Irrigation-Induced Contamination 

of Water, Sediment, and Biota in the 

Western United States—Synthesis of 

Data from the National Irrigation 

Water Quality Program

U.S. GEOLOGICAL SURVEY

Professional Paper 1655

U.S. GEOLOGICAL SURVEY

U.S. FISH AND WILDLIFE SERVICE

BUREAU OF RECLAMATION

BUREAU OF INDIAN AFFAIRS
(Back of Cover)
U.S. GEOLOGICAL SURVEY PROFESSIONAL PAPER

2003

Irrigation-Induced Contamination of Water, 

Sediment, and Biota in the Western United States—

Synthesis of Data from the National Irrigation Water 

Quality Program

1655

By RALPH L. SEILER, JOSEPH P. SKORUPA, DAVID L. NAFTZ, and B. THOMAS NOLAN 

NATIONAL IRRIGATION WATER QUALITY PROGRAM
U.S. DEPARTMENT OF THE INTERIOR

GALE A. NORTON, Secretary

U.S. GEOLOGICAL SURVEY

CHARLES G. GROAT, Director

Any use of trade, product, or firm names in this publication is for

descriptive purposes only and does not imply endorsement by the

U.S. Government

Library of Congress Cataloging-in-Publication Data

Irrigation-induced contamination of water, sediment, and biota in the western United

States-synthesis of data from the National Irrigation Water Quality Program / Ralph L.

Seiler ... [et al.].

p. cm. -- (U.S. Geological Survey professional paper ; 1655)

Includes bibliographical references (p. ).

ISBN 0-607-89024-X

1. Selenium--Environmental aspects--West (U.S.) 2. Irrigation--Environmental

aspects--West (U.S.) 3. Geographic information systems--West (U.S.) I. Seiler, R.L. II.

Series.

QH545.S45I77 2003

363.738‘4--dc21

2002044780

For sale by U.S. Geological Survey, Branch of Information Services,

Denver Federal Center, Box 25286, Denver, CO 80225–0286
CONTENTS III

Page

Abstract.................................................................................................... 1

Introduction.............................................................................................. 3

Creation of National Irrigation Water Quality Program .................... 3

Summary of National Irrigation Water Quality Program................... 3

Approach and objectives of National Irrigation Water Quality

Program Data-Synthesis Project .................................................. 5

Purpose and scope.............................................................................. 8

Acknowledgments.............................................................................. 8

Literature review ................................................................................ 8

Historical perspective................................................................... 8

Previous National Irrigation Water Quality Program evaluations 8

Methods used ........................................................................................... 10

Data collection ................................................................................... 10

Study-area selection..................................................................... 10

Site selection and sampling schedule........................................... 10

Protocols for data collection and chemical analysis .................... 10

Water ...................................................................................... 11

Sediment ................................................................................ 12

Biota....................................................................................... 13

Data synthesis .................................................................................... 13

Data base...................................................................................... 13

Availability............................................................................. 13

Introduction and structure...................................................... 13

Sources of data....................................................................... 15

Data manipulation and quality assurance .............................. 15

Data interpretation ....................................................................... 16

Statistical methods ................................................................. 16

Geochemical methods............................................................ 17

Geographic Information Systems .......................................... 17

Biological methods ................................................................ 17

Data synthesis .......................................................................................... 18

National Irrigation Water Quality Program Data Base ...................... 18

Descriptive summary ................................................................... 18

Statistical bias .............................................................................. 18

Limitations of data ............................................................................. 19

Sampling-site agreement.............................................................. 21

Matrix selection ........................................................................... 21

Species selection .......................................................................... 21

Tissue selection............................................................................ 21

Identification of contaminants in water and sediment ....................... 22

Standards, criteria, and guidelines used....................................... 22

Summary statistics and comparison to criteria ............................ 23

National Irrigation Water-Quality Program study-area 

comparisons ........................................................................... 28

Dissolved solids ..................................................................... 28

Arsenic ................................................................................... 28

Boron ..................................................................................... 30

Molybdenum.......................................................................... 30

Selenium ................................................................................ 30

Surface water ................................................................... 30

Ground water ................................................................... 34

Bottom sediment .............................................................. 34

Uranium ................................................................................. 34

Pesticides ............................................................................... 34

CONTENTS

Page

Data Synthesis—Continued

Contaminant associations ............................................................ 36

Relation between major-ion and trace-element concentrations in surface water ....................................................... 37

Relation between contaminant concentrations in surface

 water and bottom sediment ............................................. 40

Relation between total- and filtered-contaminant concentrations in surface water ....................................................... 44

Conceptual model of selenium contamination in irrigated areas............. 44

Selenium biogeochemistry................................................................. 46

Enrichment of selenium in rocks ....................................................... 46

Enrichment of selenium in soils and ground water in irrigated areas 47

Enrichment of selenium in water in fish and wildlife habitat............ 48

Incorporation of selenium into the food chain and wildlife 

exposure to it................................................................................ 48

Implications of the conceptual model................................................ 49

Physical and chemical processes involving selenium in NIWQP areas .. 49

Selenium concentrations.................................................................... 49

Sources of selenium........................................................................... 49

Distribution of seleniferous rocks in the Western United States . 49

Sources of selenium in National Irrigation Water Quality 

Program Study areas .............................................................. 52

Climate............................................................................................... 54

Hydrology .......................................................................................... 54

Effects of terminal water bodies on selenium concentrations ..... 54

Effects of upstream sources of selenium ..................................... 57

Temporal changes in selenium concentrations .................................. 58

Interrelation of geology, climate, and hydrology............................... 59

Selenium in biota ..................................................................................... 63

Effects of selenium on animals.......................................................... 63

Selenium guidelines........................................................................... 63

Summary statistics and comparison with guidelines ......................... 64

National Irrigation Water Quality Program study-area comparisons 65

Sample bias.................................................................................. 65

Plants............................................................................................ 67

Aquatic invertebrates ................................................................... 67

Fish............................................................................................... 67

Birds............................................................................................. 67

Liver....................................................................................... 67

Eggs ....................................................................................... 68

Taxonomic and feeding guild analysis of avian-egg data.................. 72

Prediction of selenium contamination of water ....................................... 74

Prediction using geologic, climatic, and hydrologic data.................. 74

Broad geographic areas................................................................ 75

Individual locations...................................................................... 78

Limitations of these predictive tools............................................ 80

Prediction using logistic-regression model........................................ 80

Prediction using major-ion chemistry................................................ 84

Geochemical and statistical tools used in model development.... 84

Creation of classification model .................................................. 84

Application of classification model ............................................. 85

Recommendations for use of predictive tools.................................... 86

III
IV CONTENTS

ILLUSTRATIONS

Page

FIGURES 1–2. Maps showing:

1. Location of 191 areas in the Western United States evaluated for irrigation-drainage problems by National Irrigation Water 

Quality Program task group........................................................................................................................................................ 4

2. Location of 26 National Irrigation Water Quality Program study areas selected because of potential irrigation-drainage 

water-quality problems ............................................................................................................................................................... 6

3. Diagram showing structure and linking attributes of National Irrigation Water Quality Program data base ................................................ 14

4. Histogram of molybdenum concentrations in surface-water samples from National Irrigation Water Quality Program study areas........... 24

5–10. Box plots showing concentrations of constituents from source-water sites and from sites in and downstream from irrigated areas:

5. Total dissolved solids in surface water ............................................................................................................................................. 28

6. Arsenic in surface water, ground water, and bottom sediment ......................................................................................................... 29

7. Boron in surface water, ground water, and bottom sediment ........................................................................................................... 31

8. Molybdenum in surface water, ground water, and bottom sediment................................................................................................ 32

9. Selenium in source water used for irrigation and in surface water, ground water, and bottom sediment ........................................ 33

10. Uranium in surface water, ground water, and bottom sediment ....................................................................................................... 35

11. Graph showing relation between uranium and total dissolved-solids concentrations in ground water ......................................................... 36

12. Box plots showing concentrations of total DDT (DDT plus metabolites) in surface water and bottom sediment in National

Irrigation Water Quality Program study areas ........................................................................................................................................... 36

13. Graph showing principal-components analysis results, showing trace-element loadings and study-area scores .......................................... 37

14–19. Graph showing relation between:

14. Median molybdenum and boron concentrations in surface water from National Irrigation Water Quality Program study areas ... 38

15. Boron and molybdenum, boron and chloride, and molybdenum and chloride concentrations in surface-water sampled from two 

National Irrigation Water Quality Program study areas ............................................................................................................. 39

16. Median concentrations of selected trace elements in surface water and in <0.062-millimeter-fraction bottom sediment in

National Irrigation Water Quality Program study areas ............................................................................................................. 41

17. Concentrations of selected trace elements in surface water and in <0.062-millimeter-fraction bottom sediment at individual

data-collection sites .................................................................................................................................................................... 42

18. Trace-element concentrations in bottom sediment and product of percentage organic carbon in <0.062-millimeter-fraction

bottom sediment and trace-element concentrations in surface water ......................................................................................... 43

19. Concentrations of aluminum, arsenic, boron, molybdenum, and selenium in filtered and in unfiltered surface water ................... 45

20. Map showing location of areas in Western United States where potentially seleniferous rocks form bedrock............................................. 51

21. Box plot showing statistical summary of selenium concentrations in filtered surface-water samples showing association of Upper

Cretaceous marine sedimentary rocks and elevated selenium concentrations .......................................................................................... 52

22. Map showing evaporation index in Western United States ............................................................................................................................ 56

23. Box plot showing statistical summary of selenium concentrations in filtered surface-water samples from lakes, ponds, and marshes,

demonstrating association of elevated selenium concentrations and terminal hydrologic systems .......................................................... 57

24–26. Graphs showing:

24. Selenium concentrations in surface water, Sun River area, Montana, 1990–93............................................................................... 58

25. Seasonal changes in selenium concentrations in deep and shallow wells near North Roadside Pond, middle Green River 

Basin, Utah, 1988–93 ................................................................................................................................................................. 59

26. Selenium concentrations in Rasmus Lee Lake, Kendrick Reclamation Project, Wyoming, during March–April 1988 and

March–April 1989 ...................................................................................................................................................................... 59

27. Graph showing relations between selenium concentrations, climate data, and geology for 26 National Irrigation Water Quality

Program study areas .................................................................................................................................................................................. 62

28–30. Box plots showing:

28. Selenium concentrations in plants, aquatic invertebrates, fish, birds (livers and eggs) in National Irrigation Water Quality

Program study areas.................................................................................................................................................................... 66

29. Geometric-mean concentrations of selenium in sets of avian eggs from National Irrigation Water Quality Program study areas . 69

30. Geometric-mean concentrations of selenium in sets of waterbird eggs from three feeding guilds.................................................. 74

Page

Avian-egg risk assessment ...................................................................... 86

Environmental contamination and risk assessment........................... 86

Choice of risk metric......................................................................... 87

Reasons for focus on selenium.......................................................... 88

Teratogenic-risk assessment.............................................................. 90

Multielement teratogenic-response data ........................................... 92

Relevance of arsenic and mercury..................................................... 94

Teratogenic-response curves for selenium exposure......................... 96

Page

National Irrigation Water Quality Program teratogenic-risk 

assessment................................................................................... 97

National Irrigation Water Quality Program embryo-viability 

assessment................................................................................... 99

Demographic context of projected avian effects............................... 104

Relation between selenium in water, sediment, and biota ...................... 107

Summary and conclusions....................................................................... 112

References cited ...................................................................................... 115
CONTENTS V

Page

FIGURES 31–32. Maps showing locations of:

31. Areas in Western United States that are identified as susceptible to irrigation-induced selenium contamination on basis of

Upper Cretaceous or Tertiary marine sedimentary bedrock and evaporation index greater than 2.5........................................ 76

32. National Irrigation Water Quality Program study areas, test areas, and areas in Western United States susceptible to 

irrigation-induced selenium contamination ............................................................................................................................... 77

33. Decision tree for prediction of likelihood of selenium contamination and classification of test areas and 26 National Irrigation

Water Quality Program study areas used to derive decision tree.............................................................................................................. 79

34–36. Graph showing:

34. Relation between total dissolved-solids concentration and probability that selenium concentration exceeds 5 micrograms 

per liter (U.S. Environmental Protection Agency chronic criterion for selenium) .................................................................... 83

35. Three-dimensional principal-component score plot showing grouping of water samples from National Irrigation Water 

Quality Program study areas into three distinct clusters............................................................................................................ 85

36. Relation between facies-1 water samples and water samples having selenium concentrations greater than or equal to 

3 micrograms per liter, a guideline for protection of semi-aquatic wildlife .............................................................................. 85

37. Photographs showing typical selenium-induced terata of avian embryos..................................................................................................... 91

38. Box plots comparing trace-element concentrations in stilt eggs containing normal and deformed embryos............................................... 93

39. Graph showing exploratory factor-analysis loadings, showing association of trace elements in eggs and the presence of terata ............... 94

40. Box plots comparing mercury concentrations in stilt and avocet eggs from National Irrigation Water Quality Program study areas to 

concentrations in stilt eggs from the Tulare Lake Bed area in California ................................................................................................ 95

41. Graph showing teratogenic-response functions for ducks, stilts, and avocets .............................................................................................. 97

42. Graph showing teratogenic-response functions for stilts at Kesterson Reservoir during 1983–85 and in Tulare Lake Bed area 

during 1987–95 ......................................................................................................................................................................................... 97

43. Box plot showing statistical summary of projected magnitude of selenium-induced hen effects at sites with one or more eggs

exceeding stilt standard (intermediate sensitivity) for embryotoxicity..................................................................................................... 102

44. Graph showing magnitude of predicted hen effects at 79 National Irrigation Water Quality Program data-collection sites where 

selenium concentrations in eggs are sufficient to reduce hatchability...................................................................................................... 104

45. Graph showing relation between effected eggs and effected hens for black-necked stilts during 1983–85 at Kesterson Reservoir and

during 1988–89 in basin that includes Tulare Lake Bed area in California ............................................................................................. 105

46. Graph showing relations among selenium concentrations in surface water and bottom sediment (<0.062-millimeter fraction),

and invertebrate tissue............................................................................................................................................................................... 107

47. Graph showing relation between selenium concentrations in surface water at nesting site and in sets of avian eggs, during

April-July 1987–92 ................................................................................................................................................................................... 109

48. Graph showing relation between study-area rankings for selenium in water and plants, water and aquatic invertebrates, water and 

fish, and water and reproductive effects on birds...................................................................................................................................... 111

Page

TABLE 1. Reconnaissance and detailed studies concerning National Irrigation Water Quality Program study areas ........................................................... 7

2. Minimum analytical reporting limits for trace elements in water, bottom sediment, and biota............................................................................. 11

3. Minimum analytical reporting limits for pesticides in water, bottom sediment, and biota .................................................................................... 12

4. Water and bottom-sediment data-collection sites in National Irrigation Water Quality Program study areas....................................................... 18

5. Description of surface-water analyses in National Irrigation Water Quality Program data base ........................................................................... 18

6. Description of ground-water analyses in National Irrigation Water Quality Program data base ........................................................................... 19

7. Description of bottom-material analyses in National Irrigation Water Quality Program data base ....................................................................... 19

8. Description of biological-sample analyses in National Irrigation Water Quality Program data base .................................................................... 20

9. Taxa collected for inorganic analysis in National Irrigation Water Quality Program study areas.......................................................................... 20

10. Contaminant standards, criteria, and guidelines used by data-synthesis team to evaluate National Irrigation Water Quality Program 

water and sediment data ..................................................................................................................................................................................... 22

11–14. Summary statistics for:

11. Filtered inorganic constituents in water samples from National Irrigation Water Quality Program study areas....................... 25

12. Inorganic constituents in bottom-sediment samples from National Irrigation Water Quality Program study areas................. 26

13. Selected pesticides in surface-water samples from National Irrigation Water Quality Program study areas ............................. 27

14. Selected pesticides in bottom-sediment samples from National Irrigation Water Quality Program study areas ....................... 27

15. Classification of National Irrigation Water Quality Program study areas by selenium content of surface water in and downstream from

irrigated areas ..................................................................................................................................................................................................... 50

16. Generalized bedrock geology of National Irrigation Water Quality Program study areas..................................................................................... 53

TABLES
VI CONTENTS

Page

Tables—Continued

17. Summary description of climate in National Irrigation Water Quality Program study areas................................................................................. 55

18. Summary description of degree of selenium contamination and physical characteristics of National Irrigation Water Quality Program 

study areas .......................................................................................................................................................................................................... 61

19. Selenium concentrations in biota that may adversely affect sensitive fish or aquatic birds................................................................................... 63

20. Summary statistics for selenium in plants, invertebrates, fish, and bird livers and eggs from National Irrigation Water Quality Program 

study areas .......................................................................................................................................................................................................... 65

21. Classification of National Irrigation Water Quality Program study areas by selenium content of avian eggs....................................................... 70

22. Taxonomic distribution of National Irrigation Water Quality Program avian-egg samples analyzed for selenium............................................... 73

23. Amount of land in Western United States where bedrock consists of Upper Cretaceous or Tertiary marine sedimentary rocks and where

evaporation index exceeds four threshold values ............................................................................................................................................... 75

24. Matrix of Spearman correlation coefficients (r) for variables considered in logistic-regression models............................................................... 81

25. Results of logistic-regression modeling of selenium in surface-water samples from National Irrigation Water Quality Program study areas .... 83

26. Background levels and toxic thresholds for contaminants in avian eggs ............................................................................................................... 88

27. Toxic exposures of contaminants to avian eggs from National Irrigation Water Quality Program study areas .................................................... 89

28. Correlation matrix for Tulare Lake Basin multielement-response sample............................................................................................................. 94

29. Number of avian embryos analyzed for selenium content and assessed for teratogenesis .................................................................................... 96

30. Predicted rates of avian teratogenesis in National Irrigation Water Quality Program study areas......................................................................... 98

31. Power analysis of avian data for detecting selenium-induced terata in National Irrigation Water Quality Program study areas.......................... 100

32. Calculation of probability of hen effect, Belle Fourche Reclamation Project (C), South Dakota.......................................................................... 102

33. Summary of predicted avian embryotoxicity in National Irrigation Water Quality Program study areas ............................................................. 103

34. Effected hens and effected eggs of black-necked stilts, Tulare Lake Bed area, California.................................................................................... 104

35. Study-area rankings, summary statistics for selenium concentrations in water, plants, aquatic invertebrates, and fish, and reproductive

effects on birds.................................................................................................................................................................................................... 110

CONVERSION FACTORS, VERTICAL DATUM, AND WATER-QUALITY UNITS

Multiply By To obtain

acre 4,047 square meter

bar 100 kilopascal

cubic foot per second (ft3/s) 0.02832 cubic meter per second

foot (ft) 0.3048 meter

inch (in.) 25.40 millimeter

inch per year (in/yr) 25.40 millimeter per year

mile (mi) 1.609 kilometer

pint 0.4732 liter

pound per day (lb/d) 0.4536 kilogram per day

square mile (mi2) 2.590 square kilometer

Abbreviated water-quality units used in this report: Additional abbreviated units used in this report:

mg/L, milligram per liter μg/g, microgram per gram

μg/L, microgram per liter μm, micrometer

ng/L, nanogram per liter mm, millimeter

pg/L, picograms per liter μg/kg, microgram per kilogram

mg/kg, milligram per kilogram

ECx, Concentration that has an effect on x percent of the

organisms tested. The EC50, is the concentration

that affects 50 percent of the organisms tested.

Unless otherwise noted, all trace-element concentrations in biological samples are in dry weight. Dry-weight and wet-weight concentrations in 

biological tissue can be converted from one to the other by using these equations:

Dry-weight concentration = (wet-weight concentration) / [1 − (percent moisture/100)]

Wet-weight concentration = (dry-weight concentration) x [1 − (percent moisture/100)]

Sea level: In this report, "sea level" refers to the National Geodetic vertical Datum of 1929 (NGVD of 1929, formerly called Sea-Level Datum of 1929),

which is derived from a general adjustment of the first-order leveling networks of the United States and Canada.
 1

ABSTRACT

In October 1985 the U.S. Department of the Interior (DOI), through the 

National Irrigation Water Quality Program (NIWQP), began a series of field 

investigations at 26 areas in the Western United States to determine whether irrigation drainage has had harmful effects on fish, wildlife, and humans or has 

reduced beneficial uses of water. In 1992 NIWQP initiated the Data Synthesis 

Project to evaluate data collected during the field investigations. Geologic, climatologic, and hydrologic data were evaluated and water, sediment, and biota 

from the 26 areas were analyzed to identify commonalities and dominant factors 

that result in irrigation-induced contamination of water and biota.

Data collected for the 26 area investigations have been compiled and merged 

into a common data base. The structure of the data base is designed to enable 

assessment of relations between contaminant concentrations in water, sediment, 

and biota. The data base is available to the scientific community through the 

World Wide Web at URL <http://www.usbr.gov/niwqp>. Analysis of the data base 

for the Data Synthesis included use of summary statistics, factor analysis, and 

logistic regression. A Geographic Information System was used to store and 

analyze spatially oriented digital data such as land use, geology and evaporation 

rates.

In the U.S. Department of the Interior (DOI) study areas, samples of water, 

bottom sediment, and biota were collected for trace-element and pesticide analysis. Contaminants most commonly associated with irrigation drainage were 

identified by comparing concentrations in water with established criteria. For 

surface water, the criteria used were typically chronic criteria for the protection 

of freshwater aquatic life. Because ground water can discharge to the surface 

where wildlife can be exposed to it, the criteria used for ground water were both 

the maximum contaminant levels (MCL’s) for drinking water and the chronic 

criteria for the protection of freshwater aquatic life. 

Data collected by the NIWQP studies indicated that, in surface water, filtered 

and unfiltered samples had nearly the same concentrations of arsenic, boron, 

molybdenum, and selenium for concentrations greater than about 10 micrograms per liter. Therefore, in this concentration range, filtered concentrations 

can be directly compared to biological-effect levels developed for unfiltered 

samples. In the range of 1 to 10 micrograms per liter there may be a tendency 

for unfiltered arsenic concentrations to be greater than filtered concentrations. 

For selenium, however, the data suggest differences from equality in that range 

result from analytical imprecision and not a general tendency for unfiltered concentrations to be greater than filtered concentrations. This relation may not be 

true in lentic, nutrient-rich waters because in such settings algae can bioaccumulate large amounts of selenium and other trace elements.

Selenium was the trace element in surface water that most commonly 

exceeded chronic criteria for the protection of freshwater aquatic life; more than 

40 percent of the selenium concentrations in surface-water samples exceeded 

the U.S. Environmental Protection Agency (USEPA) aquatic-life chronic criterion (5 micrograms per liter). In 12 of the 26 areas at least 25 percent of the surface water-samples had selenium concentrations that either equaled or exceeded 

the chronic criterion (5 micrograms per liter). More than 28 percent of boron 

concentrations and almost 17 percent of the molybdenum concentrations 

exceeded the aquatic life criteria established by the State of California (550 and 

19 micrograms per liter, respectively). In ground water, more than 22 percent 

of the arsenic concentrations and more than 35 percent of the selenium concentrations exceeded the MCL (10 and 50 micrograms per liter, respectively). Few 

samples of uranium in surface water exceeded a criterion for the protection of 

aquatic life (300 micrograms per liter), but 44 percent of the uranium concentrations in ground water exceeded the MCL (30 micrograms per liter). Molybdenum, selenium and uranium were the trace elements most commonly found 

in bottom-sediment samples that exceeded the upper limit of the 95th percentile 

expected range in soils of the Western United States. Selenium is the only trace 

element for which ecological sediment guidelines are used in this report. Selenium concentrations commonly exceeded the ecological sediment guideline of 

two micrograms per gram.

DDT and its degradation products DDD and DDE were the most common 

pesticide residues found in surface water at concentrations exceeding criteria.

However, almost all the samples exceeding the criteria were from a single 

study area, the Owyhee–Vale Reclamation Project areas in Oregon and Idaho. 

The organochlorine pesticide chlordane was detected in 30 percent of the bottom-sediment samples, and undegraded DDT was detected in 21 percent. DDT 

or its degradation products were detected in all 21 study areas where bottomsediment samples were analyzed for organochlorine pesticides.

A principal-components analysis indicated that elevated selenium concentrations in surface water are not associated with elevated boron, molybdenum, 

or arsenic concentrations. The occurrence of selenium is associated with sulfate and uranium. The association of boron and molybdenum with chloride 

suggests that evaporative processes control their concentrations. Arsenic is not 

associated with any other measured trace element and is associated negatively 

with selenium.

This report focuses on selenium because it was the trace element most frequently found at concentrations exceeding criteria for the protection of aquatic 

life. Selenium concentrations in water are dynamic, and, at a given site, the 

selenium concentration can change by an order of magnitude during a year and 

from one year to another. In some areas, selenium contamination may not occur 

during normal or wet periods. However, during a drought, reduced water deliveries may result in selenium contamination by evaporative concentration.

Marine sedimentary rocks, especially those of Late Cretaceous age, are 

likely to be seleniferous. Irrigation of soils derived from them can contribute 

large amounts of selenium to drainwater; shallow wells in and near irrigated 

areas contained hundreds to thousands of micrograms per liter of selenium. 

The median selenium concentration in surface-water samples from NIWQP 

sites associated with Upper Cretaceous marine sedimentary rocks is 7 micrograms per liter (range less than 1 to 8,300 micrograms per liter) and from sites 

not associated with such rocks is 0.4 micrograms per liter (range less than 1 to 

390 micrograms per liter). 

Irrigation-induced selenium contamination has been observed only in arid 

or semiarid areas. In those NIWQP study areas having local geologic sources 

of selenium, typically more than 25 percent of the surface-water samples 

exceed the chronic criterion for selenium if the evaporation rate is 3.0 times 

greater than the annual precipitation. 

Irrigation-Induced Contamination of Water, Sediment, and Biota in the 

Western United States—Synthesis of Data from the National Irrigation Water 

Quality Program

By Ralph L. Seiler, U.S. Geological Survey; 

Joseph P. Skorupa, U.S. Fish and Wildlife Service; and

David L. Naftz and B. Thomas Nolan, U.S. Geological Survey
2 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

In terminal water bodies, selenium accumulates and is not flushed out. In 

both terminal and flow-through lakes and ponds, the median selenium concentrations in surface water for samples collected from June through August are 

nearly the same (1.0 and 0.8 micrograms per liter, respectively). However, the 

75th-percentile selenium concentration for terminal water bodies (24 μg/L) is 

significantly higher than for flow-through systems (4 μg/L). 

Selenium concentrations in biota were compared with concentrations that 

have been demonstrated to have adverse effects on similar species (the effect 

level) or to have adverse effects on another species if consumed (the dietary 

effect level). Twenty-five percent of the plant samples had selenium concentrations exceeding the dietary effect level (3 micrograms per gram dry weight) 

whereas more than 57 percent of the invertebrate samples and 61 percent of the 

fish samples exceeded the dietary effect level. Of the more than 2,000 bird eggs 

collected, 44 percent had selenium concentrations exceeding 6 micrograms per 

gram, a threshold value for reduced hatchability. In 14 areas, selenium concentrations in eggs from some populations of birds exceeded 6 micrograms per 

gram. Selenium-caused deformities of bird embryos were found in four of the 

NIWQP study areas; however, most study areas were not systematically surveyed for such deformities.

Eggs were sampled from 34 species of birds belonging to 10 orders. Nearly 

all the eggs collected come from aquatic species of birds, with American coots, 

mallards, and American avocets being the three species most frequently collected. Of the 34 species, at least one set of eggs from 16 species had a geometric-mean selenium concentration of at least 12.5 micrograms per gram, a highrisk threshold. All three species of grebes yielded at least one set of high risk 

eggs, as did four of five species of shorebirds and five of eleven species of waterfowl. Egg-set data were examined to determine if some feeding guilds are more 

at risk to selenium poisoning than others. Analysis of data for waterbird eggs 

from the study areas where the 75th percentile selenium concentration in surface 

water exceeded 5 μg/L suggests that herbivorous birds may bioaccumulate less 

selenium than insect- and fish-eating birds. For birds from these study areas, 

selenium concentrations for 39 percent of the egg sets from herbivorous birds 

fell in the normal range (less than 3 μg/g) while only 7 and 0 percent, respectively, of egg sets from insect- and fish-eating birds fall in the normal range. 

Although herbivorous birds may bioaccumulate less selenium, it does not 

appear that any waterbird feeding guilds are particularly well buffered from 

exposure to selenium contamination.

Predictive tools were developed to aid managers in identifying specific land 

areas at risk for irrigation-induced selenium contamination. The tools range 

from identifying broad geographic regions where selenium contamination is 

likely, to assessing the probability that selenium concentrations in a specific 

stream or lake exceed the chronic criterion for selenium. 

A geographic information system was used to prepare a map that identifies 

land areas in the Western United States that are susceptible to selenium contamination if irrigated. On the basis of the 75th percentile, selenium concentration 

in surface water, 12 of the 26 NIWQP study areas were classified as contaminated, two as seleniferous, and 12 as uncontaminated. The map correctly identified both seleniferous areas and 10 of 12 selenium-contaminated areas as 

susceptible; 10 of 12 uncontaminated areas were correctly identified as not 

being susceptible. About 160,000 square miles are identified as being susceptible; of that area, about 4,100 square miles have been identified by satellite imagery as actively being irrigated.

Principal-components analysis and pattern-recognition techniques indicate 

that major-ion chemistry of water samples alone can be used to identify selenium- and nonselenium-producing areas in the Western United States. Water 

samples composed of simple salts of sulfate typically have concentrations of 

selenium that exceed 3 μg/g, whereas samples composed of simple salts of chloride or carbonate typically have low selenium concentrations. Weathering of 

soils that contain reduced-sulfur minerals, such as pyrite, mobilizes sulfur and 

selenium because selenium commonly substitutes for sulfur in these minerals.

In areas where the bedrock is composed of Upper Cretaceous marine sedimentary rocks, logistic regression of data from the NIWQP sites indicates that 

if the dissolved-solids concentration equals 1,000 milligrams per liter, the probability is about 69 percent that the selenium concentration will exceed 5 micrograms per liter, the U.S. Environmental Protection Agency chronic criterion. In 

areas where the bedrock is not composed of such rocks, the probability is only 

about 10 percent. 

The avian-egg data within the biotic data base were used to make a quantitative toxicological risk assessment. Of the 23 study areas where avian eggs 

were sampled, 14 areas yielded at least one egg containing 6 μg/g selenium, the 

threshold for embryotoxicity. However, only 6 of the study areas yielded eggs 

containing enough selenium to expect selenium-induced teratogenesis of duck 

embryos. Predicted probabilities of discovering embryo teratogenesis matched 

field observations in 13 of the 14 study areas reporting results of embryo 

assessments.

Bird eggs were collected from 161 individual sampling sites and at 79 of 

those sites selenium concentrations in one or more eggs exceeded 6 μg/g. At 

the 79 sites where biological effects are expected on the basis of selenium concentrations in the eggs, the median rate was 3.9 percent of the hens losing at 

least one egg to selenium-induced embryotoxicity. This corresponds to about 

1.2 percent selenium-induced egg inviability among otherwise viable eggs. 

Across all NIWQP study areas, the overall rate of hens projected to lose at least 

one egg to selenium-induced embryotoxicity is estimated to be 1.9 percent, 

which corresponds to about 0.3 percent selenium-induced egg inviability 

among otherwise viable eggs. After accounting for increased mortality of selenium poisoned-hatchlings due to other factors such as weather and predators, 

it was estimated that that increases of 0.3 and 1.2 percent in inviable eggs

would cause approximately a 1.4 and 5.4 percent depression in nesting success.

Regional surveys of nesting success among ducks revealed that duck populations commonly exist near their demographic break-even point. The vulnerable demographic condition of North American duck populations during the 

mid-1960’s to mid-1980’s was primarily due to noncontaminant factors, such 

as poor-quality nesting habitat and dry climatic cycles. Under such conditions, 

rates of 1.4- to 5.4-percent depression in nesting success caused by exposure to 

selenium from irrigation projects can be crucial for avian populations already 

close to their demographic break-even point. Even the worst-case levels of contaminant effects, however, could be tolerated by populations of ducks existing 

just modestly above demographic break-even points. This suggests the biotic 

risk to ducks could be addressed by reducing irrigation-induced water pollution 

but more effectively by restoring high-quality (more predator-safe) nesting 

habitat.

An analysis at the nesting-site level was made of the relation between selenium content of water and bird eggs. Eggs from 93 bird populations were collected from nesting sites where the water sample collected during April–July 

contained less than 5 μg/L selenium. The average selenium concentration in 

egg sets was embryotoxic in 19 of the 93 populations. Of the populations collected at sites where the selenium in the water was less than 1 microgram per 

liter, only four of 54 populations contained embryotoxic concentrations. Eggs 

from 65 populations of birds were collected from nesting sites where selenium 

concentrations in water samples collected during April–July equaled or 

exceeded 5 μg/L, and 55 of those 65 populations contained embryotoxic concentrations of selenium in the eggs. 

An analysis at the study-area level was made of the relation between selenium contamination of water and selenium contamination of the food chain 

and egg loss due to selenium poisoning. Most food organisms, particularly 

aquatic invertebrates and fish, contained potentially harmful amounts of selenium in study areas where selenium concentrations in more than 25 percent of 

the water samples exceed 5 μg/L. The analysis also indicates that some hens 

are predicted to lose eggs to selenium poisoning in all study areas where selenium concentrations in more than 25 percent of the water samples exceed 5 

μg/L. These results suggest that areas where selenium contamination of the 

food chain and loss of eggs to selenium poisoning is occurring may be identified using the same methods developed to identify areas where selenium contamination of water is likely to occur.
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 3

INTRODUCTION

CREATION OF NATIONAL IRRIGATION WATER-QUALITY 

PROGRAM

In the early 1980’s, incidents of mortality, congenital deformities, and reproductive failures in waterfowl were discovered 

in Kesterson National Wildlife Refuge, western San Joaquin 

Valley, Calif. The cause of these adverse biological effects was 

determined to be selenium carried by irrigation drainwater into 

areas used by wildlife (Ohlendorf, Hoffman, and others, 1986). 

The U.S. Congress and environmental groups wanted to determine if what happened at Kesterson National Wildlife Refuge 

was an aberration or if it was symptomatic of a larger problem 

that might occur elsewhere in the Nation. To answer this specific question and to address general concerns about the quality 

of irrigation drainage and its potential harmful effects on 

humans, fish, and wildlife, the U.S. Department of the Interior 

(DOI) implemented the National Irrigation Water Quality Program (NIWQP) in October 1985. The objective of the NIWQP 

was to identify the nature and extent of irrigation-induced 

water-quality problems in the Western United States and to 

remediate those water-quality problems resulting in risk to 

humans or to DOI trust responsibilities1. 

SUMMARY OF NATIONAL IRRIGATION WATER-QUALITY 

PROGRAM

The DOI formed an interbureau group known as the “Task 

Group on Irrigation Drainage,” which included members from 

the Bureau of Indian Affairs (BIA), Bureau of Reclamation 

(BOR), U.S. Fish and Wildlife Service (USFWS), and the U.S. 

Geological Survey (USGS). The purpose of the task group was 

to prepare a comprehensive plan for reviewing irrigation-drainage concerns for which DOI may have responsibility.

The scope of the management strategy committed the program to identifying and addressing irrigation-induced waterquality and contamination problems in DOI irrigation and 

drainage facilities, National Wildlife Refuges, and other migratory-bird or endangered-species management areas that may 

receive drainwater from these DOI facilities, and public and private drinking-water supplies that may be affected by drainwater 

from these facilities.

A five-phase approach was developed for the identification 

and subsequent assessment and response to problems that were 

identified:

1 The NIWQP generally is not responsible for remediation of 

water-quality problems caused by anthropogenic chemicals such as 

pesticides. However, the presence of these chemicals can affect 

endangered species or migratory birds and thus, to that extent, bear on 

NIWQP decisions to begin site remediation. For this reason, a 

substantial amount of data on these anthropogenic chemicals were 

collected and are presented in this report.

Phase 1: Site Identification—identify sites requiring attention 

under the scope of the management strategy

Phase 2: Reconnaissance Investigations—determine from 

existing information and reconnaissance investigations 

whether irrigation drainage has caused or has the potential to 

cause harmful effects on human health, fish, or wildlife, or to 

impair beneficial uses of water

Phase 3: Detailed Studies—conduct intensive studies to 

determine the extent, magnitude, effects, and causes of 

contamination problems if reconnaissance investigations or 

new information indicates a high potential for harmful effects

Phase 4: Planning for Remediation—develop a coordinated 

plan of action with appropriate Federal, State, and local 

agencies to address identified problems

Phase 5: Remediation—implement corrective action for those 

areas and activities in which the DOI has authority and 

resources once a plan has been developed and authorized.

The DOI has constructed or manages more than 600 irrigation-drainage facilities and national wildlife refuges in 17 

Western States. In 1985–86, NIWQP made a comprehensive 

survey of these DOI projects. To facilitate evaluation of these 

areas, many smaller areas were eliminated from consideration 

and other areas were grouped together. As a result, desk evaluations of previously collected water-quality, biological, and 

geological data and other pertinent information were done for 

191 areas in the Western United States (fig. 1), of which 26 

were selected for reconnaissance investigations (fig. 2) on the 

basis of known or strongly suspected irrigation-induced problems identified during phase 1. Field investigations were made 

by interagency study teams consisting of a USGS scientist as 

team leader and additional USGS, USFWS, BOR, and BIA 

professionals.

Reconnaissance investigations (phase 2) were made in 26 

areas (table 1; fig. 2) in 14 of the 17 contiguous Western States. 

Alfalfa is the principal crop in most of the study areas; however, in some areas, cotton or foods such as onions, corn, 

wheat, and rice are the principal crops. Study-area sizes differ 

greatly. For example, in the Columbia River Basin in Washington, a total of 575,000 acres was irrigated in 1991 (Embrey and 

Block, 1995), but from 1968 through 1977, the average was 

only 4,425 acres in the Vermejo Project area in New Mexico 

(Bartolino and others, 1996).

Reports describing results of NIWQP investigations for 

the 26 study areas are listed in table 1. In 9 of the 26 areas, 

reconnaissance investigations confirmed that irrigation drainage had caused significant harmful effects. Subsequent detailed 

investigations (phase 3) by NIWQP personnel were undertaken 

to determine the extent, magnitude, effects, and causes of contamination problems in eight of these areas. In the ninth area, 

the Tulare Lake Bed area of California (Y in fig. 2 and table 1),
4 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

FIGURE 1. Location of 191 areas in the western United States evaluated for irrigation-drainage problems by National Irrigation Water 

Quality Program task group. Modified after National Research Council (1991).

IDAHO

CALIFORNIA

ARIZONA

OKLAHOMA

WASHINGTON

OREGON

CALIFORNIA

NEVADA

ARIZONA

UTAH

IDAHO

MONTANA

WYOMING

COLORADO

NEW

MEXICO

TEXAS

OKLAHOMA

KANSAS

NEBRASKA

SOUTH

DAKOTA

NORTH

DAKOTA

EXPLANATION

Bureau of Reclamation

Evaluated area

Bureau of Indian Affairs

0 250 500 MILES

0 250 500 KILOMETERS

GULF

O F

MEXICO

P

A

C I F I C

O

C

E

A

N

MEXICO

CANADA
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 5

the equivalent of a detailed investigation was undertaken by a 

separate Federal/State cooperative research program—the San 

Joaquin Valley Drainage Program. The program was established in 1984 as a joint Federal–State task force to investigate 

problems associated with the drainage of irrigated agricultural 

land in the western and southern parts of the San Joaquin Valley 

(San Joaquin Valley Drainage Program, 1990); it focused 

equally on developing options for managing a large volume of 

agricultural drainage water and on identification of associated 

toxic hazards. Additional data for some of the sites used during 

the Tulare Lake Bed area reconnaissance investigation 

(Schroeder and others, 1988) were presented by Moore and others (1990). Biological data collected for the San Joaquin Valley 

Drainage Program were used extensively in the current study 

(see section titled “Avian-Egg Risk Assessment”).

In 1992, after the detailed studies, the remedial part of the 

NIWQP began. Planning for remediation (phase 4) began in 

four areas identified by reconnaissance investigations and 

detailed studies: the Salton Sea area in California (U in fig. 2 

and table 1), the middle Green River Basin in Utah (N); the 

Stillwater Wildlife Management Area in Nevada (W); and the 

Kendrick Reclamation Project in Wyoming (H). Remedial planning subsequently was started in two other areas, the Gunnison 

River Basin–Grand Valley Project in Colorado (F) and the San 

Juan River area in New Mexico (V). A decision on remedial 

planning is pending for the Sun River area in Montana (X). 

Actual remediation activities are in progress in the middle 

Green River Basin, the Kendrick Reclamation Project, the Stillwater Wildlife Management Area, and the San Juan River area 

either by NIWQP participants or by other Federal, State, or 

local entities in the areas.

The results of the reconnaissance investigation of the Vermejo Project area in New Mexico (Z) were not available in time 

to be included in the analysis of factors common to seleniumcontaminated areas, although Vermejo Project area data were 

used in the comparisons of contaminant concentrations among 

the study areas.

An additional 13 areas were found to have the potential for 

irrigation-induced contamination and were selected for fieldscreening investigations. Although the same general protocols 

as the reconnaissance investigations were used in these fieldscreening investigations, fewer samples were collected and the 

resulting data were not available for use in the data analysis. 

However, data from some of the areas were used to test conclusions after the data analysis was completed.

APPROACH AND OBJECTIVES OF NATIONAL IRRIGATION 

WATER QUALITY PROGRAM DATA-SYNTHESIS PROJECT

Early in the planning of NIWQP, the interagency task group 

realized that a synthesis of the data collected by the reconnaissance and detailed investigations would be an important component of the overall program. A review of NIWQP by the 

National Research Council (National Research Council, 1991) 

supported the need for a systems analysis to explore the linkages and thoroughly address the many dimensions of irrigation-induced water-quality problems. They noted that many of 

the areas with contamination problems have common characteristics and that DOI should rigorously seek to identify such 

commonalities (National Research Council, 1991, p. 2)

The NIWQP has now completed reconnaissance investigations of 26 areas and field-screening investigations of an additional 13 areas in the 17 Western States. Nationally, only DOI 

areas have been the focus of investigation, although NIWQP 

has provided funding for USFWS investigations at several nonDOI areas. The National Research Council (1991, p. 2) noted 

that the knowledge gained during the NIWQP investigations 

can be used to forewarn of other problems in the region, 

whether on public or private land.

The data collected by the NIWQP investigations provide a 

unique opportunity to identify common characteristics of the 

sites and the physical, chemical, and biological factors that 

result in water-quality problems. In April 1992, NIWQP initiated the Data-Synthesis Project, a 5-year effort to evaluate data 

collected by the completed and ongoing NIWQP investigations. 

The data synthesis consisted of an evaluation of data from 

completed and ongoing NIWQP investigations; no new data 

were collected for the synthesis. The overall objective of the 

synthesis was to identify common features of contaminated 

areas and dominant biologic and physical factors that result in 

contamination of water and biota in irrigated areas of the Western United States. 

Specific objectives of the data-synthesis project were:

• To construct a comprehensive relational data base containing all data collected by the reconnaissance and 

detailed investigations

• To use the data base to identify the principal contaminants and to make comparisons among the 26 study areas 

using descriptive statistics

• To use the data base to identify how the physical setting 

and geochemical and biological processes are related to 

the magnitude, seasonality, and extent of contamination 

problems

• To develop tools for predicting biological risk from measurements of contaminant concentrations in water and 

sediment

• To identify common features of contaminated areas and 

use this information to develop the capability to predict 

where irrigation-drainage problems are likely to occur 

so that potential problem areas, whether or not they 

are within the purview of the DOI, can be identified
6 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

FIGURE 2. Location of 26 National Irrigation Water Quality Program study areas (A–Z) selected because of potential irrigation-drainage 

water-quality problems. Also shown are data-collection sites within study areas. For base credit, see figure 1.

EXPLANATION

T

0 250 500 MILES

0 250 500 KILOMETERS

X

A

B

C

D

E

F

G

H

J

K

L

M

N

O

Q

R

S

T

U

V

W

Y

Z

P

I

WASHINGTON

OREGON

CALIFORNIA

NEVADA

ARIZONA

UTAH

IDAHO

MONTANA

WYOMING

COLORADO

NEW

MEXICO

TEXAS

OKLAHOMA

KANSAS

NEBRASKA

SOUTH

DAKOTA

NORTH

DAKOTA

GULF

O F

MEXICO

P

A

C I F I C

O

C

E

A

N

MEXICO

CANADA
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 7

TABLE 1. Reconnaissance and detailed studies concerning National Irrigation Water Quality Program study areas

[Symbol: —, detailed study not done] 1

1 Used in figure 2 to show locations of study areas

Study area References

Identifier1 Name Reconnaissance studies Detailed studies

A American Falls Reservoir, Idaho Low and Mullins, 1990 —

B Angostura Reclamation Unit, South Dakota Greene and others, 1990 —

C Belle Fourche Reclamation Project, South Dakota Roddy and others, 1991 —

D Columbia River Basin, Washington Embry and Block, 1995 —

E Dolores–Ute Mountain area, Colorado Butler and others, 1995 —

F Gunnison River Basin–Grand Valley Project, Colorado Butler and others, 1991 Butler and others, 1994, 1996

G Humboldt River area, Nevada Seiler and others, 1993 —

H Kendrick Reclamation Project, Wyoming Peterson and others, 1988 See, Naftz, and others, 1992; See,

Peterson, and Ramirez, 1992.

I Klamath Basin Refuge Complex, California–Oregon Sorenson and Schwarzbach, 1991 MacCoy, 1994; Dileanis and

others, 1996.

J Lower Colorado River valley, California–Arizona Radtke and others, 1988 —

K Lower Rio Grande valley, Texas Wells and others, 1988 —

L Malheur National Wildlife Refuge, Oregon Rinella and Schuler, 1992 —

M Middle Arkansas River Basin, Colorado–Kansas Mueller and others, 1991 —

N Middle Green River Basin, Utah Stephens and others, 1988 Peltz and Waddell, 1991; Stephens

and others, 1992.

O Middle Rio Grande, New Mexico Ong and others, 1992 —

P Milk River Basin, Montana Lambing and others, 1988 —

Q Owyhee–Vale Reclamation Project areas, Oregon–Idaho Rinella and others, 1994 —

R Pine River area, Colorado Butler and others, 1993 —

S Riverton Reclamation Project, Wyoming Peterson and others, 1991 —

T Sacramento Refuge Complex, California Dileanis and others, 1992 —

U Salton Sea area, California Setmire and others, 1990 Schroeder and others, 1993; 

Setmire and others, 1993.

V San Juan River area, New Mexico Blanchard and others, 1993 Thomas and others, 1997

W Stillwater Wildlife Management Area, Nevada Hoffman and others, 1990 Rowe and others, 1991; Lico,

1992; Hallock and Hallock,

1993; Hoffman, 1994; Tuttle

and Thodal, 1998.

X Sun River area, Montana Knapton and others, 1988 Lambing and others, 1994; Nimick

and others, 1996.

Y Tulare Lake Bed area, California Schroeder and others, 1988 Moore and others, 1990

Z Vermejo Project area, New Mexico Bartolino and others, 1996 —
8 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

PURPOSE AND SCOPE

This report describes the results of the data-synthesis project. 

Concentrations of contaminants in water, sediment, and biota 

are compared with criteria and the most important contaminants 

associated with irrigation drainage are identified. Information 

on hydrology, climate, geology, geochemistry, and biology are 

integrated and evaluated as an interdependent system. Some 

previously published results (Seiler, 1998; Skorupa, 1998; 

Naftz and Jarman, 1998; Nolan and Clark, 1997) are summarized and the concepts developed in greater detail.

The emphasis in the report is on selenium and the principal 

determinants used for evaluating whether irrigation drainage is 

having adverse environmental effects are selenium concentrations in water and bird eggs. The interrelation of geology, climate, and hydrology in determining whether selenium 

contamination occurs in irrigated areas is explored and methods 

are developed to predict where selenium contamination will 

occur. Selenium concentrations in biota are compared with criteria and the relation between selenium concentrations in biota, 

water, and sediment are explored. A risk assessment for birds 

evaluates (1) which contaminants are associated with overt 

embryonic deformities, and (2) the overall demographic significance of irrigation-induced selenium contamination on bird 

populations in the western United States.

ACKNOWLEDGMENTS

The authors are indebted to colleagues in DOI who participated in NIWQP investigations. They provided detailed information about the study areas, as well as help and 

encouragement in the building of the data base. We also 

acknowledge the reviews and helpful suggestions from many 

people who made the effort to critically examine this extensive 

report.

The authors want to express their gratitude to Richard Engberg, NIWQP Program Manager, DOI, and Herman Feltz, 

USGS Bureau Coordinator. They have been unflagging in their 

support of the authors since the inception of the data synthesis 

in 1992.

LITERATURE REVIEW

Many reports on selenium have been published; to provide a 

complete review of all the many aspects of selenium is beyond 

the scope of this report. This review is limited to describing the 

discovery of selenium as an important contaminant in the Western United States and emphasizes publications concerning 

sources of the selenium found in irrigated areas in the Western 

United States and processes involved in determining selenium 

concentrations associated with irrigation drainage. Reports that 

describe the geochemistry, biochemistry, or toxicology of selenium, for example, are not reviewed here. A comprehensive 

review of publications reporting field measurements of exposure and response to selenium, macrocosm and mesocosm 

experiments, and selected experimental studies of captive biota 

was presented elsewhere by the NIWQP (U.S. Department of 

the Interior, 1998) 

HISTORICAL PERSPECTIVE

Since the beginning of irrigated agriculture, water-quality 

issues have constrained the management of irrigation projects. 

Salt buildup in the root zone requires the application of more 

water than plants need so that salts do not accumulate in the 

soil. The excess water is necessary to maintain the salt balance 

in the soil.

Except in rare circumstances, deep percolation of the excess 

water causes rises in the local ground-water table. Managing 

this excess salty water to keep it out of the root zone has 

required the construction of drains and facilities for disposing 

of the water. Until the early 1980’s, the primary water-quality 

concerns for drainwater were salinity, nutrients, and pesticides. 

After the experience at Kesterson National Wildlife Refuge, 

the presence of selenium and other trace elements in drainwater 

was recognized as an important water-quality issue (Ohlendorf, 

Hoffman, and others, 1986; Presser and Ohlendorf, 1987; Tanji 

and Valoppi, 1989). 

During the early 1930’s selenium in pasturage was determined to be the cause of the so-called alkali disease, a disease 

of cavalry horses in South Dakota first described and reported 

by Madison (1860). For more than 1,000 years seleniferous 

pasturage has been known to kill horses; Marco Polo described 

symptoms of ill horses similar to those of horses in South 

Dakota (Wright, 1948). Wilcox (1944) attributed the loss of 

General Custer’s cavalry at the Battle of the Little Big Horn to 

selenium poisoning of horses, which delayed the arrival of his 

backup troops. Because of frequent illness and death of cattle 

in the Western United States, investigations were undertaken to 

determine the cause and extent of the disease.

A series of U.S. Department of Agriculture Technical Bulletins (Byers, 1935, 1936; Byers and others, 1938; Williams and 

others, 1940, 1941; Lakin and Byers, 1941) describes investigations made during the 1930’s and 1940’s about the sources, 

distribution, and effects of selenium. The series provides 

numerous chemical analyses of soils and plants for many areas 

in the United States, Mexico, and Canada. Many of the areas 

investigated by the NIWQP for irrigation-induced water-quality problems were investigated for seleniferous soils and selenium-accumulating plants in that bulletin series. The first 

report (Byers, 1935) contains an extensive section about selenium in the Belle Fourche Irrigation Area in South Dakota, one 

of the first areas investigated as part of NIWQP.
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 9

Comprehensive summaries of the findings from this era of 

research began appearing in the mid-1940’s and continued into 

the early-1960’s (Moxon and Rhian, 1943; Trelease and Beath, 

1949; Rosenfield and Beath, 1957, 1964; Anderson and others, 

1961). Many of the important concepts about causes of selenium contamination and areas where it can be expected were 

known more than 65 years ago. Byers (1935, p. 45) documented 

the extent of knowledge about selenium sources and seleniumassociated problems in the 1930’s:

“The source of selenium in soils has been shown 

to be sulphide minerals occurring in the soil-parent 

materials. So far as yet known the seleniferous soilforming material is, for the most part, shales of the 

Cretaceous period. Soils derived from these shales, or 

from other seleniferous materials, may retain 

sufficient selenium to produce toxic vegetation when 

the mean annual rainfall is insufficient to produce 

percolation through the soil profile.”

