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Regionalization of hydrological model parameters using gradient boosting machine

文献类型：期刊论文

作者Song, Zhihong2,4; Xia, Jun1,2,4; Wang, Gangsheng2,3,4; She, Dunxian2,4; Hu, Chen2,4; Hong, Si2,4
刊名HYDROLOGY AND EARTH SYSTEM SCIENCES
出版日期2022-01-31
卷号26期号:2页码:505-524
ISSN号1027-5606
DOI10.5194/hess-26-505-2022
通讯作者Xia, Jun(xiajun666@whu.edu.cn) ; Wang, Gangsheng(wanggs@whu.edu.cn)
英文摘要The regionalization of hydrological model parameters is key to hydrological predictions in ungauged basins. The commonly used multiple linear regression (MLR) method may not be applicable in complex and nonlinear relationships between model parameters and watershed properties. Moreover, most regionalization methods assume lumped parameters for each catchment without considering within-catchment heterogeneity. Here we incorporated the Penman- Monteith-Leuning (PML) equation into the Distributed Time Variant Gain Model (DTVGM) to improve the mechanistic representation of the evapotranspiration (ET) process. We calibrated six key model parameters, grid by grid across China, using a multivariable calibration strategy which incorporates spatiotemporal runoff and ET datasets (0.25 degrees; monthly) as reference. In addition, we used the gradient boosting machine (GBM), a machine learning technique, to portray the dependence of model parameters on soil and terrain attributes in four distinct climatic zones across China. We show that the modified DTVGM could reasonably estimate the runoff and ET over China using the calibrated parameters but performed better in humid rather than arid regions for the validation period. The regionalized parameters by the GBM method exhibited better spatial coherence relative to the calibrated grid-by-grid parameters. In addition, GBM outperformed the stepwise MLR method in both parameter regionalization and gridded runoff simulations at a national scale, though the improvement pertaining to watershed streamflow validation is not significant due to most of the watersheds being located in humid regions. We also revealed that the slope, saturated soil moisture content, and elevation are the most important explanatory variables to inform model parameters based on the GBM approach. The machine-learning-based regionalization approach provides an effective alternative to deriving hydrological model parameters from watershed properties, particularly in ungauged regions.
WOS关键词UNGAUGED CATCHMENTS ; VEGETATION DYNAMICS ; CLIMATE-CHANGE ; LARGE-SCALE ; RESPONSE CHARACTERISTICS ; CONTINUOUS STREAMFLOW ; GLOBAL OPTIMIZATION ; FILTERING METHOD ; WATER-BALANCE ; RIVER-BASIN
资助项目National Natural Science Foundation of China[41890823] ; National Natural Science Foundation of China[418777159] ; National Key Research and Development Program of China[2017YFA0603702] ; Excellent Young Scientists Fund
WOS研究方向Geology ; Water Resources
语种英语
WOS记录号WOS:000751424000001
出版者COPERNICUS GESELLSCHAFT MBH
资助机构National Natural Science Foundation of China ; National Key Research and Development Program of China ; Excellent Young Scientists Fund
源URL[http://ir.igsnrr.ac.cn/handle/311030/170306]  
专题中国科学院地理科学与资源研究所
通讯作者Xia, Jun; Wang, Gangsheng
作者单位1.Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Beijing 10010, Peoples R China
2.Wuhan Univ, Hubei Key Lab Water Syst Sci Sponge City Construc, Wuhan 430072, Peoples R China
3.Wuhan Univ, Inst Water Carbon Cycles & Carbon Neutral, Wuhan 430072, Peoples R China
4.Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China
推荐引用方式
GB/T 7714		 Song, Zhihong,Xia, Jun,Wang, Gangsheng,et al. Regionalization of hydrological model parameters using gradient boosting machine[J]. HYDROLOGY AND EARTH SYSTEM SCIENCES,2022,26(2):505-524.
APA Song, Zhihong,Xia, Jun,Wang, Gangsheng,She, Dunxian,Hu, Chen,&Hong, Si.(2022).Regionalization of hydrological model parameters using gradient boosting machine.HYDROLOGY AND EARTH SYSTEM SCIENCES,26(2),505-524.
MLA Song, Zhihong,et al."Regionalization of hydrological model parameters using gradient boosting machine".HYDROLOGY AND EARTH SYSTEM SCIENCES 26.2(2022):505-524.

入库方式： OAI收割

来源：地理科学与资源研究所

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