Articles | Volume 25, issue 2
https://doi.org/10.5194/hess-25-1069-2021
© Author(s) 2021. 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-25-1069-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
02 Mar 2021
Research article |
| 02 Mar 2021
| 02 Mar 2021
Behind the scenes of streamflow model performance
Laurène J. E. Bouaziz
CORRESPONDING AUTHOR
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, the Netherlands
Department Catchment and Urban Hydrology, Deltares, Boussinesqweg 1, 2629 HV Delft, the Netherlands
Fabrizio Fenicia
Eawag, Überlandstrasse 133, 8600 Dübendorf, Switzerland
Guillaume Thirel
Université Paris-Saclay, INRAE, UR HYCAR, 92160 Antony, France
Tanja de Boer-Euser
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, the Netherlands
Joost Buitink
Hydrology and Quantitative Water Management Group, Wageningen University and Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Claudia C. Brauer
Hydrology and Quantitative Water Management Group, Wageningen University and Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Jan De Niel
Hydraulics division, Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40, 3001 Leuven, Belgium
Benjamin J. Dewals
Hydraulics in Environmental and Civil Engineering (HECE), University of Liège, Allée de la Découverte 9, 4000 Liège, Belgium
Gilles Drogue
Université de Lorraine, LOTERR, 57000 Metz, France
Benjamin Grelier
Université de Lorraine, LOTERR, 57000 Metz, France
Lieke A. Melsen
Hydrology and Quantitative Water Management Group, Wageningen University and Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Sotirios Moustakas
Hydraulics division, Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40, 3001 Leuven, Belgium
Jiri Nossent
Flanders Hydraulics Research, Berchemlei 115, 2140 Antwerp, Belgium
Vrije Universiteit Brussel (VUB), Department of Hydrology and Hydraulic Engineering, Pleinlaan 2, 1050 Brussels, Belgium
Fernando Pereira
Flanders Hydraulics Research, Berchemlei 115, 2140 Antwerp, Belgium
Eric Sprokkereef
Ministry of Infrastructure and Water Management, Zuiderwagenplein 2, 8224 AD Lelystad, the Netherlands
Jasper Stam
Ministry of Infrastructure and Water Management, Zuiderwagenplein 2, 8224 AD Lelystad, the Netherlands
Albrecht H. Weerts
Department Catchment and Urban Hydrology, Deltares, Boussinesqweg 1, 2629 HV Delft, the Netherlands
Hydrology and Quantitative Water Management Group, Wageningen University and Research, P.O. Box 47, 6700 AA Wageningen, the Netherlands
Patrick Willems
Hydraulics division, Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40, 3001 Leuven, Belgium
Vrije Universiteit Brussel (VUB), Department of Hydrology and Hydraulic Engineering, Pleinlaan 2, 1050 Brussels, Belgium
Hubert H. G. Savenije
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, the Netherlands
Markus Hrachowitz
Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, the Netherlands
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Cited
25 citations as recorded by crossref.
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- Improving continental hydrological models for future climate conditions via multi-objective optimisation W. Sharples et al. 10.1016/j.envsoft.2024.106018
- A Pareto‐Based Sensitivity Analysis and Multiobjective Calibration Approach for Integrating Streamflow and Evaporation Data P. Yeste et al. 10.1029/2022WR033235
- The value of satellite soil moisture and snow cover data for the transfer of hydrological model parameters to ungauged sites R. Tong et al. 10.5194/hess-26-1779-2022
- Which range of streamflow data is most informative in the calibration of an hourly hydrological model? M. Saadi & C. Furusho-Percot 10.1080/02626667.2023.2277835
- It Takes a Village to Run a Model—The Social Practices of Hydrological Modeling L. Melsen 10.1029/2021WR030600
- A hydrography upscaling method for scale-invariant parametrization of distributed hydrological models D. Eilander et al. 10.5194/hess-25-5287-2021
- Megafloods in Europe can be anticipated from observations in hydrologically similar catchments M. Bertola et al. 10.1038/s41561-023-01300-5
- Using the classical model for structured expert judgment to estimate extremes: a case study of discharges in the Meuse River G. Rongen et al. 10.5194/hess-28-2831-2024
- Does Flash Flood Model Performance Increase with Complexity? Signature and Sensitivity-Based Comparison of Conceptual and Process-Oriented Models on French Mediterranean Cases A. Haruna et al. 10.3390/hydrology9080141
- Evaluation of overland flow modelling hypotheses with a multi‐objective calibration using discharge and sediment data A. de Lavenne et al. 10.1002/hyp.14767
- Enhancing rainfall–runoff model accuracy with machine learning models by using soil water index to reflect runoff characteristics S. Iamampai et al. 10.2166/wst.2023.424
- Multi-decadal fluctuations in root zone storage capacity through vegetation adaptation to hydro-climatic variability have minor effects on the hydrological response in the Neckar River basin, Germany S. Wang et al. 10.5194/hess-28-4011-2024
- Reduction of vegetation-accessible water storage capacity after deforestation affects catchment travel time distributions and increases young water fractions in a headwater catchment M. Hrachowitz et al. 10.5194/hess-25-4887-2021
- Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe B. Naz et al. 10.5194/gmd-16-1617-2023
- Catchment response to intense rainfall: Evaluating modelling hypotheses P. Astagneau et al. 10.1002/hyp.14676
- Future changes in annual, seasonal and monthly runoff signatures in contrasting Alpine catchments in Austria S. Hanus et al. 10.5194/hess-25-3429-2021
- Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods Y. Yang & T. Chui 10.5194/hess-25-5839-2021
- Ecosystem adaptation to climate change: the sensitivity of hydrological predictions to time-dynamic model parameters L. Bouaziz et al. 10.5194/hess-26-1295-2022
- Pulling the rabbit out of the hat: Unravelling hidden nitrogen legacies in catchment‐scale water quality models S. Lutz et al. 10.1002/hyp.14682
- Understanding each other's models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, and communication C. Telteu et al. 10.5194/gmd-14-3843-2021
- Catchment response to climatic variability: implications for root zone storage and streamflow predictions N. Tempel et al. 10.5194/hess-28-4577-2024
- Stable water isotopes and tritium tracers tell the same tale: no evidence for underestimation of catchment transit times inferred by stable isotopes in StorAge Selection (SAS)-function models S. Wang et al. 10.5194/hess-27-3083-2023
- A signature‐based approach to quantify soil moisture dynamics under contrasting land‐uses R. Araki et al. 10.1002/hyp.14553
- A multi‐objective approach to select hydrological models and constrain structural uncertainties for climate impact assessments D. Saavedra et al. 10.1002/hyp.14446
25 citations as recorded by crossref.