When selenium was identified as the cause of the alkali disease, investigators began to study the effects of selenium on 

poultry. Even before the 20th century, farmers in western South 

Dakota and northern Nebraska knew that eggs from hens on 

their farms did not hatch satisfactorily (Peters, 1904). Franke 

and others (1936), by injecting selenite into the air cell of eggs 

before incubation, produced birds having deformed or missing 

beaks, eyes, and legs. Poley and others (1937) demonstrated 

that the toxic effects of selenium are not passed on to chicken 

embryos if selenium is removed from the diet of the mother hen 

for 6 days prior to egg laying. It is likely investigators from the 

1930’s would have quickly recognized selenium as the cause of 

the deformities in birds from Kesterson National Wildlife Refuge.

The fact that irrigation water leaches selenium from soils also 

was known in the 1930’s; at the time, irrigation was considered 

a remedial method (Byers and others, 1938, p. 71):

“Data are given which show that irrigation is a 

remedial measure for seleniferous soils and that 

irrigation drainage waters remove soluble selenium 

from soils which contain it.” 

Nearly 45 years later, in the early 1980’s, the harmful effects of 

drainwater derived from application of irrigation water to 

seleniferous agricultural soils on aquatic birds and fish were 

discovered at Kesterson National Wildlife Refuge in San 

Joaquin Valley, Calif.

Although the ponds at Kesterson National Wildlife Refuge 

were constructed in 1971 for use in regulating drain flow, two 

other purposes were served—to dispose of and evaporate agricultural drainwater and to provide wildlife habitat. The inflow 

was entirely freshwater until 1978 (National Research Council, 

1989). During 1974–80, Kesterson Reservoir supported a 

warm-water fishery typical of the Central Valley in California. 

Species found in the reservoir included largemouth and striped 

bass, bluegill, white catfish, black bullhead, green sunfish, 

carp, and mosquitofish (Bureau of Reclamation, 1986). By 

1981, the water supply was exclusively irrigation drainwater. 

The delivery of seleniferous drainwater eventually resulted in 

the collapse of the warm-water fishery and the only fish persisting in the refuge were pollution-tolerant mosquitofish (Skorupa, 1998). Hundreds of adult birds died and nesting birds had 

complete reproductive failure. 

The magnitude of irrigation-related water-quality problems 

at Kesterson National Wildlife Refuge and elsewhere in the 

Western United States was presented to the general public in a 

series of news articles in The Sacramento Bee starting in September 1985. That newspaper reported that dangerous levels of 

selenium being flushed from BOR project service areas into 

wildlife refuges in seven states. The House Committee on Interior and Insular Affairs held hearings to allow DOI to comment 

on the newspaper reports. At the request of the Committee, 

DOI scientists undertook a preliminary assessment of selenium 

contamination caused by irrigation drainage from BOR project 

areas. Their report indicated that evidence of elevated levels of 

selenium was found at many of the sites, but no evidence could 

be found to confirm widespread ill effects alleged in the newspaper reports (Deason, 1986). 

PREVIOUS NATIONAL IRRIGATION WATER QUALITY PROGRAM 

EVALUATIONS

Data from seven of the initial NIWQP reconnaissance investigations were evaluated by Sylvester and others (1988), who 

identified several factors involved in determining the concentration of contaminants associated with irrigation drainage:

• Yearly variations in precipitation and streamflow

• Presence or absence of geologic sources of trace elements

• Arid to semiarid climate

• Presence of topographically closed drainage basins

• Amount and relative contribution of irrigation 

drainage to wetlands, ponds, and refuges.

Feltz and others (1991) summarized selenium concentrations in water, bottom sediment, and biota for 20 of the study 

areas. They concluded that elevated concentrations of selenium 

associated with irrigation drainage can be either localized or 

widespread within a given study area.

Engberg and Sylvester (1993) analyzed data from 20 of the 

areas. They recognized the potential for selenium contamination in approximately 12 percent of all irrigated lands in the 17 

Western States. They identified possible source material for 

selenium in each of the 20 areas and concluded that data from 

these areas supported the earlier conclusions of Sylvester and 

others (1988).
10 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

Presser and others (1994) analyzed published data and some 

unpublished data for 20 of the study areas. Selenium concentrations in water, bottom sediment, and biota for the 20 areas were 

summarized. The importance of Cretaceous marine sedimentary rocks in irrigated areas as direct and indirect sources of 

selenium was discussed. They also discussed how assessments 

of contamination can be affected by the time of sampling, not 

only by the season of sampling but also in what particular year 

samples were collected. 

Presser (1994b) described and summarized 11 biogeochemical processes involved in the transport of selenium from rock to 

waterfowl at Kesterson National Wildlife Refuge. Subsequent 

data analysis has shown that these processes probably occur in 

most of the areas investigated by the NIWQP that were contaminated with selenium.

Lemly and others (1993) and Lemly (1993b) summarized 

published information about the NIWQP investigations. In 

addition to describing NIWQP investigations, they proposed 

actions to stop further drainage-related degradation of arid wetlands, including restoration of freshwater inflows to wetlands, 

and recognition of irrigation drainwater as a class of pollution 

subject to regulation under the National Pollution Discharge 

Elimination System permitting process.

The focus of previous analyses of the NIWQP data has been 

selenium. In this report data from other trace elements and pesticides are examined to evaluate whether this exclusive focus on 

selenium is warranted. In addition, the ideas and conceptual 

models related to selenium that have been presented in earlier 

NIWQP data evaluations are re-evaluated in this report and 

developed to provide tools for managers who must manage irrigation drainage. Earlier reports have noted the presence of elevated selenium concentrations in biota and biological effects in 

some NIWQP areas (Lemly, 1995; Hren and Feltz, 1998; Van 

Derveer and Canton, 1997). In this report biological data for 

selenium concentrations in birds are placed in their demographic context to answer what the effects of selenium in irrigated areas are on birds at the population level.

METHODS USED

DATA COLLECTION

STUDY-AREA SELECTION

Areas for reconnaissance investigations were selected by the 

NIWQP Manager for DOI and Bureau Coordinators, who are 

representatives of the USGS, USFWS, BOR, and BIA. The fundamental criterion for selection was the known occurrence, or 

high probability of, irrigation-induced contamination by or of a 

DOI area. Although NIWQP originated as a direct result of the 

events related to selenium contamination at Kesterson National 

Wildlife Refuge, the NIWQP has not restricted itself to the 

investigation of selenium. NIWQP investigations also have documented and addressed irrigation-induced contamination 

resulting from increases in salinity and in concentrations of unionized ammonia, pesticides, and toxic trace elements other 

than selenium (for example, arsenic, boron, molybdenum, and 

uranium). NIWQP responsibility has been limited in scope to 

contaminants that are mobilized by the application of irrigation 

water and that are adversely affecting humans or biota in DOImanaged areas.

SITE SELECTION AND SAMPLING SCHEDULE

Workplans were developed by individual study teams and 

reviewed by the NIWQP Program Manager and Bureau Coordinators. The Program Manager and Bureau Coordinators provided guidance, but the sampling sites were selected by the 

individual study teams. All study areas included reference 

sites, typically on streams or rivers that provided water to the 

area for irrigation.

Some reconnaissance-study teams primarily selected sites 

that already had associated data, typically the larger streams in 

an area. Other reconnaissance-study teams selected sites that 

they believed were most likely to be contaminated, or they 

actively searched for contaminated sites. Some study teams 

concentrated on streams, canals, and drains but did not sample 

lakes and ponds. Some of the study teams that did sample lakes 

selected large reservoirs rather than smaller lakes or ponds.

In some areas water and sediment samples were not collected in the same locations as the biological samples. Biological sampling was done opportunistically and therefore was not 

directed to the same extent as water and sediment sampling. In 

some areas, water and sediment sampling emphasized streams, 

canals, and drains and few samples were collected from wildlife-habitat areas (see section titled “Limitations of Data,” p. 

19).

In some of the initial NIWQP reconnaissance investigations, 

for example the Lower Colorado River valley (J) and Milk 

River Basin (P; fig. 2), almost all water samples were collected 

during a single sampling round in the middle of the irrigation 

season, when it was assumed that effects from irrigation would 

be greatest. Bed-sediment samples were collected after a prolonged period of low or steady flows. Bureau coordinators realized from initial results that seasonal changes in contaminant 

concentrations could mask contamination. Therefore, in subsequent investigations, samples were collected before the irrigation season began, during the irrigation season, and after the 

irrigation season ended. 

PROTOCOLS FOR DATA COLLECTION AND CHEMICAL 

ANALYSIS

To enhance comparability of results among the study areas, 

the investigations were guided by a common protocol for 

obtaining and analyzing data. Chemical analyses were made by 

the same laboratories according to consistent analytical protocols throughout the life of the program.
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 11

Samples of water, bottom sediment, and biota were collected 

in each of the study areas. Water samples were analyzed for 

major constituents except during two of the early reconnaissance investigations. Samples of each medium were analyzed 

for trace elements, including arsenic, barium, boron, cadmium, 

chromium, copper, lead, mercury, molybdenum, nickel, selenium, silver, uranium, vanadium, and zinc. Stable isotopes of 

water were measured during some of the studies, particularly 

the detailed investigations.

Pesticide analyses were done at the discretion of the study 

teams on the basis of usage in the study areas. Of the 26 study 

teams, 24 sampled bottom sediment for organochlorine pesticides and polychlorinated biphenyls, 7 analyzed some bottomsediment samples for organophosphate pesticides, and 5 analyzed bottom sediment for herbicides. Surface-water samples 

were analyzed for herbicides in 17 areas and for organochlorine 

pesticides in 6 areas. 

WATER

All NIWQP water samples were collected and analyzed 

according to standard U.S. Geological Survey methods (U.S. 

Geological Survey, 1977; Fishman and Friedman, 1989). Waterquality samples were collected at stream sites by using depthintegrating samplers and methods described by Ward and Harr 

(1990). Where depths were too shallow to use samplers, representative water samples were collected in sample-collection 

bottles from the centroid of flow or from several verticals across 

the stream.

Alkalinity, pH, dissolved oxygen, and specific conductance 

typically were measured in the field. Most trace-element samples were preserved with nitric acid after filtering in the field 

through a 0.45-micrometer cellulose-nitrate plate filter or 0.45-

micrometer polyether-sulfone capsule filter. Samples for dissolved mercury were preserved with nitric acid and potassium 

dichromate. Samples for nutrient analysis were preserved with 

mercuric chloride and were chilled on ice and refrigerated until 

the samples were analyzed.

Water-quality analyses were done at the USGS National 

Water-Quality Laboratory. Minimum analytical reporting limits 

for trace elements and pesticides in water are presented in tables 

2 and 3. Minimum reporting limits for many of the pesticides 

analyses were reduced during later investigations and therefore 

are presented as a range in table 3. Inorganic substances in surface and ground water were analyzed according to methods 

described by Fishman and Friedman (1989). Most trace elements were measured by using inductively coupled plasmaemission spectroscopy after preconcentration. Arsenic and selenium were analyzed using hydride generation and atomicabsorption spectrometry. Mercury was analyzed using coldvapor atomic-absorption spectrometry.

Water samples for pesticide analyses were collected by dipping hexane-rinsed and baked borosilicate-glass bottles 

directly into the stream. These unfiltered samples were chilled 

on ice for transportation to the laboratory. Organochlorine and 

organophosphate pesticides in water were extracted by using 

hexane and were analyzed according to methods described by 

Wershaw and others (1987). Organophosphate compounds 

were determined on a gas chromatograph using flame-photometric detectors, and organochlorine compounds were determined on a gas chromatograph using electron-capture 

detectors. Chlorophenoxy acid herbicides in water were 

extracted by using either diethyl or methyl t-butyl ether from 

acidified water samples (Wershaw and others, 1987). The 

extracted herbicides were hydrolyzed to the free acids and then 

converted to their methyl esters, which were determined by gas 

chromatography using electron-capture detectors.

1 Minimum concentration of substance that can be identified, 

measured, or reported with laboratory-determined level of confidence 

that analyte concentrations are greater than zero. Analyses subject to 

interference from other substances or properties of sample have higher 

analytical reporting limit.

TABLE 2. Minimum analytical reporting limits for trace 

elements in water, bottom sediment, and biota

[Abbreviation and symbol: μg/L, micrograms per liter; μg/g, 

micrograms per gram; —, not applicable or not determined]

Constituent

Reporting limit1

Trace

element in

water

(μg/L)

Trace element in

bottom sediment

Trace

element in

tissue

(μg/g,

dry weight)

μg/g,

dry weight Percent

Aluminum 1 — 0.05 5

Antimony 1 0.1 — —

Arsenic 1 .1 — .5

Barium 1 1 — .5

Beryllium 1 1 — .1

Boron 10 .4 — 1.5

Cadmium 1 .1 — .1

Chromium 1 1 — .5

Copper 1 1 — .5

Iron 3 — .05 10

Lead 1 4 — .5

Manganese 1 4 — 4

Mercury .1 .2 — .1

Molybdenum 1 2 — .5

Nickel 1 2 — .5

Selenium 1 .1 — .5

Silver 1 .1 — —

Uranium 1 .05 — —

Zinc 1 4 — 20
12 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

SEDIMENT

Bottom-sediment samples for analysis of inorganic constituents were taken from the upper 2 to 4 in. of sediment deposited 

in streams, marshes, lakes, and drainage ditches. Several samples were collected at each site and thoroughly mixed in a glass 

container to make a composite sample. Subsamples for inorganic analysis were placed in pint-sized plastic freezer cartons 

for shipment to the laboratory. 

Inorganic analyses of bottom sediment were done by the 

USGS Environmental Geochemistry Laboratory. Minimum 

analytical reporting limits for trace elements in bottom sediment are presented in table 2. The analytical methods used 

were presented by Severson and others (1987). In the laboratory, wet samples were air dried, disaggregated, and run 

through a 2-mm sieve. Material greater than 2 mm was discarded. Samples were split into two fractions and sieved again. 

Typically, two size fractions were submitted for analysis—a 

fine fraction (< 0.062 mm), and a coarse fraction (< 2 mm), 

which may have included fine material. In analyzing sediment 

samples, some study teams analyzed only one of the two fractions.

The sediment samples then were digested with hydrochloric 

acid, hydrofluoric acid, perchloric acid, and aqua regia (a 

hydrochloric–nitric acid mix). After digestion, the extracts 

were processed by methods described by Severson and others 

(1987). Mercury was determined by cold-vapor atomic-absorption spectroscopy, arsenic and selenium by continuous-flow 

hydride-generation atomic-absorption spectroscopy, uranium 

and thorium by delayed-neutron-activation analysis, and 40 

other elements (including boron after a hot-water extraction) 

by inductively coupled argon-plasma atomic-emission spectroscopy.

Bottom-sediment samples for pesticide analyses were collected by using stainless-steel equipment. Minimum analytical 

reporting limits for pesticides in bottom sediment are presented 

in table 3. In the field, the sediment was composited in a stainless-steel bowl, and then native water from the site where the 

samples were collected was used to run sediment subsamples 

through a 2-mm stainless-steel sieve. The samples were stored 

in pretreated baked glass jars and were chilled on ice for transportation to the laboratory.

Bottom sediment was analyzed for pesticides at the USGS 

National Water Quality Laboratory. Organochlorine and organophosphate pesticides in sediment were extracted using hexane and were analyzed according to methods described by 

Wershaw and others (1987). Organophosphate compounds 

were determined on a gas chromatograph using flame-photometric detectors, and organochlorine compounds were determined on a gas chromatograph using electron-capture 

detectors. Total organic-carbon content of the sediment also 

was measured in some samples.

Chlorophenoxy acid herbicides in sediment were extracted 

by using either diethyl or methyl t-butyl ether from an acidified 

slurry of the sediment sample and water (Wershaw and others, 

1987). The extracted herbicides were hydrolyzed to the free 

acids and then converted to their methyl esters, which were 

determined by gas chromatography using electron-capture 

detectors.

TABLE 3. Minimum analytical reporting limits for 

pesticides in water, bottom sediment, and biota

[Abbreviation and symbol: μg/L, micrograms per liter; μg/g, 

micrograms per gram; —, not determined]

Constituent

Reporting limit1

1 Minimum concentration of substance that can be identified, 

measured, or reported with laboratory-determined level of confidence that 

analyte concentrations are greater than zero. Analyses subject to 

interference from other substances or properties of sample have higher 

analytical reporting limit.

Pesticide

in water2

(μg/L)

2 For some pesticides minimum reporting limits were reduced 

during later investigations, therefore the reporting limit is presented as a 

range.

Pesticide

in bottom sediment

(μg/g,

wet weight)

Pesticide

in tissue

(μg/g,

wet weight)

Organochlorine pesticides

Aldrin 0.010–0.001 0.1 0.01

Chlordane .10 1 .01

DDT .010–.001 .1 .01

DDE .010–.001 .1 .01

DDD .010–.001 .1 .01

Dieldrin .010–.001 .1 .01

Endosulfan .010–.001 .1 .01

Endrin .010–.001 .1 .01

Lindane .010–.001 .1 .01

Methoxychlor .01 .1 .01

Mirex .01 .1 .01

Toxaphene 1.0 10 .01

Organophosphate and carbamate pesticides

Diazinon 0.01 0.1 0.5

Dicamba .01 — .01

Malathion .01 .1 .5

Parathion .01 .1 .5

Propazine .10 — —

Sevin 2.0–.5 — —

Trithion .01 .1 —

 Chlorphenoxy acid herbicides

2,4D 0.01 0.1 0.01

2,4,5T .01 .1 .01

Silvex .01 .1 —
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 13

BIOTA

Samples were collected, prepared, packaged, stored, and 

shipped for analysis according to standard procedures outlined 

by U.S. Fish and Wildlife Service (1986). All handling of biological samples involved sample contact only with forceps, sterilized dissection tools, plastic gloves or bags, aluminum foil, or 

sterilized plastic or glass jars.

Aquatic vascular plants and algae were collected by handpicking. Samples of rooted vascular plants, such as cattails, 

were rinsed extensively with native water to remove sediment. 

The plant samples were then placed in cleaned jars, weighed, 

and frozen. 

Invertebrates were collected by using a kick-net or by handpicking. Lake plankton samples were collected by using a 

plankton tow net. Invertebrate groups were commonly composited to obtain sufficient material for analysis. The invertebrate 

samples then were placed in cleaned jars, weighed, and frozen. 

Fish were collected by using electroshocking equipment and 

seine or gill nets. Fish were rinsed, weighed, measured for 

length, and immediately frozen on dry ice until stored in a 

freezer. Whole-body samples were composited by species into 

groups of three or more fish. Fillet and egg samples were taken 

from individual fish and were not composited. Fish samples for 

analysis of inorganic contaminants were frozen in plastic bags. 

Fish samples for analysis of organic compounds were wrapped 

in aluminum foil and placed in plastic bags.

Tissue samples, including muscle, liver, and eggs, were collected from bird species in most study areas. Adult birds were 

shot using steel pellets and hatchlings were netted. Specimens 

were refrigerated and tissues removed and frozen within 24 

hours. In some instances, to ensure sufficient material for analysis, tissues from two or three individual birds were composited 

into a single sample.

Nests were located and bird eggs removed. Eggs were opened 

and the embryos examined for developmental abnormalities. 

After examination, eggs were placed in cleaned jars, weighed, 

and frozen. In some instances, small eggs were composited to 

provide sufficient material for analysis.

Biological tissues were shipped to one of several different 

laboratories for analysis. These laboratories were contracted by 

the analytical control facility at the USFWS Patuxent Wildlife 

Research Center. Trace-element analyses were done by contract 

laboratories. Although some pesticides were analyzed at the 

USFWS facility, most were analyzed by contract laboratories. 

The USFWS facility was responsible for quality assurance and 

quality control of the biological analyses done by contract laboratories. Minimum analytical reporting limits for trace elements and pesticides in biota are presented in tables 2 and 3. 

Analyses for most trace elements in biological tissues were 

done by using inductively coupled argon-plasma atomic-emission spectrometry after complete digestion of the sample by 

using nitric and perchloric acids. Analyses for arsenic and selenium in biological tissues were done by using hydride-generation atomic-absorption spectrometry. Analysis for mercury was 

done by flameless cold-vapor atomic-absorption spectrometry. 

Plant- and animal-tissue samples for organochlorine pesticide 

analysis were extracted by using hexane and analyzed by using 

packed- or capillary-column electron-capture gas chromatography. In most cases, lipid content also was measured in biological material analyzed for organochlorine pesticides.

Typically, 10 percent of the samples collected as part of a 

NIWQP investigation were field blanks or replicates for quality assurance. Blank samples were used to detect sample contamination introduced during collection, preparation, and 

shipping. Field replicate samples were collected to detect variability due to sampling method and laboratory variability. Split 

samples were used to check the precision of analytical results 

reported by the laboratory.

DATA SYNTHESIS

A data base of chemical, physical, and biological data collected during the 26 NIWQP investigations was created, and 

the data were analyzed by using a geographic information system (GIS) and statistical and geochemical methods. Information on how to obtain the data base and the methods used by the 

data-synthesis team in creating the data base and the procedures used in analyzing the data are described in this section. 

DATA BASE

AVAILABILITY

The data base has been made available to the scientific community (and public) through the World Wide Web on the 

NIWQP home page, at URL <http://www.usbr.gov/niwqp>. 

(Additional information about the program can be located by 

using search engines to find the term “NIWQP” on the Internet.)

For personal-computer-based systems, the data base is available as dBase III files and ASCII (tab-delimited) tables. For 

personal-computer-based system and UNIX-based workstations that use Ingres or Oracle, SQL scripts and data files are 

available to create the tables and views.

INTRODUCTION AND STRUCTURE

The data base was designed as a relational data base so that 

relations among contaminant concentrations in biota, water, 

and bottom sediment could be explored. In addition to chemical data, the completed data base contains geological, hydrological, climatological, and cultural data that can be used to 

describe the 26 NIWQP study areas and specific data-collection sites within those areas. These data were collected and 

merged into a common data base maintained by using the 
14 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

Ingres relational data-base management system on UNIX workstations. All data-collection sites are georeferenced by latitude 

and longitude so that the data sets can be included in a GIS.

Seiler and Skorupa’s (2001) data dictionary describes the 

structure (fig. 3) and variables in the data base. Critical linking 

variables are area, subarea, and site-identification fields. Each 

of the 26 NIWQP areas was assigned a short identifying name 

in the area field. If an area had been divided into hydrologically 

distinct subareas, each of the subareas was assigned a short 

identifying name in the subarea field. The area table includes 

information about the geology, climate, land-use, and other 

physiographic and cultural data.

The data dictionary (Seiler and Skorupa, 2001) is available 

on-line as a portable document format (pdf) file at URL 

<http://pubs.water.usgs.gov/ofr00513>. 

The site table links the analytical chemical data with the area 

table and contains specific information about each site where 

data were collected. Data in this table include the type and name 

of the site; altitude, latitude, and longitude; and a field indicating whether the site is a reference site or a site in or downstream 

from an irrigated area. Each data-collection site for water and 

bottom sediment was assigned a unique site-identification number based on the latitude and longitude or the downstream-order 

number assigned by the USGS. 

Different components of the chemical analyses are stored in 

different tables. For example, for every analysis, field values 

such as discharge, specific conductance, and temperature are 

stored in the data base, but only about 1 percent of the analyses 

have available pesticide data. The different tables are linked by 

site identification, matrix, date, and time fields; these fields 

form a unique combination that allows the different tables and 

components of a chemical analysis to be recombined.

Biological samples commonly were not collected in exactly 

the same place as the water and sediment samples. So that relations among contaminant concentrations in water, sediment, 

and biota could be explored, biological sites were assigned the 

site-identification number of the nearest appropriate site where 

surface water or sediment had been collected. For instance, fish 

samples from a stream or pond could be assigned the site-identification number for a stream site several miles upstream or 

downstream or for a site at the pond outflow. Biological samples were not always assigned a site-identification number. For 

example, bird samples from a pond where no water samples 

had been collected were not assigned a site-identification number. Of the 8,217 inorganic samples of biological material in 

the data base, 3,248 were not linked to a site where water and 

sediment data were collected (fig. 3.) and 351 of the 1,088 

organic samples of biological material were not linked.

FIGURE 3.—Structure and linking attributes of National Irrigation Water Quality Program (NIWQP) data base. n, number of records. Note that 3,248 of 

the 8,217 samples in the INORGBIO table are not linked to other tables through the site-id variable and 351 samples in the ORGBIO table are not 

linked.

EXPLANATION

WATER

FIELD INORGBIO

n = 8,217

ORGBIO TAXON

Site_id, samp-date, samp-time, matrix

SEDIMENT NUTRIENT ISOTOPE INORG ORG

Site_id

BIOTA

FIELD

n = 8,293

ORGBM

n = 245

ORGBIO TAXON

ISOTOPE

AREA

INORGBM

n = 754

 

ORGBIO

n = 1,088

TAXONOMY

n = 243

Matrix Group

Linking attribute Descriptions of attributes are 

 provided in Seiler and Skorupa, 2001

Data table Contents of data tables 

 are listed in Seiler and Skorupa, 2001

WATER BOTTOM BIOTA

Area, Sub_area

SITE

n = 1,264

Site_id

AREAS

n = 26

NUTRIENT

n = 3,078

SED

n = 101

ORGANICS

n = 321

INORG

n = 8,304

ISOTOPES

n = 1,640
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 15

The following example shows how the structure of the data 

base allows the relations between different types of data to be 

explored. Data stored in the INORGBIO table (fig. 3) indicate 

that the average selenium concentration for 16 American 

Avocet eggs collected in June 1988 from Rasmus Lee Lake was 

72 μg/g. The USGS site identification number of the nearest 

appropriate site where water samples were collected, 

‘424435106370300’, is stored with the biological data as the 

attribute ‘siteid’. The attribute ‘siteid’ links the biological data 

to physical and chemical data in other tables. Using the value 

for the siteid attribute from the INORGBIO table to locate samples in the FIELD table indicates that on June 22, 1988 at 1240 

a water sample was collected at the site and that the specific 

conductance of the water was 10,100 microsiemens per centimeter (μS/cm) at 25 oC. Data in the INORG table indicate that 

arsenic, selenium, and uranium concentrations were 2, 120, and 

35 μg/L, respectively, for that site at that date and time. Data for 

that siteid in the SITE table indicate the site is in the Kendrick 

Reclamation Project area (H) and that the bedrock at the site has 

been mapped as Upper Cretaceous marine sedimentary rocks 

belonging to the Austin and Eagle Ford Groups. Data for the 

Kendrick Reclamation Project area in the AREA table indicate 

free water surface evaporation (FWSE) in the area is 42-45 

inches per year, that alfalfa is the principal crop, and that the 

irrigation season typically begins at the beginning of May.

SOURCES OF DATA

All area-specific chemical and biological data collected by 

the NIWQP were reported in USGS publications (table 1). 

These publications were the principal sources of data for information describing the study areas and individual sampling sites 

within the study areas. Information on evaporation rates and 

geologic units in the areas were obtained by overlaying climatic 

and geologic maps on maps of the individual data-collection 

sites. Some area-specific information, such as the typical start 

and end of the irrigation season and whether specific lakes are 

terminal or flowthrough, was obtained directly from members 

of the study teams.

The chemical data collected were stored in several different 

data bases. Chemical data for all constituents in water and pesticides in sediment were stored in the USGS National Water 

Information System (NWIS) data base. Inorganic data for sediment were stored in a USGS Geologic Division data base, and 

data for biological material were stored in personal-computer 

spreadsheets and data bases in USFWS field offices. Data from 

these separate data bases were collected and merged into a common data base on UNIX workstations.

Information describing sites where water-quality and most 

bottom-sediment samples were collected was stored in NWIS. 

This site information was included automatically during retrievals of chemical analyses. For some analyses of bottom sediment, information describing the data-collection sites was not 

stored in NWIS. Site information for those sites was manually 

added to the site table.

NWIS is maintained on computers in the USGS Water 

Resources Division District Offices. Chemical data for water 

were retrieved from NWIS maintained in the State where the 

NIWQP investigations were done. For all sites sampled during 

a NIWQP investigation, chemical data collected during 1986–

93 were retrieved by using lists of site-identification numbers 

obtained from the published reports or from the study-team 

members.

Chemical analyses of bottom sediment were obtained principally from USGS reports (Severson and others, 1987; Harms 

and others, 1990; Stewart and others, 1992). Tables of ASCII 

data were created from these reports, either by scanning the 

tables and using optical character-recognition software or by 

using the floppy disk provided with the report. Some unpublished data were provided from the analyzing laboratory as 

ASCII tables.

Although biological data are stored in USFWS field offices, 

data from field offices were not entered in the data-synthesis 

data base because of difficulties in collating data from multiple 

formats and because of uneven verification. Biological data 

were entered by creating personal-computer spreadsheets from 

the original laboratory analytical-results sheets. Some additional data were obtained from the published reports and 

USFWS study-team members. Data in the spreadsheets then 

were incorporated into the Ingres data base.

DATA MANIPULATION AND QUALITY ASSURANCE

Data files for water and bottom sediment were manipulated 

and verified using P-STAT (P-STAT, Inc., 1990) statistical software on the USGS Water Resources Division Nevada District 

Prime computer. Subsequently the P-STAT files were processed into structured query language (SQL) scripts to create 

Ingres relational-data-base tables.

Data manipulation involved checking the data against published and draft reports to verify that all analyses were in the 

data base. Erroneous entries were removed from the NIWQP 

data base when found. A study-team member was informed of 

any problems found in the data base. Where data from the quality-assurance samples had been mixed with data from environmental samples, the two types of data were manipulated to 

separate them. Analyses that were not collected as part of a 

DOI investigation but were collected at a DOI site were 

included in the NIWQP data base but were flagged because the 

data were not verified against published reports. 
16 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

Personal-computer-based spreadsheets containing biological 

data were converted to P-STAT files for ordering of columns 

and adding site-identification numbers and other types of data 

cleanup or manipulation. These P-STAT files were then converted to an Ingres relational data base.

Quality assurance was done by checking all parameters for 20 

percent of all DOI water samples from each area against published data. All discrepancies between published reports and the 

data base were investigated and resolved, in some instances by 

calling the study-team leader. In most study areas, less than one 

percent of the checked values in the NIWQP database differed 

from published values. For example, when more than 2,000 data 

values from 93 analyses from the Stillwater Wildlife Management Area (Nevada) were checked, 11 discrepancies were 

found. In the San Juan River area (New Mexico) only one discrepancy was found in more than 400 individual data values 

from 11 analyses. The small percentage of errors discovered 

indicate that the data base accurately reflects the published data.

All discrepancies were checked carefully for evidence of systematic errors. In some cases errors were found and corrected in 

the programs that manipulated the data from the NWIS database. For example, in one case data manipulation resulted in 

loss of “<“ symbols from one parameter. In another case, most 

of the land-surface altitudes in the Salton Sea area were wrong 

because the programs did not handle altitudes less than zero correctly. In other cases, published data were missing from the 

NIWQP, and NWIS, databases. In those cases the missing data 

were added to the NIWQP database and the study area teamleader was advised that published data were missing from the 

NWIS database. Many of the discrepancies found resulted from 

word-processing and verification errors in published reports; 

the data in the NIWQP and NWIS databases were correct. For 

example, the negative sign in δD and δ18O values in one report 

had been converted to “<“ symbols.

Because relatively few (only about 700) bottom-sediment 

samples were analyzed, all selenium, arsenic, and molybdenum 

concentrations in the <0.062-mm fraction were checked against 

published values. Very few errors in these values were found, 

only 5 in more than 1,000 values checked differed from published values. In addition, all constituent concentrations were 

checked against published values for two randomly selected 

samples from each area. If these checks revealed a disproportionately high number of errors in a study area, then all values 

for all analyses from that study area were checked. 

Because the biological part of the data base was created from 

the original laboratory reports, those reports were checked 

against published reports for accuracy of wet-weight to dryweight conversions, for sampling dates, and in some instances 

to verify the taxonomic identification of individual samples. 

Also, after data from each original laboratory report had been 

entered into the master biotic data base (according to uniform 

conventions for data rounding and the reporting of values below 

detection limits), each datum cell was verified individually for 

keypad-entry errors by independent members of the data-entry 

team. Thus, ultimately each datum entry was reviewed for 

accuracy by no fewer than three members of the data-entry 

team.

DATA INTERPRETATION

STATISTICAL METHODS

Summary statistics used consisted of resistant measures of 

central tendency (median) and spread (interquartile range). 

Because many of the measured contaminant concentrations are 

less than the reporting limit and because most constituents have 

multiple reporting limits, robust methods (Helsel and Hirsch, 

1992) such as log-normal maximum-likelihood estimation 

were used to compute estimates of summary statistics. 

Box plots were used to compare and contrast the distribution 

of contaminant concentrations among the different study areas. 

Box plots show only the individual data points if eight or fewer 

data points are available. The lower limit of the box plots is the 

reporting limit; that is, censored data values (concentrations 

that are less than the reporting limit) are not shown. Whiskers 

on the boxes are drawn to the 10th and 90th percentiles; observations less than the 10th percentile and greater than the 90th

percentile are plotted individually.

For comparing contaminant concentrations among the different study areas, all NIWQP samples except quality-assurance samples were used to calculate summary statistics. Where 

characterization of contaminant concentrations at an individual 

site was necessary, the most recent analysis made was selected 

instead of calculating the median or average concentration for 

the site. This decision was made because, at many sites, a combination of a limited number of analyses and censored data 

meant an accurate estimate of the median could not be made 

even using robust methods.

The software package Pirouette (Infometrix, 1992) was used 

for pattern-recognition, principal-components analysis (PCA), 

and classification modeling of data. These methods are used 

often for multivariate analysis of large data bases (Meglen and 

Sistko, 1985).

PCA is a mathematical technique for reducing a complex 

system of correlations into fewer dimensions. Meglen (1991) 

describes the use of PCA to examine large multivariate data 

bases. A matrix of correlations is obtained from the original 

data set and then decomposed using eigen analysis into two 

matrices—the scores matrix and the loadings matrix. The 

scores and loading matrices are used to derive the best, mutually independent axes (principal components) that describe the 

data set. The scores matrix shows relations among the samples, 

and the loadings matrix contains information about relations 

among the variables (Meglen, 1991).
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 17

Blythe-Still-Casella binomial confidence intervals (Blythe 

and Still, 1983; Casella, 1987) were calculated using the software package StatXact4 (Cytel Software Corp., 2000), as were 

all Fisher Exact test statistics. 

GEOCHEMICAL METHODS

Geochemical analysis of data was done principally by using 

the computer program SNORM (Bodine and Jones, 1986). For 

each analysis, the program calculates the salt norm, which is the 

quantitative ideal equilibrium assemblage that would crystallize 

if water evaporated completely at 25°C and at a pressure of 1 bar 

with atmospheric concentrations of carbon dioxide (Bodine and 

Jones, 1986). Characterization of water composition by an 

assemblage of salts provides more information about solute origin and subsequent interaction than major cation–anion predominance graphs provide. Salt-norm data can be useful in 

evaluating the similarities and differences in water from 

geochemically different environments.

GEOGRAPHIC INFORMATION SYSTEMS

A GIS was used to store and analyze spatially oriented digital 

data. GIS data layers (sets of spatially located digital data) 

showing land use, mean annual precipitation, free-water-surface evaporation, and geology of the United States were 

obtained or created to use for data analysis. Seiler and others 

(1999) provided a list of the coverages and details of their creation.

BIOLOGICAL METHODS

Data management and data analysis for risk assessment of 

selenium concentrations in bird eggs relied primarily on two 

software packages, Quattro Pro and Statistica. Quattro Pro is a 

general-purpose spreadsheet program, and Statistica is a comprehensive, modular data-management, statistical-analysis, and 

graphics program. Statistica conventions and operations are 

described in a manual by StatSoft (1995). Data analyses for the 

risk assessment were derived primarily through use of five Statistica modules: (1) Data Management, (2) Basic Statistics, (3) 

Nonparametric Statistics, (4) Nonlinear Estimation, and (5) 

Factor Analysis. 

Pairwise correlations of concentrations of inorganic constituents in bird eggs are presented in a correlation-matrix format 

(Sokal and Rohlf, 1995; StatSoft, 1995). Data were log transformed prior to calculation of pairwise product–moment correlation coefficients, and the matrix cells were populated with the 

probability values (p-values) for the corresponding correlation 

coefficient. Because the correlation matrix is an initial screening of the data for potential associations that might merit further 

examination, a slightly relaxed p-value criterion of p < 0.10 was 

used to identify potentially significant pairwise correlations.

Exploratory factor analysis was done using the Factor Analysis module of the Statistica program. Factor analysis was performed on untransformed chemical data. The principalcomponents method was used for factors extraction and an 

upper limit of the top two factors exceeding the Kaiser criterion 

(eigenvalues > 1) was imposed for factor selection and graphical display of factor loadings. Factor analysis is a technique for 

simultaneously examining the interrelations or structure of 

multiple variables. Here, factor analysis is used to provide a 

graphical display of the relative degree to which different inorganic constituents appeared to be, or not to be, highly correlated with terata of avian embryos. Afifi and Clark (1996) 

provided a detailed statistical explanation of factor analysis, 

and the Statistica algorithms and options for completing factor 

analysis are documented in the manual by StatSoft (1995).

Logistic-regression analysis was used to examine exposureresponse or dose-response relations. Logistic regressions were 

performed using the Nonlinear Estimation module of Statistica. The applicable general logistic model with one independent variable is

p = e(β0 + β1X) : [1 + e(β0 + β1X)] (1) 

where p is probability of a specific outcome,

β0,1 are regression coefficients, and

X is the independent variable.

 This model is particularly useful for describing the relation 

between a continuous independent variable (such as the concentration of a chemical in the egg) and a binary or binomial 

response variable (such as the presence or absence of an 

adverse effect). As used here, the model estimates the probability of response associated with particular exposures or doses of 

a chemical. Therefore, logistic regression can be quite useful 

for risk assessment. Both the regression coefficients (β0 and 

β1) and the precision (standard errors) of the coefficients are 

calculated by Statistica. In addition, Statistica can be used to 

plot the logistic-regression curves and to test model significance (by a model chi-square calculation). Documentation for 

the Statistica logistic-regression algorithms and conventions 

was presented by StatSoft (1995).

Power analysis was done to assess the power of the NIWQP 

studies to detect at least one teratogenic (deformed) embryo. 

Power analysis is based on an equation derived from the laws 

of binomial probability. Sokal and Rohlf (1995) provided a relevant review of these laws.
18 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

DATA SYNTHESIS

NATIONAL IRRIGATION WATER QUALITY PROGRAM 

DATA BASE

DESCRIPTIVE SUMMARY

A descriptive summary of the contents of the NIWQP data 

base is provided in tables 4 through 9. The number and types of 

individual data-collection sites are summarized in table 4. Water

and, bottom sediment were collected at 1,264 sites within the 26 

NIWQP study areas. Of these sites, 705 of the sites were on rivers, streams, canals, or drains; 211 sites were lakes, reservoirs, 

and ponds; and 348 sites were wells, springs, and subsurface 

drains. Of the 348 ground water sites, 315 were wells and 14 

were springs. Nineteen subsurface drains were sampled, all 

from the middle Green River Basin (N).

There are 130 reference sites. Reference sites are upstream 

from irrigated land within a study area. Of the 130 reference 

sites, 39 are source-water sites. Source water sites are surface 

water sites upstream from irrigated lands which represent the 

water used for irrigation.

Biological samples were collected at 685 sites. At 366 of the 

sites water and bottom sediment samples were also collected. At 

319 sites only biological samples were collected.

Information about the number and types of analyses and analytes from surface-water sites is summarized in table 5. The data 

base contains results from almost 4,000 surface-water samples 

collected as part of the NIWQP investigations and more than 

3,000 surface-water samples that were collected for other purposes at the same sites. The number and types of analyses and 

analytes from ground-water sites are summarized in table 6 and 

from bottom-sediment sites, in table 7. The number and types of 

biological analyses in the data base are summarized in tables 8 

and 9.

Analytes listed in tables 5 through 8 represent classes of 

chemicals that are in the data base but are not the only analytes 

in the data base. For example, analyses are available for more 

than 100 pesticides and organochlorine compounds, although 

table 5 lists only three pesticides (DDT, parathion, and 2,4-D).

STATISTICAL BIAS

The NIWQP data base is statistically biased. One type of bias 

results from sampling sites not having been selected randomly. 

The general project design called for a focus primarily on drains 

although at least one reference site and at least one receiving 

stream also were to be sampled. In a few areas, however, many 

of the sampling sites selected were along main channels of large 

rivers because they had been used in earlier investigations and 

therefore had a longer record of sampling for comparison. Preferential selection of main-channel sampling sites increases the 

number of samples having low contaminant concentrations in 

TABLE 5. Description of surface-water analyses in National 

Irrigation Water Quality Program data base

Description

Number of samples

analyzed1

1 A total of 3,869 surface-water samples were analyzed during National 

Irrigation Water Quality Program investigations. Analytical results for an 

additional 3,634 surface-water samples from the same sites but which were not 

collected as part of a National Irrigation Water Quality Program investigation 

are in the database but were not included in this summary description.

Filtered

sample

Unfiltered

sample

Representative Inorganic Analyses

Major-element chemical analyses 1,661 0

Residue on evaporation at 180 degrees Celsius 1,662 0

Arsenic 1,285 146

Boron 1,783 108

Copper 1,163 88

Molybdenum 1,221 38

Selenium 2,057 545

Uranium 634 247

Zinc 1,288 87

Stable isotopes (deuterium and oxygen–18) 0 221

Representative Nutrient Analyses

Ammonia 782 0

Nitrite/nitrate 1,408 458

Phosphate 562 0

Representative Pesticides Analyses

DDT 0 110

Parathion 0 167

2,4–D 0 178

TABLE 4. Water and bottom-sediment data-collection sites 

in National Irrigation Water Quality Program study areas

Type of data-collection site 1

1 Of these sites, 130 are reference sites, and 39 are sourcewater sites.

Number

of sites

Rivers, streams, canals, surface drains 705

Lakes, reservoirs, ponds 211

Wells 315

Springs 14

Subsurface drains 19

Total.......................................... 1

1,264
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 19

the data set. Sites on main channels of large rivers tend to have 

lower contaminant concentrations than drains or ponds because 

of lower evaporation rates and greater dilution of added contaminants. This type of sample bias has occurred in the Lower 

Colorado River valley and San Juan River area reconnaissance 

investigations. In the Lower Colorado River valley, 8 of the 

11 samples were from the main stem of the Colorado River or 

near it on river diversions. In the San Juan River area, almost 

25 percent of the samples within and downstream from irrigated areas were from main-channel river sites (Seiler, 1995).

In some reconnaissance investigations, contaminants were 

deliberately being sought, and sites were selected because of 

the likelihood that contaminant concentrations would be high. 

Preferential selection of such sampling sites increases the number of samples having high contaminant concentrations in the 

data set. 

Another type of bias results from sites not being sampled at 

the same frequency. During process-oriented investigations, 

typically the most contaminated sites were sampled more frequently than the least contaminated sites. A bias toward sampling the most contaminated sites increases the number of 

samples having high contaminant concentrations. This type of 

sample bias is seen in data from the Kendrick Reclamation 

Project investigation, in which 20 percent of the 568 surfacewater samples were from a single site in a selenium-contaminated lake.

In spite of potential problems caused by bias, the data set was 

used to determine the degree of contamination in each study 

area. Areas where sample bias could noticeably raise or lower 

summary descriptors of contaminant concentrations were identified by examining the data set. During data analysis, the 

potential effects of sample bias in an area were considered. For 

example, tools for identifying contaminated areas were not 

rejected during their construction if their predictions failed to 

match observations in areas where sample bias occurred.

The data base cannot be used to describe baseline conditions 

in the Western United States because the 26 study areas were 

selected due to known, suspected, or potential irrigationinduced contamination. Selenium was not the only contaminant that resulted in study areas having been selected for investigation. Some areas were selected because of previously 

known salinity, arsenic, or pesticide problems. 

LIMITATIONS OF DATA

Data collection in the reconnaissance and detailed investigations was not specifically designed to meet the objectives of the 

data-synthesis project. The data collected depended on individual project objectives. For the purposes of this data synthesis, 

the data collected during the reconnaissance and detailed investigations are less than ideal in six principal ways.

TABLE 6. Description of ground-water analyses in 

National Irrigation Water Quality Program data base

Description

Number of

samples

analyzed1

1 A total of 789 ground-water samples were analyzed during 

National Irrigation Water Quality Program investigations. 

Analytical results for an additional 125 ground-water samples from 

the same sites but were not collected as part of a National Irrigation 

Water Quality Program investigation are in the database but were not 

included in the summary description.

Representative Inorganic Analyses2

2 All samples were filtered except those for stable isotopes.

Major-element chemical analyses 441

Residue on evaporation at 180 degrees Celsius 441

Arsenic 348

Boron 483

Copper 149

Molybdenum 344

Selenium 695

Uranium 116

Zinc 192

Stable isotopes (deuterium and oxygen–18) 398

Representative Nutrient Analyses2

Ammonia 196

Nitrite/nitrate 307

Phosphate 104

TABLE 7. Description of bottom-material analyses in 

National Irrigation Water Quality Program data base

[Abbreviation and symbol: mm, millimeter; <, less than]

Description

Number of samples analyzed

Fine fraction

(<0.062 mm)

Coarse fraction

(<2 mm)

Representative Inorganic Analyses1

1 A total of 707 samples were analyzed for inorganic 

constituents, 349 in the fine fraction and 358 in the <2mm 

fraction.

Arsenic 345 358

Boron 298 355

Copper 345 358

Molybdenum 345 358

Selenium 346 358

Uranium 326 256

Zinc 345 358

Representative Organic Analyses2

2 A total of 245 samples were analyzed for pesticides, all 

in the <2mm fraction.

DDT 0 223

Parathion 0 36

2,4–D 0 16
20 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

1

Used in figure 2 to show locations of study areas.

TABLE 9. Taxa collected for inorganic analysis in National Irrigation Water Quality Program study areas

[Symbol: —, none]

Study area Number of taxa

Identifier1 Name Plants Plankton Aquatic

invertebrates Fish Amphibiansand reptiles Birds Mammals Total

A American Falls Reservoir, Idaho 3 — 6 28 — 35 — 72

B Angostura Reclamation Unit, South Dakota 8 — 6 146 — 8 — 168

C Belle Fourche Reclamation Project, South Dakota 11 — 8 128 — 18 — 165

D Columbia River Basin, Washington 18 — 9 74 — 53 — 154

E Dolores–Ute Mountain area, Colorado 54 1 61 198 — 34 1 349

F Gunnison River Basin–Grand Valley Project, Colorado 48 — 35 608 4 213 1 910

G Humboldt River area, Nevada 37 — 10 42 — 93 — 182

H Kendrick Reclamation Project, Wyoming 53 — 45 84 — 484 — 666

I Klamath Basin Refuge Complex, California–Oregon 15 — 42 42 — 112 — 211

J Lower Colorado River valley, California–Arizona 28 — — 31 — 12 — 71

K Lower Rio Grande valley, Texas 2 — 5 23 7 1 — 38

L Malheur National Wildlife Refuge, Oregon 5 — 7 11 — 62 — 85

M Middle Arkansas River Basin, Colorado–Kansas 8 — 7 59 — 110 — 184

N Middle Green River Basin, Utah 285 — 80 232 1 631 3 1,232

O Middle Rio Grande, New Mexico 8 — 12 20 — 45 — 85

P Milk River Basin, Montana 10 5 10 2 — 23 — 50

Q Owyhee–Vale Reclamation Project areas, Oregon–Idaho 31 — 24 45 — 29 — 129

R Pine River area, Colorado 40 4 48 184 — 41 7 324

S Riverton Reclamation Project, Wyoming 16 — 9 73 — 46 — 144

T Sacramento Refuge Complex, California 8 — 30 27 — 71 — 136

U Salton Sea area, California 42 — 39 41 8 294 — 424

V San Juan River area, New Mexico 15 — 15 86 9 63 — 188

W Stillwater Wildlife Management Area, Nevada 268 — 54 93 — 629 — 1,044

X Sun River area, Montana 46 — 163 72 6 784 — 1,071

Y Tulare Lake Bed area, California 9 — 11 33 — 22 — 75

Z Vermejo Project area, New Mexico 18 — 14 28 — — — 60

Total ................................................... 1,086 10 750 2,410 35 3,913 12 8,217

TABLE 8. Description of biologicalsample analyses in National Irrigation 

Water Quality Program data base

Description Number of samples

analyzed

Representative Inorganic Analyses1

1 A total of 8,217 samples were analyzed for 

inorganic constituents.

Arsenic 7,706

Mercury 7,671

Selenium 8,127

Representative Organic Analyses2

2 A total of 1,088 samples were analyzed for 

organic constituents.

DDT 853

Arochlor 1254 183
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 21

SAMPLING-SITE AGREEMENT

The first limitation of the data is that water and biological 

samples commonly were not collected in the same locations. 

For example, plants, insects, fish, and bird tissue, but no water 

samples may have been collected from a particular pond. This 

lack of complementary data was particularly problematic for 

data from the reconnaissance investigations. Typically, during 

detailed investigations after confirming that contamination was 

occurring, USGS and USFWS personnel collected additional 

samples from sites known to be contaminated. To address this 

limitation and evaluate relations between contaminant concentrations in water and biota, biological sampling sites were 

assigned the site-identification number of the nearest appropriate site where surface water or sediment were collected.

MATRIX SELECTION

A second limitation of the data results from the data being 

used in multiple ways. The USGS typically collected 0.45-μm 

filtered water samples to determine “dissolved” contaminant 

concentrations for trace elements in water. The USFWS, however, uses total-contaminant concentrations in water because 

most water-based criteria for assessing potential effects on 

aquatic life are based on toxicity tests that use whole-water concentrations. The reason for the lack of comparable methods is 

that the sampling was done for different purposes. Comparison 

of contaminant concentrations with biological effect levels is 

only one use of the data. Water-quality data also commonly are 

used by the USGS in geochemical-speciation models, which 

require that concentrations are measured using filtered samples. 

Comparisons between biological effect levels based on 

whole-water samples and contaminant concentrations determined for filtered water are problematic. However, for some 

contaminants, total- and filtered-contaminant concentrations in 

water are nearly identical, thus allowing for comparison to biological effect levels (see section titled “Relation Between Totaland Filtered-Contaminant Concentrations in Surface Water,” 

p. 44). Also, at least for metals, the U.S. Environmental Protection Agency (USEPA) recommends that dissolved-contaminant 

concentrations be used to set and measure compliance with 

water-quality standards and criteria. The agency also determined correction factors that express the percentage of “dissolved” to total-recoverable metal (M.G. Protho, U.S. 

Environmental Protection Agency, written commun., 1993).

SPECIES SELECTION

A third limitation of the data is that the taxa collected were 

not consistent. For example, no birds were collected during the 

lower Rio Grande valley and Vermejo Project area reconnaissance studies or during the San Juan River area detailed investigation. In some areas, no shorebirds were collected, and in one 

area, only blackbirds were collected. Although in some areas 

specific taxa may not have been present, the inconsistency also 

can result from other factors. In some areas, the taxa collected 

depended on the background and specific interests of the field 

biologist in charge of sample collection.

A fourth limitation is that in some areas samples were not 

collected from all important feeding guilds. The principal 

exposure route to higher organisms for most contaminants is 

through the food they eat rather than the water they drink or live 

in. Because some feeding guilds are not particularly susceptible to contaminated water, biological problems would be 

missed if samples were collected only from those feeding 

guilds. For example, red-winged blackbirds are probably poor 

indicators of selenium contamination of the wetlands they live 

in because they consume seeds, spiders, and terrestrial insects. 

