Hydrological Simulation Study in Gansu Province of China Based on Flash Flood Analysis

被引:0
|
作者
Zhang, Bingyu [1 ]
Wei, Yingtang [2 ]
Liu, Ronghua [1 ]
Tian, Shunzhen [3 ]
Wei, Kai [2 ]
机构
[1] China Inst Water Resources & Hydropower Res, Beijing 100038, Peoples R China
[2] Gansu Ganlan Water Resources & Hydropower Survey &, Lanzhou 730030, Peoples R China
[3] Gansu Prov Water Conservancy Bur, Lanzhou 730030, Peoples R China
基金
中国国家自然科学基金;
关键词
Gansu Province Flash Flood Warning; forecasting platform; CNFF; spatiotemporally mixed runoff method; Geomorpho-Climatic Instantaneous Unit Hydrograph (GIUH); sensitivity analysis; Regionalized Sensitivity Analysis (RSA); applicability evaluation; UNCERTAINTIES; EUROPEEN; SYSTEM; MODEL; SHE;
D O I
10.3390/w16030488
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The calibration and validation of hydrological model simulation performance and model applicability evaluation in Gansu Province is the foundation of the application of the flash flood early warning and forecasting platform in Gansu Province. It is difficult to perform the simulation for Gansu Province due to the fact that it covers a wide range, from north to south, with multiple climate types and diverse landforms. The China Flash Flood Hydrological Model (CNFF) was implemented in this study. A total of 11 model clusters and 289 distributed hydrological models were divided based on hydrology, climate, and land-use factors, among others. A spatiotemporally mixed runoff method and the Event-Specific Geomorphological Instantaneous Unit Hydrograph (GIUH) were applied based on large-scale fast parallel computation. To improve model calibration and validation efficiency, the RSA method (Regionalized Sensitivity Analysis) was used for CNFF model parameter sensitivity analysis, which could reduce the number of model parameters that need to be adjusted during the calibration period. Based on the model sensitivity analysis results, the CNFF was established in Gansu Province to simulate flood events in eight representative watersheds. The average NSE, REQ, and ET were 0.76 and 0.73, 9.1% and 12.6%, and 1.2 h and 1.7 h, respectively, in the calibration and validation period. In general, the CNFF model shows a good performance in multiple temporal and spatial scales, thus providing a scientific basis for flash flood early warning and analysis in Gansu Province.
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页数:15
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