PICO
Despite recent advances in hydrology research and technology developments in the Anthropocene, our understanding of human–water interactions in both large- and local-scale hydrologic systems remains elusive. This knowledge gap is mainly due to the complexity of human influences, limited and patchy records on human activities, and the inadequacy of conventional modelling approaches that are often designed for natural catchments assuming stationarity conditions. As a result, the accuracy and reliability of hydrological forecasting in these human-influenced catchments are significantly affected. Given the large populations potentially affected by water-related hazards in these areas, there is an urgent need for more focused attention and research.
This session will explore recent advances in hydrological forecasting (e.g. floods, droughts, moisture-driven landslides, and compound or cascading hydro-hazards) for urban and regulated catchments. We invite abstracts focusing on (but not limited to) the following topics:
• Developments and applications of advanced statistical, process-based, and machine learning models for forecasting hydroclimatic extremes in urban or regulated catchment
• Recent developments in data acquisition for capturing human activities (or proxy data), including in-situ measurements, remote sensing, paleoclimatic record, and social media, as well as hydrological dataset analytics and integration
• Novel quantitative approaches for human impacts (of any type) on the water cycle and hydrological processes
• Impact-based assessment of water-related risks, such as economic impacts, human health and safety, social and community impacts, and environmental impacts
• Uncertainty quantifications and risk assessments of singular and compound hydro-hazards under climate non-stationarity
• Improved visualization and effective communication methods designed for early warnings and short-to-long-range predictions of rare and ‘record-breaking' extreme events.
Compound flooding poses significant global socio-economic and infrastructure risks that are projected to intensify due to climate change and anthropogenic development. These compound floods, where multiple interacting drivers amplify flood extents and depths, are the most widespread and catastrophic natural hazards, in particular in urban catchments, inflicting billions of dollars in damages and jeopardizing livelihoods and critical resources. A primary challenge in addressing these events lies in the incomplete understanding of the nonlinear and complex climatic, hydrological, and hydrodynamic processes involved in compound flooding, which often leads to ineffective flood management strategies. This gap in knowledge also limits the development of suitable tools and methods for accurate flood characterization and modeling. Given the massive and escalating impacts of such events, there is a clear need for a more comprehensive understanding of the key drivers that shape flood dynamics, including uncertainties related to climate, human activity, and natural systems. Although significant advances have been made in developing physically-based dynamic models for flood simulation, these models often fall short in terms of accuracy and reliability, and remain computationally intensive for operational use. These challenges stem from an incomplete understanding of flood processes, uncertainties in predictability, and limitations in model assumptions. This presentation addresses these challenges by proposing an integrated framework that incorporates human activity, hydrological factors, topography, river morphology, and land use to enhance our understanding of riverine, coastal, and compound flood generation. It also highlights strategies for improving flood forecasting and inundation modeling through the integration of state-of-the-art process-based models, data assimilation, and machine learning, while considering cascading uncertainties in both model predictions and real-world applications.
How to cite: Moradkhani, H.: From Complexity to Clarity: An Integrative Framework for Enhancing Flood Forecasting in Urban Catchments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7506, https://doi.org/10.5194/egusphere-egu25-7506, 2025.
Estuary and coastal regions face the dual impacts of river flooding and storm surges, posing serious threats to the lives and properties of residents. The Guangdong-Hong Kong-Macao Greater Bay Area in China, an economically developed and densely populated region with a complex river network, is frequently affected by flood disasters. In recent years, rapid development in the Greater Bay Area, coupled with human activities such as sand mining and dredging, has significantly altered riverbed morphology, leading to a pronounced trend of incision. On one hand, riverbed incision increases the cross-sectional area, allowing for greater flood discharge; on the other hand, it changes the hydrodynamic conditions of the rivers, resulting in rising water levels in certain areas despite the incision.
This study employs a one-dimensional river hydrodynamic model and a storm surge model to simulate the impacts of river flooding and storm surges under various topographic conditions on flood disasters in the Greater Bay Area. The results indicate that the nonlinear interactions between floods and tides amplify the hazard of compound flooding in the mid-to-lower river network region. Furthermore, the severity of this hazard intensifies as the strength of flood-tide compound events increases.
How to cite: Chen, Y., Jiang, J., He, S., Zeng, Z., and Wang, Z.: Intensive human activities causing riverbed incision have increased the danger of compound flood in the PRD., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4381, https://doi.org/10.5194/egusphere-egu25-4381, 2025.
