Journal Articles

Wu, J, Liang, Q, Chen, H (2026) Neural approximation of Riemann fluxes for transient shallow water flows with wetting and drying, Digital Water, 4(1), p.2725229, ISSN: 2837-5807. DOI: 10.1080/28375807.2026.2725229.

Wu, J, Liang, Q, Chen, H, Duan, H (2026) Rimnet: a deep finite volume method with hybrid neural–numerical Riemann flux approximation in the 1D shallow water equations, Journal of Hydroinformatics, jh2026095, ISSN: 1464-7141. DOI: 10.2166/hydro.2026.095.

Huang, Y, Chen, H, Liu, S, Zhao, G, Fu, X, Zhang, H, Liang, Z, Liu, Y (2026) Temporal dynamics of turbidity in China's lakes and reservoirs: Distinct trajectories and corresponding driving mechanisms, International Journal of Applied Earth Observation and Geoinformation, 149, ISSN: 1569-8432. DOI: 10.1016/j.jag.2026.105287.

Hill, B, Liang, Q, Chen, H, Bosher, L (2026) From reach to catchment-scale impacts: High-resolution hydrodynamic modelling of Nature-based solutions in the Cocker Catchment, UK, Journal of Hydrology: Regional Studies, 64, 103178, DOI: 10.1016/j.ejrh.2026.103178.

Chen, H, Liang, Q, Zhao, J, Maharjan, SB (2025) Assessing national exposure to and impact of glacial lake outburst floods considering uncertainty under data sparsity, Hydrology and Earth System Sciences (HESS), 29(3), pp.733-752, ISSN: 1027-5606. DOI: 10.5194/hess-29-733-2025.

Qin, H, Liang, Q, Chen, H, De-Silva, V (2024) A coupled human and natural systems (CHANS) framework integrated with reinforcement learning for urban flood mitigation, Journal of Hydrology, 643, 131918, ISSN: 0022-1694. DOI: 10.1016/j.jhydrol.2024.131918.

Qin, H, Liang, Q, Chen, H, De-Silva, V (2024) A two-way coupled CHANS model for flood emergency management, with a focus on temporary flood defences, Environmental Modelling and Software, 181, 106166, ISSN: 1364-8152. DOI: 10.1016/j.envsoft.2024.106166.

Qin, H, Liang, Q, Chen, H, De-Silva, V (2024) A high‐performance Coupled Human and Natural Systems (CHANS) model for flood risk assessment and reduction, Water Resources Research, 60(7), e2023WR036269, ISSN: 0043-1397. DOI: 10.1029/2023wr036269.

Wells, H, Hillier, J, Garry, FK, Dunstone, N, Clark, MR, Kahraman, A, Chen, H (2024) Climatology and convective mode of severe hail in the United Kingdom, Atmospheric Research, 309(2024), 107569, ISSN: 0169-8095. DOI: 10.1016/j.atmosres.2024.107569.

Hill, B, Chen, H, Liang, Q, Bosher, L, Vann, J (2024) Monitoring solutions for remote locations: A data gathering approach for remote nature-based solution sites, Nature-Based Solutions, 5, pp.100120-100120, DOI: 10.1016/j.nbsj.2024.100120.

Hill, B, Liang, Q, Bosher, L, Chen, H, Nicholson, A (2023) A systematic review of natural flood management modelling: approaches, limitations, and potential solutions, Journal of Flood Risk Management, 16(3), e12899, ISSN: 1753-318X. DOI: 10.1111/jfr3.12899.

Cui, Y, Liang, Q, Xiong, Y, Wang, G, Wang, T, Chen, H (2023) Assessment of Object-Level Flood Impact in an Urbanized Area Considering Operation of Hydraulic Structures, Sustainability, 15(5), pp.4589-4589, DOI: 10.3390/su15054589.

Wang, X, Liu, Z, Chen, H (2022) Investigating Flood Impact on Crop Production under a Comprehensive and Spatially Explicit Risk Evaluation Framework, Agriculture, 12(4), pp.484-484, DOI: 10.3390/agriculture12040484.

Xing, Y, Chen, H, Liang, Q, Ma, X (2022) Improving the performance of city-scale hydrodynamic flood modelling through a GIS-based DEM correction method, Natural Hazards, 112(3), pp.2313-2335, ISSN: 0921-030X. DOI: 10.1007/s11069-022-05267-1.

