Benchmarking data-driven rainfall-runoff models in Great Britain: a comparison of LSTM-based models with four lumped conceptual models

Lees, T; Buechel, M; Anderson, B; Slater, L; Reece, S; Coxon, G; Dadson, SJ

AI Collection

Journal article

Benchmarking data-driven rainfall-runoff models in Great Britain: a comparison of LSTM-based models with four lumped conceptual models

Abstract:: Long short-term memory (LSTM) models are recurrent neural networks from the field of deep learning (DL) which have shown promise for time series modelling, especially in conditions when data are abundant. Previous studies have demonstrated the applicability of LSTM-based models for rainfall–runoff modelling; however, LSTMs have not been tested on catchments in Great Britain (GB). Moreover, opportunities exist to use spatial and seasonal patterns in model performances to improve our understanding of hydrological processes and to examine the advantages and disadvantages of LSTM-based models for hydrological simulation. By training two LSTM architectures across a large sample of 669 catchments in GB, we demonstrate that the LSTM and the Entity Aware LSTM (EA LSTM) models simulate discharge with median Nash–Sutcliffe efficiency (NSE) scores of 0.88 and 0.86 respectively. We find that the LSTM-based models outperform a suite of benchmark conceptual models, suggesting an opportunity to use additional data to refine conceptual models. In summary, the LSTM-based models show the largest performance improvements in the north-east of Scotland and in south-east of England. The south-east of England remained difficult to model, however, in part due to the inability of the LSTMs configured in this study to learn groundwater processes, human abstractions and complex percolation properties from the hydro-meteorological variables typically employed for hydrological modelling.

Publication status:: Published

Peer review status:: Peer reviewed

Actions

Email

Email this record

Send the bibliographic details of this record to your email address.

Your Email
Please enter the email address that the record information will be sent to.

-
Your message (optional)
Please add any additional information to be included within the email.
Share
Cite

Cite this record

APA Style

Lees, T., Buechel, M., Anderson, B., Slater, L., Reece, S., Coxon, G., & Dadson, S. J. (2021). Benchmarking data-driven rainfall-runoff models in Great Britain: a comparison of LSTM-based models with four lumped conceptual models. Hydrology and Earth System Sciences, 25(10), 5517–5534.

MLA Style

Lees, T, et al. “Benchmarking Data-Driven Rainfall-Runoff Models in Great Britain: a Comparison of LSTM-Based Models with Four Lumped Conceptual Models.” Hydrology and Earth System Sciences, vol. 25, no. 10, 2021, pp. 5517–34.

Chicago Style

Lees, T, M Buechel, B Anderson, et al. 2021. “Benchmarking Data-Driven Rainfall-Runoff Models in Great Britain: a Comparison of LSTM-Based Models with Four Lumped Conceptual Models.” Hydrology and Earth System Sciences 25 (10): 5517–34.
Print