Articles | Volume 25, issue 10
Research article
21 Oct 2021
Research article |  | 21 Oct 2021

Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models

Thomas Lees, Marcus Buechel, Bailey Anderson, Louise Slater, Steven Reece, Gemma Coxon, and Simon J. Dadson

Data sets

Catchment attributes and hydro-meteorological timeseries for 671 catchments across Great Britain (CAMELS-GB) G. Coxon, N. Addor, J. P. Bloomfield, J. Freer, M. Fry, J. Hannaford, N. J. K. Howden, R. Lane, M. Lewis, E. L. Robinson, T. Wagener, and R. Woods

Climate Hydrology and Ecology Research Support System Meteorology Dataset for Great Britain (1961-2015) [CHESS-met] v1.2 E. Robinson, E. Blyth, D. Clark, E. Comyn-Platt, J. Finch, and A. Rudd

Model code and software

Benchmarking Data-Driven Rainfall-Runoff Models in Great Britain: A comparison of LSTM-based models with four lumped conceptual models T. Lees and R. Lane

tommylees112/neuralhydrology: Benchmarking Data Driven Rainfall-Runoff Models in Great Britain (benchmarking) F. Kratzert, T. Lees, M. Gauch, D. Klotz, B. Jenkins, G. Nearing, and M. Visser

Short summary
We used deep learning (DL) models to simulate the amount of water moving through a river channel (discharge) based on the rainfall, temperature and potential evaporation in the previous days. We tested the DL models on catchments across Great Britain finding that the model can accurately simulate hydrological systems across a variety of catchment conditions. Ultimately, the model struggled most in areas where there is chalky bedrock and where human influence on the catchment is large.