Because of their invertebrate diet and where they forage, however, shorebirds such as black-necked stilts and American 

avocets are good indicators of selenium contamination of wetlands. Herbivorous birds, such as ducks or American coots, 

may be good indicators of contamination from trace elements 

such as arsenic that accumulate more in plant than insect tissue. 

The data collected by the reconnaissance and detailed investigations would have been more useful for the data synthesis if 

all reconnaissance and detailed investigations had collected 

representative fish and birds from specific feeding guilds. If a 

specific member of a guild was unavailable in an area, substitutions of another species from the same feeding guild could 

have been made.

TISSUE SELECTION

A fifth limitation of the data is lack of consistency among 

study areas in the types of tissue that were sampled. Typically, 

liver tissue was sampled in adult and juvenile birds, but, in one 

area, a combination of liver and kidney tissue was analyzed. 

Meaningful comparison of data between study areas is difficult 

when tissue types do not match because it is not known 

whether observed differences in contaminant concentrations is 

due to tissue differences or habitat or exposure differences.

A sixth limitation of the data when assessing the effects of 

selenium is that few or no bird eggs were collected in some of 

the study areas. In 21 of the 26 study areas, 10 or more bird 

eggs were analyzed but in two study areas, no eggs were collected. Egg tissue is useful to collect for two reasons. Survivor 

bias does not affect eggs because the most contaminated eggs 

are just as likely to be collected as the least contaminated eggs. 

In addition, the selenium in the egg is likely to have originated 

in the area where the egg was collected. With few exceptions, 

adult birds typically establish residency in an area before laying their eggs; they rest, court, mate, make their nest, and then 

lay eggs. In the time between arrival of the mating pair and egg 

laying, selenium accumulated in other areas is depurated fairly 

quickly from the adult birds. Organochlorine pesticides are not 

rapidly depurated, however, so the lack of egg data would not 

be as important if pesticides were being assessed. 
22 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

IDENTIFICATION OF CONTAMINANTS IN WATER AND 

SEDIMENT

STANDARDS, CRITERIA, AND GUIDELINES USED

Standards, criteria, and guidelines from multiple sources 

were used to evaluate contaminant concentrations in water and 

sediment. The values used for comparison by the data synthesis 

team and the sources for the values are given in table 10. For 

surface-water sites, comparisons were made principally with 

the USEPA freshwater aquatic-life chronic criteria (U.S. Environmental Protection Agency, 1986b, 1987). Because freshwater aquatic-life criteria have not been promulgated by the 

USEPA for boron, molybdenum, and uranium, the criteria used 

for those elements were from other sources. Boron and molybdenum were compared to criteria promulgated by the California 

State Water Resources Control Board (1988) for regulation of 

agricultural drainage to the San Joaquin River. Uranium concentrations were compared to the proposed Canadian waterquality objective for the protection of aquatic life (Environment 

Canada, 1983).

For aluminum, the chronic criterion listed in table 10 is valid 

only within a pH range of 6.5 to 9.0. Aluminum concentrations 

were not compared to criteria if the pH of the sample was not 

within that range or was not measured.

For cadmium, chromium(III), copper, lead, and zinc, the 

acute- and chronic-criteria values listed in table 10 assume that 

the hardness of the water is 100 mg/L. For silver, the acute-criterion also assumes that the hardness is 100 mg/L. Because, for 

these trace elements, applicable criteria depend on the water 

hardness, comparisons to criteria for a sample can be made only 

if analytical results for calcium and magnesium are available. 

(Water hardness was calculated from the calcium and magnesium concentrations, and measured trace elements were compared to criteria calculated on the basis of that hardness.)

The chronic criteria for arsenic and chromium depend on 

their oxidation state, which was not measured as part of NIWQP 

investigations. Concentrations of these trace elements were 

compared to applicable criteria by assuming that all the element 

was in the most toxic oxidation state. Therefore, the percentage 

of exceedances determined is a maximum. Considering thermodynamics, most of the arsenic in oxygenated surface waters 

would be in the less toxic oxidation state, arsenic(V), and most 

chromium would be in the most toxic state, chromium(VI). The 

percentage of exceedances determined for chromium therefore 

is assumed to be representative of what would have been determined if the oxidation state had been measured.

The above-listed metals vary substantially in their tendencies 

to bioaccumulate, however, the USEPA aquatic-life criteria do 

not account for this. The criteria are produced using a testing 

protocol that does not include dietary exposure, therefore the 

criteria listed in table 10 will be overestimates of the toxicity

TABLE 10. Contaminant standards, criteria, and guidelines used by 

data-synthesis team to evaluate National Irrigation Water Quality 

Program water and sediment data

[Abbreviations and symbol: μg/L, microgram per liter; μg/g, microgram per gram; 

—, no standard, criterion, or guideline]

Chemical

constituent

Maximum

contaminant

level1

(μg/L)

1 Drinking-water standard for protection of human health. From U.S. 

Environmental Protection Agency (1996) except as noted.

Acute

criterion2

(μg/L)

2 Criterion for protection of aquatic life. From U.S. Environmental Protection 

Agency (1986b) except as noted. 

Chronic

criterion2

(μg/L)

Qualitative

sediment

guideline3

(μg/g)

3 Guideline is upper limit of 95 percent expected range in soils from Western 

United States from Shacklette and Boerngen (1984).

 Inorganic constituents

Aluminum — 4750

4 Criterion is pH dependent; value shown is valid for pH = 6.5–9.0.

487 —

Arsenic 510

5 In October 2001 the Maximum Contaminant Level was lowered from 50 to 10 

micrograms per liter (U.S. Environmental Protection Agency, 2001).

— — 22

Arsenic(III) — 360 180 —

Arsenic(V) — 6850

6 Data are insufficient to develop criteria; value shown is Lowest Observed-Effect 

Level.

— —

Boron 7600

7 Criterion is lifetime health advisory (U.S. Environmental Protection Agency, 

1996).

— 8550

8 Chronic criterion from California State Water Resources Control Board (1988).

—

Cadmium 5 93.9

9 Criterion is hardness dependent; value shown is valid for hardness of 100 

milligrams per liter CaCO3).

9

1.1 —

Chromium 100 — — 200

Chromium(III) — 91,700 9210 —

Chromium(VI) — 16 11 —

Copper 1,300 918 912 90

Lead 15 982 93.2 55

Molybdenum 740 — 819 4.

Selenium 50 20 105/3

10 The value 5 μg/L is from U.S. Environmental Protection Agency 1987 and is 

the chronic criterion for the protection of freshwater aquatic life (fish and aquatic 

invertebrates). The value 3 μg/L is a guideline used for the protection of wildlife (semiaquatic birds and mammals).

111.4/2

11 The value of 1.4 μg/g is the qualitative sediment guideline and the value of 2 

μg/g is the ecological sediment guideline (Lemly, 2002).

Silver 7100 94.1 .12 —

Uranium 1230

12 Criterion from by U.S. Environmental Protection Agency (2000).

— 13300

13 Recommended Canadian water-quality objective for protection of aquatic life 

(Environment Canada, 1983).

5.3

Zinc 72,000 9120 9110 180

 Pesticides

DDD — 60.6 — —

DDE — 61,050 — —

DDT — 141.1

14 Criterion for total DDT (sum DDD + DDE + DDT). From U.S. Environmental 

Protection Agency (1980).

140.001 —

Endrin 2 .18 .0023 —

Lindane .2 2 .08 —

Malathion — — .1 —

Toxaphene 3 15.73

15 Criterion from U.S. Environmental Protection Agency (1986a).

15.0002 —
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 23

thresholds to the extent that the metals tend to bioaccumulate. 

The USEPA criteria for selenium are an exception to this generalization. The selenium criteria were based on field data rather 

than on the standard bioassay protocol. In the case of selenium, 

the standard protocol produced a chronic criterion of 9.7 μg/L 

(selenate basis), as opposed to the field-based adopted criterion 

of 5 μg/L (U.S. Environmental Protection Agency, 1987).

For selenium in water, two criteria were used. The first is the 

USEPA chronic criterion for selenium (5 μg/L), a general standard for the protection of freshwater aquatic life (U.S. Environmental Protection Agency, 1987). USEPA’s aquatic-life criteria 

were derived to protect fish and other instream aquatic life but 

neglect semi-aquatic wildlife (amphibians, reptiles, birds, and 

mammals), which are more sensitive to the effects of bioaccumulation (Peterson and Nebeker 1992).The second criterion 

used is 3 μg/L. This criterion has been derived independently by 

several authors for wildlife exposed to contaminated lentic ecosystems. Three μg/L is the low end of a range given by Skorupa 

and Ohlendorf (1991), who stated that water containing 3 to 20 

μg/L total-recoverable selenium is hazardous to some species of 

birds under some circumstances. Peterson and Nebeker (1992) 

report that dissolved selenium concentrations in water greater 

than 2.1 μg/L appear to produce adverse effects in some species 

of wildlife. Based on bioenergetic modelling, DuBowy (1989) 

concluded that a criterion of 2.8 μg/L for dissolved selenium 

was required to protect mallard ducks from adverse reproductive effects. Based on a sediment selenium model, Van Derveer 

and Canton (1997) calculated a 3 μg/L criterion for protection 

of aquatic life in lentic-aquatic systems rich in organic carbon, 

such as Kesterson Reservoir, California. Van Derveer and Canton’s (1997) result agreed with prior field observations at the 

Highway 158 arm of Belews Lake, North Carolina, where fish 

sublethal effects were associated with 3-4 μg/L (U.S. Department of the Interior, 1998).

The criteria used for trace elements and pesticides in groundwater samples are USEPA freshwater aquatic-life chronic criterion and MCL’s promulgated for drinking water. For boron, 

molybdenum, silver, and zinc, drinking-water MCL’s have not 

been promulgated, and concentrations of these elements in 

ground water were compared to the lifetime health advisory. 

Although comparisons were made to drinking-water standards, 

most of the ground-water sites are subsurface drains or observation wells. Few of the NIWQP ground-water sites are sources of 

drinking water. Although wildlife are not directly exposed to 

ground water, ground water discharges to surface drains and 

wildlife may be exposed to it there. For arsenic, the current 

MCL of 10 μg/L was used. In January 2001 the MCL for arsenic 

was lowered from 50 μg/L to 10 μg/L (U.S. Environmental Protection Agency, 2001).

Concentrations of pesticides in surface-water samples were 

compared to acute and chronic criteria for the protection of 

freshwater aquatic life and to chronic criteria for protection of 

wildlife. For total DDT (the sum of measured DDD, DDE, and 

DDT) these criteria range from 9.2 pg/L for full dietary protection of wildlife (U. S. Environmental Protection Agency, 1995) 

to 1,050 μg/L for protection of aquatic life from brief exposure 

(U. S. Environmental Protection Agency, 1980). For endrin, 

lindane, and malathion, U.S. Environmental Protection Agency 

(1996) criteria were used; for toxaphene, U.S. Environmental 

Protection Agency (1986a) criteria were applied.

Concentrations of trace metals in bottom sediment were 

compared to the upper limit of the 95th-percentile expected 

range of values in soils of the Western United States west of the 

96th parallel. About 97.5 percent of randomly collected soil 

samples in the Western United States should fall below this 

value which is called the qualitative sediment guideline in this 

report. The sediment guideline is calculated from data presented in Shacklette and Boerngen (1984, p. 6). Data described 

by Shacklette and Boerngen (1984) are for soil samples collected at depths of about 8 in., whereas the bottom-sediment 

samples collected for the NIWQP are from water bodies and 

were shallower, typically from depths of only 2 to 4 in. Because 

the horizons sampled are not the same, the soil data of Shacklette and Boerngen (1984) are considered guidelines and are 

used here for qualitative comparison only.

For selenium, in addition to the qualitative sediment guideline, an ecological sediment guideline is also utilized for comparisons. The ecological sediment guideline is an estimate of 

the toxicity threshold for fish and wildlife based on field observations of bioaccumulation from sediment to the food chain. 

The most recent review of such data concluded that a value of 

2 μg/g in sediment emerges as the threshold beyond which bioaccumulation in the benthic-invertebrate food chain exceeds 

the dietary toxicity threshold for fish of 3 μg/g (Lemly, 2002).

SUMMARY STATISTICS AND COMPARISON TO CRITERIA

The distribution of molybdenum concentrations in environmental surface water samples from the NIWQP study areas 

shown in figure 4 is typical of water-quality data. The histogram shows that the statistical distribution is truncated on the 

left by the detection limit and is highly skewed to the right by 

outlier values. Because this distribution is typical of waterquality data, median or 75th-percentile contaminant concentrations (rather than the mean concentration) is used herein to 

characterize the different study areas. The mean concentration 

of a contaminant is not a good estimate of the central tendency 

because the mean is not resistant to the magnitude of a small 
24 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

number of points. In the data set, for example, the two greatest 

concentrations of molybdenum in surface water are 5,000 and 

56,000 μg/L. With those two samples included, the mean 

molybdenum concentration is 75.9 μg/L; with the two samples 

excluded the mean molybdenum concentration is 35.5 μg/L. 

The median value (4 μg/L) and the 75th-percentile value (10 

μg/L), in contrast, are the same with or without those two samples. 

In this report, statistical summaries of data usually are compared with criteria, rather than comparing individual data points 

with criteria. Because they are not resistant to effects of outliers, 

mean values are rarely used in this report to summarize data. 

Instead, the median or 75th percentile concentration is used to 

summarize chemical concentrations in water and sediment for 

comparison with criteria. For biological tissues, the geometric 

mean is used to summarize chemical concentrations. For data 

skewed to the right, the geometric mean is usually quite close to 

the median (Helsel and Hirsch, 1992).

Summary statistics were computed for trace element and pesticide concentrations in surface water, ground water, or bottom 

sediment (tables 11 through 14). Only samples collected for 

NIWQP investigations were used. The data set was not manipulated to remove bias. The percentage of samples that had concentrations exceeding the detection limit and the percentage 

that had concentrations exceeding criteria were computed. The 

results of the mercury analyses were not included in this study 

because of possible contamination of samples by sampling 

equipment and containers used in the 1980’s (David A. Rickert, 

U.S. Geological Survey, written commun., 1994). 

Summary statistics used in this section include all nonreplicate environmental sample analyses in the NIWQP data base 

for samples collected at NIWQP sites during DOI investigations. Samples that were collected before or after a DOI investigation are in the data base but were excluded from the 

statistical analysis. Summary statistics for surface-water and 

bottom-sediment constituents were based on samples collected 

at streams, lakes, and surface drains. Summary statistics for 

ground-water constituents were based on samples collected at 

wells, springs, and underground drains.

Selenium was the trace element most commonly found in 

surface water at concentrations exceeding its chronic criterion 

(table 11); more than 40 percent of the samples exceeded the 

USEPA chronic criterion. One reason that such a large percentage of the samples exceeded the chronic criterion for selenium 

is that many of the NIWQP study areas were selected for investigation because of known or suspected selenium contamination.

Additional bias towards selenium as the most important contaminant results because using the entire data set gives extra 

weight to those sites where more samples were collected. After 

selenium contaminated sites were identified, more followup 

samples were collected at contaminated sites than at uncontaminated sites. This type of bias can be removed by using only one 

sample per site. The most recent sample collected was selected 

at each of the 802 surface-water sites monitored as part of the 

NIWQP. At 286 of the 802 sites (36 percent), the most recent 

sample collected either exceeded or equaled the USEPA 

chronic criterion for selenium. Thus, selenium remains an 

important contaminant even after removing bias caused by 

repeat sampling at contaminated sites.

Boron and molybdenum were the next most commonly 

found trace elements that exceeded applicable criteria in surface-water samples. More than 28 percent of boron concentrations and almost 17 percent of molybdenum concentrations 

exceeded the chronic criteria established by California. The 

types of sample bias found for selenium are not found for boron 

or molybdenum in the NIWQP data base. Multiple repeat analyses were not made for these elements because of their elevated 

concentrations, and none of the NIWQP study areas were 

selected for investigation on the basis of their occurrence.

The chronic criterion for arsenic depends on its oxidation 

state: Arsenic(III) is much more toxic than arsenic(V). 

Although arsenic concentration was measured, the amount of 

the total that was in the form of arsenic(III) was not determined

FIGURE 4. Molybdenum concentrations in surface-water samples from 

National Irrigation Water Quality Program study areas. Two largest 

molybdenum concentrations (5,000 and 56,000 micrograms per liter) are 

not shown. Bin interval for histogram is 10 micrograms per liter. Centraltendency summary statistics were calculated by log-probability methods. n, 

number of observations.

0 500 1,000 1,500 2,000 2,500

0

25

50

75

100

125

150

175

200

1,000

1,025

1,050

Median = 4 

Median = 4 micrograms per liter

 micrograms per liter

 micrograms per liter

Mean = 35.5 micrograms per liter

Mean = 75.9 

75th percentile = 10 micrograms per liter

75th percentile = 10 micrograms per liter

Central tendency (n = 1,404 ) based on all data

Central tendency (n =1,402 ) based on all but two largest values (not plotted)

NUMBER OF OBSERVATIONS

MOLYBDENUM CONCENTRATION, IN MICROGRAMS PER LITER
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 25

TABLE 11. Summary statistics for filtered inorganic constituents in water samples from National Irrigation Water 

Quality Program study areas 

[Abbreviations and symbol: μg/L, micrograms per liter; NC, not calculated because values for more than 80 percent of samples are below 

reporting limit; —, not applicable; <, less than]

Property or 

constituent

Summary statistics1

1 Summary statistics are for nonreplicate, filtered environmental samples collected as part of a NIWQP investigation.

Exceedance (percent)

Relative to:

Median2

2 The median and interquartile range were computed using adjusted log-normal maximum-likelihood methods (Helsel and Cohn, 1988). The 

interquartile range describes the spread of the data. It measures the range of the central 50 percent of the data and is defined as the 75th percentile 

minus the 25th percentile.

Interquartile

range2 Range ofobserved values

Detects

(percent)

Total number

of observations

Chronic

criterion MCL3

3 Relative to maximum contaminant level (U.S. Environmental Protection Agency, 1996) except as noted.

Surface Water (streams, lakes, surface drains)

pH (standard units) 8.3 0.6 3.3 – 11 — 3,620 — —

Aluminum (μg/L) 9.9 17 <10 – 1,000 54.8 188 43.2 (n = 156)

4 n is the number of observations out of the total number of observations for which ancillary data (such as pH or hardness) needed for 

comparison with criterion were available.

—

Arsenic (μg/L) 2.0 11 <1 – 7,500 80.8 1,285 3.5 —

Boron (μg/L) 220 550 <10 – 260,000 98.2 1,783 28.4 —

Cadmium (μg/L) NC NC <1 – 660 15.7 1,166 43.2(n = 1,059) —

Chromium (μg/L) .41 1.0 <1 – 15,000 26.9 1,244 4.1(n = 1,134) —

Copper (μg/L) 2.1 3.4 <1 – 140 73.3 1,163 41.0(n = 1,053) —

Lead (μg/L) NC NC <5 – 74 9.1 1,113 42.1(n = 1,003) —

Molybdenum (μg/L) 4.0 10 <1 – 56,000 82.6 1,221 16.7 —

Selenium (μg/L) 2.0 12 <1 – 8,300 67.2 2,057 40.1 —

Silver (μg/L) NC NC <1 – 5 6.9 332 4.4(n = 223) —

Uranium (μg/L) 9.0 26 <1 – 470 95.6 634 51.6

5 Relative to recommended Canadian water-quality objective for protection of aquatic life (Environment Canada, 1983).

—

Zinc (μg/L) 6.6 13 <10 – 19,000 66.5 1,288 43.1(n = 1,151) —

Ground Water (wells, springs, subsurface drains)

pH (standard units) 7.3 0.5 5.0 – 9.1 — 760 — —

Aluminum (μg/L) 21 31 <10 – 370 73.0 174 7.5 —

Arsenic (μg/L) 2.0 6.0 <1 – 1,400 79.9 348 10.6 22.4

Boron (μg/L) 1,400 1,720 10 – 120,000 100 483 76.6 674.5

6 Relative to lifetime health advisory (U.S. Environmental Protection Agency, 1996).

Cadmium (μg/L) NC NC <1 – 100 16.3 147 43.7 (n=134) 1.4

Chromium (μg/L) .87 1.8 <1 – 50 40.4 193 40 (n=180) 0

Copper (μg/L) 4.0 9.0 <1 – 180 84.6 149 47.4 (n=136) 0

Lead (μg/L) NC NC <5 – 18 9.5 168 41.3 (n=155) 0.6

Molybdenum (μg/L) 17 27 <1 – 28,000 95.6 344 44.2 620.6

Selenium (μg/L) 26 80 <1 – 16,000 82.9 694 70.2 35.6

Silver (μg/L) NC NC <1 – 1 15.0 40 43.4 (n=29) 60

Uranium (μg/L) 20 133 <1 – 1,500 94.0 116 9.5 744.0

7 Relative to maximum contaminant level (U.S. Environmental Protection Agency, 2000).

Zinc (μg/L) 20 34 <10 – 15,000 81.8 192 414.4 (n=181) 63.1
26 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

as part of the NIWQP. Slightly less than 4 percent of the arsenic 

values exceeded the criteria for arsenic(III). The actual percentage of exceedances might have been much less than this had 

arsenic species been measured because arsenic(V) would be 

expected from thermodynamic considerations to be the predominant species in oxygenated surface water.

Median concentrations of most trace elements were greater in 

ground water than in surface water, and fewer samples had concentrations less than the detection limit (table 11). Trace-element concentrations are typically higher in ground water than 

surface water because ground water commonly is in close contact for long periods with aquifer material containing trace elements. Another reason for the higher values may be sample 

bias. Few reference sites were selected for ground water; most 

ground-water sites were either underground drains or wells in 

irrigated areas. 

In ground-water samples, boron was the trace element that 

most frequently exceeded the MCL or lifetime health advisory 

(table 11). The median boron concentration in NIWQP groundwater samples was more than twice the lifetime health advisory. 

Forty-four percent of the uranium samples exceeded the MCL, 

more than 35 percent of the selenium samples exceeded the 

MCL, and 22 percent of the arsenic samples exceeded the MCL

(table 11). Although most of the wells NIWQP sampled are not 

used as sources of drinking water, these results indicate there 

may be human-health concerns in some farming areas for 

households that depend on shallow wells for their drinking 

water. 

Except for molybdenum, selenium and uranium, trace elements in bottom sediment generally do not exceed the qualitative sediment guidelines (table 12). Selenium is the only trace 

element for which ecological sediment guidelines are used in 

this report. Selenium concentrations commonly exceeded both 

the qualitative and ecological sediment guidelines.

For arsenic and molybdenum, most of the samples that 

exceed the guideline are from one study area— the Stillwater 

Wildlife Management Area in Nevada (W). A high percentage 

of bottom-sediment samples had molybdenum concentrations 

less than the detection limit, but molybdenum was almost 

always detected in ground water. Molybdenum has a high 

geochemical mobility and thus a tendency to enter into solution 

in water under normal earth-surface conditions (Hem, 1985).

In surface water and bottom sediment, DDT and degradation 

products of DDT were the pesticides most commonly found at 

concentrations exceeding the reporting limit (tables 13 and 14). 

The only pesticides to exceed criteria in surface water were 

malathion and DDT (and its degradation products). A single 

sample of malathion from Lower Rio Grande valley in Texas

TABLE 12. Summary statistics for inorganic constituents in bottom-sediment samples from 

National Irrigation Water Quality Program study areas 1 2 3 4

[Abbreviations and symbol: NC, not calculated because values for more than 80 percent of samples are below 

reporting limit; μg/g, micrograms per gram; —, not determined because a soils guideline has not been established 

for the constituent; <, less than]

1 Summary statistics are for nonreplicate, fine fraction (<0.062 mm) environmental samples collected as part of 

a NIWQP investigation. 2 The median and interquartile range were computed using adjusted log-normal maximum-likelihood methods 

(Helsel and Cohn, 1988). The interquartile range describes the spread of the data. It measures the range of the central 50 

percent of the data and is defined as the 75th percentile minus the 25th percentile. 3 Relative to qualitative sediment guideline (table 10) except as noted.

4 Relative to ecological sediment guideline (see text for discussion).

Chemical

constituent

Summary statistics1

Exceedance3

Median (percent) 2

(μg/g)

Interquartile

range2

(μg/g)

Range of

observed values

(μg/g)

Detects

(percent)

Total

number of

observations

Aluminum 62,000 20,000 20,000–98,000 100 345 —

Arsenic 6.5 4.9 <10 – 370 96.8 345 5.4

Boron 1.7 2.5 <.4 – 390 95.0 298 —

Cadmium NC NC <2 – 2 .7 280 —

Chromium 54 27 2 – 270 100 345 2.3

Copper 22 14 5 – 520 100 345 1.2

Lead 17 5 <4 – 470 98.6 345 1.7

Molybdenum .55 1.4 <2 – 73 20.9 345 13

Selenium .6 1.5 <.1 – 85 98.3 346 422.5

Silver NC NC <1 – <1 0 265 —

Uranium 4.0 3.5 .25 – 56.6 100 326 27.6

Zinc 77 36 23 – 510 100 345 2.3
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 27

TABLE 13. Summary statistics for selected pesticides in surface-water samples from

National Irrigation Water Quality Program study areas

[Abbreviations and symbols: NC, not calculated because values for more than 80 percent of samples 

are below reporting limit; ng/L, nanograms per liter; —, not determined because applicable criterion 

has not been established for the constituent; <, less than]

Chemical

constituent

Summary statistics1

1 Summary statistics are for nonreplicate, environmental surface-water samples collected as part of a 

NIWQP investigation.

Median2

(ng/L)

2 The median and interquartile range were computed using adjusted log-normal maximum-likelihood 

methods (Helsel and Cohn, 1988). The interquartile range describes the spread of the data. It measures the 

range of the central 50 percent of the data and is defined as the 75th percentile minus the 25th percentile. 

Interquartile

range2

(ng/L)

Range of

observed

values

(ng/L)

Detects

(percent)

Total number

of

observations

Exceedance3

(percent)

3 Exceedance relative to chronic criteria (table 10).

DDD 0.3 0.7 <1 – 10 21 107 —

DDE NC NC <1 – 38 15 107 —

DDT NC NC <1 – 36 15 107 421

4 Because exceedance was calculated by using total DDT (sum DDD + DDE + DDT), exceedance may 

be greater than detects.

Endrin NC NC <1 – 20 9.0 108 0

Lindane NC NC <1 – 2 1.8 107 0

Malathion NC NC <10 – 710 4.8 168 .6 

Toxaphene NC NC <1,000 – <1,000 0 107 0

1Computed using adjusted log-normal maximum-likelihood methods (Helsel and Cohn, 1988).

TABLE 14. Summary statistics for selected pesticides in bottom-sediment samples 

from National Irrigation Water Quality Program study areas

[NC, not calculated because values for more than 80 percent of samples are below reporting limit]

Chemical

constituent

Summary statistics

Median1 Interquartile

range1

Range of

observed

values Detects

(percent)

Total

number of

observations

(micrograms per kilogram)

Organochlorine pesticides

Aldrin NC NC <0.1 – 1.5 6 219

Chlordane 0.49 0.88 <1 – 30 30 221

DDD .14 1.1 <.1 – 24 55 221

DDE .5 1.8 <.1 – 67 81 220

DDT .007 .07 <.1 – 86 21 219

Endrin NC NC <.1 – 1.0 3 221

Lindane NC NC <.1 – 4.7 1 220

Methoxychlor NC NC <.1 – 45 5 215

Mirex NC NC <.1 – 0.5 .5 221

Toxaphene NC NC <10 – 40 2 221

Organophosphate pesticides

Malathion NC NC <0.1 – <0.1 0 36

Parathion NC NC <.1 – .1 2 36

Trithion NC NC <.1 – <.1 0 36

Herbicides

2,4-D NC NC <0.1 – <0.1 0 16

Silvex NC NC <.1 – <.1 0 16

2,4,5-T NC NC <.1 – <.1 0 16
28 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

exceeded the chronic criterion. Twenty-one percent of totalDDT concentrations in surface water exceeded the chronic criterion, however, most of the samples that exceeded the chronic 

criterion were from one area: the Owyhee area in Oregon and 

Idaho. 

Degradation products of DDT were detected in 81 percent of 

the bottom-sediment samples and in about 20 percent of those 

surface-water samples that were analyzed for the degradation 

products. Although none of the samples exceeded 1,050 μg/L 

DDE, the lowest observed-effect level (LOEL) (U.S. Environmental Protection Agency, 1986b), DDE concentrations thousands of times less than the LOEL can adversely affect wildlife 

because of its bioaccumulation. In addition, DDE and several 

other organochlorine compounds can modulate the endocrine 

system and affect reproduction in invertebrates, fish, and wildlife (U.S. Environmental Protection Agency, 1997). Kelce and 

others (1995) concluded that “the reported increased incidence 

of developmental male reproductive system abnormalities in 

wildlife and humans may reflect antiandrogenic activity of the 

persistent DDT metabolite p,p’–DDE * * * ”

NATIONAL IRRIGATION WATER QUALITY PROGRAM STUDYAREA COMPARISONS

Box plots of contaminant concentrations in surface water, 

ground water, and bottom sediment were prepared for the 26 

study areas. Contaminant concentrations from an area were 

compared to regulatory and proposed criteria and to concentrations from other areas. Summary statistics used in this section 

include all nonreplicate sample analyses in the NIWQP data 

base for samples collected at NIWQP sites during DOI investigations. Data values from samples that were collected before or 

after a DOI investigation are in the data base but were excluded 

from the statistical analysis. 

DISSOLVED SOLIDS

The dissolved-solids concentrations at source water sites 

in almost all the areas were typically less than 500 mg/L and 

most were less than 1,000 mg/L (fig. 5A). Source water sites are 

surface water sites upstream from irrigated lands which represent the water used for irrigation. For 9 of the 25 study areas 

analyzed (A, B, D, I, J, Q, R, S, and T), dissolved-solids concentrations in surface-water samples did not exceed 3,000 mg/L, 

even after irrigation (fig. 5B), and in only 2 of the 25 areas (Y 

and Z) was the median greater than 3,000 mg/L (fig. 5B). The 

greatest dissolved-solids concentrations were found in arid 

areas having terminal lakes or ponds. In 4 of the 26 areas (H, W, 

Y, and Z), more than 10 percent of the samples had dissolvedsolids concentrations exceeding 10,000 mg/L.

ARSENIC

Arsenic concentrations in surface water potentially exceed 

the chronic criterion in only 3 of the 26 areas, Humboldt River 

area, Malheur National Wildlife Refuge area, and Stillwater 

Wildlife Management Area (G, L, and W). Although arsenic 

concentrations determined as part of the NIWQP cannot be 

compared directly to the chronic criteria because the oxidation 

state of the arsenic was not determined, even if all the arsenic

FIGURE 5. Concentrations of total dissolved solids in surface-water samples 

from source-water sites and from sites in and downstream from irrigated 

lands in National Irrigation Water Quality Program (NIWQP) study areas. 

Data points are from non-replicate environmental samples collected as part 

of a NIWQP investigation. 

Upper decile (90th percentile)

Upper quartile (75th percentile)

Lower quartile (25th percentile)

Median

Lower decile (10th percentile)

Point outside lower decile

EXPLANATION

Point outside upper decile

100

1,000

10,000

100 ,000

300,000

B

DISSOLVED-SOLIDS CONCENTRATION, 

IN MILLIGRAMS PER LITER

Downstream water

NUMBER OF ANALYSES

1412104847

339

34

193

65

30

1418414812334521235275

187103

9

16

NIWQP STUDY AREA

ABCDE FGH I J K LMNOPQRS T UVWXY Z

0323455733105116243131039402

10

100

1,000

3,000

A

DISSOLVED-SOLIDS CONCENTRATION, 

IN MILLIGRAMS PER LITER

Source water

NUMBER OF ANALYSES

NIWQP STUDY AREA

A CDE FGH I J K LMNOPQRS T UVWXY Z B
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 29

were in the most toxic oxidation state, only a low percentage of 

the NIWQP samples would exceed the criterion. The MCL for 

arsenic was lowered from 50 to 10 μg/L in January 2001 (U.S. 

Environmental Protection Agency, 2001). The median arsenic 

concentration exceeded the current 10-μg/L MCL in surface 

water (fig. 6) in four areas (G, I, Q, and W), and in 7 areas 25 

percent or more of the samples exceeded the MCL. All the 

ground-water samples from two areas (G and W) and one of 

two ground-water samples from the middle Rio Grande area 

(O) exceeded the MCL. In three areas with high arsenic concentrations in surface water (I, L, and Q), arsenic concentrations were not determined in ground water.

FIGURE 6. Concentrations of arsenic in surface water, ground water, and bottom sediment from sites in and downstream from irrigated lands in National 

Irrigation Water Quality Program (NIWQP) study areas. USEPA, U.S. Environmental Protection Agency. Data points are from non-replicate 

environmental samples collected as part of a NIWQP investigation.

A BC DE FGH I J K LMNOPQRS T UVWX Y Z

0000320221000051422010000

0

161642012

4

121048471043460201013141418543812332121233475185859

7530141762789145127116121011111617242368

A BCDE FGH I J K LMNOPQRS T UVWX Y Z

A B

C

ARSENIC CONCENTRATION,

IN MICROGRAMS PER GRAM, DRY WEIGHT

ARSENIC CONCENTRATION,

IN MICROGRAMS PER LITER

ARSENIC CONCENTRATION,

IN MICROGRAMS PER LITER

Surface Water Ground Water

Bottom Sediment (<0.062mm fraction)

NUMBER OF ANALYSES NUMBER OF ANALYSES

NIWQP STUDY AREA

NIWQP STUDY AREA

NUMBER OF ANALYSES

NIWQP STUDY AREA

10,000

1,000

100

10

1

1,000

100

10

1

10,000

1,000

100

10

1

Upper decile (90th percentile)

Upper quartile (75th percentile)

Lower quartile (25th percentile)

Median

USEPA chronic criterion

Qualitative sediment guideline

Maximum contaminant level

Lower decile (10th percentile)

Point outside lower decile

EXPLANATION

Point outside upper decile

Current

Previous Previous

Current

ABCD FGH I J K LMNOPQRS TUVWXY Z E
30 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

The highest arsenic concentrations in surface water typically 

were found in terminal lakes in areas where irrigated land is 

associated with rocks and soils of volcanic origin. In the Stillwater Wildlife Management Area (W), seepage of impounded 

irrigation water and natural evaporation has created small, isolated ponds having arsenic concentrations exceeding 30 mg/L 

(Tuttle and Thodal, 1998). 

All of the arsenic concentrations in ground-water samples 

from the Stillwater Wildlife Management Area exceed the current MCL; however, few, if any, of the wells sampled as part of 

the NIWQP are used as drinking-water sources. In the Stillwater 

Wildlife Management Area, 55 percent of the ground-water 

samples had arsenic concentrations that exceeded 180 μg/L, 

which could affect wildlife adversely in areas where ground 

water discharges to surface drains.

The highest concentrations of arsenic in bottom sediment 

were in the Belle Fourche Reclamation Project (C), Humboldt 

River area (G), and Stillwater Wildlife Management Area (W). 

Elevated arsenic concentrations in the Humboldt River area 

were expected because abandoned arsenic mills and associated 

tailings are found along the edge of a lake in the Humboldt 

Wildlife Management Area (Seiler and others, 1993). 

BORON

In 6 of the 26 study areas (G, K, N, U, W, and Y), the median 

boron concentration in surface water exceeded the aquatic-life 

chronic criterion of 550 μg/L (fig. 7A). Boron is extremely soluble and boron concentrations can have a wide range. In the 

Stillwater Wildlife Management Area (W) and Tulare Lake Bed 

area (Y), boron concentrations ranged from approximately 200 

μg/L to more than 100,000 μg/L. Three of the four evaporation 

ponds sampled as part of the Tulare Lake Bed area reconnaissance investigation had boron concentrations exceeding 

100,000 μg/L; the greatest concentration was 140,000 μg/L 

(Schroeder and others, 1988). Typically, the study areas having 

the highest boron concentrations have lakes or ponds in terminal 

sinks where boron is evaporatively concentrated.

In 4 of the 13 areas where ground water was analyzed for 

boron (H, N, U, and W), more than 50 percent of the samples 

exceeded the lifetime health advisory (fig. 7B). In 4 other areas 

(E, M, P, and R) all boron concentrations were less than the 

health advisory. Overall, almost 75 percent of the boron concentrations in ground water exceeded the health advisory. Sampling 

bias is a substantial reason for the high percentage of exceedances. More than 60 percent of the samples were collected from 

2 areas (U and W) where boron concentrations typically 

exceeded the health advisory.

In bottom sediment, the median concentration was commonly 2 to 8 μg/g (fig. 7C). Concentrations of boron in sediment exceeding 50 μg/g are found in areas where elevated 

concentrations of boron are found in surface and ground water.

MOLYBDENUM

In 3 of the 26 areas ( G, W, and Y), the median molybdenum 

concentration in surface water exceeded the aquatic-life 

chronic criterion of 19 μg/L (fig. 8A). The study areas having 

the highest molybdenum concentrations have lakes or ponds in 

terminal sinks. Similar to boron, the range of molybdenum 

concentrations is wide. For example, in the Stillwater Wildlife 

Management Area (W), molybdenum concentrations span a 

range of more than three orders of magnitude.

In 3 of the 26 areas (N, R, and W), more than 25 percent of 

the ground-water samples exceeded the lifetime health advisory for molybdenum (fig. 8B). In the Stillwater Wildlife Management Area (W), molybdenum in more than 50 percent of the 

samples exceeded the advisory. Only a few percent of bottomsediment samples exceeded the qualitative sediment guideline 

for molybdenum (fig. 8C) and more than half were from the 

Stillwater Wildlife Management Area.

SELENIUM

SURFACE WATER

Selenium concentrations in the source water used for irrigation in the NIWQP areas typically are less than the reporting 

limit of 1 μg/L. Source-water sites are upstream of irrigated 

land, and hence are unaffected by irrigation in the study area. 

In 23 of the 26 areas, samples were taken of the source water 

used for irrigation and in only 7 of these areas (B, C, F, H, J, M, 

and U) did a sample of source water contain selenium concentrations exceeding 1 μg/L (fig. 9A). Commonly, where the 

source water did contain measurable amounts of selenium, 

concentrations ranged from 1 to 3 μg/L. However, in 2 of the 7 

areas (F and M), even some source-water samples contained 

selenium concentrations exceeding 5 μg/L. Selenium sources 

available to the water used for irrigation can include irrigation 

drainwater from areas upstream or drainage from seleniferous 

rocks in the watershed. Selenium concentrations in source 

water exceeding 5 μg/L are very unusual, and according to 

Hamilton’s (1999) historical analysis of selenium in the Colorado River Basin, are more likely linked to both land-use practices and native geology than to native geology alone.

The importance of irrigation in determining selenium concentrations was examined by comparing selenium concentrations at reference sites and at sites effected by irrigation. 

Selenium concentrations were measured at 802 individual 

sites—693 sites in and downstream from irrigated land and 109 

reference sites. The most recent non-replicate selenium value 

measured at each site was selected to represent the selenium 

concentration at the site. For the reference sites, the median 

concentration was <1 μg/L and the 75th percentile was 2 μg/L. 

For the sites in and downstream from irrigated land, the median 

concentration was 2 μg/L and the 75th percentile was 15 μg/L.
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 31

FIGURE 7. Concentrations of boron in surface water, ground water, and bottom sediment from sites in and downstream from irrigated areas in 

National Irrigation Water Quality Program (NIWQP) study areas. Chronic criterion is from California State Water Resources Control Board (1988). 

Data points are from non-replicate environmental samples collected as part of a NIWQP investigation.

A BC DE FGH I J K LMNOPQRS T UVWX Y Z

1

10

100

1,000 7

530135627891351271151191111151482368

0

00

0

3

6

0

0

0

0

0

0

0

0

0

6

2

22

5

15

2

2

10

23 2

64

35

1412104847312342276510141418753812334521235475

184

78916

2

A BCDE FGH I J K LMNOPQRS T UVWX Y Z A BCDE FGH I J K LMNOPQRS T UVWX Y Z

10

100

1,000

10,000

100,000

10

100

1,000

10,000

100,000

A B

C

200,000 200,000

0.4

BORON CONCENTRATION,

IN MICROGRAMS PER GRAM,DRY WEIGHT

BORON CONCENTRATION,

IN MICROGRAMS PER LITER

Surface water Ground water

Bottom sediment (<0.062mm fraction)

NUMBER OF ANALYSES NUMBER OF ANALYSES

NIWQP STUDY AREA

NIWQP STUDY AREA

NIWQP STUDY AREA

NUMBER OF ANALYSES

Upper decile (90th percentile)

Upper quartile (75th percentile)

Lower quartile (25th percentile)

Median

California State Water

Resources Control Board

(1988) chronic criterion

Lifetime health advisory

Lower decile (10th percentile)

Point outside lower decile

EXPLANATION

Point outside upper decile
32 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

FIGURE 8. Concentrations of molybdenum in surface water, ground water, and bottom sediment from sites in and downstream from irrigated lands in 

National Irrigation Water Quality Program (NIWQP) study areas. Some NIWQP areas either show no plotted data because data were below reporting limit 

or were not plotted because no analyses of the given type were done. USEPA, U.S. Environmental Protection Agency. Data points are from non-replicate 

environmental samples collected as part of a NIWQP investigation.

A B C D E F G H I J K L MN O P Q R S T U V WX Y Z

2

10

100 7

530141762789145127116121011111617232368

14121048471213460131014141854301233432123347518626916

000035622100005142201000146064502

ABCDE FGH I J K LMNOPQRS T UVWXY Z ABCDE FGH I J K LMNOPQRS T UVWXY Z 1

10

100

100,000

10,000

1,000

1

10

100

10,000

1,000

A B

C

MOLYBDENUM CONCENTRATION, 

IN MICROGRAMS PER GRAM,

DRY WEIGHT 

MOLYBDENUM CONCENTRATION, 

IN MICROGRAMS PER LITER

Upper decile (90th percentile)

Upper quartile (75th percentile)

Lower quartile (25th percentile)

Median

Chronic criterion

Qualitative sediment guideline

Lifetime health advisory

Lower decile (10th percentile)

Point outside lower decile

EXPLANATION

Point outside upper decile

NUMBER OF ANALYSES NUMBER OF ANALYSES

NUMBER OF ANALYSES

NIWQP STUDY AREA NIWQP STUDY AREA

NIWQP STUDY AREA

Surface water Ground water

Bottom sediment (<0.062mm fraction)
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 33

FIGURE 9. Concentrations of selenium in source water used for irrigation and in surface water, ground water, and bottom sediment from sites in and 

downstream from irrigated areas in National Irrigation Water Quality Program (NIWQP) study areas. USEPA, U.S. Environmental Protection Agency. Data 

points are from non-replicate environmental samples collected as part of a NIWQP investigation.

03234407133115104243131138402

00003662220000522122010002210645202

14121048473433432613101414181963812334321235475184120916

7530

14

176278914512711612101 11 116172 4

4 2

68

ABC E D FGH I J K LMNOPQRS T UVWXY Z ABC E D FGH I J K LMNOPQRS T UVWXY Z 0.1

1

10

100

ABCDE FGH I J K LMNOPQRS TUVWXY Z

1

A CDE FGH I J K LMNOPQRS T UVWXY Z B 1

10

2

3

4

5

6

7

8

9

A B

C

SELENIUM CONCENTRATION, IN MICROGRAMS PER LITER

SELENIUM CONCENTRATION,

IN MICROGRAMS PER GRAM, DRY WEIGHT

SELENIUM CONCENTRATION,

IN MICROGRAMS PER LITER

USEPA acute criterion

Upper decile (90th percentile)

Upper quartile (75th percentile)

Lower quartile (25th percentile)

Median

USEPA chronic criterion

Qualitative sediment guideline

Ecological sediment guideline

Maximum contaminant level

Lower decile (10th percentile)

Point outside lower decile

EXPLANATION

Point outside upper decile

NUMBER OF ANALYSES NUMBER OF ANALYSES

NIWQP STUDY AREA NIWQP STUDY AREA

NUMBER OF ANALYSES NUMBER OF ANALYSES

NIWQP STUDY AREA NIWQP STUDY AREA

Ground water D Bottom sediment (<0.062mm fraction)

Source water Surface water

20,000

10,000

1,000

100

10

1

10,000

1,000

100

10
34 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

In 12 of the 26 areas, the median selenium concentration in 

surface water of the streams, canals, drains, ponds, and lakes in 

and downstream from irrigated land was less than the reporting 

limit (1 μg/L) (fig. 9B). In 3 of these 12 areas (I, L, and P), selenium was not detected in any surface-water samples.

In 12 of the 26 areas at least 25 percent of the surface water 

samples had selenium concentrations that either equaled or 

exceeded the U.S. Environmental Protection Agency aquaticlife chronic criterion (5 μg/L). In 6 areas (F, H, M, N, U, and Y), 

50 percent or more of the surface-water samples exceeded the 

chronic criterion. In 6 other areas (C, E, R, S, X, and Z), between 

25 and 50 percent of the samples equaled or exceeded the 

chronic criterion.

Typically, the range of selenium concentrations in contaminated areas spans several orders of magnitude. In the 

Kendrick Reclamation Project (H) and in the middle Green 

River Basin (N), selenium concentrations in surface water 

ranged from less than 1 μg/L to more than 5,000 μg/L.

GROUND WATER

Wells were sampled for selenium in only 13 of the 26 areas 

(fig. 9C). In 7 of those 13 areas (F, H, N, R, U, W, and X), selenium concentrations in some samples exceeded the MCL and in 

3 of those 7 areas (H, R, and U), selenium concentrations 

exceeded the MCL in more than 50 percent of the samples. 

Because selenium in ground water ultimately discharges to the 

surface where wildlife may be exposed to it, selenium concentrations in ground water were also compared with criteria for the 

protection of aquatic life. In more than 70 percent of the wellwater samples, selenium concentrations exceeded the chronic 

criterion. Samples from subsurface drains were collected in 

only one area, the middle Green River area (N). The median 

selenium concentration in the subsurface-drain samples was 25 

μg/L (range 4 to 75 μg/L).

In the Kendrick Reclamation Project (H) and in the Middle 

Green River Basin (N), some well waters contained selenium 

concentrations exceeding 10,000 μg/L. Two of the wells in the 

middle Green River Basin were at one time used as sources of 

drinking water; selenium in one of them ranged from 83 to 90 

μg/L. Although their data were not included in the NIWQP data 

base, See, Peterson, and Ramirez (1992) reported that selenium 

concentrations in 49 domestic wells (sampled by the Natrona 

County Department of Health) in the vicinity of the Kendrick 

Reclamation Project ranged from less than 5 to 1,700 μg/L.

BOTTOM SEDIMENT

In 15 of the 25 areas where fine fraction (<0.062 mm) bottom-sediment samples were collected (fig. 9D), at least some of 

the samples exceeded the ecological sediment guideline value 

of 2 mg/kg for selenium (Lemly, 2002). In four areas (F, H, N, 

and Z) selenium concentrations in more than 50 percent of the 

bottom sediment samples exceed the ecological sediment 

guideline. 

URANIUM

Leaching of soil and rock by irrigation water can produce 

concentrations of dissolved uranium that may threaten nearby 

drinking water supplies (Zielinski and others, 1995). Because 

the USEPA has not promulgated any criterion for uranium for 

the protection of freshwater aquatic life, the criterion used was 

the proposed Canadian Water-Quality Objective of 300 μg/L 

and the MCL of 30 μg/L. These values for uranium are based 

on its potential chronic toxicity (kidney damage) in mammals, 

not on its radioactivity. Haseltine and Sileo (1983) indicated 

that metallic uranium in the diet did not cause kidney damage 

or weight loss in American black ducks (Anas rubripes); their 

study was the only one found on the effects of uranium on wildlife.

Few surface-water samples exceeded the Canadian WaterQuality Objective for uranium for the protection of aquatic life 

(fig. 10A). Ground water in only 10 of the 26 areas was sampled for uranium. Individual uranium concentrations exceeding 

the MCL were measured in 4 of those 10 areas (H, M, U, and 

W; fig. 10B) and the median uranium concentration exceeded 

the MCL in 2 areas (U and W). Although uranium commonly 

exceeds the MCL in some areas, uranium alone would not 

restrict use of the water. All samples that exceeded the criterion 

for uranium also exceeded the National Secondary Drinking 

Water standard for total dissolved solids (fig. 11).

In the fine (< 0.062-mm) fraction, uranium in more than 25 

percent of the bottom-sediment samples exceeded the qualitative sediment guideline (5.3 μg/g). In 15 of the 25 study areas 

where bottom sediment was sampled, at least one bottom-sediment sample exceeded the guideline for uranium (fig. 10C); in 

5 of the 25 study areas (F, G, N, V, and W), the median uranium 

concentration exceeded the guideline.

PESTICIDES

Pesticides were sampled in surface water at the discretion of 

the study teams and were sampled in only 7 of the 26 areas (D,

J, K, N, P, Q, and U). DDT’s degradation products, DDE and 

DDD, were detected more frequently than undegraded DDT, 

which was found in surface water only in the one area (Q). 

Although 22 of the 107 samples (21 percent) analyzed for total 

DDT (sum of DDD + DDE + DDT) exceeded the chronic criterion, 18 of those 22 samples were from the Owyhee–Vale 

Reclamation Project areas (Q; fig. 12A). Five additional samples for the Salton Sea area (U), four of which exceeded the 

chronic criterion, are shown in figure 12 but were not included 

in the calculation of the percent exceedances in table 13. The 

samples from the Salton Sea area were collected from NIWQP 

sites and analyzed by USGS personnel during the reconnaissance investigation, but were not analyzed as part of the 

NIWQP investigations. 
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 35

FIGURE 10. Concentrations of uranium in surface water, ground water, and bottom sediment from sites in and downstream from irrigated areas in 

National Irrigation Water Quality Program (NIWQP) study areas. MCL, maximum contaminant level; USEPA, U.S. Environmental Protection Agency. 

Data points are from non-replicate environmental samples collected as part of a NIWQP investigation. 1 4 1 2 1 0

0472928401391491824201202216239221281650

0000002500005322010002206 0500

7530131762789145127116111011111616112368

ABCD E FGH I J K LMNOPQRS TUVWXY Z

A B CD E F GH I J K L MNOP QR S T U VWX Y Z A B CD E F GH I J K L MNOP QR S T U VWX Y Z

A B

C

URANIUM CONCENTRATION,

IN MICROGRAMS PER GRAM, DRY WEIGHT

URANIUM CONCENTRATION,

IN MICROGRAMS PER LITER

Upper decile (90th percentile)

Upper quartile (75th percentile)

Lower quartile (25th percentile)

Median

Canadian waterquality objective

Qualitative sediment guideline

MCL

Lower decile (10th percentile)

Point outside lower decile

EXPLANATION

Point outside upper decile

NUMBER OF ANALYSES NUMBER OF ANALYSES

NIWQP STUDY AREA

NIWQP STUDY AREA NIWQP STUDY AREA

NUMBER OF ANALYSES

Surface water Ground water

Bottom sediment (<0.062mm fraction)

1,000

100

10

1

100

10

1

0.1

100

1,000

2,000

10

1
36 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

Erosion of agricultural soils is the probable source of DDT 

now found in surface water. DDT or its degradation products 

were detected in bottom-sediment samples in all 21 areas in 

which they were sampled (fig. 12B). In 7 of these 21 areas (D, 

J, K, Q, T, U, and Y) total DDT concentrations exceeding 10 

μg/kg were measured in the bottom sediment, and in 2 areas (Q 

and U) the median concentration exceeded 10 μg/kg.

CONTAMINANT ASSOCIATIONS

What contaminants are associated in surface water? What are 

the relations between concentrations in water and bottom sediment? What are the relations between filtered- and total-contaminant concentrations in water? Answers to these questions 

can provide information about sources of contaminants and the 

processes that affect their concentrations. Relations between 

selenium concentrations in biota, water, and sediment are discussed in the section titled “Relation Between Selenium in 

Water, Sediment, and Biota,” p. 107. 

