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

• URL: http://arxiv.org/abs/2307.02497v1
• Date: Tue, 4 Jul 2023 08:27:52 GMT
• Title: Multi-gauge Hydrological Variational Data Assimilation: Regionalization Learning with Spatial Gradients using Multilayer Perceptron and Bayesian-Guided Multivariate Regression
• Authors: Ngo Nghi Truyen Huynh, Pierre-Andr\'e Garambois, Fran\c{c}ois Colleoni, Benjamin Renard, H\'el\`ene Roux (IMFT)
• Abstract summary: This contribution presents a novel seamless regionalization technique for learning complex regional transfer functions designed for high-resolution hydrological models. 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.
• Score: 0.3281128493853064
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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