Urban flood risk has surged in recent years due to unsustainable urban development, changes of hydrological processes and frequent occurrence of extreme weather events. Addressing this challenge requires capturing the dynamic interactions between human and natural systems. This study presents an innovative Coupled Human And Natural Systems (CHANS) modelling framework which integrates high-performance hydrodynamic and agent-based models to simulate real-time flood-human interactions at high spatial resolution. The framework is enhanced with a reinforcement learning (RL) module to support AI-guided flood risk management, including optimal resource allocation during emergencies.
Applied to the 2015 Desmond flood in the Eden Catchment (UK) and urban flooding in Can Tho City (Vietnam), the CHANS framework demonstrates its capacity to replicate household-level responses and assess flood mitigation strategies, such as early warnings, sandbag distributions, temporary flood defence and mobile pump deployments. Results show that early warnings combined with temporary defences reduced inundation by 30% in Carlisle, saving up to £30 million. RL-guided mobile pump strategies in Can Tho outperformed traditional methods, improving flood mitigation efficiency by up to 4× during post-flooding events.
By incorporating human behaviour, decision-making, and AI optimisation, the CHANS framework provides a robust tool for enhancing flood risk management strategies, contributing to more resilient and adaptive disaster response planning.
How to cite: Qin, H. and Liang, Q.: A high-performance Coupled Human And Natural Systems (CHANS) modelling framework for flood risk assessment and emergency management, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20729, https://doi.org/10.5194/egusphere-egu25-20729, 2025.
Floods are attracting global attention; 23% (1.81 billion) of the world’s population live in areas directly exposed to floods. Floods are the most frequent and one of the costliest disasters worldwide, with 1.3 trillion USD loss in the past 30 years. Coastal urban areas require greater attention due to their increased vulnerability to Sea Level Rise (SLR), cyclones, storm surges and other related challenges. In Asia, India is one of the severely affected countries by floods. With a coastline of 7500km, Indian coastal urban areas such as Surat, Mumbai, Kolkata, Chennai, Kochi and Vishakapatanam face urban flood issues annually. Several criteria influence urban flood Risk, each contributing its share to the overall flood risk. A comprehensive output of the analysis of all these criteria/layers provides a platform to assess flood risk mitigation potential. Flood resilience strategies formulated at a broader administrative level are often implemented on a smaller scale, such as wards or blocks.
A uniform approach may not be advantageous for all the wards due to the diverse challenges across all the wards. Even with a similar overall risk index, the action needed will differ based on the severity of the individual risk criteria contributing to the risk. An Impact-based risk assessment analyses individual criteria layers, which can provide specific insights into the challenges and needs of the local context. The scientific approach in analysis is advantageous for impact-based assessment. The categorization of wards into different zones for the individual risk criteria makes it beneficial in developing custom-made actionable strategies.
For current study, Kochi Municipal Corporation (Kochi City), the commercial capital of Kerala, is selected as the Area of Interest (AOI). The city adjoins seacoast on the west-side, with backwaters entering the land mass, giving a distinctive landscape. Kochi is divided into 74 administrative wards, ranging from 17.38sqkm (Ward-03) to 595.67sqkm (Ward-29). The distinctive physical and social characteristics across the wards in the AOI call for a more specific approach to flood management strategies.
For Kochi City, eleven significant criteria under Hazard, Exposure, and Vulnerability components contributing to the urban flood risk are identified and analyzed spatially for the AOI. The wards are divided into five different zones based on the risk level. The zoning considers the individual criteria indices, hazard index, exposure index, vulnerability index and overall risk index. The individual criteria indices help identify more specific challenges and needs at the local level based on the socio-economic and environmental situation. The existing flood mitigation measures and management strategies are considered in the preparation of actionable solutions. Depending on the need and urgency, actionable solutions for different priority levels are formulated for each risk zone.
The approach is multifaceted, considering the overall risk and analyzing the specific issues associated with the individual or a group of wards with similar contexts. The prioritization of the actions offers an opportunity to better allocate the resources to risk zones that need immediate actions. Understanding the impacts specific to the wards helps develop targeted strategies to reduce the specific challenges, thereby enhancing overall resilience.