FIGURE 11. Relation between uranium and total dissolved-solids 

concentrations in ground water in National Irrigation Water Quality Program 

(NIWQP) study areas. Shaded area shows where uranium samples would 

exceed maximum contaminant level (MCL) without exceeding standard for 

dissolved solids. Data points are from non-replicate environmental samples 

collected as part of a NIWQP investigation.

FIGURE 12. Concentrations of total DDT (DDT plus metabolites) in surface 

water and bottom sediment in National Irrigation Water Quality Program 

(NIWQP) study areas. USEPA, U.S. Environmental Protection Agency. 

Data points are from non-replicate environmental samples collected as part 

of a NIWQP investigation.

URANIUM CONCENTRATION, IN MICROGRAMS PER LITER

DISSOLVED-SOLIDS CONCENTRATION,

IN MILLIGRAMS PER LITER

10,000

10 100 1,000 10,000 100,000

1,000

100

10

1

0.1

EXPLANATION

National Secondary Drinking Water Standard for total dissolved solids

Maximum contaminant level for uranium

Ground-water analysis

1

10

100

0.1

1

10

100

200

B

SUM OF DDT, DDD, AND DDE,

IN NANOGRAMS PER LITER

SUM OF DDT, DDD, AND DDE,

IN MICROGRAMS PER KILOGRAM,

DRY WEIGHT

000510000011170061021000500000

200211724037104527120141181314919082

ABC JKLM O RST DEFGH I N PQ UVWXY Z

ABC DE FGH I J K LMNOPQRS T UVWXY Z

A Surface water

Bottom sediment

NIWQP STUDY AREA

USEPA chronic criterion

Upper decile (90th percentile)

Upper quartile (75th percentile)

Lower quartile (25th percentile)

Median

Lower decile (10th percentile)

Point outside lower decile

EXPLANATION

Point outside upper decile

Chronic criterion

falls on axis

(3)

NUMBER OF ANALYSES

NUMBER OF ANALYSES

NIWQP STUDY AREA
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 37

RELATION BETWEEN MAJOR-ION AND TRACE-ELEMENT 

CONCENTRATIONS IN SURFACE WATER

A resources manager needs to know whether the existence of 

selenium problems in an area indicates that some other element, 

such as uranium or boron, also is likely to be a problem there. 

To examine contaminant associations in surface water, principal-components analysis (PCA) was applied to median concentrations of trace elements (arsenic, boron, molybdenum, 

selenium, and uranium), major constituents (calcium, magnesium, sodium, chloride, and sulfate), and alkalinity for each of 

the NIWQP study areas. Data from only 21 of the 26 NIWQP 

areas were used in the principal-components analysis because 

data on uranium in surface water were not collected in 3 areas 

(D, Q, and Z; fig. 10A) and major-constituent data were not collected in 2 areas (K and P).

Median concentrations for the PCA were determined for constituent concentrations in environmental, non-replicate surfacewater samples from sites within and downstream from irrigated 

areas. In calculating medians, trace-element data from an analysis were included even if the analytical suite was incomplete 

and major constituents were not determined. The number of 

analyses used to calculate median concentrations of the trace 

elements arsenic, boron, molybdenum, and selenium is shown 

in figures 6 through 10. To reduce the effects that scale and 

extreme values in the data set have on the statistical analysis, all 

median concentrations were ranked, and the rank replaced the 

actual values. For example, the highest median concentrations 

for selenium (110 μg/L; fig. 9) and chloride (9,600 mg/L) were 

assigned values of 21 and the lowest median concentrations 

were assigned values of 1.

Two principal components best explain the data set. The first 

two principal components account for 95 percent of the total 

variance. The loadings for the first two principal components 

were plotted to evaluate the occurrence of distinct clusters (fig. 

13A). The principal-components loadings form two distinct 

groups of major constituents and trace elements and show 

arsenic as an outlier.

The principal-components analysis shows that selenium is 

associated with calcium, magnesium, sodium, sulfate, and uranium (fig. 13A). Presser (1994a, p. 144) noted that in fluids issuing from rocks in the mountains along the west side of the San 

Joaquin Valley, Calif., only sodium sulfate and magnesium sulfate waters contained selenium concentrations greater than 3 

μg/L. The association of selenium and sulfate likely is the result 

of weathering of marine shales containing selenium-bearing 

reduced-sulfur minerals (Presser, 1994a; Naftz, 1996a). Pyrite 

is considered to be a main source of sulfur in shales (Presser and 

Swain, 1990), and much of the selenium in the Earth’s crust 

occurs in sulfide minerals (Berrow and Ure, 1989). The processes of acid generation during the weathering of pyrite and 

subsequent neutralization and cation exchange with clay minerals may explain the association of calcium, magnesium, and 

sodium with selenium. 

FIGURE 13. Principal-components analysis results. Letters (A–Z) identify 

National Irrigation Water Quality Program study areas (fig. 2). A, majorconstituents and trace-element loadings; shaded areas show occurrence of 2 

clusters with arsenic as an outlier. B, study-area scores; dividing line 

separates areas associated with Upper Cretaceous marine sedimentary rocks 

from those not so associated. Shaded area shows study areas having irrigated 

land that lies on Upper Cretaceous marine sedimentary rocks. Data from five 

areas (D, K, P, Q, and Z) not used because uranium or major-ion data were 

not collected.

0.26 0.27 0.28 0.29 0.30 0.31 0.32

0.4

0.6

_

_

_

_

_

_

0.2

0.0

0.2

0.4

0.6

0.8

0 10 20 30 40 50 60 70

15

10

5

0

5

10

15

20

U

Y

H

B

L

I

A

W

O

G

G

R

X

T

F

N

C

V

E

J

M

S

SECOND-PRINCIPALCOMPONENT LOADING SECOND-PRINCIPAL- COMPONENT SCORE

FIRST-PRINCIPAL-COMPONENT SCORE

Arsenic

Alkalinity

Selenium

Sodium

Chloride

Uranium

Sulfate 

Magnesium

Calcium

Molybdenum

Boron

Major constituents and trace elements

Areas

FIRST-PRINCIPAL-COMPONENT LOADING

EXPLANATION

Principal component loading

 for water-quality constituents

Principal component score for

 National Irrigation Water

 Quality Program study area

 Letter indicates study area

A

B
38 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

The importance of geology in explaining the NIWQP data is 

indicated by a grouping of geologically related study areas on a 

plot of principal-components scores (fig. 13B). All study areas 

below the dividing line are either directly or indirectly associated with Upper Cretaceous marine sedimentary rocks (Seiler, 

1995); the eight areas in the cluster (B, C, E, F, H, M, N, and S) 

have irrigated land that lies on Upper Cretaceous marine sedimentary rocks.

Sulfate and selenium are closely associated on the principalcomponents loadings plot (fig. 13A). This association is a result 

of the similar chemical and physical properties of sulfur and 

selenium. The close association of sulfate and selenium may 

have caused previous investigators to attribute cases of livestock 

poisoning to selenium when it was actually due to sulfate. 

O’Toole and others (1996) concluded that many field cases of 

“blind staggers” in livestock that previously had been attributed 

to selenium were caused by malignant catarrhal fever, or 

(polioencephalomalacia), a disease that is closely associated 

with excess dietary sulfate in water, feed, and some weeds.

Selenium and uranium appear to be associated (fig. 13A). 

Such an association is probably because of their similar redox 

chemistry. Both are insoluble under reducing conditions and 

soluble under oxidizing conditions (Drever, 1988), and both 

commonly occur in sediment rich in organic material. Elevated 

selenium concentrations are common in sedimentary rocks 

associated with uranium roll-front deposits in the Western 

United States (Levinson, 1980). Naftz and Rice (1989) 

observed that in situ mining techniques to extract uranium can 

increase selenium concentrations in ground water. In Wyoming 

uranium mines, the injection of an alkaline, oxidizing lixiviant 

to mobilize uranium mobilizes selenium in the ore deposit as 

well. Similarly, percolation of alkaline, oxygen-containing irrigation water through soil could mobilize uranium and selenium. 

Application of irrigation water can result in elevated uranium 

concentrations without selenium concentrations being elevated 

if the soils are uraniferous but not seleniferous. This occurs at 

Stillwater Wildlife Management Area (W), which has among 

the lowest selenium but highest uranium concentrations of the 

26 study areas (figs. 9 and 10).

Boron and molybdenum are associated with each other (figs. 

13A and 14), and both are associated with chloride (fig. 13A). 

Their association with chloride suggests that evaporative processes control their concentrations. Boron and chloride are conservative and increase together in all 26 areas. However, the 

association of molybdenum and chloride is complex. In some 

areas, molybdenum is positively associated with chloride and in 

other areas it is negatively associated (fig. 15).

Arsenic is not associated with any of the other measured 

trace elements (fig. 13A) and is negatively associated with selenium. The negative association may result in part from differing adsorption chemistry of the two elements. Masscheleyn 

and others (1991) reported that, under highly oxidizing conditions, arsenic solubility is low and selenium solubility is high, 

probably because arsenic(V) is strongly adsorbed to iron oxyhydrides whereas selenium(VI) adsorption is minimal. 

Another potential reason for the negative association is differing geologic sources. Where arsenic concentrations are high in 

water, geologic source materials tend to be relatively high in 

volcanic glass or are iron-oxide-bearing igneous rocks (Welch 

and others, 1988); in contrast, igneous rocks tend to have low 

concentrations of selenium (Berrow and Ure, 1989). The four 

areas having the highest median arsenic concentrations (G, I, 

Q, and W) all lie on, or are adjacent to, Tertiary volcanic rocks

and are not associated with Upper Cretaceous marine rocks. 

FIGURE 14. Relation between median molybdenum and boron concentrations 

in surface water from National Irrigation Water Quality Program (NIWQP) 

study areas. Letter identifies study area (fig. 2).

 

A

B

C

D

E

F

G

H

I

J

K

L

M

O N

P

Q

R

S

T

U

V

W

X

Y

Y

Z

MEDIAN MOLYBDENUM CONCENTRATION,

IN MICROGRAMS PER LITER

MEDIAN BORON CONCENTRATION,

IN MICROGRAMS PER LITER

1,000

100

10

10 100 1,000 10,000 100,000

1

0.1

Method detection limit

EXPLANATION

Data point for National Irrigation Water Quality

 Program study area Letter indicates study area
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 39

FIGURE 15. Relations between boron and molybdenum, boron and chloride, and molybdenum and chloride 

concentrations in surface-water samples from two National Irrigation Water Quality Program (NIWQP) study areas, 

Humboldt River area (NIWQP area G; Nevada) and Belle Fourche Reclamation Project (NIWQP area C; South 

Dakota). Data points are from non-replicate environmental samples collected as part of a NIWQP investigation. 

1 10 100 10

100

1 10 100

1 10 100

10 100 100

1 100

1,000

1,000

1,000

1,000

1,000

1,000

10,000 10,000

1,000

100

1,000

1,000

10,000 10,000

10,000

10,000

10 10 100

10 100 1

10

100

MOLYBDENUM, 

IN MICROGRAMS PER LITER

MOLYBDENUM, 

IN MICROGRAMS PER LITER

Humboldt River area 

Nevada

Belle Fourche Reclamation Project

South Dakota

CHLORIDE, 

IN MICROGRAMS PER LITER

CHLORIDE, 

IN MICROGRAMS PER LITER

CHLORIDE, 

IN MICROGRAMS PER LITER

CHLORIDE, 

IN MICROGRAMS PER LITER

BORON, 

IN MICROGRAMS PER LITER

BORON, 

IN MICROGRAMS PER LITER

BORON, 

IN MICROGRAMS PER LITER

BORON, 

IN MICROGRAMS PER LITER

MOLYBDENUM, 

IN MICROGRAMS PER LITER

MOLYBDENUM, 

IN MICROGRAMS PER LITER

A B

C D

E F

0.1

1

100

10

EXPLANATION

Surface-water analysis
40 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

RELATION BETWEEN CONTAMINANT CONCENTRATIONS IN 

SURFACE WATER AND BOTTOM SEDIMENT

Comparisons were made among arsenic, boron, molybdenum, selenium, and uranium concentrations in surface water 

and bottom sediment. Comparisons were made at both the 

study-area level and the data-collection-site level. For comparisons at the study-area level (fig. 16), median concentrations of 

the constituents in surface water and in the fine (< 0.062-mm) 

fraction of bottom sediment at sites in and downstream from 

irrigated lands were determined. Median concentrations less 

than the reporting limit were estimated by adjusted log-normal 

maximum-likelihood methods (Helsel and Cohn, 1988). In 

some instances, too few values were greater than the reporting 

limit, such as for molybdenum in bottom sediment, to make an 

estimate of the median concentration. In such instances, data 

from the study area were not plotted.

For comparisons at the level of individual sites (fig. 17), the 

most recent analysis made was selected to represent surfacewater and bottom-sediment contaminant concentrations at the 

site. For surface water, typically three or four surface-water 

analyses were made at a data-collection site. Typically, samples 

for bottom-sediment analysis were collected only once at a 

data-collection site during a NIWQP investigation. Because of 

the small number of samples at most sites, robust methods could 

not be used to characterize the central tendency at the site level, 

especially where the data included censored values. Censored 

values were not plotted in figure 17.

Linear regression of median concentrations of contaminants 

in NIWQP study areas indicates a significant relation (p < 0.05) 

between boron, selenium, and uranium concentrations in surface water and bottom sediment after log transformation. The pvalue is a significance level, or probability, and has a range of 0 

to 1. A p-value of 0.05 means a 5-percent likelihood that concentrations in water and bottom sediment are not related or that 

the observed relation is simply due to chance. Statistical tradition uses a value of 0.05 for the significance level, but other values may be used depending on the objectives of the 

investigators (Helsel and Hirsch, 1992). The arsenic, boron, 

molybdenum, selenium, and uranium concentrations in surface 

water at individual sites also are significantly related to the concentrations in bottom sediment (p < 0.05) at the site after log 

transformation (fig. 17).

Two reasons are possible for the relation between boron concentrations in surface water and bottom sediment (figs. 16 and 

17). Boron is an essential nutrient for higher plants (Vymazal, 

1995). In sediment high in organic matter, decomposing plant 

matter may contribute large amounts of boron to the sediment. 

Reported boron concentrations in aquatic and wetland plants 

were tabulated by Vymazal (1995). Concentrations from 1 to 

50 μg/g were reported most commonly, but concentrations 

exceeding 2,500 μg/g have been reported for a species of duckweed from Michigan (Glandon and McNabb, 1978). 

The relation between boron concentrations in surface water 

and bottom sediment also may be an artifact of analytical procedures. Some of the boron attributed to the bottom sediment 

actually originates as boron dissolved in the water associated 

with the bottom sediment and remains behind as the sample is 

dried. Because percentage moisture was not measured as part 

of the analysis, the actual amount of boron attributable to residual water cannot be estimated. However, it is likely to be substantial for some samples. From simple mathematics, if the 

moisture content of the sample is 50 percent, then the amount 

of boron contributed to a gram of sediment by the water would 

be 0.1 percent of the boron concentration in the water. In some 

samples, residual boron from the water could contribute from 

ten to more than a hundred micrograms to the bottom sediment. 

Although a similar effect at elevated concentrations can be 

expected for other trace elements, this effect is most likely to 

be important for boron because it commonly attains concentrations in water greater than 1,000 μg/L (figs. 7A, B).

Because selenium bioaccumulates and is incorporated into 

living tissue, many investigators have observed a relation 

between bottom-sediment selenium concentration and organiccarbon content (Weres and others, 1989; Presser and others, 

1994; Van Derveer and Canton, 1997). Van Derveer and Canton (1997), using data from Colorado streams and four NIWQP 

study areas (B, C, H, and W), reported a highly significant relation between selenium concentration in bottom sediment and 

the product of selenium in water and the total organic-carbon 

content of bottom sediment. Although a similar strong relation 

for selenium holds for data from all NIWQP sites (fig. 18), no 

such relation holds for arsenic, molybdenum, and uranium. For 

boron, the relation shown in figure 18 may be an artifact of the 

analytical process.
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 41

FIGURE 16. Relation between median concentrations of selected trace elements in surface water and in <0.062-millimeter-fraction bottom 

sediment in National Irrigation Water Quality Program study areas. Derivation of below-reporting-limit values is explained in text.

0.1 1 10 100 1,000

10

100

1,000

10,000

100,000

1 2 3 4 5 6 7 8 9 10

1

10

100

1,000

0.1 1 10

0.1

1

10

100

1,000

0.1 1 10

0.1

1

10

100

1 10 100

1

10

100

1,000

MEDIAN TRACE-ELEMENT CONCENTRATION IN SURFACE WATER, IN MICROGRAMS PER LITER

MEDIAN TRACE ELEMENT CONCENTRATION

IN <0.062 mm FRACTION BOTTOM SEDIMENT,

IN MICROGRAMS PER GRAM, DRY WEIGHT

MEDIAN TRACE ELEMENT CONCENTRATION

IN <0.062 mm FRACTION BOTTOM SEDIMENT,

IN MICROGRAMS PER GRAM, DRY WEIGHT

Arsenic Boron

Selenium

Uranium

Molybdenum

A B

C D

E

Reporting limit

Data point for National

 Irrigation Water Quality

 Program study area

 Letter indicates study area

Note: β0 and β1 are linear regression coefficients

 for the equation ( log10 y = β0 + β1 log10 x ) and r2 is

 the coefficient of determination

β0 = -0.14

β1 = 0.82

r

2 = 0.10

β0 = 2.16

β1 = 0.92

r

2 = 0.70

β0 = 0.72

β1 = 0.34

r

2 = 0.34

β0 = 0.38

β1 = 1.12

r

2 = 0.46

β0 = 0.36

β1 = 1.14

r

2 = 0.38

Regression line

EXPLANATION

W

Q

I

A

K L

U

O

Y

P

S H X

T

V

C

Y

W

G

U

I

E

F

A F B

M

V E

G Y

W G

K

U

N

L

P

X

C

B V H

M

Q

V

F

O

A

S

T

I

E J

Y

H

N

F

C

V

M

B

V L

X

U

S R E

Q

K

A

A

Y

S

M

W N H

B

C

U

F

G

V

R

P

X

K J

D

A

T

L

I
42 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

FIGURE 17. Relation between concentrations of selected trace elements in surface water and in <0.062-millimeter-fraction bottom sediment at 

individual data-collection sites. Data points are from non-replicate environmental samples collected as part of a NIWQP investigation. 

0.1 1 10 100

10

100

1,000

10,000

100,000

1

10

100

1,000

Note: β0 and β1 are linear regression coefficients

 for the equation ( log10 y = β0 + β1 log10 x ) and r2 is

 the coefficient of determination

β0 = -0.21

β1 = 1.08

r

2 = 0.21

β0 = 2.19

β1 = 1.09

r

2 = 0.64

β0 = 0.42

β1 = 1.35

r

2 = 0.27

β0 = 0.60

β1 = 0.59

r

2 = 0.29

β0 = 0.56

β1 = 0.73

r

2 = 0.18

1 10 100 0.1 1 10 100

1,000

1

10

100

1,000

1 10 100

1

10

100

1,000

10,000

0.1 1 10 100

0.1

1

10

100

1,000

TRACE-ELEMENT CONCENTRATION IN SURFACE WATER, IN MICROGRAMS PER LITER

TRACE ELEMENT CONCENTRATION

IN <0.062 mm FRACTION BOTTOM SEDIMENT,

IN MICROGRAMS PER GRAM, DRY WEIGHT

TRACE ELEMENT CONCENTRATION

IN <0.062 mm FRACTION BOTTOM SEDIMENT,

IN MICROGRAMS PER GRAM, DRY WEIGHT

Reporting limit

Data point for sampling site National

 Irrigation Water Quality Program 

 study area

Arsenic Boron

Selenium

Uranium

Molybdenum

A B

C D

E

Regression line

EXPLANATION
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 43

FIGURE 18. Relation between trace-element concentrations in bottom sediment and product of percentage organic carbon in <0.062-

millimeter-fraction bottom sediment and trace-element concentrations in surface water. Data points are from non-replicate environmental 

samples collected as part of a NIWQP investigation. 

0.1 1 10 100 1,000

0.1

1

10

100

1 10 100

1

10

100

0.1 1 10 100 1,000

1

10

100

1,000

1 10 100 1,000 10,000 100,000

1

10

100

1,000

10,000

0.1 1 10 100 1,000

0.1

1

10

100

TRACE-ELEMENT CONCENTRATION IN BOTTOM SEDIMENT, IN MICROGRAMS PER GRAM, DRY WEIGHT

PRODUCT OF TRACE-ELEMENT

CONCENTRATION IN WATER

(MICROGRAMS PER LITER) AND

ORGANIC CARBON (PERCENT, DRY WEIGHT)

IN <0.062 mm FRACTION BOTTOM SEDIMENT

PRODUCT OF TRACE-ELEMENT

CONCENTRATION IN WATER

(MICROGRAMS PER LITER) AND

ORGANIC CARBON (PERCENT, DRY WEIGHT)

IN <0.062 mm FRACTION BOTTOM SEDIMENT

Arsenic Boron

Selenium

Uranium

Molybdenum

A B

C D

E

Regression line

Data point for sampling site National

 Irrigation Water Quality Program 

 study area

Note: β0 and β1 are linear regression coefficients

 for the equation ( log10 y = β0 + β1 log10 x ) and r2 is

 the coefficient of determination

EXPLANATION

β0 = -0.76

β1 = 0.17

r

2 = 0.24

β0 = -0.92

β1 = 0.48

r

2 = 0.57

β0 = 0.38

β1 = 0.21

r

2 = 0.31

β0 = -0.21

β1 = 0.70

r

2 = 0.54

β0 = 0.23

β1 = 0.16

r

2 = 0.10
44 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

RELATION BETWEEN TOTAL- AND FILTERED-CONTAMINANT 

CONCENTRATIONS IN SURFACE WATER

The USFWS typically measures total-contaminant concentrations in unfiltered water samples when developing relations 

between biological effect and environmental-contaminant concentrations; however, the USGS typically measures contaminant concentrations for trace elements in filtered-water samples. 

Determining the relation between contaminant concentrations 

in filtered and unfiltered water is necessary so that comparisons 

can be made between biological-effect levels determined by the 

USFWS and contaminant concentrations measured by the 

USGS.

Some study teams measured both total- and filtered-water 

trace-element concentrations in surface-water samples they collected. Comparison of the two types of concentrations in surface 

water for arsenic, boron, molybdenum, and selenium indicates 

that they were nearly the same (fig. 19). Filtered-water aluminum concentrations, however, are typically much less than total 

concentrations. These results indicate that for concentrations 

greater than about 10 μg/L, arsenic, boron, molybdenum, and 

selenium concentrations in filtered samples may be directly 

compared to biological-effect levels developed by using unfiltered samples.

For arsenic, the relation likely does not apply in the range of 

1 to 10 μg/L. Of 153 samples in which filtered arsenic concentrations were in the range of 1 to 10 μg/L, filtered concentrations 

equaled unfiltered concentrations in 87 samples. In 18 samples 

filtered concentrations were greater than unfiltered concentrations and in 48 samples unfiltered concentrations were greater 

than filtered concentrations. This suggests that, in this range, 

there may be a tendency for unfiltered concentrations to be 

greater than filtered concentrations.

For selenium, the relation likely applies in the range of 1 to 

10 μg/L. Of 216 samples in which filtered selenium concentrations were in the range 1 to 10 μg/L, filtered concentrations 

equaled unfiltered concentrations in 136 samples. In 40 samples, filtered concentrations were greater than unfiltered concentrations, and in 40 other samples unfiltered concentrations 

were greater than filtered concentrations. This suggests differences from equality result from analytical imprecision and not 

a general tendency for unfiltered concentrations to be greater 

than filtered concentrations.

The relation between trace-element concentrations in total 

and filtered samples is expected given typical suspended-sediment concentrations in the water and trace-element concentrations in sediment. The median aluminum concentration in the 

fine (< 0.062-mm) fraction of bottom-sediment samples collected by the NIWQP was 6.2 percent (table 12). Assuming that 

this fine fraction is representative of suspended sediment, a suspended-sediment concentration of only 10 mg/L would contribute 620 μg of aluminum to a liter of water. However, the median 

selenium concentration in the fine fraction of bottom-sediment 

samples collected by the NIWQP is 0.6 μg/g (table 12). If this 

fraction is representative of suspended sediment, 2,000 mg/L 

suspended sediment would contribute only 1.2 μg of selenium 

to a liter of water.

For those elements that bioaccumulate, total concentrations 

could be higher than filtered-water concentrations in lentic, 

nutrient-rich waters with large algal populations. Fujii (1988) 

reported total selenium concentrations in five evaporation 

ponds in the Tulare Lake Bed area that averaged 1.7 times 

greater than corresponding filtered concentrations. Algae are 

important because they can accumulate large amounts of selenium in their tissues. Bennet and others (1986) demonstrated 

that in the laboratory the single-celled freshwater algae Chlorella pyrenoidosa could accumulate selenium to concentrations 

of 2,600 to 3,100 μg/g dry weight. One mg/L of algae containing this much selenium would add 2.6 to 3.1 μg/L of particulate 

selenium to the water. Settings where algae have the potential 

to substantially affect the relation between filtered- and totalselenium concentrations should be apparent because water 

containing 1 mg/L of algae probably would be discolored. 

Chlorophyll a constitutes 1-2 percent of the dry weight of 

planktonic algae (Franson, 1995), hence, water containing 1 

mg/L of algae would contain 10-20 μg/L of chlorophyll a. 

Water containing 10-15 μg/L of chlorophyll a can be discolored 

and water containing 20-30 μg/L of chlorophyll a will be 

deeply discolored (Raschke, 1993).

CONCEPTUAL MODEL OF SELENIUM 

CONTAMINATION IN IRRIGATED AREAS

The emphasis of the rest of this report is on selenium for two 

reasons. First, discussion in the section titled “Summary Statistics and Comparison to Criteria” (p. 23), indicated that in the 

NIWQP data set selenium is the contaminant associated with 

irrigation drainage that most often exceeds the chronic criterion for the protection of aquatic life. More than 40 percent of 

the surface water samples collected during NIWQP investigations exceeded the criterion. Second, wildlife are extremely 

sensitive to selenium. Thresholds for dietary toxicity in vertebrate animals are only slightly greater than nutritionally optimal levels, therefore, relatively small perturbations in the 

dietary exposure of vertebrate animals are potentially harmful. 

This section of the report presents a conceptual model of 

why soils in irrigated areas of the Western United States are 

seleniferous; how irrigation mobilizes selenium, how the climate and hydrology of an area interact to determine whether 

selenium contamination of water occurs, and how selenium 

enters the food chain. The conceptual model includes a brief 

description of selenium biogeochemistry and describes processes of: (1) selenium enrichment in marine rocks principally 

through biological processes; (2) selenium enrichment in soil 

and ground water in irrigated areas and its subsequent transport 

to areas supporting fish and wildlife; (3) selenium enrichment 

in water and sediment through hydrologic processes in wildlife 

habitat; and (4) selenium incorporation into the food chain and, 

ultimately, exposure of wildlife to it.
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 45

FIGURE 19. Relation between concentrations of aluminum, arsenic, boron, molybdenum, and selenium in filtered and in unfiltered surface water. 

Data points are from non-replicate environmental samples collected as part of a NIWQP investigation.

1 10 100 10,000 1,000

1,000

100

10

10 100 1,000 10,000

1

1 10 100 1,000

1 10 100 1,000

CONCENTRATION IN FILTERED WATER, IN MICROGRAMS PER LITER 

CONCENTRATION IN UNFILTERED WATER,

IN MICROGRAMS PER LITER

CONCENTRATION IN UNFILTERED WATER,

IN MICROGRAMS PER LITER

Arsenic

Boron Molybdenum

B

D

E Selenium

C

EXPLANATION

Line of equal concentration

Surface-water analysis

100 1,000 10,000

A Aluminum

1

10

100

1,000

10,000

10

10,000

1,000

100

1,000

100

10

10

n= 17 n= 210

n= 99 n= 30

n= 363

1

1,000

100

10

Note: n is the number of samples.
46 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

SELENIUM BIOGEOCHEMISTRY

Selenium biogeochemistry is reviewed in numerous articles 

and is summarized here. Selenium is a member of the sulfur 

family (Group VIA of the periodic table), and as such selenium 

and sulfur have similar chemical properties. Selenium has three 

common oxidation states: Se+6 (SeO4

-2, selenate), Se+4 (SeO3-2, 

selenite), and Se-2 (selenide). Less common in nature is elemental selenium (Se0

). 

Selenium geochemistry is very dependent on the pH and 

redox conditions of the environment. In wetlands the forms of 

selenium found in the water column and underlying sediment 

are commonly different because the water contains dissolved 

oxygen whereas sediments, particularly wetland sediments, are 

often anoxic. In water, the oxyanions of selenium, selenate and 

selenite, are most common. Selenate is the most soluble because 

selenite has a strong affinity for clays and iron oxyhydroxides 

and readily forms stable, insoluble complexes. In water 

selenides would be rather rare except possibly in organic compounds. In anoxic wetland sediments, selenides and elemental 

selenium are most common. Anaerobic bacteria in sediments 

are capable of reducing selenate to elemental selenium. 

Selenides are found in both metallic and organic forms. 

Selenide substitutes for sulfur in many metallic sulfide minerals 

such as pyrite (FeS2). Much of the selenium in the crust of the 

earth is found in these sulfide minerals.

Selenates are readily taken up by organisms and biotransformed into organic forms such as seleno-amino acids and dimethylselenide. The essential requirement for selenium in 

animals is attributed to enzymes containing selenium, in particular glutathione peroxidase. Although some algae have an 

essential growth requirement for selenium, no essential organoselenium compounds have been reported for either algae or 

higher plants (Vyzamal, 1995). 

ENRICHMENT OF SELENIUM IN ROCKS

Selenium is found in many geologic units. The distribution of 

selenium in rocks of the United States and the world have been 

reviewed by Rosenfeld and Beath (1964) and Berrow and Ure 

(1989). The average selenium content of the crust of the Earth 

is about 0.05 mg/kg (Fortescue, 1992). Igneous rocks tend to 

have low contents of selenium whereas relatively large contents 

are found in all sedimentary rocks (Berrow and Ure, 1989). 

In the interior of the Western United States, selenium commonly is associated with fine-grained marine sedimentary rocks 

of Late Cretaceous age (Anderson and others, 1961; Howard, 

1977). Lakin and Byers (1941, p. 2) concluded that

“All areas of soils derived from material of 

Cretaceous age are then open to suspicion of the 

presence of harmful quantities of selenium * * * ” 

Trelease and Beath (1949, p. 95) noted that about 80 percent 

of all plant specimens containing more than 50 mg/kg selenium 

had been collected from Cretaceous units. In the Diablo Range 

along the west side of San Joaquin Valley, Calif., Tidball and 

others (1991, p. 109) noted that marine rocks of Late Cretaceous to Oligocene age are variously seleniferous, whereas 

nonmarine rocks of Pliocene and younger age tend to be nonseleniferous. 

Understanding general mechanisms by which selenium is 

incorporated into rocks is important because irrigation of soils 

derived from those rocks can contribute selenium to areas supporting fish and wildlife. Until relatively recently, the emphasis 

has been on physical processes as mechanisms for selenium 

enrichment of marine rocks of Cretaceous age. More recently, 

however, biological processes occurring in marine environments have been proposed as the principal driving mechanism 

behind selenium enrichment in marine rocks of all ages.

One source of selenium in ancient marine deposits is the 

direct deposition of particulate matter derived either from erosion of seleniferous deposits on the continental mass or from 

fallout of seleniferous atmospheric dusts and particles. Several 

authors have proposed that Cretaceous oceans were enriched in 

selenium originating from contemporaneous volcanic activity. 

Selenium is present in volcanic gases because volatile selenium 

escapes along with volatile sulfur from cooling rocks and from 

volcanoes (Berrow and Ure, 1989). The presence of selenium 

in volcanic gases led Byers and others (1936, p. 823) to conclude that selenium in Cretaceous marine rocks was primarily 

from rainout of volcanic gases. Soils and sediment near volcanic activity are relatively rich in selenium because selenium is 

likely to be present in the atmosphere in a particulate form and 

is subject to removal by rain relatively close to its point of origin (Berrow and Ure, 1989). Toward the close of the Late Cretaceous, in what is now the Western United States, violent 

volcanic activity continued while a large inland sea and the 

Rocky Mountains formed (Trelease and Beath, 1949). Berrow 

and Ure (1989, p. 219) stated that much of the selenium in Cretaceous sedimentary rocks must have been derived from volcanic dusts and gases emitted into the atmosphere because 

igneous rocks tend to have low concentrations of selenium. 

Rosenfield and Beath (1964, p. 12) concluded that erosion of 

seleniferous volcanic rocks and rainout of volcanic dust and 

gases were the explanation for the distribution of selenium in 

different geological units. 

These physical explanations for selenium enrichment in Cretaceous rocks are incomplete because they do not consider biological processes. In fact, biological processes causing 

deposition of selenium in marine sediments likely are of 

greater importance than physical processes. Presser (1994a) 

suggested the bioaccumulation of selenium in ancient seas and 

later deposition and diagenesis of the seleniferous organic matter is a primary mechanism of selenium enrichment in ancient 

sedimentary deposits. Planktonic organic matter carries minor 
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 47

elements to the sea floor directly and also drives redox reactions 

that determine the suite of elements which precipitate from bottom water (Piper, 1994). Piper (1994, p. 110) concluded that the 

biologic cycle is the single dominating influence on the minorelement composition of virtually all sedimentary rocks having 

high contents of marine phases such as organic matter, apatite, 

silica, calcite, and dolomite.

Any marine deposit laid down in a shallow, productive environment may be seleniferous. Selenium bioaccumulates in 

marine phytoplankton to concentrations several thousand times 

greater than that in the seawater. Selenium concentrations in 

present-day seawater average about 0.09 μg/L (Hem, 1985) and 

in marine phytoplankton are 3 mg/g (Brumsack, 1986). Because 

selenium stimulates the growth of some marine phytoplankton 

and is a required mineral nutrient for some algae (Vymazal, 

1995), bioaccumulation and deposition of seleniferous organic 

matter would have occurred even if selenium concentrations in 

ancient oceans were not enriched by volcanic activity. Sindeeva 

(1964, p. 291) noted that selenium was present in sizeable 

amounts in recently deposited sea-bottom sediments in the Bering Sea, the Arctic Ocean, the Caribbean, the northern part of 

the Atlantic Ocean, and the Gulf of California. The Phosphoria 

Formation in the Northwestern United States is a marine sedimentary deposit of Permian age that contains high concentrations of selenium throughout the deposit (Piper and others, 

2000).

Tertiary continental sedimentary deposits derived by the 

reworking of Upper Cretaceous marine sedimentary rocks 

exposed by mountain uplifts and subsequently eroded also may 

be seleniferous. Trelease and Beath (1949, p. 96) commented 

that it is not surprising many Tertiary rocks are seleniferous 

because they represent rock debris derived from erosion of Cretaceous and older rocks during uplifting associated with the 

development of the Rocky Mountains. Whether continental sedimentary deposits are seleniferous depends on several factors: 

whether the parent rock was seleniferous, whether reduced selenium in the rock was exposed to the strongly oxidizing conditions required to mobilize the selenium, and whether the rock 

was leached enough to remove any selenium.

ENRICHMENT OF SELENIUM IN SOILS AND GROUND 

WATER IN IRRIGATED AREAS

Application of irrigation water to seleniferous soils can mobilize selenium and create hydraulic gradients that cause the discharge of seleniferous ground water into drains and other 

surface-water bodies. Because drainage from agricultural areas 

can be a principal source of selenium to areas supporting fish 

and wildlife, an understanding of the processes by which 

seleniferous rocks cause soils and ground water in agricultural 

areas to become seleniferous is important. General mechanisms 

by which soils and ground water in irrigated areas become 

enriched in selenium are described below. 

In the simplest mechanism, selenium in soil originates in a 

seleniferous soil-parent rock beneath the soils. Soils are 

seleniferous because selenium-containing minerals remain in 

the soil following pedochemical weathering of the parent rock. 

Ground water becomes seleniferous because water percolating 

through the soil and rock reacts with the selenium-containing 

minerals and dissolves selenium. In areas where a seleniferous 

soil-parent rock is beneath the soils, application of irrigation 

water accelerates the weathering processes and mobilizes more 

selenium than would occur naturally. Areas having soils that 

may become seleniferous by this natural weathering process 

can be idenitifed by geologic maps showing the bedrock distribution of seleniferous sedimentary rocks and deposits.

A second mechanism involves transport of selenium from 

upland areas in the mountains surrounding irrigated areas. 

Presser (1994a, b) describes how seleniferous sedimentary 

deposits tens of miles upland from irrigated land can contribute 

selenium to land downslope through processes of active weathering, alluvial-fan building, and local drainage. Ground water 

moving through seleniferous rocks and deposits can discharge 

to streams, and dissolved selenium can be carried in surface 

water to downgradient areas. Evaporation of ground water in 

seleniferous rocks or deposits can wick selenium salts to the 

surface, and subsequently surface water can strip selenium 

from the surface exposures. Erosion of seleniferous rocks or 

deposits upland can carry insoluble selenium to downgradient 

areas by mass wasting and subsequent transport of suspended 

sediment in surface water. Areas having soils that may become 

seleniferous by this process can be identified by geologic and 

topographic maps because they are adjacent to and downslope 

from seleniferous sediments and deposits.

The third mechanism involves soils and ground water in an 

area becoming seleniferous by selenium being transported into 

the area by surface water. Unlike the second mechanism, the 

geologic source of the selenium can be hundreds of miles 

upstream from the contaminated area. Areas having soils that 

may become seleniferous by this process can be identified by 

geologic and watershed maps because they will be downstream 

from seleniferous sediments and deposits.

Application of oxygenated water to irrigated areas weathers 

reduced selenium in the soil. In addition, application of nitrate 

fertilizers has been shown to enhance the oxidation of selenium 

(Wright, 1999) and inhibit reduction of selenium oxyanions 

(Benson, 1998). Oxyanions of selenium accumulate in the root 

zone through evapotranspiration of the applied water. To prevent salt buildup from harming the crops, excess water must be 

applied to fields to flush the salts from the root zone. This 

excess water carries selenium from the root zone to the water 

table and results in enrichment of selenium in the ground water. 

Drains installed in irrigated areas to lower the water table and 

prevent waterlogging of the soil carry seleniferous ground 

water to surface water bodies and, ultimately, to wildlife areas.
48 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

Climate strongly influences the selenium content of the 

ground water in irrigated areas. In humid areas, leaching of the 

soils by rainfall during geologic time before irrigation began 

would have removed much of the soluble selenium produced by 

weathering. In arid areas, any soluble selenium produced by 

weathering would remain in the soil profile because rainfall is 

insufficient to leach the soils. Trelease and Beath (1949, p. 56) 

noted that soils supporting seleniferous vegetation have been 

found only in areas where the mean annual rainfall is less that 

20 in., and so is insufficient to leach soil of soluble selenium 

compounds.

Evapotranspiration consumes water and, hence, selenium 

salts released by weathering processes become concentrated in 

the soil water and ground water. Typically, drying of the soil 

causes a moisture gradient between the soil surface and the 

water table. As a result, water evaporates directly from the water 

table. Evaporation also increases ground-water loss by its 

effects on the transpiration rate of plants. Because roots restrict 

which minerals enter the plant, removal of water from the subsurface by plants can enrich contaminant concentrations in the 

remaining water.

ENRICHMENT OF SELENIUM IN WATER IN FISH AND 

WILDLIFE HABITAT

Wildlife may be exposed to selenium in ground water from 

irrigated areas when it discharges to seeps or surface drains. 

Wildlife congregate near the seeps or lakes or ponds receiving 

irrigation drainage. Whether selenium concentrations in areas 

supporting fish and wildlife become elevated above those in the 

water entering the area depends on the climate and hydrology of 

the area.

Climate is important in two ways. First, areas having higher 

amounts of precipitation have greater, diffuse inflows of freshwater, which dilute contaminants. Second, high rates of evaporation leads to evaporative enrichment of contaminant 

concentrations in surface and ground water. Direct evaporation 

from a water body causes contaminant concentrations to 

increase by removing water and leaving the contaminant in the 

remaining water. Walker Lake, a terminal lake in western 

Nevada, provides an example of the amount of water that can be 

lost through evaporation and its effect on concentration: During 

1987–94, nearly all surface-water inflow to the lake was 

diverted for agricultural use. During the same period, evaporative losses caused a water-level decline of almost 26 ft and a dissolved-solids concentration increase of about 4,000 mg/L 

(Thomas, 1995).

The presence of terminal lakes or ponds is an important factor 

in determining whether selenium enrichment of the water 

occurs in an area. Selenium concentrations increase in lakes and 

ponds through evaporative enrichment, however, in terminal 

lakes or ponds, selenium is not flushed out during normal spring 

runoff. Elevated concentrations in the water eventually may 

result because the selenium from several seasons remains, 

although biological processes may transfer selenium in the 

water column to the sediment.

The occurrence of elevated concentrations of selenium in 

terminal ponds is biologically significant. In general, ponds are 

more attractive nesting and foraging habitat to waterbirds than 

drains because drains and canals typically have steeper sides 

than ponds. Also, the flow velocity in canals is usually too swift 

to allow for nesting. Even in selenium-contaminated ponds, 

lush growths of emergent vegetation can develop and create 

attractive nesting areas. Hartshorn (1985) observed that Kesterson Reservoir looked surprisingly alive and that some of the 

shallow ponds were choked with cattails.

INCORPORATION OF SELENIUM INTO THE FOOD CHAIN 

AND WILDLIFE EXPOSURE TO IT

Aquatic cycling of selenium in wetlands and its incorporation into food chains is described in detail by Lemly and Smith 

(1987) and those concepts are summarized here. Microorganisms, algae, and higher plants take up selenate directly from the 

water into the cell interior where it is reduced and incorporated 

in organoselenium compounds. Death and decay of the organisms carries the reduced selenium compounds into the sediments, as does the fecal matter from consumers of the 

microorgansims and plants. Selenium is also removed from the 

water column through reduction of selenate to selenite and subsequent settling following adsorption onto clays and particulates. Tanji (1989) observed that evaporation increased 

chloride concentrations twofold in a series of ponds in the San 

Joaquin Valley but selenium concentrations remained nearly 

constant, indicating that up to 50 percent of the selenium in the 

water column was removed to the sediments.

Once in the sediments, selenium is effectively mobilized in 

most aquatic systems into food chains. Selenium is directly 

taken up by rooted plants and bottom-dwelling invertebrates 

and detrital-feeding fish and wildlife. Selenium also is released 

from sediments back into the water column following oxidation resulting from plant roots, microorganisms and the burrowing activity of benthic invertebrates. 

Wildlife in wetlands receiving seleniferous water receive 

potentially toxic doses of selenium when they consume food 

organisms that have sequestered large amounts of selenium in 

their tissues. Waterborne selenium, per se, is not very toxic to 

wildlife, rather toxic exposure principally occurs through the 

food chain (U.S. Department of the Interior, 1998). Most of the 

combined waterborne and dietary uptake of selenium occurs at 

the primary producer and primary consumer (phytoplankton 

and zooplankton) levels (Lemly, 1996c). Because of bioconcentration, tissues of food organisms contain selenium in concentrations tens to thousands of times greater than the 

concentrations in their food or water. Selenium can accumulate 

in tissues of food-chain organisms to levels that are toxic to 

predators such as fish and birds without effects on the growth, 

reproduction, or survivial of the food-chain organisms (Lemly, 

1996c).
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 49

IMPLICATIONS OF THE CONCEPTUAL MODEL

An important implication of the conceptual model is that it 

suggests irrigated areas with potential selenium problems may 

be identifiable a priori on the basis of selenium chemistry, and 

the area’s geology, climate, and hydrology. Basic knowledge of 

selenium chemistry, and its similarity to sulfur, may be useful in 

identifying water types and levels of salinity where selenium 

concentrations in water reach harmful levels. The association of 

selenium with marine rocks suggests geologic maps may be 

useful in identifying irrigated areas where soils may be seleniferous. If selenium is present in an ecosystem, knowledge of the 

climate and hydrology may allow predictions to be made on 

whether evaporative processes will be sufficient to concentrate 

selenium in water to harmful levels.

Another important implication of the conceptual model is 

that some feeding guilds may be more at risk than others. 

Because of bioaccumulation, selenium concentrations often 

increase between trophic levels. Plants, being at a lower trophic 

level, likely will contain less selenium than aquatic invertebrates and fish which consume plant matter and/or detritus. As 

a result, top-level consumers, such as insectivorous and piscivorous birds and fish, usually will be exposed to more selenium 

through their diets than birds and fish consuming plant matter 

and/or detritus. Hence, within an area receiving irrigation drain 

water from seleniferous soils, predatory birds and fish may be 

at more risk than foraging birds and fish.

Ideas developed in the conceptual model were tested in the 

following sections to determine whether they were consistent 

with data collected from the NIWQP study areas. Physical and 

biological processes involving selenium in NIWQP study areas 

were analyzed, and to the extent possible, results from the analysis were used to develop tools for use by managers. Because of 

the types of data collected during NIWQP investigations, analysis of the data emphasizes water and birds rather than sediment, food-chain organisms, and fish. Biological data were 

analyzed, however, to determine if certain food-chain organisms and feeding guilds are buffered from the effects of selenium contamination in wetlands. 

The remainder of the report also addresses issues not raised 

in the conceptual model. A risk assessment addresses whether 

other contaminants associated with irrigation drainage could be 

the cause of observed deformities of bird embryos, and what the 

effects of selenium are at the population level. The final section 

of the report addresses the relation between selenium levels in 

water, sediment and biota and the relation between selenium 

levels in water and predicted numbers of hens losing eggs to 

selenium poisoning.

PHYSICAL AND CHEMICAL PROCESSES 

INVOLVING SELENIUM IN NIWQP AREAS

SELENIUM CONCENTRATIONS

A single selenium concentration was assigned to each study 

area to compare with criteria and to represent that area’s degree 

of selenium contamination. The 75th percentile of the selenium 

concentrations from surface-water sites in and downstream 

from irrigated land (table 15) was selected as this value. The 

75th-percentile selenium concentration is the value that is 

exceeded by 25 percent of the samples from an area. The 

median concentration was not used because many of the investigations were done only at a reconnaissance level; an area 

would have to be extremely contaminated before 50 percent of 

the samples would exceed criterion. Additionally, the 75th percentile can accommodate more nondetects than the median. If 

25 percent of the samples had detectable concentrations of 

selenium, then the 75th percentile is known exactly. The 75th 

percentile is conservative in that aquatic birds, for instance, 

probably are exposed to selenium-contaminated water in an 

area where 25 percent of the samples from that area exceed criteria. 

Areas were classified as contaminated or seleniferous if the 

75th percentile exceeded water-quality criteria. Of the 26 areas, 

12 were ranked as contaminated (C, E, F, H, M, N, R, S, U, X, 

Y, and Z; table 15) because selenium concentrations in 25 percent or more of the surface-water samples exceeded the chronic 

criterion for the protection of freshwater aquatic life (5 μg/L; 

U.S. Environmental Protection Agency, 1987). Of the remaining 14 areas, 2 were classified as seleniferous (B and V) 

because selenium concentrations in 25 percent or more of the 

surface-water samples exceeded the guideline for protection of 

wildlife, 3 μg/L (table 10). Even though normal background 

concentrations of selenium in uncontaminated freshwater ecosystems are less than 1 μg/L (U.S. Department of Interior, 

1998), for this report areas were classified as uncontaminated 

if the 75th percentile was less than 3 μg/L.

SOURCES OF SELENIUM

DISTRIBUTION OF SELENIFEROUS ROCKS IN THE WESTERN 

UNITED STATES

The King and Beikman (1974) 1:2,500,000-scale geologic 

map of the United States was used as the source of geological 

information about NIWQP study areas in the Western United 

States because the geologic units are consistent at a national 

scale. King and Beikman (1974) mapped Quaternary deposits 

in the Western United States only if they are thick and extensive; instead of mapping surficial glacial deposits, they showed 

the limits of glaciation. Thus, older bedrock may be covered by 

younger glacial deposits not shown on their map.
50 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

TABLE 15. Classification of National Irrigation Water Quality Program study areas by selenium content of surface water in 

and downstream from irrigated areas

[Area classification: C, contaminated (75th percentile of selenium concentrations equals or exceeds 5 micrograms per liter); S, seleniferous (75th 

percentile of selenium concentration equals or exceeds 3 but is less than 5 micrograms per liter); UC, uncontaminated (75th percentile of selenium 

concentrations is less than 3 micrograms per liter). Abbreviation and symbol: μg/L, microgram per liter; <, less than]

Study area Selenium concentration1

(μg/L)

1 Analyses of non-replicate, filtered surface-water samples collected during a NIWQP investigation from study area in and downstream from irrigated 

lands.

Number of

observations

Area

classification

Area 

ranking2

2 Ranking of study area from most to least contaminated on the basis of 75th percentile selenium concentration.

Identifier3

3 Used in figure 2 to show locations of study areas.

Name 4

4 This value was based on a limited number of samples from one pond system. A better regional value for the Tulare Lake Bed area is provided by Moore 

and others (1990), who report an acreage-weighted geometric-mean selenium concentration of 49 μg/L for 7,224 acres of evaporation ponds in the area.

Median 75th

percentile Range

A American Falls Reservoir, Idaho <1 1.0 <1 – 6 14 UC 18.5

B Angostura Reclamation Unit, South Dakota 3.5 4.5 <1 – 6 12 S 13

C Belle Fourche Reclamation Project, South 

Dakota

3.5 5.0 2 – 11 10 C 11.5

D Columbia River Basin, Washington <1 <1 <1 – 4 48 UC 23

E Dolores–Ute Mountain area, Colorado 2.0 7.0 <1 – 88 47 C 8

F Gunnison River Basin–Grand Valley Project, 

Colorado

13 35 <1 – 380 343 C 4

G Humboldt River area, Nevada <1 2.0 <1 – 4 34 UC 16

H Kendrick Reclamation Project, Wyoming 12 64 <1 – 5,300 236 C 3

I Klamath Basin Refuge Complex, 

California–Oregon

<1 <1 <1 – <1 13 UC 23

J Lower Colorado River valley, California–

Arizona

1.0 2.0 <1 – 2 13 UC 16

K Lower Rio Grande valley, Texas <1 1.0 <1 – 2 14 UC 18.5

L Malheur National Wildlife Refuge, Oregon <1 <1 <1 – <1 14 UC 23

M Middle Arkansas River Basin, Colorado–Kansas 6.5 10 2 – 52 18 C 5

N Middle Green River Basin, Utah 29 73 <1 – 8,300 196 C 2

O Middle Rio Grande, New Mexico <1 <1 <1 – 1 38 UC 23

P Milk River Basin, Montana <1 <1 <1 – <1 12 UC 23

Q Owyhee–Vale Reclamation Project areas, 

Oregon–Idaho

2.0 2.0 <1 – 5 33 UC 16

R Pine River area, Colorado 2.0 6.0 <1 – 94 43 C 9.5

S Riverton Reclamation Project, Wyoming 3.0 5.0 <1 – 12 21 C 11.5

T Sacramento Refuge Complex, California <1 <1 <1 – 5 23 UC 23

U Salton Sea area, California 5.0 8.0 1 – 10 54 C 6

V San Juan River area, New Mexico <1 3.0 <1 – 67 75 S 14

W Stillwater Wildlife Management Area, Nevada <1 <1 <1 – 21 184 UC 23

X Sun River area, Montana 2.0 7.5 <1 – 190 120 C 7

Y Tulare Lake Bed area, California 110 4

265 <1 – 390 9 C 1

Z Vermejo Project area, New Mexico 2.0 6.0 <1 – 23 16 C 9.5
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 51

Although the King and Beikman (1974) map is out of print, a 

digital version of it, created by Schruben and others (1994), 

is available <http://geo-nsdi.er.usgs.gov/metadata/digitaldata/11/metadata.faq.html> and was used for this study to identify the geological units associated with each site where water 

or sediment samples were collected. A structural and lithologic 

map of the United States by Bayer (1983) is based on the King 

and Beikman (1974) map and remains in print.

