Articles | Volume 11, issue 4
https://doi.org/10.5194/gmd-11-1591-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-11-1591-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
19 Apr 2018
Development and technical paper |
| 19 Apr 2018
| 19 Apr 2018
Continuous state-space representation of a bucket-type rainfall-runoff model: a case study with the GR4 model using state-space GR4 (version 1.0)
Irstea, UR HYCAR, 1 rue Pierre-Gilles de Gennes, 92160 Antony, France
Guillaume Thirel
Irstea, UR HYCAR, 1 rue Pierre-Gilles de Gennes, 92160 Antony, France
Charles Perrin
Irstea, UR HYCAR, 1 rue Pierre-Gilles de Gennes, 92160 Antony, France
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Cited
22 citations as recorded by crossref.
- Towards more realistic runoff projections by removing limits on simulated soil moisture deficit K. Fowler et al. 10.1016/j.jhydrol.2021.126505
- Impacts of Uncontrolled Operator Splitting Methods on Parameter Identification, Prediction Uncertainty, and Subsurface Flux Representation in Conceptual Hydrological Models B. Woldegiorgis et al. 10.1029/2022WR033250
- Exploring parsimonious daily rainfall-runoff model structure using the hyperbolic tangent function and Tank model J. Song et al. 10.1016/j.jhydrol.2019.04.054
- Vers une plus grande flexibilité temporelle du modèle opérationnel de prévision des crues GRP J. Viatgé et al. 10.1051/lhb/2019017
- Global BROOK90 R Package: An Automatic Framework to Simulate the Water Balance at Any Location I. Vorobevskii et al. 10.3390/w12072037
- On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al. 10.5194/hess-28-5011-2024
- Generating interpretable rainfall-runoff models automatically from data T. Dantzer & B. Kerkez 10.1016/j.advwatres.2024.104796
- Flood prediction using parameters calibrated on limited discharge data and uncertain rainfall scenarios J. Reynolds et al. 10.1080/02626667.2020.1747619
- The numerical error of the Xinanjiang model J. Zhao et al. 10.1016/j.jhydrol.2023.129324
- 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
- Evaluating the Applicability of Drainage Routing Schemes for Paddy Fields J. Song et al. 10.1061/(ASCE)IR.1943-4774.0001491
- Comparative analysis of lumped and semi-distributed hydrological models in humid Mediterranean environments S. Abbenante et al. 10.1080/02626667.2024.2413422
- Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations W. Knoben et al. 10.5194/gmd-12-2463-2019
- Numerical daemons of hydrological models are summoned by extreme precipitation P. La Follette et al. 10.5194/hess-25-5425-2021
- Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al. 10.5194/hess-28-2505-2024
- Identifying Climate and Human Impact Trends in Streamflow: A Case Study in Uruguay R. Navas et al. 10.3390/w11071433
- Technical note: Hydrology modelling R packages – a unified analysis of models and practicalities from a user perspective P. Astagneau et al. 10.5194/hess-25-3937-2021
- Hydrological modelling at multiple sub-daily time steps: Model improvement via flux-matching A. Ficchì et al. 10.1016/j.jhydrol.2019.05.084
- On the potential of variational calibration for a fully distributed hydrological model: application on a Mediterranean catchment M. Jay-Allemand et al. 10.5194/hess-24-5519-2020
- Assessing Hydrological Simulations with Machine Learning and Statistical Models E. Rozos 10.3390/hydrology10020049
- Multi-dimensional hydrological–hydraulic model with variational data assimilation for river networks and floodplains L. Pujol et al. 10.5194/gmd-15-6085-2022
- Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times S. Donegan et al. 10.5194/hess-25-4159-2021
22 citations as recorded by crossref.
- Towards more realistic runoff projections by removing limits on simulated soil moisture deficit K. Fowler et al. 10.1016/j.jhydrol.2021.126505
- Impacts of Uncontrolled Operator Splitting Methods on Parameter Identification, Prediction Uncertainty, and Subsurface Flux Representation in Conceptual Hydrological Models B. Woldegiorgis et al. 10.1029/2022WR033250
- Exploring parsimonious daily rainfall-runoff model structure using the hyperbolic tangent function and Tank model J. Song et al. 10.1016/j.jhydrol.2019.04.054
- Vers une plus grande flexibilité temporelle du modèle opérationnel de prévision des crues GRP J. Viatgé et al. 10.1051/lhb/2019017
- Global BROOK90 R Package: An Automatic Framework to Simulate the Water Balance at Any Location I. Vorobevskii et al. 10.3390/w12072037
- On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al. 10.5194/hess-28-5011-2024
- Generating interpretable rainfall-runoff models automatically from data T. Dantzer & B. Kerkez 10.1016/j.advwatres.2024.104796
- Flood prediction using parameters calibrated on limited discharge data and uncertain rainfall scenarios J. Reynolds et al. 10.1080/02626667.2020.1747619
- The numerical error of the Xinanjiang model J. Zhao et al. 10.1016/j.jhydrol.2023.129324
- 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
- Evaluating the Applicability of Drainage Routing Schemes for Paddy Fields J. Song et al. 10.1061/(ASCE)IR.1943-4774.0001491
- Comparative analysis of lumped and semi-distributed hydrological models in humid Mediterranean environments S. Abbenante et al. 10.1080/02626667.2024.2413422
- Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v1.2: an open-source, extendable framework providing implementations of 46 conceptual hydrologic models as continuous state-space formulations W. Knoben et al. 10.5194/gmd-12-2463-2019
- Numerical daemons of hydrological models are summoned by extreme precipitation P. La Follette et al. 10.5194/hess-25-5425-2021
- Metamorphic testing of machine learning and conceptual hydrologic models P. Reichert et al. 10.5194/hess-28-2505-2024
- Identifying Climate and Human Impact Trends in Streamflow: A Case Study in Uruguay R. Navas et al. 10.3390/w11071433
- Technical note: Hydrology modelling R packages – a unified analysis of models and practicalities from a user perspective P. Astagneau et al. 10.5194/hess-25-3937-2021
- Hydrological modelling at multiple sub-daily time steps: Model improvement via flux-matching A. Ficchì et al. 10.1016/j.jhydrol.2019.05.084
- On the potential of variational calibration for a fully distributed hydrological model: application on a Mediterranean catchment M. Jay-Allemand et al. 10.5194/hess-24-5519-2020
- Assessing Hydrological Simulations with Machine Learning and Statistical Models E. Rozos 10.3390/hydrology10020049
- Multi-dimensional hydrological–hydraulic model with variational data assimilation for river networks and floodplains L. Pujol et al. 10.5194/gmd-15-6085-2022
- Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times S. Donegan et al. 10.5194/hess-25-4159-2021
Latest update: 14 Dec 2024
Short summary
Many rainfall–runoff models are based on stores. However, the differential equations that describe the stores' evolution are rarely presented in literature.
This represents an issue when the temporal resolution changes. In this work, we propose and evaluate a state-space version of a simple rainfall–runoff model within a robust resolution scheme. The results show that the proposed model performs equally well or slightly better than the original one and is independent of the temporal resolution.
Many rainfall–runoff models are based on stores. However, the differential equations that...
