Abstract
This study investigated the potential of random forest (RF) algorithms for regionalizing the parameters of an hourly hydrological model. The relationships between model parameters and climate/landscape catchment descriptors were multidimensional and exhibited nonlinear features. In this case, machine-learning tools offered the option of efficiently handling such relationships using a large sample of data. The performance of the regionalized model using RF was assessed in comparison with local calibration and two benchmark regionalization approaches. Two catchment sets were considered: (1) A target pseudo-ungauged catchment set was composed of 120 urban ungauged catchments and (2) 2105 gauged American and French catchments were used for constructing the RF. By using pseudo-ungauged urban catchments, we aimed at assessing the potential of the RF to detect the specificities of the urban catchments. Results showed that RF-regionalized models allowed for slightly better streamflow simulations on ungauged sites compared with benchmark regionalization approaches. Yet, constructed RFs were weakly sensitive to the urbanization features of the catchments, which prevents their use in straightforward scenarios of the hydrological impacts of urbanization.
Highlights
IntroductionHydrological models are used for various purposes to represent the water cycle and processes in a defined space–time domain
Results showed that random forest (RF)-regionalized models allowed for slightly better streamflow simulations on ungauged sites compared with benchmark regionalization approaches
As the ML algorithms are increasingly applied for various purposes in hydrology, we aimed at exploring the capacity of the RF algorithm in regionalizing hourly hydrological model parameters
Summary
Hydrological models are used for various purposes to represent the water cycle and processes in a defined space–time domain. In order to realistically describe the water movement within the spatially delimited domain (e.g., a hydrological catchment), explicit physically-based and distributed approaches are useful to better track the spatial and temporal variability and the non-linearity of the hydrological processes. This usually ends up in dealing with highly parameterized hydrological models compared to the level of data availability and modelling constraints, which results in high degrees of freedom and parameter uncertainty [1]. More parsimonious modelling tools are achieved by seeking effective process representation through implicitly describing the modelled domain, which generally comes at the cost of sacrificing exhaustive spatial description and, to a certain extent, losing parameter interpretability
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