Upper Cretaceous marine sedimentary rocks are the most 

widespread geologic source of selenium in the Western United 

States. These rocks are commonly seleniferous (Lakin and 

Byers, 1941) and form the bedrock in more than 300,000 mi2

, 

or 17 percent of the total land area, in the 17 Western States 

(fig. 20). Soils derived from these fine-grained sedimentary 

rocks commonly provide good soils for agricultural development and commonly are irrigated in the arid and semiarid 

regions of the Western United States.

FIGURE 20. Areas in Western United States where potentially seleniferous rocks form bedrock. Geology from King and 

Beikman (1974). For base credit, see figure 1. 

120°

45°

40°

35°

30°

115° 110° 105° 100°

95°

EXPLANATION

Tertiary marine sedimentary deposits

Tertiary continental sedimentary deposits

Upper Cretaceous marine sedimentary rocks

0 250 500 MILES

0 250 500 KILOMETERS
52 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

If biological processes are the principal reason for selenium 

enrichment of marine sediments as Piper (1994) and Presser 

(1994b) suggest, marine sedimentary deposits of Tertiary age as 

well as Cretaceous age likely are seleniferous. In the Coast 

Ranges of California, Presser (1994b) identified Upper Cretaceous–Paleocene, Eocene–Oligocene, and Miocene marine 

deposits that are seleniferous. In addition to organic selenium 

concentrated by bioaccumulation, as sea levels declined during 

the early Tertiary, particulate matter derived by erosion of 

seleniferous Cretaceous sedimentary deposits exposed during 

the recession of the sea would have been deposited in the Tertiary marine sediments. Sindeeva (1964) concluded that selenium dissolved and suspended in river water precipitates 

immediately upon entering the ocean. Marine sedimentary 

deposits of Tertiary age form the bedrock beneath almost 

84,800 mi2

 of land in the Western United States (fig. 20), which 

is about 4.6 percent of the total land area.

Depending on their history, Tertiary continental sedimentary 

deposits may be seleniferous. Continental sedimentary deposits 

of Tertiary age (fig. 20) form the bedrock in about 366,000 mi2, 

or about 20 percent of the total land area, in the Western United 

States.

SOURCES OF SELENIUM IN NATIONAL IRRIGATION WATER 

QUALITY PROGRAM STUDY AREAS

The map units from the King and Beikman (1974) geologic 

map are listed in table 16 by study area and are ranked by the 

number of sites that are within a particular geologic unit in each 

area. The most common geologic units forming bedrock in each 

of the study areas range from Cretaceous to Quaternary in age 

and consist mainly of marine or continental sedimentary deposits.

Of the 26 NIWQP study areas, 12 have sampling sites located 

where Upper Cretaceous marine sedimentary rocks form nearsurface bedrock (table 16). Seventy-fifth percentile selenium 

concentrations in surface water in these twelve areas (B, C, E, F, 

H, M, N, P, R, V, X, and Z) ranged from less than 1 to 73 μg/L 

(table 15). To further assess the importance of Cretaceous sedimentary rocks in determining the selenium concentration at the 

NIWQP sites, individual surface-water sites were classified on 

the basis of their association with Upper Cretaceous marine sedimentary rocks. The most recent selenium value measured at 

each site was selected to represent the selenium concentration at 

the site, and box plots of the data were prepared (fig. 21). The 

importance of Cretaceous sedimentary rocks to selenium concentration is apparent: the median selenium concentration for 

sites associated with Cretaceous sedimentary rocks is 7 μg/L

(range less than 1 to 8,300 μg/L); for sites not associated, the 

median is estimated to be 0.4 μg/L (range less than 1 to 

390 μg/L).

FIGURE 21. Statistical summary of selenium concentrations in filtered 

surface-water samples showing association of Upper Cretaceous marine 

sedimentary rocks and elevated selenium concentration. Summary 

statistics below the reporting limit computed using log-normal 

maximum-likelihood methods. USEPA, U.S. Environmental Protection 

Agency. Each surface-water sampling site was represented by the 

selenium concentration in the most recent non-replicate, filtered sample 

collected at the site as part of a NIWQP investigation.

417 273

1

10

100

1,000

10,000

NUMBER OF SAMPLING SITES

GEOLOGIC AGE OF ROCKS ASSOCIATED

WITH SAMPLING SITE

EXPLANATION

 REPRESENTATIVE SELENIUM CONCENTRATION

AT SAMPLING SITE, IN MICROGRAMS PER LITER

Upper decile (90th percentile)

Upper quartile (75th percentile)

USEPA chronic criterion

Lower quartile (25th percentile)

Median

Point outside upper decile

Upper CretaceousNonCretaceous

Median

value

is 7

micrograms

per liter

Median

value

is 0.4

microgram

per liter
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 53

1Used in figure 2 to show locations of study areas.

TABLE 16. Generalized bedrock geology of National Irrigation Water Quality Program study areas

[Geologic units within each study area are ranked according to relative number of individual data-collection sites having 

indicated geology. Geologic symbols and descriptions used are adapted from compilation by King and Beikman (1974). 

Generally in decreasing-age order, these geologic units include Early Proterozoic igneous and metamorphic rocks (Xg, granitic 

rocks; Xm, orthogneiss and paragneiss); upper Paleozoic stratified, mainly marine, sedimentary rocks (uPz); Upper Cretaceous 

stratified, mainly marine, sedimentary rocks (uK1, the Woodbine and Tuscaloosa Groups, locally including some Lower 

Cretaceous rocks not mapped separately; uK2, the Austin and Eagle Ford Groups; uK3, the Taylor Group; and uK4, the Navarro 

Group); Paleocene continental sedimentary deposits (Txc); Eocene continental sedimentary deposits (Tec); the Eocene Jackson 

Group (Te3); Miocene volcanic rocks (Tmf, felsic; Tmv, nonfelsic); Pliocene continental sedimentary deposits (Tpc); Pliocene 

nonfelsic volcanic rocks (Tpv); thick and widespread Quaternary stratified sedimentary sequences (Q); Quaternary nonfelsic 

volcanic rocks (Qv); and Holocene stratified sedimentary deposits, Great Plains only (Qh). Geologic units exclude Pleistocene 

glacial deposits, which blanket large parts of northern interior States]

Study area Ranked geologic units

Identifier1 Name 1 2 3 4

A American Falls Reservoir, Idaho Q

B Angostura Reclamation Unit, South Dakota uK3 uK2 uK1

C Belle Fourche Reclamation Project, South Dakota uK2 uK3

D Columbia River Basin, Washington Q Tmv

E Dolores–Ute Mountain area, Colorado uK1 uK2

F Gunnison River Basin–Grand Valley Project, Colorado uK2 uK1

G Humboldt River area, Nevada Q

H Kendrick Reclamation Project, Wyoming uK2

I Klamath Basin Refuge Complex, California–Oregon Q Tpv

J Lower Colorado River valley, California–Arizona Q Xm Xg uPz

K Lower Rio Grande valley, Texas Qh Te3

L Malheur National Wildlife Refuge, Oregon Q Tmv Tmf

M Middle Arkansas River Basin, Colorado–Kansas uK2 uK3 Q Tpc

N Middle Green River Basin, Utah:

Ouray subarea

Pariette subarea

Stewart subarea

Tec

Tec

uK2

uK3

O Middle Rio Grande, New Mexico Tpc

P Milk River Basin, Montana uK3

Q Owyhee–Vale Reclamation Project areas, Oregon–Idaho Q Tmv Tpc

R Pine River area, Colorado Tec uK4 uK3

S Riverton Reclamation Project, Wyoming Tec

T Sacramento Refuge Complex, California Q

U Salton Sea area, California Qv

V San Juan River area, New Mexico uK2 uK4 Txc

W Stillwater Wildlife Management Area, Nevada Q

X Sun River area, Montana uK2 uK3

Y Tulare Lake Bed area, California Q

Z Vermejo Project area, New Mexico uK3
54 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

Whether lower Tertiary geologic units are large contributors 

of selenium to the study areas is difficult to assess from the 

NIWQP data set because there are fewer sites and most of those 

sites are also associated with Upper Cretaceous geologic units. 

Of the 26 study areas, 5 include extensive land areas where the 

surficial rocks are of Paleocene or Eocene age (table 16). Seventy-fifth percentile selenium concentrations in surface water in 

these five areas (K, N, R, S, and V) ranged from 1 to 73 μg/L 

(table 15). Selenium contamination occurs in four of those five 

areas. Of those four areas, three (N, R, and V) also contain some 

Upper Cretaceous marine sedimentary rocks, and in the fourth 

area (S), Upper Cretaceous marine sedimentary rocks are found 

in the uplands adjacent to irrigated lands. 

The best evidence of the importance of lower Tertiary geologic units in NIWQP study areas is in the Ouray subarea of the 

middle Green River Basin (N). In the Ouray subarea selenium 

concentrations in surface water ranged from less than one to 93 

μg/L and in ground water ranged from less than one to 9,300 

μg/L. All data-collection sites are in areas where Eocene deposits form the bedrock and the nearest exposure of Upper Cretaceous rocks is about 14 mi upstream. Trelease and Beath (1949, 

p. 80) noted that seleniferous plants occur in parts of the Eocene 

Uinta Formation, which is exposed in the area (Stephens and 

others, 1992).

In 6 of the 26 NIWQP areas, selenium is derived mainly from 

nearby upland source areas or is transported in from great distances upstream in the water used for irrigation. Upper Cretaceous and locally Tertiary marine sedimentary rocks and 

deposits are found in mountain areas upland from irrigated land 

in three of these areas (S, T, and Y), and the water destined for 

use in irrigation passes over or through Upper Cretaceous or 

Tertiary marine sedimentary rocks and deposits many miles 

upstream from irrigated land in the other three areas (J, K, and 

U). Seventy-fifth percentile selenium concentrations in surface 

water in these six areas ranged from less than 1 to 265 μg/L 

(table 15).

None of the NIWQP areas show evidence of widespread selenium contamination from volcanic sources. Berrow and Ure 

(1989, p. 221) stated that igneous rocks in general contain low 

amounts of selenium but that volcanic tuffs (or volcanic ash) in 

particular may contain much higher amounts. Some of the surficial rocks are volcanic in three of the study areas (D, I, and Q), 

and volcanic rocks surround or are upstream from two additional areas (G and W). Seventy-fifth percentile selenium concentrations in surface water in these five areas ranged from less 

than 1 to 2 μg/L (table 15).

CLIMATE

The potential for selenium contamination to occur is related 

to the potential for water to be concentrated by evaporation and 

to the availability of freshwater to dilute contaminants. A summary of precipitation and evaporation data for the study areas is 

presented in table 17.

Evaporation data were presented in many ways in the 

NIWQP reconnaissance- and detailed-studies reports (table 1). 

Some reports presented evaporation rates but others did not. 

Some presented Class A pan-evaporation rates, some presented 

potential evapotranspiration rates, and others presented FWSE 

rates or water-loss rates from a local reservoir. The FWSE rate 

was selected to describe evaporation rates (table 17) so that a 

consistent measure of evaporation could be used for all study 

areas. Evaporation rates for each area were determined from a 

map of FWSE rates for the United States (Farnsworth and others, 1982).

A measure of study-area aridity that incorporates information about both evaporation and precipitation rates was chosen 

because both affect selenium contamination. In the past others 

used the ratio of evaporation to precipitation to express aridity 

on climate maps of the United States. Transereau (1905) first 

used rainfall–evaporation ratios to map climatic zones and to 

interpret the distribution of forest centers in the Eastern United 

States. In the current study, a number called the evaporation 

index (EI) was used; the index was derived by dividing FWSE 

by precipitation. EI, which essentially is the inverse of 

Transereau’s (1905) rainfall–evaporation ratio, increases as the 

aridity of an area increases. In the Western United States, 

because the amount of evaporation is commonly greater than 

the amount of precipitation, EI typically is greater than 1. In 

about 56 percent of the land area in the Western United States, 

the EI is greater than 2.5 (fig. 22); that is, annual evaporation is 

more than 2.5 times greater than annual precipitation.

HYDROLOGY

EFFECTS OF TERMINAL WATER BODIES ON SELENIUM 

CONCENTRATIONS

To assess the effect of terminal water bodies on selenium 

concentrations, lakes, ponds, and marshes in the NIWQP study 

areas were classified as flow-through or terminal. (Terminal 

lakes have no outlet, or, defined more narrowly, do not have 

flow-through on an annual basis.) The selenium value last measured during June–August at each site was selected to represent 

the selenium concentration at the site. Although the median 

selenium concentration in terminal water bodies and in flowthrough water bodies is nearly the same, 1.0 and 0.8 μg/L

respectively (fig. 23), the 75th-percentile selenium concentration for terminal water bodies (24 μg/L) is significantly higher 

than for flow-through systems (4 μg/L). In flow-through systems, the selenium load is moved through either continuously 

or episodically, reducing selenium concentrations and thereby 

ameliorating existing selenium problems or decreasing the 

potential for selenium problems.
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 55

TABLE 17. Summary description of climate in National Irrigation Water Quality Program study areas

[Symbols: —, not applicable; <, less than] 1 2 3 4 5 6 7 8

1 Used in figure 2 to show locations of study areas.

2 As published in reconnaissance and detailed investigation reports (table 1) or midpoint of range if range of values was presented. 3 As published in reconnaissance and detailed investigation reports (table 1). 4 Precipitation during year of data collection for study areas where all data were collected during a short period in climatically unusual year. See section 

“Interrelation of Geology, Climate, and Hydrology,” p. 59, in text for discussion. 5 Determined from map by Farnsworth and others (1982).

6 Mean annual free-water-surface evaporation divided by mean annual precipitation. 7 Ranking of study area from most to least arid on the basis of evaporation index.

8 Calculated using precipitation during year of data collection rather than mean annual precipitation. See section “Interrelation of Geology, Climate, and 

Hydrology,” p.59, in text for discussion.

Study area Precipitation

(inches) Free-watersurface

evaporation5

(inches)

Evaporation

index6 Arearanking7

Identifier1 Name Mean

annual2 Range3 Singleyear4

A American Falls Reservoir, Idaho 10.9 — — 41 3.7 17.5

B Angostura Reclamation Unit, South Dakota 16.4 — — 46 2.8 22.5

C Belle Fourche Reclamation Project, South Dakota 14.4 — — 40 2.8 22.5

D Columbia River Basin, Washington 8 6 – 10 — 40 5.0 9.5

E Dolores–Ute Mountain area, Colorado 12 8 – 16 — 53 4.4 13

F Gunnison River Basin–Grand Valley Project, Colorado 10 8 – 12 — 50 5.4 9.5

G Humboldt River area, Nevada 5.5 — — 45 8.1 5

H Kendrick Reclamation Project, Wyoming 12 — — 44 3.7 17.5

I Klamath Basin Refuge Complex, California–Oregon 13 — — 39 3.0 20.5

J Lower Colorado River valley, California–Arizona 4.5 <4 – 5 — 85 18.9 2

K Lower Rio Grande valley, Texas 25.9 21.8 – 30 — 57 2.2 25

L Malheur National Wildlife Refuge, Oregon 10 — — 43 4.3 14

M Middle Arkansas River Basin, Colorado–Kansas 14.6 11.8 – 17.5 — 58 4.0 15

N Middle Green River Basin, Utah 7.6 — — 43 5.7 8

O Middle Rio Grande, New Mexico 9.4 — — 64 6.8 7

P Milk River Basin, Montana 12 — 21.4 40 8

1.9 26

Q Owyhee–Vale Reclamation Project areas, Oregon–

Idaho

9.5 9 – 10 — 43 4.5 12

R Pine River area, Colorado 14 12 – 16 — 49 3.5 19

S Riverton Reclamation Project, Wyoming 8.1 — 3.8 40 4.9 11

T Sacramento Refuge Complex, California 18.5 15 – 22 — 48 2.6 24

U Salton Sea area, California 3 — — 73 24.5 1

V San Juan River area, New Mexico 7.6 6.6 – 8.5 — 56 7.4 6

W Stillwater Wildlife Management Area, Nevada 5.3 5.2 – 5.4 — 53 10.0 4

X Sun River area, Montana 12 — — 36 3.0 20.5

Y Tulare Lake Bed area, California 5.5 4–7 — 61 11.2 3

Z Vermejo Project area, New Mexico 13.8 — — 54 3.9 16
56 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

FIGURE 22. Evaporation index in Western United States. Climate data from Farnsworth and others (1982) and G.H. Taylor 

(Oregon State Climatologist, written commun., 1994). For base credit, see figure 1.

120°

45°

40°

35°

30°

115° 110° 105° 100°

95°

EXPLANATION

Evaporation index:

2.5 3.0 3.5 5.0

0 250 500 MILES

0 250 500 KILOMETERS
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 57

Almost two-thirds of the study areas contain individual terminal lakes or ponds, although in most areas, the system as a 

whole would be considered flow-through. An example of this is 

Rasmus Lee Lake in the Kendrick Reclamation Project (H).

This small lake is terminal but the North Platte River flows 

through the Kendrick Reclamation Project area.

In the Stillwater Wildlife Management Area and the Salton 

Sea area (W and U), the entire basins are terminal sinks. Within 

an area, although some lakes may be flow-through and other 

lakes terminal, a lake’s classification may change because of 

changes in precipitation or in the water-distribution system. The 

Humboldt River area (G) is a terminal sink except for rare 

floods when flows terminate in an adjacent terminal sink. Alternatively, a lake may be flow-through under normal circumstances but become terminal during a drought. 

In the Tulare Lake Bed Area (Y), drain water is managed as 

if the basin were terminal. Drain water from the area is not 

allowed to discharge to the San Joaquin River, rather the drain 

water is stored in terminal ponds where it is consumed by evaporation.

EFFECTS OF UPSTREAM SOURCES OF SELENIUM

If selenium is transported in the water used for irrigation into 

an area, selenium problems may occur in that area even without 

a local source of selenium. Sources of selenium can include 

irrigation drainage from upstream areas. For example, the Colorado River ultimately receives drainwater containing selenium from irrigation projects along the Green, San Juan, and 

Gunnison Rivers. The Colorado River thereby imports selenium into the Salton Sea area (U). 

Other possible sources include natural drainage from 

seleniferous rocks or discharges from mining or oil-field operations. The Rio Grande passes over Upper Cretaceous and Tertiary marine sedimentary rocks and deposits hundreds of miles 

upstream from irrigated lands in the lower Rio Grande Valley 

near Brownsville, Texas. The Vermillion Creek Basin in Colorado and Wyoming is characterized by large expanses of 

unvegetated, highly erodible Mancos Shale, a seleniferous 

Upper Cretaceous marine sedimentary rock. Low-altitude 

spring runoff from this area provides a significant natural 

source of selenium in the middle Green River Basin (N; James 

Yahnke, Bureau of Reclamation, written commun., 1998). In 

Nevada, after it flows through spoils from an abandoned gold 

mine, a small creek has selenium concentrations in excess of 35 

μg/L when the discharge is 1.5 ft3/s (Independence Mining 

Company data on file at the U.S. Forest Service office in Elko, 

Nev.). Flow in the creek discharges to the North Fork Humboldt River, more than 200 mi upstream from where the water 

is used for irrigation in the Humboldt River area (G). 

The significance of a single source of selenium in the budget 

of large rivers is illustrated by a sewage treatment plant in 

northern Utah. Seepage from the sewage lagoons for the town 

of Vernal passes through Mancos Shale and discharges to Ashley Creek, a tributary to the Green River. The seepage mobilizes large amounts of selenium. A sample of seepage 

downgradient from the sewage lagoons contained 16,000 μg/L 

of selenium (Stephens and others, 1992, p. 155). Leakage from 

the sewage lagoons is a principal source of selenium in the 

Ashley Creek Basin (Stephens and others, 1992). Seven measurements made during the reconnaissance and detailed investigations (Stephens and others, 1988; Peltz and Waddell, 1991) 

showed an average selenium load of about 7.5 lb/d from Ashley 

Creek where it discharges into the Green River. Almost 200 mi 

downstream, during 1985–94 , the mean selenium load for the 

Green River near the confluence with the Colorado River was 

53.0 lb/d (Engberg, 1999). Thus, about 14 percent of the selenium load of the Green River could originate from a single site 

on a small stream.

FIGURE 23. Statistical summary of selenium concentrations in filtered 

surface-water samples from lakes, ponds, and marshes, demonstrating 

association of elevated selenium concentrations and terminal hydrologic 

systems. Summary statistics below the reporting limit computed using lognormal maximum-likelihood methods. USEPA, U.S. Environmental 

Protection Agency. Each lake, pond, or marsh sampling site was represented 

by the selenium concentration in most recent, non-replicate, filtered sample 

collected at the site as part of a NIWQP investigation during the months 

June–August. 

62 78

1

10

100

1,000

10,000

USEPA chronic criterion

Terminal Flow-through

EXPLANATION

HYDROLOGY

NUMBER OF SAMPLING SITES

SELENIUM IN FILTERED WATER,

IN MICROGRAMS PER LITER

Upper quartile (75th percentile)

Upper decile (90th percentile)

Point outside upper decile

Median

value

is 1

microgram

per liter

Median

value

is 0.8

microgram

per liter
58 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

TEMPORAL CHANGES IN SELENIUM CONCENTRATIONS

Selenium concentrations at a site can change from hour to 

hour, month to month, or year to year. All the samples from a 

few of the early reconnaissance investigations were collected 

during the middle of the irrigation season in a single year, and 

no information about temporal changes in contaminant concentrations was obtained. Later reconnaissance-level investigations, during which samples were collected throughout the 

entire irrigation season, provided information about seasonal 

changes in selenium concentrations. Only in the detailed investigations (table 1) were samples collected over a sufficiently 

long time and frequently enough to define monthly and annual 

changes in selenium concentrations.

Selenium concentrations at a site can show large changes during a year. The lowest selenium concentrations might be 

expected to occur always during the irrigation season, but this is 

not necessarily so. Selenium concentrations in water samples 

from Lake Creek of the Sun River area (X) vary seasonally (fig. 

24A) and correlate negatively with discharge (fig. 24B). The 

highest concentrations during 1990–92 were in samples collected during the winter, whereas during 1993, the highest concentrations were in samples collected during the spring and 

summer. Flow in Lake Creek is a mixture of precipitation runoff 

and inflow of highly seleniferous seepage from nonirrigated 

lands in the basin and water pumped into Lake Creek from an 

adjacent basin. The pumped water consists of irrigation return 

flow, native flow, and runoff as well as seepage from nonirrigated lands. Thus, selenium concentration in Lake Creek at any 

time depends on the relative contribution of water from several 

sources within and outside the basin.

Samples of ground water from wells in the middle Green 

River Basin (N) also showed rapid seasonal changes (fig. 25). 

This effect was more pronounced in the shallow well, where 

selenium concentrations decreased from 410 to 7 μg/L during 

the 3.5 months of the 1988 irrigation season. Selenium concentrations returned to almost the original concentration by the 

beginning of the 1989 irrigation season.

Selenium concentrations can change greatly from one year to 

the next. Selenium concentrations in Rasmus Lee Lake, Wyo., 

during March–April in 1988 and 1989 are shown in figure 26. 

Selenium concentrations were nearly 10 times lower in 1989 

than they had been in 1988. Large increases in precipitation also 

can result in nondetection of selenium in an area that normally 

would be expected to be contaminated. For example, selenium 

has been measured in surface- and ground-water samples from 

the Milk River Basin (P), an area of seleniferous sedimentary 

rocks, yet selenium was not detected in a single surface-water 

sample collected during the reconnaissance investigation of that 

area. Presser and others (1994) cited work by Everett Pitt of 

Northern Montana College (Havre, Mont.) in which he reported 

selenium concentrations of 70 to 100 μg/L in a creek in the Milk

FIGURE 24. Selenium concentrations in surface water, Sun River area, 

Montana, 1990–93. A, Irrigation and nonirrigation seasons; calendar years are 

divided into months. B, Discharge at Lake Creek data-collection site near 

Power, Mont.

1990 1991 1992

1

10

100

0.01 0.1 1 10 100 1,000

1

10

100

1,000

DISCHARGE, IN CUBIC FEET PER SECOND 

CALENDAR YEAR

SELENIUM IN FILTERED WATER, IN MICROGRAMS PER LITER

1990 1991 1992 1993

A

B

EXPLANATION

Typical irrigation season in Sun River area, Montana

Discrete measurement at Lake Creek data-collection site

 in Sun River area, Montana

1,000
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 59

River Basin. Lake Bowdoin in that basin had selenium concentrations of 6 μg/L in 1985, although when the NIWQP samples 

were collected in 1986 selenium concentrations were less than 

1 μg/L (Lambing and others, 1988). Furthermore, many saline 

seeps that contain unusually high concentrations of trace elements, particularly selenium, were mapped in the Milk River 

Basin by Miller and Bergantino (1983). Although the distribution of fallowing dryland fields also temporarily affects selenium concentrations, the principal reason selenium was not 

detected in surface water during the Milk River Basin reconnaissance investigation is probably because of dilution during 

the period of sample collection. All the samples for the area 

were collected during June or August in a flood year when the 

annual rainfall was nearly twice the normal amount (table 17).

INTERRELATION OF GEOLOGY, CLIMATE, AND 

HYDROLOGY

Geologic units were compiled into three main groups based 

on the geologic map by King and Beikman (1974): (1) Upper 

Cretaceous stratified sedimentary sequences that were deposited mainly in marine environments [hereafter referred to as 

Upper Cretaceous marine sedimentary deposits or rocks]; (2) 

Paleocene to Pliocene mainly marine, stratified sedimentary 

sequences [hereafter referred to as Tertiary marine sedimentary 

deposits or rocks]; and (3) Paleocene to Pliocene continental 

sedimentary deposits [hereafter referred to as Tertiary continental sedimentary deposits or rocks].

FIGURE 25. Seasonal changes in selenium concentrations in deep and shallow 

wells near North Roadside Pond, middle Green River Basin, Utah, 1988–93. 

Gaps in connecting lines indicate periods of no record.

FIGURE 26. Selenium concentrations in Rasmus Lee Lake, Kendrick Reclamation Project, Wyoming, during March–

April 1988 and March–April 1989. Gaps in connecting lines indicate periods of no record.

 

 

 

SELENIUM IN FILTERED WATER,

IN MICROGRAMS PER LITER

CALENDAR YEAR

10,000

1988 1989 1990 1991 1992 1993

100

10

1

EXPLANATION

Typical irrigation season in middle 

 Green River Basin, Utah

Well NR3D Depth, 42.5 feet 

Well NR3S Depth, 7 feet 

1,000

1

10

100

1

10,000

,000

SELINIUM IN UNFILTERED WATER,

IN MICROGRAMS PER LITER

MARCH APRIL

EXPLANATION

Year

 1988

 1989

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29
60 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

The 26 study areas were classified into groups primarily on 

the basis of their association or lack of association with Upper 

Cretaceous marine sedimentary rocks (table 18):

• areas not associated with Upper Cretaceous marine sedimentary 

rocks

• areas where the bedrock beneath irrigated land consists mainly 

of Upper Cretaceous marine sedimentary rocks

• areas where the bedrock beneath irrigated land is a combination 

of Upper Cretaceous marine sedimentary rocks and Tertiary 

continental sedimentary deposits

• areas where the bedrock in mountains upland from irrigated land 

includes Upper Cretaceous marine sedimentary rocks or 

includes both Upper Cretaceous and Tertiary marine sedimentary rocks

• areas where rivers upstream from irrigated land traverse Upper 

Cretaceous marine sedimentary rocks or Upper Cretaceous and 

Tertiary marine sedimentary rocks.

Upper Cretaceous marine sedimentary rocks were found to 

be either directly or indirectly associated with all 12 areas classified as selenium contaminated (table 18). Upper Cretaceous 

marine sedimentary rocks are of particular importance because 

these rocks generally are seleniferous and because they are 

common in the NIWQP study areas. Eight study areas have no 

direct or indirect association with Upper Cretaceous marine 

sedimentary rocks and none of them were classified as seleniferous or selenium contaminated.

NIWQP study areas containing Tertiary continental sedimentary deposits were not classified separately because, in all but 

two areas, they also are associated with Upper Cretaceous 

marine sedimentary rocks. Tertiary marine sedimentary deposits, although known to be important sources of selenium in the 

San Joaquin Valley in California, were not assigned to a separate group because of their minor importance in the NIWQP 

data set. 

The climate of the study areas was represented by the mean 

annual precipitation and the EI (table 18). For the Milk River 

Basin (P) and the Riverton Reclamation Project (S), the precipitation during the year of data collection was substantially different from the mean annual precipitation. All data for the Milk 

River Basin reconnaissance investigation was collected in June 

or August 1986, a year in which there was almost twice the normal amount of precipitation (table 17). Much more water than 

normal also was delivered to the area in the months prior to data 

collection. Selenium was not detected in any surface-water samples from the basin owing to dilution from above-normal precipitation. Because of the unusual amount of dilution that year, 

the precipitation for the year of data collection was used for data 

analysis and calculation of the evaporation index instead of the 

mean annual precipitation in the Milk River Basin. 

In the Riverton Reclamation Project (S), precipitation during 

the year of data collection was less than half the normal amount 

(table 17). However, because stored water was available in the 

months prior to data collection, more water than normal was 

delivered to the area to offset the effects of drought. Because 

less-than-average precipitation did not reduce the amount of 

water delivered to the Riverton Reclamation Project, the average precipitation was used to calculate the evaporation index.

The hydrology of the study areas was characterized by the 

presence of upstream selenium sources or by the presence of 

terminal lakes or ponds in the area (table 18).

The relation between selenium concentrations in surface 

water and two measures of aridity in study areas where irrigated lands overlie Upper Cretaceous marine sedimentary bedrock is shown in figure 27A,B. The upward trend in selenium 

concentration correlates with increasing aridity. Data from the 

Kendrick Reclamation Project (H) and San Juan River area (V) 

were not used in the statistical analysis of the relation between 

selenium and the climatic variables because of known sample 

bias (discussed in section “Statistical Bias,” p. 18). Because all 

selenium values for surface-water samples from the Milk River 

Basin (P) were less than the detection limit, data from that area 

also were excluded.

As Barnes (1985) suggested, in areas where marine sedimentary rocks comprise the bedrock, annual precipitation 

between 12 and 20 in. separates seleniferous from nonseleniferous areas. To test the predictive capability of a statistically 

significant regression between the logarithms of annual precipitation and selenium concentration (adjusted r

2 = 0.83, p < 

0.001), it was recomputed by using only data from areas where 

the precipitation was between 12 and 20 in. The relation was 

not significant (adjusted r

2

 = 0.06, p > 0.29) when outliers for 

areas F and N were removed.

The statistically significant relation between the logarithms 

of EI and selenium concentration (adjusted r2 = 0.70, p = 

0.003) is still evident even when outliers F and N are removed 

(adjusted r

2

 = 0.26, p = 0.14). For EI, 26 percent of the variance 

is explained when the outliers are removed, but the percentage 

is even lower for precipitation, at 6 percent of the variance 

explained after outliers are removed. For this reason, EI rather 

than precipitation was chosen as the variable to represent climate. 

For the study areas where irrigated lands overlie Upper Cretaceous sedimentary bedrock, a regression on the data in figure 

27B was used to identify what the EI would be if the 75th percentile of the selenium concentrations exceeds the chronic criterion. Excluding data from two areas (H and V) because of 

sample bias and one area (P) because of non-detects, the 

regression indicated that when the 75th percentile exceeded 3 

μg/L, the EI was about 2.5 and when it exceeded 5 μg/L, the EI 

was about 3.0. 
TABLE 18. Summary description of degree of selenium contamination and physical characteristics of National Irrigation Water Quality Progra

[Abbreviation and symbols: μg/L, micrograms per liter; —, no data; <, less than] 1 2 3 4 5 6 7

1 Used in figure 2 to show locations of study areas.2 Analyses of filtered surface-water samples collected from study area in and downstream from irrigated lands.3 C, contaminated (75th percentile of selenium concentrations equals or exceeds 5 micrograms per liter). S, seleniferous (75th percentile of selenium concentrations equals or exceeds 3 but is less 

UC, uncontaminated (75th percentile of selenium concentrations is less than 3 micrograms per liter). See section "Selenium Concentration," p. 49.

4 After King and Beikman (1974): Tc, mainly Tertiary continental sedimentary deposits; Tm, Tertiary marine sedimentary deposits; uKm, mainly Upper Cretaceous marine sedimentary rocks.5 Mean annual free-water-surface evaporation divided by mean annual precipitation.6 Water body identified as providing water used for irrigation.7 Calculated using precipitation during year of data collection rather than mean annual precipitation.

Study area

Selenium seventyfifth-percentile

concentration2

(μg/L)

Studyarea

classification3 Geology4

Climate 

Identifier1 Name

Mean

annual

precipitation

(inches)

Evaporation

index5

Maxim

conc

sou

A American Falls Reservoir, Idaho 1.0 UC Irrigated lands not associated with uKm 10.9 3.7 B Angostura Reclamation Unit, South Dakota 4.5 S Irrigated lands underlain by uKm 16.4 2.8 C Belle Fourche Reclamation Project, South Dakota 5 C Irrigated lands underlain by uKm 14.4 2.8 D Columbia River Basin, Washington <1 UC Irrigated lands not associated with uKm 8 5.0 

E Dolores–Ute Mountain area, Colorado 7.0 C Irrigated lands underlain by uKm 12 4.4 

F Gunnison River Basin–Grand Valley Project, Colorado 35 C Irrigated lands underlain by uKm 10 5.0 

G Humboldt River area, Nevada 2.0 UC Irrigated lands not associated with uKm 5.5 8.1 

H Kendrick Reclamation Project, Wyoming 64 C Irrigated lands underlain by uKm 12 3.7 

I Klamath Basin Refuge Complex, California–Oregon <1 UC Irrigated lands not associated with uKm 13 3.0 

J Lower Colorado River valley, California–Arizona 2.0 UC Rivers traverse uKm upstream from 

irrigated land. 

4.5 18.9 

K Lower Rio Grande valley, Texas 1.0 UC Rivers traverse uKm and Tm upstream from

irrigated land.

25.9 2.2 

L Malheur National Wildlife Refuge, Oregon <1 UC Irrigated lands not associated with uKm 10 4.3 

M Middle Arkansas River Basin, Colorado–Kansas 10 C Irrigated lands underlain by uKm and Tc 14.6 4.0 

N Middle Green River Basin, Utah 73 C Irrigated lands underlain by uKm and Tc 7.6 5.7 

O Middle Rio Grande, New Mexico <1 UC Irrigated lands underlain by Tc, not 

associated with uKm.

9.4 6.8 

P Milk River Basin, Montana <1 UC Irrigated lands underlain by uKm 721.4 71.9 

Q Owyhee–Vale Reclamation Project areas, Oregon–Idaho 2.0 UC Irrigated lands not associated with uKm 9.5 4.5 

R Pine River area, Colorado 6.0 C Irrigated lands underlain by uKm and Tc 14 3.5 

S Riverton Reclamation Project, Wyoming 5.0 C Bedrock includes uKm in mountains upland

from irrigated lands underlain by Tc

8.1 4.9 

T Sacramento Refuge Complex, California <1 UC Bedrock includes uKm in mountains upland

from irrigated land

18.5 2.6 

U Salton Sea area, California 8.0 C Rivers traverse uKm, upstream from 

irrigated land

3 24.5 

V San Juan River area, New Mexico 3.0 S Irrigated lands underlain by uKm and Tc 7.6 7.4 

W Stillwater Wildlife Management Area, Nevada <1 UC Irrigated lands not associated with uKm 5.3 10.0 

X Sun River area, Montana 7.5 C Irrigated lands underlain by uKm 12 3.0 Y Tulare Lake Bed area, California 265 C Bedrock includes uKm and Tm in mountains 

upland from irrigated land

5.5 11.2 

Z Vermejo Project area, New Mexico 6.0 C Irrigated lands underlain by uKm 13.8 3.9 
FIGURE 27. Relations between selenium concentrations, climate data, and geology for 26 National Irrigation Water Quality Program study areas.

EVAPORATION INDEX EVAPORATION INDEX

E

T

S

Bedrock in mountains upland from includes Upper Cretaceous or both Cretaceous and Tertiary marine sed

Bedrock beneath irrigated lands consist of bothUpper Cretaceous marine and Tertiarycontinental sedimentary rocks

Bedrock beneath irrigated lands consist of bothUpper Cretaceous marine and Tertiarycontinental sedimentary rocks

100

300 300100

10

1

<1

300

300

1 10 

D

K

J

U

10

1

<1

Rivers providing water for irrigation traverseUpper Cretaceous or both Upper Cretaceous andTertiary marine sedimentary rocks upstream fromstudy area

1 10 20 30

10

<1

C

A

Q

G

I L DO W

1

No association with Upper Cretaceous marinesedimentary rocks

1 10 20 30

H

F

N

E

M

R Z

B

V

C

X

P

100300

10

1

<1

EVAPORATION INDEX

EVAPORATION INDEX

B

1 10 20 30

1

10

100

<1 P

V

X

M

N

H

F

E

C

B

R Z

ANNUAL PRECIPITATION, IN INCHES

SEVENTY-FIFTH-PERCENTILE SELENIUM

CONCENTRATION IN FILTERED WATER,

IN MICROGRAMS PER LITER

SEVENTY-FIFTH-PERCENTILE SELENIUM

CONCENTRATION IN FILTERED SURFACE WATER,

IN MICROGRAMS PER LITER

U.S. Environmental Protecti (1987) chronic criterion foWildlife protection criterion Climatic guideline (see text fRegression line Points H, Pin regression. See text for disc

EXPLANATION

Data point for National Irrig Program study area Lett

A

A

100

2 3 4 5 6 789 2 3 4 5 6 789 2 3 4 5 6 789

1 2 3 4 5 6 7 8 910 20 30 2 3 4 5 6 789

β0 = -0.854

β1 = 3.213r2 = 0.719 EI = 2.5

EI = 3.5

EI = 2.5

EI = 3.5

EI = 2.5

EI = 3.5

EI = 2.5

EI = 3.5

Precipitation=

12 inches

Precipitation=

20 inches
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 63

Where there is no association with Upper Cretaceous marine 

sedimentary rocks, increasing aridity was not associated with 

increases in selenium concentration (fig. 27C), and the 75th percentile of the selenium concentrations did not exceed the 

USEPA chronic criterion of 5 μg/L. In areas where selenium is 

imported into the area in irrigation water after passing over 

Upper Cretaceous or Tertiary marine sedimentary deposits 

upstream (J, K, and U), or areas where Upper Cretaceous or Tertiary marine sedimentary deposits are exposed upland from irrigated lands (S, T, and Y), the 75th percentile of the selenium 

concentrations can exceed the USEPA chronic criterion in arid 

environments (fig. 27D,E).

SELENIUM IN BIOTA

EFFECTS OF SELENIUM ON ANIMALS

In vertebrates, selenium is a required micronutrient and is an 

essential part of several enzymes (for example, cellular glutathione peroxidase) and other proteins having unknown functions (Maas, 1998). Selenium is an important antioxidant, 

which helps to explain its role in preventing a number of diseases. In humans, selenium deficiency is a major factor in Keshan disease, which causes congestive heart failure in infants and 

young children. The disease was endemic in certain regions of 

China but has been eliminated almost entirely by supplying 

sodium selenite pills to those at risk (Maas, 1998).

Selenium is unusual among the required micronutrients in 

that toxic amounts are not substantially greater than required 

amounts. For example, it is required as a nutrient in the diet of 

fish at concentrations of about 0.1 to 0.5 μg/g but becomes toxic 

at concentrations greater than 3 μg/g (Lemly, 1998). In the 

1930’s, poisoning of livestock in the north-central United States 

was related to consuming seleniferous forage. Chronic selenosis in cattle and horses (alkali disease) involves loss of hair in 

the mane and tail and distinctive cracks in the hooves that can 

cause lameness (O’Toole and Raisbeck, 1998). 

In birds, the embryo is the life stage most sensitive to selenium poisoning (Heinz, 1996). Selenium poisoning leads to the 

development of deformed chicks that cannot hatch. The principal deformity of embryos of mallard dams exposed to selenium 

was arrested development of the lower beak and spoonbill narrowing with lateral deviation of the upper beak (O’Toole and 

Raisbeck, 1998). In fish, the most sensitive stage is the larval 

stage (Lemly, 1998), and deformities occur as the larval fish use 

the selenium-contaminated yolk sac (Lemly, 1998). Typical 

deformities in fish include abnormal curvature of the spine, 

deformed or missing fins and eyes, and deformed mouths.

Sublethal selenium poisoning of adult birds causes cachexia 

(weight loss in the presence of adequate nutrition) and lethargy 

(Ohlendorf and others, 1988; Albers and others, 1996). The 

unhealthy birds are more susceptible to predation and are less 

able to make long migrations, establish nests, and raise chicks. 

General debility and poor body weight caused by selenium was 

believed to be the reason coots failed to nest at Kesterson in 

1984 and 1985 (Ohlendorf and others, 1988); their body weight 

averaged 25 percent below normal regardless of whether they 

were found dead or still alive and active.

SELENIUM GUIDELINES

Selenium concentrations in biological material were compared to concentrations (table 19) that have been demonstrated 

to have adverse effects on the species itself or on a similar species (an effect level) or that can have adverse effects on another 

species if it is consumed (a dietary effect level). For plants and 

invertebrates, selenium concentrations are compared only to 

the dietary effect level of 3 μg/g. Hilton and others (1980), 

Hamilton and others (1990), Hamilton and others (1996), and

TABLE 19. Selenium concentrations in biota that 

may adversely affect sensitive fish or aquatic birds

1 2 3 4 5 6

 

7

[Abbreviation and symbol: μg/g, micrograms per gram; —, 

no data] 

1 Concentrations known to have adverse effects on a 

species.2 Selenium concentration in an organism that can have 

adverse effects on another species if consumed. Lemly (1996c) 

identified 3 micrograms per gram as toxic threshold concentration 

for selenium in aquatic food-chain organisms consumed by fish 

and wildlife.

3 Lemly (1996c) identified 4 micrograms per gram as 

threshold concentration that affects health and reproductive 

success of freshwater and anadromous fish. U.S. Department of 

the Interior (1998) estimated that 4 to 6 micrograms per gram was 

threshold range for reproductive failure in sensitive species such 

as bluegill.

4 U.S. Department of the Interior (1998) concluded that 

liver concentrations greater than 30 micrograms per gram are very 

likely to be associated with reproductive impairment.

5 The effect levels used for both individual eggs and egg 

sets are the same. See text for discussion. 6 CH2M Hill (2002) identified 12.5 μg/g as the 

concentration at which 10 percent of mallard eggs became 

inviable. See text for discussion.

7 Six micrograms per gram is a threshold concentration for 

increased risk of egg inviability. Skorupa (1998, 1999) identified 

the upper boundary of safe exposure levels for stilt eggs as 

approximately 6 micrograms per gram. CH2M Hill (2002) 

identified 6.4 μg/g as the lower 95 percent confidence interval for 

the EC10 in mallard eggs. See text for discussion.

Tissue Effect level1

(μg/g, dry weight)

Dietary effect level2

(μg/g, dry weight)

Plants — 3

Invertebrates — 3

Whole fish 34–6 3

Birds:

Liver 430 —

5Egg

High risk

Threshold

612.5

76

—

—
64 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

Lemly (1996c, p. 435) identified a toxic threshold of about 3 

μg/g for selenium in aquatic food-chain organisms consumed by 

fish and wildlife. DeForest and others (1999) proposed a higher 

threshold value of about 10 μg/g for warmwater food chains. 

Hamilton (in press) supports continued use of the 3 ug/g threshold value, however, and argues that DeForest and others (1999) 

reached their conclusion based on an incomplete review of the 

scientific literature. In selenium-normal (uncontaminated) environments where waterborne selenium is less than 1 μg/L, selenium concentrations in freshwater algae typically range from 

0.1 to 1.5 μg/g and in freshwater macrophytes, typically range 

from 0.1 to 2.0 μg/g (U.S. Department of the Interior, 1998). 

Background selenium concentrations in aquatic invertebrates 

range from 0.4 to 4.5 μg/g and typically are less than 2 μg/g 

(U.S. Department of the Interior, 1998).

The selenium concentration at which fish become toxic to 

predators that consume them is near the level at which selenium 

begins to have reproductive effects in the fish themselves. Selenium concentrations in whole-body fish samples are compared 

to dietary effect levels (3 μg/g) and to concentrations associated 

with adverse biological effects on the fish themselves. Selenium 

concentrations in whole-body fish samples from selenium-normal environments range from < 1 to 4 μg/g and are typically 

<2 μg/g (U.S. Department of the Interior, 1998). Selenium concentrations in gravid females of 4 to 6 μg/g, only slightly greater 

than the normal range, can affect reproduction of sensitive fish 

species through transfer of the selenium from the parent to the 

egg (U.S. Department of the Interior, 1998). Transfer of selenium to the developing fish embryo during yolk-sac absorption 

can cause death of the fry within a few days after hatching 

(Lemly, 1996c).

Selenium concentrations in bird-liver tissue from seleniumnormal environments are typically less than 10 μg/g (U.S. 

Department of the Interior, 1998). Selenium concentrations can 

be much higher in mercury-contaminated environments 

because selenium protects against mercury poisoning and therefore mercury-challenged organisms accumulate more selenium 

than normal. In mercury-normal environments, selenium concentrations greater than 30 μg/g are likely to be associated with 

reproductive impairment (U.S. Department of the Interior, 

1998).

Selenium concentrations in eggs from selenium-normal environments commonly average less than 3 μg/g and the maximum 

concentrations are usually less than 5 μg/g (U.S. Department of 

the Interior, 1998). The threshold guideline for seleniuminduced embryotoxic risk used in this report is 6 μg/g egg selenium (dry weight). The choice of this guideline is based on the 

findings from large-sample field studies of black-necked stilts 

reported by Skorupa (1998, 1999). Skorupa (1999) reported 

that this number was an estimate of the EC03 (3 percent effects 

concentration) for individual-level emryotoxicity. In other 

words, any individual egg with 6 or more μg/g selenium would 

have a 3 percent or greater chance of being inviable. This is 

viewed as the best existing estimate of the true threshold separating no effects concentrations from the lowest detectable 

effect concentration.

Recently, Fairbrother and others (1999) reviewed laboratory 

data for mallard ducks and estimated that the EC10 for selenium embryotoxic effects was 16 μg/g. That analysis was 

extended by CH2M Hill (2002) and resulted in an estimate of 

12.5 μg/g egg selenium for the EC10 with 95 percent confidence boundaries of 6.4 to 16.5 μg/g. The lower end of the 95-

pecent confidence limit is comparable to the 6 μg/g guideline 

used in this report. CH2M HILL’s (2002) EC10 estimate of 12.5 

μg/g egg selenium is used in this report as guideline for highrisk selenium-exposure. In practice, however, none of the 

applicable Federal wildlife laws (such as the Migratory Bird 

Treaty Act or Endangered Species Act) allow any foreseeable, 

human-caused mortality of protected populations, let alone 10 

percent mortality. Therefore, the value of 6 μg/g is used in this 

report as the primary guideline for evaluating selenium concentrations in eggs. 

Selenium concentrations in bird eggs are compared to applicable criteria in two ways—as individual eggs and as sets of 

eggs representing a distinct breeding population of birds. Population-level thresholds are commonly lower than individuallevel thresholds because they are based on population averages 

even though the maximum values determine when hens in the 

population begin to show a toxic response (U.S. Department of 

the Interior, 1998). However, the 6 μg/g guideline is used for 

both purposes in this report to avoid confusion regarding individual versus population levels of analysis. For sets of avian 

eggs it is not uncommon for the maximum individual value in 

the set to be at least twice the mean. In the NIWQP data set the 

maximum was at least twice the mean in twenty percent of the 

eggsets that contained 10 or more individual eggs. Therefore, 

when the population mean exceeds the guideline of 6 μg/g egg 

selenium there is a high probability that some eggs in that population will exceed 12.5 μg/g. 

SUMMARY STATISTICS AND COMPARISON WITH 

GUIDELINES

Summary statistics were computed for selenium concentrations in plant, invertebrate, fish, and bird tissues (table 20) and 

were compared to effect levels. The categories used to group 

species are broad. Thus, the plant category groups together 

samples from algae, pondweed, and cattail roots, and the invertebrates category groups clams, insects, and crayfish. However, 

only whole-body fish samples are included in the fish category; 

fish-tissue samples from many study areas included fillets (and 

rarely gonads), but these tissues were not compared to the 

guidelines.
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 65

Plant and animal tissues are clearly different in the percentage of samples where selenium was detected. Only 79 percent 

of the plant samples contained selenium concentrations greater 

than the reporting limit. This may be because, except for some 

hyperaccumulating plants, selenium is not a required micronutrient for higher plants. Selenium was detected in 92 percent of 

the invertebrate samples and in more than 99 percent of the samples from fish and birds. Because selenium is a required micronutrient for animals, selenium is expected to be found in most 

animal tissues.

Twenty-five percent of the plant samples had selenium concentrations exceeding the dietary effect level, whereas 57 percent of the invertebrate samples and 61 percent of the fish 

samples exceeded it (table 20). These data suggest species 

whose food chains are based on invertebrates and fish may be at 

greater risk than species whose food chains are based on plants.

Exceptions to this generalization may occur; DuBowy (1989) 

found that, sometimes, due to the low caloric content of plants, 

herbivorous marsh birds must consume so much mass that they 

get a higher dietary dose of selenium than insectivorous marsh 

birds feeding on a lower mass of calorically rich, but more contaminated, insects.

About 44 percent of the bird-egg samples exceeded the 

threshold effect level of 6 μg/g for individual eggs, and about 16 

percent of the bird-liver samples exceeded the effect level of 30 

μg/g. Elevated concentrations of selenium in the liver should be 

taken only as an indication that further study is warranted (U.S. 

Department of the Interior, 1998). 

NATIONAL IRRIGATION WATER QUALITY PROGRAM 

STUDY-AREA COMPARISONS

Box plots for selenium concentrations in plant, invertebrate, 

fish, and bird tissues in the 26 areas are shown in figure 28. 

Effect levels for selenium toxicity are shown in the figure, but 

effect levels for selenium deficiency are not shown. Because 

selenium is a required mineral nutrient in animals, adverse 

effects from selenium deficiency also can occur.

SAMPLE BIAS

The exceedance data (table 20) are strongly affected by sample bias because samples were collected preferentially from the 

detailed study areas, which were typically selected for additional study because of selenium contamination. Therefore, the 

44-percent exceedance value for bird eggs (table 20), for example, cannot be extrapolated to mean that 44 percent of the bird 

eggs in the Western United States contain potentially toxic 

amounts of selenium. Of the 497 eggs that contained selenium 

concentrations exceeding the high-risk level of 12.5 μg/g, 

almost 79 percent were from the Kendrick Reclamation Project 

(H) and the middle Green River Basin (N).

Another type of bias, called survivor bias, can skew the 

results of tissue studies toward lower contaminant concentrations because animals having high contaminant loads are less 

likely to be sampled than animals with low contaminant loads. 

Methods for collecting free-ranging fish and birds typically 

gather only specimens that were alive at the time of collection. 

Therefore, only the population of survivors is being sampled. 

TABLE 20. Summary statistics for selenium in plants, invertebrates, fish, and 

bird livers and eggs from National Irrigation Water Quality Program study 

areas

[Abbreviation and symbol: μg/g, micrograms per gram; —, no data]

Category

of biota

1 2 3 4 5

1 Relative to dietary effect level of 3 micrograms per gram.

2 Relative to effect level of 6 micrograms per gram.

3 Eighty tissue samples in the database are a combination of liver and kidney tissue. They were 

not used in this summary because effect levels have not been established for that combination of 

tissues.

4 Relative to effect level of 30 micrograms per gram.

5 The first value is relative to threshold level of 6 micrograms per gram. The second value is 

relative to the high-risk level of 12.5 micrograms per gram.

