Articles | Volume 14, issue 4
Geosci. Model Dev., 14, 1885–1897, 2021
https://doi.org/10.5194/gmd-14-1885-2021
Geosci. Model Dev., 14, 1885–1897, 2021
https://doi.org/10.5194/gmd-14-1885-2021
Model description paper
09 Apr 2021
Model description paper | 09 Apr 2021

Rapid development of fast and flexible environmental models: the Mobius framework v1.0

Magnus Dahler Norling et al.

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Cited articles

Ahnert, K. and Mulansky, M.: Odeint – Solving Ordinary Differential Equations in C++, AIP Conference Proceedings, 1389, 1586, https://doi.org/10.1063/1.3637934, 2011. 
Beven, K.: Rainfall-Runoff Modelling, The Primer, Second Edition, Wiley-Blackwell, New Jersey, 2012. 
Blair, G. S., Beven, K., Lamb, R., Bassett, R., Cauwenberghs, K., Hankin, B., Dean, G., Hunter, N., Edwards, L., Nundloll, V., Samreen, F., Simm, W., and Towe, R.: Models of everywhere revisited: A technological perspective, Environ. Model. Softw., 122, 104521, https://doi.org/10.1016/j.envsoft.2019.104521, 2019. 
Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta, H. V., Wagener, T., and Hay, L. E.: Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models, Water Resour. Res., 44, W00B02, https://doi.org/10.1029/2007wr006735, 2008. 
Clark, M. P., Nijssen, B., Lundquist, J. D., Kavetski, D., Rupp, D. E., Woods, R. A., Freer, J. E., Gutmann, E. D., Wood, A. W., Brekke, L. D., Arnold, J. R., Gochis, D. J., and Rasmussen, R. M.: A unified approach for process-based hydrologic modeling: 1. Modeling concept, Water Resour. Res., 51, 2498–2514, https://doi.org/10.1002/2015WR017198, 2015. 
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Short summary
In order to allow researchers to quickly prototype and build models of natural systems, we have created the Mobius framework. Such models can, for instance, be used to ask questions about what the impacts of land-use changes are to water quality in a river or lake, or the response of biogeochemical systems to climate change. The Mobius framework makes it quick to build models that run fast, which enables the user to explore many different scenarios and model formulations.