Accurate and scalable hydrologic models are essential building blocks of
several important applications, from water resource management to timely flood
warnings. However, as the climate changes, precipitation and rainfall-runoff
pattern variations become more extreme, and accurate training data that can
account for the resulting distributional shifts become more scarce. In this
work we present a novel family of hydrologic models, called HydroNets, which
leverages river network structure. HydroNets are deep neural network models
designed to exploit both basin specific rainfall-runoff signals, and upstream
network dynamics, which can lead to improved predictions at longer horizons.
The injection of the river structure prior knowledge reduces sample complexity
and allows for scalable and more accurate hydrologic modeling even with only a
few years of data. We present an empirical study over two large basins in India
that convincingly support the proposed model and its advantages.

本研究提出了一种名为 HydroNets 的新型水文模型，该模型利用河流网络结构，采用深度神经网络模型，以识别流域特异的降雨径流信号和上游网络动态的组合。实证研究在印度的两个大流域中建立的 HydroNets 模型证明了该模型与传统水文模型相比具有从少量数据中获取更准确预测数据的优势。