Summary statistics Exceedance

(percent)

Median

(μg/g,

dry weight)

Interquartile

range

(μg/g,

dry weight)

Number of

samples

Detects

(percent)

Dietary

effect Effect

Plants 1.0 2.7 1,086 79.3 125.0 —

Invertebrates 3.4 6.2 751 92.0 157.1 —

Whole fish 3.9 5.1 2,177 99.6 161.3 232.4

Birds:

Liver 10 16.1 31,234 99.8 — 415.7

Egg 5.2 9.2 2,055 99.5 — 544.1/24.2

Egg sets 4.3 6.1 517 -- -- 537.1/13.3
66 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

FIGURE 28. Selenium concentrations in plants, aquatic invertebrates, fish, and birds (livers and eggs) in National 

Irrigation Water Quality Program (NIWQP) study areas. Typical background level (upper limit) of selenium 

concentrations from U.S. Department of the Interior (1998). Some NIWQP areas either show no plotted data 

because data were below reporting limit or were not plotted because no analyses of the given type were done. 28146 128 74178 542 42411931231159206 20

2451767027424993723320

66896 13 51 04 54 205778 01 21 02 44 893 03 91 55 41631 11 4

13810291591374003120479399159192315201281392620120

3811185448375315282582858103140168421526846918

A Plants B Aquatic invertebrates

C Fish D Bird egg

SELENIUM CONCENTRATION IN TISSUE, IN MICROGRAMS PER GRAM, DRY WEIGHT

Upper decile (90th percentile)

Upper quartile (75th percentile)

Lower quartile (25th percentile)

Median

Effect level

Typical background level

Dietary effect level

Lower decile (10th percentile)

Point outside lower decile

EXPLANATION

Point outside upper decile

NIWQP STUDY AREA

NIWQP STUDY AREA NIWQP STUDY AREA

NIWQP STUDY AREA

NUMBER OF ANALYSES NUMBER OF ANALYSES

NUMBER OF ANALYSES NUMBER OF ANALYSES

2 2082 494 35 36 51 5101 57 91243 01 41 093 15 111250

0

3751641 0

E Bird liver

NIWQP STUDY AREA

NUMBER OF ANALYSES

0.1

1

10

100

0.1

0.1

1

10

100

1,000

1,000

1

10

100

1,000

0.1

1

10

100

0.1

1

10

100

1,000

1,000

ABCDE FGH I J LMNOPQRS TUVWXY K Z

A C B DE FGH I J K LMNOPQRS TUVWXY Z

ABCDE FGH I J K LMNOPQRS TUVWXY Z

ABCDE FGH I J K LMNOPQRS T UVWXY Z ABCDE FGH I J K LMNOPQRS T UVWXY Z
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 67

This can be an important issue with regard to assessing effects 

of selenium exposure because individual and taxonomic sensitivity to selenium is quite variable (Lemly, 1993c; Albers and 

others, 1996; Green and Albers, 1997; O’Toole and Raisbeck, 

1998; Skorupa, 1998). Furthermore, survivors can be devoid of 

the histologic lesions typical of fatally poisoned individuals 

even when survivors possess the same level of tissue selenium 

that proved fatal to more-sensitive individuals (Albers and others, 1996). Thus, survivor bias can skew risk assessments 

because of falsely concluding that a given level of exposure is 

insufficient to induce toxicity (as evident in histologic lesions) 

or because of underestimating levels of a population’s exposure 

to selenium.

As a hypothetical example of underestimating levels of exposure because of survivor bias, suppose that the probability-density function for exposure of a population of animals was 

uniform across a range of 0 to 100 μg/g selenium in tissue and 

that selenium-induced mortality increased by 20-percent increments with every 20-μg/g increase in selenium in tissue. For this 

hypothetical situation, a population having an actual mean 

exposure of 50 μg/g tissue selenium would be assessed as having a mean exposure of only 22 μg/g selenium in tissue on the 

basis of sampling only the survivors—a negative bias of more 

than 50 percent. Clearly, as the mortality-response function 

steepens, sampling highly exposed survivors becomes less 

likely and exposure surveys become more skewed. An important aspect of survivor bias is that it always affects estimates of 

toxic sensitivity and estimates of population exposure in the 

same direction: Because both sensitivity and exposure are 

underestimated, the overall bias toward underestimating true 

risk is compounded.

PLANTS

As primary producers, aquatic plants are important sources of 

food for higher trophic-level organisms. Most plant samples had 

selenium concentrations less than the dietary effect level of 3 

μg/g (fig. 28A; table 20), and the median concentration for most 

of the areas was less than 1.5 μg/g, the concentration typical of 

selenium-normal environments. In 4 of the 26 study areas (F, H, 

M, and N); however, more than 25 percent of the samples 

exceeded the dietary effect level and in 3 of these areas (H, M, 

and N), more than half exceeded it. The two areas having the 

highest median selenium concentrations in plant tissue were the 

Kendrick Reclamation Project (H) and the middle Green River 

Basin (N).

AQUATIC INVERTEBRATES

Aquatic invertebrates are a large component of the diet of 

some fish and birds. Only one terrestrial-invertebrate sample 

(earthworms) was collected; that sample was not included in the 

statistical analysis that follows. In 9 of the 25 study areas analyzed, the median selenium concentration in aquatic invertebrate tissue was less than 2 μg/g, which is typical of seleniumnormal environments (fig. 28B). In 17 areas, more than onequarter of the samples exceeded the dietary effect level, and in 

13 of these 17 areas, more than one-half exceeded it. The three 

areas having the highest median selenium concentrations in 

invertebrate tissue were the Kendrick Reclamation Project (H), 

middle Green River Basin (N), and Tulare Lake Bed area (Y). 

FISH

In 10 of the 26 study areas, the median selenium concentration in whole-body fish tissue was less than 2 μg/g, which is 

typical of selenium-normal environments (fig. 28C), and in five 

areas it was above background but less than the dietary effect 

level. In 14 areas, at least some fish samples exceeded the effect 

level of 6 μg/g, and in 7 areas, the median selenium concentration equaled or exceeded the effect level. The areas having the 

highest median selenium concentrations in fish tissue were the 

middle Arkansas River Basin (M), middle Green River Basin 

(N), Salton Sea area (U), and Sun River area (X). In three areas 

(F, N, and X), several samples were collected that had selenium 

concentrations that were ten times greater than the effect level.

BIRDS

In the NIWQP studies, selenium concentrations were measured in bird eggs (fig. 28D) or in adult or juvenile bird-liver 

tissue (fig. 28E). In two of the areas [middle Rio Grande (O) 

and San Juan River (V)] a combination of liver and kidney tissue were collected. For several reasons, bird-liver tissue is not 

optimum for determining whether selenium has adverse effects 

on the birds of an area: If adult birds are sampled, additional 

evidence is needed to prove that the birds did not just arrive 

from another area and bring the selenium with them. In adults, 

selenium poisoning has nonspecific effects such as emaciation, 

which makes determining the effect level for selenium from 

liver concentrations difficult (Heinz and others, 1988). Also, in 

contaminated areas, survivor bias can be a problem. Further, 

selenium concentrations in liver tissue can be elevated in selenium-normal environments that are contaminated by mercury 

(U.S. Department of the Interior, 1998). Selenium concentrations in liver tissue are more reliable for identifying populations that are not at risk from selenium toxicity than they are for 

identifying poisoned populations (U.S. Department of the Interior, 1998).

LIVER

Selenium concentrations in liver tissue typically show a wide 

range of concentrations, even in uncontaminated areas. In selenium-normal environments, the selenium concentration in liver 

is typically less than 10 μg/g but can be much higher in mercury-contaminated environments (U.S. Department of the Interior, 1998). In the American Falls Reservoir (A), Malheur 

National Wildlife Refuge (L), and Owyhee–Vale Reclamation 
68 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

Project areas (Q), all of which were rated uncontaminated on 

the basis of selenium concentrations in surface water (table 15), 

selenium concentrations in liver show almost an order of magnitude range. In 3 of the 22 study areas analyzed, none of the 

selenium concentrations in liver-tissue samples exceeded the 

background concentration (fig. 28E); the median selenium concentration in 10 areas were within background levels; and the 

median concentration in all but 3 areas was less than the effect 

level. Even within the areas considered to be the most contaminated on the basis of selenium concentrations in surface water, 

some tissue samples were within the range of selenium concentrations found in tissue samples from uncontaminated areas. 

The three study areas having median selenium concentrations 

that equaled or exceeded the effect level (30 μg/g) were the 

Gunnison River Basin–Grand Valley Project (F), Kendrick Reclamation Project (H), and Tulare Lake Bed area (Y). Although 

the median selenium concentration in the Middle Green River 

Basin (N) only slightly exceeded background levels, almost 25 

percent of the samples exceeded the effect level.

EGGS

Under NIWQP, more than 2,000 bird eggs were analyzed for 

contaminant concentrations. Sampling plans at many NIWQP 

study areas intentionally focused on the collection of bird eggs 

because earlier studies at Kesterson National Wildlife Refuge 

demonstrated the importance of evaluating embryo viability and 

the presence of terata. In 12 study areas, the median selenium 

concentration was within the background range (<3 μg/g), and 

in almost all study areas, individual eggs were within this range 

(fig. 28D). In 15 of the 26 study areas, some eggs equaled or 

exceeded the 6 μg/g threshold effect level and in 11 areas some 

eggs equaled or exceeded the 12.5-μg/g high-risk effect level. In 

the Gunnison River Basin–Grand Valley Project (F), Kendrick 

Reclamation Project (H), Middle Green River Basin (N), and 

Tulare Lake Bed area (Y), 20 percent or more of the selenium 

concentrations exceeded the 12.5-μg/g high-risk effect level. 

Bird embryos having deformities typical of selenium poisoning 

were discovered in all four of these areas. 

Reduced hatchability of bird eggs is an example of harm to 

wildlife populations that can be related statistically to the chemical content of the eggs. To analyze the data at the population 

level, eggs are grouped into sets and the geometric-mean selenium concentration for each set is calculated. A set is a species–

site–year permutation that conceptually represents the eggs 

from a distinct breeding population of birds. For example, the 

16 American avocet eggs that were collected in 1988 in the Kendrick Reclamation Project (H) at the Rasmus Lee Lake site represent a set; the 86 American avocet eggs that were collected at 

the same site in 1989 represent another set of eggs. In some 

cases, a single egg or composite sample represents the set for a 

particular breeding population of birds. 

Box plots were prepared for geometric-mean selenium concentrations in sets of bird eggs in the study areas (fig. 29). 

Ranges of selenium concentrations in bird eggs that might be 

found in selenium-deficient areas are not shown in figure 29. In 

6 of the 24 areas where bird eggs were collected, all geometricmean selenium concentrations for egg sets were less than 3 

μg/g, which is the expected concentration in selenium-normal 

environments; in 9 areas, most of the geometric-mean selenium 

concentrations were less than 3 μg/g (fig. 29). Fifty-five percent of the egg sets in the Kendrick Reclamation Project (H) 

had geometric-mean selenium concentrations exceeding 12.5 

μg/g and 33 percent in the Gunnison River Basin–Grand Valley 

Project (F) exceeded that level.

Study areas were classified as normal, elevated, and embyrotoxic based on the maximum geometric-mean selenium content of an eggset from the study area. Fourteen areas were 

classified as embryotoxic (table 21) because the geometricmean selenium concentration exceeded the 6 μg/g threshold 

level in at least some of the egg sets. Six areas were classified 

as normal because all geometric-mean selenium concentrations 

were less than 3 μg/g. Four areas were classified as elevated 

because the maximum geometric-mean selenium concentration was greater than 3 μg/g but less than 6 μg/g 

When irrigation-induced contamination is severe, effects on 

wildlife populations can be overtly evident. The confirmed 

presence of deformed embryos at Kesterson Reservoir and at 4 

of the 26 study areas (F, H, N, and Y) provide dramatic evidence 

of selenium effects. Deformities of bird embryos are a clear, 

unambiguous indication that wildlife is being harmed by a contaminant. However, tens to hundreds of embryos must be 

examined to determine if deformities are occurring in an area, 

but in most study areas few or no embryos were examined. If 

the geologic and climatic conditions described in the sections 

“Sources of Selenium,” p. 49 and “Climate,” p. 54, characterize 

these areas, deformities may be occurring even though none 

were observed.

Embryos having multiple overt deformities like those from 

Kesterson Reservoir were not found in 22 of the 26 areas. The 

apparent lack of clearly evident effects on aquatic biota and 

wildlife does not necessarily mean that biological effects were 

not occurring in the 22 areas. The apparent lack of biological 

effects in the Gunnison River Basin–Grand Valley Project (F; 

Lemly and others, 1993; Butler and others, 1994) was interpreted by Canton and Van Derveer (1997, p. 1258) to mean that 

biological effects were not occurring even though selenium 

concentrations were elevated in the water. A deformed embryo 

having symptoms of selenium toxicosis later was found in the 

area, which indicates the danger in assuming that a lack of evidence means a lack of biological effects. 
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 69

FIGURE 29. Geometric-mean concentrations of selenium in sets of avian eggs from National Irrigation Water Quality Program 

(NIWQP) study areas. Each set represents a distinct population of breeding birds.

A B C D E F GH I J K L MN OP Q R S T U V WX Y Z

0.1

1

10

100

200

Normal Elevated Embryotoxic

GEOMETRIC-MEAN SELENIUM CONCENTRATION IN

EGG SETS, IN MICROGRAMS PER GRAM, DRY WEIGHT 

CLASSIFICATION OF SELENIUM CONCENTRATIONS

NUMBER OF EGG-SETS

[Total number of individual eggs shown in parentheses] 7 (13) 2 (8) 5 (10) 7 (29) 10 (15) 48 (91) 5 (37) 18 (400) 5 (31) 1 (2) 0 (0) 13 (47) 3 (9) 175 (399) 8 (15) 3 (9) 9 (19) 9 (23) 7 (15) 6 (20) 9 (128) 9 (13) 24 (92) 134 (620)

1(12)0 (0)

Upper quartile (75th percentile)

Lower quartile (25th percentile)

Median

Lower decile (10th percentile)

Point outside lower decile

EXPLANATION

Upper decile (90th percentile)

Point outside upper decile

NIWQP STUDY AREA
70 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

TABLE 21. Classification of National Irrigation Water Quality Program study areas by selenium content of avian eggs

[Symbols: —, not applicable; <, less than; >, greater than] 1 2 3 4 5 6

1 Used in figure 2 to show locations of study areas.

2 Greatest geometric-mean selenium concentration of an egg set from the study area. 3 According to greatest selenium concentration of the egg sets from study area: Elevated, 3 to 6 micrograms per gram; embryotoxic, greater than 6 micrograms per gram; 

normal, less than 3 micrograms per gram. 4 Ranking of 24 study areas from most to least contaminated on the basis of the greatest observed selenium concentration in a set of bird eggs. 5 Yes, birds and embryos observed having multiple overt deformities and selenium toxicosis confirmed by tissue analysis; possible, deformed birds and embryos observed but 

selenium toxicosis not identifed as cause; no, deformed birds and embryos not observed. 6 Data for two Yuma clapper rail eggs were collected from the study area during the National Irrigation Water Quality Program investigation (William G. Kepner, U.S. Fish 

and Wildlife Service, written commun., 1989) but were not published in the report by Radke and others (1988). That data is presented here but was not used in the subsequent section 

“Avian-egg Risk Assessment,” p. 86.

Study area

Number of eggs

analyzed for

selenium content

Greatest

selenium

concentration2

(micrograms per 

gram)

Area

classification3 Arearanking4

Number

of

embryos

assessed7

7 For some study areas, reports describing investigations did not specify number of embryos assessed. This uncertainty is reflected herein by use of symbols “<,” or “>” 

preceding number of assessed embryos. See text for discussion. Sample sizes in this table may also differ from those listed for ‘Birds’ in table 9 because that table includes samples 

of all types of bird tissue and excludes eggs that were examined but not chemically analyzed.

Seleniumrelated bird

deformities5

Identifier1 Name Individuals Sets

A American Falls Reservoir, Idaho 13 7 2.0 Normal 22.5 0 No

B Angostura Reclamation Unit, South Dakota 8 2 2.5 Normal 21 0 No

C Belle Fourche Reclamation Project, South Dakota 10 5 9.5 Embryotoxic 12 0 No

D Columbia River Basin, Washington 29 7 3.3 Elevated 16.5 <58 No

E Dolores–Ute Mountain area, Colorado 15 10 18 Embryotoxic 5.5 <15 No

F Gunnison River Basin–Grand Valley Project, Colorado 91 48 42 Embryotoxic 4 >65 Yes

G Humboldt River area, Nevada 37 5 3.2 Elevated 18 <37 No

H Kendrick Reclamation Project, Wyoming 400 18 121 Embryotoxic 1 137 Yes

I Klamath Basin Refuge Complex, California–Oregon 31 5 1.9 Normal 24 0 No

J Lower Colorado River valley, California–Arizona6 2 1 12 Embryotoxic 10 0 No

K Lower Rio Grande valley, Texas 0 0 — — -- 0 No

L Malheur National Wildlife Refuge, Oregon 47 13 2.7 Normal 20 <47 No

M Middle Arkansas River Basin, Colorado–Kansas 9 3 7.8 Embryotoxic 13 0 No

N Middle Green River Basin, Utah 399 175 74 Embryotoxic 2 173 Yes

O Middle Rio Grande, New Mexico 15 8 2.0 Normal 22.5 0 No

P Milk River Basin, Montana 9 3 2.9 Normal 19 0 No

Q Owyhee–Vale Reclamation Project areas, Oregon–

Idaho

19 9 3.3 Elevated 16.5 0 No

R Pine River area, Colorado 23 9 14 Embryotoxic 9 <23 No

S Riverton Reclamation Project, Wyoming 15 7 17 Embryotoxic 7 0 No

T Sacramento Refuge Complex, California 20 6 3.9 Elevated 15 18 No

U Salton Sea area, California 128 9 6.2 Embryotoxic 14 65 Possible

V San Juan River area, New Mexico 13 9 18 Embryotoxic 5.5 7 Possible

W Stillwater Wildlife Management Area, Nevada 92 24 9.9 Embryotoxic 11 109 Possible

X Sun River area, Montana 620 134 68 Embryotoxic 3 759 Possible

Y Tulare Lake Bed area, California 12 1 16 Embryotoxic 8 93 Yes

Z Vermejo Project area, New Mexico 0 0 — — -- 0 No
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 71

O’Toole and Raisbeck (1998, p. 383-384) commented that 

several authors had predicted that selenium-induced problems 

in waterfowl were likely in the Stillwater Wildlife Management 

Area (W) but that the detailed study by Hoffman (1994) did not 

report terata in the area. However, reduced hatchability caused 

by selenium may be occurring in one part of the study area. The 

highest selenium concentrations (median 8.9 μg/g, n = 31) in 

eggs sampled near the Stillwater Wildlife Management Area 

were from the Fernley Wildlife Management Area, which is 

about 35 mi west of the main study area. Hallock and others 

(1993) reported the results of an experiment in which 42 eggs 

from the Stillwater Wildlife Management Area and 12 eggs 

from the Fernley Wildlife Management Area were collected and 

incubated in the laboratory to assess hatchability. Of the 42 eggs 

from the low-selenium Stillwater Wildlife Management Area, 2 

(4.8 percent) failed to hatch. The Blyth-Still-Casella 95-percent 

confidence interval for that outcome is 0.85-15.7 percent. Of the 

12 eggs from the Fernley Wildlife Management Area, 2 (16.7

percent) failed to hatch. Thus, the 16.7 percent observed fail-tohatch rate for Fernley eggs falls outside the upper confidence 

boundary for the Stillwater eggs. Although the two rates are not 

conclusively distinguishable by statistical hypothesis testing 

because of low statistical power associated with the small sample size from Fernley, the association of lower hatchability with 

the area having the highest selenium concentrations in eggs 

raises the possibility that selenium may have had an effect even 

though terata were not found. The small sample size makes it 

difficult to determine whether selenium affected hatchability; 

even if one-quarter of the eggs from the Fernley area had failed 

to hatch, the differences in the rates would not have been statistically significant.

Selenium-caused embryonic deformities are found only 

when two conditions are met: high-enough ambient selenium 

concentrations to cause deformities and intensive-enough sampling effort to find deformed embryos if present. For instance, 

calculations using the binomial theorem show that in an area 

where selenium is causing a Kesterson-like 5 percent of the 

embryos to be deformed, almost 60 eggs containing assessable 

embryos must be collected to attain a 95-percent probability 

that one or more of the eggs contains a deformed embryo. If 

only 10 assessable eggs are collected, the probability is 60 percent that none of the eggs will contain a deformed embryo. In 

20 of the areas, fewer than 50 eggs were analyzed for selenium 

content (table 21). In only 8 of the 26 study areas is it certain 

that 10 or more eggs were assessed for embryo status (table 21). 

In several areas the number of embryos assessed for terata was 

not specified in reconnaissance- and detailed-study reports 

(table 1). Embryos can be assessed without being submitted for 

chemical analysis, or eggs submitted for chemical analysis may 

not contain assessable embryos. Uncertainty in the number of 

assessed embryos is reflected by use of “<” or “>” symbols in 

table 21. For instance, in the Columbia River Basin (D), 58 eggs 

were analyzed for trace elements and organochlorine compounds, but the number of embryos assessed is listed as <58 

because some eggs did not contain assessable embryos. The 

number of eggs that could be analyzed and examined for terata 

during reconnaissance investigations was limited in part 

because of the need to balance the number of areas that could 

be investigated with the degree of detail of the investigations.

Statements that deformities were not observed cannot be 

considered evidence that deformities are not occurring unless 

information is given about the intensity of the sampling effort. 

Consider the two following statements:

(1) No deformed embryos were found.

(2) No deformed embryos were found in 300 eggs with assessable embryos. The deformity rate in the area is 1 percent 

or less because otherwise the probability is 95 percent that 

one or more deformed embryos would have been found.

Only the second statement can be considered evidence that 

deformities are not occurring in an area. 

Thus, the low selenium concentrations in surface water in the 

Malheur National Wildlife Refuge (L; table 15) combined with 

the lack of observed deformities in 47 eggs probably means 

that deformities are not occurring at an elevated rate. But in the 

San Juan River area (V), selenium concentrations in surface 

water were elevated and no deformities were observed in the 7 

egg samples. An appropriate conclusion for this area could be 

that deformities may have been occurring at rates of as much as 

20 percent, but if only 7 eggs were examined, 21 percent of the 

time no deformity would be found.

Because some birds are more susceptible to selenium contamination in wetlands than others, further discussion of how 

to interpret this type of negative result (deformities were not 

found) is warranted. Knowledge of which species were collected is important in analyzing the results because statistically 

significant results may be biologically meaningless if the only 

birds collected are not susceptible to selenium poisoning. For 

example, in the Belle Fourche Reclamation Project (C), eggs of 

several species of birds, including red-winged blackbirds, were 

collected. The four blackbird eggs collected had selenium concentrations in the normal range, which might be expected even 

in a contaminated area. Red-winged blackbird diet is principally seeds, spiders, and insects (Welty, 1975), which, being 

terrestrial, would contain low concentrations of selenium even 

in areas where wetlands contain elevated concentrations of 

selenium. However, the three grebe eggs collected in the Belle 

Fourche Reclamation Project contained embryotoxic amounts 

of selenium, which might be expected because grebes eat fish 

that live in the contaminated water. Although the selenium concentrations in the grebe eggs were much less than values associated with teratogenesis, even if 20 percent of the grebe 

embryos in the area were deformed, the probability is greater 

than 50 percent that collecting only three grebe eggs would not 

reveal a deformity. 
72 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

Most of the 26 NIWQP studies do not provide definitive data 

on whether deformities are occurring because only studies that 

detected deformities, or sampled large numbers of embryos 

(~ >100) without finding deformities, provide adequate data. In 

only five study areas (H, N, W, X, Y) were more than 100 

embryos examined. A deformed embryo was detected in one 

additional study area (F). Thus, of the 24 study areas from 

which bird eggs were collected, definitive data for assessing teratogenic effects were collected from only six. For those six 

areas, selenium-induced teratogenic effects were confirmed in 

four areas (F, H, N, and Y). In all four areas, teratogenic effects 

would have been expected a priori because selenium concentrations in at least some of the collected eggs exceeded concentrations associated with embryo deformities in laboratory 

experiments on mallard ducks (Heinz and others, 1989).

Because all twenty-four NIWQP studies that included collection of avian eggs were specifically designed to asses selenium 

concentrations in these eggs, ultimately, NIWQP must rely on 

egg selenium concentrations as a surrogate measure of selenium-induced adverse effects for most of the study areas. Furthermore, because egg viability (hatchability) is a much more 

sensitive endpoint than embryo teratogenesis for seleniuminduced effects (Skorupa, 1999), the most prudent approach is 

to compare egg selenium concentrations to pre-existing guidelines for assessing risk of selenium-induced egg inviability, also 

known as embryotoxicity (i.e. the death of an embryo regardless 

of whether the embryo was deformed or normal at the time of 

death).

TAXONOMIC AND FEEDING GUILD ANALYSIS OF AVIANEGG DATA

Eggs were sampled from 34 species of birds belonging to 10 

orders (table 22). Geographically, the most extensively sampled 

orders were the Anseriformes (represented in NIWQP samples 

by geese and ducks), Gruiformes (represented in NIWQP samples by coots), and Charadriiformes (represented in NIWQP 

samples by avocets, killdeer, and stilts). Samples from those 

three orders were collected from 21 (81 percent) of the 26 

NIWQP study areas. On the basis of numbers of sets of eggs 

collected, American coots (n = 65), mallards (n = 61), and 

American avocets (n = 44) were the three species most frequently sampled and also were the only species whose eggs 

were collected from 10 or more different NIWQP study areas. 

Every NIWQP study area that was sampled for avian eggs was 

represented by at least one of four groups of birds: ducks, recurvirostrids (avocets and stilts), coots, and blackbirds. From a 

regional risk-assessment perspective, those are the four groups 

of birds for which it would be most useful to have rigorous 

embryonic exposure-response curves. Fortunately, statistically 

rigorous exposure-response curves based on field data are available for two of those taxonomic groups, ducks (U.S. Department of the Interior, 1998) and recurvirostrids (Skorupa, 1998). 

Of the 34 species of birds sampled during NIWQP investigations, sets of eggs having a geometric-mean selenium content 

of at least 12.5 μg/g, a high-risk threshold (CH2M HILL, 

2002), were documented for 16 species (table 22). All three 

species of grebes yielded at least one high-risk set of eggs, as 

did four of five species of shorebirds. Among waterfowl 

(Anseriformes), five of eleven species yielded at least one highrisk set of eggs. By comparison, 24 of the 34 species sampled 

for the NIWQP yielded one or more sets of eggs having a geometric-mean selenium content of at least 6 μg/g, the threshold 

for embryotoxicity associated with intermediate avian sensitivity to selenium (Skorupa, 1998). Among 10 species of ducks, 

only American wigeon did not yield at least one set of potentially selenium-effected eggs; however, only one wigeon egg 

was sampled. 

Egg-set data were examined to determine if some feeding 

guilds are more at risk to selenium poisoning than others. 

Waterbird taxa for which eggs were collected (table 22) were 

classified into one of three feeding guilds (herbivore, insectivore, and piscivore) using dietary information from Martin and 

others (1951), Storer and Nuechterlein (1992), Parsons and 

Master (2000), Robinson and others (1999), and Jackson and 

Jackson (2000). For the following analysis, nine taxa whose 

diet is almost exclusively restricted to one of the three guilds 

were used. American Coot and Canada Goose were classified 

as herbivores; American Avocet, Common Snipe, Killdeer, and 

Black-necked Stilt were classified as insectivores; and Western 

Grebe, Snowy Egret, and Black-necked Night Heron were classified as piscivores. Eggs forming 157 sets from these taxa 

were collected from study areas where the 75th percentile selenium concentration in surface water exceeded 5 μg/L (table 

15). The median geometric-mean selenium concentration in 

the egg sets increases with trophic level of the dietary items 

(fig. 30A). Geometric-mean selenium concentration in nearly 

50 percent of egg sets from insectivorous birds and more than 

75 percent of egg sets from fish-eating birds exceed 6 μg/g. 

Selenium concentrations for 39 percent of the egg sets from 

herbivorous birds fall in the normal range (less than 3 μg/g) 

while only 7 and 0 percent, respectively, of egg sets from 

insect- and fish-eating birds fall in the normal range.

These results suggest that herbivorous birds may bioaccumulate less selenium than insect- and fish-eating birds, however, these results may just be an artifact of sampling. Water 

samples were not collected at nesting sites for nearly 2/3 of the 

157 egg sets and, hence, the apparent lower risk for herbivorous 

birds could result from a larger percentage of their eggs being 

collected at sites where water is uncontaminated. To test this 

alternative explanation, data were plotted for 32 egg sets where 

selenium concentrations in water were measured and were 

greater than 5 μg/L (fig. 30B). These data also indicate herbivorous birds bioaccumulate less selenium than insect-eating 

birds. Selenium concentrations for 14 percent of the egg sets
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 73

1Sample may consist of individual egg or individual composite of several eggs, depending on size of particular species’ eggs. 2Set is distinct species-by-site-by-year permutation that conceptually represents eggs sampled for distinct breeding population of birds; may be either 

single egg or composite. 3Measured in any egg set for each species; geometric means greater than 20 μg/g indicate high potential for reproductive impairment.

TABLE 22. Taxonomic distribution of National Irrigation Water Quality Program avian-egg samples analyzed for selenium

[Taxonomic nomenclature according to American Ornithologists’ Union (1998). Tabulated data exclude 13 samples that were not identified to 

species level. Abbreviation: μg/g, micrograms per gram]

Species Number of

study areas

Number of

samples1 Numberof sets2

Maximum geometricmean selenium3 (μg/g, 

dry weight)

Anseriformes (Screamers, Swans, Geese, and Ducks)

Anas acuta (northern pintail) 4 49 15 8.5

Anas americana (american wigeon) 1 1 1 1.4

Anas clypeata (northern shoveler) 3 59 22 12

Anas cyanoptera (cinnamon teal) 6 60 21 37

Anas discors (blue-winged teal) 2 7 5 9.0

Anas platyrhynchos (mallard) 16 170 61 52

Anas strepera (gadwall) 5 143 39 21

Aythya affinis (lesser scaup) 1 49 19 9.3

Aythya americana (redhead) 4 46 13 20

Branta canadensis (canada goose) 3 175 24 19

Oxyura jamaicensis (ruddy duck) 3 20 6 9.6

Charadriiformes (Shorebirds, Gulls, Auks, and Allies)

Charadrius vociferus (killdeer) 3 13 9 21

Gallinago gallinago (common snipe) 3 7 6 21

Himantopus mexicanus (black-necked stilt) 4 142 15 33

Larus californicus (california gull) 1 2 1 2.8

Phalaropus tricolor (Wilson’s phalarope) 1 6 2 12

Recurvirostra americana (american avocet) 10 300 44 86

Ciconiiformes (Herons, Ibises, Storks, and Allies)

Ardea herodias (great blue heron) 1 6 2 2.7

Botaurus lentiginosus (american bittern) 1 1 1 5.3

Egretta thula (snowy egret) 1 3 2 6.9

Nycticorax nycticorax (black-crowned night-heron) 4 32 9 18

Falconiformes (Diurnal Birds of Prey):

Circus cyaneus (northern harrier) 2 3 3 5.2

Galliformes (Gallinaceous Birds):

Colinus virginianus (northern bobwhite) 1 1 1 4.1

Phasianus colchicus (ring-necked pheasant) 1 1 1 7.0

Gruiformes (Cranes, Rails, And Allies):

Fulica americana (american coot) 15 344 65 49

Passeriformes (Passerine Birds)

Agelaius phoeniceus (red-winged blackbird) 7 45 20 18

Euphagus cyanocephalus (Brewer’s blackbird) 1 2 1 4.5

Xanthocephalus xanthocephalus (yellow-headed blackbird) 4 43 26 21

Pelecaniformes (Totipalmate Swimmers)

Pelecanus erythrorhynchos (american white pelican) 1 5 1 2.8

Phalacrocorax auritus (double-crested cormorant) 2 11 3 2.4

Podicipediformes (Grebes)

Aechmophorus occidentalis (western grebe) 4 21 5 25

Podiceps nigricollis (eared grebe) 3 242 12 121

Podilymbus podiceps (pied-billed grebe) 5 28 17 74

Strigriformes (Owls)

Asio flammeus (short-eared owl) 1 1 1 3.4
74 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

from herbivorous birds fall in the normal range and the median 

selenium concentration for herbivorous birds is substantially 

lower than for insectivorous birds. Even if herbivorous birds 

bioaccumulate less selenium than insectivorous and piscivorous 

birds, however, it does not indicate they are at less risk. It is both 

the degree of bioaccumulation and the species sensitivity to 

selenium exposure that determines the magnitude of toxic 

effects. 

Although herbivorous birds may bioaccumulate less selenium, a minimum of 86 percent of the eggs from all feeding 

guilds exceeded normal background values. Thus, at a gross 

level of examination, it does not appear that any waterbird feeding guilds are particularly well buffered from exposure to selenium contamination. This agrees with results of food-chain 

studies at selenium-contaminated wetlands, which have consistently found food-chain uptake of selenium to be pervasive 

throughout aquatic food webs (Saiki and Lowe, 1987; Hothem 

and Ohlendorf, 1989; Schuler and others, 1990). Because 

nearly all the NIWQP eggs come from aquatic species of birds, 

analyzing the data set in aggregate should introduce minimal 

bias due to taxonomic variation in the samples from each 

NIWQP study area. This would not be true if certain waterbird 

feeding guilds appeared to be strongly buffered from selenium 

exposure or if some study areas included many samples of terrestrial birds. In summary, mixed-species collections of waterbird eggs from the NIWQP study areas normally should 

accurately reflect local contaminant conditions. Increasing 

levels of selenium contamination should normally lead to comparable increases in mean selenium content of mixed-species 

egg collections.

PREDICTION OF SELENIUM CONTAMINATION OF 

WATER

An important goal of the data synthesis was the creation of 

tools to help managers identify areas for which they are responsible that may be at risk from irrigation-induced selenium contamination. The tools range in scope from identifying broad 

geographic areas where selenium contamination is likely to 

assessing the probability that selenium concentrations in a specific lake exceed the USEPA chronic criterion for selenium. 

The predictive tools presented in this section were described in 

detail by Naftz (1996a), Nolan and Clark (1997), Seiler (1995), 

and Seiler and others (1999). A summary of the predictive tools 

and a discussion of when it is appropriate to use each of the 

tools follows.

PREDICTION USING GEOLOGIC, CLIMATIC, AND 

HYDROLOGIC DATA

The severity of selenium contamination is related to geology, 

climate, and hydrology (see sections titled “Sources of Selenium,” p. 49; “Climate,” p. 54; and “Hydrology,” p. 54). These 

types of data are readily available and can be used to predict the 

likelihood of selenium contamination. Two types of management tools have been prepared. The first is a map based on geologic and climatic data that may be used to identify broad 

geographic areas where selenium contamination is likely; 

Seiler and Skorupa (1995) and Seiler and others (1999) discussed the derivation and use of this tool. The second management tool is a decision tree in which answers to questions about 

the geology, hydrology and climate are used to predict the likelihood that a specific irrigation project is contaminated. Seiler 

(1995) discussed the derivation and use of this tool.

The EI is used extensively in applying these two tools. The 

critical EI values selected are empirical and were based on an 

examination of data for those study areas where there is a 

known geologic source of selenium (fig. 27B). Although additional information may require revision of the numbers in the FIGURE 30. Geometric mean concentrations of selenium in sets of waterbird 

eggs from three feeding guilds.

NUMBER OF EGG SETS

85 60 11

WATERBIRD FEEDING GUILD

Herbivore Insectivore Piscivore

WATERBIRD FEEDING GUILD

Herbivore Insectivore Piscivore

GEOMETRIC-MEAN SELENIUM CONCENTRATION

IN EGG SETS (IN MICROGRAMS PER GRAM) 

GEOMETRIC-MEAN SELENIUM CONCENTRATION

IN EGG SETS (IN MICROGRAMS PER GRAM) 

0.1

1

10

100

NUMBER OF EGG SETS

14 17 1

200

0.1

1

10

100

200

A Egg sets from waterbird species from study areas classified

as regionally contaminated (table 15) on the basis of the

75th percentile selenium concentrations

B Sub set of data in fig. 30A showing egg sets from waterbird 

species where site-specific water samples were collected and 

selenium concentrations were greater than 5 μg/L

EMBRYOTOXIC ELEVATED NORMAL EMBRYOTOXIC ELEVATED NORMAL
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 75

future, an EI value of 2.5 was considered important because it 

was intermediate between the value for an uncontaminated area 

(P, EI = 1.9, fig. 27B) and those for the areas with the lowest EI’s 

where selenium contamination occurred (B and C; EI = 2.8, fig. 

27B). An EI value of 3.5 was considered important because in 

all but 1 of the 12 study areas, if the EI exceeded 3.5, more than 

25 percent of the selenium concentrations exceed the chronic 

criterion for selenium. 

BROAD GEOGRAPHIC AREAS

Broad geographic areas of the Western United States susceptible to irrigation-induced selenium contamination can be identified by creating a map based on geology and climate. Areas in 

the Western United States susceptible to irrigation-induced 

selenium contamination are identified in figure 31. The map 

was created by using a GIS to overlay geology data—areas 

where Upper Cretaceous or Tertiary marine sedimentary rocks 

form the bedrock (fig. 20) and climate data—areas where the EI 

is greater than 2.5 (fig. 22). Seiler and others (1999) presented 

specific details about how the map was created. The GIS geology data layer was created by manipulating data from the geologic map of the United States, compiled by King and Beikman 

(1974) and later digitized by Schruben and others (1994). The 

GIS climate data layer, showing areas where the EI exceeded 

2.5, was created by manipulating evaporation and precipitation 

data in the GIS.

About 160,000 mi2 of land was identified as susceptible to 

selenium contamination if irrigated (fig. 31; table 23). Examination of satellite imagery indicated that about 4,100 mi2 of 

land that is actively irrigated for agriculture in the Western 

United States was found to be within areas mapped as susceptible to irrigation-induced selenium contamination (Seiler and 

others, 1999). The greater the EI, the more likely are selenium 

problems in areas having sources of selenium. Approximately 

52,600 mi2

 of land was identified as being the most susceptible 

to selenium contamination because of EI values greater than 

3.5.

Irrigated land that is adjacent to areas mapped as susceptible 

to selenium contamination should be considered potentially 

susceptible because selenium can be transported from source 

areas in mountains to irrigated areas in adjacent valleys 

through processes of active weathering, alluvial-fan building, 

and local drainage. In California, all areas mapped as susceptible are in mountain ranges where no agricultural irrigation was 

done. Another reason to consider land as provisionally susceptible if it is adjacent to land mapped as susceptible is that the 

bedrock in the area may be seleniferous if derived by reworking 

seleniferous marine sedimentary deposits. The NIWQP data 

clearly indicate that selenium contamination can develop in 

areas of Tertiary continental sedimentary bedrock if Upper 

Cretaceous marine sedimentary rocks are nearby.

The susceptibility map correctly classified 22 of the 26 

NIWQP study areas (fig. 32). Ten of 12 areas classified as 

uncontaminated were correctly identified. The first uncontaminated area incorrectly identified was the Sacramento Refuge 

Complex in California (T) which is adjacent to an area mapped 

as susceptible. It has one of the lowest EI values (2.6) of the 

NIWQP areas; however, being near seleniferous rocks may not 

result in contamination if the EI is low. In the second area, the 

Milk River Basin in Montana (P), all surface water samples 

were collected during a flood year. Data from Lambing and 

others (1988) and Presser and others (1994) indicate this area 

is contaminated during more normal circumstances.

The two areas classified as seleniferous and 10 of the 12 

areas classified as contaminated (table 15) were identified correctly because they are on or adjacent to areas mapped as susceptible (figs. 31 and 32). The two contaminated areas that 

were incorrectly identified are the Salton Sea (U) and Sun 

River (X) area. The Salton Sea area could not be identified by 

the criteria used to construct the map because selenium is 

brought into the area in water used for irrigation. The Sun River 

area was not identified correctly because the climate map used 

to construct figures 31 and 32 are inaccurate in central Montana 

(Seiler and others, 1999).

The validity of the map (fig. 31) was assessed by plotting 

several independent test areas where selenium investigations 

have been done and determining whether the map correctly 

identified the selenium-contaminated areas. Kesterson 

National Wildlife Refuge (in San Joaquin Valley in California), 

an area of known selenium contamination resulting from irrigation drainage (Ohlendorf, Hoffman, and others, 1986), was 

correctly identified as susceptible because it is adjacent to an 

area mapped as susceptible (fig. 32). 

Selenium contamination was discovered in 1996 at Red 

Rock Ranch (fig. 32), an experimental agroforestry farm south 

of Kesterson Reservoir. Irrigation drainwater at the Red Rock 

TABLE 23. Amount of land in Western United States 

where bedrock consists of Upper Cretaceous or 

Tertiary marine sedimentary rocks and where 

evaporation index exceeds four threshold values

Threshold value

for evaporation

index

Area in 

Western United States

(square miles)

Percentage

of total area

of Western

United States

Greater than 2.5 160,000 8.7

Greater than 3.0 83,300 4.5

Greater than 3.5 52,600 2.9

Greater than 5.0 17,300 0.9
76 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

FIGURE 31. Areas in Western United States that are identified as susceptible to irrigation-induced selenium contamination 

on basis of Upper Cretaceous or Tertiary marine sedimentary bedrock (fig. 20) and evaporation index greater than 2.5 (fig. 

22). For base credit, see figure 1.

120°

45°

40°

35°

30°

115° 110° 105° 100°

95°

EXPLANATION

Evaporation index in areas where geologic units are mainly 

 Upper Cretaceous or Tertiary marine sedimentary deposits:

2.5 3.0 3.5 5.0

0 250 500 MILES

0 250 500 KILOMETERS
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 77

FIGURE 32. National Irrigation Water Quality Program study areas, test areas, and areas in Western United States 

susceptible to irrigation-induced selenium contamination. For locations and descriptions of study areas (A–Z), 

see figure 2 and table 1.

120°

45°

40°

35°

30°

115° 110° 105° 100°

95°

EXPLANATION

National Irrigation Water Quality Program study areas:

Area classified as uncontaminated and where some bird eggs contain 

 embryotoxic concentrations of selenium

Area classified as seleniferous or contaminated and where some bird 

 eggs contain embryotoxic concentrations of selenium

Area or site where surface water is not selenium contaminated

Area or site where surface water is selenium contaminated

W

Y

Test areas and other areas or sites discussed in text: West

Oakes

Scofield

Area classified as uncontaminated and where bird eggs do not contain 

 embryotoxic concentrations of selenium

Area classified as seleniferous or contaminated but where bird eggs do 

 not contain embryotoxic concentrations of selenium

T

B

Area classified as uncontaminated and where bird eggs were not

 collected

Area classified as contaminated and where bird eggs were not collected

K

Z

0 250 500 MILES

0 250 500 KILOMETERS

2.5 3.0 3.5 5.0

Evaporation index in areas where geologic units are mainly 

 Upper Cretaceous or Tertiary marine sedimentary deposits:

West

Oakes

Wind

River

Kesterson

Reservoir

Red Rock

Ranch

Kesterson

Reservoir

Walker

River

Scofield

Emery

Red Rock

Ranch

West

Oakes

Wind

River

Kesterson

Reservoir

Red Rock

Ranch

Kesterson

Reservoir

Walker

River

Scofield

Emery

Red Rock

Ranch

A

B

D

G

K

L

M

O

Q

T

U

Z

P

I

A

B

D

G

K

L

M

O

Q

T

U

Z

P

I

X

C

E

F

H

J

N

R

S

V

W

Y

X

C

E

F

H

J

N

R

S

V

W

Y
78 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

Ranch was reused for irrigation of salt-tolerant plants and ultimately contained extremely high concentrations of selenium—

1,600 μg/L in the water used for the last crop and more than 

11,000 μg/L in shallow evaporation ponds (Skorupa, 1998). 

Black-necked stilts nesting in the area were exposed to the 

water and almost 60 percent of the eggs in the population contained deformed embryos (Skorupa, 1998). The Red Rock 

Ranch is adjacent to an area mapped as susceptible. Data from 

Kesterson Reservoir and Red Rock Ranch indicate the importance of provisionally treating areas as susceptible if they are 

adjacent to areas mapped as susceptible.

In the mid-1980’s, the West Oakes Irrigation Area of North 

Dakota (fig. 32) was investigated for potential selenium contamination because of its association with sediments of Cretaceous age. The West Oakes Irrigation Area was identified 

correctly as being in an area not susceptible to irrigationinduced selenium problems; selenium concentrations in surface 

water were less than 2 μg/L, and the greatest selenium concentration in ground water was 4 μg/L (Goolsby and others, 1989). 

The validity of the map also was assessed by using NIWQP 

data other than the data analyzed to make the susceptibility 

maps. In 1992–93, the NIWQP collected water samples from 

the Wind River Indian Reservation in Wyoming (fig. 32). This 

area is on and adjacent to land mapped as susceptible to selenium contamination, a classification that is supported by data 

from Grasso and others (1995). Of 28 water samples collected 

from the Little Wind Irrigation Unit in 1993, 6 had selenium 

concentrations that equaled or exceeded 5 μg/L, the maximum 

being 17 μg/L. Of 25 water samples collected during a subsequent investigation of the Little Wind River Irrigation Unit in 

1995 (Clark and Sadler, 1996), 6 had selenium concentrations 

that exceed 5 μg/L, the maximum being 49 μg/L.

Because biological data were not analyzed to create the susceptibility map, those data can provide an independent test of 

the map. Areas were classified as embryotoxic, elevated, or normal on the basis of the selenium content of bird eggs from the 

areas (table 21). In 14 of the 24 study areas where bird eggs 

were collected, the geometric-mean selenium concentration in 

at least one set of avian eggs was classified as embryotoxic 

(table 21). The map identified 10 of those 14 areas as susceptible to irrigation-induced selenium contamination (fig. 32). The 

four areas classified as embryotoxic in table 21 that were not 

identified from the map are the Lower Colorado River valley (J)

in California and Arizona, the Stillwater Wildlife Management 

Area in Nevada (W), the Salton Sea area (U), and the Sun River 

Area in Montana (X). Even though they are not identified on the 

susceptibility map, elevated selenium concentrations in water 

are known to occur in the Stillwater Wildlife Management Area 

(W), the Salton Sea area (U), and Sun River areas (X) and likely 

occur in backwater areas in the Lower Colorado River valley (J; 

Seiler and others, 1999). The Lower Colorado River valley (J)

and the Salton Sea area (U) were not identified as susceptible 

because selenium is brought into the areas in Colorado River 

water. In the Stillwater Wildlife Management Area (W), selenium is not a general problem; all of the embryotoxic eggs are 

from two isolated ponds whose source of selenium is not 

known (Seiler and others, 1999). The Sun River area (X) was 

not identified because the climate map used to construct figures 

31 and 32 is inaccurate in central Montana (Seiler and others, 

1999).

Two areas that were not classified as embryotoxic are on 

areas mapped as susceptible (fig. 32). The Milk River Basin (P) 

is classified as normal although it probably is contaminated 

most years (see section titled “Temporal Changes in Selenium 

Concentration,” p. 58). The Angostura Reclamation Unit (B) is 

classified as normal, but only eggs from an unidentified species 

of blackbird were collected. Species of blackbirds that nest in 

the area primarily consume terrestrial seeds and insects and 

thus may not show evidence of selenium problems. The diet of 

the rusty blackbird, which may winter in the area, is mostly 

aquatic beetles and their larvae (Martin and others, 1951); however, it does not nest in the area.

INDIVIDUAL LOCATIONS

The susceptibility map has the advantages that it is graphical, is easy to use, and is amenable to use with land-use overlays. However, not all areas susceptible to irrigation-induced 

selenium contamination are identified because important 

hydrologic information about terminal ponds and information 

about upstream sources of selenium are not readily mappable.

Seiler (1995) presented a decision tree (fig. 33) that uses 

answers to questions about the geology, climate, and hydrology 

of a location to provide an estimate of the likelihood that selenium contamination will occur there. The decision tree ranks a 

target irrigation area into one of four classes on the basis of the 

likelihood of significant problems resulting from selenium 

contamination: selenium problem is unlikely; selenium problem is possible; selenium problem is likely; or selenium problem is very probable. From these four classes, a manager can 

assess the need to collect additional information.

To rank the likelihood that an area is contaminated, four 

basic questions about the geology, climate, and hydrology of 

the area must be answered:

(1) Are irrigated lands on or near an area where Upper 

Cretaceous or Tertiary marine sedimentary deposits form 

the near-surface bedrock?

(2) What is the EI of the area?

(3) Are terminal ponds or lakes in the area?

(4) Is a source of selenium upstream?

Answers to the first question can be obtained by plotting the 

location of the target area on a geologic map of the area.

Calculating the EI for the second question requires obtaining 

the annual FWSE rate from the evaporation map by 

Farnsworth and others (1982) and the average annual
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 79

precipitation from records of individual weather stations or 

from State-scale precipitation maps such as those by Moody 

and others (1986). Answers to the last two questions can be 

obtained by examining topographic and geologic maps of the 

area and the watershed supplying it.

According to the study-area classifications determined using 

the decision tree (fig. 33), selenium contamination is unlikely in 

9 of the 26 areas, and table 15 shows these 9 areas as uncontaminated. In 17 of the 26 areas, the likelihood of selenium contamination ranged from possible to very probable. In 12 of those 17 

areas, selenium concentrations in more than 25 percent of the 

surface-water samples exceeded 5 μg/L, the USEPA chronic criterion for the protection of aquatic life. In five areas (H, N, V, Y, 

and Z) a selenium problem was ranked as very probable; those 

areas were ranked as the first, second, third, ninth, and fourteenth most contaminated areas on the basis of their 75th percentile selenium concentration (table 15).

Biological data collected as part of the NIWQP were used to 

assess the reliability of the decision tree. Selenium contamination was classified as being very probable in 5 of the 26 areas, 

and waterfowl deformities resembling those at Kesterson Reservoir were found in 3 of those 5 areas. Bird eggs were collected 

in eight of the nine areas where selenium contamination was 

classified as unlikely in figure 33; selenium concentrations in 

these eggs were within the normal range in five of the eight 

areas and were only slightly elevated in the other three (table 

21). Bird eggs were collected in 16 of the 17 areas where the 

likelihood of selenium contamination ranged from possible to 

very probable; selenium concentrations in the eggs from 14 of 

these 16 areas were in the embryotoxic range. 

Samples of two yuma clapper rail eggs collected near 

Havasu National Wildlife Refuge in Arizona contained an 

average of 12.5 μg/g selenium (William G. Kepner, U.S. Fish 

and Wildlife Service, written commun., 1989), a concentration 

used in this report as a guideline for high-risk selenium-exposure (table 19). This supports the classification of the Lower 

Colorado River valley (J) as susceptible even though none of 

the water samples from the area exceeded the chronic criterion 

for selenium. The area was classified as uncontaminated (table 

15); however, most of the samples on which that classification 

was based were from the main stem of the Colorado River. 

Birds frequenting biologically-productive areas in oxbows 

along the river that rarely receive inflow likely are exposed to 

water with higher selenium concentrations than those in the 

Colorado River itself. Water samples were not collected from 

these backwater areas as part of the NIWQP investigation.

FIGURE 33. Prediction of likelihood of selenium contamination and classification of test areas and 26 National Irrigation Water Quality Program study areas used to 

derive decision tree. Letter identifies study area (fig. 2). Modified from Seiler (1995) and published with permission of American Society of Agronomy.

Does area

have terminal

lakes or ponds,

and is evaporation

index ≥ 8?

Selenium

problem

very

probable

Selenium

problem

unlikely

Does area

have major

upstream

selenium

source?

Selenium

problem

possible

Selenium

problem

likely

Is evaporation

index ≥ 4.5?

Does area

have terminal

lakes or ponds?

Does area

have terminal

lakes or ponds?

Does area

have terminal

lakes or ponds?

Is evaporation

index ≥ 2.5?

Is evaporation

index ≥ 3.5?

Is evaporation

index ≥ 3.5?

Selenium

problem

unlikely

Selenium

problem

possible

Selenium

problem

likely

Selenium

problem

unlikely

Selenium

problem

very

probable

Selenium

problem

likely

Selenium

problem

likely

Selenium

problem

possible

No Yes

No

No

Yes Yes

Yes

Yes Yes

Yes Yes Yes

Yes Yes

No

No

No No

No

No No

No

Are

areas of

Upper Cretaceous

or Tertiary marine

sedimentary deposits

upland from

irrigated

lands?

