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Tackling the difficult problem of estimating spatially distributedhydrological parameters, especially for floods on ungauged watercourses, thiscontribution presents a novel seamless regionalization technique for learningcomplex regional transfer functions designed for high-resolution hydrologicalmodels. The transfer functions rely on: (i) a multilayer perceptron enabling aseamless flow of gradient computation to employ machine learning optimizationalgorithms, or (ii) a multivariate regression mapping optimized by variationaldata assimilation algorithms and guided by Bayesian estimation, addressing theequifinality issue of feasible solutions. The approach involves incorporatingthe inferable regionalization mappings into a differentiable hydrological modeland optimizing a cost function computed on multi-gauge data with accurateadjoint-based spatially distributed gradients.

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