# Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network

arXiv: https://arxiv.org/abs/2010.07921 · HTML: https://mlsanity.com/paper/2010.07921/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 6 of 6)

- [Flood forecasting with machine learning models in an operational framework](https://mlsanity.com/paper/2111.02780/) (2021)
- [The data synergy effects of time-series deep learning models in hydrology](https://mlsanity.com/paper/2101.01876/) (2021)
- [Continental-scale streamflow modeling of basins with reservoirs: towards a coherent deep-learning-based strategy](https://mlsanity.com/paper/2101.04423/) (2021)
- [Using Machine Learning to Discover Parsimonious and Physically-Interpretable Representations of Catchment-Scale Rainfall-Runoff Dynamics](https://mlsanity.com/paper/2412.04845/) (2024)
- [Hierarchical Conditional Multi-Task Learning for Streamflow Modeling](https://mlsanity.com/paper/2410.14137/) (2024)
- [Hapi: A Multivariable Land-Surface Transformer for Medium-Range Hydrological Forecasting at Continental Scale](https://mlsanity.com/paper/2609.22702/) (2026)

## Abstract (arXiv)

Long Short-Term Memory Networks (LSTMs) have been applied to daily discharge prediction with remarkable success. Many practical scenarios, however, require predictions at more granular timescales. For instance, accurate prediction of short but extreme flood peaks can make a life-saving difference, yet such peaks may escape the coarse temporal resolution of daily predictions. Naively training an LSTM on hourly data, however, entails very long input sequences that make learning hard and computationally expensive. In this study, we propose two Multi-Timescale LSTM (MTS-LSTM) architectures that jointly predict multiple timescales within one model, as they process long-past inputs at a single temporal resolution and branch out into each individual timescale for more recent input steps. We test these models on 516 basins across the continental United States and benchmark against the US National Water Model. Compared to naive prediction with a distinct LSTM per timescale, the multi-timescale architectures are computationally more efficient with no loss in accuracy. Beyond prediction quality, the multi-timescale LSTM can process different input variables at different timescales, which is especially relevant to operational applications where the lead time of meteorological forcings depends on their temporal resolution.