Are

irrigated

lands on either

Upper Cretaceous or

Tertiary marine

sedimentary

deposits?

A,D,I,K,L,O,Q G,W J,U T S Y P E,F,M,R B,C,X H,N,V,Z
80 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

Data from Kesterson Reservoir, California and the West 

Oakes Irrigation area, North Dakota, two areas not included in 

NIWQP studies, were used to assess the reliability of the decision tree. The likelihood of selenium contamination at Kesterson was ranked as very probable by the decision tree and as 

unlikely at West Oakes. Both these classifications are supported 

by reports describing the areas (Goolsby and others, 1989; 

Presser, 1994b). Although the Vermejo Project area (Z) was 

investigated as part of NIWQP, the data were not available during construction of the decision tree. The likelihood of selenium 

contamination at the Vermejo Project area was ranked as very 

probable, a ranking that is supported by the classification of the 

area as contaminated (table 15); also, Lusk and others (1991) 

stated that selenium concentrations in birds were at levels that 

may have been causing reproductive impairment.

LIMITATIONS OF THESE PREDICTIVE TOOLS

Many factors and processes that influence the actual amount 

or extent of selenium contamination in a given area are not considered in these management tools. For instance, hydrologic 

information such as the presence of terminal water bodies was 

not used in preparing the maps, and the amount of irrigated land 

in an area that is on Upper Cretaceous or Tertiary marine sedimentary rocks is not considered in the decision tree. Also not 

considered are the selenium content of the soils and chemically 

or microbiologically mediated redox reactions, all of which can 

affect how much selenium is transported from irrigated lands to 

wetlands or to other receiving water. These types of factors and 

processes purposely were not incorporated into the decision tree 

because such site-specific information commonly is not available to a resources manager performing an initial screening of 

areas to decide which warrant further investigation.

A fundamental assumption of the tools is that irrigated soils 

are derived from the underlying bedrock as generalized on the 

geologic map by King and Beikman (1974). Both the susceptibility maps and decision tree are constructed as if Upper Cretaceous and Tertiary marine sedimentary rocks were the only 

geologic source of selenium. Although these sediments are 

probably the most regionally important geologic source of selenium, users of the tools should be aware that marine sedimentary rocks of other ages, for example, the Phosphoria Formation 

of Permian age, may be important locally. Sediments that were 

deposited in similar environments or were formed by reworking 

Cretaceous deposits also may be seleniferous.

In the Western United States, glacial deposits of Pleistocene 

age conceal extensive areas of bedrock in Montana, North and 

South Dakota, and Nebraska. Selenium may not be a problem if 

the glacial deposits are thick and are not derived from Cretaceous rocks. Selenium may be a problem, however, in areas 

where the glacial deposits are derived from Cretaceous marine 

sedimentary rocks even though the underlying bedrock is not 

seleniferous.

The reliability of the susceptibility map depends on how 

accurately the maps used in the analysis portray the distribution 

of the critical geologic and climatic factors. Errors in the map 

of the EI result from interpolating values between contour 

intervals on the precipitation and evaporation maps. Errors 

from interpolation are greatest when the contouring intervals 

are large and are widely spaced on the map. Most of the error 

in the EI map results from interpolation errors in the precipitation map, where the contour intervals are typically 10 in. On 

the FWSE map, the contour interval is 5 in. except where the 

evaporation rate is greater than 80 in/yr. Inaccuracies in the EI 

map reduce the reliability of the map of susceptible areas in 

east-central Montana. Some areas in Montana that probably are 

susceptible to irrigation-induced selenium contamination are 

not identified on the map. For example, the Sun River area (X) 

is not mapped as susceptible (fig. 32) even though its EI 

exceeds 2.5 and irrigated lands are on Upper Cretaceous 

marine sedimentary rocks (table 18). 

The map identifies areas on the basis of average climatic 

conditions. Under drought conditions, areas not identified on 

the map may be susceptible to irrigation-induced selenium 

contamination. Similarly, problems may not occur under flood 

conditions in areas mapped as susceptible. This is demonstrated by the Milk River Basin (P), which was mapped as susceptible (fig. 32); under normal circumstances this area 

probably is contaminated (see section titled “Temporal 

Changes in Selenium Concentrations”). However, for the 

NIWQP investigation, all data were collected during a wet year 

and selenium was not detected in any of the surface-water samples.

The map misses areas where selenium is brought into the 

area in water used for irrigation. This is demonstrated by the 

Lower Colorado River valley (J) and the Salton Sea area (U), 

which were not mapped as susceptible. Eggs from the Lower 

Colorado River valley were ranked as embryotoxic (table 21), 

and the Salton Sea area was classified as contaminated (table 

15). These areas were not identified by the map because selenium is imported into the areas in Colorado River water.

PREDICTION USING LOGISTIC-REGRESSION MODEL

Logistic regression was used to predict the probability of 

exceeding the chronic criterion for selenium in surface water at 

a specific site. Nolan and Clark (1997) discussed the development and application of this predictive model.

Logistic regression differs from classical multivariate statistical methods in that the modeled response is the probability of 

being in a category, rather than the observed quantity of a 

response variable (Helsel and Hirsch, 1992). Logistic-regression models were validated by using a simulated data set of 

“unknowns.” Composite NIWQP data were partitioned into 

two halves and a logistic-regression model developed by using 
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 81

half the data set. The resulting model parameters were used with 

the other (simulated unknown) half of the data set to calculate 

theoretical probabilities of exceeding the chronic criterion for 

selenium in surface water. Results of this calculation were compared to observed probabilities.

The types of sites used in the logistic regression were 

streams, lakes, and surface drains; reference sites and groundwater sites were excluded. River sites on large (main-stem) 

streams originating outside an investigation area were included 

in two of the six models tested. The other four models included 

only small streams, lakes, and surface drains where local irrigation practices and geologic sources were assumed to be the primary influence on water quality. Data from the Vermejo Project 

area (Z) reconnaissance investigation and the Sun River area (X) 

detailed investigation were excluded because they had not been 

compiled regionally when the regression models were being 

developed and therefore lacked ancillary information necessary 

for the analysis.

The median selenium and total dissolved-solids (TDS) concentrations from each sampling site were calculated. Censored 

values for selenium were converted to one-half the detection 

limit (0.5 μg/L) before determining sampling-site median concentrations. Use of the median concentration at a sampling site 

reduced the influence of sites that were sampled intensively. 

Additionally, the median concentrations for selenium and TDS 

were log transformed to improve model performance.

Spearman correlation coefficients were computed to screen 

selenium concentrations, geologic source, TDS, and EI for possible use in logistic-regression models. TDS and EI were 

included in the correlation analysis as indicators of evaporative 

effects in semiarid to arid climates typical of the Western United 

States. The Spearman correlation coefficient for selenium and 

TDS from the local surface-water sites (r = 0.45; table 24) indicated that selenium concentrations in water samples from 

streams, lakes, and surface drains positively correlated with 

salinity. Similarly, Fujii and others (1988) and Tanji and Valoppi 

(1989) showed a positive relation between selenium and salinity 

in shallow ground water of the San Joaquin Valley in California.

The presence or absence of Upper Cretaceous marine sedimentary deposits had a significant effect on surface-water selenium concentration (fig. 21). A binary geologic variable (BGV) 

was created indicating the presence (BGV = 1) or absence (BGV

= 0) of Upper Cretaceous marine sedimentary deposits composing bedrock in irrigated areas. The Spearman correlation coefficient for surface-water selenium concentration and the BGV 

was moderately high (r = 0.64; table 24). Source geology was 

represented in the regression by the BGV.

Spearman correlation coefficients (table 24) were greatest for 

BGV (r = 0.64) and TDS (r = 0.45). The positive correlations 

indicate increasing surface-water selenium concentration in the 

presence of Cretaceous sedimentary rocks and with increasing

TDS concentration. EI and selenium correlated negatively (r = 

-0.33), however. EI is a poor predictor of the selenium potential 

without a significant local source of selenium in an area. The 

EI correlation coefficient likely shows the influence of samples 

from the Salton Sea area (U), which is a closed lake in a warm, 

dry climate having a high evaporation potential. Although 

imported water used for irrigation contains some selenium, no 

significant local geologic source of selenium is present in the 

area. At 8 μg/L, the 75th-percentile selenium concentration of 

surface-water samples from the Salton Sea area was comparatively low. By comparison, the 75th-percentile selenium concentration in samples from the middle Green River Basin (N), 

an area having a significant, local geologic source of selenium, 

was 73 μg/L. TDS concentration is a better indicator of evaporative effects on selenium concentrations than EI is at individual sampling locations. TDS concentration indicates how 

evapoconcentrated a sample actually is and reflects local and 

temporal conditions, whereas EI indicates only the potential for 

evapoconcentration in a region. Finally, because TDS and EI 

correlated positively (r = 0.20; p = .0001), use of both in a 

regression model could result in multicolinearity problems. For 

modeling purposes, TDS concentration alone was assumed to 

contain sufficient climatic information to predict the selenium 

potential of surface water in semiarid to arid settings.

The fundamental assumption of logistic regression is that the 

natural logarithm of the odds ratio (probability of being in a 

response category) is linearly related to the explanatory variables (Afifi and Clark, 1996). Logistic-regression model-fitting 

criteria used in this study included the Akaike Information Criterion, the percentage correct responses, model sensitivity, and 

the partial-likelihood ratio. The Akaike Information Criterion 

measures model error and includes a penalty for too many variables (Helsel and Hirsch, 1992). The smaller the Akaike Information Criterion, the better the model. The percentage correct 

responses and model sensitivity are based on comparison of 

observed selenium levels and selenium responses predicted by 

the logistic-regression model. An exceedance is defined herein 

as a surface-water selenium concentration greater than the 

USEPA chronic criterion of 5 μg/L. The proportion of correct 

responses is the sum of the number of observed exceedances 

TABLE 24. Matrix of Spearman correlation coefficients (r) for

variables considered in logistic-regression models

[Based on data from local surface-water sites (excluding mainstem and reference 

sites) and on medians of log-transformed data from sampling sites]

Selenium

Total

dissolved

solids

Evaporation

index

Binary

geologic

variable

Selenium 1.00 0.45 −0.33 0.64

Total dissolved solids 0.45 1.00 0.20 0.33

Evaporation index −0.33 0.20 1.00 −0.31

Binary geologic variable 0.64 0.33 −0.31 1.00
82 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

correctly predicted by the model as exceedances and the number 

of observed nonexceedances correctly predicted as nonexceedances divided by the sum of observed exceedances and nonexceedances. Model sensitivity is the number of observed 

exceedances correctly predicted as exceedances divided by the 

number of observed exceedances. Higher values of these two 

criteria indicate better models.

The partial-likelihood ratio is used to compare nested models 

to determine the significance of adding one or more new variables to a model (Helsel and Hirsch, 1992). A nested model 

contains all explanatory variables in the original model plus one 

or more additional explanatory variables. Nonnested models 

have the same number of explanatory variables. If the partiallikelihood ratio is greater than the value of a chi-square distribution having degrees of freedom equal to the number of additional variables in the new model, then the more complex model 

is significantly improved over the original.

Logistic-regression models were screened for different data 

sets: local surface-water sites, all seasons (327 sites); local and 

main-stem surface-water sites, all seasons (379 sites); and local 

sites, spring and summer only (276 sites). Reference sites were 

excluded from the logistic-regression models.

Nested logistic-regression models developed by using data 

from local surface-water sites were compared. In nested models, a single new variable is added at each step (table 25). Performance criteria for model 2 indicated that significant 

improvement resulted from addition of the BGV. The percentage of correct responses improved from 67.0 to 84.4, and model 

sensitivity improved from 29.4 to 80.7 percent, compared to 

model 1, which had only TDS as an explanatory variable for the 

local surface-water model. Positive regression coefficients for 

both explanatory variables indicated that selenium concentration increased with increasing TDS concentration and with the 

presence of Upper Cretaceous marine sedimentary deposits. 

Additionally, the model had a partial-likelihood ratio of 111.09, 

significantly greater than the theoretical chi-square value of 

3.841 [significance level (a) = 0.05]. Similar improvement 

occurred between models 3 and 4, which included local and 

main-stem sampling sites, and between seasonal local models 5 

and 6.

The all-season local model using variables TDS and BGV 

(model 2 in table 25) was considered optimal on the basis of 

model-fitting criteria. Model 2 was more accurate than its seasonal counterpart (model 6) and had greater sensitivity than the 

version that included main-stem sites (model 4). However, 

model 4, which was just as accurate and nearly as sensitive as 

model 2, reliably predicted the likelihood of selenium contamination of large (main-stem) and small (local) streams, regardless of season, and was considered more robust because it had 

wider applicability.

The following equation for predicting the probability of 

exceeding the USEPA chronic criterion was produced by logistic regression (Afifi and Clark, 1996):

p = elogit(p) : (1 + elogit(p)) , (2) 

where p is the probability of exceeding the USEPA chronic 

criterion; and

 e is the base of the natural logarithms having the 

approximate value of 2.71828.

Logit(p) is calculated by substituting the regression 

coefficients (table 25) into the following equation:

logit(p) = + [ 1 X log10(TDS)] + [ 2 X (BGV)] , (3) 

where , 1 and 2 are regression coefficients;

TDS is total dissolved solids, in milligrams per liter; and

BGV is the binary geologic variable, which has a value of 

0 or 1.

For model 2, if Cretaceous sedimentary rocks are present, 

equation 3 simplifies to equation 4 : 

logit(p) = 1.6629 X log10(TDS) - 4.184 . (4) 

If Cretaceous sedimentary rocks are not present, equation 3 

simplifies to equation 5: 

logit(p) = 1.6629 X log10(TDS) - 7.2647 . (5) 

Logistic-regression results for model 2 are shown in figure 34, 

from which the probability that selenium concentrations 

exceed the chronic criterion for selenium may be estimated. 

The two following examples show how the regression equations are used to estimate probability.

For a site in an area where Upper Cretaceous marine sedimentary deposits form the bedrock and the TDS is 1,000 mg/L, 

the probability that the selenium concentration exceeds the 

USEPA chronic criterion is calculated from equations 2 and 4 

(based on model 2). In this case, log10(TDS) = 3.000 and from 

equation 4 logit(p) = 0.805. By substituting this value for 

logit(p) into equation 2, probability can be calculated: p = 

0.691. This value indicates an approximately 69-percent probability that the selenium concentration in the water exceeds 5 

μg/L.

Given the same TDS (1,000 mg/L) in an area where Upper 

Cretaceous marine sedimentary rocks do not form the bedrock, 

the probability that the selenium concentration exceeds the 

USEPA chronic criterion is calculated from equations 2 and 5. 

As in the first case, log10(TDS) = 3.000, but in this second case, 

logit(p) = -2.276. By substituting this value for logit(p) into 

equation 2, probability can be calculated: p = 0.093, or about 9 

percent.

α β β

α β β
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 83

Logistic-regression models were validated by using a data set 

of simulated “unknowns” created by partitioning the local surface-water data set into halves. Model parameters developed by 

using half of the data set were used with the other (simulatedunknowns) half of the data to compute theoretical probabilities 

of exceeding the USEPA chronic criterion and then were compared to observed probabilities. The percentage of correct 

responses decreased only slightly, from 87.0 to 84.7 percent, for 

the simulated-unknowns data set compared to the other half of 

the data set; also, model sensitivity increased slightly, from 

78.8 to 80.3 percent. Because the model performed well when 

using the simulated-unknowns data set, logistic regression 

seems capable of predicting the probability selenium concentrations will exceed 5 μg/L in unsampled irrigation-drainage 

areas having a semiarid to arid climate.

TABLE 25. Results of logistic-regression modeling of selenium in surface-water samples from National

Irrigation Water Quality Program study areas

[Based on medians of log-transformed selenium and total-dissolved-solids data from local and mainstem sampling sites (excluding reference 

sites). Symbol: —, not applicable]

Model

number

Explanatory

variables

Number of

observations1

1 Median selenium concentrations at sampling sites.

Regression coefficients Partiallikelihood

ratio2

2 χ2

0.95, 1= 3.841 

Correct

responses

(percent)

Model

sensitivity3

(percent)

3 The number of observed exceedances correctly predicted as exceedances divided by the number of observed exceedances.

α β1 β2

Local sites, all seasons

1 Total dissolved solids 327 −6.9510 2.0345 0 — 67.0 29.4

2 Total dissolved solids and

binary geologic variable

327 −7.2647 1.6629 3.0807 111.093 84.4 80.7

Local and main-stem sites, all seasons

3 Total dissolved solids 379 −7.3544 2.1636 0 — 68.1 29.5

4 Total dissolved solids and

binary geologic variable

379 −8.4961 2.0492 2.7709 103.911 84.7 78.7

Local sites, spring and summer only

5 Total dissolved solids 276 −7.9041 2.3472 0 — 71.0 46.9

6 Total dissolved solids and

binary geologic variable

276 −8.8008 2.0962 3.0749 90.032 83.7 83.7

FIGURE 34. Relation between total dissolved-solids concentration and 

probability that selenium concentration exceeds 5 micrograms per liter (U.S. 

Environmental Protection Agency (1987) chronic criterion for selenium). 

Results based on model 2.

100 1,000 10,000 100,000 0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

95-percent 

confidence

limits

PROBABILITY OF SELENIUM CONCENTRATION IN SURFACE WATER

EXCEEDING 5 MICROGRAMS PER LITER

TOTAL DISSOLVED-SOLIDS CONCENTRATION,

IN MILLIGRAMS PER LITER

Cretaceous

Non-Cretaceous
84 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

A limitation of the model is that it does not consider components of TDS and their relation to the association of TDS and 

selenium. Saline waters high in sulfate likely are high in selenium because the two elements are chemically similar and selenium substitutes for sulfur in many sulfide minerals. The Upper 

Cretaceous Panoche Formation of California was thought to be 

a source of selenium because of its high salt content. Seeps from 

the Panoche Formation, however, produce a saline water high in 

chloride but low in selenium (Presser, 1994a).

PREDICTION USING MAJOR-ION CHEMISTRY

A fourth predictive model and management tool was developed to aid in the identification and prediction of seleniferous 

areas. The input to this model is major-ion concentrations in 

surface-water samples. The model uses geochemical modeling, 

pattern-recognition analysis, and classification modeling techniques to develop a predictive tool for identifying areas having 

surface water that may pose a selenium hazard. Background 

information on the geochemical and statistical tools used in the 

predictive model, model development, and model application 

(Naftz, 1996a,b) are summarized in the following sections. 

GEOCHEMICAL AND STATISTICAL TOOLS USED IN MODEL 

DEVELOPMENT

The geochemical tool used in model development was the 

computer program SNORM (Bodine and Jones, 1986); it was 

used in combination with the statistical tools of pattern-recognition analysis and classification modeling (Wold and Sjostrom, 

1977; Wold and others, 1984; Meglen and Sistko, 1985; 

Meglen, 1988, 1990, 1991; Conny and Meglen, 1990). In combination, these tools provide a method to predict whether surface-water samples might contain selenium concentrations that 

exceed 3 μg/L, the criterion used for protection of wildlife.

The computer program SNORM (Bodine and Jones, 1986) 

transforms a standard water analysis (major-ion concentrations) 

into the normative salt assemblage and corresponding simple-salt concentrations. For each water sample, the 12 different 

simple-salts concentrations calculated by using the SNORM 

program represent the minerals or salts that would remain after 

complete evaporation of a water sample. 

After the simple salts were calculated for each of the almost 

2,000 NIWQP water samples, statistical tools were applied to 

the resulting simple-salts data matrix. These tools were used to 

identify the hydrochemical facies characteristic of samples having concentrations of selenium exceeding the predetermined 

screening level of 3 μg/L. The software package Pirouette (Infometrix, 1992) was used for pattern-recognition analysis and 

classification modeling of the simple-salts data. To aid in data 

interpretation and classification modeling, the statistical methods principal-components analysis and soft independent modeling by class analogy were applied. 

CREATION OF CLASSIFICATION MODEL

Salt norms were computed by using SNORM for 1,962 samples from 23 NIWQP study areas. Not all chemical analyses in 

the NIWQP data base could be used because SNORM requires 

complete chemical analyses. Because not all of the NIWQP 

surface-water samples were analyzed for major ions, only 

selected samples from the cumulative NIWQP data base could 

be used; analyses of samples from 3 of the 26 study areas (B, 

K, and P) did not qualify for inclusion.

The first step in developing the classification model was to 

determine if a specific combination of amounts and types of 

simple salts were correlated with selenium concentrations that 

exceed 3 μg/L. Selenium concentrations of 3 μg/L appear to be 

a toxicity threshold that can produce adverse effects in semiaquatic wildlife in lentic environments.

Three principal components best explained the simple-salts 

data set. The principal-components scores for the 1,962 water 

samples were plotted in three dimensions to evaluate the occurrence of distinct data clusters that could indicate common 

geochemical processes controlling surface-water chemistry in 

NIWQP study areas. The scores grouped into three distinct 

clusters, called facies 1, 2, and 3. The boundaries drawn around 

the clusters of principal-components scores aid in the visualization of the data (fig. 35) and indicate possible commonalities in geochemical processes. 

The simple-salts association represented by facies 1 is most 

characteristic of water derived from the weathering of marine 

shales containing reduced and oxidized sulfur-bearing mineral 

phases. Because selenium commonly substitutes for sulphur in 

mineral structures, facies-1 water samples contain the most 

selenium, consistently exceeding 3 μg/L. The median selenium 

concentration for facies-1 samples is 10 μg/L compared to 

median values of 2 μg/L for facies 2 samples and less than 1 

μg/L for facies 3 samples. A bivariate plot comparing the percentage of facies-1 water samples from each study area to the 

percentage of water samples having selenium concentrations of 

at least 3 μg/L (fig. 36) was used to confirm that facies-1 water 

samples contain selenium concentrations greater than or equal 

to 3 μg/L. For the 23 study areas, the percentage of facies-1 

samples positively correlates with selenium concentrations of 

at least 3 μg/L (r

2

 = 0.77, n = 23; fig. 36). In the Sun River area 

(X), for example, approximately 55 percent of the surfacewater samples were classified as facies-1 and approximately 45 

percent of the samples contained 3 or more μg/L of selenium.

Pattern-recognition analysis of the simple-salts data calculated by the SNORM geochemical program indicated that selenium-producing areas could be identified on the basis of 

SNORM results. Soft independent modeling by class analogy 

(Wold and Sjostrom, 1977) was applied to the SNORM simple-salts data set (derived from NIWQP data) to construct a 

classification model that could be used to determine if sele-
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 85

nium concentrations in surface water in the area are likely to 

exceed 3 μg/L. The training data set used to construct and optimize the classification model was a slightly modified version of 

the data set used in the pattern-recognition analysis of the 

NIWQP simple-salts data. This modified training data included 

data from 1,755 of the 1,962 samples collected in 23 study 

areas. Class assignments were consistent with the facies classification used during the pattern-recognition analysis. A 95-percent probability was used to define class inclusion. Specific 

parameters were evaluated during application of the soft independent modeling by class analogy to the training data set to 

ensure an optimal model for classification of the test data set. 

Two principal components were retained in each of the three 

classes, and the homogeneity of each class was ensured by 

visual inspection of the principal-components scores by class. 

Acceptable class separation during classification modeling of 

the training set was indicated by the residuals between classes 

and total modeling power (Naftz, 1996a). 

APPLICATION OF CLASSIFICATION MODEL

The performance of the classification model was tested by 

using data from more than 2,000 samples of surface water from 

Wyoming and Utah compiled from the U.S. Geological Survey 

NWIS data base. Data from those two States were selected 

because of the variety of geologic settings represented and 

because of the documented presence of selenium in surface-water samples. The classification model was successful in 

identifying water samples having selenium concentrations of at 

least 3 μg/L; more than 75 percent of the samples from the test 

data set having such elevated selenium concentrations were 

classified in facies 1 and over 80 percent of the samples having 

selenium concentrations less than 3 μg/L were classified in 

facies 2 or 3. The demonstrated use of the classification model 

in differentiating samples that contained selenium from those 

that did not indicates that the model can be applied successfully 

to areas where selenium concentrations have not been determined.

FIGURE 35. Three-dimensional principal-components scores plot showing 

grouping of water samples from National Irrigation Water Quality Program study 

areas into three distinct clusters. Scores based on principal-components (PC1, 

PC2, and PC3) analysis of simple-salts concentrations. Distinct clusters are 

classified as facies 1, 2, and 3.

FIGURE 36. Relation between facies-1 water samples and water samples having 

selenium concentrations greater than or equal to 3 micrograms per liter, a 

guideline for protection of semi-aquatic wildlife (table 10). Letter identifies 

National Irrigation Water Quality Program study area (fig. 2).

0

0 10 40 60 80 100 20 30 50 70 90

100

90

80

70

60

50

40

30

20

10

L A W

R

V

T G

Q

J

Z

X

N

S

E

C

U

H Y

Y

F

M

I

D

O

PERCENTAGE OF WATER SAMPLES FROM STUDY AREA HAVING SELENIUM

 CONCENTRATIONS GREATER THAN OR EQUAL TO 3 MICROGRAMS PER LITER

PERCENTAGE OF FACIES-1 WATER SAMPLES FROM STUDY AREA 

r 2 = 0.7746 (n = 23)

EXPLANATION

Data point for National

 Irrigation Water Quality

 Program study area

 Letter indicates study area

PC 3

PC 1

PC 2

FACIES 1

FACIES 3

FACIES 2

EXPLANATION

Surface-water analysis
86 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

Numerous circumstances contribute to the lack of selenium 

data for water samples. Examples of areas where selenium data 

are inadequate and where use of the classification model could 

be applied successfully include the following circumstances: 

(1) Selenium data were not collected. (2) Selenium data were 

collected, but the analytical reliability is questionable. (3) Selenium data were collected, but the detection level is high.

RECOMMENDATIONS FOR USE OF PREDICTIVE TOOLS

The following discussion presents recommendations on the 

best ways to apply the predictive tools described in this report. 

The section describes advantages and disadvantages of each 

method and how they can be used alone or in combination with 

other methods to help managers identify specific areas where 

selenium may be a problem.

Geochemical methods using major-ion chemistry are suitable 

for areas of the world where the principal geologic sources of 

selenium have not been identified or for parts of the United 

States where seleniferous rocks other than Upper Cretaceous 

sedimentary rocks are the soil-parent material for irrigated 

lands. Watersheds where selenium may be a problem are identified by a geochemical and statistical analysis of the results of 

water analyses for which selenium data were not collected. The 

major advantage of the method is that it depends only on the 

chemical nature of selenium, and thus no knowledge of the local 

geology or hydrology is needed. The major disadvantage is the 

high level of technical knowledge required to use the geochemical and statistical programs required by this method.

The maps identifying areas susceptible to irrigation-induced 

contamination (figs. 31 and 32) are specific to the Western 

United States. Their principal advantage is that the risk of selenium contamination in broad geographic regions can be evaluated quickly without the need to collect new information. 

Furthermore, land-use overlays can be created to aid Federal, 

State, and local water managers to evaluate whether areas for 

which they are responsible are susceptible to selenium contamination. Seiler and others (1999) presented maps that show locations of irrigated agricultural lands, BOR project areas, and 

National Wildlife Refuges in relation to susceptible areas. Disadvantages of the maps include the small scale at which they 

were produced, the lack of consideration of some hydrologic 

factors, and the specificity to the Western United States.

Like the susceptibility maps, the decision tree (fig. 33) is specific to the Western United States; however, unlike the maps, it 

can be modified easily. The principal advantages of using the 

decision tree are that it considers more of the factors that determine whether irrigation might cause contamination and that it 

ranks the likelihood that an area is or has the potential to be contaminated. Also, by slightly modifying the questions, the 

approach can be used in areas where seleniferous rocks other 

than those of Cretaceous age are known to be the soil-parent 

material. Disadvantages are that the decision tree is more timeconsuming to use and using the decision tree requires more 

information about an area than is required for using the maps.

The logistic-regression method is most useful after a decision has been made that more sampling is warranted in an area. 

It is valuable in selecting sampling sites during an initial reconnaissance. After the applicability of the regression to a specific 

area has been determined, it can be used directly. Site-specific 

regressions also can be developed for use by wildlife managers; 

a site-specific regression could be used to manage a wetland 

unit on a real-time basis by obtaining dissolved-solids or specific-conductance values for it.

The most effective way to use the tools is in conjunction with 

each other. After preliminary identifications of susceptible 

areas are made using the maps, geochemical methods, or 

knowledge of the presence of seleniferous sedimentary deposits not of Cretaceous age, the decision tree can be used to further evaluate the risk of contamination. 

Because these tools provide a cost-effective way to identify 

areas where irrigation is likely to cause selenium contamination, resources can be applied more efficiently to investigate 

those areas at highest risk. These tools, however, do not eliminate the need for site-specific evaluations to determine whether 

selenium contamination is in fact occurring, and if it is occurring, to what extent. 

AVIAN-EGG RISK ASSESSMENT

ENVIRONMENTAL CONTAMINATION AND RISK 

ASSESSMENT

Water and sediment samples can be used to provide direct 

measures of environmental contamination and are essential for 

assessing ultimate sources of contamination. In the NIWQP 

study areas, selenium was the contaminant most frequently 

detected at elevated concentrations in surface water (table 11). 

Although the water and sediment data bases provide excellent 

documentation of contamination (or lack thereof), these environmental media provide only rudimentary measures of biotic 

risk. Risk is a function of exposure (Norton and others, 1992; 

Barnthouse, 1994), and the relation between levels of abiotic 

contamination and biotic exposure can be highly variable (Skorupa and Ohlendorf, 1991; Ohlendorf and others, 1993; Lemly, 

1997b; Van Derveer and Canton, 1997). Therefore, bioticexposure surveys are required for reliable toxicological-risk 

assessment (Keith, 1996).

The NIWQP biotic data base, summarized by taxa in table 9, 

provides an extensive basis for hazard assessment, the simplest 

form of risk assessment (Suter, 1993). Hazard assessment is 

categorical and consists primarily of comparing exposure data 

to hazard threshold points to determine whether a potential for 

hazard is or is not indicated (Lemly, 1996b). Reviews of toxic 
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 87

threshold points for selenium by Heinz (1996) and Lemly 

(1996c) provided relatively rigorous criteria for hazard assessment. Ideally such assessment is based on a broad array of environmental and biotic media; a protocol based on that principle 

was proposed by Lemly (1995, 1996a). Lemly’s protocol uses a 

quantitative rating system, but ultimately it is a categorical hazard-assessment procedure. A high rating strongly indicates that 

a site should be categorized as hazardous, whereas a low score 

strongly indicates that a site should be categorized as safe. 

Lemly’s protocol for rating the hazard posed at any given study 

site is a useful screening technique, but the ratings cannot be 

translated into direct estimates of toxic response.

The risk assessment presented here does not use the hazardassessment approach for two reasons: First, most of the original 

NIWQP reconnaissance reports already have presented basic 

categorical assessments of hazard. Second, the biotic data base 

does not provide a consistent basis for applying a standard 

approach such as Lemly’s protocol. Effort applied to sampling 

aquatic invertebrates, fish, and birds—the primary biotic media 

for Lemly’s protocol—was uneven across NIWQP studies 

(table 9). Further, Lemly specifically identifies fish eggs as an 

ideal medium for hazard assessment, yet, eggs comprised only 

0.7 percent of NIWQP’s 2,410 fish tissue samples. By comparison, more than 50 percent of the 3,913 bird samples consisted 

of eggs.

More rigorous risk assessment, as opposed to simple hazard 

assessment, depends on quantitative techniques to estimate 

actual probabilities (or magnitudes) of toxic effect (for example, 

Bartell and others, 1992; Suter, 1993). The fundamental prerequisites for doing toxicological-risk assessment include (1) standardized and unbiased estimates of biotic exposure and (2) 

well-characterized exposure-response functions (Bartell and 

others, 1992; Burger and Gochfeld, 1992; Norton and others, 

1992; Barnthouse, 1994; Solomon, 1996). The biotic components of the NIWQP studies were focused predominantly on 

surveying contaminant exposure. No systematic attempt was 

made by NIWQP biologists to collect data according to a standardized measure of toxic response. 

Several studies included bioassay toxicity testing (Hallock 

and Hallock, 1993; Dileanis and others, 1996) or attempted to 

quantify precisely the rates of avian-embryo teratogenesis or 

mortality (Schroeder and others, 1988; See, Naftz, and others, 

1992; Stephens and others, 1992; Nimick and others, 1996), but 

none of these studies produced statistically rigorous and 

broadly applicable exposure-response functions. Thus, 

although the NIWQP biotic data base provides a reasonable 

exposure survey, NIWQP-specific toxic-response functions 

were not developed. Long-term studies in the San Joaquin Valley initiated during the San Joaquin Valley Drainage Program, 

however, did include a large and systematically collected body 

of response data that, when supplemented with the sparse 

NIWQP response data, provides statistically rigorous and 

broadly applicable exposure-response functions for avianembryonic exposure to selenium (Skorupa, 1998). Thus, 

NIWQP biotic-exposure data were combined with San Joaquin 

Valley Drainage Program toxic-response data to provide a 

quantitative toxicological-risk assessment consistent with the 

National Research Council (1993) standard of “ * * * a probabilistic statement of the ‘outcome’ associated with an ecological receptor being exposed to some form of stress * * * ”.

CHOICE OF RISK METRIC

Avian-embryonic exposure to selenium and response to that 

exposure, in the forms of teratogenesis and embryo viability, 

were the chosen risk metrics. Although this choice was dictated 

by the general lack of well-characterized toxic-response functions for other risk metrics, these risk metrics are also the most 

appropriate. In addition, NIWQP sampling plans intentionally 

focused on the collection of bird eggs (among other biota) 

because earlier studies at Kesterson had demonstrated the 

importance of evaluating embryo viability and the occurrence 

of terata.

Avian eggs (embryos) have several essential advantages over 

other biotic tissues for assessing risk. The sensitivity of 

amphibians and reptiles to aquatic selenium contamination 

simply is not known, thereby making those taxa inappropriate 

as a risk metric. Fish and birds are the two taxa of animals 

clearly most sensitive to aquatic selenium contamination 

(Ohlendorf, 1989; Lemly, 1996a,c; Skorupa, 1998); using less 

sensitive taxa, such as aquatic invertebrates or mammals, 

would be inappropriate as a risk metric. Of the various fish lifestages that could be sampled, embryonic and larval generally 

are considered to be the most sensitive; for birds, the embryonic life-stage is the most sensitive. Also, for both fish and 

birds, selenium-induced reproductive impairment is one of the 

most sensitive toxic endpoints (Heinz, 1996; Lemly, 1996c). 

As indicated in the previous section, the NIWQP biotic data 

base is nearly devoid of records for fish eggs but records for 

bird eggs are abundant.

The focus on bird eggs is not meant to imply that reproductive risks should be the sole source of concern for biotic effects 

in NIWQP study areas. Researchers concluded that for birds 

(Tully and Franke, 1935; Heinz and Fitzgerald, 1993), fish 

(Lemly, 1993a, 1996d), and mammals (Ghosh and others, 

1993), the sensitivity of adults to selenium poisoning greatly 

increases during the winter as compared to more thermoneutral 

seasons of the year, such as the breeding season. Furthermore, 

immunobiological effects of selenium exposure eventually 

may prove to be the most sensitive of all toxic endpoints 

(Whiteley, 1989; Fairbrother and Fowles, 1990; Schamber and 

others, 1995). Unfortunately, field data quantifying the direct 

toxicity of selenium for nonbreeding fish and wildlife are rare. 

Indirect, immunobiologically mediated toxicity has not been 

studied sufficiently to provide a well-developed basis for either 

simple hazard assessment or quantitative risk assessment. 
88 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

The other candidate risk metrics for the NIWQP biotic data 

base were whole-body analyses for fish and liver analyses for 

birds. Avian hepatotoxicity generally is considered inferior to 

avian embryotoxicity as a reliable toxic endpoint for selenosis 

(Heinz, 1996). Moller (1996) suggested that selenium may 

become elevated in bird livers either as an adaptive response to 

oxidative stress or by excessive dietary exposure to selenium. In 

the former instance, high concentrations of selenium in liver tissue are not indicators of toxicity, and in the latter instance they 

are. No comparable circumstances for producing “false positives” are known for selenium concentrations in avian eggs.

Avian eggs, as a risk metric, have much less potential than 

avian livers or whole-body fish residues to be compromised by 

survivor bias because reproductive impairment occurs at levels 

of exposure to selenium much less than the levels required to 

cause hen mortality (Heinz, 1996). Eggs containing dead or live 

embryos are equally likely to be sampled by biologists if eggs 

are collected at random from complete clutches. In other words, 

neither biologists nor incubating adult birds distinguish 

between eggs containing live or dead embryos. In addition, the 

egg provides a standardized embryonic exposure environment, 

an easily quantifiable exposure unit, a uniform age of initial 

exposure, a relatively uniform duration of exposure for eggs at 

comparable stages of incubation, and a standardized season of 

exposure. Therefore, for many reasons, avian eggs are clearly 

the optimal risk metric for the NIWQP biotic data base.

REASONS FOR FOCUS ON SELENIUM

Comprehensive field and laboratory research completed to 

determine the cause(s) of avian reproductive impairment at 

Kesterson National Wildlife Refuge led to the conclusion that 

selenium poisoning alone was sufficient to explain the congenital deformities and reproductive failure of waterfowl at Kesterson National Wildlife Refuge (Ohlendorf, Hoffman, and 

others, 1986; Heinz and others, 1989; Ohlendorf, 1989; U.S. 

Fish and Wildlife Service, 1990). The evidence for selenium 

poisoning was so strong that Suter (1993) referred to the 

Kesterson case study as one of just a few “gold standards” for 

establishing causation in a retrospective ecological-risk assessment. Thus, selenium was the primary contaminant of concern 

at Kesterson, and the Kesterson case provided the impetus for 

the NIWQP. The NIWQP, however, was a broad regional survey that possibly could turn up additional contaminant problems associated with irrigation and applicable to specific 

localities.

Authors of the NIWQP reconnaissance reports identified 

nine contaminants of greatest concern, including arsenic, 

boron, cadmium, copper, mercury, molybdenum, selenium, 

zinc, and DDT (U.S. Department of the Interior, 1998). Background levels and toxic threshold levels for avian eggs were 

compiled for arsenic, boron, cadmium, copper, mercury, 

molybdenum, selenium, zinc, and DDE (table 26). Selenium

TABLE 26. Background levels and toxic thresholds for contaminants in avian eggs

[Abbreviation and symbol: DW, Dry weight; —, not known.]

Constituent

1 2 3 4 5 6 7 8 9 10

1 T.J. Kubiak (U.S. Fish and Wildlife Service, written commun., 1991), based on poultry data reported by 

Romanoff and Romanoff (1949).

2 Based on poultry data (Evans and others, 1953; Moore and others, 1954).

3 Based on sodium arsenate data for mallards (Stanley and others, 1994).

4 Unpublished field data for ducks and shorebirds sampled at reference sites in San Joaquin River Basin 

(R.L. Hothem and D.P. Welsh, U.S. Fish and Wildlife Service, written commun., 1990) and Tulare Basin (U.S. 

Fish and Wildlife Service, unpublished data) in California.

5 Based on data for mallards (Stanley and others, 1996).

6 Based on poultry data (Leach and others, 1979).

7 Based on poultry data (Puls, 1988) and waterfowl data (Haseltine and others, 1980).

8 Based on poultry data (Puls, 1988).

9 Based on data for mallards (Heinz, 1979). 

10 Based on poultry data (Lepore and Miller, 1965).

Background

level

(μg/g DW)

Toxic threshold

Dose-response

threshold

11 12 13 14

11 Based on waterbird data (Skorupa and Ohlendorf, 1991).

12 Based on black-necked stilts field data (Skorupa, 1998).

13 Concentrations are in μg/g wet weight. 

14 Based on avian data from Blus (1996).

No observed

effect level

Lowest

observed effect

level

Arsenic:

Organic

Inorganic

1

0.25

—

2

1.3

31.8

2

2.8

33.6

—

—

Boron 1,41.0 522 538 —

Cadmium 60.15 — — —

Copper 75.5 — — —

Mercury 80.1 — — 93.0

Molybdenum 10.25 1023 33 —

Selenium 111.9 — — 126.0

Zinc 1,850 — — —

DDE13 140.3 — — 143.7
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 89

was overwhelmingly the most often identified constituent of 

concern, being named in 15 of the 26 reconnaissance reports. 

Other constituents of concern were identified in fewer reconnaissance reports (generally six to nine reports). The tabulated 

toxic-exceedance rates for each contaminant of greatest concern 

in each study area clearly show that selenium is the most prevalent of the hazardous constituents associated with irrigation in 

the Western United States, as sampled by the NIWQP (table 

27). For all NIWQP contaminants of greatest concern, except 

selenium and DDE, the median toxic exceedance rate for the 

sampled study areas (n = 23) was 0. For the NIWQP contaminants of greatest concern, except mercury, selenium, and DDE, 

the maximum rates of toxic exceedance across the sampled 

study areas were less than 2.2 percent. Thus, for avian eggs, the 

only contaminants potentially warranting regional-level risk 

assessment are mercury, selenium, and DDE.

Although 7 of the 26 NIWQP study areas yielded one or 

more avian eggs exceeding the hazard threshold for mercury (3 

μg/g), the highest exceedance rate was only 10.6 percent. The 

maximum individual value observed in any study area, 8.5 μg/g 

mercury, was less than three times the hazard threshold. Therefore, the mercury profile is best described as a few eggs, only 

slightly exceeding the hazard threshold, in about one-fourth of 

the study areas sampled. By contrast, 15 study areas yielded 

one or more avian eggs exceeding the hazard threshold for selenium (6 μg/g), and three areas had exceedance rates greater 

than 80 percent. The maximum individual value of 160 μg/g 

selenium was more than 25 times the hazard threshold. Clearly, 

selenium was a much more dominant contaminant of concern 

than mercury in the NIWQP study areas (table 27). 

TABLE 27. Toxic exposures of contaminants to avian eggs from National Irrigation Water Quality Program study areas 

[Symbol: —, not analyzed.]

Study area Toxic exceedance1

(percent)

1 Relative to no observed effect level (table 26). For constituents without well established toxicity guidelines (cadmium, copper, and zinc), exceedance was relative to ten times the 

normal background level.

Identifier2

2 Used in figure 2 to show locations of study areas.

Name Arsenic Boron Cadmium Copper Mercury Molybdenum Selenium Zinc DDE

A American Falls Reservoir, Idaho 0 — — 0 7.7 — 0 0 23.1

B Angostura Reclamation Unit, South Dakota 0 0 0 0 0 0 0 0 0

C Belle Fourche Reclamation Project, South Dakota 0 0 0 0 0 0 40.0 0 0

D Columbia River Basin, Washington 0 — — — 0 — 0 0 3.4

E Dolores–Ute Mountain area, Colorado 0 0 0 0 0 0 13.3 0 0

F Gunnison River Basin–Grand Valley Project, 

Colorado 0 0 0 0

0

0 84.6 0 12.5

G Humboldt River area, Nevada 0 0 0 0 0 0 0 0 —

H Kendrick Reclamation Project, Wyoming 0 0 .3 .3 .3 0 89.7 0 4.0

I Klamath Basin Refuge Complex, California–Oregon 0 0 — 0 0 0 0 0 12.7

J Lower Colorado River valley, California–Arizona — — — — — — — — —

K Lower Rio Grande valley, Texas — — — — — — — — —

L Malheur National Wildlife Refuge, Oregon 2.1 0 0 0 10.6 0 0 0 30.0

M Middle Arkansas River Basin, Colorado–Kansas 0 0 0 0 0 0 66.7 0 0

N Middle Green River Basin, Utah .6 0 0 .3 .3 0 48.0 0 0

O Middle Rio Grande, New Mexico 0 0 0 0 0 0 0 0 0

P Milk River Basin, Montana 0 0 0 0 0 — 0 0 0

Q Owyhee–Vale Reclamation Project areas, Oregon–

Idaho 0 0 0 0 5.3 0 0 0 0

R Pine River area, Colorado 0 0 0 0 0 — 21.7 0 0

S Riverton Reclamation Project, Wyoming 0 0 — 0 0 0 46.7 0 0

T Sacramento Refuge Complex, California 0 — — 0 0 — 5.0 0 10.5

U Salton Sea area, California 0 0 0 0 0 0 11.7 0 38.8

V San Juan River area, New Mexico 0 0 0 0 0 0 7.7 0 —

W Stillwater Wildlife Management Area, Nevada 1.1 0 0 0 4.3 0 40.2 0 —

X Sun River area, Montana .4 .7 0 0 .7 0 30.3 0 8.3

Y Tulare Lake Bed area, California 0 0 0 0 0 0 91.7 0 0

Z Vermejo Project area, New Mexico — — — — — — — — —

Median 0 0 0 0 0 0 11.7 0 3.4

Maximum 2.1 .7 .3 .3 10.6 0 91.7 0 38.8
90 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

Although DDE appears to pose a greater hazard than mercury 

in the NIWQP study areas, it is nonetheless still of much less 

regional concern than is selenium. A separate risk assessment 

for DDE is beyond the scope of this report. Additionally, 

because of DDE’s high degree of persistence in avian tissues 

(Blus, 1996), it is a less appropriate focal contaminant for risk 

analysis than is selenium. Hen exposure to DDE as reflected in 

the data for birds’ eggs may have occurred outside the NIWQP 

study areas, whereas selenium exposure as documented in the 

egg data set is unlikely to have occurred outside the study areas 

(Heinz, 1993, 1996).

In summary, the reasons for focusing on selenium in this risk 

assessment include the well-documented causative linkage 

between selenium and the avian reproductive impairment at 

Kesterson National Wildlife Refuge, the widespread occurrence 

of waterborne selenium contamination in the NIWQP study 

areas (table 15), the dominant position of selenium as a contaminant of concern in eggs from NIWQP study areas (table 26), 

and selenium’s differential propensity (compared to DDE) to 

reflect localized contaminant conditions.

TERATOGENIC-RISK ASSESSMENT

Selenium is a well-documented causative agent for avian teratogenesis (Franke and Tully, 1935, 1936; Gruenwald, 1958; 

Hoffman and Heinz, 1988; Hoffman and others, 1988). Therefore, the incidence of deformed embryos is a response variable 

that can be used to assess biotic effects of environmental selenium exposure. However, to precisely define what is being 

counted as a teratogenic response is important when addressing 

the issue of avian teratogenesis. Measured incidences of teratogenesis depend on what types of abnormalities are included 

under the umbrella of terata, or malformations, or deformities. 

Results also depend on an investigator’s method of examining 

specimens for abnormalities. 

Herein, teratogenesis refers to the incidence of irreversible 

major structural deformities that are overtly obvious upon 

superficial external inspection of an avian embryo. Lemly 

(1993c, 1997a) presented comparable criteria for fish larvae. 

Teratogenic response as used here was described in such terms 

as “monstrosities” in early scientific literature (Franke and 

Tully, 1936; Franke and others, 1936). In practice in the 

NIWQP studies, major deformities of the eyes, bill, or limbs 

were almost the only criteria used for qualifying terata. Nonstructural abnormalities, even though externally visible, such as 

hydrocephaly, generalized edema, subcutaneous hemorrhaging, 

and cloudy eyes did not qualify as teratogenic responses for the 

assessments discussed here. Deformities of organs or other 

internal soft tissues also were not included. 

The intentionally conservative definition of teratogenesis 

used herein includes only the most pronounced and easily identified types of terata. This is done for two reasons: First, by these 

criteria, observers having highly variable levels of experience 

can examine sets of avian embryos and produce highly consistent and comparable assessments of teratogenic response. 

These selenium-caused “monstrosities” are not normally a 

matter of observer interpretation; they obviously exist (fig. 37) 

or do not. Second, only counting pronounced forms of terata 

eliminates the more general types of embryonic abnormalities 

that have multiple potential sources of causation (O’Toole and 

Raisbeck, 1998). Eliminating such abnormalities from the definition of teratogenic response was expected to reduce at least 

some of the background noise that could obscure the seleniuminduced teratogenic-response curve. As a combined suite, 

alternative causes are few for the severe multiple, overt, structural deformities of the eyes, bill, and limbs that are typical of 

selenium poisoning (fig. 37; Franke and Tully, 1935; Ohlendorf, Hoffman, and others, 1986; Presser and Ohlendorf, 1987; 

Hoffman and Heinz, 1988; Hoffman and others, 1988; Ohlendorf and others, 1988; Howard, 1989; Ohlendorf, 1989; 

Bobker, 1993; Ohlendorf and Hothem (1995); Ohlendorf, 

1996; O’Toole and Raisbeck, 1998). The spatulate narrowing 

of the upper bill (beak) and hypoplasia (reduction) of the lower 

bill in embryos (fig. 37) is reported to be a “distinctive feature” 

of selenium-induced teratogenesis among ducks (O’Toole and 

Raisbeck, 1998). Among shorebirds, such as stilts and avocets, 

characteristic features of selenium-induced teratogenesis 

include absence of eyes, malformation of the bill and limbs, 

and in the most severe cases, exencephaly.

The characteristics of terata that might be caused by other 

common contaminants in NIWQP areas are distinct from those 

caused by selenium. In mallards, external applications of 1 mg 

of methylmercury per egg caused minor skeletal aberrations 

and incomplete ossification (Hoffman and Moore, 1979). 

Higher doses caused micromelia (abnormally small and malformed extremities), gastroschisis (congenital opening of the 

ventral abdominal wall), and eye and brain defects. No terata 

were detected in chicks or dead embryos following separate 

injections of DDT, DDD, and DDE into chicken eggs, although 

typical DDT neurological poisoning symptoms were observed 

(Abou-Donia and Menzel, 1968). Detailed descriptions of 

avian terata induced by maternally delivered arsenic have not 

yet been reported in the scientific literature because even unrealistically high dietary dosing of hens has not induced sufficient rates of teratogenesis for detailed characterization. At the 

concentrations measured in eggs at NIWQP sites, arsenic is 

functionally nonteratogenic (Evans and others, 1953; Moore 

and others, 1954; Stanley and others, 1994).

Teratogenesis, especially when conservatively defined, is a 

relatively insensitive response variable for selenium poisoning. 

For example, Stanley and others (1996) studied the reproductive performance of captive game-farm mallards fed seleniumtreated diets and found a 34-percent depression in egg hatchability even though the dietary selenium treatment (7 mg/kg as
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 91

FIGURE 37. Typical selenium-induced terata of avian embryos.

A

B

C

Gadwall (Kesterson Reservoir, California) with

arrested development of lower bill, spoonbill

narrowing of upper bill, and missing eyes

Northern Pintail (Tulare Lake Bed area, California) with

arrested development of lower bill, spoonbill narrowing

of upper bill, and missing eyes

D

E

Mallard (Grand Valley Project, Colorado) with

arrested development of lower bill, missing

right leg, and intestinal tract outside body

Black-necked stilt (Kesterson Reservoir,

California) with missing eyes, malformed bill,

limb deformities and exencephaly

Redhead (middle Green River Basin, Utah) with

spoonbill narrowing of upper bill

F American Avocet (Kendrick Reclamation Project,

Wyoming) with club foot and malformed bill
92 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

selenomethionine) was not sufficient to induce embryo teratogenesis. Although estimates of teratogenesis thresholds are conservative measures of threshold points for reproductive effects, 

teratogenesis as a response variable is much easier to measure 

in the field than egg hatchability. Teratogenesis assessments can 

be made by collecting eggs during one visit, assessing the status 

of the embryos inside the collected eggs, and chemically analyzing the egg contents (including the embryo). To relate egg 

hatchability to egg chemistry requires the marking of nests, collection of a sample egg from each nest, and multiple return visits to find each nest and monitor the fate of the sibling eggs that 

were left in the nest at the time the sample eggs were collected. 

Even then, only the data from nests incubated to full term are 

useful. Given the typical rates of nest predation, nest flooding, 

and nest abandonment (Ohlendorf and others, 1989; Hothem 

and Welsh, 1994b), an observer may have to monitor a ratio of 

5 to 10 nests for every usable full-term nest record. Therefore, 

field assessments of egg viability (hatchability) as a function of 

egg chemistry are much less frequently attempted than teratogenesis assessments.

