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

Abstract

This paper presents the Modular Assessment of Rainfall-Runoff Models Toolbox (MARRMoT): A modular open-source toolbox containing documentation and model code based on 46 existing conceptual hydrologic models. The toolbox is developed in MATLAB and works with Octave. MARRMoT models are based solely on traceable published material and model documentation, not on already-existing computer code. Models are implemented following several good practices of model development: The definition of model equations (the mathematical model) is kept separate from the numerical methods used to solve these equations (the numerical model) to generate clean code that is easy to adjust and debug; the implicit Euler time-stepping scheme is provided as the default option to numerically approximate each model's ordinary differential equations in a more robust way than (common) explicit schemes would; threshold equations are smoothed to avoid discontinuities in the model's objective function space; and the model equations are solved simultaneously, avoiding the physically unrealistic sequential solving of fluxes. Generalized parameter ranges are provided to assist with model inter-comparison studies. In addition to this paper and its Supplement, a user manual is provided together with several workflow scripts that show basic example applications of the toolbox. The toolbox and user manual are available from span classCombining double low line"uri"https://github.com/wknoben/MARRMoT/span (last access: 30 May 2019; a hrefCombining double low line"https://doi.org/10.5281/zenodo.3235664"https://doi.org/10.5281/zenodo.3235664). Our main scientific objective in developing this toolbox is to facilitate the inter-comparison of conceptual hydrological model structures which are in widespread use in order to ultimately reduce the uncertainty in model structure selection

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