- Predicting streamflow with LSTM networks using global datasets K. Wilbrand et al. 10.3389/frwa.2023.1166124
- Improving continental hydrological models for future climate conditions via multi-objective optimisation W. Sharples et al. 10.1016/j.envsoft.2024.106018
- A Pareto‐Based Sensitivity Analysis and Multiobjective Calibration Approach for Integrating Streamflow and Evaporation Data P. Yeste et al. 10.1029/2022WR033235
- The value of satellite soil moisture and snow cover data for the transfer of hydrological model parameters to ungauged sites R. Tong et al. 10.5194/hess-26-1779-2022
- Which range of streamflow data is most informative in the calibration of an hourly hydrological model? M. Saadi & C. Furusho-Percot 10.1080/02626667.2023.2277835
- It Takes a Village to Run a Model—The Social Practices of Hydrological Modeling L. Melsen 10.1029/2021WR030600
- A hydrography upscaling method for scale-invariant parametrization of distributed hydrological models D. Eilander et al. 10.5194/hess-25-5287-2021
- Megafloods in Europe can be anticipated from observations in hydrologically similar catchments M. Bertola et al. 10.1038/s41561-023-01300-5
- Using the classical model for structured expert judgment to estimate extremes: a case study of discharges in the Meuse River G. Rongen et al. 10.5194/hess-28-2831-2024
- Does Flash Flood Model Performance Increase with Complexity? Signature and Sensitivity-Based Comparison of Conceptual and Process-Oriented Models on French Mediterranean Cases A. Haruna et al. 10.3390/hydrology9080141
- Evaluation of overland flow modelling hypotheses with a multi‐objective calibration using discharge and sediment data A. de Lavenne et al. 10.1002/hyp.14767
- Enhancing rainfall–runoff model accuracy with machine learning models by using soil water index to reflect runoff characteristics S. Iamampai et al. 10.2166/wst.2023.424
- Multi-decadal fluctuations in root zone storage capacity through vegetation adaptation to hydro-climatic variability have minor effects on the hydrological response in the Neckar River basin, Germany S. Wang et al. 10.5194/hess-28-4011-2024
- Reduction of vegetation-accessible water storage capacity after deforestation affects catchment travel time distributions and increases young water fractions in a headwater catchment M. Hrachowitz et al. 10.5194/hess-25-4887-2021
- Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe B. Naz et al. 10.5194/gmd-16-1617-2023
- Catchment response to intense rainfall: Evaluating modelling hypotheses P. Astagneau et al. 10.1002/hyp.14676
- Future changes in annual, seasonal and monthly runoff signatures in contrasting Alpine catchments in Austria S. Hanus et al. 10.5194/hess-25-3429-2021
- Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods Y. Yang & T. Chui 10.5194/hess-25-5839-2021
- Ecosystem adaptation to climate change: the sensitivity of hydrological predictions to time-dynamic model parameters L. Bouaziz et al. 10.5194/hess-26-1295-2022
- Pulling the rabbit out of the hat: Unravelling hidden nitrogen legacies in catchment‐scale water quality models S. Lutz et al. 10.1002/hyp.14682
- Understanding each other's models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, and communication C. Telteu et al. 10.5194/gmd-14-3843-2021
- Catchment response to climatic variability: implications for root zone storage and streamflow predictions N. Tempel et al. 10.5194/hess-28-4577-2024
- Stable water isotopes and tritium tracers tell the same tale: no evidence for underestimation of catchment transit times inferred by stable isotopes in StorAge Selection (SAS)-function models S. Wang et al. 10.5194/hess-27-3083-2023
- A signature‐based approach to quantify soil moisture dynamics under contrasting land‐uses R. Araki et al. 10.1002/hyp.14553
- A multi‐objective approach to select hydrological models and constrain structural uncertainties for climate impact assessments D. Saavedra et al. 10.1002/hyp.14446
Latest update: 20 Nov 2024
Short summary
We quantify the differences in internal states and fluxes of 12 process-based models with similar streamflow performance and assess their plausibility using remotely sensed estimates of evaporation, snow cover, soil moisture and total storage anomalies. The dissimilarities in internal process representation imply that these models cannot all simultaneously be close to reality. Therefore, we invite modelers to evaluate their models using multiple variables and to rely on multi-model studies.
We quantify the differences in internal states and fluxes of 12 process-based models with...
