{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T02:52:59Z","timestamp":1774925579414,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2243227"],"award-info":[{"award-number":["U2243227"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Watershed hydrological modeling methods are currently the predominant approach for flood forecasting. Digital elevation model (DEM) data, a critical input variable, significantly influence the accuracy of flood simulations, primarily due to their resolution. However, there is a paucity of research exploring the relationship between DEM resolution and flood simulation accuracy. This study aims to investigate this relationship by examining three watersheds of varying scales in southern Jiangxi Province, China. Utilizing the Liuxihe model, a new-generation physically based distributed hydrological model (PBDHM), we collected and collated data, including DEM, land use, soil type, and hourly flow and rainfall data from monitoring stations, covering 22 flood events over the last decade, to conduct model calibration and flood simulation. DEM data were processed into seven resolutions, ranging from 30 m to 500 m, to analyze the impact of DEM resolution on flood simulation accuracy. The results are as follows. (1) The Nash\u2013Sutcliffe efficiency coefficients for the entire set of flood events were above 0.75, demonstrating the Liuxihe model\u2019s strong applicability in this region. (2) The DEM resolution of the Anhe and Dutou watersheds lost an average of 7.9% and 0.8% accuracy when increasing from 30 m to 200 m, with further losses of 37.9% and 10.7% from 200 m to 300 m. Similarly, the Mazhou watershed showed an average of 8.4% accuracy loss from 30 m to 400 m and 20.4% from 400 m to 500 m. These results suggest a threshold where accuracy sharply declines as DEM resolution increases, and this threshold rises with watershed scale. (3) Parameter optimization in the Liuxihe model significantly enhanced flood simulation accuracy, effectively compensating for the reduction in accuracy caused by increased DEM resolution. (4) The optimal parameters for flood simulation varied with different DEM resolutions, with significant changes observed in riverbed slope and river roughness, which are highly sensitive to DEM resolution. (5) Changes in DEM resolution did not significantly impact surface flow production. However, the extraction of the water system and the reduction in slope were major factors contributing to the decline in flood simulation accuracy. Overall, this study elucidates that there is a threshold range of DEM resolution that balances data acquisition efficiency and computational speed while satisfying the basic requirements for flood simulation accuracy. This finding provides crucial decision-making support for selecting appropriate DEM resolutions in hydrological forecasting.<\/jats:p>","DOI":"10.3390\/rs16163105","type":"journal-article","created":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T11:14:41Z","timestamp":1724325281000},"page":"3105","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Study of the Effect of DEM Spatial Resolution on Flood Simulation in Distributed Hydrological Modeling"],"prefix":"10.3390","volume":"16","author":[{"given":"Hengkang","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]},{"given":"Yangbo","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"560","DOI":"10.1038\/s41586-020-2478-3","article-title":"Current European flood-rich period exceptional compared with past 500 years","volume":"583","author":"Kiss","year":"2020","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4382","DOI":"10.1002\/2016GL068070","article-title":"Dominant flood generating mechanisms across the United States","volume":"43","author":"Berghuijs","year":"2016","journal-title":"Geophys. Res. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"103568","DOI":"10.1016\/j.ijdrr.2023.103568","article-title":"Towards flood risk reduction: Commonalities and differences between urban flood resilience and risk based on a case study in the Pearl River Delta","volume":"86","author":"Zheng","year":"2023","journal-title":"Int. J. Disaster Risk Reduct."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e1517","DOI":"10.1002\/wat2.1517","article-title":"Operational and emerging capabilities for surface water flood forecasting","volume":"8","author":"Speight","year":"2021","journal-title":"WIREs Water"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1002\/wat2.1137","article-title":"Continental and global scale flood forecasting systems","volume":"3","author":"Emerton","year":"2016","journal-title":"WIREs Water"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"310","DOI":"10.1111\/geoj.12103","article-title":"Improving flood forecasts for better flood preparedness in the UK (and beyond)","volume":"180","author":"Stephens","year":"2014","journal-title":"Geogr. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2695","DOI":"10.1007\/s11269-014-0637-8","article-title":"Hydrological Modeling of Large river Basins: How Much is Enough?","volume":"28","author":"Johnston","year":"2014","journal-title":"Water Resour. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1023\/A:1003728419934","article-title":"Integrated modelling of hydrological processes and nutrient dynamics at the river basin scale","volume":"410","author":"Krysanova","year":"1999","journal-title":"Hydrobiologia"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.jhydrol.2016.03.026","article-title":"An overview of current applications, challenges, and future trends in distributed process-based models in hydrology","volume":"537","author":"Fatichi","year":"2016","journal-title":"J. Hydrol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1016\/j.jhydrol.2015.02.013","article-title":"Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications","volume":"523","author":"Song","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_11","unstructured":"Liou, E.Y.S., and James, L.D. Opset: Program for computerized selection of watershed parameter values for the stanford watershed model. 1971."},{"key":"ref_12","first-page":"1941","article-title":"Hydrological model comparison and assessment: Criteria from catchment scales and temporal resolution","volume":"61","author":"Gao","year":"2016","journal-title":"Hydrol. Sci. J."},{"key":"ref_13","first-page":"W01429","article-title":"Characterization of watershed model behavior across a hydroclimatic gradient","volume":"44","author":"Wagener","year":"2008","journal-title":"Water Resour. Res."},{"key":"ref_14","first-page":"746","article-title":"The improved Xinanjiang model","volume":"17","author":"Li","year":"2005","journal-title":"J. Hydrodyn."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1002\/hyp.3360050103","article-title":"Digital terrain modelling: A review of hydrological, geomorphological, and biological applications","volume":"5","author":"Moore","year":"1991","journal-title":"Hydrol. Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"103662","DOI":"10.1016\/j.advwatres.2020.103662","article-title":"A review of SWAT applications, performance and future needs for simulation of hydro-climatic extremes","volume":"143","author":"Tan","year":"2020","journal-title":"Adv. Water Resour."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.jhydrol.2012.06.057","article-title":"Assessing the effects of urbanization on annual runoff and flood events using an integrated hydrological modeling system for Qinhuai River basin, China","volume":"464\u2013465","author":"Du","year":"2012","journal-title":"J. Hydrol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e1673","DOI":"10.1002\/wat2.1673","article-title":"Investigating the effects of channelization in the Silala River: A review of the implementation of a coupled MIKE-11 and MIKE-SHE modeling system","volume":"11","author":"Lagos","year":"2024","journal-title":"WIREs Water"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"597","DOI":"10.1061\/(ASCE)WR.1943-5452.0000066","article-title":"Using a Distributed Hydrologic Model to Evaluate the Location of Urban Development and Flood Control Storage","volume":"136","author":"Fang","year":"2010","journal-title":"J. Water Resour. Plan. Manag.-ASCE"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"14415","DOI":"10.1029\/94JD00483","article-title":"A simple hydrologically based model of land surface water and energy fluxes for general circulation models","volume":"99","author":"Liang","year":"1994","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1061\/(ASCE)HE.1943-5584.0000286","article-title":"Liuxihe Model and Its Modeling to River Basin Flood","volume":"16","author":"Chen","year":"2011","journal-title":"J. Hydrol. Eng."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1016\/j.jhydrol.2015.06.008","article-title":"Flood hazard risk assessment model based on random forest","volume":"527","author":"Wang","year":"2015","journal-title":"J. Hydrol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1007\/s11069-008-9277-8","article-title":"Flood risk analyses\u2014How detailed do we need to be?","volume":"49","author":"Apel","year":"2009","journal-title":"Nat. Hazards"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1831","DOI":"10.1016\/j.advwatres.2007.02.005","article-title":"Evaluation of on-line DEMs for flood inundation modeling","volume":"30","author":"Sanders","year":"2007","journal-title":"Adv. Water Resour."},{"key":"ref_25","first-page":"26","article-title":"Effect of DEM data sources and resolutions on watershed flood simulations","volume":"42","author":"Li","year":"2023","journal-title":"J. Hydroelectr. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1714","DOI":"10.1002\/hyp.7306","article-title":"Resample or not?! Effects of resolution of DEMs in watershed modeling","volume":"23","author":"Dixon","year":"2009","journal-title":"Hydrol. Process."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Rocha, J., Duarte, A., Silva, M., Fabres, S., Vasques, J., Revilla-Romero, B., and Quintela, A. (2020). The Importance of High Resolution Digital Elevation Models for Improved Hydrological Simulations of a Mediterranean Forested Catchment. Remote Sens., 12.","DOI":"10.3390\/rs12203287"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"3159","DOI":"10.1007\/s11269-016-1338-2","article-title":"TOPMODEL for Streamflow Simulation of a Tropical Catchment Using Different Resolutions of ASTER DEM: Optimization Through Response Surface Methodology","volume":"30","author":"Suliman","year":"2016","journal-title":"Water Resour. Manag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1111\/j.1752-1688.2003.tb04420.x","article-title":"Water quality model output uncertainty as affected by spatial resolution of input data","volume":"39","author":"Cotter","year":"2003","journal-title":"J. Am. Water Resour. Assoc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.compenvurbsys.2009.11.002","article-title":"Effects of DEM sources on hydrologic applications","volume":"34","author":"Li","year":"2010","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1007\/s11269-007-9204-x","article-title":"Characterization and evaluation of elevation data uncertainty in water resources modeling with GIS","volume":"22","author":"Wu","year":"2008","journal-title":"Water Resour. Manag."},{"key":"ref_32","first-page":"624","article-title":"Effects of DEM source and resolution on the HEC-HMS hydrological simulation","volume":"26","author":"Gao","year":"2015","journal-title":"Adv. Water Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.jhydrol.2003.09.028","article-title":"Regionalisation of catchment model parameters","volume":"287","author":"Merz","year":"2004","journal-title":"J. Hydrol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0022-1694(02)00075-6","article-title":"A macro-scale and semi-distributed monthly water balance model to predict climate change impacts in China","volume":"268","author":"Guo","year":"2002","journal-title":"J. Hydrol."},{"key":"ref_35","first-page":"699","article-title":"Effects of DEM resolution on the TOPMODEL","volume":"19","author":"Sun","year":"2008","journal-title":"Adv. Water Sci."},{"key":"ref_36","first-page":"107","article-title":"Model I: Theory and Methods","volume":"49","author":"Chen","year":"2010","journal-title":"Acta Sci. Nat. Univ. Sunyatseni"},{"key":"ref_37","first-page":"105","article-title":"Liuxihe Model II: Parameter Deriving","volume":"49","author":"Chen","year":"2010","journal-title":"Acta Sci. Nat. Univ. Sunyatseni"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"04017039","DOI":"10.1061\/(ASCE)HE.1943-5584.0001569","article-title":"Evaluating the Uncertainties in the SWAT Model Outputs due to DEM Grid Size and Resampling Techniques in a Large Himalayan River Basin","volume":"22","author":"Kumar","year":"2017","journal-title":"J. Hydrol. Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3907","DOI":"10.5194\/essd-13-3907-2021","article-title":"The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019","volume":"13","author":"Yang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"375","DOI":"10.5194\/hess-20-375-2016","article-title":"Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization","volume":"20","author":"Chen","year":"2016","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"735","DOI":"10.5194\/hess-21-735-2017","article-title":"Large-watershed flood forecasting with high-resolution distributed hydrological model","volume":"21","author":"Chen","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.5194\/hess-21-1279-2017","article-title":"Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model","volume":"21","author":"Li","year":"2017","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.5194\/hess-23-1505-2019","article-title":"Predicting floods in a large karst river basin by coupling PERSIANN-CCS QPEs with a physically based distributed hydrological model","volume":"23","author":"Li","year":"2019","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"104990","DOI":"10.1016\/j.catena.2020.104990","article-title":"Elaborate simulations and forecasting of the effects of urbanization on karst flood events using the improved Karst-Liuxihe model","volume":"197","author":"Li","year":"2021","journal-title":"Catena"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Gu, Y., Chen, Y., Sun, H., and