Xing, Y, Shao, D, Liang, Q, Chen, H, Ma, X, Ullah, I (2021) Investigation of the drainage loss effects with a street view based drainage calculation method in hydrodynamic modelling of pluvial floods in urbanized area, Journal of Hydrology, 605, 127365, ISSN: 0022-1694. DOI: 10.1016/j.jhydrol.2021.127365.

Chen, H, Zhao, J, Liang, Q, Maharjan, SB, Joshi, SP (2021) Assessing the potential impact of glacial lake outburst floods on individual objects using a high-performance hydrodynamic model and open-source data, Science of the Total Environment, 806(Part 3), 151289, ISSN: 0048-9697. DOI: 10.1016/j.scitotenv.2021.151289.

Zhao, J, Chen, H, Liang, Q, Xia, X, Xu, J, Hoey, T, Barrett, B, Renaud, FG, Bosher, L, Zhou, X (2021) Large-scale flood risk assessment under different development strategies: the Luanhe River Basin in China, Sustainability Science, 17(4), pp.1365-1384, ISSN: 1862-4065. DOI: 10.1007/s11625-021-01034-6.

Chen, H, Liang, Q, Liang, Z, Liu, Y, Ren, T (2020) Extraction of connected river networks from multi-temporal remote sensing imagery using a path tracking technique, Remote Sensing of Environment, 246, 111868, ISSN: 0034-4257. DOI: 10.1016/j.rse.2020.111868.

Liang, Z, Dong, F, Qian, SS, Liu, Y, Chen, H, Lu, W (2020) Ecoregional or site-specific lake nutrient criteria? Evidence from ecological fallacy, Ecological Indicators, 111, pp.105989-105989, ISSN: 1470-160X. DOI: 10.1016/j.ecolind.2019.105989.

Ren, T, Liang, Z, Chen, H, Liu, Y (2019) Clustering of Lake Variables Based on Pattern Recognition Method, Beijing Daxue Xuebao Ziran Kexue Ban Acta Scientiarum Naturalium Universitatis Pekinensis, 55(2), pp.335-341, ISSN: 0479-8023. DOI: 10.13209/j.0479-8023.2019.001.

Chen, H, Liang, Q, Liang, Z, Liu, Y, Xie, S (2019) Remote-sensing disturbance detection index to identify spatio-temporal varying flood impact on crop production, Agricultural and Forest Meteorology, 269-270, pp.180-191, ISSN: 0168-1923. DOI: 10.1016/j.agrformet.2019.02.002.

Liang, Z, Liu, Y, Chen, H, Ji, Y (2019) Is ecoregional scale precise enough for lake nutrient criteria? Insights from a novel relationship-based clustering approach, Ecological Indicators, 97, pp.341-349, ISSN: 1470-160X. DOI: 10.1016/j.ecolind.2018.10.034.

Liang, Z, Qian, SS, Wu, S, Chen, H, Liu, Y, Yu, Y, Yi, X (2019) Using Bayesian change point model to enhance understanding of the shifting nutrients-phytoplankton relationship, Ecological Modelling, 393, pp.120-126, ISSN: 0304-3800. DOI: 10.1016/j.ecolmodel.2018.12.008.

Liang, Z, Chen, H, Ren, T, Yi, X, Zhu, X, Liu, Y, Guo, H (2018) Exploring the applicability of regional nutrient criteria, Huanjing Kexue Xuebao Acta Scientiae Circumstantiae, 38(7), pp.2545-2553, ISSN: 0253-2468. DOI: 10.13671/j.hjkxxb.2018.0018.

Chen, H, Liang, Z, Liu, Y, Jiang, Q, Xie, S (2018) Effects of drought and flood on crop production in China across 1949–2015: spatial heterogeneity analysis with Bayesian hierarchical modeling, Natural Hazards, 92(1), pp.525-541, ISSN: 0921-030X. DOI: 10.1007/s11069-018-3216-0.

Chen, H, Liang, Q, Liu, Y, Xie, S (2018) Hydraulic correction method (HCM) to enhance the efficiency of SRTM DEM in flood modeling, Journal of Hydrology, 559, pp.56-70, ISSN: 0022-1694. DOI: 10.1016/j.jhydrol.2018.01.056.

