Journal Articles

Li, Z, Feng, H, Sun, B, Gualtieri, C, Liang, Q, Zhang, W, Wang, F (Accepted for publication) Influence of shape on the incipient motion of exposed microplastics, Journal of Hydrology, 671, pp.135254-135254, ISSN: 0022-1694. DOI: 10.1016/j.jhydrol.2026.135254.

Tong, X, Liang, Q, Wang, G (2026) High-performance modelling of urban non-point-source pollutant dynamics: a full-process approach, Advances in Water Resources, 211, 105241, ISSN: 0309-1708. DOI: 10.1016/j.advwatres.2026.105241.

Wang, T, Su, G, Wang, S, Fan, Y, Terence Yang, J-Y, Xu, X, Wang, Z, Wu, J, Liang, Q, Su, Y, Zha, J, Liu, L, Arhonditsis, G (2025) China’s enhanced wastewater treatment capacity may accelerate greenhouse gas emissions from rural domestic pollution, npj Clean Water, 9(1), 10, ISSN: 2059-7037. DOI: 10.1038/s41545-025-00540-9.

Lin, L, Zeng, Z, Tang, C, Xie, Y, Liang, Q (2025) Robust and Fast Sensing of Urban Flood Depth with Social Media Images Using Pre-Trained Large Models and Simple Edge Training, Hydrology, 12(11), pp.307-307, DOI: 10.3390/hydrology12110307.

Cao, L, Tao, A, Zeng, J, Liu, J, Wang, G, Zheng, J, Liang, Q (2025) Corrigendum to “Key differences of swells around China induced by two calculation domains” [Applied Ocean Research, Volume 165, 16 October 2025, 104804], Applied Ocean Research, ISSN: 0141-1187. DOI: 10.1016/j.apor.2025.104828.

Cao, L, Tao, A, Zeng, J, Liu, J, Wang, G, Zheng, J, Liang, Q (2025) Key differences of swells around China induced by two calculation domains, Applied Ocean Research, 165, ISSN: 0141-1187. DOI: 10.1016/j.apor.2025.104804.

Xue, W, Wu, Z, Xu, H, Wang, H, Liang, Q, Ma, C, Zhou, Y, Xu, S (2025) Comprehensive risk assessment of urban flood process based on dynamic weights and lumped impact parameters, Journal of Hydrology, 662(Part A), 133903, ISSN: 0022-1694. DOI: 10.1016/j.jhydrol.2025.133903.

Zhou, Y, Liu, H, Wu, Z, Liang, Q, Zhou, J, Xu, H, Wang, H, Zhang, X, Xue, W (2025) Reducing the losses to urban floods with a refined material allocation framework, Sustainable Cities and Society, 130, 106573, ISSN: 2210-6707. DOI: 10.1016/j.scs.2025.106573.

Tong, X, Liang, Q, Sander, G, Wang, G, Lai, X (2025) A Physically Based Model for Non-Point Source Pollutant Wash-Off Process Over Impervious Surfaces, Water Resources Research, 61(6), e2024WR038791, ISSN: 0043-1397. DOI: 10.1029/2024WR038791.

Xing, Y, Liang, Q, Shao, D, Ullah, I (2025) Effects of urban topographical features on drainage efficiency for pluvial flash flood occurrence, Natural Hazards, 121(12), pp.14513-14529, ISSN: 0921-030X. DOI: 10.1007/s11069-025-07358-1.

Ming, X, Liang, Q, Jiang, J (2025) Large-scale high-resolution hydrodynamic modelling of urban floods: Some practical considerations, Journal of Hydro-environment Research, 59, 100655, ISSN: 1570-6443. DOI: 10.1016/j.jher.2025.100655.

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.

Li, Z, Wang, B, Zhang, L, Liang, Q, Sun, B, Wang, F (2025) Characterization of hydrodynamics around plates shaped like dragonfly wings as a sediment reduction measure in a sewer system, Water Research, 274, 123152, ISSN: 0043-1354. DOI: 10.1016/j.watres.2025.123152.

Chong, Y, Chen, G, Dijkstra, T, Liang, Q, Xia, X, Yue, D, Meng, X (2025) A two-layer model for simulating river blockages induced by tributary debris flows, Computers and Geotechnics, 179, 107046, ISSN: 0266-352X. DOI: 10.1016/j.compgeo.2024.107046.

Chen, X, Wang, Z, Yang, H, Liang, Q, Li, J, Cai, Y (2024) Assessing dynamic flood vulnerability variations in urban functional zones using dynamic population data and a PSO-based weighting approach, International Journal of Disaster Risk Reduction, 116, 105154, DOI: 10.1016/j.ijdrr.2024.105154.

Wu, Z, Yang, H, Cai, Y, Yu, B, Liang, C, Duan, Z, Liang, Q (2024) Intelligent Monitoring Applications of Landslide Disaster Knowledge Graphs Based on ChatGLM2, Remote Sensing, 16(21), pp.4056-4056, DOI: 10.3390/rs16214056.

Xiong, Y, Liang, Q, Zheng, J, Wang, G, Tong, X (2024) Simulation of the full‐process dynamics of floating vehicles driven by flash floods, Water Resources Research, 60(10), e2023WR036739, ISSN: 0043-1397. DOI: 10.1029/2023wr036739.

Chen, X, Liang, B, Li, J, Cai, Y, Liang, Q (2024) Comprehensive Assessment of Large-Scale Regional Fluvial Flood Exposure Using Public Datasets: A Case Study from China, ISPRS International Journal of Geo-Information, 13(10), pp.357-357, DOI: 10.3390/ijgi13100357.

Wei, ZL, Shang, YQ, Liang, Q, Xia, XL (2024) A coupled hydrological and hydrodynamic modeling approach for estimating rainfall thresholds of debris-flow occurrence, Natural Hazards and Earth System Sciences, 24(10), pp.3357-3379, ISSN: 1561-8633. DOI: 10.5194/nhess-24-3357-2024.

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.

Wang, W, Yang, H, Huang, S, Wang, Z, Liang, Q, Chen, S (2024) Trivariate copula functions for constructing a comprehensive atmosphere-land surface-hydrology drought index: A case study in the Yellow River basin, Journal of Hydrology, 642, 131784, ISSN: 0022-1694. DOI: 10.1016/j.jhydrol.2024.131784.

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.

Cao, L, Liu, S, Zeng, J, Qin, S, Zhang, Z, Wang, G, Zheng, J, Liang, Q, Tao, A (2024) Long-Term Trends of Extreme Waves Based on Observations from Five Stations in China, Journal of Marine Science and Application, ISSN: 1671-9433. DOI: 10.1007/s11804-024-00436-z.

Zhou, Y, Wu, Z, Liang, Q, Xu, H, Wang, H, Xue, W (2024) Threshold and real-time initiation mechanism of urban flood emergency response under combined disaster scenarios, Sustainable Cities and Society, 108(2024), 105512, ISSN: 2210-6707. DOI: 10.1016/j.scs.2024.105512.

Wang, H, Wang, G, Fu, R, Zheng, J, Wang, P, Yu, F, Liang, Q (2024) Graphics processing unit (GPU)-enhanced nonhydrostatic model with grid nesting for global tsunami propagation and coastal inundation, Physics of Fluids, 36(4), 046607, ISSN: 1070-6631. DOI: 10.1063/5.0203639.

Yang, H, Cui, X, Cai, Y, Wu, Z, Gao, S, Yu, B, Wang, Y, Li, K, Duan, Z, Liang, Q (2024) Coupling Downscaling and Calibrating Methods for Generating High-Quality Precipitation Data with Multisource Satellite Data in the Yellow River Basin, Remote Sensing, 16(8), 1318, DOI: 10.3390/rs16081318.

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.

Wang, W, Du, Q, Yang, H, Jin, P, Wang, F, Liang, Q (2024) Drought patterns and multiple teleconnection factors driving forces in China during 1960–2018, Journal of Hydrology, 631, 130821, ISSN: 0022-1694. DOI: 10.1016/j.jhydrol.2024.130821.

Hergibo, P, Liang, Q, Phillips, TN, Xie, Z (2023) A quadtree-based adaptive moment-of-fluid method for interface reconstruction with filaments, Journal of Computational Physics, 499, 112719, ISSN: 0021-9991. DOI: 10.1016/j.jcp.2023.112719.

Xia, X, Thorkildsen Jarsve, K, Dijkstra, T, Liang, Q, Meng, X, Chen, G (2023) An integrated hydrodynamic model for runoff-generated debris flows with novel formulation of bed erosion and deposition, Engineering Geology, 326, 107310, ISSN: 0013-7952. DOI: 10.1016/j.enggeo.2023.107310.

Wang, H, Wang, G, Zheng, J, Liang, Q, Tao, A (2023) Advancements in nearshore wave modeling: A unified one-layer nonhydrostatic approach, Physics of Fluids, 35(7), 076610, ISSN: 1070-6631. DOI: 10.1063/5.0159266.

Tong, X, Lai, X, Liang, Q (2023) An improved non-point source pollution model for catchment-scale hydrological processes and phosphorus loads, Journal of Hydrology, 621, pp.129588-129588, ISSN: 0022-1694. DOI: 10.1016/j.jhydrol.2023.129588.