How to cite: Mukherjee, M. and Abdul Salam, H.: Impact-Based Flood Risk Assessment for Actionable Resilience Strategies for Kochi, an Indian Coastal City., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3565, https://doi.org/10.5194/egusphere-egu25-3565, 2025.
Flood forecasting is particularly challenging in complex basins influenced by man-made structures and regulations. To enhance forecasting accuracy, a coupled hydrological and 1D/2D hydrodynamic model was developed to simulate flood process in mountainous streams, plains river networks, hydraulic control structures and flood detention areas. Applied to the Puyang River Basin, a densely populated region characterized by hills and plains, the coupled model integrates Xin’anjiang model with Muskingum routing module to estimate upstream mountainous flow discharge, and employs 1D /2D hydrodynamic model to simulate flood processes in rivers and overland areas. This coupled framework, encompassing a 2,500 km² catchment area, includes 7 river branches, 7 dams, 1 flood detention area, and over 100 floodgates and pumps, incorporating real-time flood control operations. Calibration and validation with over 30 years of observed flood data demonstrates over 85% acceptability, confirming the model’s robustness and reliability. Therefore the coupled model become a feasible tool to monitor and forecasting flood process in a complex catchment with many regulated structures. However, comprehensive datasets, including long-term records of precipitation, evaporation, water level, and discharge, as well as detailed topographic and infrastructure data, are critical for accurate calibration and forecasting.
This approach facilitates real-time monitoring and prediction in complex, regulated basins. Discussions on flood scenarios considering disaster-inducing factors, flood control strategies, and optimized structural operations are addressed, providing a framework adaptable to other similarly complex river basins.
How to cite: zhong, H., shang, H., and duan mu, X.: Flood forecasting in the Complex River Basin Affected by Man-made Hydraulic Structures, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3606, https://doi.org/10.5194/egusphere-egu25-3606, 2025.
Groundwater from karst aquifers supplies freshwater for 25% of the world population. Worldwidely, groundwater level has been descending, spring discharge has declined, and some springs have dried up due to climate changes and anthropogenic activities. Spring discharge as a proxy, can reflects the state of karst hydrological processes. Thus, simulation of spring discharge is vital in water resources development, utilization and management.
The forming processes of spring discharge in a basin include surface water convergence, dictated by terrains, and groundwater diffusion, controlled by heterogeneous aquifers. Consideration of the physical processes can better understand karst hydrological processes. Many machine learning models have recently been used to simulate karst spring processes, however, without considering the physical mechanisms. This paper develops a graph neural network (GNN) embedded with a heat kernel (HK) model to depict rainfall-runoff converging and groundwater diffusing processes in data insufficient area and finally realize spring discharge modeling. Application of the model to Niangziguan Springs, China, demonstrates that the GNN with the second-order HK has better metric performance than the first-order model in forecasting multi-time step spring discharge processes. The optimal graph structure of the model varies with the forecasting time step. The structure of one- and two-step forecasting is an information flow graph, which mainly describes the convergence of surface flow, while the structure of three- and four-step forecasting is a groundwater flow graph that stresses groundwater diffusion. The facts reveal that surface water convergence is completed within two months, and groundwater diffusion mainly happens between three and four months. GNN with HK is robust in depicting the karst hydrological processes with interpretability.
How to cite: Hao, Y.: A graph neural network-based model for spring discharge forecasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5283, https://doi.org/10.5194/egusphere-egu25-5283, 2025.
Hydrologic forecast merging (HFM) is critical in enhancing forecast accuracy by addressing uncertainties from model structures and parameters. This study integrates forecasts from spatially large-scale and locally calibrated models to improve reservoir inflow predictions through a dynamic weight estimation approach. The method uses time-series features (TSFs) of streamflow and Bayesian model averaging (BMA) for dynamic weight estimation. The conceptual HBV-EC model is set up on the spatially large Moose River basin in Canada in a semi-distributed fashion, while the GR4J, HYMOD, and SACSMA models are implemented to simulate inflow for the Mesomikenda Lake Dam within the large basin. Both large and local-scale models are calibrated using Canadian Precipitation Analysis (CaPA). Using the Global Deterministic Prediction System (GDPS) dataset, reservoir inflow forecasts are generated up to ten days ahead by applying the calibrated models. Then, the dynamic merging approach is applied to improve inflow forecast accuracy, and the outcomes are compared with the traditional fixed weights merging method. Results show that while large-scale models generally underperform compared to local-scale models, nonetheless, they provide better fits in specific hydrograph segments. Merging inflow forecasts using the dynamic weight estimation approach shows higher accuracy than the fixed-weight method. Overall, the findings indicate the utility of merging large-scale model forecasts with the local one through the dynamic weight estimation method, offering water resource managers more reliable and precise forecasts for better decision-making.
