Improving 2D hydraulic modelling in floodplain areas with ICESat-2 data: A case study in the Upstream Yellow River
文献类型：期刊论文
| 作者 | Frias, Monica Coppo1,4,5,7; Liu, Suxia3,4; Mo, Xingguo3,4; Druce, Daniel1; Yamazaki, Dai6; Musaeus, Aske Folkmann1,5,7; Nielsen, Karina2; Bauer-Gottwein, Peter5,7 |
| 刊名 | REMOTE SENSING OF ENVIRONMENT
 |
| 出版日期 | 2025-12-15 |
| 卷号 | 331页码:115008 |
| 关键词 | Flood inundation DEM correction Machine learning ANN Hydraulic modelling FABDEM ICESat-2 Remote sensing Sentinel-2 2D hydraulic model |
| ISSN号 | 0034-4257 |
| DOI | 10.1016/j.rse.2025.115008 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Reliable flood inundation modelling in complex river systems that are poorly instrumented is often limited by inaccuracies in open source DEMs, particularly near river channels and vegetated regions. This study proposes a methodology to correct and enhance resolution of satellite based DEMs in floodplain areas with ICESat-2 land elevation, Sentinel-2 MSI imagery, and a simple artificial neural network (ANN). FabDEM (30-m) is selected as the base DEM, and the ANN is trained to correct elevation errors at 10-m resolution using spectral bands from Sentinel-2 and ICESat-2 ATL03 elevation. The corrected ANN DEM reduces the mean squared error by 7 cm on average and up to 38 cm in the areas closer to the main river channel. MIKE 21 is used to simulate 2D flood extent maps for four different events, that consider in-situ discharge values at high, medium and low flow, comparing modelled flood extent with observed surface water extent (SWE) maps derived from Sentinel-2 at the selected dates. To ensure that improvements are attributed to DEM corrections rather than hydraulic parametrization, simulations are performed with different uniform values of the Gauckler-Strickler coefficient Ks, which are kept consistent across FabDEM and ANN DEM based scenarios. The critical success index (CSI), F1-score and bias are calculated for simulations with FabDEM and ANN DEM. Across all events, the ANN DEM improves flood simulation accuracy, increasing the Critical Success Index (CSI) and F1 score by up to 19 % and 13 %, respectively, and reducing bias by up to 25 %. This workflow demonstrates a scalable and efficient approach to improve hydraulic model inputs in data-scarce floodplain environments, offering valuable insights for flood risk assessment and water resource management in remote regions. |
| URL标识 | 查看原文 |
| WOS关键词 | LARGE-SCALE ; WATER ; RESOLUTION |
| WOS研究方向 | Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001568168100002 |
| 出版者 | ELSEVIER SCIENCE INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/216067]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 通讯作者 | Frias, Monica Coppo |
| 作者单位 | 1.DHI, DK-2970 Horsholm, Denmark; 2.Tech Univ Denmark, Dept Geodesy & Earth Observat, DK-2800 Lyngby, Denmark; 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China; 4.Univ Chinese Acad Sci, Sino Danish Coll, Beijing 100049, Peoples R China; 5.Univ Copenhagen, Dept Geosci & Nat Resource Management, DK-1958 Frederiksberg C, Denmark 6.Univ Tokyo, Inst Ind Sci, Tokyo 1538505, Japan; 7.Tech Univ Denmark, Dept Environm & Resource Engn, DK-2800 Lyngby, Denmark; |
| 推荐引用方式 GB/T 7714 | Frias, Monica Coppo,Liu, Suxia,Mo, Xingguo,et al. Improving 2D hydraulic modelling in floodplain areas with ICESat-2 data: A case study in the Upstream Yellow River[J]. REMOTE SENSING OF ENVIRONMENT,2025,331:115008. |
| APA | Frias, Monica Coppo.,Liu, Suxia.,Mo, Xingguo.,Druce, Daniel.,Yamazaki, Dai.,...&Bauer-Gottwein, Peter.(2025).Improving 2D hydraulic modelling in floodplain areas with ICESat-2 data: A case study in the Upstream Yellow River.REMOTE SENSING OF ENVIRONMENT,331,115008. |
| MLA | Frias, Monica Coppo,et al."Improving 2D hydraulic modelling in floodplain areas with ICESat-2 data: A case study in the Upstream Yellow River".REMOTE SENSING OF ENVIRONMENT 331(2025):115008. |
入库方式： OAI收割
来源：地理科学与资源研究所
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