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

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

doi:https://doi.org/10.5194/hess-25-5517-2021

Articles | Volume 25, issue 10

https://doi.org/10.5194/hess-25-5517-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-25-5517-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

Supplement

https://doi.org/10.5194/hess-25-5517-2021-supplement

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 https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9

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 https://doi.org/10.5285/b745e7b1-626c-4ccc-ac27-56582e77b900

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 https://doi.org/10.5281/zenodo.4555820

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 https://doi.org/10.5281/zenodo.5541446

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.