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
Correspondence to: E. H. Sutanudjaja
([email protected])
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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