HydroNets: Leveraging River Structure for Hydrologic Modeling
Abstract
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.
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.
Research Areas