Liang, Z, Chen, H, Wu, S, Zhang, X, Yu, Y, Liu, Y (2018) Exploring Dynamics of the Chlorophyll a-Total Phosphorus Relationship at the Lake-Specific Scale: a Bayesian Hierarchical Model, Water, Air, & Soil Pollution, 229(1), 21, ISSN: 0049-6979. DOI: 10.1007/s11270-017-3678-9.

Liang, Z, Wu, S, Chen, H, Yu, Y, Liu, Y (2018) A probabilistic method to enhance understanding of nutrient limitation dynamics of phytoplankton, Ecological Modelling, 368, pp.404-410, ISSN: 0304-3800. DOI: 10.1016/j.ecolmodel.2017.11.004.

Li, B, Chen, H, Li, N, Wu, Z, Wen, Z, Xie, S, Liu, Y (2017) Spatio-temporal shifts in the archaeal community of a constructed wetland treating river water, Science of The Total Environment, 605-606, pp.269-275, ISSN: 0048-9697. DOI: 10.1016/j.scitotenv.2017.06.221.

Chen, H, Liang, Z, Liu, Y, Liang, Q, Xie, S (2017) Integrated remote sensing imagery and two-dimensional hydraulic modeling approach for impact evaluation of flood on crop yields, Journal of Hydrology, 553, pp.262-275, ISSN: 0022-1694. DOI: 10.1016/j.jhydrol.2017.08.001.

Yang, X, Chen, H, Wang, Y, Xu, C-Y (2016) Evaluation of the effect of land use/cover change on flood characteristics using an integrated approach coupling land and flood analysis, Hydrology Research, 47(6), pp.1161-1171, ISSN: 0029-1277. DOI: 10.2166/nh.2016.108.

Zeng, XT, Huang, GH, Chen, HL, Li, YP, Kong, XM, Fan, YR (2016) A simulation-based water-environment management model for regional sustainability in compound wetland ecosystem under multiple uncertainties, Ecological Modelling, 334, pp.60-77, ISSN: 0304-3800. DOI: 10.1016/j.ecolmodel.2016.04.021.

Zeng, X, Yang, X, Yu, L, Chen, H (2015) A Mix Inexact-Quadratic Fuzzy Water Resources Management Model of Floodplain (IQT-WMMF) for Regional Sustainable Development of Dahuangbaowa, China, Water, 7(6), pp.2771-2795, DOI: 10.3390/w7062771.

Conferences

Hill, B, Liang, Q, Chen, H, Bosher, L (2025) A large-scale, high-resolution, 2D hydrodynamic modelling approach to represent natural flood management features. In , River Flow Proceedings of the 12th International Conference on Fluvial Hydraulics River Flow 2024, pp.1087-1091, DOI: 10.1201/9781003475378-158.

Datasets

Chen, H, Liang, Q, Zhao, J, Bikash Maharjan, S (2025) Supplementary information files for "Assessing national exposure to and impact of glacial lake outburst floods considering uncertainty under data sparsity", DOI: 10.17028/rd.lboro.29109089.

Chen, H, Liang, Q, Liang, Z, Liu, Y, Ren, T (2021) Supplementary information files for Extraction of connected river networks from multi-temporal remote sensing imagery using a path tracking technique, DOI: 10.17028/rd.lboro.12656828.

