GIS-SWIAS: Tool to Summarize Seawater Intrusion Status and Vulnerability at Aquifer Scale

Abstract

In this paper, we introduce GIS-SWIAS, a novel generalized ArcGIS ArcToolbox that helps to analyze seawater intrusion (SWI) status and vulnerability at aquifer scale (SWIAS). It is a user-friendly tool that can be applied to any aquifer and is fully integrated in the ArcGIS environment, which is a widely available software tool. It is the first ArcGIS tool with these characteristics focusing on SWI analyses that we can find in the literature. GIS-SWIAS is able to deal with georeferenced information; it is easy to introduce the required data (inputs) and to efficiently perform the demanding computational operations required. Its outputs are in the form of shapes, reports, and images (maps, conceptual cross sections, and time series of lumped indices) to summarize the magnitude, intensity, and temporal evolution of SWI within an aquifer for specific dates or by showing statistics for a chosen time period. It can be applied to assess historical SWI dynamic in cases where there is no groundwater flow model. In those cases, the spatial distribution is assessed by applying simple interpolation techniques. Nevertheless, if we want a rational quantitative analysis of the sustainability of alternative management scenarios to the SWI problem, the GIS-SWIAS tool requires that information on hydraulic head and chloride concentration distribution is generated from simulations of their impacts by a calibrated density-dependent flow model. In such cases, adaptation strategies to potential future scenarios—whose distributed impacts have to be propagated within the previously calibrated models—could usefully be analyzed and compared using this tool. Given all these ways that the GIS-SWIAS tool can be applied, it provides a valuable tool for both the researcher and technician to assess SWI dynamics and aquifer resilience under different scenarios. It can support the decision-making process by helping to make a rational selection of sustainable management strategies. Its performance for the analyses of historical and potential future scenarios has been tested and confirmed in two case studies described in previous research works.