How to cite: Sheikh, M. R. and Coulibaly, P.: Enhancing reservoir inflow predictions through dynamic forecast merging, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13731, https://doi.org/10.5194/egusphere-egu25-13731, 2025.
Digital Elevation Models (DEMs) are among the most critical factors influencing the performance of flood modeling. In many regions worldwide, freely available satellite-derived global DEMs are often the only accessible source of topographic data. Extensive research has focused on improving freely available DEMs to support catchment-scale flood modelling, particularly in low-lying areas. However, relatively little attention has been given to high-mountain and rugged terrains, such as the Himalayas. In these environments, the low resolution of open-access DEMs often fails to capture key hydrological features, such as narrow valleys and streams, leading to suboptimal performance of hydrodynamic models. This study uses Glacial Lake Outburst Floods (GLOFs) — widely recognised as one of the most devastating natural hazards in the Himalayas — as a case study. We evaluate the performance of five contemporary 1 arc-second (~30 m) DEMs: FABDEM, Copernicus DEM, NASADEM, AW3D30, and SRTM. The evaluation is conducted by analysing differences in simulated inundation areas, water depths, flow velocities, and flow arrival times for GLOFs using a GPU-based high-performance hydrodynamic model. To address the limitations of freely available DEMs, this study proposes a novel method for hydrological correction in DEMs to improve the accuracy of GLOF modelling. GLOF events are simulated using the original and hydrologically corrected DEMs, followed by a comparative analysis to assess the simulation accuracy and performance of the different DEMs. The results demonstrate that the corrected DEMs yield significant improvements in modelling accuracy, highlighting the potential of this approach for more reliable flood hazard and risk assessments in high-mountain environments.
How to cite: Chen, H. and Liang, Q.: Evaluate and Enhance the Efficiency of Freely Available Global DEMs for Flood Modeling in High-Mountain Environments: A Case Study of Glacial Lake Outburst Floods (GLOFs), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4194, https://doi.org/10.5194/egusphere-egu25-4194, 2025.
Widespread floods are floods that co-occur in space over a large geographical area, typically caused by prolonged or extreme rainfall, which affects extensive regions and even whole countries. The improvement of early warning systems, particularly improving the skill of seasonal and sub-seasonal (S2S) forecast is imperative to improve our preparedness and reduce loss of life, property damage, and environmental disruption caused by spatially co-occurring floods. The aim of this study is to forecast the (sub-)seasonal probabilities of widespread flooding across the highly anthropogenically regulated Yangtze River Basin in China using deep learning techniques. For that we test three contrasting state-of-the-art deep learning architectures for predicting sequential time series: recurring (i.e., Long short-term memory, LSTM), convolutional (i.e., dilated convolutional neural network, dCNN) and transformer-based networks (i.e., Informer). We use monthly antecedent precipitation and large scale climatic indices to forecast widespread floods severity index for different lead times at S2S timescale. In our study the widespread flood severity is estimated as the sum of daily maximum streamflow that exceeds local (i.e., gauge-specific) 2-year return period within the given months for the period 1961-2018 across 40 sub-catchments in the Yangtze River Basin. The three deep learning models are trained on the whole Yangtze River basin and four distinct hydroclimatic regions, intending to provide a deeper understanding on regional variability of large-scale atmospheric drivers of widespread flooding. Our preliminary results for the LSTM-based models indicate that in the case one-month-ahead forecasts, the seasonal patterns of widespread flooding are captured accurately for the whole Yangtze Basin and for the four individual regions. However, the models tend to underestimate flood severity under extreme conditions. In the next steps, we plan to extend lead time to three months and compare the performance of three different architectures mentioned above with the aim to enhance the accuracy of early warning systems for widespread floods.
How to cite: Zhang, Y., Merz, R., Zhang, Z., and Tarasova, L.: Comparing different deep learning architectures for S2S forecasting of widespread floods in the Yangtze River Basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6919, https://doi.org/10.5194/egusphere-egu25-6919, 2025.