Liu, J. (2022). Remote Sensing-Supported Flood Forecasting of Urbanized Watersheds\u2014A Case Study in Southern China. Remote Sens., 14.","DOI":"10.3390\/rs14236129"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Li, J.Y., Chen, Y.B., Zhu, Y.Z., and Liu, J. (2023). Study of Flood Simulation in Small and Medium-Sized Basins Based on the Liuxihe Model. Sustainability, 15.","DOI":"10.3390\/su151411225"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zhu, Y.Z., Chen, Y.B., Zhao, Y.J., Zhou, F., and Xu, S.C. (2023). Application and Research of Liuxihe Model in the Simulation of Inflow Flood at Zaoshi Reservoir. Sustainability, 15.","DOI":"10.3390\/su15139857"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Xu, S.C., Chen, Y.B., Xing, L.X., and Li, C. (2021). Baipenzhu Reservoir Inflow Flood Forecasting Based on a Distributed Hydrological Model. Water, 13.","DOI":"10.3390\/w13030272"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2763","DOI":"10.1029\/1999WR900154","article-title":"A reformulation of Horton\u2019s laws for large river networks in terms of statistical self-similarity","volume":"35","author":"Peckham","year":"1999","journal-title":"Water Resour. Res."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"587","DOI":"10.1177\/0309133309360627","article-title":"Strahler\u2019s Physical geography, New York: Wiley (1951; 1960; 1969; 1975)","volume":"34","author":"Day","year":"2010","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2111","DOI":"10.1007\/s11269-016-1275-0","article-title":"Comparative Study of Three Updating Procedures for Real-Time Flood Forecasting","volume":"30","author":"Liu","year":"2016","journal-title":"Water Resour. Manag."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.2136\/sssaj1999.6351063x","article-title":"Relationship between the Hydraulic Conductivity Function and the Particle-Size Distribution","volume":"63","author":"Arya","year":"1999","journal-title":"Soil Sci. Soc. Am. J."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/S0022-1694(03)00181-1","article-title":"Hydrologic process simulation of a semiarid, endoreic catchment in Sahelian West Niger. 1. Model-aided data analysis and screening","volume":"279","author":"Peugeot","year":"2003","journal-title":"J. Hydrol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/S0022-1694(03)00182-3","article-title":"Hydrologic process simulation of a semiarid, endoreic catchment in Sahelian West Niger. 2. Model calibration and uncertainty characterization","volume":"279","author":"Cappelaere","year":"2003","journal-title":"J. Hydrol."},{"key":"ref_55","first-page":"1481","article-title":"Parameter optimization of WEP model and its application to the upstream of Han River","volume":"40","author":"Lei","year":"2009","journal-title":"J. Hydraul. Eng."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.chemolab.2015.08.020","article-title":"Particle swarm optimization (PSO). A tutorial","volume":"149","author":"Marini","year":"2015","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5208","DOI":"10.1016\/j.amc.2010.12.053","article-title":"Particle swarm optimization: Hybridization perspectives and experimental illustrations","volume":"217","author":"Thangaraj","year":"2011","journal-title":"Appl. Math. Comput."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1007\/s10462-015-9445-7","article-title":"Particle swarm optimization with crossover: A review and empirical analysis","volume":"45","author":"Engelbrecht","year":"2016","journal-title":"Artif. Intell. Rev."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1007\/s11831-022-09849-x","article-title":"25 Years of Particle Swarm Optimization: Flourishing Voyage of Two Decades","volume":"30","author":"Nayak","year":"2023","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.cnsns.2018.04.019","article-title":"A new collection of real world applications of fractional calculus in science and engineering","volume":"64","author":"Sun","year":"2018","journal-title":"Commun. Nonlinear Sci. Numer. Simul."},{"key":"ref_61","first-page":"101139","article-title":"Analysis of runoff generation driving factors based on hydrological model and interpretable machine learning method","volume":"42","author":"Wang","year":"2022","journal-title":"J. Hydrol.-Reg. Stud."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/16\/3105\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:41:39Z","timestamp":1760110899000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/16\/3105"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,22]]},"references-count":61,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["rs16163105"],"URL":"https:\/\/doi.org\/10.3390\/rs16163105","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,22]]}}}