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Figure 1. U.S. Geological Survey hydrologist and able assistant surveying Upper Fremont Glacier in the Wind River Range of Wyoming. This glacier is a source of irrigation water to the Riverton Reclamation Project in central Wyoming.

As an example, concern is increasing about the quality of surface and subsurface water draining from irrigated land and the potential effects of irrigation drainage on human health, fish, and wildlife. Incidences of mortality, congenital defects, and reproductive failures in waterfowl were discovered by the U.S. Fish and Wildlife Service in 1983 at Kesterson National Wildlife Refuge in western San Joaquin Valley, California. Because of concern about possible adverse effects from irrigation drainage elsewhere in the United States, the U.S. Department of the Interior (DOI) implemented the National Irrigation Water Quality Program (NIWQP) in 1985 and to date (1996) has evaluated 26 DOI irrigation projects in the Western United States (fig. 2).

Initial evaluation of the data from the NIWQP study areas indicates that selenium is the constituent most commonly found at elevated concentrations in water, bottom sediment, and biota (Feltz and others, 1991; Presser and others, 1994; and Presser, 1994). Because of these consistently elevated concentrations of selenium, factors controlling the occurrence of selenium in water need to be better defined. Water-quality data collected from the NIWQP study areas throughout the Western United States provide an opportunity to determine important and regionally significant geochemical sources and processes that control the concentration of selenium in irrigation drainwater. This information can then be used to predict where other "selenium problem areas" might occur in other parts of the Western United States and can be extrapolated to irrigated areas around the world.

The geochemical computer program SNORM (Bodine and Jones, 1986) was the "geochemical tool" used to calculate concentrations of simple salts from the results of the chemical analyses of surface-water samples from NIWQP study sites. The 12 different simple-salt concentrations calculated with the SNORM program for each water sample are representative of the minerals ("salts") that would remain after a water sample was completely evaporated. To evaporate the thousands of water samples collected at all the NIWQP study areas and determine the amount and type of salt remaining would be quite labor intensive; instead, the SNORM computer program simulates this evaporation almost instantly. The abundance and type of simple salts calculated with the SNORM program provides useful geochemical information on what kind of minerals (selenium or non-selenium bearing) the water might have contacted and partially dissolved. This is analogous to determining where a person lives on the basis of his or her accent.

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Figure 3. Results of pattern-recognition modeling of the National Irrigation Water Quality Program data base.

The individual points in figure 3A show where the simple salts for each of the NIWQP water samples plot on these newly defined axes. The points are grouped into three distinct clusters when viewed in three dimensions and are classified as groups 1, 2, and 3. Water samples in group 1 have the greatest percentage of selenium concentrations higher than 5 micrograms per liter (\f5 m\f4 g/L) (fig. 3B). Group 1 samples are distinguished from group 2 and 3 samples by elevated concentrations of the sulfate simple salts and sodium chloride simple salt combined with very low concentrations of the calcium carbonate simple salt. On the basis of the simple salt "chemical fingerprint" for group 1 samples, selenium is mobilized during irrigation by the oxidation of sulfide minerals or re-solution of sulfate salts that have selenium substituted for sulfate in the mineral structure.

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Figure 4. Proportion of samples from each National Irrigation Water Quality Program study area falling within the group 1 scores cluster and groups 2 and 3 scores cluster shown in figure 3A.

Simple-salt groupings that identify water samples with elevated selenium concentrations also are evident at individual NIWQP study areas. The majority of study areas with at least 25 percent of water samples belonging to the group 1 simple-salt association consistently contained the greatest percentage of samples with selenium concentrations higher than 2.0 \f5 m\f4 g/L (fig. 4). With the exception of the Riverton Reclamation Project, Tulare Lake Bed Area, and Salton Sea Area, all study areas have irrigated soils developed on upper Cretaceous marine sediments. These sediments contain the necessary sulfide minerals required for sulfide weathering reactions, and the resulting simple-salt "chemical fingerprint" for group 1 sites. Most study areas with at least 75 percent of water samples classified in groups 2 or 3 consistently contain the greatest percentage of samples with selenium concentrations less than 2.0 \f5 m\f4 g/L (fig. 4) and do not have irrigated soils developed from upper Cretaceous marine sediments. On the basis of these pattern-recognition modeling results, the simple-salt "chemical fingerprint" of water samples can help differentiate samples with elevated selenium concentrations from samples with low selenium concentrations.

The first step in building a SIMCA classification model is to "train" the model to recognize the sample groups to be classified by the simple-salt concentrations. The data are referred to as a training data set and consist of the NIWQP data set used in the pattern-recognition modeling described previously, with each of the samples classified according to groups 1, 2, or 3 (fig. 3A). After the classification model is constructed and optimized with the training data set, the model is ready to classify unknown samples in what is termed a "test" data set.

The test data set used to evaluate the performance of the SIMCA model consisted of data from more than 2,000 samples of surface water from Wyoming and Utah compiled from the National Water Data Storage and Retrieval (WATSTORE) System of the U.S. Geological Survey. Data from Wyoming and Utah were selected because of the diverse geologic environments represented in these areas and the documented presence of selenium in surface-water samples. The model classified more than 75 percent of the samples with elevated selenium concentrations (higher than 2.0 \f5 m\f4 g/L) correctly into group 1. More than 80 percent of the samples with low selenium concentrations were correctly classified into groups 2 or 3.

The following publications contain additional information on the occurrence of selenium in irrigation drainwater and the geochemical and statistical tools used to identify and predict the occurrence of selenium-producing landscapes. Details on pattern-recognition and classification modeling of the NIWQP data sets can be found in Naftz (in press).

Feltz, H.R., Sylvester, M.A., and Engberg, R.A., 1991, Reconnaissance investigations of the effects of irrigation drainage on water quality, bottom sediment, and biota in the Western United States, in Mallard, G.E., and Aronson, D.A., eds., Proceedings of the U.S. Geological Survey Toxic Substance Hydrology Program, Monterey, Calif., March 11-15, 1991: U.S. Geological Survey Water-Resources Investigations Report 91-4034, p. 319-323.

Meglen, R.R., and Sistko, R.J., 1985, Evaluating data quality in large data bases using pattern-recognition techniques, in Breen, J.J., and Robinson, P.E., eds., Environmental Application of Chemometrics, American Chemical Society Symposium Series 292, Wash., D.C., American Chemical Society, p. 18-33.

National Research Council, 1989, Irrigation- induced water quality problems, Wash., D.C., National Academy Press, 157 p.

Presser, T.S., 1994, The Kesterson effect: Environmental Management, v. 18, no. 3, p. 437-455.

Presser, T.S., Sylvester, M.A., and Low, W.H., 1994, Bioaccumulation of selenium from natural geologic sources in Western States and its potential consequences: Environmental Management, v. 18, no. 3, p. 423-436.

Wold, Svante, and Sjostrom, Michael, 1977, SIMCA: A method for analyzing chemical data in terms of similarity and analogy, in Kowalski, B.R., ed., Chemometrics Theory and Application, American Chemical Society Symposium Series 52, Wash., D.C., American Chemical Society, p. 243-282.