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.

Zhong, B, Wang, Z, Yang, H, Xu, H, Gao, M, Liang, Q (2022) Parameter optimization of SWMM model using integrated Morris and GLUE methods, Water, 15(1), 149, DOI: 10.3390/w15010149.

Guan, X, Xia, C, Xu, H, Liang, Q, Ma, C, Xu, S (2022) Flood risk analysis integrating of Bayesian-based time-varying model and expected annual damage considering non-stationarity and uncertainty in the coastal city, Journal of Hydrology, 617(Part B), 129038, ISSN: 0022-1694. DOI: 10.1016/j.jhydrol.2022.129038.

Lin, L, Tang, C, Liang, Q, Wu, Z, Wang, X, Zhao, S (2022) Rapid urban flood risk mapping for data-scarce environments using social sensing and region-stable deep neural network, Journal of Hydrology, 617(Part A), 128758, ISSN: 0022-1694. DOI: 10.1016/j.jhydrol.2022.128758.

Xiong, Y, Liang, Q, Zheng, J, Stolle, J, Nistor, I, Wang, G (2022) A fully coupled hydrodynamic-DEM model for simulating debris dynamics and impact forces, Ocean Engineering, 255, 111468, ISSN: 0029-8018. DOI: 10.1016/j.oceaneng.2022.111468.

Su, X, Liang, Q, Xia, X (2022) A new GPU-accelerated coupled discrete element and depth-averaged model for simulation of flow-like landslides, Environmental Modelling & Software, 153, 105412, ISSN: 1364-8152. DOI: 10.1016/j.envsoft.2022.105412.

Ma, Y, Xia, X, Liang, Q, Wan, H (2022) Investigating the Impact of Spatial Distribution of Sustainable Drainage System (SuDS) Components on Their Flood Mitigation Performance in Communities with High Groundwater Levels, Water, 14(9), pp.1367-1367, DOI: 10.3390/w14091367.

Zhao, J and Liang, Q (2022) Novel variable reconstruction and friction term discretisation schemes for hydrodynamic modelling of overland flow and surface water flooding, Advances in Water Resources, 163, 104187, ISSN: 0309-1708. DOI: 10.1016/j.advwatres.2022.104187.

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.

Ming, X, Liang, Q, Dawson, R, Xia, X, Hou, J (2022) A quantitative multi-hazard risk assessment framework for compound flooding considering hazard inter-dependencies and interactions, Journal of Hydrology, 607, 127477, ISSN: 0022-1694. DOI: 10.1016/j.jhydrol.2022.127477.

Zhou, X, Moinuddin, M, Renaud, F, Barrett, B, Xu, J, Liang, Q, Zhao, J, Xia, X, Bosher, L, Huang, S, Hoey, T (2022) Development of an SDG interlinkages analysis model at the river basin scale: a case study in the Luanhe River Basin, China, Sustainability Science, 17(4), pp.1405-1433, ISSN: 1862-4065. DOI: 10.1007/s11625-021-01065-z.

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.

Liu, K, Ke, L, Wang, J, Jiang, L, Richards, KS, Sheng, Y, Zhu, Y, Fan, C, Zhan, P, Luo, S, Cheng, J, Chen, T, Ma, R, Liang, Q, Madson, A, Song, C (2021) Ongoing drainage reorganization driven by rapid lake growths on the Tibetan Plateau, Geophysical Research Letters, 48(24), e2021GL095795, ISSN: 0094-8276. DOI: 10.1029/2021GL095795.

Nian, T, Li, D, Liang, Q, Wu, H, Guo, X (Accepted for publication) Multi-phase flow simulation of landslide dam formation process based on extended coupled DEM-CFD method, Computers and Geotechnics, 140, ISSN: 0266-352X. DOI: 10.1016/j.compgeo.2021.104438.

Su, X, Xia, X, Liang, Q, Hou, J (2021) A coupled discrete element and depth-averaged model for dynamic simulation of flow-like landslides, Computers and Geotechnics, 141, 104537, ISSN: 0266-352X. DOI: 10.1016/j.compgeo.2021.104537.

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.

Hou, J-M, Shi, B-S, Liang, Q, Tong, Y, Kang, Y-D, Zhang, Z-A, Bai, G-G, Gao, X-J, Yang, X (2021) A graphics processing unit-based robust numerical model for solute transport driven by torrential flow condition, Journal of Zhejiang University Science A, 22(10), pp.835-850, ISSN: 1673-565X. DOI: 10.1631/jzus.A2000585.

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.

Zhao, W, Xia, X, Su, X, Liang, Q, Liu, X, Ju, N (2021) Movement process analysis of the high-speed long-runout Shuicheng landslide over 3-D complex terrain using a depth-averaged numerical model, Landslides, 18(9), pp.3213-3226, ISSN: 1612-510X. DOI: 10.1007/s10346-021-01695-5.

Han, H, Hou, J, Bai, G, Li, B, Wang, T, Li, X, Gong, J, Gao, X, Su, F, Wang, Z, Liang, Q (2021) A deep learning technique-based automatic monitoring method for experimental urban road inundation, Journal of Hydroinformatics, 23(4), pp.764-781, ISSN: 1464-7141. DOI: 10.2166/hydro.2021.156.

Wang, G, Liang, Q, Shi, F, Zheng, J (2021) Analytical and numerical investigation of trapped ocean waves along a submerged ridge, Journal of Fluid Mechanics, 915, A54, ISSN: 0022-1120. DOI: 10.1017/jfm.2020.1039.

Jiang, J, Liang, Q, Xia, X, Hou, J (Accepted for publication) A coupled hydrodynamic and particle-tracking model for full-process simulation of nonpoint source pollutants, Environmental Modelling & Software, 136, pp.104951-104951, ISSN: 1364-8152. DOI: 10.1016/j.envsoft.2020.104951.

Özgen-Xian, I, Xia, X, Liang, Q, Hinkelmann, R, Liang, D, Hou, J (2021) Innovations towards the next generation of shallow flow models, Advances in Water Resources, 149, 103867, ISSN: 0309-1708. DOI: 10.1016/j.advwatres.2021.103867.

Pramanik, M, Chowdhury, K, Rana, MJ, Bisht, P, Pal, R, Szabo, S, Pal, I, Behera, B, Liang, Q, Padmadas, SS, Udmale, P (2020) Climatic influence on the magnitude of COVID-19 outbreak: a stochastic model-based global analysis, International Journal of Environmental Health Research, ISSN: 0960-3123. DOI: 10.1080/09603123.2020.1831446.

Kabir, S, Patidar, S, Xia, X, Liang, Q, Neal, J, Pender, G (2020) A deep convolutional neural network model for rapid prediction of fluvial flood inundation, Journal of Hydrology, 590, 125481, ISSN: 0022-1694. DOI: 10.1016/j.jhydrol.2020.125481.

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, pp.111868-111868, ISSN: 0034-4257. DOI: 10.1016/j.rse.2020.111868.

Zounemat-Kermani, M, Matta, E, Cominola, A, Xia, X, Zhang, Q, Liang, Q, Hinkelmann, R (2020) Neurocomputing in surface water hydrology and hydraulics: A review of two decades retrospective, current status and future prospects, Journal of Hydrology, 588, pp.125085-125085, ISSN: 0022-1694. DOI: 10.1016/j.jhydrol.2020.125085.

Ming, X, Liang, Q, Xia, X, Li, D, Fowler, HJ (2020) Real‐time flood forecasting based on a high‐performance 2D hydrodynamic model and numerical weather predictions, Water Resources Research, 56(7), e2019WR025583, ISSN: 0043-1397. DOI: 10.1029/2019wr025583.

Wang, F, Wang, Z, Yang, H, Di, D, Zhao, Y, Liang, Q (2020) A new copula-based standardized precipitation evapotranspiration streamflow index for drought monitoring, Journal of Hydrology, 585, pp.124793-124793, ISSN: 0022-1694. DOI: 10.1016/j.jhydrol.2020.124793.

Wang, F, Wang, Z, Yang, H, Di, D, Zhao, Y, Liang, Q (2020) Utilizing GRACE-based groundwater drought index for drought characterization and teleconnection factors analysis in the North China Plain, Journal of Hydrology, 585, pp.124849-124849, ISSN: 0022-1694. DOI: 10.1016/j.jhydrol.2020.124849.

Jiang, L, Hu, Y, Xia, X, Liang, Q, Soltoggio, A, Kabir, S (2020) A multi-scale mapping approach based on a deep learning CNN model for reconstructing high-resolution urban DEMs, Water, 12(5), 1369, DOI: 10.3390/w12051369.

Wang, F, Wang, Z, Yang, H, Di, D, Zhao, Y, Liang, Q, Hussain, Z (2020) Comprehensive evaluation of hydrological drought and its relationships with meteorological drought in the Yellow River basin, China, Journal of Hydrology, 584, pp.124751-124751, ISSN: 0022-1694. DOI: 10.1016/j.jhydrol.2020.124751.

Hou, J, Li, B, Tong, Y, Ma, L, Ball, J, Luo, H, Liang, Q, Xia, J (2020) Cause analysis for a new type of devastating flash flood, Hydrology Research, 51(1), pp.1-16, ISSN: 1998-9563. DOI: 10.2166/nh.2019.091.

