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Large-scale baseflow index prediction using hydrological modelling, linear and multilevel regression approaches

文献类型：期刊论文

作者Zhang, Junlong6; Zhang, Yongqiang7; Song, Jinxi8,9; Cheng, Lei1; Paul, Pranesh Kumar7; Gan, Rong2; Shi, Xiaogang4; Luo, Zhongkui3; Zhao, Panpan5
刊名JOURNAL OF HYDROLOGY
出版日期2020-06-01
卷号585页码:14
关键词Baseflow separation BFI Multilevel regression Hydrological models Linear regression
ISSN号0022-1694
DOI10.1016/j.jhydrol.2020.124780
通讯作者Zhang, Yongqiang(zhangyq@igsnrr.ac.cn) ; Song, Jinxi(jinxisong@nwu.edu.cn)
英文摘要Baseflow is critical for water balance budget, water resources management, and environmental evaluation. Prediction of baseflow index (BFI), the ratio of baseflow to total streamflow, has a great significance in unravelling the baseflow characteristics for large scale trajectory. Therefore, this study compares BFI predictive performance derived from a new multilevel regression approach along with two other commonly used approaches: hydrological modelling (SIMHYD, a simplified version of the HYDROLOG model, and Xinanjiang model), and linear regression (traditional linear regression, and alternative traditional regression considers the second-order interaction). The multilevel regression approach does not only group the catchments into the four climate zones (arid, tropics, equiseasonal and winter rainfall), but also considers inter-catchment and interclimate zone variances. Likewise, calibration and two regionalisation techniques namely spatial proximity and integrated similarity are used to obtain the BFI from hydrological modelling approach. Correspondingly, the traditional linear regression technique estimates BFI establishing linear regressions between catchment attributes and four climate zones. Then, all the three approaches are evaluated against combined average estimation from four well-parameterised baseflow separation methods (Lyne-Hollick (LH), United Kingdom Institute of Hydrology (UKIH), Chapman-Maxwell (CM) and Eckhardt (ECK)) at 596 catchments across Australia for 1980-2012. The findings show that the multilevel regression has greatly improved the performance of BFI prediction in comparison to other methods. In particular, the two calibrated and regionalised hydrological models perform worst in predicting BFI with a Nash-Sutcliffe Efficiency (NSE) of - 8.44 and - 2.58 along with an absolute percent bias (PBIAS) of 81% and 146% (overestimation of baseflow), respectively. However, the traditional linear regression remains in intermediate position with the NSE of 0.57 and bias of 25. In addition, alternative traditional regression also shows very close proximity. In contrast, the multilevel regression approach shows the best performance with the NSE of 0.75 and bias of 19%. The study also demonstrates that the multilevel regression approach can improve BFI prediction, and shows potential for being used in the prediction of other hydrological signatures in large-scale.
WOS关键词FLOW DURATION CURVES ; RAINFALL-RUNOFF MODEL ; COLORADO RIVER-BASIN ; SEPARATION METHODS ; RECESSION ANALYSIS ; SPATIAL INTERPOLATION ; CATCHMENT PROPERTIES ; XINANJIANG MODEL ; SMALL WATERSHEDS ; UNITED-STATES
资助项目CAS Pioneer Hundred Talents Program and Supporting Fund of the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences[YJRCPT2019-101] ; National Natural Science Foundation of China, China[51379175] ; National Natural Science Foundation of China, China[51679200] ; Chinese Scholarship Council
WOS研究方向Engineering ; Geology ; Water Resources
语种英语
WOS记录号WOS:000544230000042
出版者ELSEVIER
资助机构CAS Pioneer Hundred Talents Program and Supporting Fund of the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences ; National Natural Science Foundation of China, China ; Chinese Scholarship Council
源URL[http://ir.igsnrr.ac.cn/handle/311030/162442]  
专题中国科学院地理科学与资源研究所
通讯作者Zhang, Yongqiang; Song, Jinxi
作者单位1.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China
2.Univ Technol Sydney, Sch Life Sci, Sydney, NSW 2007, Australia
3.Zhejiang Univ, Coll Environm & Resource Sci, Hangzhou 310058, Peoples R China
4.Univ Glasgow, Sch Interdisciplinary Studies, Dumfries DG1 4ZL, Scotland
5.North China Univ Water Resource & Elect Power, Inst Water Conservancy, Zhengzhou 450045, Peoples R China
6.Shandong Normal Univ, Coll Geog & Environm, Jinan 250358, Peoples R China
7.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China
8.Northwest Univ, Coll Urban & Environm Sci, Shaanxi Key Lab Earth Surface Syst & Environm Car, Xian 710127, Peoples R China
9.Chinese Acad Sci, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Shaanxi, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Junlong,Zhang, Yongqiang,Song, Jinxi,et al. Large-scale baseflow index prediction using hydrological modelling, linear and multilevel regression approaches[J]. JOURNAL OF HYDROLOGY,2020,585:14.
APA Zhang, Junlong.,Zhang, Yongqiang.,Song, Jinxi.,Cheng, Lei.,Paul, Pranesh Kumar.,...&Zhao, Panpan.(2020).Large-scale baseflow index prediction using hydrological modelling, linear and multilevel regression approaches.JOURNAL OF HYDROLOGY,585,14.
MLA Zhang, Junlong,et al."Large-scale baseflow index prediction using hydrological modelling, linear and multilevel regression approaches".JOURNAL OF HYDROLOGY 585(2020):14.

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