Multi-gauge Hydrological Variational Data Assimilation: Regionalization Learning with Spatial Gradients using Multilayer Perceptron and Bayesian-Guided Multivariate Regression
This addresses the challenge of flood prediction in ungauged regions, which is critical for water resource management and disaster preparedness, though it appears incremental as it builds on existing variational data assimilation and machine learning methods.
The paper tackles the problem of estimating spatially distributed hydrological parameters for floods on ungauged watercourses by presenting a novel seamless regionalization technique that learns complex regional transfer functions for high-resolution hydrological models, achieving optimization through multi-gauge data with accurate adjoint-based gradients.
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.