Evaluation of a GIS-linked model of salt loading to groundwater

Citation
Dl. Corwin et al., Evaluation of a GIS-linked model of salt loading to groundwater, J ENVIR Q, 28(2), 1999, pp. 471-480
Citations number
21
Categorie Soggetti
Environment/Ecology
Journal title
JOURNAL OF ENVIRONMENTAL QUALITY
ISSN journal
00472425 → ACNP
Volume
28
Issue
2
Year of publication
1999
Pages
471 - 480
Database
ISI
SICI code
0047-2425(199903/04)28:2<471:EOAGMO>2.0.ZU;2-8
Abstract
The ability to assess through prognostication the impart of nonpoint source (NPS) pollutant loads to groundwater, such as salt loading, is a key eleme nt in agriculture's sustainability by mitigating deleterious environmental impacts before they occur. The modeling of NPS pollutants in the vadose zon e is well suited to the integration of a geographic information system (GIS ) because of the spatial nature of NPS pollutants, The GIS-linked, function al model TETrans was evaluated for its ability to predict salt loading to g roundwater in a 2396 ha study area of the Broadview Water District located on the westside of central California's San Joaquin Valley, Model input dat a were obtained from spatially-referenced measurements as opposed to previo us NPS pollution modeling effort's reliance upon generalized information fr om existing spatial databases (e.g., soil surveys) and transfer functions. The simulated temporal and spatial changes in the loading of salts to drain age waters for the study period 1991-1996 were compared to measured data, A comparison of the predicted and measured cumulative salt loads in drainage waters for individual drainage sumps showed acceptable agreement for manag ement applications. An evaluation of the results indicated the practicality and utility of applying a one-dimensional, GIS-linked model of solute tran sport in the vadose zone to predict and visually display salt loading over thousands of hectares. The display maps provide a visual toot for assessing the potential impact of salinity upon groundwater, thereby providing infor mation to make management decisions for the purpose of minimizing environme ntal impacts without compromising future agricultural productivity.