Analysis and comparison of the flood simulations with the routing model CaMa-Flood at different spatial resolutions in the CONUS

IF 4.8 2区 环境科学与生态学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Environmental Modelling & Software Pub Date : 2025-02-01 DOI:10.1016/j.envsoft.2024.106305
Ruijie Jiang , Hui Lu , Kun Yang , Hiroshi Cho , Dai Yamazaki
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Abstract

Accurate flood modelling is crucial for disaster prevention. Fine-resolution global routing models can offer more detailed flood information, but balancing model efficiency with accuracy remains challenging. This study examines the conditions under which a fine-resolution model outperforms a coarser one, using the CaMa-Flood model at 0.05°, 0.083°, 0.1°, and 0.25° resolutions across the contiguous United States. The results indicate finer resolution does not improve the simulation of flood timing, but better simulates the daily river discharge and flood peak flow due to better representation of the river network in small rivers. Notably, the improvement in daily discharge simulation is greater than that in peak flow. Nevertheless, uncertainties in channel parameters mean that a more detailed river network does not necessarily yield better flood simulations. For rivers with upstream drainage areas greater than 500 km2, a 0.25° model is sufficient if high-precision channel parameters are unavailable.
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CONUS中不同空间分辨率下CaMa-Flood路由模型的洪水模拟分析与比较
准确的洪水模型对防灾至关重要。精细分辨率的全局路由模型可以提供更详细的洪水信息,但平衡模型的效率和准确性仍然是一个挑战。本研究考察了精细分辨率模型优于粗分辨率模型的条件,在美国各地使用了0.05°、0.083°、0.1°和0.25°分辨率的CaMa-Flood模型。结果表明,更精细的分辨率并没有改善洪水时间的模拟,但由于更好地代表了小河流的河网,因此可以更好地模拟河流的日流量和洪峰流量。值得注意的是,日流量模拟的改进大于峰值流量模拟。然而,河道参数的不确定性意味着更详细的河网不一定能产生更好的洪水模拟。对于上游流域面积大于500 km2的河流,如果无法获得高精度的河道参数,0.25°模型就足够了。
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来源期刊
Environmental Modelling & Software
Environmental Modelling & Software 工程技术-工程:环境
CiteScore
9.30
自引率
8.20%
发文量
241
审稿时长
60 days
期刊介绍: Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.
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