Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks

TLDR

Rainfall‑runoff modelling is a key hydrological challenge with approaches ranging from physically based to fully data‑driven models. The study proposes using LSTM networks for rainfall‑runoff modelling, demonstrating regional applicability and transfer of learned process understanding to improve single‑catchment performance. The authors trained LSTM models on 241 CAMELS catchments, leveraging the network’s capacity to capture long‑term storage and snow effects, and benchmarked performance against the SAC‑SMA + Snow‑17 model. The LSTM approach outperformed the SAC‑SMA + Snow‑17 model, confirming its potential for hydrological applications. Abstract.

Abstract

Abstract. Rainfall–runoff modelling is one of the key challenges in the field of hydrology. Various approaches exist, ranging from physically based over conceptual to fully data-driven models. In this paper, we propose a novel data-driven approach, using the Long Short-Term Memory (LSTM) network, a special type of recurrent neural network. The advantage of the LSTM is its ability to learn long-term dependencies between the provided input and output of the network, which are essential for modelling storage effects in e.g. catchments with snow influence. We use 241 catchments of the freely available CAMELS data set to test our approach and also compare the results to the well-known Sacramento Soil Moisture Accounting Model (SAC-SMA) coupled with the Snow-17 snow routine. We also show the potential of the LSTM as a regional hydrological model in which one model predicts the discharge for a variety of catchments. In our last experiment, we show the possibility to transfer process understanding, learned at regional scale, to individual catchments and thereby increasing model performance when compared to a LSTM trained only on the data of single catchments. Using this approach, we were able to achieve better model performance as the SAC-SMA + Snow-17, which underlines the potential of the LSTM for hydrological modelling applications.