Articles | Volume 23, issue 10
https://doi.org/10.5194/hess-23-4011-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-4011-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Benchmarking the predictive capability of hydrological models for river flow and flood peak predictions across over 1000 catchments in Great Britain
School of Geographical Sciences, University of Bristol, Bristol, BS8 2NQ, UK
Gemma Coxon
School of Geographical Sciences, University of Bristol, Bristol, BS8 2NQ, UK
Jim E. Freer
School of Geographical Sciences, University of Bristol, Bristol, BS8 2NQ, UK
Cabot Institute, University of Bristol, Bristol, BS8 2NQ, UK
Thorsten Wagener
Faculty of Engineering, University of Bristol, Bristol, BS8 2NQ,
UK
Cabot Institute, University of Bristol, Bristol, BS8 2NQ, UK
Penny J. Johnes
School of Geographical Sciences, University of Bristol, Bristol, BS8 2NQ, UK
Cabot Institute, University of Bristol, Bristol, BS8 2NQ, UK
John P. Bloomfield
British Geological Survey, Maclean Building, Wallingford, OX10 8BB, UK
Sheila Greene
Trinity College Dublin, Dublin, Ireland
Christopher J. A. Macleod
The James Hutton Institute, Craigiebuckler, Aberdeen, AB15 8QH, UK
Sim M. Reaney
Department of Geography, Durham University, Durham, DH1 3LE, UK
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- Perceptual perplexity and parameter parsimony K. Beven & N. Chappell 10.1002/wat2.1530
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24 citations as recorded by crossref.
- Adjusting for Conditional Bias in Process Model Simulations of Hydrological Extremes: An Experiment Using the North Wyke Farm Platform S. Curceac et al. 10.3389/frai.2020.565859
- Knowledge gaps in our perceptual model of Great Britain's hydrology T. Wagener et al. 10.1002/hyp.14288
- Incorporating Uncertainty Into Multiscale Parameter Regionalization to Evaluate the Performance of Nationally Consistent Parameter Fields for a Hydrological Model R. Lane et al. 10.1029/2020WR028393
- Improving streamflow prediction in the WRF-Hydro model with LSTM networks K. Cho & Y. Kim 10.1016/j.jhydrol.2021.127297
- Reproducing different types of changes in hydrological indicators with rainfall-runoff models C. Massmann 10.2166/nh.2020.073
- Towards integrated flood inundation modelling in groundwater-dominated catchments S. Collins et al. 10.1016/j.jhydrol.2020.125755
- Exploring the role of hydrological pathways in modulating multi-annual climate teleconnection periodicities from UK rainfall to streamflow W. Rust et al. 10.5194/hess-25-2223-2021
- A review of hydrologic signatures and their applications H. McMillan 10.1002/wat2.1499
- Challenges in modeling and predicting floods and droughts: A review M. Brunner et al. 10.1002/wat2.1520
- Benchmarking data-driven rainfall–runoff models in Great Britain: a comparison of long short-term memory (LSTM)-based models with four lumped conceptual models T. Lees et al. 10.5194/hess-25-5517-2021
- CCAM: China Catchment Attributes and Meteorology dataset Z. Hao et al. 10.5194/essd-13-5591-2021
- Grappling with uncertainties in physical climate impact projections of water resources R. Dankers & Z. Kundzewicz 10.1007/s10584-020-02858-4
- Flood spatial coherence, triggers, and performance in hydrological simulations: large-sample evaluation of four streamflow-calibrated models M. Brunner et al. 10.5194/hess-25-105-2021
- PISCO_HyM_GR2M: A Model of Monthly Water Balance in Peru (1981–2020) H. Llauca et al. 10.3390/w13081048
- Evaluating the performance of random forest for large-scale flood discharge simulation L. Schoppa et al. 10.1016/j.jhydrol.2020.125531
- Simultaneous Calibration of Hydrologic Model Structure and Parameters Using a Blended Model R. Chlumsky et al. 10.1029/2020WR029229
- Hydrological signatures describing the translation of climate seasonality into streamflow seasonality S. Gnann et al. 10.5194/hess-24-561-2020
- When does a parsimonious model fail to simulate floods? Learning from the seasonality of model bias P. Astagneau et al. 10.1080/02626667.2021.1923720
- A Brief Analysis of Conceptual Model Structure Uncertainty Using 36 Models and 559 Catchments W. Knoben et al. 10.1029/2019WR025975
- Perceptual perplexity and parameter parsimony K. Beven & N. Chappell 10.1002/wat2.1530
- Performance dependence of multi-model combination methods on hydrological model calibration strategy and ensemble size Y. Wan et al. 10.1016/j.jhydrol.2021.127065
- CAMELS-GB: hydrometeorological time series and landscape attributes for 671 catchments in Great Britain G. Coxon et al. 10.5194/essd-12-2459-2020
- Why do we have so many different hydrological models? A review based on the case of Switzerland P. Horton et al. 10.1002/wat2.1574
- Assessing the performance and robustness of two conceptual rainfall-runoff models on a worldwide sample of watersheds T. Mathevet et al. 10.1016/j.jhydrol.2020.124698
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Latest update: 06 Jun 2023
Short summary
We evaluated four 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 south-eastern 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 four hydrological model structures and their parameters on over 1100 catchments...