No abstract

The groundwater vulnerability (GV) assessment for contamination is an effective technique for the planning, policy, and decision-making, as well as for sustainable groundwater resource protection and management. The GV depends strongly on local hydrogeological settings and land-use conditions that may vary in response to the activities of agricultural development. In this study, a modified DRASTIC model, which employs an additional factor of land use coupled with the analytic hierarchy process (AHP) theory, was used to quantify the spatial and temporal variation of GV and groundwater contamination risk in the Pingtung groundwater basin. The results show that the GV slightly decreased due to the decrease in agricultural areas under the change of land use over two decades (1995–2017). The yearly changes or a shorter period of observations incorporated with the accurate land-use map in DRASTIC parameters could improve GV maps to obtain a better representation of site-specific conditions. Meanwhile, the maps of yearly contamination risk indicated that the counties of Jiuru and Ligang are at high risk of nitrate pollution since 2016. In other agriculture-dominated regions such as Yanpu, Changzhi, and Gaoshu in the Pingtung groundwater basin, the climate conditions influence less the temporal variations of groundwater contamination risk. The results of this study are expected to support policy-makers to adopt the strategies of sustainable development for groundwater resources in local areas.

No abstract

The study proposes a stochastic approach to quantify the uncertainty of groundwater vulnerability (GV) produced by classical index-overlay methods. In the analysis, the physical-based MODFLOW model has been integrated with the DRASTIC method and modified by the analytical hierarchy process (AHP) technique. Specifically, the flow fields from the MODFLOW model provide the parameters of depth to water and the associated hydraulic conductivity (K) for the DRASTIC method. The integrated loops between the MODFLOW and DRASTIC models enable the evaluations of GV maps by considering sources of uncertainty in geological parameters and stress changes in an aquifer system. In illustrating the approach for practical implementations, the study considers the uncertainty produced by the heterogeneity of K in the Pingtung Plain groundwater basin in southern Taiwan. Different degrees of K heterogeneity were assessed to quantify the impact of the K heterogeneity on the GV mappings. Results show that quantification of parameter uncertainty from the GW model can improve the accuracy and reliability of the GV map. The stochastic GV maps have accounted for the source of the K uncertainty. There are significant discrepancies in GV values in the spatial distribution and intensity in all GV classes. The results clarify the potential risk of groundwater contaminations in the Pingtung Plain groundwater basin.