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.

A geostatistical analysis of soil salinity in an agricultural area in the San Joaquin Valley included measurements of electrical conductivity of soil paste extract (ECe) and water content of soil samples supplemented by surface measurements of apparent electrical conductivity (EMH). Prediction of soil salinity at unsampled points by cokriging loge(ECe) and EMH is worthwhile because EMH measurements are quicker than soil sampling. This work studies how patterns of loge(ECe) predicted by cokriging with EMH are influenced by variation in gravimetric water content (W). The data are mean EMH = 1.00 ± 0.13 dS m−1 for 2378 locations, mean loge(ECe) = 1.40 ± 0.29 dS m−1, and mean gravimetric W = 0.260 ± 0.003, both averaged for four samples from 0.3‐m intervals to 1.2‐m depth for 315 locations. The coefficient of determination (R2) for EMH vs. loge(ECe) increased with depth from 0.05 to 0.54 whereas the R2 for EMH vs. W decreased from 0.48 to 0.28. A gray‐scale EMH map contained nine out of 56 quarter‐section boundaries coinciding with step variations in EMH. The t‐statistics for differences in mean W were six of nine significant at 0.001 and nine of nine at 0.05, but mean loge(ECe) had only two of nine at 0.05, implying that W caused EMH steps. Water‐affected EMH impaired prediction of ECe at depth by cokriging, because near‐surface variations in W masked ECe. Two subareas were defined, one where management factors, such as irrigation, controlled EMH, causing steps, and one where near‐surface W varied less, making cokriging predictions more reliable.

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Furrow irrigation, using siphon tubes and earthen head ditches and tailwater ditches, is the most common method of irrigating field crops in the San Joaquin Valley. Its operation requires only one irrigator.

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