Other

Chen, H, Tong, X, Liang, Q (2026) Reconstructing Flow Connectivity and Channel Conveyance in LiDAR-Derived Terrain for National-Scale High-Resolution Flood Modelling, LiDAR-derived digital elevation models (DEMs) are increasingly adopted in hydrodynamic flood modelling; however, their direct use, particularly in complex urban environments, remains problematic. Although LiDAR provides high-resolution surface information and supports the generation of bare-earth digital terrain models (DTMs), unresolved flow-permeable structures such as bridges, culverts, and elevated transport infrastructure, together with micro-scale urban features including narrow river channels, pathways, kerbs, and missing submerged channel bathymetry, systematically distort flow connectivity and channel conveyance. These deficiencies introduce structural biases into flood simulations, yet existing studies typically address individual features in isolation, limiting transferability and large-scale applicability.This study reframes LiDAR DEM preprocessing as a process-based investigation into how unresolved terrain features bias flood hydraulics and introduces an automated, physically consistent terrain reconstruction framework that explicitly targets these bias mechanisms. The framework is implemented at the national scale using the 2 m LiDAR-derived DTM for England.Three dominant sources of hydrodynamic bias are addressed. First, flow-permeable structures, including bridges, culverts, and elevated transport infrastructure, are systematically identified using observed water surface information and river network data, and the terrain beneath these structures is reconstructed using interpolation-based techniques to restore hydraulic connectivity. Second, impermeable urban features, such as buildings and kerbs, are selectively elevated while preserving longitudinal connectivity along roads and pathways, ensuring realistic overland flow routing. Third, submerged river bathymetry is reconstructed using empirical relationships between river width and water depth to recover channel conveyance absent from bare-earth DTMs.The resulting terrain dataset is directly applicable to hydrodynamic flood modelling without manual intervention. Sensitivity analyses across multiple historical flood events demonstrate that restoring flow connectivity and reconstructing channel bathymetry exert distinct and flow-regime-dependent controls on simulated flood extent, water levels, and discharge. In particular, unresolved flow-permeable structures predominantly govern urban inundation patterns, whereas missing bathymetry represents the primary source of error in channel hydraulics.By systematically isolating and correcting key terrain-induced bias mechanisms, this study provides generalisable insights into the process sensitivity of catchment and urban flood models to DEM representation and offers a scalable pathway for improving large-scale flood simulations using LiDAR data.. DOI: 10.5194/egusphere-egu26-22261.

Tong, X, Liang, Q, Chen, H, Zong, Y (2026) A Coupled Human And Natural System (CHANS) Framework for Human Mobility during Flood Events, Climate change and rapid urbanisation have intensified the frequency and consequences of extreme flood events. During floods, transportation systems may fail, leading to traffic breakdowns, prolonged exposure, and cascading impacts on emergency response and wider urban functioning. Flood risk is therefore shaped not only by the physical dynamics of inundation, but also by how people perceive, respond to, and adapt their mobility under evolving hazard conditions, exemplifying a Coupled Human And Natural System (CHANS). This tight coupling between hazard evolution and human response makes it essential to represent hazard-human interactions at the event timescale, particularly for reliable flood forecasting, early warning, and emergency preparedness. However, capturing adaptive human mobility under dynamically changing flood conditions remains a major challenge, especially within a CHANS modelling framework.Agent-based modelling (ABM) has been increasingly applied to represent human behaviour during floods, often coupled with hydrodynamic inundation models. However, most existing implementations rely on offline or weakly coupled co-simulation, in which flood dynamics and human behaviour are computed in separate platforms and synchronised through frequent data exchange. Such data-exchange-driven approaches become increasingly expensive when high-frequency updates are required, limiting their capability to represent real-time feedback between flood evolution and human mobility.In this study, we present a CHANS modelling framework built upon the GPU-accelerated High Performance Integrated hydrodynamic Modelling System (HiPIMS) for predicting flood hydrodynamics, fully coupled with an agent-based module within the same computational framework to represent human mobility. This enables seamless simulation of interacting flood conditions and human responses. Human mobility is represented by autonomous agents within a unified architecture that supports pedestrians, cyclists, and vehicles. Mobility agents exhibit heterogeneous behavioural attributes, including risk aversion, awareness, compliance, and patience, and interact within a shared, dynamically evolving flood environment.The framework is demonstrated through an urban case study in Newcastle upon Tyne, with data from the Urban Observatory for model validation. Further simulations are conducted for light, medium, and heavy rainfall scenarios to analyse adaptive transport responses under different flood conditions.By supporting large numbers of agents and real-time hazard-human interactions within a single computational environment, the proposed framework enables systematic analysis of human adaptive behaviour and system-level disruption during flood events. This work provides a new methodological basis for characterising flood risk in a coupled human and natural systems context, with clear implications for early warning, emergency response planning, and integrated flood forecasting.. DOI: 10.5194/egusphere-egu26-10058.

