干旱区生态环境知识资源中心(Arid): Regionalization of hydrological model parameters using gradient boosting machine

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DOI	10.5194/hess-26-505-2022
	Regionalization of hydrological model parameters using gradient boosting machine
	Song, Zhihong; Xia, Jun; Wang, Gangsheng; She, Dunxian; Hu, Chen; Hong, Si
通讯作者	Xia, J ; Wang, GS
来源期刊	HYDROLOGY AND EARTH SYSTEM SCIENCES
ISSN	1027-5606
EISSN	1607-7938
出版年	2022
卷号	26 期号:2 页码:505-524
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.
类型	Article
语种	英语
开放获取类型	gold, Green Submitted
收录类别	SCI-E
WOS记录号	WOS:000751424000001
WOS关键词	UNGAUGED CATCHMENTS ; VEGETATION DYNAMICS ; CLIMATE-CHANGE ; LARGE-SCALE ; RESPONSE CHARACTERISTICS ; CONTINUOUS STREAMFLOW ; GLOBAL OPTIMIZATION ; FILTERING METHOD ; WATER-BALANCE ; RIVER-BASIN
WOS类目	Geosciences, Multidisciplinary ; Water Resources
WOS研究方向	Geology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376495
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],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.