Articles | Volume 12, issue 6
https://doi.org/10.5194/gmd-12-2463-2019
© 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/gmd-12-2463-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
25 Jun 2019
Model description paper |
| 25 Jun 2019
| 25 Jun 2019
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
Wouter J. M. Knoben
CORRESPONDING AUTHOR
Department of Civil Engineering, University of Bristol, Bristol, BS8 1TR, UK
Jim E. Freer
School of Geographical Science, University of Bristol, Bristol, BS8
1BF, UK
Keirnan J. A. Fowler
Department of Infrastructure Engineering, University of Melbourne,
Melbourne, Parkville VIC 3052, Australia
Murray C. Peel
Department of Infrastructure Engineering, University of Melbourne,
Melbourne, Parkville VIC 3052, Australia
Ross A. Woods
Department of Civil Engineering, University of Bristol, Bristol, BS8 1TR, UK
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Cited
82 citations as recorded by crossref.
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- Hydrological signatures describing the translation of climate seasonality into streamflow seasonality S. Gnann et al. 10.5194/hess-24-561-2020
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- Unlocking the potential of stochastic simulation through Bluecat: Enhancing runoff predictions in arid and high‐altitude regions J. Jorquera & A. Pizarro 10.1002/hyp.15046
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- Disaggregated monthly hydrological models can outperform daily models in providing daily flow statistics and extrapolate well to a drying climate A. John et al. 10.1016/j.jhydrol.2021.126471
- A multiple hydrograph separation technique for identifying hydrological model structures and an interpretation of dominant process controls on flow duration curves C. Leong & Y. Yokoo 10.1002/hyp.14569
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- How to Tailor My Process‐Based Hydrological Model? Dynamic Identifiability Analysis of Flexible Model Structures T. Pilz et al. 10.1029/2020WR028042
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- TOSSH: A Toolbox for Streamflow Signatures in Hydrology S. Gnann et al. 10.1016/j.envsoft.2021.104983
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- Advancing objective functions in hydrological modelling: Integrating knowable moments for improved simulation accuracy A. Pizarro & J. Jorquera 10.1016/j.jhydrol.2024.131071
- A multi‐objective approach to select hydrological models and constrain structural uncertainties for climate impact assessments D. Saavedra et al. 10.1002/hyp.14446
- A self-identification Neuro-Fuzzy inference framework for modeling rainfall-runoff in a Chilean watershed Y. Morales et al. 10.1016/j.jhydrol.2020.125910
- Are gridded precipitation datasets a good option for streamflow simulation across the Juruá river basin, Amazon? F. Satgé et al. 10.1016/j.jhydrol.2021.126773
- Comprehensive uncertainty analysis for surface water and groundwater projections under climate change based on a lumped geo-hydrological model F. Ejaz et al. 10.1016/j.jhydrol.2023.130323
- The impact of hydrological model structure on the simulation of extreme runoff events G. van Kempen et al. 10.5194/nhess-21-961-2021
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- Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty 10.1016/j.jhydrol.2023.130498
- Impact of the Mean Daily Air Temperature Calculation on the Rainfall-Runoff Modelling N. Bezak et al. 10.3390/w12113175
- Setting expectations for hydrologic model performance with an ensemble of simple benchmarks W. Knoben 10.1002/hyp.15288
- Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability L. Trotter et al. 10.5194/gmd-15-6359-2022
- Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model J. Aerts et al. 10.5194/hess-26-4407-2022
- 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
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- 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
- Estimating rainfall–runoff modeling using the rainfall prognostic model-based artificial framework with a well-ordered selective genetic algorithm S. Kumar et al. 10.2166/hydro.2022.009
78 citations as recorded by crossref.