Wu, J, Liang, Q, Chen, H (2026) A Structure-Preserving Neural Flux Surrogate for Efficient Shallow-Water Modelling, High-resolution shallow-water (SW) models are critical for flood and inundation forecasting, yet their operational efficiency is often bottlenecked by the computational cost of repeatedly solving intercell Riemann problems. While emerging machine-learning surrogates (e.g, PINNs and neural operators) can accelerate PDE prediction, they often struggle to meet the rigorous requirements of hydrodynamic modelling. Specifically, these end-to-end models generally enforce physics only via soft constraints, leading to non-physical mass leakage and error accumulation over long durations. They also suffer from spectral bias, which hinders the sharp capture of discontinuities and wet–dry fronts. Furthermore, they typically lack cross-geometry generalizability, requiring costly retraining when boundary conditions or mesh resolutions change. This study proposes a structure-preserving hybrid strategy that integrates deep learning into a classical Godunov-type finite-volume (FV) solver. Rather than approximating the global solution map, we employ a neural network as a local, plug-in surrogate specifically for intercell flux evaluation. This network learns a discretization-aware operator, mapping local reconstructed interface states to normal-aligned numerical fluxes. Crucially, by embedding this learned surrogate within the standard FV backbone—retaining CFL-controlled time marching and wetting–drying treatments—the hybrid solver strictly enforces mass conservation through rigorous flux-difference assembly. Because the model learns local interface physics rather than global flow patterns, it exhibits strong cross-resolution generalization: a model trained on a specific grid can be deployed directly on different mesh densities and unseen initial conditions without retraining. This work establishes a scalable pathway for integrating deep learning into hydrodynamic solvers, combining the computational speed of machine learning with the reliability and conservation properties of numerical mechanics.. DOI: 10.5194/egusphere-egu26-15780.

Jiang, J, Chen, H, Tong, X, Varley, D, Liang, Q (2026) A Fully Integrated Hydrodynamic-NBS Model for City-Scale Flood Risk Assessment under Extreme Rainfall, Urban Nature-based Solutions (NbS) are critical for flood mitigation, yet existing modelling approaches have limitations in explicitly capturing their performance during extreme events. Conventional approaches typically couple NbS modules with two-dimensional (2D) surface flow models as separate executables. This "loose coupling" fails to capture the two-way dynamic interactions between surface runoff and NbS features, especially when systems approach full saturation or exceed their design capacity.This study introduces HiPIMS-NbS, a high-performance, GPU-accelerated modelling framework that embeds physical behaviours of multi-layer NbS directly into high-resolution 2D shallow water computations. Unlike traditional models with predetermined drainage areas, HiPIMS-NbS calculates flow directions and surface-NbS exchange rates dynamically across the 2D domain. Surface ponding depths influence NbS infiltration rates while NbS storage regulates surface water availability within the unified GPU-accelerated computational framework, achieving dynamic two-way coupling.The model was validated against SWMM benchmarks and field-scale bioretention experiments. To demonstrate city-scale applications, HiPIMS-NbS was applied to the 2012 "Toon Monsoon" flood event in Newcastle upon Tyne, UK, to evaluate various NbS implementation scenarios. Results demonstrate that the model achieves the computational efficiency required for city-scale simulations while capturing key NbS behaviours under realistic overflow conditions. This integrated approach provides a robust modelling basis for urban planners to optimise NbS placement and design for the extreme rainfall events projected under changing climate.. DOI: 10.5194/egusphere-egu26-17989.

Li, X, Liang, Q, Chen, H (2026) Decoding Chinese Ancient Culture-related Nature-based Solutions for Flood-Resilience Using Modern Informatics, Climate change has intensified extreme rainfall events, while rapid urban expansion has reduced rainwater infiltration. Together, these processes have disrupted urban hydrological systems and increased the frequency and severity of urban flooding, posing growing threats to lives and property. Conventional flood mitigation strategies largely depend on extensive grey infrastructure, such as pipes and tunnels, designed to rapidly evacuate stormwater. However, many of these systems were developed in the last century and are increasingly economically and ecologically unsustainable under intensifying rainfall extremes. In response, Nature-based Solutions (NbS) have gained prominence as sustainable approaches that work with natural processes to enhance flood resilience. Although NbS are often framed as a modern response to climate change, similar principles have long existed in traditional ecological and planning practices. However, Traditional Ecological Knowledge (TEK) is frequently regarded as fragmented or highly context-specific, which limits its systematic integration into contemporary flood resilience frameworks. As a result, it remains unclear whether such historically grounded practices can be translated into generalisable, scientifically testable principles applicable to modern NbS design and flood risk assessment.Here, we present a systematic interpretation of flood management strategies in ancient Chinese civilisation through the lens of Feng Shui. Feng Shui is an indigenous planning philosophy centred on the concept of harmony between humans and nature and has been widely applied in traditional village site selection and layout. This study focuses specifically on the local water management principles embedded within Feng Shui. We synthesise ancient texts and classical literature to reconstruct traditional water-planning concepts and relate them to contemporary hydrological and geomorphological theory. Using spatial statistical and mathematical fitting analyses across more than 300 historical villages, we demonstrate the consistency and non-site-specificity of these principles. Furthermore, hydrodynamic simulations of a representative village show that Feng Shui–inspired water systems can effectively reduce flood depths and peak flows under present-day extreme rainfall scenarios, through mechanisms such as distributed storage, controlled diversion, and flow-path reorganisation.Together, these results indicate that traditional village planning embodied core principles analogous to those underpinning modern NbS. Our findings provide quantitative evidence for the scientific basis, adaptability, and flood mitigation effectiveness of traditional ecological knowledge. More broadly, this study demonstrates a methodological pathway for translating TEK into scientifically grounded frameworks by integrating historical analysis, spatial statistics, and numerical modelling, highlighting its potential relevance for contemporary flood resilience assessment and NbS design.. DOI: 10.5194/egusphere-egu26-6015.

