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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2018-635
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2018-635
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 18 Jan 2019

Research article | 18 Jan 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across a large-sample of catchments in Great Britain

Rosanna A. Lane1, Gemma Coxon1, Jim E. Freer1,3, Thorsten Wagener2,3, Penny J. Johnes1,3, John P. Bloomfield4, Sheila Greene5, Christopher J. A. Macleod6, and Sim M. Reaney7 Rosanna A. Lane et al.
  • 1School of Geographical Sciences, University of Bristol, Bristol, BS8 2NQ, United Kingdom
  • 2Faculty of Engineering, University of Bristol, Bristol, BS8 2NQ, United Kingdom
  • 3Cabot Institute, University of Bristol, Bristol, BS8 2NQ, United Kingdom
  • 4British Geological Survey, Maclean Building, Wallingford, OX10 8BB, United Kingdom
  • 5Trinity College Dublin, Dublin, Ireland
  • 6The James Hutton Institute, Craigiebuckler, Aberdeen, AB15 8QH, United Kingdom
  • 7Department of Geography, Durham University, Durham, DH1 3LE, United Kingdom

Abstract. Benchmarking model performance across large samples of catchments is useful to guide 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 multiple, 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 1100 catchments. Model performance was then evaluated using a standard performance metric for daily flows, and more novel performance metrics for peak flows considering parameter uncertainty.

Our results show that simple, lumped hydrological models were able to produce adequate simulations across most of Great Britain, with median Nash–Sutcliffe efficiency scores of 0.72–0.78 across all 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 Scotland and southeast 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 for assessing daily or peak flows, demonstrating the value of using an ensemble of model structures.

This research demonstrates what conceptual lumped models can achieve as a performance benchmark, as well as providing 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.

Rosanna A. Lane et al.
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Rosanna A. Lane et al.
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Short summary
We evaluated 4 hydrological model structures and their parameters on over 1100 catchments across Great Britain, considering modelling uncertainties. Models performed well for most catchments, but failed in parts of Scotland and southeast England. Failures were often linked to inconsistencies in the water balance. This research shows what conceptual lumped models can achieve, gives insights into where and why these models may fail, and provides a benchmark of national modelling capability.
We evaluated 4 hydrological model structures and their parameters on over 1100 catchments across...
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