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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, Aifeng1,2; Qi, Shanshan1,2; Wang, Gangsheng3
2022
Source PublicationSCIENCE OF THE TOTAL ENVIRONMENT
ISSN0048-9697
Volume802Pages:12
Corresponding AuthorLv, Aifeng(lvaf@163.com) ; Wang, Gangsheng(wang.gangsheng@gmail.com)
AbstractQuantifying 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.
KeywordInland basin Climatic variation Human activities Precipitation products Predictions in ungauged basins (PUBs) SWAT
DOI10.1016/j.scitotenv.2021.149831
WOS KeywordCLIMATE-CHANGE IMPACTS ; HAIHE RIVER-BASIN ; HYDROLOGICAL EVALUATION ; BUDYKO HYPOTHESIS ; WATER-RESOURCES ; STREAMFLOW ; SWAT ; PRODUCTS ; VARIABILITY ; CATCHMENTS
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China (NSFC)[41671026] ; Important Science & Technology Specific Projects of Qinghai Province[2019-SF-A4-1] ; National Natural Science Foundation of Qinghai Province[2019-ZJ-7020] ; Excellent Young Scientists Fund of NSFC
Funding OrganizationNational Natural Science Foundation of China (NSFC) ; Important Science & Technology Specific Projects of Qinghai Province ; National Natural Science Foundation of Qinghai Province ; Excellent Young Scientists Fund of NSFC
WOS Research AreaEnvironmental Sciences & Ecology
WOS SubjectEnvironmental Sciences
WOS IDWOS:000701773100005
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/166001
Collection中国科学院地理科学与资源研究所
Corresponding AuthorLv, Aifeng; Wang, Gangsheng
Affiliation1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China
Recommended Citation
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]. SCIENCE OF THE TOTAL ENVIRONMENT,2022,802:12.
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,802,12.
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):12.
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