Recurring annual floods creates havoc to the life and property of millions of people around the world. Along with damaging the natural habitat of the living beings, floods terminally affects the growth and development of crops. Accurate flood inundation forecasting plays a crucial role in analysing the risk associated with crop damage due to flooding. Keeping this in view, this study attempts to simulate the flood inundation extent and depth with a daily lead-time of up to 3 days. The Mahanadi River delta in eastern India, which is one of the highly flood prone river deltas in the world is considered as the study area. The rainfall forecasts for the river basin are first bias-corrected using a newly developed bias-correction technique employing copula functions and self-organizing maps. Forcing the hydro-meteorological inputs to a conceptual hydrological model, the discharge forecasts up to 3 days lead-time are obtained. For further improvement, the errors in the discharge forecasts are updated using the state-of-the-art deep learning model, Long-Short Term Memory. Finally, the forecasted inundation depth and extent are simulated by forcing the hydrodynamic 1D-2D MIKE FLOOD model with the improved daily discharge forecasts as the upstream inflow boundary conditions. The hydrological model-simulated discharges after performing error updation are found to be reasonably accurate with a Nash-Sutcliffe Efficiency of >0.90. More than 50% of the observed flood inundated area are found to coincide with the model simulated inundations.
How to cite: Chatterjee, C., Khatun, A., and Sahoo, B.: Simulating Daily Flood Inundation Forecasts for a Large Flood-Prone River Delta in Eastern India, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11793, https://doi.org/10.5194/egusphere-egu25-11793, 2025.
Accurate flood forecasting is of great significance for flood prevention and mitigation, protection of residents' lives and properties, as well as rational utilization and protection of water resources. To improve the accuracy and reliability of flood forecasting, a deep learning flood process probabilistic forecasting model VD-LSTM-Bootstrap based on the vector direction of the flood process is constructed by coupling the runoff process vectorization method and Bootstrap interval prediction method in the input and output layers of the LSTM model, respectively. Jingle and Lushi watersheds were selected as the study areas, and the model was trained and validated based on 50 and 20 measured flood data according to the 7:3 division ratio, respectively. The results show that, compared with LSTM, the VD-LSTM model has better overall forecasting performance, with NSE above 0.8, RE less than 15%, and RMSE and bias smaller; The discharge simulation results of the VD-LSTM are in better agreement with the measured discharge process lines, and the problems of underestimation of the flood peaks and hysteresis of the model are improved; In terms of probabilistic forecasting, the confidence intervals provided by the VD-LSTM-Bootstrap model exhibit high reliability, with coverage rates in the Jingle and Lushi basins at 90.1%, 85.5%, 80.3%, and 91.7%, 86.2%, 81.6%, respectively, which are above the corresponding confidence level.
How to cite: Xie, T.: Study on Machine Learning Method Based on Vector Direction of Flood Process for Flood Forecasting, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14887, https://doi.org/10.5194/egusphere-egu25-14887, 2025.
The accuracy and precision of grid-based 2D hydrodynamic modeling of pluvial and fluvial urban flooding scenarios are highly sensitive to the spatial resolution of the digital elevation model (DEM) employed. The use of a 1-meter resolution DEM can significantly improve the performance of 2D models relative to coarser resolutions. The sole 1m DEM with extensive coverage of the continental United States is provided by the US Geological Survey (USGS) through the 3DEP program. This dataset is produced by Lidar which is used to generate bare-earth elevations, particularly important for the modeling of urban coastal floodplains.
The challenge in using the USGS 1m DEM for hydrologic modeling is that waterways above a certain width are “hydroflattened”, with the elevation of channels reflecting an averaged elevation of the water surface and providing no information about the underlying channel bathymetry. As river bathymetry data is sparse and inconsistent, this presents an issue. One approach to this problem is adjusting the roughness coefficient, or Manning’s n, of the DEM water surface to reflect very low friction such that discharge “sits” on top of the “solid” base stage of the river and is transported downstream with low resistance. One issue with this approach is that the hydraulic radius is significantly smaller compared to that obtained using the actual channel area, potentially biasing results.
