Preprints
https://doi.org/10.5194/hess-2021-382
https://doi.org/10.5194/hess-2021-382

  09 Aug 2021

09 Aug 2021

Review status: this preprint is currently under review for the journal HESS.

Regionalization of hydrological model parameters using gradient boosting machine

Zhihong Song1,2, Jun Xia1,2,3, Gangsheng Wang1,2, Dunxian She1,2, Chen Hu1,2, and Si Hong1,2 Zhihong Song et al.
  • 1State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, 430072, China
  • 2Hubei Key Laboratory of Water System Science for Sponge City Construction, Wuhan University, Wuhan, 430072, China
  • 3Key Laboratory of Water Cycle and Related Land Surface Processes, Chinese Academy of Sciences, Beijing, 10010, China

Abstract. 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 process. We calibrated six key model parameters grid-by-grid across China using a multivariable calibration strategy, which incorporates spatiotemporal runoff and evapotranspiration (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 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 national scale, though the improvement is not significant pertaining to watershed streamflow validation 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 by using watershed properties in ungauged regions.

Zhihong Song et al.

Status: open (until 21 Oct 2021)

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Zhihong Song et al.

Zhihong Song et al.

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Short summary
We performed a machine learning approach to regionalize parameters of a China-wide hydrological model by linking six model parameters with ten physical attributes (terrain and soil properties). The results show the superiority of machine-learning-based regionalization approach compared with the traditional linear regression method in ungauged regions. We also obtained the relative importance of attributes against model parameters.