Li, Q, Liang, Q, Xia, X (2020) A novel 1D-2D coupled model for hydrodynamic simulation of flows in drainage networks, Advances in Water Resources, 137, pp.103519-103519, ISSN: 0309-1708. DOI: 10.1016/j.advwatres.2020.103519.

Xiong, Y, Mahaffey, S, Liang, Q, Rouainia, M, Wang, G (2019) A new 1D coupled hydrodynamic discrete element model for floating debris in violent shallow flows, Journal of Hydraulic Research, ISSN: 0022-1686. DOI: 10.1080/00221686.2019.1671513.

Cui, Y, Liang, Q, Wang, G, Zhao, J, Hu, J, Wang, Y, Xia, X (2019) Simulation of Hydraulic Structures in 2D High-Resolution Urban Flood Modeling, Water, 11(10), pp.2139-2139, DOI: 10.3390/w11102139.

Dang, C-H, Wang, J, Liang, Q (2019) Inflows/outflows driven particle dynamics in an idealised lake, Journal of Hydrodynamics, 31(5), pp.873-886, ISSN: 1001-6058. DOI: 10.1007/s42241-019-0070-9.

Xia, X, Liang, Q, Ming, X (2019) A full-scale fluvial flood modelling framework based on a high-performance integrated hydrodynamic modelling system (HiPIMS), Advances in Water Resources, 132, 103392, ISSN: 0309-1708. DOI: 10.1016/j.advwatres.2019.103392.

Lin, L, Wu, Z, Liang, Q (2019) Urban flood susceptibility analysis using a GIS-based multi-criteria analysis framework, Natural Hazards, 97(2), pp.455-475, ISSN: 0921-030X. DOI: 10.1007/s11069-019-03615-2.

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.

Wang, G, Liang, Q, Zheng, J, Wan, P (2019) A new multilayer nonhydrostatic formulation for surface water waves, Journal of Coastal Research, 35(3), pp.693-710, ISSN: 0749-0208. DOI: 10.2112/JCOASTRES-D-18-00022.1.

Xiong, Y, Liang, Q, Park, H, Cox, D, Wang, G (2019) A deterministic approach for assessing tsunami-induced building damage through quantification of hydrodynamic forces, Coastal Engineering, 144, pp.1-14, ISSN: 0378-3839. DOI: 10.1016/j.coastaleng.2018.11.002.

Liu, Z, Zhang, H, Liang, Q (2018) A coupled hydrological and hydrodynamic model for flood simulation, Hydrology Research, ISSN: 0029-1277. DOI: 10.2166/nh.2018.090.

Jiang, L, Ling, D, Zhao, M, Wang, C, Liang, Q, Liu, K (2018) Effective identification of terrain positions from gridded DEM data using multimodal classification integration, ISPRS International Journal of Geo-Information, 7(11), DOI: 10.3390/ijgi7110443.

Hou, J, Wang, R, Liang, Q, Li, Z, Huang, MS, Hinkelmann, R (2018) Efficient surface water flow simulation on static Cartesian grid with local refinement according to key topographic features, Computers & Fluids, 176, pp.117-134, ISSN: 0045-7930. DOI: 10.1016/j.compfluid.2018.03.024.

Hou, J, Guo, K, Liu, F, Han, H, Liang, Q, Tong, Y, Li, P (2018) Assessing Slope Forest Effect on Flood Process Caused by a Short-Duration Storm in a Small Catchment, Water, 10(9), pp.1256-1256, DOI: 10.3390/w10091256.

Xia, X and Liang, Q (2018) A new efficient implicit scheme for discretising the stiff friction terms in the shallow water equations, Advances in Water Resources, 117, pp.87-97, ISSN: 0309-1708. DOI: 10.1016/j.advwatres.2018.05.004.

Wang, G, Fu, D, Zheng, J, Liang, Q, Zhang, Y (2018) Analytic study on long wave transformation over a seamount with a pit, Ocean Engineering, 154, pp.167-176, ISSN: 0029-8018. DOI: 10.1016/j.oceaneng.2018.02.012.

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.

Hou, J, Li, G, Li, G, Liang, Q, Zhi, Z (2018) Application of efficient high-resolution hydrodynamic model to simulations of flood propagation, Shuili Fadian Xuebao Journal of Hydroelectric Engineering, 37(2), pp.96-107, ISSN: 1003-1243. DOI: 10.11660/slfdxb.20180210.

Wang, G, Zheng, J, Liang, Q (2018) Accuracy of depth-integrated nonhydrostatic wave models, Ocean Engineering, 149, pp.217-225, ISSN: 0029-8018. DOI: 10.1016/j.oceaneng.2017.12.015.

Xia, X and Liang, Q (2018) A new depth-averaged model for flow-like landslides over complex terrains with curvatures and steep slopes, Engineering Geology, 234, pp.174-191, ISSN: 0013-7952. DOI: 10.1016/j.enggeo.2018.01.011.

Xia, X, Liang, Q, Ming, X, Hou, J (2018) Reply to comment by Lu et al. on “An efficient and stable hydrodynamic model with novel source term discretization schemes for overland flow and flood simulations”, Water Resources Research, 54(1), pp.628-630, ISSN: 0043-1397. DOI: 10.1002/2017WR021696.

Xiong, Y, Liang, Q, Mahaffey, S, Rouainia, M, Wang, G (2018) A novel two-way method for dynamically coupling a hydrodynamic model with a discrete element model (DEM), Journal of Hydrodynamics, 30(5), pp.966-969, ISSN: 1001-6058. DOI: 10.1007/s42241-018-0081-y.

Xing, Y, Liang, Q, Wang, G, Ming, X, Xia, X (2018) City-scale hydrodynamic modelling of urban flash floods: the issues of scale and resolution, Natural Hazards, ISSN: 0921-030X. DOI: 10.1007/s11069-018-3553-z.

Hou, J-M, Liang, Q-H, Wang, G, Hinkelmann, R (2017) Preface for special section on flood modeling and resilience, Water Science and Engineering, 10(4), pp.265-266, ISSN: 1674-2370. DOI: 10.1016/j.wse.2017.12.008.

Yang, H-B, Li, E-C, Zhao, Y, Liang, Q-H (2017) Effect of water-sediment regulation and its impact on coastline and suspended sediment concentration in Yellow River Estuary, Water Science and Engineering, 10(4), pp.311-319, ISSN: 1674-2370. DOI: 10.1016/j.wse.2017.12.009.

Hou, J-M, Wang, R, Jing, H-X, Zhang, X, Liang, Q-H, Di, Y-Y (2017) An efficient dynamic uniform Cartesian grid system for inundation modeling, Water Science and Engineering, 10(4), pp.267-274, ISSN: 1674-2370. DOI: 10.1016/j.wse.2017.12.004.

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.

Xia, X, Liang, Q, Ming, X, Hou, J (2017) An efficient and stable hydrodynamic model with novel source term discretization schemes for overland flow and flood simulations, Water Resources Research, 53(5), pp.3730-3759, ISSN: 0043-1397. DOI: 10.1002/2016WR020055.

Birkinshaw, SJ, Guerreiro, SB, Nicholson, A, Liang, Q, Quinn, P, Zhang, L, He, B, Yin, J, Fowler, HJ (2017) Climate change impacts on Yangtze River discharge at the Three Gorges Dam, Hydrology and Earth System Sciences, 21(4), pp.1911-1927, DOI: 10.5194/hess-21-1911-2017.

LIANG, Q, SMITH, L, XIA, X (2016) New prospects for computational hydraulics by leveraging high-performance heterogeneous computing techniques, Journal of Hydrodynamics, Ser. B, 28(6), pp.977-985, ISSN: 1001-6058. DOI: 10.1016/S1001-6058(16)60699-6.

Lai, X, Liang, Q, Huang, Q, Jiang, J, Lu, XX (2016) Numerical evaluation of flow regime changes induced by the Three Gorges Dam in the Middle Yangtze, Hydrology Research, 47(S1), pp.149-160, ISSN: 0029-1277. DOI: 10.2166/nh.2016.158.

Guan, M and Liang, Q (2016) A two-dimensional hydro-morphological model for river hydraulics and morphology with vegetation, Environmental Modelling and Software, 88, pp.10-21, ISSN: 1364-8152. DOI: 10.1016/j.envsoft.2016.11.008.

Amouzgar, R, Liang, Q, Clarke, P, Yasuda, T, Mase, H (2016) Computationally Efficient Tsunami Modeling on Graphics Processing Units (GPUs), International Journal of Offshore and Polar Engineering, 26(2), pp.154-160, ISSN: 1053-5381. DOI: 10.17736/ijope.2016.ak10.

Ferrer-Boix, C and Liang, Q (2016) A numerical approach for analysing the performance of a sewage screening chamber, Urban Water Journal, 13(4), pp.360-371, ISSN: 1573-062X. DOI: 10.1080/1573062x.2014.993991.

LIANG, Q, CHEN, K-C, HOU, J, XIONG, Y, WANG, G, QIANG, J (2016) Hydrodynamic modelling of flow impact on structures under extreme flow conditions, Journal of Hydrodynamics, Ser. B, 28(2), pp.267-274, ISSN: 1001-6058. DOI: 10.1016/S1001-6058(16)60628-5.

