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.

利用可学习的复杂区域转换函数进行高分辨率水文模型的新奇无缝区域化技术，解决估计空间分布水文参数的难题，特别是针对未计量水道上的洪水问题。

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

多规模水文变分数据同化：利用多层感知机和贝叶斯指导多变量回归进行空间梯度区域化学习