干旱区生态环境知识资源中心(Arid): Large-scale baseflow index prediction using hydrological modelling, linear and multilevel regression approaches

Arid

DOI	10.1016/j.jhydrol.2020.124780
	Large-scale baseflow index prediction using hydrological modelling, linear and multilevel regression approaches
	Zhang, Junlong; Zhang, Yongqiang; Song, Jinxi; Cheng, Lei; Paul, Pranesh Kumar; Gan, Rong; Shi, Xiaogang; Luo, Zhongkui; Zhao, Panpan
通讯作者	Zhang, YQ ; Song, JX
来源期刊	JOURNAL OF HYDROLOGY
ISSN	0022-1694
EISSN	1879-2707
出版年	2020
卷号	585
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.
英文关键词	Baseflow separation BFI Multilevel regression Hydrological models Linear regression
类型	Article
语种	英语
开放获取类型	Green Accepted
收录类别	SCI-E
WOS记录号	WOS:000544230000042
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
WOS类目	Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Engineering ; Geology ; Water Resources
来源机构	中国科学院地理科学与资源研究所 ; 西北农林科技大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/324870
作者单位	[Zhang, Junlong] Shandong Normal Univ, Coll Geog & Environm, Jinan 250358, Peoples R China; [Zhang, Yongqiang; Paul, Pranesh Kumar] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China; [Song, Jinxi] Northwest Univ, Coll Urban & Environm Sci, Shaanxi Key Lab Earth Surface Syst & Environm Car, Xian 710127, Peoples R China; [Song, Jinxi] Chinese Acad Sci, Inst Soil & Water Conservat, State Key Lab Soil Eros & Dryland Farming Loess P, Yangling 712100, Shaanxi, Peoples R China; [Cheng, Lei] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China; [Gan, Rong] Univ Technol Sydney, Sch Life Sci, Sydney, NSW 2007, Australia; [Shi, Xiaogang] Univ Glasgow, Sch Interdisciplinary Studies, Dumfries DG1 4ZL, Scotland; [Luo, Zhongkui] Zhejiang Univ, Coll Environm & Resource Sci, Hangzhou 310058, Peoples R China; [Zhao, Panpan] North China Univ Water Resource & Elect Power, Inst Water Conservancy, Zhengzh...
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]. 中国科学院地理科学与资源研究所, 西北农林科技大学,2020,585.
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.
MLA	Zhang, Junlong,et al."Large-scale baseflow index prediction using hydrological modelling, linear and multilevel regression approaches".JOURNAL OF HYDROLOGY 585(2020).