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- HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists R. Rigon et al. 10.5194/hess-26-4773-2022
- Multi-model ensemble benchmark data for hydrological modeling in Japanese river basins Y. Sawada et al. 10.3178/hrl.16.73
- Marching in step: The importance of matching model complexity to data availability in terrestrial biosphere models X. Feng 10.1111/gcb.15090
- How Robust Is a Multi-Model Ensemble Mean of Conceptual Hydrological Models to Climate Change? T. Kimizuka & Y. Sawada 10.3390/w14182852
- Contrasting hydrological responses to climate change in two adjacent catchments dominated by karst and nonkarst Y. Chang et al. 10.1016/j.jhydrol.2023.130013
- Historical development of rainfall‐runoff modeling M. Peel & T. McMahon 10.1002/wat2.1471
- Confidence intervals of the Kling-Gupta efficiency J. Vrugt & D. de Oliveira 10.1016/j.jhydrol.2022.127968
- RavenR v2.1.4: an open-source R package to support flexible hydrologic modelling R. Chlumsky et al. 10.5194/gmd-15-7017-2022
- Mimicry of a Conceptual Hydrological Model (HBV): What's in a Name? K. Jansen et al. 10.1029/2020WR029143
- OpenForecast: An Assessment of the Operational Run in 2020–2021 G. Ayzel & D. Abramov 10.3390/geosciences12020067
- Seamless Integration of Rainfall Spatial Variability and a Conceptual Hydrological Model Y. Zhou et al. 10.3390/su13063588
- TATOO – Python Topographic Analysis Tool Library for semi-automated setup of high-resolution integrated hydrologic models J. Mitterer 10.1016/j.envsoft.2022.105406
- Exploring parameter (dis)agreement due to calibration metric selection in conceptual rainfall–runoff models E. Muñoz-Castro et al. 10.1080/02626667.2023.2231434
- The numerical error of the Xinanjiang model J. Zhao et al. 10.1016/j.jhydrol.2023.129324
- Partitioning of Precipitation Into Terrestrial Water Balance Components Under a Drying Climate H. Gardiya Weligamage et al. 10.1029/2022WR033538
- When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling Y. Song et al. 10.5194/hess-28-3051-2024
- The PAVICS-Hydro platform: A virtual laboratory for hydroclimatic modelling and forecasting over North America R. Arsenault et al. 10.1016/j.envsoft.2023.105808
- Experimental Coupling of TOPMODEL with the National Water Model: Effects of Coupling Interface Complexity on Model Performance D. Kim et al. 10.1111/1752-1688.12953
- Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system A. Newman et al. 10.5194/hess-25-5603-2021
- SuperflexPy 1.3.0: an open-source Python framework for building, testing, and improving conceptual hydrological models M. Dal Molin et al. 10.5194/gmd-14-7047-2021
- Hydrologically informed machine learning for rainfall–runoff modelling: towards distributed modelling H. Herath et al. 10.5194/hess-25-4373-2021
- A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting M. Jahangir et al. 10.1016/j.jhydrol.2023.129269
- Modelling water quantity and quality for integrated water cycle management with the Water Systems Integrated Modelling framework (WSIMOD) software B. Dobson et al. 10.5194/gmd-17-4495-2024
- Symptoms of Performance Degradation During Multi‐Annual Drought: A Large‐Sample, Multi‐Model Study L. Trotter et al. 10.1029/2021WR031845
- Reliability of SM2RAIN precipitation datasets in comparison to gauge observations and hydrological modelling over arid regions F. Satgé et al. 10.1002/joc.6704
- Automatic Model Structure Identification for Conceptual Hydrologic Models D. Spieler et al. 10.1029/2019WR027009
- Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise W. Knoben & D. Spieler 10.5194/hess-26-3299-2022
- Hydrological signatures describing the translation of climate seasonality into streamflow seasonality S. Gnann et al. 10.5194/hess-24-561-2020
- Simple Catchments and Where to Find Them: The Storage-Discharge Relationship as a Proxy for Catchment Complexity F. Jehn et al. 10.3389/frwa.2021.631651
