Regionalization of hydrological model parameters using gradient boosting machine

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 regiona...

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Vydané v:	Hydrology and earth system sciences Ročník 26; číslo 2; s. 505 - 524
Hlavní autori:	Song, Zhihong, Xia, Jun, Wang, Gangsheng, She, Dunxian, Hu, Chen, Hong, Si
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Katlenburg-Lindau Copernicus GmbH 31.01.2022 Copernicus Publications
Predmet:	Analysis Arid regions Arid zones Calibration Climatic zones Datasets Elevation Evapotranspiration Evapotranspiration processes Heterogeneity Hydrologic models Hydrology Learning algorithms Machine learning Mathematical models Modelling Moisture content Neural networks Parameters Performance evaluation Properties Regression analysis Runoff Saturated soils Soil Soil moisture Soil moisture content Stream discharge Stream flow Variables Water content Watersheds China
ISSN:	1607-7938, 1027-5606, 1607-7938
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Shrnutí:	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∘; 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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1607-7938 1027-5606 1607-7938
DOI:	10.5194/hess-26-505-2022