Hou, J, Liang, Q, Xia, X (2015) Robust absorbing boundary conditions for shallow water flow models, Environmental Earth Sciences, 74(11), pp.7407-7422, ISSN: 1866-6280. DOI: 10.1007/s12665-015-4743-6.

Liang, Q, Xia, X, Hou, J (2015) Efficient urban flood simulation using a GPU-accelerated SPH model, Environmental Earth Sciences, 74(11), pp.7285-7294, ISSN: 1866-6280. DOI: 10.1007/s12665-015-4753-4.

Liang, Q, Hou, J, Amouzgar, R (2015) Simulation of Tsunami Propagation Using Adaptive Cartesian Grids, Coastal Engineering Journal, 57(4), ISSN: 2166-4250. DOI: 10.1142/s0578563415500163.

Wang, G, Zheng, J-H, Liang, Q-H, Zhang, W, Huang, C (2015) Theoretical analysis of harbor resonance in harbor with an exponential bottom profile, China Ocean Engineering, 29(6), pp.821-834, ISSN: 0890-5487. DOI: 10.1007/s13344-015-0058-3.

Hinkelmann, R, Liang, Q, Aizinger, V, Dawson, C (2015) Robust shallow water models, Environmental Earth Sciences, 74(11), pp.7273-7274, ISSN: 1866-6280. DOI: 10.1007/s12665-015-4764-1.

Xia, X and Liang, Q (2015) A GPU-accelerated smoothed particle hydrodynamics (SPH) model for the shallow water equations, Environmental Modelling & Software, 75, pp.28-43, ISSN: 1364-8152. DOI: 10.1016/j.envsoft.2015.10.002.

Hou, J, Liang, Q, Li, Z, Wang, S, Hinkelmann, R (2015) Numerical error control for second-order explicit TVD scheme with limiters in advection simulation, Computers & Mathematics with Applications, 70(9), pp.2197-2209, ISSN: 0898-1221. DOI: 10.1016/j.camwa.2015.08.022.

Liang, Q and Smith, LS (2015) A high-performance integrated hydrodynamic modelling system for urban flood simulations, Journal of Hydroinformatics, 17(4), pp.518-533, ISSN: 1464-7141. DOI: 10.2166/hydro.2015.029.

Liang, Q, Hou, J, Xia, X (2015) Contradiction between the C‐property and mass conservation in adaptive grid based shallow flow models: cause and solution, International Journal for Numerical Methods in Fluids, 78(1), pp.17-36, ISSN: 0271-2091. DOI: 10.1002/fld.4005.

Smith, L, Liang, Q, James, P, Lin, W (2015) Assessing the utility of social media as a data source for flood risk management using a real-time modelling framework, Journal of Flood Risk Management, 10(3), pp.370-380, ISSN: 1753-318X. DOI: 10.1111/jfr3.12154.

Zhang, L, Liang, Q, Wang, Y, Yin, J (2015) A robust coupled model for solute transport driven by severe flow conditions, Journal of Hydro-environment Research, 9(1), pp.49-60, ISSN: 1570-6443. DOI: 10.1016/j.jher.2014.04.005.

Kesserwani, G and Liang, Q (2015) RKDG2 shallow-water solver on non-uniform grids with local time steps: Application to 1D and 2D hydrodynamics, Applied Mathematical Modelling, 39(3-4), pp.1317-1340, ISSN: 0307-904X. DOI: 10.1016/j.apm.2014.08.009.

Hou, J, Liang, Q, Zhang, H, Hinkelmann, R (2015) An efficient unstructured MUSCL scheme for solving the 2D shallow water equations, Environmental Modelling & Software, 66, pp.131-152, ISSN: 1364-8152. DOI: 10.1016/j.envsoft.2014.12.007.

Smith, LS, Liang, Q, Quinn, PF (2015) Towards a hydrodynamic modelling framework appropriate for applications in urban flood assessment and mitigation using heterogeneous computing, Urban Water Journal, 12(1), pp.67-78, ISSN: 1573-062X. DOI: 10.1080/1573062x.2014.938763.

Lu, D, Wang, B, Wang, Y, Zhou, H, Liang, Q, Peng, Y, Roskilly, T (2015) Optimal operation of cascade hydropower stations using hydrogen as storage medium, Applied Energy, 137, pp.56-63, ISSN: 0306-2619. DOI: 10.1016/j.apenergy.2014.09.092.

Hou, J, Liang, Q, Zhang, H, Hinkelmann, R (2014) Multislope MUSCL method applied to solve shallow water equations, Computers & Mathematics with Applications, 68(12), pp.2012-2027, ISSN: 0898-1221. DOI: 10.1016/j.camwa.2014.09.018.

Lai, X, Liang, Q, Yesou, H, Daillet, S (2014) Variational assimilation of remotely sensed flood extents using a 2-D flood model, Hydrology and Earth System Sciences, 18(11), pp.4325-4339, DOI: 10.5194/hess-18-4325-2014.

Lai, X, Liang, Q, Jiang, J, Huang, Q (2014) Impoundment Effects of the Three-Gorges-Dam on Flow Regimes in Two China’s Largest Freshwater Lakes, Water Resources Management, 28(14), pp.5111-5124, ISSN: 0920-4741. DOI: 10.1007/s11269-014-0797-6.

Zhang, H, Wang, Y, Liang, Q, Smith, LS, Kilsby, CG (2014) Non-negative depth reconstruction for a two-dimensional partial inertial inundation model, Journal of Hydroinformatics, 16(5), pp.1158-1177, ISSN: 1464-7141. DOI: 10.2166/hydro.2014.131.

Wang, G, Zheng, J, Liang, Q, Zheng, Y (2014) Analytical solutions for oscillations in a harbor with a hyperbolic-cosine squared bottom, Ocean Engineering, 83, pp.16-23, ISSN: 0029-8018. DOI: 10.1016/j.oceaneng.2014.03.027.

Hou, J, Simons, F, Liang, Q, Hinkelmann, R (2014) An improved hydrostatic reconstruction method for shallow water model, Journal of Hydraulic Research, 52(3), pp.432-439, ISSN: 0022-1686. DOI: 10.1080/00221686.2013.858648.

Smith, LS and Liang, Q (2013) Towards a generalised GPU/CPU shallow-flow modelling tool, Computers & Fluids, 88, pp.334-343, ISSN: 0045-7930. DOI: 10.1016/j.compfluid.2013.09.018.

Xia, X, Liang, Q, Pastor, M, Zou, W, Zhuang, Y-F (2013) Balancing the source terms in a SPH model for solving the shallow water equations, Advances in Water Resources, 59, pp.25-38, ISSN: 0309-1708. DOI: 10.1016/j.advwatres.2013.05.004.

Hou, J, Liang, Q, Simons, F, Hinkelmann, R (2013) A stable 2D unstructured shallow flow model for simulations of wetting and drying over rough terrains, Computers & Fluids, 82, pp.132-147, ISSN: 0045-7930. DOI: 10.1016/j.compfluid.2013.04.015.

Lai, X, Jiang, J, Liang, Q, Huang, Q (2013) Large-scale hydrodynamic modeling of the middle Yangtze River Basin with complex river–lake interactions, Journal of Hydrology, 492, pp.228-243, ISSN: 0022-1694. DOI: 10.1016/j.jhydrol.2013.03.049.

Li, G, Gao, J, Liang, Q (2013) A well‐balanced weighted essentially non‐oscillatory scheme for pollutant transport in shallow water, International Journal for Numerical Methods in Fluids, 71(12), pp.1566-1587, ISSN: 0271-2091. DOI: 10.1002/fld.3726.

Duran, A, Liang, Q, Marche, F (2013) On the well-balanced numerical discretization of shallow water equations on unstructured meshes, Journal of Computational Physics, 235, pp.565-586, ISSN: 0021-9991. DOI: 10.1016/j.jcp.2012.10.033.

Hou, J, Liang, Q, Simons, F, Hinkelmann, R (2013) A 2D well-balanced shallow flow model for unstructured grids with novel slope source term treatment, Advances in Water Resources, 52, pp.107-131, ISSN: 0309-1708. DOI: 10.1016/j.advwatres.2012.08.003.

Zhang, C, Liang, Q, Yin, J (2013) A first-order adaptive solution to rapidly spreading flood waves, Progress in Computational Fluid Dynamics, An International Journal, 13(1), pp.1-1, ISSN: 1468-4349. DOI: 10.1504/pcfd.2013.050645.

Liang, Q (2012) A simplified adaptive Cartesian grid system for solving the 2D shallow water equations, International Journal for Numerical Methods in Fluids, 69(2), pp.442-458, ISSN: 0271-2091. DOI: 10.1002/fld.2568.

Kesserwani, G and Liang, Q (2012) Dynamically adaptive grid based discontinuous Galerkin shallow water model, Advances in Water Resources, 37, pp.23-39, ISSN: 0309-1708. DOI: 10.1016/j.advwatres.2011.11.006.

