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

doi:10.3390/w13020196

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所

	Improving Runoff Simulation and Forecasting with Segmenting Delay of Baseflow from Fast Surface Flow in Montane High-Vegetation-Covered Catchments
	Li, You 4,5; Wang, Genxu 1; Liu, Changjun 2; Lin, Shan 4,5; Guan, Minghong 4,5; Zhao, Xuantao 2,3
刊名	WATER
	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
DOI	10.3390/w13020196
通讯作者	Wang, Genxu(wanggx@scu.edu.cn)
产权排序	1
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
出版者	MDPI
WOS记录号	WOS:000611744000001
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
内容类型	期刊论文
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.Sichuan Univ, Coll Water Resource & Hydropower, State Key Lab Hydraul & Mt River Engn, Chengdu 610065, Peoples R China 2.China Inst Water Resources & Hydropower Res, Res Ctr Flood & Drought Disaster Reduct, Beijing 100038, Peoples R China 3.North China Univ Water Resources & Elect Power, Sch Water Conservancy, Zhengzhou 450045, Peoples R China 4.Univ Chinese Acad Sci, Beijing 100049, 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.