We devise a simplified set of experiments using Manning’s equation for a rectangular channel to efficiently calibrate and validate a 2D hydrologic model based on the USGS 1m DEM. We present the results of a case study of the Charles River watershed in Eastern Massachusetts, USA. The Charles is 129km long and passes through 23 cities and towns before draining into Massachusetts Bay, terminating at the highly urbanized core of the Boston metropolitan area.
Stream gage measurements are used to estimate 100-year return levels for daily average discharge and surface water elevation; these values and Manning’s n (roughness) reported in the literature are then used to estimate channel depth assuming simplified geometry. This in turn is used to estimate Manning’s n for the hydroflattened water surface, which is then substituted into the 2D model. Finally, 2D flood simulation results are evaluated against US Federal Emergency Management Agency (FEMA) 100-year inundation extent maps. This is done using four validation metrics: Probability of Detection, False Alarm Ratio, Critical Success Index, and Bias. This provides a simple and computationally efficient calibration and validation methodology for 2D gridded hydrodynamic models in the absence of known channel bathymetry and roughness; while a number of effective approaches have seen widespread use in different data availability and modeling contexts, our simplest possible methodology is generalizable across a broad range of scenarios and watersheds. This can be particularly useful when 2D inundation mapping is called for in interdisciplinary research contexts in which information on the spatiotemporally explicit evolution of floods is required, such as in the simulation of dynamic disruption and recovery of infrastructure systems in urbanized watersheds during extreme precipitation events.
How to cite: Watson, J., Beighley, E., and Ganguly, A.: Simple and efficient calibration and validation of gridded 2D hydrodynamic models using 1D models, stream gage measurements, and inundation extent maps, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15300, https://doi.org/10.5194/egusphere-egu25-15300, 2025.
The existing parameter calibration methods are based on the objective function surface to find the optimal value. This kind of method has the problems such as unstable result, poor convergence performance, low efficiency and failure to find the global optimal value. Based on the analysis of the objective function structure and the information that it provides for the parameter calibration, the essential problems existing in the present method was found in this paper. And the paper also found that the information provided by the parameter function surface is more direct and effective than by the objective function surface. Furthermore, the nonlinear model function can be linearized by the system response relationship between the increment of the dependent variables and the increment of parameters. Based on these researches, this paper proposed the system response parameter calibration method based on the parametric function surface. Firstly, the method is verified by an ideal model. The results showed that all the sensitive parameters could reach the real values not influenced by the different initial parameter values with higher convergence speed and accuracy, which verified that the method is feasible. Lastly, the XAJ Model parameters based on Shaowu basin measured data were also calibrated by the method. The results showed that the stable optimal parameter values could also be quickly got. So the system response parameter calibration method is an effective parameters optimization method.
How to cite: Zhao, L., Bao, W., Ren, M., and Ning, Y.: System Response Parameter Calibration Method and its Application on Hydrological Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5518, https://doi.org/10.5194/egusphere-egu25-5518, 2025.
Hydrometeorological extremes, such as intense rainfall, prolonged droughts, and extreme temperature fluctuations, are increasingly impacting river systems worldwide. These events not only alter hydrological regimes but also significantly influence water quality, presenting challenges for ecosystems, water resource management, and public health. This study explores the interplay between hydrometeorological extremes and river water quality, focusing on nutrient loading, sediment transport, dissolved oxygen levels, and contaminant mobilization. Using case studies from diverse climatic regions, we investigate how extreme events disrupt physical, chemical, and biological processes within river systems. Intense rainfall events, for instance, are shown to exacerbate nutrient runoff and sediment resuspension, leading to eutrophication and habitat degradation. Conversely, drought conditions amplify salinity, temperature, and pollutant concentrations due to reduced dilution capacity. The role of antecedent conditions, event frequency, and catchment characteristics in moderating these impacts will be also evaluated. Hopefully through a combination of field observations, remote sensing data, and hydrological modeling, this work will provide a comprehensive assessment of how future shifts in extreme event patterns, driven by climate change, could shape river water quality dynamics. The research findings will underline the urgent need for adaptive water quality management strategies that incorporate the projected increase in hydrometeorological extremes, emphasizing ecosystem resilience and the protection of water resources.
How to cite: Jiang, J., He, S., Chen, Y., Wang, Z., Lai, C., Wu, X., and Zeng, Z.: Potential effects of hydrometeorological extremes on river water quality, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2564, https://doi.org/10.5194/egusphere-egu25-2564, 2025.