Kesserwani, G and Liang, Q (2012) Influence of Total-Variation-Diminishing Slope Limiting on Local Discontinuous Galerkin Solutions of the Shallow Water Equations, Journal of Hydraulic Engineering, 138(2), pp.216-222, ISSN: 0733-9429. DOI: 10.1061/(asce)hy.1943-7900.0000494.

Kesserwani, G and Liang, Q (2012) Locally Limited and Fully Conserved RKDG2 Shallow Water Solutions with Wetting and Drying, Journal of Scientific Computing, 50(1), pp.120-144, ISSN: 0885-7474. DOI: 10.1007/s10915-011-9476-4.

Wang, Y, Liang, Q, Kesserwani, G, Hall, JW (2011) A positivity-preserving zero-inertia model for flood simulation, Computers & Fluids, 46(1), pp.505-511, ISSN: 0045-7930. DOI: 10.1016/j.compfluid.2011.01.026.

Alias, NA, Liang, Q, Kesserwani, G (2011) A Godunov-type scheme for modelling 1D channel flow with varying width and topography, Computers & Fluids, 46(1), pp.88-93, ISSN: 0045-7930. DOI: 10.1016/j.compfluid.2010.12.009.

Liang, Q (2011) A structured but non‐uniform Cartesian grid‐based model for the shallow water equations, International Journal for Numerical Methods in Fluids, 66(5), pp.537-554, ISSN: 0271-2091. DOI: 10.1002/fld.2266.

Wang, J and Liang, Q (2011) Testing a new adaptive grid‐based shallow flow model for different types of flood simulations, Journal of Flood Risk Management, 4(2), pp.96-103, ISSN: 1753-318X. DOI: 10.1111/j.1753-318x.2011.01094.x.

Liang, Q (2011) A Coupled Morphodynamic Model for Applications Involving Wetting and Drying, Journal of Hydrodynamics, 23(3), pp.273-281, ISSN: 1001-6058. DOI: 10.1016/s1001-6058(10)60113-8.

Wang, Y, Liang, Q, Kesserwani, G, Hall, JW (2011) A 2D shallow flow model for practical dam-break simulations, Journal of Hydraulic Research, 49(3), pp.307-316, ISSN: 0022-1686. DOI: 10.1080/00221686.2011.566248.

Kesserwani, G and Liang, Q (2011) A conservative high‐order discontinuous Galerkin method for the shallow water equations with arbitrary topography, International Journal for Numerical Methods in Engineering, 86(1), pp.47-69, ISSN: 0029-5981. DOI: 10.1002/nme.3044.

Liang, Q (2011) Dynamically adaptive simulation of coastal hydrodynamics, International Journal of Offshore and Polar Engineering, 21(1), pp.50-55, ISSN: 1053-5381.

Kesserwani, G and Liang, Q (2010) Well-balanced RKDG2 solutions to the shallow water equations over irregular domains with wetting and drying, Computers & Fluids, 39(10), pp.2040-2050, ISSN: 0045-7930. DOI: 10.1016/j.compfluid.2010.07.008.

Kesserwani, G and Liang, Q (2010) A discontinuous Galerkin algorithm for the two-dimensional shallow water equations, Computer Methods in Applied Mechanics and Engineering, 199(49-52), pp.3356-3368, ISSN: 0045-7825. DOI: 10.1016/j.cma.2010.07.007.

Kesserwani, G, Vazquez, J, Rivière, N, Liang, Q, Travin, G, Mosé, R (2010) New Approach for Predicting Flow Bifurcation at Right-Angled Open-Channel Junction, Journal of Hydraulic Engineering, 136(9), pp.662-668, ISSN: 0733-9429. DOI: 10.1061/(asce)hy.1943-7900.0000222.

Liang, Q (2010) Flood Simulation Using a Well-Balanced Shallow Flow Model, Journal of Hydraulic Engineering, 136(9), pp.669-675, ISSN: 0733-9429. DOI: 10.1061/(asce)hy.1943-7900.0000219.

Liang, Q (2010) A Well-Balanced and Non-Negative Numerical Scheme for Solving the Integrated Shallow Water and Solute Transport Equations, Communications in Computational Physics, 7(5), pp.1049-1075, ISSN: 1815-2406. DOI: 10.4208/cicp.2009.09.156.

REN, M, WANG, B, LIANG, Q, FU, G (2010) Classified real-time flood forecasting by coupling fuzzy clustering and neural network, International Journal of Sediment Research, 25(2), pp.134-148, ISSN: 1001-6279. DOI: 10.1016/s1001-6279(10)60033-9.

Liang, Q, Wang, Y, Archetti, R (2010) A well-balanced shallow flow solver for coastal simulations, International Journal of Offshore and Polar Engineering, 20(1), pp.41-47, ISSN: 1053-5381.

Kesserwani, G, Liang, Q, Vazquez, J, Mosé, R (2010) Well‐balancing issues related to the RKDG2 scheme for the shallow water equations, International Journal for Numerical Methods in Fluids, 62(4), pp.428-448, ISSN: 0271-2091. DOI: 10.1002/fld.2027.

Liang, Q and Marche, F (2009) Numerical resolution of well-balanced shallow water equations with complex source terms, Advances in Water Resources, 32(6), pp.873-884, ISSN: 0309-1708. DOI: 10.1016/j.advwatres.2009.02.010.

Liang, Q and Borthwick, AGL (2009) Adaptive quadtree simulation of shallow flows with wet–dry fronts over complex topography, Computers & Fluids, 38(2), pp.221-234, ISSN: 0045-7930. DOI: 10.1016/j.compfluid.2008.02.008.

Liang, Q, Taylor, PH, Borthwick, AGL (2009) Particle mixing and reactive front motions in chaotic but closed shallow flows, Computers & Fluids, 38(2), pp.382-392, ISSN: 0045-7930. DOI: 10.1016/j.compfluid.2008.04.011.

Liang, Q, Du, G, Hall, JW, Borthwick, AG (2008) Flood Inundation Modeling with an Adaptive Quadtree Grid Shallow Water Equation Solver, Journal of Hydraulic Engineering, 134(11), pp.1603-1610, ISSN: 0733-9429. DOI: 10.1061/(asce)0733-9429(2008)134:11(1603).

Ning, DZ, Zang, J, Liang, Q, Taylor, PH, Borthwick, AGL (2008) Boussinesq cut‐cell model for non‐linear wave interaction with coastal structures, International Journal for Numerical Methods in Fluids, 57(10), pp.1459-1483, ISSN: 0271-2091. DOI: 10.1002/fld.1656.

Liang, Q and Borthwick, AGL (2008) Simple treatment of non‐aligned boundaries in a Cartesian grid shallow flow model, International Journal for Numerical Methods in Fluids, 56(11), pp.2091-2110, ISSN: 0271-2091. DOI: 10.1002/fld.1615.

Liang, Q (2008) Simulation of Shallow Flows in Nonuniform Open Channels, Journal of Fluids Engineering, 130(1), 011205, ISSN: 0098-2202. DOI: 10.1115/1.2829593.

Zang, J, Ning, D, Liu, S, Liang, Q, Taylor, PH, Taylor, RE, Borthwick, AGL (2007) Boussinesq cut-cell model for wave scattering from cylinder in shallow water, International Journal of Offshore and Polar Engineering, 17(4), pp.266-269, ISSN: 1053-5381.

Liang, Q, Zang, J, Borthwick, AGL, Taylor, PH (2007) Shallow flow simulation on dynamically adaptive cut cell quadtree grids, International Journal for Numerical Methods in Fluids, 53(12), pp.1777-1799, ISSN: 0271-2091. DOI: 10.1002/fld.1363.

Liang, Q, Taylor, PH, Borthwick, AGL (2007) Particle mixing and reactive front motion in unsteady open shallow flow – Modelled using singular value decomposition, Computers & Fluids, 36(2), pp.248-258, ISSN: 0045-7930. DOI: 10.1016/j.compfluid.2006.01.018.

Liang, Q, Borthwick, AGL, Taylor, PH (2006) Wind-induced chaotic advection in shallow flow geometries. Part II: Non-circular basins, Journal of Hydraulic Research, 44(2), pp.180-188, ISSN: 0022-1686. DOI: 10.1080/00221686.2006.9521674.

Liang, Q, Borthwick, AGL, Taylor, PH (2006) Wind-induced chaotic advection in shallow flow geometries. Part I: Circular basins, Journal of Hydraulic Research, 44(2), pp.170-179, ISSN: 0022-1686. DOI: 10.1080/00221686.2006.9521673.

Liang, Q, Borthwick, AGL, Taylor, PH (2005) Chaotic mixing in a basin due to a sinusoidal wind field, International Journal for Numerical Methods in Fluids, 47(8-9), pp.871-877, ISSN: 0271-2091. DOI: 10.1002/fld.880.

Liang, Q, Borthwick, AGL, Stelling, G (2004) Simulation of dam‐ and dyke‐break hydrodynamics on dynamically adaptive quadtree grids, International Journal for Numerical Methods in Fluids, 46(2), pp.127-162, ISSN: 0271-2091. DOI: 10.1002/fld.748.

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.

Jiang, J and Liang, Q (2025) Multivariate Analysis of Compound Flooding in Can Tho City, Vietnam. In , Proceedings of the IAHR World Congress, pp.880-882.

