Articles | Volume 15, issue 9
Hydrol. Earth Syst. Sci., 15, 2913–2935, 2011
https://doi.org/10.5194/hess-15-2913-2011
Hydrol. Earth Syst. Sci., 15, 2913–2935, 2011
https://doi.org/10.5194/hess-15-2913-2011

Research article 15 Sep 2011

Research article | 15 Sep 2011

Large-scale groundwater modeling using global datasets: a test case for the Rhine-Meuse basin

E. H. Sutanudjaja1, L. P. H. van Beek1, S. M. de Jong1, F. C. van Geer1,3, and M. F. P. Bierkens1,2 E. H. Sutanudjaja et al.
  • 1Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, The Netherlands
  • 2Unit Soil and Groundwater Systems, Deltares, Utrecht, The Netherlands
  • 3Netherlands Organization for Applied Scientific Research TNO, Utrecht, The Netherlands

Abstract. The current generation of large-scale hydrological models does not include a groundwater flow component. Large-scale groundwater models, involving aquifers and basins of multiple countries, are still rare mainly due to a lack of hydro-geological data which are usually only available in developed countries. In this study, we propose a novel approach to construct large-scale groundwater models by using global datasets that are readily available. As the test-bed, we use the combined Rhine-Meuse basin that contains groundwater head data used to verify the model output. We start by building a distributed land surface model (30 arc-second resolution) to estimate groundwater recharge and river discharge. Subsequently, a MODFLOW transient groundwater model is built and forced by the recharge and surface water levels calculated by the land surface model. Results are promising despite the fact that we still use an offline procedure to couple the land surface and MODFLOW groundwater models (i.e. the simulations of both models are separately performed). The simulated river discharges compare well to the observations. Moreover, based on our sensitivity analysis, in which we run several groundwater model scenarios with various hydro-geological parameter settings, we observe that the model can reasonably well reproduce the observed groundwater head time series. However, we note that there are still some limitations in the current approach, specifically because the offline-coupling technique simplifies the dynamic feedbacks between surface water levels and groundwater heads, and between soil moisture states and groundwater heads. Also the current sensitivity analysis ignores the uncertainty of the land surface model output. Despite these limitations, we argue that the results of the current model show a promise for large-scale groundwater modeling practices, including for data-poor environments and at the global scale.

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