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This article in JEQ

  1. Vol. 28 No. 2, p. 471-480
    Received: Dec 17, 1997

    * Corresponding author(s): dcorwin@ussl.ars.usda.gov
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Evaluation of a GIS-Linked Model of Salt Loading to Groundwater

  1. D. L. Corwin *,
  2. M. L. K. Carrillo,
  3. P. J. Vaughan,
  4. J. D. Rhoades and
  5. D. G. Cone
  1. Broadview Water District, P.O. Box 95, Firebaugh, CA 93622.



The ability to assess through prognostication the impact of nonpoint source (NPS) pollutant loads to groundwater, such as salt loading, is a key element in agriculture's sustainability by mitigating deleterious environmental impacts before they occur. The modeling of NPS pollutants in the vadose zone is well suited to the integration of a geographic information system (GIS) because of the spatial nature of NPS pollutants. The GIS-linked, functional model TETrans was evaluated for its ability to predict salt loading to groundwater in a 2396 ha study area of the Broadview Water District located on the westside of central California's San Joaquin Valley. Model input data were obtained from spatially-referenced measurements as opposed to previous NPS pollution modeling effort's reliance upon generalized information from existing spatial databases (e.g., soil surveys) and transfer functions. The simulated temporal and spatial changes in the loading of salts to drainage 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 management applications. An evaluation of the results indicated the practicality and utility of applying a one-dimensional, GIS-linked model of solute transport in the vadose zone to predict and visually display salt loading over thousands of hectares. The display maps provide a visual tool for assessing the potential impact of salinity upon groundwater, thereby providing information to make management decisions for the purpose of minimizing environmental impacts without compromising future agricultural productivity.

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