- Unlocking the potential of stochastic simulation through Bluecat: Enhancing runoff predictions in arid and high‐altitude regions J. Jorquera & A. Pizarro 10.1002/hyp.15046
- Ground truthing global-scale model estimates of groundwater recharge across Africa C. West et al. 10.1016/j.scitotenv.2022.159765
- Lumped geohydrological modelling for long-term predictions of groundwater storage and depletion F. Ejaz et al. 10.1016/j.jhydrol.2021.127347
- Disaggregated monthly hydrological models can outperform daily models in providing daily flow statistics and extrapolate well to a drying climate A. John et al. 10.1016/j.jhydrol.2021.126471
- A multiple hydrograph separation technique for identifying hydrological model structures and an interpretation of dominant process controls on flow duration curves C. Leong & Y. Yokoo 10.1002/hyp.14569
- Exploring the Implications of Modeling Choices on Prediction of Irrigation Water Savings C. Eluwa et al. 10.1029/2021WR031618
- How to Tailor My Process‐Based Hydrological Model? Dynamic Identifiability Analysis of Flexible Model Structures T. Pilz et al. 10.1029/2020WR028042
- Improving flood forecasting in Narmada river basin using hierarchical clustering and hydrological modelling D. Mehta et al. 10.1016/j.rineng.2023.101571
- An Exploration of Bayesian Identification of Dominant Hydrological Mechanisms in Ungauged Catchments C. Prieto et al. 10.1029/2021WR030705
- Assessing rainfall-runoff models for climate change: simple and differential split-sample tests for conceptual and artificial intelligence models N. Behfar et al. 10.1080/02626667.2024.2345151
- A priori selection of hydrological model structures in modular modelling frameworks: application to Great Britain M. Kiraz et al. 10.1080/02626667.2023.2251968
- Evaluating the parameter sensitivity and impact of hydrologic modeling decisions on flood simulations A. Alexander et al. 10.1016/j.advwatres.2023.104560
- A history of TOPMODEL K. Beven et al. 10.5194/hess-25-527-2021
- RoGeR v3.0.5 – a process-based hydrological toolbox model in Python R. Schwemmle et al. 10.5194/gmd-17-5249-2024
- Simultaneous Calibration of Hydrologic Model Structure and Parameters Using a Blended Model R. Chlumsky et al. 10.1029/2020WR029229
- A step toward global-scale applicability and transferability of flow duration curve studies: A flow duration curve review (2000–2020) C. Leong & Y. Yokoo 10.1016/j.jhydrol.2021.126984
- Ground Truthing Global-Scale Model Estimates of Groundwater Recharge Across Africa C. West et al. 10.2139/ssrn.4184338
- The Modeling Toolkit: how recruitment strategies for modeling positions influence model progress L. Melsen 10.3389/frwa.2023.1149590
- The eWaterCycle platform for open and FAIR hydrological collaboration R. Hut et al. 10.5194/gmd-15-5371-2022
- How well do the multi-satellite and atmospheric reanalysis products perform in hydrological modelling L. Gu et al. 10.1016/j.jhydrol.2022.128920
- Profiling and Pairing Catchments and Hydrological Models With Latent Factor Model Y. Yang & T. Chui 10.1029/2022WR033684
- Challenges in modeling and predicting floods and droughts: A review M. Brunner et al. 10.1002/wat2.1520
- Can model structure families be inferred from model output? J. Remmers et al. 10.1016/j.envsoft.2020.104817
- Identification of Dominant Hydrological Mechanisms Using Bayesian Inference, Multiple Statistical Hypothesis Testing, and Flexible Models C. Prieto et al. 10.1029/2020WR028338
- Error correction method based on deep learning for improving the accuracy of conceptual rainfall-runoff model W. Wenchuan et al. 10.1016/j.jhydrol.2024.131992
- 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
- Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data S. Gharari et al. 10.1029/2020WR027948
- Nested Cross-Validation for HBV Conceptual Rainfall–Runoff Model Spatial Stability Analysis in a Semi-Arid Context M. El Garnaoui et al. 10.3390/rs16203756
