# HydroNets: Leveraging River Structure for Hydrologic Modeling

arXiv: https://arxiv.org/abs/2007.00595 · HTML: https://mlsanity.com/paper/2007.00595/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 14 of 14)

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- [Toward Routing River Water in Land Surface Models with Recurrent Neural Networks](https://mlsanity.com/paper/2404.14212/) (2024)
- [Hierarchically Disentangled Recurrent Network for Factorizing System Dynamics of Multi-scale Systems: An application on Hydrological Systems](https://mlsanity.com/paper/2407.20152/) (2024)
- [Physics-Guided Fair Graph Sampling for Water Temperature Prediction in River Networks](https://mlsanity.com/paper/2412.16523/) (2024)
- [AIFL: A Global Daily Streamflow Forecasting Model Using a Deterministic LSTM Pre-trained on ERA5-Land and Fine-tuned on IFS](https://mlsanity.com/paper/2602.16579/) (2026)

## Abstract (arXiv)

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.