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Improving Runoff Simulation and Forecasting with Segmenting Delay of Baseflow from Fast Surface Flow in Montane High-Vegetation-Covered Catchments

文献类型：期刊论文

作者Li, You1,5; Wang, Genxu4; Liu, Changjun3; Lin, Shan1,5; Guan, Minghong1,5; Zhao, Xuantao2,3
刊名WATER
出版日期2021
卷号13期号:2页码:196
关键词SVSMRG-SBS model LSTM surface flow baseflow semi-distributed hydrological model montane catchment runoff simulation flood simulation
DOI10.3390/w13020196
产权排序1
通讯作者Wang, Genxu(wanggx@scu.edu.cn)
文献子类Article
英文摘要Due to the complicated terrain conditions in montane catchments, runoff formation is fast and complicated, making accurate simulation and forecasting a significant hydrological challenge. In this study, the spatiotemporal variable source mixed runoff generation module (SVSMRG) was integrated with the long short-term memory (LSTM) method, to develop a semi-distributed model (SVSMRG)-based surface flow and baseflow segmentation (SVSMRG-SBS). Herein, the baseflow was treated as a black box and forecasted using LSTM, while the surface flow was simulated using the SVSMRG module based on hydrological response units (HRUs) constructed using eco-geomorphological units. In the case study, four typical montane catchments with different climatic conditions and high vegetation coverage, located in the topographically varying mountains of the eastern Tibetan Plateau, were selected for runoff and flood process simulations using the proposed SVSMRG-SBS model. The results showed that this model had good performance in hourly runoff and flood process simulations for montane catchments. Regarding runoff simulations, the Nash-Sutcliffe efficiency coefficient (NSE) and correlation coefficient (R2) reached 0.8241 and 0.9097, respectively. Meanwhile, for the flood simulations, the NSE ranged from 0.5923 to 0.7467, and R2 ranged from 0.6669 to 0.8092. For the 1-, 3-, and 5-h baseflow forecasting with the LSTM method, it was found that model performances declined when simulating the runoff processes, wherein the NSE and R2 between the measured and modeled runoff decreased from 0.8216 to 0.8087 and from 0.9095 to 0.8871, respectively. Similar results were found in the flood simulations, the NSE and R2 values declined from 0.7414-0.5885 to 0.7429-0.5716 and from 0.8042-0.6547 to 0.7936-0.6067, respectively. This means that this new model achieved perfect performance in montane catchment runoff and flood simulation and forecasting with 1-, 3-, 5-h steps. Therefore, as it considers vegetation regulation, the SVSMRG-SBS model is expected to improve runoff and flood simulation accuracy in montane high-vegetation-covered catchments.
电子版国际标准刊号2073-4441
资助项目National Natural Science Foundation of China[41790431] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDA23090201] ; Spatiotemporal Variable Source Mixed Runoff Generation Model and Mechanism of Innovation Team Project[JZ0145B2017]
WOS研究方向Environmental Sciences & Ecology ; Water Resources
语种英语
WOS记录号WOS:000611744000001
出版者MDPI
资助机构National Natural Science Foundation of China ; Strategic Priority Research Program of the Chinese Academy of Sciences ; Spatiotemporal Variable Source Mixed Runoff Generation Model and Mechanism of Innovation Team Project
源URL[http://ir.imde.ac.cn/handle/131551/55840]  
专题中国科学院水利部成都山地灾害与环境研究所
通讯作者Wang, Genxu
作者单位1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
2.North China Univ Water Resources & Elect Power, Sch Water Conservancy, Zhengzhou 450045, Peoples R China
3.China Inst Water Resources & Hydropower Res, Res Ctr Flood & Drought Disaster Reduct, Beijing 100038, Peoples R China
4.Sichuan Univ, Coll Water Resource & Hydropower, State Key Lab Hydraul & Mt River Engn, Chengdu 610065, Peoples R China
5.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China
推荐引用方式
GB/T 7714		 Li, You,Wang, Genxu,Liu, Changjun,et al. Improving Runoff Simulation and Forecasting with Segmenting Delay of Baseflow from Fast Surface Flow in Montane High-Vegetation-Covered Catchments[J]. WATER,2021,13(2):196.
APA Li, You,Wang, Genxu,Liu, Changjun,Lin, Shan,Guan, Minghong,&Zhao, Xuantao.(2021).Improving Runoff Simulation and Forecasting with Segmenting Delay of Baseflow from Fast Surface Flow in Montane High-Vegetation-Covered Catchments.WATER,13(2),196.
MLA Li, You,et al."Improving Runoff Simulation and Forecasting with Segmenting Delay of Baseflow from Fast Surface Flow in Montane High-Vegetation-Covered Catchments".WATER 13.2(2021):196.

入库方式： OAI收割

来源：成都山地灾害与环境研究所

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