Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Geosci. Model Dev., 10, 3913-3929, 2017
https://doi.org/10.5194/gmd-10-3913-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 4.0 License.
Model description paper
27 Oct 2017
GLOFRIM v1.0 – A globally applicable computational framework for integrated hydrological–hydrodynamic modelling
Jannis M. Hoch1,2, Jeffrey C. Neal3, Fedor Baart2, Rens van Beek1, Hessel C. Winsemius2,4, Paul D. Bates3, and Marc F. P. Bierkens1,2 1Department of Physical Geography, Utrecht University, P.O. Box 80115, 3508 TC Utrecht, the Netherlands
2Deltares, P.O. Box 177, 2600 MH Delft, the Netherlands
3School of Geographical Sciences, University of Bristol, University Road, Bristol, BS8 1SS, UK
4Institute for Environmental Studies, VU University, De Boelelaan 1087, 1081 HV, Amsterdam, the Netherlands
Abstract. We here present GLOFRIM, a globally applicable computational framework for integrated hydrological–hydrodynamic modelling. GLOFRIM facilitates spatially explicit coupling of hydrodynamic and hydrologic models and caters for an ensemble of models to be coupled. It currently encompasses the global hydrological model PCR-GLOBWB as well as the hydrodynamic models Delft3D Flexible Mesh (DFM; solving the full shallow-water equations and allowing for spatially flexible meshing) and LISFLOOD-FP (LFP; solving the local inertia equations and running on regular grids). The main advantages of the framework are its open and free access, its global applicability, its versatility, and its extensibility with other hydrological or hydrodynamic models. Before applying GLOFRIM to an actual test case, we benchmarked both DFM and LFP for a synthetic test case. Results show that for sub-critical flow conditions, discharge response to the same input signal is near-identical for both models, which agrees with previous studies. We subsequently applied the framework to the Amazon River basin to not only test the framework thoroughly, but also to perform a first-ever benchmark of flexible and regular grids on a large-scale. Both DFM and LFP produce comparable results in terms of simulated discharge with LFP exhibiting slightly higher accuracy as expressed by a Kling–Gupta efficiency of 0.82 compared to 0.76 for DFM. However, benchmarking inundation extent between DFM and LFP over the entire study area, a critical success index of 0.46 was obtained, indicating that the models disagree as often as they agree. Differences between models in both simulated discharge and inundation extent are to a large extent attributable to the gridding techniques employed. In fact, the results show that both the numerical scheme of the inundation model and the gridding technique can contribute to deviations in simulated inundation extent as we control for model forcing and boundary conditions. This study shows that the presented computational framework is robust and widely applicable. GLOFRIM is designed as open access and easily extendable, and thus we hope that other large-scale hydrological and hydrodynamic models will be added. Eventually, more locally relevant processes would be captured and more robust model inter-comparison, benchmarking, and ensemble simulations of flood hazard on a large scale would be allowed for.

Citation: Hoch, J. M., Neal, J. C., Baart, F., van Beek, R., Winsemius, H. C., Bates, P. D., and Bierkens, M. F. P.: GLOFRIM v1.0 – A globally applicable computational framework for integrated hydrological–hydrodynamic modelling, Geosci. Model Dev., 10, 3913-3929, https://doi.org/10.5194/gmd-10-3913-2017, 2017.
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Short summary
To improve flood hazard assessments, it is vital to model all relevant processes. We here present GLOFRIM, a framework for coupling hydrologic and hydrodynamic models to increase the number of physical processes represented in hazard computations. GLOFRIM is openly available, versatile, and extensible with more models. Results also underpin its added value for model benchmarking, showing that not only model forcing but also grid properties and the numerical scheme influence output accuracy.
To improve flood hazard assessments, it is vital to model all relevant processes. We here...
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