Knowledge Resource Center for Ecological Environment in Arid Area
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 (corresponding author),Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China. ; Xia, J ; Wang, GS (corresponding author),Wuhan Univ, Hubei Key Lab Water Syst Sci Sponge City Construc, Wuhan 430072, Peoples R China. ; Xia, J (corresponding author),Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Beijing 10010, Peoples R China. ; Wang, GS (corresponding author),Wuhan Univ, Inst Water Carbon Cycles & Carbon Neutral, Wuhan 430072, Peoples R China. |
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 |
作者单位 | [Song, Zhihong; Xia, Jun; Wang, Gangsheng; She, Dunxian; Hu, Chen; Hong, Si] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China; [Song, Zhihong; Xia, Jun; Wang, Gangsheng; She, Dunxian; Hu, Chen; Hong, Si] Wuhan Univ, Hubei Key Lab Water Syst Sci Sponge City Construc, Wuhan 430072, Peoples R China; [Xia, Jun] Chinese Acad Sci, Key Lab Water Cycle & Related Land Surface Proc, Beijing 10010, Peoples R China; [Wang, Gangsheng] Wuhan Univ, Inst Water Carbon Cycles & Carbon Neutral, 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],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. |
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