- Simulation of water flow management by the flood control facilities in the adjacent river basins V. Antonov et al. 10.1088/1742-6596/1400/7/077049
- Changes in hydrological regime in High Arctic non-glaciated catchment in 1979–2020 using a multimodel approach M. Osuch et al. 10.1016/j.accre.2022.05.001
- The Representation of Hydrological Dynamical Systems Using Extended Petri Nets (EPN) M. Bancheri et al. 10.1029/2019WR025099
- TOSSH: A Toolbox for Streamflow Signatures in Hydrology S. Gnann et al. 10.1016/j.envsoft.2021.104983
- Numerical daemons of hydrological models are summoned by extreme precipitation P. La Follette et al. 10.5194/hess-25-5425-2021
- Improving performance of bucket-type hydrological models in high latitudes with multi-model combination methods: Can we wring water from a stone? A. Todorović et al. 10.1016/j.jhydrol.2024.130829
- 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
- Exploring Hydrologic Model Process Connectivity at the Continental Scale Through an Information Theory Approach G. Konapala et al. 10.1029/2020WR027340
- Advancing objective functions in hydrological modelling: Integrating knowable moments for improved simulation accuracy A. Pizarro & J. Jorquera 10.1016/j.jhydrol.2024.131071
- A multi‐objective approach to select hydrological models and constrain structural uncertainties for climate impact assessments D. Saavedra et al. 10.1002/hyp.14446
- A self-identification Neuro-Fuzzy inference framework for modeling rainfall-runoff in a Chilean watershed Y. Morales et al. 10.1016/j.jhydrol.2020.125910
- Are gridded precipitation datasets a good option for streamflow simulation across the Juruá river basin, Amazon? F. Satgé et al. 10.1016/j.jhydrol.2021.126773
- Comprehensive uncertainty analysis for surface water and groundwater projections under climate change based on a lumped geo-hydrological model F. Ejaz et al. 10.1016/j.jhydrol.2023.130323
- The impact of hydrological model structure on the simulation of extreme runoff events G. van Kempen et al. 10.5194/nhess-21-961-2021
- Towards more realistic runoff projections by removing limits on simulated soil moisture deficit K. Fowler et al. 10.1016/j.jhydrol.2021.126505
- Generative deep learning for probabilistic streamflow forecasting: Conditional variational auto-encoder M. Jahangir & J. Quilty 10.1016/j.jhydrol.2023.130498
- Impact of the Mean Daily Air Temperature Calculation on the Rainfall-Runoff Modelling N. Bezak et al. 10.3390/w12113175
- Setting expectations for hydrologic model performance with an ensemble of simple benchmarks W. Knoben 10.1002/hyp.15288
- Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability L. Trotter et al. 10.5194/gmd-15-6359-2022
- Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model J. Aerts et al. 10.5194/hess-26-4407-2022
4 citations as recorded by crossref.
- 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
- Identifying Causal Interactions Between Groundwater and Streamflow Using Convergent Cross‐Mapping G. Bonotto et al. 10.1029/2021WR030231
- 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
- Estimating rainfall–runoff modeling using the rainfall prognostic model-based artificial framework with a well-ordered selective genetic algorithm S. Kumar et al. 10.2166/hydro.2022.009
Discussed (preprint)
Latest update: 07 Nov 2024
Short summary
Computer models are used to predict river flows. A good model should represent the river basin to which it is applied so that flow predictions are as realistic as possible. However, many different computer models exist, and selecting the most appropriate model for a given river basin is not always easy. This study combines computer code for 46 different hydrological models into a single coding framework so that models can be compared in an objective way and we can learn about model differences.
Computer models are used to predict river flows. A good model should represent the river basin...
