2019
DOI: 10.5194/hess-2018-635
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Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across a large-sample of catchments in Great Britain

Abstract: 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 acro… Show more

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Cited by 8 publications
(8 citation statements)
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References 60 publications
(90 reference statements)
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“…These include differing quality and scales of input data and streamflow observations and large heterogeneity in hydrological behavior (possibly requiring more than one specialized model). Yet, this heterogeneity may in fact provide us with the opportunity to improve our understanding of differences in model adequacy and “benchmarking” model performance (Lane et al, 2019; Newman et al, 2017; Seibert, Vis, Lewis, & van Meerveld, 2018), and to draw most needed conclusions on the robustness of generalizations and on estimation uncertainty (Gupta et al, 2014; McMillan et al, 2016).…”
Section: Discussionmentioning
confidence: 99%
“…These include differing quality and scales of input data and streamflow observations and large heterogeneity in hydrological behavior (possibly requiring more than one specialized model). Yet, this heterogeneity may in fact provide us with the opportunity to improve our understanding of differences in model adequacy and “benchmarking” model performance (Lane et al, 2019; Newman et al, 2017; Seibert, Vis, Lewis, & van Meerveld, 2018), and to draw most needed conclusions on the robustness of generalizations and on estimation uncertainty (Gupta et al, 2014; McMillan et al, 2016).…”
Section: Discussionmentioning
confidence: 99%
“…Lastly, future works should focus more on improving the processbased approach while maintaining the core principles of the multiple hydrograph separation technique in which the structures were developed in order to maintain its flexibility. For example, using recession curve power functions instead of exponential functions (Tashie et al, 2020) or using nonlinear storage methods for baseflow estimations (e.g., Ceola et al, 2010;Cheng et al, 2020;Kirchner, 2009) in each tank especially in snow dominated catchments (e.g., Lane et al, 2019).…”
Section: Advantages and Limitations Of The Structural Frameworkmentioning
confidence: 99%
“…There are perceptions about the meaning of NSE values, e.g. NSE ≥ 0.5 indicates acceptable model performance Moriasi et al, 2007) or acceptable parameter sets (Freer et al, 1996;Lane et al, 2019), the NSE = 0.6 as a threshold for acceptable model runs (Choi & Beven, 2007), NSE ≥ 0.75 indicates good model performance (Moriasi et al, 2007), etc.…”
Section: On the Role Of Error Metricsmentioning
confidence: 99%