Liang, Q, Tong, X, Chen, H, Jiang, J (2026) Towards national-scale hydrodynamic flood modelling: feasibility, resolution sensitivity, and computational trade-offs, Surface water flooding (SWF), when intense rainfall overwhelms drainage systems and inundates streets, homes, and infrastructure, is the most widespread form of flooding in the UK and a rapidly escalating global hazard under climate change and growing urban exposure. In England alone, around 3.2 million properties are at risk. In the extreme cases, SWF can develop rapidly with little or no warning and exhibit highly dynamic, fast-moving flow conditions, behaving like an “inland tsunami”, with debris-laden flood waves overwhelming streets, vehicles, and buildings within minutes. It can paraly transport, disrupting essential services, and, in some cases, cause catastrophic loss of life. Recent events have highlighted the deadly consequences of such flooding, including the July 2021 Zhengzhou (China) flood with nearly 400 fatalities, the 2021 floods in Germany and Belgium with over 200 deaths, the October 2024 flash floods in Valencia, Spain causing 237 fatalities, and widespread cyclone- and monsoon-driven flooding across South and Southeast Asia in 2025 causing more than 1,000 deaths and displacing millions.At large spatial scales, SWF does not occur as isolated local events. Intense rainfall may occur simultaneously or sequentially over wide areas, and interconnected river networks, drainage systems, and infrastructure can couple multiple local flood processes into a single, spatially extensive flood system. Understanding and predicting such large-scale, interacting flood dynamics is therefore essential for both national-scale risk assessment and real-time forecasting.Numerical modelling provides an indispensable tool for representing SWF processes. However, due to their highly transient, shock-like behaviour, hydrological or simplified hydraulic approaches are often insufficient. Fully hydrodynamic models solving the two-dimensional shallow water equations with shock-capturing capability are required, but their computational cost has historically limited their application to city or local-catchment scales. Scaling such models to regional or national extents is not a simple domain enlargement problem, but introduces coupled challenges related to computational demand, terrain resolution, and modelling strategy. As a result, fundamental questions remain regarding the feasibility of national-scale hydrodynamic modelling, the computational resources required, the sensitivity of flood hazard metrics to DEM resolution, and the trade-offs between alternative large-scale simulation strategies.To address these questions, we conduct a national-scale hydrodynamic flood modelling experiment over England using the High-Performance Integrated hydrodynamic Modelling System (HiPIMS) accelerated by multi-GPU computing. Event-based simulations are performed over the England at DEM resolutions of 10 m, 20 m, and 40 m to systematically quantify resolution effects on flood hazard representation and associated computational costs. The experimental design also enables comparison between alternative national-scale modelling strategies, including domain-wide versus partitioned simulations.The results delineate the practical feasibility limits, resolution sensitivity, and performance trade-offs of national-scale hydrodynamic flood modelling, and provide quantitative guidance on the computational and data requirements for moving towards national-to-street-scale, physics-based surface water flood forecasting and risk assessment.. DOI: 10.5194/egusphere-egu26-5294.

Chen, H and Liang, Q (2025) 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), 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. . DOI: 10.5194/egusphere-egu25-4194.