Articles | Volume 25, issue 10
https://doi.org/10.5194/hess-25-5517-2021
https://doi.org/10.5194/hess-25-5517-2021
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

Related authors

Hydrological concept formation inside long short-term memory (LSTM) networks
Thomas Lees, Steven Reece, Frederik Kratzert, Daniel Klotz, Martin Gauch, Jens De Bruijn, Reetik Kumar Sahu, Peter Greve, Louise Slater, and Simon J. Dadson
Hydrol. Earth Syst. Sci., 26, 3079–3101, https://doi.org/10.5194/hess-26-3079-2022,https://doi.org/10.5194/hess-26-3079-2022, 2022
Short summary
Nonstationary weather and water extremes: a review of methods for their detection, attribution, and management
Louise J. Slater, Bailey Anderson, Marcus Buechel, Simon Dadson, Shasha Han, Shaun Harrigan, Timo Kelder, Katie Kowal, Thomas Lees, Tom Matthews, Conor Murphy, and Robert L. Wilby
Hydrol. Earth Syst. Sci., 25, 3897–3935, https://doi.org/10.5194/hess-25-3897-2021,https://doi.org/10.5194/hess-25-3897-2021, 2021
Short summary
A pan-African high-resolution drought index dataset
Jian Peng, Simon Dadson, Feyera Hirpa, Ellen Dyer, Thomas Lees, Diego G. Miralles, Sergio M. Vicente-Serrano, and Chris Funk
Earth Syst. Sci. Data, 12, 753–769, https://doi.org/10.5194/essd-12-753-2020,https://doi.org/10.5194/essd-12-753-2020, 2020
Short summary

Related subject area

Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024,https://doi.org/10.5194/hess-28-2107-2024, 2024
Short summary
Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain
Marcus Buechel, Louise Slater, and Simon Dadson
Hydrol. Earth Syst. Sci., 28, 2081–2105, https://doi.org/10.5194/hess-28-2081-2024,https://doi.org/10.5194/hess-28-2081-2024, 2024
Short summary
Impacts of spatiotemporal resolutions of precipitation on flood event simulation based on multimodel structures – a case study over the Xiang River basin in China
Qian Zhu, Xiaodong Qin, Dongyang Zhou, Tiantian Yang, and Xinyi Song
Hydrol. Earth Syst. Sci., 28, 1665–1686, https://doi.org/10.5194/hess-28-1665-2024,https://doi.org/10.5194/hess-28-1665-2024, 2024
Short summary
A network approach for multiscale catchment classification using traits
Fabio Ciulla and Charuleka Varadharajan
Hydrol. Earth Syst. Sci., 28, 1617–1651, https://doi.org/10.5194/hess-28-1617-2024,https://doi.org/10.5194/hess-28-1617-2024, 2024
Short summary
Multi-model approach in a variable spatial framework for streamflow simulation
Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue
Hydrol. Earth Syst. Sci., 28, 1539–1566, https://doi.org/10.5194/hess-28-1539-2024,https://doi.org/10.5194/hess-28-1539-2024, 2024
Short summary

Cited articles

Addor, N. and Melsen, L.: Legacy, rather than adequacy, drives the selection of hydrological models, Water Resour. Res., 55, 378–390, 2019. a
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017. a
Alvarez-Garreton, C., Mendoza, P. A., Boisier, J. P., Addor, N., Galleguillos, M., Zambrano-Bigiarini, M., Lara, A., Puelma, C., Cortes, G., Garreaud, R., McPhee, J., and Ayala, A.: The CAMELS-CL dataset: catchment attributes and meteorology for large sample studies – Chile dataset, Hydrol. Earth Syst. Sci., 22, 5817–5846, https://doi.org/10.5194/hess-22-5817-2018, 2018. a
Bengio, Y., Simard, P., and Frasconi, P.: Learning Long-Term Dependencies with Gradient Descent is Difficult, IEEE T. Neural. Networ., 5, 157–166, 1994. a
Beven, K.: A manifesto for the equifinality thesis, J. Hydrol., 320, 18–36, 2006a. a
Download
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.