Estimating basin-scale groundwater storage change component in data-scarce area of Bandung Basin, West Java, Indonesia, using groundwater modeling and GRACE data

Changes in groundwater storage are dominantly influenced by anthropogenic and climatic factors. In global and/or regional scale groundwater storage change studies, storage changes are often estimated using gravimetric satellite data (GRACE). However, the applicability of such analysis at basin-scale is still limited due to its relatively coarse spatial resolution of 1° x 1°. Combination of GRACE data with groundwater level monitoring observations, where available, and numerical modeling might yield more accurate results for catchment-scale studies.

In this study, we estimate the basin-scale groundwater storage change component in the data-scarce area of the Bandung groundwater basin in West Java, Indonesia, using MODFLOW. We parameterize the model using hydraulic conductivity data obtained from slug-tests, pumping-tests, and laboratory analysis. There is some historical groundwater level observation available to compare the model outputs against. The model is forced by recharge calculated with a distributed hydrological model (wflow_sbm). Groundwater abstraction is estimated based upon population density and other known water demands as reported in earlier studies. We simulate the period of 2005 to 2018, starting from an initial steady-state assumed to exist prior to 2005.

We compare the groundwater storage change observed in the model to that derived from GRACE data, which was calculated by subtracting the soil moisture change derived from the wflow_sbm simulation from the total terrestrial water storage change. The results show how the groundwater storage change estimated from the groundwater flow model can mimic both the dynamic and magnitude of that derived from GRACE in combination with wflow_sbm. The capability of groundwater modeling to estimate basin-scale groundwater storage change, validated by GRACE, unravel the opportunity of using such methods to predict the behavior of groundwater storage dynamics to the future impact of anthropogenic and climatic factor and to assist in deriving basin-scale groundwater policies and management strategies in data-scarce areas.