripple through supply chains and financial systems [2-3]. This study emphasises the importance of protecting critical assets in Downtown London, a vital business hub, to mitigate economic and social impacts during floods. Through a watershed-based approach, Downtown London, a vibrant business hub with numerous critical assets, has been selected as the case study area. The district contains key commercial buildings and infrastructure that are vital to economic and social continuity. Using Digimap and Verisk, essential commercial buildings and critical assets are pinpointed based on their usage and significance. These tools facilitate generating an accurate map of assets requiring priority attention during flood events.
The proposed decision support system (DSS) is developed to aid risk management authorities, including policy-makers, decision-makers, and technical staff. The system operates on two key bases. Real-time population density data for critical assets is obtained using Google API. This data helps evaluate the human vulnerability component during flood scenarios. A flood forecasting system is integrated to predict water levels at 15-minute intervals for the coming hours. This system provides granular and actionable insights into evolving flood conditions. For each critical asset, two risk values are computed: one based on population density and another on forecasted water levels. These values are combined to derive a dynamic risk level for each time step, enabling authorities to respond effectively. The integration of real-time data and predictive modeling in the DSS offers a comprehensive framework for flood risk assessment. By prioritising critical assets based on dynamic risk levels, authorities can implement targeted preparedness and response measures such as early warnings and evacuation plans. This approach ensures both human safety and economic resilience. The findings have demonstrated the feasibility of applying real-time data and cutting-edge modeling to enhance urban flood resilience. By combining flood risk maps, real-time population density, and a comprehensive prioritisation framework, this approach provides a promising tool for urban planners and emergency responders to protect critical business assets and ensure economic continuity during flood events.
References
[1] Bakhtiari, V., Piadeh, F., Behzadian, K. and Kapelan, Z. (2023). A critical review for the application of cutting-edge digital visualisation technologies for effective urban flood risk management. Sustainable Cities and Society, p.104958.
[2] Bakhtiari, V., Piadeh, F., Chen, A.S. and Behzadian, K. (2024). Stakeholder analysis in the application of cutting-edge digital visualisation technologies for urban flood risk management: A critical review. Expert Systems with Applications, 236, p.121426.
[3] Piadeh, F., Behzadian, K. and Alani, A.M. (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, p.127476.
How to cite: Bakhtiari, V. and Piadeh, F.: Asset-based Dynamic Flood Risk Assessment: Case Study of London Downtown, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3504, https://doi.org/10.5194/egusphere-egu25-3504, 2025.
Social media applications have emerged as reliable communication channels, especially when traditional methods falter [1]. Their integration into emergency management presents significant advantages, including enhanced situational awareness during unfolding events, rapid dissemination of news and alerts to broader audiences, and improved coordination among decision-makers and stakeholders [2]. Both remote sensing and social media data offer distinct advantages in large-scale flood monitoring and near-real-time flood monitoring [3]. To better understand these advantages and challenges, a comprehensive review and analysis of the literature on the application of social media in this field was conducted. Social media facilitates participatory and collaborative structures, enabling collective knowledge-building in public information and warning systems. To realise this vision, the authors examined, 73 studies conducted from 2014 to 2024 to systematically evaluate the current literature surrounding communication on social media and the latest research in social media informatics related to disasters. This review identified key challenges within existing studies. The articles included 23 related to pluvial floods, 12 related to fluvial floods, 17 related to storm floods and 21 paper that were unspecified The majority of the studies were conducted in China, followed by the United States. Various software platforms, including Twitter, YouTube, and other social media networks, were analysed. Data extraction from these platforms was performed using Python programming. The study periods ranged from 1 to 3,650 days. These findings serve as guidance for researchers examining the relationship between social media and disaster management. They aim to develop the use of social networks during disasters, analyse patterns, and create programming to identify best practices for utilising social media in times of crisis. In the future, a mapping framework and tool can be developed to automatically extract information from social media through text and image analysis. By integrating this data with other available information sources, it will be possible to generate more accurate inundation maps in real-time. It is essential to recognise that information about floods obtained from social media may be incomplete during communication interruptions. To address this issue, future research should prioritise integrating big data from urban Internet of Things networks and improving communication infrastructure repairs. By adopting this strategy, we can collect more comprehensive disaster information to enhance flood emergency response effectiveness.
References
[1] Piadeh, F., Behzadian, K. and Alani, A.M. (2022). A critical review of real-time modelling of flood forecasting in urban drainage systems. Journal of Hydrology, 607, p.127476.
[2] Piadeh, F., Ahmadi, M., Behzadian, K. (2020). A Novel Planning Policy Framework for the Recognition of Responsible Stakeholders in the of Industrial Wastewater Reuse Projects. Journal of Water Policy, 24 (9), pp. 1541–1558.
[3] Bakhtiari, V., Piadeh, F., Chen, A., Behzadian, K. (2024). Stakeholder Analysis in the Application of Cutting-Edge Digital Visualisation Technologies for Urban Flood Risk Management: A Critical Review. Expert Systems with Applications, p.121426.
How to cite: Panahi, A., Karkhaneh, N., and Piadeh, F.: Community-based flood early warning system: Current practice and Future directions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3526, https://doi.org/10.5194/egusphere-egu25-3526, 2025.
About 90% of extreme precipitation in the midlatitudes can be assoicated with front boundaries. Therefore, it is important to identifiy frontal locations for short term weather forecasting or long-term prediction of precipitation in climatology. Deep learning (DL) refers to machine learning alogrithms that use multiple layers of neural networks to derive features from input data. It is generaly useful to process 2-dimensional image data. One of the potential advantages of employing DL to detect surface weather fronts is that the developed DL based functions can be applied to automatic detection of surface weather fronts for climate models. However, justifications of its applicability on climate models are needed.
In this study, we developed deep learning based methodology to detect surface weather fronts. Specifically, a U-shape convolutional network (U-net) based deep learning model is developed to predict surface weather fronts over Japan and surrounding sea in summer (June, July, and August). We justify the applicability of the deep learning model in predicting surface fronts in summer on outputs from large-scale Global Climate Models (i.e. GCMs) from two aspects. First, the coarse resolution of GCMs (e.g., 1.25 degrees) can capture the general morphological features of surface fronts. Second, models trained in a colder climate are applied to predict fronts in a warmer climate with some decrease in predicted peak frequency of fronts, but the general features of the spatial distribution of fronts can be represented by the deep-learning model predictions. We also see that the locations of peak frequency tend to move slightly more southwesterly in a slant zone within the belt region between 25N to 40N as climate warms in the future.
How to cite: Mao, Y. and Yamada, T.: Applicability of deep learning based detection of surface weather fronts on large scale climate models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19530, 2025.
