Articles | Volume 15, issue 16
https://doi.org/10.5194/gmd-15-6359-2022
© Author(s) 2022. 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-15-6359-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
26 Aug 2022
Development and technical paper |
| 26 Aug 2022
| 26 Aug 2022
Modular Assessment of Rainfall–Runoff Models Toolbox (MARRMoT) v2.1: an object-oriented implementation of 47 established hydrological models for improved speed and readability
Department of Infrastructure Engineering, University of Melbourne, Melbourne, Parkville VIC 3052, Australia
Wouter J. M. Knoben
Centre for Hydrology, University of Saskatchewan, Canmore, Alberta T1W 3G1, Canada
Keirnan J. A. Fowler
Department of Infrastructure Engineering, University of Melbourne, Melbourne, Parkville VIC 3052, Australia
Margarita Saft
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
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Cited
15 citations as recorded by crossref.
- Setting expectations for hydrologic model performance with an ensemble of simple benchmarks W. Knoben https://doi.org/10.1002/hyp.15288
- Development of a model-agnostic isotope tracer simulator T. Holmes et al. https://doi.org/10.1080/02626667.2025.2556917
- Advancing objective functions in hydrological modelling: Integrating knowable moments for improved simulation accuracy A. Pizarro & J. Jorquera https://doi.org/10.1016/j.jhydrol.2024.131071
- Unlocking the potential of stochastic simulation through Bluecat: Enhancing runoff predictions in arid and high‐altitude regions J. Jorquera & A. Pizarro https://doi.org/10.1002/hyp.15046
- Selecting a conceptual hydrological model using Bayes' factors computed with replica-exchange Hamiltonian Monte Carlo and thermodynamic integration D. Mingo et al. https://doi.org/10.5194/gmd-18-1709-2025
- Evaluating the parameter sensitivity and impact of hydrologic modeling decisions on flood simulations A. Alexander et al. https://doi.org/10.1016/j.advwatres.2023.104560
- On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al. https://doi.org/10.5194/hess-28-5011-2024
- Efficacy of evapotranspiration products on improving streamflow prediction in ungauged catchments under various hydrological models and climates S. Wu et al. https://doi.org/10.1016/j.jhydrol.2026.135857
- Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis (CAMELS-SPAT): streamflow observations, forcing data and geospatial data for hydrologic studies across North America W. Knoben et al. https://doi.org/10.5194/hess-29-5791-2025
- A flexible, differentiable framework for neural-enhanced hydrological modeling: Design, implementation, and applications with HydroModels.jl X. Jing et al. https://doi.org/10.1016/j.envsoft.2025.106802
- Multi-station Joint Runoff Forecasting Using Graph Neural Network Coupled with Spatial Connectivity of Hydrological Stations Q. Wang & S. Zhu https://doi.org/10.1007/s11004-024-10167-0
- Combining uncertainty quantification and entropy-inspired concepts into a single objective function for rainfall-runoff model calibration A. Pizarro et al. https://doi.org/10.5194/hess-29-4913-2025
- GHydroMod-SIM-DAI v1.0: A new distributed hydrological model capable of dynamically recognizing runoff generation mechanisms and accounting for aboveground and underground anthropogenic impacts Y. Wang et al. https://doi.org/10.1016/j.jhydrol.2025.134184
- Multivariate calibration can increase simulated discharge uncertainty and model equifinality S. Pool et al. https://doi.org/10.5194/hess-30-2797-2026
- Symptoms of Performance Degradation During Multi‐Annual Drought: A Large‐Sample, Multi‐Model Study L. Trotter et al. https://doi.org/10.1029/2021WR031845
15 citations as recorded by crossref.
- Setting expectations for hydrologic model performance with an ensemble of simple benchmarks W. Knoben https://doi.org/10.1002/hyp.15288
- Development of a model-agnostic isotope tracer simulator T. Holmes et al. https://doi.org/10.1080/02626667.2025.2556917
- Advancing objective functions in hydrological modelling: Integrating knowable moments for improved simulation accuracy A. Pizarro & J. Jorquera https://doi.org/10.1016/j.jhydrol.2024.131071
- Unlocking the potential of stochastic simulation through Bluecat: Enhancing runoff predictions in arid and high‐altitude regions J. Jorquera & A. Pizarro https://doi.org/10.1002/hyp.15046
- Selecting a conceptual hydrological model using Bayes' factors computed with replica-exchange Hamiltonian Monte Carlo and thermodynamic integration D. Mingo et al. https://doi.org/10.5194/gmd-18-1709-2025
- Evaluating the parameter sensitivity and impact of hydrologic modeling decisions on flood simulations A. Alexander et al. https://doi.org/10.1016/j.advwatres.2023.104560
- On the importance of discharge observation uncertainty when interpreting hydrological model performance J. Aerts et al. https://doi.org/10.5194/hess-28-5011-2024
- Efficacy of evapotranspiration products on improving streamflow prediction in ungauged catchments under various hydrological models and climates S. Wu et al. https://doi.org/10.1016/j.jhydrol.2026.135857
- Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis (CAMELS-SPAT): streamflow observations, forcing data and geospatial data for hydrologic studies across North America W. Knoben et al. https://doi.org/10.5194/hess-29-5791-2025
- A flexible, differentiable framework for neural-enhanced hydrological modeling: Design, implementation, and applications with HydroModels.jl X. Jing et al. https://doi.org/10.1016/j.envsoft.2025.106802
- Multi-station Joint Runoff Forecasting Using Graph Neural Network Coupled with Spatial Connectivity of Hydrological Stations Q. Wang & S. Zhu https://doi.org/10.1007/s11004-024-10167-0
- Combining uncertainty quantification and entropy-inspired concepts into a single objective function for rainfall-runoff model calibration A. Pizarro et al. https://doi.org/10.5194/hess-29-4913-2025
- GHydroMod-SIM-DAI v1.0: A new distributed hydrological model capable of dynamically recognizing runoff generation mechanisms and accounting for aboveground and underground anthropogenic impacts Y. Wang et al. https://doi.org/10.1016/j.jhydrol.2025.134184
- Multivariate calibration can increase simulated discharge uncertainty and model equifinality S. Pool et al. https://doi.org/10.5194/hess-30-2797-2026
- Symptoms of Performance Degradation During Multi‐Annual Drought: A Large‐Sample, Multi‐Model Study L. Trotter et al. https://doi.org/10.1029/2021WR031845
Saved (final revised paper)
Latest update: 08 Jul 2026
Short summary
MARRMoT is a piece of software that emulates 47 common models for hydrological simulations. It can be used to run and calibrate these models within a common environment as well as to easily modify them. We restructured and recoded MARRMoT in order to make the models run faster and to simplify their use, while also providing some new features. This new MARRMoT version runs models on average 3.6 times faster while maintaining very strong consistency in their outputs to the previous version.
MARRMoT is a piece of software that emulates 47 common models for hydrological simulations. It...