Xiong, Y, Liang, Q, Tong, X, Zheng, J, Wang, G (2022) Modelling Floating Debris Transport in a Flash Flood Event. In , Proceedings of the IAHR World Congress, pp.327-334, DOI: 10.3850/IAHR-39WC2521716X2022121.

Wang, G, Yu, H, Zheng, J, Liang, Q (2020) The ray paths of trapped waves over the submerged ridge. In , Apac 2019 Proceedings of the 10th International Conference on Asian and Pacific Coasts, pp.169-173, DOI: 10.1007/978-981-15-0291-0_24.

Cui, Y, Liang, Q, Wang, G, Hu, J, Wang, Y (2019) SIMULATION OF HYDRAULIC STRUCTURES IN 2D HIGH-RESOLUTION URBAN FLOOD MODELING. In , Proceedings of the IAHR World Congress, pp.895-903, DOI: 10.3850/38WC092019-055.

Liang, Q, Ming, X, Xia, X (2019) A HIGH-PERFORMANCE INTEGRATED HYDRODYNAMIC MODELLING SYSTEM FOR REAL-TIME FLOOD FORECASTING. In , Proceedings of the IAHR World Congress, pp.5246-5255, DOI: 10.3850/38WC092019-0969.

Wang, G, Hu, J, Zheng, JH, Liang, QH (2018) Trapped waves over the hyperbolic-cosine ocean ridge. In The 9th International Conference on APAC, Proceedings of the 9th International Conference on APAC 2017, Pasay City, Philippines, pp.44-54, ISBN: 9789813233805.

Liang, Q, Xia, X, Hou, J (2016) Catchment-scale High-resolution Flash Flood Simulation Using the GPU-based Technology. In , Procedia Engineering, pp.975-981, DOI: 10.1016/j.proeng.2016.07.585.

Rashid, AA, Liang, Q, Dawson, RJ, Smith, LS (2016) Calibrating a High-Performance Hydrodynamic Model for Broad-Scale Flood Simulation: Application to Thames Estuary, London, UK. In , Procedia Engineering, pp.967-974, DOI: 10.1016/j.proeng.2016.07.584.

Hou, J, Li, Z, Liang, Q, Li, G, Cheng, W, Wang, W, Wang, R (2016) Effects of Morphological Change on Fluvial Flood Patterns Evaluated by a Hydro-geomorphological Model. In , Procedia Engineering, pp.441-449, DOI: 10.1016/j.proeng.2016.07.536.

Xing, Y, Yang, S, Zhou, H, Liang, Q (2016) Effect of Floodplain Roughness on Velocity Distribution in Mountain Rivers. In , Procedia Engineering, pp.467-475, DOI: 10.1016/j.proeng.2016.07.539.

Liang, Q and Amouzgar, R (2016) Performance of different hardware devices for tsunami simulations. In , Proceedings of the International Offshore and Polar Engineering Conference, pp.709-715.

Xiong, Y, Liang, Q, Amouzgar, R, Cox, DT, Mori, N, Wang, G, Zheng, J (2016) High-performance simulation of tsunami inundation and impact on building structures. In , Proceedings of the International Offshore and Polar Engineering Conference, pp.732-738.

Chen, K, Liang, Q, Xiong, Y, Qiang, J, Wang, G, Zheng, J (2016) Laboratory and numerical investigation of extreme flow impact on simplified sea-crossing bridge structures. In , Proceedings of the International Offshore and Polar Engineering Conference, pp.400-407.

Qiang, J, Liang, Q, Wang, G, Zheng, J (2016) Testing a shock-capturing hydrodynamic model for storm surge simulation. In , Proceedings of the International Offshore and Polar Engineering Conference, pp.747-753.

Amouzgar, R and Liang, Q (2014) Application of a GPU based hydrodynamic model in tsunami simulations. In , Proceedings of the International Offshore and Polar Engineering Conference, pp.10-16.

Al-Bourae, Y, Downie, M, Liang, Q (2013) 3D numerical reconstruction of the hydrodynamics around an artificial reef in Loch Linnhe, Scotland. In , Proceedings of the International Offshore and Polar Engineering Conference, pp.522-528.

Liang, Q, Yamada, F, Tsujimoto, G, Zheng, J (2013) Combined physical and numerical modeling study of surge impact on structures. In , Proceedings of the International Offshore and Polar Engineering Conference, pp.648-653.

Al-Bourae, Y, Liang, Q, Downie, M (2012) Tidal simulation in loch linnhe using a finite volume shallow flow model. In , Proceedings of the International Offshore and Polar Engineering Conference, pp.801-807.

Liang, QH (2011) A new coupled model for simulating shallow flow driven morphological change. In , Aip Conference Proceedings, pp.319-322, DOI: 10.1063/1.3651908.

Liang, Q (2010) A new adaptive grid based shallow water equation solver for coastal hydrodynamic modeling. In , Proceedings of the International Offshore and Polar Engineering Conference, pp.768-774.

Wu, X, Hall, J, Liang, Q (2010) Coastal flood inundation modelling with a 2-D shallow water equation solver. In , Proceedings of the International Offshore and Polar Engineering Conference, pp.871-875.

Liang, Q, Wang, Y, Archetti, R (2009) Modeling coastal run-up using a well-balanced shallow flow solver. In , Proceedings of the International Offshore and Polar Engineering Conference, pp.771-778.

Zang, J, Ning, D, Liu, S, Liang, Q, Taylor, PH, Eatock Taylor, R, Borthwick, AGL (2007) A new Boussinesq model for wave run-up on curved structures using Cartesian cut-cell grids. In , Proceedings of the International Offshore and Polar Engineering Conference, pp.2084-2091.

Liang, Q, Zang, J, Borthwick, AGL, Taylor, PH, Shuxue, L, Smith, C (2006) Numerical simulation of non-linear wave interaction with an offshore wind turbine foundation. In , Proceedings of the Seventh 2006 ISOPE Pacific Asia Offshore Mechanics Symposium ISOPE Pacoms 2006, pp.231-236.

Hill, B, Liang, Q, Chen, H, Boscher, L (Accepted for publication) Natural flood management site-scale data: remote challenges with remote controlled solutions. In .

Chapters

Wu, XZ, Hall, JW, Liang, Q, Dawson, RJ (2015) Broadscale coastal inundation modelling. In Broad Scale Coastal Simulation New Techniques to Understand and Manage Shorelines in the Third Millennium, pp.213-232, DOI: 10.1007/978-94-007-5258-0_8.

Datasets

Tong, X, Liang, Q, Wang, G (2026) Supplementary information files for "High-performance modelling of urban non-point-source pollutant dynamics: a full-process approach", DOI: 10.17028/rd.lboro.31746427.

Wang, T, Su, G, Wang, S, Fan, Y, Terence Yang, J-Y, Xu, X, Wang, Z, Wu, J, Liang, Q, Su, Y, Zhan, J, Liu, L, Arhonditsis, G (2025) Supplementary information files for "China’s enhanced wastewater treatment capacity may accelerate greenhouse gas emissions from rural domestic pollution", DOI: 10.17028/rd.lboro.31746337.

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.

Lei Wei, Z, Quan Shang, Y, Liang, Q, Lin Xia, X (2024) Supplementary information files for "A coupled hydrological and hydrodynamic modeling approach for estimating rainfall thresholds of debris-flow occurrence", DOI: 10.17028/rd.lboro.28937207.

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

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.

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.

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.

Zhang, Y, Cheng, H, Liang, Q, Zong, Y, Shi, B (2026) Multi-Sensor Terrain Reconstruction for High-Resolution Urban Flood Modelling, High-resolution (approximately 1 m) flood modelling is increasingly recognised as essential for resolving flow pathways and hydraulic connectivity in complex urban environments. At this scale, flood dynamics are strongly controlled by micro-topographic features, including hydraulically permeable elements beneath vegetation and bridge structures, as well as small-scale obstructions such as kerbs and surface discontinuities. However, conventional Digital Terrain Model (DTM) generation approaches struggle to reliably represent such features due to vegetation occlusion and sensor-specific terrain acquisition limitations, often necessitating extensive manual intervention.This study presents a semi-automated terrain reconstruction framework that integrates Unmanned Aerial Vehicle-borne LiDAR, Unmanned Aerial Vehicle （UAV）oblique photogrammetry, handheld LiDAR Simultaneous Localization and Mapping (SLAM), and Real-Time Kinematic Global Navigation Satellite Systems (RTK-GNSS) measurements to generate flood-ready DTMs for high-resolution hydrodynamic modelling. Rather than treating multi-source datasets as interchangeable inputs, the framework explicitly exploits their differing information characteristics and spatial sensitivities to occlusion and ground accessibility. UAV LiDAR provides spatially continuous but occlusion-prone surface measurements, handheld LiDAR SLAM offers dense ground-level observations in vegetated and structurally complex areas, and RTK-GNSS provides sparse but high-accuracy elevation control.An initial DTM is established through adaptive fusion of morphologically filtered UAV-derived DTMs and SLAM-derived ground observations, supported by vegetation indices extracted from digital orthophoto maps and SLAM point-density metrics. To address residual elevation errors arising from partial occlusion and sensor limitations, a residual learning strategy based on a U-Net architecture is employed to predict local elevation corrections relative to RTK-GNSS ground truth. The learning component is explicitly constrained to operate as a local correction mechanism rather than an end-to-end terrain predictor, thereby preserving physical plausibility and spatial consistency of the reconstructed terrain.The framework is demonstrated over the Zhengzhou University campus (approximately 1 km²), encompassing diverse building typologies, vegetation densities, and pedestrian and vehicular infrastructure. The hydrodynamic relevance of the reconstructed DTM is evaluated using the High-Performance Integrated Hydrodynamic Modelling System（HiPIMS） through comparative two-dimensional simulations of historical extreme flood events. Results demonstrate that improved representation of micro-topographic controls significantly enhances simulated flow connectivity, inundation extent, and inundation timing relative to conventional terrain products, and provides a transferable workflow for campus- and neighbourhood-scale flood modelling and risk assessment and urban digital twin applications.. DOI: 10.5194/egusphere-egu26-19225.

