干旱区生态环境知识资源中心(Arid): Multi-model driven by diverse precipitation datasets increases confidence in identifying dominant factors for runoff change in a subbasin of the Qaidam Basin of China

Arid

DOI	10.1016/j.scitotenv.2021.149831
	Multi-model driven by diverse precipitation datasets increases confidence in identifying dominant factors for runoff change in a subbasin of the Qaidam Basin of China
	Lv, Aifeng; Qi, Shanshan; Wang, Gangsheng
通讯作者	Lv, AF (corresponding author), Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China. ; Wang, GS (corresponding author), Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China.
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
EISSN	1879-1026
出版年	2022
卷号	802
英文摘要	Quantifying the climatic and anthropogenic effects on hydrological processes has received considerable atten-tion. However, diverse conclusions could be drawn when different models and forcing datasets are used. This is particularly uncertain and challenging in poorly gauged arid regions. Here we aim to tackle this issue in the poorly gauged Xiangride River Basin within the Qaidam Basin, one of the three prominent inland basins in China. We applied two distinct models (Budyko Mezentsev-Choudhurdy-Yang and process-based SWAT) to a poorly-gauged inland basin in West China. The model simulations were driven by four precipitation products in-cluding Tropical Rainfall Measuring Mission (TRMM) 3B42 V7, Global Precipitation Measurement (GPM) IMERG V6, Multi-Source Weighted-Ensemble Precipitation (MSWEP) and China Meteorological Assimilation Driving Datasets (CMADS). Our results indicate that MSWEP performed best (NSE = 0.64 vs. 0.36-0.59 for other datasets) in the baseline period (2009-2012), whereas CMADS was more accurate during the impacted period (2013-2016); CMADS and GPM might underestimate the precipitation in the baseline and impacted period, re-spectively. Hydrological processes during the impacted period are presumed to be influenced by climate varia-tion and/or human activities, compared to the relatively natural status in the baseline period. We conclude that runoff decline between the two periods was mainly affected by human activities (-66 to 94%), whereas the contribution of climate variation was more likely positive. A literature survey reveals that major anthropo-genic effects in the study area includes reservoir, road construction and cropland expansion that could lead to runoff decrease. We recommend the use of process-based model (e.g., SWAT) in studies like this, as process- based models driven by high-quality remote-sensed or reanalysis climate datasets, better represents the spatiotemporal hydrological change under altered conditions, whereas the steady-state assumption of soil water for the Budyko model may not be fully satisfied during a short period. (c) 2021 Published by Elsevier B.V.
英文关键词	Inland basin Climatic variation Human activities Precipitation products Predictions in ungauged basins (PUBs) SWAT
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:000701773100005
WOS关键词	CLIMATE-CHANGE IMPACTS ; HAIHE RIVER-BASIN ; HYDROLOGICAL EVALUATION ; BUDYKO HYPOTHESIS ; WATER-RESOURCES ; STREAMFLOW ; SWAT ; PRODUCTS ; VARIABILITY ; CATCHMENTS
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
来源机构	中国科学院地理科学与资源研究所
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/364608
作者单位	[Lv, Aifeng; Qi, Shanshan] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China; [Lv, Aifeng; Qi, Shanshan] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Wang, Gangsheng] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China
推荐引用方式 GB/T 7714	Lv, Aifeng,Qi, Shanshan,Wang, Gangsheng. Multi-model driven by diverse precipitation datasets increases confidence in identifying dominant factors for runoff change in a subbasin of the Qaidam Basin of China[J]. 中国科学院地理科学与资源研究所,2022,802.
APA	Lv, Aifeng,Qi, Shanshan,&Wang, Gangsheng.(2022).Multi-model driven by diverse precipitation datasets increases confidence in identifying dominant factors for runoff change in a subbasin of the Qaidam Basin of China.SCIENCE OF THE TOTAL ENVIRONMENT,802.
MLA	Lv, Aifeng,et al."Multi-model driven by diverse precipitation datasets increases confidence in identifying dominant factors for runoff change in a subbasin of the Qaidam Basin of China".SCIENCE OF THE TOTAL ENVIRONMENT 802(2022).