Multi-gauge Hydrological Variational Data Assimilation: Regionalization Learning with Spatial Gradients using Multilayer Perceptron and Bayesian-Guided Multivariate Regression

Regionalization (MPR) method, combining descriptors upscaling Regardless of the improvements made in hydrological and pre-regionalization function in form of multilinear forward models and available data, hydrological calibration regressions, implemented within a spatially distributed remains a challenging ill-posed inverse problem faced with multiscale hydrological model (mHm), has been proposed the equifinality (Beven, 2001) of feasible solutions. Most by Samaniego et al. (2010), and later applied to other gridded calibration approaches aim to estimate spatially uniform model hydrological models in several applicative studies (e.g., parameters for a single gauged catchment, resulting in piecewise Mizukami et al. (2017); Beck et al. (2020)). In all the constant discontinuous parameters fields for adjacent above studies, state of the art optimization algorithms are catchments. Moreover, these calibrated parameter are not used, especially Shuffle Complex Evolution algorithm (SCE) transferable to ungauged locations, which represents the majority (Duan et al., 1992) in Mizukami et al. (2017) or Distributed of the global land surface (Fekete & Vörösmarty, 2007; Evolutionary Algorithms (DEAP) (Fortin et al., 2012) in Beck Hannah et al., 2011).