  Tackling the difficult problem of estimating spatially distributed
hydrological parameters, especially for floods on ungauged watercourses, this
contribution presents a novel seamless regionalization technique for learning
complex regional transfer functions designed for high-resolution hydrological
models. The transfer functions rely on: (i) a multilayer perceptron enabling a
seamless flow of gradient computation to employ machine learning optimization
algorithms, or (ii) a multivariate regression mapping optimized by variational
data assimilation algorithms and guided by Bayesian estimation, addressing the
equifinality issue of feasible solutions. The approach involves incorporating
the inferable regionalization mappings into a differentiable hydrological model
and optimizing a cost function computed on multi-gauge data with accurate
adjoint-based spatially distributed gradients.
