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

摘要

The current generation of large-scale hydrological models does not include a groundwaterflow component. Large-scale groundwater models, involving aquifers and basins of multiplecountries, are still rare mainly due to a lack of hydro-geological data which areusually only available in developed countries. In this study, we propose anovel approach to construct large-scale groundwater models by using globaldatasets that are readily available. As the test-bed, we use the combinedRhine-Meuse basin that contains groundwater head data used to verify themodel output. We start by building a distributed land surface model (30arc-second resolution) to estimate groundwater recharge and river discharge.Subsequently, a MODFLOW transient groundwater model is built and forced bythe 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 MODFLOWgroundwater 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 modelscenarios with various hydro-geological parameter settings, we observe thatthe model can reasonably well reproduce the observed groundwater head time series. However, we note that there are still some limitations in the currentapproach, specifically because the offline-coupling techniquesimplifies the dynamic feedbacks between surface water levels and groundwaterheads, and between soil moisture states and groundwater heads. Also thecurrent sensitivity analysis ignores the uncertainty of the land surfacemodel output. Despite these limitations, we argue that the results of thecurrent model show a promise for large-scale groundwater modeling practices,including for data-poor environments and at the global scale.