Abstract
Abstract. 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.
Highlights
Hydrological modeling can provide a quantitative extrapolation or prediction of runoff and water balance (Beven, 2001; He et al, 2011), which serves as the basis for water management for human livelihood, agriculture, industry, and the environment (Hobeichi et al, 2019; Montanari et al, 2013; Parajka et al, 2013b; Zhang et al, 2020)
The consideration of vegetation dynamics by the Penman– Monteith–Leuning (PML) equation in Distributed Time Variant Gain Model (DTVGM)-PML would improve the mechanistic understanding of the hydrological response under vegetation greening, which is lacking in DTVGM
We show that the gradient boosting machine (GBM) model is superior to the multiple linear regression (MLR) in predicting model parameters as a function of topographic and edaphic characteristics due to its significantly lower biases and higher spatial agreement for almost all parameters in four distinct climatic zones
Summary
Hydrological modeling can provide a quantitative extrapolation or prediction of runoff and water balance (Beven, 2001; He et al, 2011), which serves as the basis for water management for human livelihood, agriculture, industry, and the environment (Hobeichi et al, 2019; Montanari et al, 2013; Parajka et al, 2013b; Zhang et al, 2020). Hydrological models often require streamflow and/or other observations to calibrate parameters (Beck et al, 2020). It is difficult to parameterize a hydrological model at large scales (e.g., from national to global) or remote regions due to the sparse, or the lack of, observing stations. Under such circumstances, attempts have been made to use reanalysis datasets, not the observations, for model calibration and validation (Bai et al, 2018b; Dembélé et al, 2020; Huang et al, 2020; Immerzeel and Droogers, 2008; Zhang et al, 2020).
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