Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000 catchments in Great Britain

Lane, Rosanna A.; Coxon, Gemma; Freer, Jim E.; Wagener, Thorsten; Johnes, Penny J.; Bloomfield, John P. ORCID: https://orcid.org/0000-0002-5730-1723; Greene, Sheila; Macleod, Christopher J.A.; Reaney, Sim M.. 2019 Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000 catchments in Great Britain. Hydrology and Earth System Sciences, 23 (10). 4011-4032. https://doi.org/10.5194/hess-23-4011-2019

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Abstract/Summary

Benchmarking model performance across large samples of catchments is useful to guide model selection and future model development. Given uncertainties in the observational data we use to drive and evaluate hydrological models, and uncertainties in the structure and parameterisation of models we use to produce hydrological simulations and predictions, it is essential that model evaluation is undertaken within an uncertainty analysis framework. Here, we benchmark the capability of several lumped hydrological models across Great Britain by focusing on daily flow and peak flow simulation. Four hydrological model structures from the Framework for Understanding Structural Errors (FUSE) were applied to over 1000 catchments in England, Wales and Scotland. Model performance was then evaluated using standard performance metrics for daily flows and novel performance metrics for peak flows considering parameter uncertainty. Our results show that lumped hydrological models were able to produce adequate simulations across most of Great Britain, with each model producing simulations exceeding a 0.5 Nash–Sutcliffe efficiency for at least 80 % of catchments. All four models showed a similar spatial pattern of performance, producing better simulations in the wetter catchments to the west and poor model performance in central Scotland and south-eastern England. Poor model performance was often linked to the catchment water balance, with models unable to capture the catchment hydrology where the water balance did not close. Overall, performance was similar between model structures, but different models performed better for different catchment characteristics and metrics, as well as for assessing daily or peak flows, leading to the ensemble of model structures outperforming any single structure, thus demonstrating the value of using multi-model structures across a large sample of different catchment behaviours. This research evaluates what conceptual lumped models can achieve as a performance benchmark and provides interesting insights into where and why these simple models may fail. The large number of river catchments included in this study makes it an appropriate benchmark for any future developments of a national model of Great Britain.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.5194/hess-23-4011-2019
ISSN:	1607-7938
Additional Keywords:	GroundwaterBGS, Groundwater
Date made live:	23 Dec 2019 09:59 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/526335

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