Guo, M, Zong, Y, Tong, X, Liang, Q (2026) A Campus-scale Digital Twin Framework for Urban Flood Monitoring, Simulation and Management, Urban flooding poses an increasing threat to lives and property in urbanized environments under climate change and growing human exposure. Digital Twin (DT) concept provides a potential framework for integrating real-time monitoring, numerical simulation, and decision support. However, DT implementations that exploit dense senor networks and high-resolution, physics-based hydrodynamic flood models to enable real-time, bi-directional information exchange between physical and virtual systems remain limited. In this study, we develop a campus-scale DT framework that couples real-time monitoring, high-resolution hydrodynamic modeling, 3D virtual representation within a unified data and computational environment supported by embedded data-analytics capabilities. A high-resolution 3D digital campus model is reconstructed from ultra-high-resolution LiDAR point clouds to provide the geometric basis for spatial representation and data management. A dense IoT monitoring network, comprising rainfall gauges, water-level sensors, CCTV, and pipe flow meters, is deployed to acquire and transmit high-frequency hydrometeorological and hazard-related observations in real time. At the core of the framework is the dynamic coupling between real-time rainfall observations and the GPU-accelerated High-Performance Integrated hydrodynamic Modelling System (HiPIMS), which resolves surface water inundation processes at high spatial and temporal resolution. Static spatial data, real-time monitoring observations, and model outputs are ingested, harmonized, and managed within the unified data and computational environment, enabling automated model execution and coordinated system operation. Monitoring data and simulation outputs are mapped directly onto the 3D virtual environment to provide real-time visualization of spatiotemporal evolution of flooding and to support flood warning and emergency management. The reliability of flood simulations is evaluated against historical flood records and further assessed through continuous comparison with in-situ water-level observations. The framework supports near-real-time flood forecasting and systematic identification of high-risk locations, providing information for early warning and emergency decision-making. Emergency interventions, such as deployment of temporal flood defenses and mobile pumping stations, can in turn influence flood dynamics and risk; these changes are subsequently captured by the monitoring-modelling system and reflected in updated DT outputs. This establishes a closed-loop, real-time monitoring-simulation-decision-feedback cycle, forming an operational DT framework for urban flood management.. DOI: 10.5194/egusphere-egu26-19143.

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.

Zong, Y, Tong, X, Liang, Q (2026) HiPIMS-GWF: A GPU-Accelerated 3D Variably-Saturated Subsurface Solver for Integrated Flood Modeling with Groundwater Components, Groundwater influences flood dynamics by modulating subsurface saturation states and engaging in complex interactions with surface water in multiple pathways. Accurately representing these processes is essential for physically consistent flood prediction, risk assessment, and mitigation strategies. However, groundwater-related processes remain poorly resolved in most existing flood modeling frameworks, which typically employ oversimplified representations of subsurface flow. In this study, we present HiPIMS-GWF, a three-dimensional, variably saturated groundwater flow module integrated into the High-Performance Integrated Hydrodynamic Modelling System (HiPIMS). HiPIMS is a GPU-accelerated, high-performance flood model capable of simulating catchment-scale fluvial flooding driven by extreme rainfall events. The HiPIMS-GWF module provides functionality to solve the three-dimensional Richards equation using both iterative and non-iterative numerical schemes, enabling explicit representation of surface-subsurface water exchanges within a unified modeling framework. Model accuracy is evaluated against a suite of standard numerical benchmark problems, and computational scalability and efficiency are assessed on a multi-GPU computing platform. Beyond the acute phase of flooding, we are also interested in investigating the long-term impacts of flood events on groundwater and surface water systems after its recession. Because catchment-scale groundwater dynamics evolve over temporal scales that can be orders of magnitude longer than those of surface flooding, capturing the full hydrological response necessitates extended simulation capabilities beyond the time horizon of flood events. To this end, HiPIMS-GWF introduces a novel modeling flexibility: once floodwater recedes, the high-resolution, physics-based surface hydrodynamic component can be swtiched to a computationally efficient, hydrologic model tailored for long-term watershed simulation. Critically, the spatially distributed fields of water saturation and surface water depth generated by the fully physics-based simulation serve as initial conditions for the long-term mode, ensuring continuity in the representation of system states across timescales. The overall accuracy and robustness of this integrated modeling framework are validated against a real-world flood event.. DOI: 10.5194/egusphere-egu26-783.

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.

Qin, H and Liang, Q (2025) A high-performance Coupled Human And Natural Systems (CHANS) modelling framework for flood risk assessment and emergency management, 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.. DOI: 10.5194/egusphere-egu25-20729.

Wei, ZL, Shang, YQ, Liang, QH, Xia, XL (2023) Supplementary material to "A coupled hydrological and hydrodynamic modelling approach for estimating rainfall thresholds of debris-flow occurrence". DOI: 10.5194/nhess-2023-180-supplement.

Su, X, Liang, Q, Jiang, J (2023) Calibrating a 2D high-performance hydrodynamic model for fluvial process modelling along the Mekong River, The Vietnamese Mekong Delta (VMD), representing a highly complex hydrodynamic system, plays a major role in food security and socio-economic development in Vietnam. With ongoing climate change and rapid urbanization, the VMD is increasingly vulnerable to flood risk from multiple sources, e.g. driven simultaneously by fluvial, pluvial and coastal processes. It is essential to develop reliable modelling tools to simulate such compound flooding processes to support hazard risk assessment and management to inform the development of policies and effective strategies to sustain the delta development.To support reliable compound flood modelling and risk assessment in VMD, it is important to accurately predict the fluvial processes along the Mekong River. For large river and river network modelling, one-dimensional (1D) and quasi two-dimensional (2D) hydrodynamic models are commonly used. However, modelling overbank flow and flooding process over floodplains is out of the capability of these 1D or even quasi 2D models. These 1D or quasi 2D models are then integrated with a 2D inundation model through one-way coupling to predict the flooding processes in floodplains. The resulting one-way coupled models neglect the dynamic interactions between the flows in the river and floodplain as well as upstream and downstream domains, inevitable introducing model uncertainties that are difficult to quantified and controlled. Ideally, we can use a full 2D hydrodynamic model to simulate the entirely fluvial flooding process spreading from the river channels over to the floodplains. However, this approach has not been widely reported for large-scale application due to the prohibited computational cost of a 2D hydrodynamic model.In this work, we explore the possibility of calibrating a fully 2D hydrodynamic model, the High-Performance Integrated hydrodynamic Modelling System (HiPIMS), to reproduce high, medium and low flood conditions along the middle and lower reaches of Mekong River of 55 km, starting from the Kratie gauge in Cambodia to avoid tidal influence. The model is driven by inflow at Kratie and calibrated using the measurements of both water level and discharge available at 4 gauge stations (Can Tho, My Thuan, Chau Doc, Tan Chau). The Nash-Sutcliffe efficiency (NSE) is used to quantify prediction errors to support the model calibration process.. DOI: 10.5194/egusphere-egu23-16380.

Xia, X, Jarsve, K, Dijkstra, T, Liang, Q, Meng, X (2023) An integrated hydrodynamic model for flash flood and debris flow simulations, Climate change is forecasted to result in more frequent and intense storms, which in turn are likely to cause more flash floods and other hazardous processes in steep hilly and mountainous catchments. These flash floods are driven by complex and rapid overland flow responses to intense rainfall across these catchments. Where loose slope or valley-based deposits are available, flood water may mobilise these materials and transform into dynamic high-velocity, high-density debris flows that can pose significant threats to people, property, and infrastructure considerable distances away from the areas where these deposits are mobilised, exacerbating the already devastating situation caused by flooding. Hydro-dynamic models solving the full shallow water equations (SWEs) have shown great potential to reliably simulate the dynamics of overland flows and flash floods at catchment scales. However, simulating the transition from flash flood into debris flow is still technically challenging because of the difficulty of simulating erosion and deposition processes robustly. A reason is that the commonly used method for calculating erosion and deposition rate may suffer from singularity in the presence of vanishing velocity, which poses a major challenge for practical applications. In this work, we have developed a novel integrated hydrodynamic model for simulating flash floods and debris flows. Overland flows, change of debris concentration and bed elevation change are simulated simultaneously to model the transition between flash flood and debris flow. The overland flow processes are simulated by solving the full SWEs using a Godunov-type finite volume method. A novel method for calculating erosion and deposition rates is incorporated into the SWEs-based model to simulate the change of debris concentration and bed elevation change. The new method can maintain numerical stability and accuracy even in the presence of vanishing velocity. Therefore, the new model can effectively simulate the full process of rainfall-runoff-flooding turning into debris flows. Satisfactory simulation results have been obtained for both laboratory-scale and real-world test cases. The new model has the potential to be applied for flash flood/debris flow risk assessment and early warning.. DOI: 10.5194/egusphere-egu23-5869.

Jiang, J and Liang, Q (2023) Evaluating the feasibility and performance of Nature-based Solutions in Can Tho, Vietnam, Can Tho, the largest city in the Mekong River Delta, is experiencing rapid urbanisation that is causing many typical urbanisation-related issues, including the increasing flood risk. The flooding area has expanded from 30% to 50% of the total city area due to urbanisation and climate change. Due to the low topography and poor capacity of drainage systems, the city may sometimes remain inundated for up to three hours after the rain event has ended. It is essential to develop effective and also sustainable management strategies for the city to mitigate risk of flooding, especially surface water flooding caused by extreme heavy rainfall.Nature-based Solutions (NbS) are proposed and widely promoted globally as a sustainable strategy for managing flood risk and creating other benefits. For flood risk management, NbS can help a city reduce surface runoff and subsequently release pressure on drainage systems through infiltration and interception, thus mitigating flood risk. Numerical modelling has been widely used to support the design and assessment of NbS. Conventionally, NbS modelling is achieved by integrating a hydrological model with NbS simulation modules though a one-way coupling method. Such models are incapable of fully describing the rainfall-runoff-flooding processes dynamically interacting with NbS measures, and therefore can only provide limited information such as temporal and spatial variation of runoff removal rate for NbS design and evaluation.In this work, a 2D hydrodynamic flood model is adopted and further developed by coupling with compatible NbS simulation approaches to overcome the existing NbS restrictions. The new modelling framework is applied in Can Tho city to evaluate the feasibility and performance of different NbS against various evaluating metrics. The simulation results indicate that green roofs, rain gardens, and bio-retention cells can effectively reduce inundation area, flow rate, and runoff volume to protect localised infrastructure and key buildings under certain rainfall scenarios. However, dramatic change of flow velocities is observed near the key infrastructure and structures following the implementation of a rain garden, posing higher risk to pedestrians and vehicles. In-depth analysis of the hydrological performance of bio-retention cells further indicates that their designed capacity is not sufficiently exploited due to the inappropriate installation location, further demonstrating the advantage of the proposed model for better planning and design of NbS to achieve optimised performance.. DOI: 10.5194/egusphere-egu23-8422.

Qin, H and Liang, Q (2023) Development of a Coupled Human And Natural Systems (CHANS) Modelling Approach for flood risk assessment and management, Keywords: CHANS modelling, inundation model, agent-based model, risk assessment, flood risk managementFlooding is the most wide-spreading natural hazard threatening people’s lives and properties worldwide. In recent years, rapid urbanisation and more frequent weather extremes have led to increased risk of flooding, evidenced by the costly summer floods occurred in Europe and China in July 2021 and, most recently, the deadly event affecting most areas across Pakistan. Effective flood risk management is essentially needed to protect people’s lives and properties.Human activities may significantly influence flooding processes and the subsequent risk. However, few flood risk assessment and management practices directly consider human activities and social dynamics. This study aims to develop a Coupled Human And Natural System (CHANS) model to simulate the human-nature interacting processes during a flood event, which is subsequently applied to assess flood impact and evaluate the effectiveness of different disaster management options. The CHANS modelling framework is implemented by coupling the in-house High-Performance Integrated hydrodynamic Modelling System (HiPIMS) and an agent-based model built on the Flexible Large-scale Agent Modelling Environment of the Graphics Processing Unit (FLAMEGPU). The agent-based model simulates the complex behaviours of individuals and households reacting to the dynamic flooding process predicted by HiPIMS. The new CHANS modelling framework is tested by simulating the household damage caused by the 2015 Desmond flood in the 2500 km2 Eden Catchment in England, and the simulation results are consistent with the data released in government reports. The model is further applied to explore the role of early warning and sandbagging in mitigating flood impacts. The validated CHANS flood risk modelling and assessment framework is further applied to the City of Can Tho in the Vietnamese Mekong Delta to assess compound flood risk taking into account dynamic household vulnerability and explore different risk mitigation measures.. DOI: 10.5194/egusphere-egu23-9446.

Xiong, Y, Liang, Q, Wang, G (2023) Modelling the Dynamics of Multiple Floating Vehicles Driven by Transient Flood Waves, Many post-event field investigations of water-related hazards suggest that debris-enriched flow is much more destructive than water flow alone. However, the role of floating objects is rarely considered in the modelling or risk assessment approaches in practice. Existing modelling approaches are mostly focused on a single or limited pieces of debris. The interactions between the flow and multiple floating objects are not well explored and understood, and few modelling tools have been developed with the capability to simulate and predict these complex interactive processes.This work aims to present a two-way coupling numerical model for simulating the full-process dynamics of floating debris driven by flood waves, based on a finite volume shock-capturing hydrodynamic model solving the 2D shallow water equations and a 3D discrete element method (DEM) model. A multi-sphere method (MSM) is introduced to the DEM model to better capture the shape and size of floating objects. The coupled model estimates the hydrostatic and dynamic forces acting on debris directly using the high-resolution water depth and velocity predicted by the hydrodynamic model, efficiently and automatically capturing the interactive dynamics between transient water flow and floating debris. The model is able to simulate the full-process dynamics of floating debris, including vertical displacement, initiation, horizontal transport, depositing, interaction with and impact on structures.After being validated against experiment tests, the model is applied to reproduce a flash flood event in the coastal village of Boscastle, UK, in 2004. During the event, over 100 vehicles were carried by extreme water flow, which blocked and damaged a couple of downstream bridges, changed the pathway and extent of flooding, and finally moved with the flood water to the river mouth. The coupled model well predicts the flood dynamics, transport processes of floating vehicles, and their final locations. Further numerical experiments are caried out to discover and understand the process of floating debris blocking a bridge and the transport process and spatial distribution of different number of floating vehicles during the flood event. This model potentially provides a new and robust tool to more realistically assess flash flood risk and inform planning and design or urban buildings and infrastructure.. DOI: 10.5194/egusphere-egu23-5931.

Tong, X, Liang, Q, Zhao, J (2023) A high-performance integrated hydrodynamic modelling framework for large-scale multi-process simulation, Coastal cities are prone to the risks from multiple hazards, e.g, compound floods driven simultaneously by interactive fluvial, pluvial and coastal processes. The overland flow and flooding process of a compound event may further erode soil, move and carry along debris of different size, and pick up and transport pollutants to create secondary hazards to exacerbate the flood impact on assets and environment. When assessing and managing multi-hazard risk, it is essential to have a modelling tool that can depict in detail the flooding and associated processes. However, the traditional flood models seldomly consider and simulate the interactive rainfall-runoff-flooding and associated secondary hazard processes.This work aims to develop and test a high-performance portable modelling framework to simulate the flooding dynamics triggered by multiple drivers, as well as the relevant cascading processes, to support more comprehensive multi-hazard risk assessment and management. To simulate the complex flooding dynamics from multiple sources, the High-Performance Integrated hydrodynamic Modelling System (HiPIMS) developed at Loughborough University is adopted. HiPIMS solves the full 2D shallow water equations (SWEs) using a Godunov-type finite volume method, implemented with novel variable reconstruction and source term discretisation schemes to handle complex domain topography and wetting and drying to achieve stable and accurate prediction. HiPIMS is further implemented on multiple GPUs to achieve high-performance computing to support large-scale high-resolution simulations. In this work, a new version of more compatible and portable HiPIMS is developed by adopting PyTorch (https://pytorch.org) to distribute GPU threads and reconstruct input data and internal variables, making it easier for interfacing with GIS tools and data pre- and post-processing. To ensure the new HiPIMS is extendable to incorporate new modelling components to achieve multi-hazard and multi-process modelling, the main model code is encapsulated to provide interfaces with easy access to hydrodynamic information, i.e, water depths and velocities, for model coupling. Git (a distributed version control system for programmers to collaboratively develop source codes) is further employed to support long-term flexible model development and maintenance.The capability of the new HiPIMS is demonstrated and confirmed by application to 1) reproduce a surface water flood event driven by fluvial and pluvial processes across the 2500km2 Eden catchment in England; 2) initiation and propagating process of floating debris driven by highly transient flood waves; and 3) wash-off, transport and deposition of non-point-source pollutants driven by rainfall induced overland and flood flows over urban surfaces.. DOI: 10.5194/egusphere-egu23-15113.

Guan, M, Liang, Q, Hou, J (2021) Editorial: Smart Approaches to Predict Urban Flooding: Current Advances and Challenges. DOI: 10.3389/feart.2021.681751.

Liang, Q (2019) Editorial. DOI: 10.1680/jwama.2019.172.1.1.