MULTIELEMENT TERATOGENIC-RESPONSE DATA

Subsurface irrigation-drainage water commonly contains elevated concentrations of a diverse assemblage of potential inorganic contaminants. For example, San Joaquin Valley Drainage 

Program investigators designated 10 inorganic constituents to 

be of primary or probable concern and an additional 10 inorganic constituents to be of possible concern (San Joaquin Valley 

Drainage Program, 1990). Many of the other NIWQP study 

areas also were contaminated by multiple trace elements. Some 

trace elements, including cadmium and uranium, are poorly 

transferred to avian eggs regardless of a hen’s dietary exposure 

(Haseltine and Sileo, 1983; Robinson and others, 1984; Ohlendorf, 1993) and therefore could not cause an embryonic 

response. Other constituents, such as boron, are readily 

biotransferred to avian eggs (Ohlendorf, Hoffman, and others, 

1986; Smith and Anders, 1989; Ohlendorf and others, 1993; 

Setmire and others, 1993; Stanley and others, 1996). Therefore, 

even though captive-feeding trials demonstrated that selenium 

alone was sufficient to explain the avian teratogenesis observed 

at Kesterson (Heinz and others, 1987, 1989), at least some 

uncertainty has remained as to whether constituents other than 

selenium also were playing a role (in addition to or in conjunction with selenium) in causing the avian teratogenesis associated with irrigation-induced aquatic contamination. This 

uncertainty has persisted partly because a direct multielement 

evaluation of field data has never been presented.

Such a direct multielement evaluation can only be completed 

on sets of avian eggs that meet strict criteria for random sampling in the field, and random selection for chemical analysis. 

(In a typical study, more eggs are sampled in the field than are 

eventually chemically analyzed.) In addition, the eggs should 

come from sites having reasonably high rates of embryo teratogenesis, and they should all have assessable embryos (eggs in 

a stage of development sufficiently advanced that the occurrence of teratogenesis can be determined readily). The eggs 

should be analyzed for a diverse spectrum of inorganic constituents. Finally, to avoid having chemical associations masked 

by potentially large intersite variability in rates of teratogenesis, a direct multielement evaluation should be restricted to 

samples from different sites having comparably high rates of 

teratogenesis. Therefore, the data set selected for multielement 

evaluation consisted of samples collected from two sites within 

the Tulare Lake Bed area (Y) that met all these conditions. 

During 10 years of study in the Tulare Lake Bed area (Y) and 

surrounding parts of the San Joaquin Valley in California, more 

than 1,900 avian eggs were collected and chemically analyzed 

(U.S. Fish and Wildlife Service, unpublished data). A subset of 

32 black-necked stilt eggs (22 normal and 10 deformed 

embryos) from two sampling sites met the criteria for a direct 

multielement evaluation of field data for chemical associations 

with embryo teratogenesis. This subset of stilt eggs is referred 

to herein as the Tulare multielement-response sample.

Eggs in the Tulare multielement-response sample were analyzed for 17 inorganic constituents by inductively coupled 

plasma-emission spectroscopy. The constituents analyzed 

included aluminum, barium, beryllium, boron, cadmium, chromium, copper, iron, lead, magnesium, manganese, mercury, 

molybdenum, nickel, selenium, strontium, vanadium, and zinc. 

Only for barium, boron, copper, iron, magnesium, manganese, 

mercury, selenium, strontium, and zinc were the levels of contamination sufficiently high or the analytical limits of detection 

sufficiently low for at least 70 percent of the samples to contain 

quantifiable amounts of the chemical constituent. Univariate 

comparisons of the chemical profiles for eggs containing normal and deformed embryos are presented for those constituents 

in figure 38. The few values that were below detection limits 

were estimated to be one-half of the applicable detection limit.

Selenium shows the strongest separation of profiles (fig. 

38H); as expected, eggs exhibiting deformity contained significantly greater selenium (Student’s t = 3.64; p = 0.001). Eggs 

exhibiting deformities also contained significantly greater 

boron (fig. 38B), and magnesium (fig. 38E) concentrations; 

however, the concentrations of these constituents significantly 

covary with selenium concentrations (p < 0.1; table 28).

Because elements that significantly covary with boron and 

magnesium but not with selenium (such as barium and zinc) 

did not show significant differences in concentration profiles 

for normal eggs and eggs exhibiting deformity (figs. 38A, J), it 

seems likely that the significant univariate-profile differences 

observed for boron and magnesium are artifacts of their covariation with selenium. That boron is not a causative agent for 

avian teratogenesis and does not interact with selenium has 

been established empirically (Smith and Anders, 1989; Stanley 

and others, 1996). Likewise, direct or interactive teratogenic 

effects are not expected from magnesium (Birch, 1988).
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 93

FIGURE 38. Comparison of trace-element concentrations in stilt eggs containing normal and deformed embryos. 

t, student’s t statistic; p, probability of the two populations being the same based on Student’s t test. TISSUE CONCENTRATION, IN MICROGRAMS PER GRAM, DRY WEIGHT

EMBRYO STATUS

EMBRYO STATUS

NUMBER OF ANALYSES

Upper decile (90th percentile)

Upper quartile (75th percentile)

Lower quartile (25th percentile)

Median

Lower decile (10th percentile)

Point outside lower decile

EXPLANATION

Point outside upper decile

22 10

0.1

1

10

t = 0.32

p = 0.75

A Barium

22 10

100

1,000

t = 2.75

p = 0.02

E Magnesium

22 10

1

10

t = 0.45

p = 0.66

F Manganese

19 9

0.1

1

10

t = 0.45

p = 0.65

G Mercury

10

100

200 22 10

t = 3.64

p = 0.001

NORMAL DEFORMED

NORMAL DEFORMED

H Selenium

10

100

200 22 10

t = 1.59

p = 0.14

NORMAL DEFORMED

I Strontium

10

100 22 10

t = 1.02

p = 0.32

NORMAL DEFORMED

J Zinc

1

10

20 22 10

t = 2.21

p = 0.04

B Boron

22 10

1

10

t = 0.67

p = 0.51

C Copper

22 10

100

200

50

t = 0.56

p = 0.58

NORMAL DEFORMED

NORMAL DEFORMED NORMAL DEFORMED NORMAL DEFORMED

NORMAL DEFORMED

NORMAL DEFORMED

D Iron
94 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

Although eggs exhibiting deformity contained greater concentrations of strontium, the difference was not statistically significant (fig. 38I; p = 0.14). Even if the association between 

strontium and terata were significant, strontium would be an 

unlikely cause of the observed deformities. Although strontium 

is teratogenic when injected into poultry eggs, it is not teratogenic even at concentrations where 100 percent of the embryos are 

dying when eggs received the strontium through the maternal 

diet (Mraz and others, 1967). Like boron and magnesium, strontium also strongly covaried with selenium (table 28).

Exploratory factor analysis (Afifi and Clark, 1996) was 

applied to clarify the association of inorganic constituents in the 

eggs with each other and the presence of embryonic terata. 

Because all constituents are considered simultaneously in this 

analysis, the potential for site associations and chemical associations getting confounded is greatly reduced. Thus, compared 

to the univariate profiles (n = 32), data from more sites were 

available for analysis and a slightly larger subset (n = 83) of the 

Tulare multielement-response sample eggs could be used. The 

results of this analysis (fig. 39) clearly show that the association 

between selenium and terata is much tighter than the association 

between boron, magnesium, strontium, and terata. Arsenic and 

mercury were not analyzed for all of the eggs and therefore 

could not be included in the exploratory factor analysis which 

requires complete data sets.

RELEVANCE OF ARSENIC AND MERCURY

Arsenic and mercury are known agents of avian teratogenesis 

and both are known to interact with selenium (Stanley and others, 1994; Heinz and Hoffman, 1998). Although neither was 

included in the inductively coupled plasma-emission-spectroscopy scans of the Tulare multielement-response-sample eggs, 

both were analyzed by other methods in subsets of those sample 

eggs. For arsenic, a subset was analyzed by hydride-generation 

atomic-absorption spectrophotometry, and for mercury, by 

cold-vapor atomic-absorption spectrophotometry. (These sample subsets analyzed for arsenic and mercury overlapped only 

minimally with the subsets analyzed for other constituents by 

inductively coupled plasma-emission spectroscopy.)

Fewer than 1 percent of all eggs analyzed exceeded the 0.4-

μg/g detection limit for arsenic, and normal background is 

about 0.1 to 0.4 μg/g (Romanoff and Romanoff, 1949). Arsenic 

analyses consistently show that eggs having detectable concentrations of arsenic are extremely rare (Ohlendorf and others, 

1993). The maximum arsenic concentration recorded for the 

Tulare Lake Bed area (Y) was only 1.8 μg/g (Ohlendorf and 

others, 1993), an amount that would have a low probability of 

inducing teratogenesis directly and that, in combination with 

selenium, actually would suppress the incidence of teratogenesis (Stanley and others, 1994). For these reasons, teratogenic 

effects from embryonic exposure to arsenic are unlikely.

 

TABLE 28. Correlation matrix for Tulare Lake Basin multi-element-response sample

[All tabulated values are p-values or significance levels for each bivariate correlation; smaller absolute values indicate stronger correlation. Correlations 

are based on log-transformed data. Symbols: —, no significant correlation (p > 0.10); <, less than]

Variable Barium Boron Copper Iron Magnesium Manganese Selenium Strontium Zinc

Barium 0.000 −0.013 — — 0.043 0.004 — <0.001 —

Boron −0.013 0.000 — — — — 0.064 — —

Copper — — 0.000 0.003 — — — — 0.072

Iron — — 0.003 0.000 — — — — 0.099

Magnesium 0.043 — — — 0.000 — 0.009 <0.001 0.001

Manganese 0.004 — — — — 0.000 — 0.020 —

Selenium — 0.064 — — 0.009 — 0.000 0.003 —

Strontium <0.001 — — — <0.001 0.020 0.003 0.000 0.097

Zinc — — 0.072 0.099 0.001 — — 0.097 0.000

FIGURE 39. Exploratory factor-analysis loadings, showing association of 

trace elements in eggs and the presence of terata. Based on data for blacknecked stilt eggs from Tulare Lake Bed area in California (Schroeder and 

others, 1988; Ohlendorf and others, 1993; U.S. Fish and Wildlife Service, 

unpublished data).

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

Selenium

Terata

Boron

Strontium

Magnesium

Barium

Copper Zinc

Manganese

Iron

FACTOR LOADING 1

FACTOR LOADING 2

EXPLANATION

Factor loadings for the presence of deformed

 embryos and chemical constituents in tissue
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 95

Likewise, exposure to mercury in the egg was minimal 

(although detectable) in the Tulare Lake Bed area (Y) and in the 

upper San Joaquin Valley. Among 78 stilt eggs analyzed for 

mercury, the maximum observed concentration of 2.5 μg/g 

(U.S. Fish and Wildlife Service, unpublished data) is less than 

the known thresholds for direct or interactive teratogenic effects 

(3 μg/g; Heinz and Hoffman, 1998). A subset of 28 blacknecked stilt eggs that met the criteria for univariate comparison 

did not exhibit any significant difference in the mercury-concentration profiles for normal eggs versus eggs exhibiting deformities (fig. 38G). This, too, suggests that the possibility of 

teratogenic effects from embryonic exposure to mercury is 

unlikely. 

For 11 of the 26 NIWQP study areas, available stilt and 

avocet data was directly comparable to the stilt data from the 

Tulare Lake Bed area (Y) and the upper San Joaquin Valley. 

Mercury profiles for stilt and avocet eggs from these 11 

NIWQP study areas suggest that direct or interactive teratogenic effects of mercury are unlikely throughout the NIWQP 

study areas (fig. 40). Mercury was not associated with deformities in the Tulare Lake Bed area (Y; fig. 38G), and the great 

majority of the mercury concentrations from the NIWQP areas 

were less than the median mercury concentration in that area 

(fig. 40). Furthermore, only one sample from the 11 NIWQP 

study areas had a mercury concentration exceeding 3 μg/g, the 

known threshold for direct or interactive teratogenic effects. 

FIGURE 40. Comparison of mercury concentrations in stilt and avocet eggs from National Irrigation Water Quality Program 

(NIWQP) study areas to concentrations in stilt eggs from the Tulare Lake Bed area in California. Data for Tulare Lake Bed area 

are from Schroeder and others (1988) and U.S. Fish and Wildlife Service (unpublished data).

Tulare

(Y)

MERCURY CONCENTRATION, 

IN MICROGRAMS PER GRAM, DRY WEIGHT

NUMBER OF ANALYSES

NIWQP STUDY AREA

3 111 11 12 9 5 3 4 128 22 161 28

FHK LNOPQUVX

Effect level

Median for Tulare Lake Bed area

Upper decile (90th percentile)

Upper quartile (75th percentile)

Lower quartile (25th percentile)

Median

Lower decile (10th percentile)

Point outside lower decile

EXPLANATION

Point outside upper decile

10

1

0.1

0.01
96 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

In summary, teratogenic-response curves for avian-egg exposure to selenium in the San Joaquin Valley were expected to be 

free of significant confounding interaction effects and to be 

applicable to most, if not all, the other NIWQP study areas. Furthermore, examination of the multielement-response data conclusively reinforced focusing on selenium for risk assessment 

(fig. 38). Thus, not only in theory is selenium alone sufficient to 

explain the avian teratogenesis associated with irrigationinduced aquatic contamination (Hoffman and Heinz, 1988), 

selenium appears to be the only teratogenic agent operating in 

the NIWQP study areas.

TERATOGENIC-RESPONSE CURVES FOR SELENIUM 

EXPOSURE

One of the principal objectives of the San Joaquin Valley 

Drainage Program, through contracts with the USFWS Patuxent 

Wildlife Research Center, was to develop predictive criteria for 

avian selenosis (U.S. Fish and Wildlife Service, 1990). Therefore, a broad-scale program to collect response data for avian 

teratogenesis at selenium-effected and reference sites of the San 

Joaquin Valley was initiated in 1987 (Schroeder and others, 

1988; Skorupa and Ohlendorf, 1988, 1989, 1991; Ohlendorf 

and Skorupa, 1989; Moore and others, 1990; Robinson and others, 1997; Skorupa, 1998). Upon completion of the San Joaquin 

Valley Drainage Program in 1990, field sampling in the San 

Joaquin Valley continued under the sponsorship of the USFWS, 

California Department of Water Resources, BOR, and NIWQP. 

Sampling continued through the 1997 field season; the largest 

cumulative sampling effort was in the Tulare Lake Bed area (Y) 

in the lower San Joaquin Valley.

During 1972–85, about 25 shallow impoundments were constructed in the Tulare Lake Bed area to allow for evaporative 

disposal of water from subsurface irrigation drains. These facilities varied from large (>1,200-acre) multiple-celled systems 

(similar in design to Kesterson Reservoir) to small (<25-acre) 

single-celled ponds. The selenium content of water discharged 

to the different evaporation basins ranged from less than 1 μg/L 

to greater than 1,000 μg/L. Although the facilities were not 

intended to provide wildlife benefits, the ponds proved attractive to waterbirds and two species of breeding waterbirds, 

American avocets and black-necked stilts, were widespread at 

the evaporation ponds. Avian eggs spanning more than 2 orders 

of magnitude in selenium content (<1 to >100 μg/g) were available for sampling at the evaporation basins and local reference 

sites. In effect, a set of field conditions that were nearly ideal for 

documenting selenium exposure-response relations had been 

created unintentionally in this area. By 1996, the Tulare multielement-response data, supplemented with data from Kesterson 

Reservoir, from the Grasslands Water District in California, and 

from several NIWQP study areas (Ohlendorf and others, 1986; 

Stephens and others, 1992; Blanchard and others, 1993; 

Hothem and Welsh, 1994a,b; U.S. Fish and Wildlife Service, 

unpublished data), constituted a substantive basis for delineating teratogenic-response functions (table 29).

Logistic-regression response functions were developed for 

ducks, stilts, and avocets (fig. 41). The data for these functions 

were derived by assessing the condition of an embryo during 

the processing of the sample for chemical analysis and then 

matching the chemical results to the individual embryo assessments. Thus, each data point represents the exposure (chemistry) data and response (embryo-assessment) data for the same 

egg. These individually paired data produced reasonably precise response functions. (Regression-coefficient standard 

errors ranged from 10 to 30 percent.) The stilt and avocet 

curves are species specific, whereas the duck curve is a composite derived primarily from data for gadwalls, mallards, pintails, and redheads (including a few data points from ruddy 

ducks, shovelers, and canvasbacks). The response function for 

53 mallard eggs does not differ significantly from the multispecies composite function (EC50 = 29 μg/g for mallard data compared to EC50 = 30 μg/g for multispecies composite data).

TABLE 29. Number of avian embryos analyzed for selenium content 

and assessed for teratogenesis

[Symbol: —, not applicable or none]

Study area Embryos

Identifier1

1 Used in figure 2 to show locations of National Irrigation Water Quality 

Program study areas.

Name or description Avocet Stilt Duck

National Irrigation Water Quality Program study areas:

F Gunnison River Basin–Grand 

Valley Project, Colorado

— — 7

H Kendrick Reclamation Project,

Wyoming

14 — —

N Middle Green River Basin, Utah — — 2

P Milk River Basin, Montana 22 — —

V San Juan River area, New Mexico 2 — —

X Sun River area, Montana 123 — 1

Other study areas:

— Grasslands, water district2

2 From study by Hothem and Welsh (1994a, b).

4 6 41

— Honey Lake Valley, California3

3 From study by Robinson (1996).

2 1 —

— Kesterson Reservoir, California4

4 From study by Ohlendorf and others (1986).

35 80 31

— Redrock Ranch near Fresno,

California5

5 E. Van Vorts, California Regional Water Quality Board, Central Valley 

Region, written commun., 1996.

— 14 —

— Tulare Lake Bed Area, California6

6 From study by Skorupa (1998) and U.S. Department of the Interior (1998).

370 507 53

Total.............................. 572 608 135
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 97

Each function in figure 41 was generated by first calculating 

a base function for each taxon from the Tulare data. To test 

within each taxon for significant differences among the data sets 

summarized in table 29, the Tulare functions then were used to 

generate expected frequencies of teratogenesis for all other data 

sets. None of the observed frequencies of teratogenesis in those 

other data sets were significantly different from the expected 

frequencies. Therefore, all sets of data within each taxon 

(ducks, stilts, avocets) were pooled to generate the final 

response functions (fig. 41). Sufficient Kesterson and Tulare 

stilts data were available to allow comparison of the actual sitespecific response curves, and they were found to be statistically 

indistinguishable (fig. 42). In summary, in the available data no 

evidence was discernible for site-specific teratogenic response. 

All logistic-regression analyses were completed by using the 

nonlinear-estimation module of the Statistica software package 

(StatSoft, 1995). 

On the basis of the selenium-response coefficients (β0 and β1

in fig. 41) and their standard errors, all three teratogenicresponse functions in figure 41 are statistically distinct (p <0.03 

for Z-tests of all coefficients (Afifi and Clark, 1996)). On the 

basis of EC50 estimates, stilts are about twice as sensitive as 

avocets to embryonic selenium exposure, and ducks are about 

four times as sensitive as avocets. These response curves cover 

about a fourfold range of interspecies sensitivity. Until more 

species are studied, the duck, stilt, and avocet curves serve as 

best estimates for generic sensitive-, average-, and tolerantresponse functions, respectively.

The duck response curve is steep but is nonetheless consistent with experimental data for game-farm mallards. For 

instance, when duckling exposure to dietary selenium (as selenomethionine) was doubled from 40 μg/g to 80 μg/g, mortality 

increased from 12.5 percent to 100 percent (Heinz and others, 

1988). Teratogenic-response functions shown in figure 41 indicate that doubling of duck eggs’ exposure to selenium from 

about 20 μg/g to about 40 μg/g causes the incidence of teratogenesis to increase from about 5 percent to about 95 percent in 

the duck embryos. Although the life stages and endpoints differ 

in this comparison, they both show a similarly steep exposureresponse function for selenium. This close correspondence 

between experimental and field data for ducks again suggests a 

general absence of meaningful interaction effects in the field 

samples. Selenium alone appears sufficient to explain the 

shapes of the curves generated from field data. 

FIGURE 41. Teratogenic-response functions for ducks, stilts, and avocets. 

Based on 1983–96 field data. ECx, predicted concentration at which x

percent of embryos will be teratogenic; n, number of samples. 

FIGURE 42. Teratogenic-response functions for stilts at Kesterson Reservoir 

during 1983–85 and in Tulare Lake Bed area during 1987–95. β0 and β1, 

selenium-response coefficients; n, number of samples.

NATIONAL IRRIGATION WATER QUALITY PROGRAM 

TERATOGENIC-RISK ASSESSMENT

The three teratogenic-response functions presented in figure 

41 provide a rigorous basis for quantitative risk assessment. The 

true multispecies risk for freshwater aquatic birds is probably 

bounded by the extreme estimates (ducks and avocets) and perhaps is estimated best by using a stilt (or average sensitivity) 

standard for overall risk assessment. By applying all three 

response equations to the concentrations of selenium in eggs as 

documented for each NIWQP study area, a range of teratogenicrisk estimates can be generated ranging from sensitive (paralleling the duck standard) to tolerant (paralleling the avocet standard). 

GENERAL LOGISTIC MODEL

p = (e(β0+β1X)) ÷ [1+e(β0+β1X)]

Model coefficients

Duck Stilt Avocet

-8.973 -6.136 -7.479

2.341 0.578 1.179

0.2978 0.1067 0.0710

β1 Standard error:

β0 Standard error:

0.0881 0.0116 0.0144

β0

β1

0 2 10 30 50 70 90 110 0 40 60 80 100 120 0.0

0.2

0.4

0.6

0.8

0.9

0.5

0.1

0.3

0.7

1.0

PROBABILITY OF TERATOGENIC EMBRYO 

SELENIUM CONCENTRATION IN EGG, IN MICROGRAMS PER GRAM, DRY WEIGHT

Duck (n = 135)

Stilt (n = 608)

Avocet (n = 572)

EC50 = 30

EC50 = 58

EC50 = 105

p = 0.5

PREDICTIONS BASED ON MODEL

Selenium effect concentrations

(micrograms per gram, dry weight)

Duck Stilt Avocet

EC01 15 14 41

EC10 23 37 74

EC50 30 58 105

0 10 30 50 70 90 110 20 40 60 80 100 120 0.0

0.2

0.4

0.6

0.8

1.0

0.9

0.7

0.5

0.3

0.1

Kesterson Reservoir

β0 = -

-

6.63597

β1 = 0.1106 Tulare Lake Bed β0 = 6.01295

β1 = 0.1023

PROBABILITY OF TERATOGENIC EMBRYO

SELENIUM CONCENTRATION IN EGG, IN MICROGRAMS PER GRAM, DRY WEIGHT

Stilt (n = 80)

Stilt (n = 507)

p = 0.5
98 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

On the basis of logistic-regression probabilities, the predicted 

rates of embryo teratogenesis for each NIWQP study area were 

calculated and are presented in table 30. The predicted values 

were obtained by summing the probabilities and dividing by 

sample size. The results for the 23 NIWQP study areas where 

avian eggs were collected, when compared to avian sensitivity 

standards, suggest that avian exposure to selenium was elevated 

sufficiently to cause teratogenic effects at 6 study areas (26 percent) on the basis of the duck standard (sensitive), 5 (22 percent) 

on the basis of the stilt standard (average), or 2 (9 percent) on 

the basis of the avocet standard (tolerant). In summary, about 75 

percent or more of the NIWQP study areas are predicted to have 

insufficient avian exposure to selenium to induce embryo teratogenesis.

Under the worst-case risk scenario for teratogenesis (duck 

standard), the median predicted magnitude of impact at effect 

sites is 9.7 percent. It is unlikely that a median 10-percent teratogenic effect would be demographically tolerable for many 

populations of breeding ducks in the long term. Greenwood 

and others (1995) estimates that 15- to 20-percent nest success 

is the demographic break-even point for demographic sufficiency for most species of ducks. However, largely due to the 

poor quality of extant nesting habitat, rates of nest predation 

commonly are high and nest success often is near the breakeven point. For example, a 4-year study (1982–85) of ducks 

nesting throughout the Canadian prairie pothole region found 

that the best overall nest success rate was only 17 percent

TABLE 30. Predicted rates of avian teratogenesis in National Irrigation Water Quality Program study areas

[Abbreviation and symbol: B, background; μg/g, micrograms per gram; —, no avian eggs collected from study area]

Study area

Predicted rate of teratogenesis based on avian 

sensitivity standards1

(percent)

1 Rate calculated by applying logistic-response equations to concentrations of selenium in avian eggs reported for each study area. Avian 

standards based on logistic-response equations for ducks, black-necked stilts, and american avocets (fig. 43). Background: fewer than 5 deformed 

embryos per 1,000 examined, or <0.5 percent.

Identifier2

2 Used in figure 2 to show locations of study areas.

Name

Duck standard

(sensitive:

EC50 = 30 μg/g)

Stilt standard

(average:

EC50 = 58 μg/g)

Avocet standard

(tolerant:

EC50 = 105 μg/g)

A American Falls Reservoir, Idaho B B B

B Angostura Reclamation Unit, South Dakota B B B

C Belle Fourche Reclamation Project, South Dakota B B B

D Columbia River Basin, Washington B B B

E Dolores–Ute Mountain area, Colorado B B B

F Gunnison River Basin–Grand Valley Project, Colorado 7.7 1.9 B

G Humboldt River area, Nevada B B B

H Kendrick Reclamation Project, Wyoming 68.6 56.6 13.4

I Klamath Basin Refuge Complex, California–Oregon B B B

J Lower Colorado River valley, California–Arizona — — —

K Lower Rio Grande valley, Texas — — —

L Malheur National Wildlife Refuge, Oregon B B B

M Middle Arkansas River Basin, Colorado–Kansas B B B

N Middle Green River Basin, Utah 11.6 4.9 .7

O Middle Rio Grande, New Mexico B B B

P Milk River Basin, Montana B B B

Q Owyhee–Vale Reclamation Project areas, Oregon–Idaho B B B

R Pine River area, Colorado B B B

S Riverton Reclamation Project, Wyoming B B B

T Sacramento Refuge Complex, California B B B

U Salton Sea area, California 0.7 B B

V San Juan River area, New Mexico B B B

W Stillwater Wildlife Management Area, Nevada B B B

X Sun River area, Montana 1.4 .7 B

Y Tulare Lake Bed area, California 27.9 4.4 B

Z Vermejo Project area, New Mexico — — —

Median for all areas B B B

Median for “effect areas”3

3 Areas where rate of teratogenesis is above background.

9.7 3.2 B
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 99

(Greenwood and others, 1995). A more localized study of ducks 

nesting in the grasslands in central California (Hothem and 

Welsh, 1994b) also found that nest success was commonly less 

than 20 percent due to high rates of nest predation. Under such 

circumstances, a 10-percent teratogenic effect, even if highly 

duplicative of predation losses, could push local populations 

from being demographic sources to being demographic sinks. 

This is especially true considering that a 10-percent teratogenic 

effect would be supplemented by nonteratogenic embryo inviability (Stanley and others, 1996) and losses after hatching 

(Heinz and others, 1989; Ohlendorf, 1989; Williams and others, 

1989), which in turn would affect demographic sufficiency. 

However, considering the teratogenic-risk factors for all 23 

assessable NIWQP study areas, the median predicted magnitude of teratogenic effect for ducks is 0 percent (table 30). 

Therefore, throughout the Western United States, the overall 

teratogenic effect of irrigation-induced aquatic contamination 

seems unlikely to threaten the net demographic status of even 

sensitive species, although local teratogenic effects might be 

important at some irrigation-influenced aquatic habitats.

The above conclusions depend on the reliability of the teratogenesis predictions (table 30). One independent way to assess 

that reliability is to use the predictions as input data for power 

analyses and then compare the power-analysis results to actual 

observations. The power of a study to detect one or more 

deformed avian embryos is a function of the true rate of terata 

and the number of assessable embryos that are randomly sampled. Based on laws of binomial probability, this relation can be 

quantified by the following equation:

Pd = 1- (1-T)

n (6) 

where Pd is power (for detecting one or more deformed 

embryos),

T is probability of teratogenesis, and 

n is number of embryos examined.

For example, the probability of teratogenesis in the Salton 

Sea area (U) was predicted to be 0.7 percent (T = 0.007) using 

a duck standard of sensitivity and the selenium content of the 

eggs collected in that study area. A total of 65 embryos were 

examined during the NIWQP study. Thus, the predicted power 

of the study to detect one or more deformed avian embryos was 

Pd = 1-(1-0.007)65 = 0.367 (37 percent). Because 37 percent is 

less than a 50-percent chance of finding a deformed embryo, the 

prediction is that no deformities would be found. In fact, no 

deformities were found among the species whose eggs were 

sampled during the study. For this test case, because the predicted rate of teratogenesis (T = 0.007) yielded a power-analysis 

prediction that matches field observations, the predicted rate of 

teratogenesis seems to be reliable. Similar power-analysis 

results and associated predictions for all assessable NIWQP 

study areas and for all three embryo-sensitivity standards are 

shown in table 31 . 

For 13 of the 14 study areas that reported embryo-assessment data, the power-analysis predictions regarding ability to 

detect deformities matched the observed outcomes of the studies. Only the Sun River area (X) showed a disagreement 

between predicted and observed outcomes (table 31). That 

study area was unusual because most of the embryos assessed 

for teratogenesis did not come from eggs submitted for chemical analysis (Nimick and others, 1996). Out of 759 assessed 

eggs, 579 duck eggs were examined for embryo status without 

being submitted for chemical analysis. Therefore, relative to 

the sample of assessed embryos, duck eggs were underrepresented in the sampling of egg chemistry. Only 58 percent of the 

bird eggs analyzed for selenium were duck eggs while 76 percent of eggs assessed for embryo condition were duck eggs. 

This is a potentially important bias because grebe and avocet 

eggs, the other primary components of the egg-chemistry data 

set, on average contained about twice as much selenium as the 

duck eggs (11 μg/g compared to 5.5 μg/g). Thus, the predicted 

probability of finding one or more selenium-induced teratogenic embryos was based on a set of eggs having substantially 

greater selenium exposure than the set of eggs used for embryo 

assessment. As a result, the calculated probability of finding 

selenium-induced teratogenesis was overestimated for the area. 

This might explain why the predicted outcome for detection of 

selenium-induced terata was “yes” whereas the observed outcome was “no” (table 31). 

Power-analysis predictions were correct for 13 of the 14 

study areas which strongly supports the reliability of the teratogenic-risk assessments throughout the Western United States. 

According to binomial probability, the likelihood of such a 

high incidence of matches between predictions and observations by pure chance is < 0.001. The high level of predictive 

accuracy is probably due, at least in part, to the lack of toxicologically significant arsenic or mercury exposures in the 

NIWQP study areas (see section titled “Relevance of Arsenic 

and Mercury,” p. 94).

NATIONAL IRRIGATION WATER QUALITY PROGRAM 

EMBRYO-VIABILITY ASSESSMENT

Embryo teratogenesis is a relatively insensitive response 

variable for avian selenosis. Comparatively subtle physiological effects causing embryo inviability (eggs that are unable to 

hatch) can occur at exposures to selenium in the egg that are 

less than the levels required for overt structural deformities. For 

example, three recent selenium-dosing experiments on captive 

game-farm mallards found that when hens produced sets of 

eggs averaging 6.8, 13.1, 57.5, and 67.9 percent teratogenic 

embryos, the corresponding rates of embryo inviability were 

38.5, 53.0, 90.7, and 96.3 percent (Heinz and others, 1987, 

1989; Stanley and others, 1994). In these studies, rates of teratogenesis consistently underestimated the rates of total selenium-induced embryo mortality by about 30 to 40 percent. 

This outcome most recently was reaffirmed by Stanley and others (1996), when they found a 34-percent rate of embryo inviability associated with selenium exposure in the egg to be just 

below the threshold for teratogenesis. 
100 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

TABLE 31. Power analysis of avian data for detecting selenium-induced terata in National Irrigation Water Quality Program study areas 

1 2 3 4 5 6

[Upper boundary of background rates of teratogenesis conservatively estimated to be 0.5 percent. Symbols: —, not applicable; <, less than; >, greater than] 

1 Used in figure 2 to show locations of study areas.

2 For some study areas, reports describing investigations did not specify number of embryos assessed. This uncertainty is reflected herein by use of symbols “<,” or “>” 

preceding number of assessed embryos. See text for discussion. Sample sizes in this table may also differ from those listed for ‘Birds’ in table 9 because that table includes samples 

of all types of bird tissue and excludes eggs that were examined but not chemically analyzed. 3 Power determined by predicted rates of teratogenesis (see table 27) and by number of embryos assessed for deformities. 4 Detection of one or more deformities is predicted “Yes” if power calculation for stilt standard is greater than 0.5. 5 Deformities were detected, but types detected (twinning; crossed bills) typically are induced by organochlorines, which are highly elevated in this area (Setmire and others, 

1993). 6 Deformed embryos were found but did not come from eggs having elevated selenium concentrations and are most reasonably interpreted as representing normal 

background teratogenesis. The large sample of assessed embryos made it highly likely that one or more deformed embryos would be detected even if teratogenesis were occurring 

at background rates (less than about 0.5 percent).

Study area Number of 

embryos 

assessed2

Power to detect one 

or more deformities3 Detection of seleniuminduced terata

Identifier1 Name Duck

standard

Stilt

standard

Avocet

standard Predicted4 Observed

A American Falls Reservoir, Idaho 0 — — — — —

B Angostura Reclamation Unit, South Dakota 0 — — — — —

C Belle Fourche Reclamation Project, South Dakota 0 — — — — —

D Columbia River Basin, Washington <58 <0.25 <0.25 <0.25 No No

E Dolores–Ute Mountain area, Colorado <15 <.07 <.07 <.07 No No

F Gunnison River Basin–Grand Valley Project, Colorado >65 >.99 >.71 >.28 Yes Yes

G Humboldt River area, Nevada <37 <.17 <.17 <.17 No No

H Kendrick Reclamation Project, Wyoming 137 1.00 1.00 1.00 Yes Yes

I Klamath Basin Refuge Complex, California–Oregon 0 — — — — —

J Lower Colorado River valley, California–Arizona 0 — — — — —

K Lower Rio Grande valley, Texas 0 — — — — —

L Malheur National Wildlife Refuge, Oregon <47 <.21 <.21 <.21 No No

M Middle Arkansas River Basin, Colorado–Kansas 0 — — — — —

N Middle Green River Basin, Utah 173 1.00 1.00 1.00 Yes Yes

O Middle Rio Grande, New Mexico 0 — — — — —

P Milk River Basin, Montana 0 — — — — —

Q Owyhee–Vale Reclamation Project areas, Oregon–Idaho 0 — — — — —

R Pine River area, Colorado <23 <.11 <.11 <.11 No No

S Riverton Reclamation Project, Wyoming 0 — — — — —

T Sacramento Refuge Complex, California 18 .09 .09 .09 No No

U Salton Sea area, California 65 .37 .28 .28 No No5

V San Juan River area, New Mexico 7 .03 .03 .03 No No

W Stillwater Wildlife Management Area, Nevada 109 .42 .42 .42 No No

X Sun River area, Montana 759 1.00 1.00 .98 Yes No6

Y Tulare Lake Bed area, California 93 1.00 .98 .37 Yes Yes

Z Vermejo Project area, New Mexico 0 — — — — —
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 101

Owing to the low sensitivity of teratogenesis as a response 

variable, many of the NIWQP study areas that were classified as 

sites having no teratogenic effects (table 30) may still be at risk 

for depression of egg hatchability as severe as 30 to 40 percent. 

In field studies, relating egg viability directly to selenium concentrations in the egg is difficult because the viability of an egg 

is determined ultimately by whether it hatches when incubated 

to full term in the field. By the time a clutch of eggs hatches, 

however, only the failed-to-hatch eggs remain to be sampled for 

chemical analysis, and failed-to-hatch eggs would be a biased 

subset of all eggs. An indirect alternative approach is to use the 

sample-egg technique (Blus, 1982; Ohlendorf, 1993), whereby 

a sample egg is randomly selected from a clutch for chemical 

analysis and the chemical content of sampled eggs is related to 

the hatchability (viability) of the uncollected sibling eggs 

(whose fate must be monitored by repeated followup visits to 

the nest). 

The inviability-response functions so generated are at the 

level of the hen, rather than at the level of the individual egg. To 

successfully characterize an inviability-response function while 

using the sample-egg technique, contaminant exposure among 

the eggs within an individual clutch must be reasonably uniform; that is, the selenium content of the sample egg must reasonably represent the sibling eggs. At adequate sample sizes (n

> 100), gross violation of the within-clutch uniformity assumption is unlikely to produce a statistically significant response 

where none exists but would be expected to reduce the chances 

of statistically discerning, biologically authentic responses. 

Therefore, whenever a strong response function was produced 

by the sample-egg method, the selenium contents of sibling 

eggs likely were similar.

An important statistical constraint that applies to inviabilityresponse functions is that sibling eggs are not independent samples. When dealing with sibling eggs, hens are the independent 

units being sampled. Thus, hen effect, rather than embryo 

effect, is the applicable response variable for studies using the 

sample-egg technique. By comparison, teratogenesis assessments can focus at the level of the embryo because teratogenicresponse curves are generated from samples of unrelated (nonsibling) eggs. Categorical presence or absence of hen effect (a 

binomial-response variable analogous to presence or absence of 

terata in sample embryos) can be assessed by determining 

whether one or more inviable sibling eggs are present in a sample clutch. Unlike background rates of embryo teratogenesis 

that are close to 0 (0.5 percent), background rates of avian 

clutches containing one or more inviable eggs are considerably 

greater than 0 (almost 9 percent) due to the normal incidence of 

infertile eggs and other normal reproductive dysfunctions. 

Thus, the interpretation of inviability-response functions must 

account for a response baseline greater than 0. The raw probabilities of hen effect must be adjusted for such a nonzero baseline in order to assess contaminant-induced responses 

appropriately.

As part of the studies funded by the San Joaquin Valley 

Drainage Program, the incidence of inviable embryos from 

individual clutches and therefore from individual hens was 

recorded for about 300 full-term nests of black-necked stilts 

from which a random sample egg also had been removed and 

analyzed for selenium content. To calculate a hen-specific inviability-response function, 409 data points were compiled from 

studies at Kesterson National Wildlife Refuge (U.S. Fish and 

Wildlife Service, unpublished data) and a study in the Salton 

Sea area (U; U.S. Fish and Wildlife Service, unpublished data). 

An inviability-response curve was produced by logistic regression (Afifi and Clark, 1996):

p = e(-2.327 + 0.0503 X) : [1 + e(-2.327 + 0.0503 X)] , (7) 

where p is probability of one or more inviable eggs in 

sampled clutch; and

X is selenium content of random-sample egg, in 

micrograms per gram, dry weight.

At X = 0, equation 7 yields an estimate of 8.9 percent for the 

background rate of clutches containing one or more inviable 

eggs (mostly due to naturally occurring infertility). On the 

basis of an analysis of the clutch-specific inviability data for 

stilts presented by Skorupa (1998), this background rate would 

be expected to apply for selenium exposures of as much as 6 

μg/g in the egg. For exposures greater than 6 μg/g, equation 7 

would be expected to apply. These two conditions were applied 

to the NIWQP data base to calculate the predicted incidence of 

reproductively impaired hens at each study site. Those raw 

incidences were converted to estimates of selenium-induced 

rates of hen effect by correcting for the 8.9-percent normal 

background rate of effected hens. Calculation of the predicted 

incidence (selenium-effected hens) was done by using the following equation:

Ha = [(1-Hbk) - (1-Hraw)] : (1-Hbk) , (8) 

where Ha is proportion selenium-effected hens;

Hraw is predicted incidence of effected hens calculated 

using equation 7; and

Hbk is normal background rate of effected hens.

The predicted incidence (Hraw) of effected hens is based on 

selenium exposure and is calculated by using equation 7. Thus, 

if Hraw = 0.10 and Hbk = 0.089, then the estimate of seleniumeffected hens would be 

Ha = [(1-.089) -(1-.10)/(1-.089)] = [(0.911-0.900) / (0.911)] = 

0.012 . 

Normally 91.1 percent of all hens whose nests went to full 

term would hatch all their eggs. At the hypothetical study site, 

the selenium content of the eggs was high enough to predict 

that only 90 percent of full-term nests would hatch all their 

eggs. That reduction of 1.2 percent compared to normal was 

used to estimate the level of selenium-induced hen effects. This 

can be expressed in demographic terms as 12 effected hens per 

1,000 breeding hens exposed at the study site. 
102 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

As an example calculation, the selenium concentrations of 

the 10 bird eggs collected from the Belle Fourche Reclamation 

Project (table 32) were used to estimate how many breeding 

hens would lose eggs to selenium poisoning in that area. For 

instance, the probability of the blue-winged teal hen’s clutch 

(table 32) having one or more inviable eggs (based on the stilt 

standard) was estimated from equation 7:

p = e[-2.327 + (0.0503 X 8.0)] : {1 + e[-2.327 + (0.0503 X 8.0)]} = 0.127.

The mean probability for all the eggs from the study area is 

0.107, or 10.7 percent (table 32). This probability includes the 

normal background rate of egg inviability (0.089, or 8.9 percent) and must be corrected to determine the excess inviability 

caused by selenium toxicity. This is done by using equation 8 to 

calculate selenium-effected hens:

Ha = [(1 - 0.089) - (1 - 0.107)] / (1 - 0.089) = 0.0198 .

Thus, the average probability of a Belle Fourche Reclamation 

Project (C) hen’s having an inviable egg as a result of selenium 

poisoning is about 2 percent.

Characteristics of the NIWQP data base relevant to assessing 

the risk of avian embryotoxicity (egg inviability), including projected rates of selenium-induced hen effects, are presented in 

table 33. Of the 23 NIWQP study areas where avian eggs were 

sampled, 14 (61 percent) were projected to have at least some 

degree of selenium-induced depression of egg viability on the 

basis of eggs containing selenium concentrations that exceeded 

the stilt threshold for embryotoxic risk, 6 μg/g selenium (Skorupa, 1998). Of the 2,055 avian eggs sampled for the NIWQP, 

906 (44 percent) contained more than 6 μg/g of selenium. The 

median rates of exceedance were 7.7 percent for the 23 study 

areas sampled and 40.1 percent for the 14 study areas that had 

one or more eggs exceeding the threshold. 

The median projected rate of selenium-effected hens was 

only 0.9 percent for the 23 study areas where bird eggs were 

collected. For the 14 study areas where eggs containing more 

than 6 μg/g were found, the median projected rate of seleniumeffected hens was 2.2 percent. The actual rates in the study 

areas may be higher because in many of the projected no-effect 

study areas few eggs were sampled (table 33). For example, the 

Humboldt River area (G) is listed as a no-effect area, but avian 

eggs were sampled at only one site within the study area. However, subsequent sampling within that area showed that avian 

eggs having selenium concentrations exceeding 6 μg/g do 

occur there (Seiler and Tuttle, 1997).

Across the 23 study areas sampled for avian eggs by the 

NIWQP, eggs were collected from 161 individual sampling 

sites (table 33). One or more eggs exceeding the stilt threshold 

for egg inviability were collected at 79 (49 percent) of the 161 

sites. At those 79 effect sites, the median projected rate of selenium-effected hens was 3.9 percent (39 per 1,000 exposed; 

interquartile range, 1.3 to 6.5; fig. 43). An alternative method 

TABLE 32. Calculation of probability of hen effect, 

Belle Fourche Reclamation Project (C), South Dakota

Species

Selenium in

single egg

(micrograms

per gram)

Probability

of hen effects1

1 A background rate of 0.089 was used for selenium 

concentrations ≤6 micrograms per gram. For selenium concentrations 

>6 micrograms per gram, probability was calculated using equation 7.

Red-winged blackbird 2.2

1.9

1.8

3.8

0.089

.089

.089

.089

Pied-billed grebe: 9.6

10

9.0

.136

.139

.133

Mallard ...................... .1 .089

Northern pintail ......... 1.3 .089

Blue-winged teal ....... 8.0 .127

 Mean..................................... .107

FIGURE 43. Statistical summary of projected 

magnitude of selenium-induced hen effects at 

sites with one or more eggs exceeding stilt 

standard (intermediate sensitivity) for 

embryotoxicity.

79

0 .1

1

10

100

AFFECTED HENS, IN PERCENT

NUMBER OF SITES

Upper decile (90th percentile)

Upper quartile (75th percentile)

Lower quartile (25th percentile)

Median

Lower decile (10th percentile)

Point outside lower decile

EXPLANATION

Point outside upper decile
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 103

for projecting the overall rate of hen effects, which is less susceptible to the zero bias caused by low sampling intensity in 

some study areas, would be to multiply the median projection 

for hen effects at effect sites (3.9 percent) by the proportion of 

effect sites in the data base (0.49). This calculation suggests that 

a more accurate projection of the overall rate of hen effects in 

NIWQP study areas would be 19 per 1,000. Thus, across all 23 

sampled study areas, 1.9 percent of hens would be expected to 

have one or more of their eggs made inviable because of elevated selenium content.

The projected rate of overall hen effects, 1.9 percent, is substantially lower than the overall exceedance rate for embryotoxic risk, 7.7 percent, because most exceedances occur within 

the region of the inviability-response curve (eq. 6), where the 

probability is low that exceedance would cause toxicity. Of the 

79 effect sites yielding avian eggs sufficiently contaminated 

with selenium to project effects, only 8 would be expected to 

have an effects rate of a magnitude at least as large as at Kesterson National Wildlife Refuge (fig. 44). However, the most 

common finding at effect sites was a modest-magnitude effects 

rate (less than or equal to 10 percent selenium-effected hens, 

compared to greater than 30 percent at Kesterson Reservoir).

TABLE 33. Summary of predicted avian embryotoxicity in National Irrigation Water Quality Program study areas

[Abbreviation and symbol: μg/g, micrograms per gram; —, not applicable] 1 2 3 4

1 Used in figure 2 to show locations of study areas.

2 On basis of stilt standard of sensitivity, eggs having selenium concentration greater than 6 micrograms per gram, dry weight, exceed embryotoxicity threshold (Skorupa, 1998). 

Because stilts show average sensitivity to selenium poisoning (see table 30), stilt standard was chosen for general assessment of toxic effects. 3 Based on specific locations and species sampled in study area, percentage of nesting hens that would lay at least one selenium-poisoned egg. 4 Ranking of 23 study areas where bird eggs were collected from most to least contaminated on the basis of the percent of hens effected by selenium.

Study area

Number

of sites

sampled

Number of sites

where at least one

sampled egg had

greater than

6 μg/g

selenium

Total

number

of

eggs

sampled

Number of eggs

having greater

 than 6 μg/g

selenium2

Exceedance

(percent)

Predicted 

percent 

effected

hens3

Area

ranking4

Identifier1 Name

A American Falls Reservoir, Idaho 5 0 13 0 0 0 19

B Angostura Reclamation Unit, South Dakota 2 0 8 0 0 0 19

C Belle Fourche Reclamation Project, South Dakota 5 2 10 4 40.0 2.0 8

D Columbia River Basin, Washington 6 0 29 0 0 0 19

E Dolores–Ute Mountain area, Colorado 4 1 15 2 3.3 1.1 11

F Gunnison River Basin–Grand Valley Project, Colorado 16 14 91 77 84.6 8.5 4

G Humboldt River area, Nevada 1 0 37 0 0 0 19

H Kendrick Reclamation Project, Wyoming 8 7 400 359 89.8 55.6 1

I Klamath Basin Refuge Complex, California–Oregon 2 0 31 0 0 0 19

J Lower Colorado River valley, California–Arizona 0 — — — — — —

K Lower Rio Grande valley, Texas 0 — — — — — —

L Malheur National Wildlife Refuge, Oregon 3 0 47 0 0 0 19

M Middle Arkansas River Basin, Colorado–Kansas 2 2 9 6 66.6 2.6 6

N Middle Green River Basin, Utah 37 23 399 193 48.4 8.6 3

O Middle Rio Grande, New Mexico 3 0 15 0 0 0 19

P Milk River Basin, Montana 3 0 9 0 0 0 19

Q Owyhee–Vale Reclamation Project areas, Oregon–Idaho 4 0 19 0 0 0 19

R Pine River area, Colorado 3 1 23 5 21.7 1.2 10

S Riverton Reclamation Project, Wyoming 5 2 15 7 46.7 2.9 5

T Sacramento Refuge Complex, California 6 1 20 1 5.0 .2 14

U Salton Sea area, California 9 6 128 15 11.7 .7 13

V San Juan River area, New Mexico 9 1 13 1 7.7 .9 12

W Stillwater Wildlife Management Area, Nevada 7 4 92 37 40.2 1.9 9

X Sun River area, Montana 20 14 620 188 30.3 2.3 7

Y Tulare Lake Bed area, California 1 1 12 11 91.7 16.2 2

Z Vermejo Project area, New Mexico 0 — — — — — —

Totals 161 79 2,055 906 — — —

Median for 23 study areas where eggs were collected 

Median for 14 study areas where exceedance >0

7.7

40.1

0.9

2.2
104 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

DEMOGRAPHIC CONTEXT OF PROJECTED AVIAN 

EFFECTS

The ultimate biological question for the NIWQP is whether 

irrigation-induced contamination of aquatic habitats is causing 

significant biotic effects at the population level. On the basis of 

avian data, whereby the magnitude of selenium-induced hen 

effects is projected to exceed 50 percent in some areas, such as 

in the Kendrick Reclamation Project (H), significant avian 

effects do occur at the population (or subpopulation) level at 

selected localities. However, on a regional scale, the projected 

median rates of hen effects are low enough that they must be 

evaluated more carefully with regard to demographic context 

before a reasonable interpretation can be made.

The 23 NIWQP study areas were not selected at random; the 

selection criteria were intended to identify a set of study areas 

especially prone to irrigation-induced contaminant effects. 

Therefore, the magnitude of the regional effects rate projected 

in the Western United States from the NIWQP data base almost 

certainly represents a worst-case boundary more than a broadly 

applicable central tendency. Ideally, an estimate of the regional 

effects rate also should be weighted by the relative importance 

of different sampling sites to avian populations as reflected by 

variation in the density of breeding birds attracted to different 

sampling sites. In the absence of such information, unweighted 

estimates of effect (table 33; figs. 43 and 44) must be used. 

That is equivalent to assuming complete independence of spatial variability of contamination from spatial variability of 

avian distributions across the aquatic habitats of the NIWQP 

study areas. Because birds are not aware of the contaminants of 

primary concern when choosing habitat, and in the absence of 

overt contaminant effects on the structure or makeup of the 

habitat, such an untested assumption nonetheless may be reasonable.

Another complication is that a binomial hen-specific measure of reproduction (hens effected or not) cannot be evaluated 

directly for demographic significance. Knowing the percentage 

of hens producing one or more inviable eggs is not the same as 

knowing the percentage of eggs that are expected to be inviable. Only the latter expectation can be placed directly into 

demographic context. However, this complication can be overcome by using nest-monitoring records for stilts nesting in the 

Tulare Lake Bed area (U.S. Fish and Wildlife Service, unpublished data). The data show a strong relation (r2 = 0.83) 

between the number of effected eggs and the number of 

effected hens (table 34; fig. 45); such a relation for stilts 

expressed as the ratio of percentage effected eggs to percentage 

effected hens should range from slightly greater than 0.25 to 

1.0 in a diminishing incremental-effects (or semilogarithmic) 

pattern as a function of exposure (as a function of hen effects). 

Under normal background conditions, only a few of the stilt 

hens producing inviable eggs (due to natural infertility) would 

be expected to lay morethan one inviable egg. (Most effected

FIGURE 44. Magnitude of predicted hen effects at 79 National Irrigation 

Water Quality Program data-collection sites where selenium 

concentrations in eggs are sufficient to reduce hatchability.

TABLE 34. Effected hens and effected eggs of black-necked stilts, 

Tulare Lake Bed area, California

[All data collected in 1988–89 from Westfarmers evaporation-pond system (U.S. 

Fish and Wildlife Service, unpublished data)]

Nestingneighborhood

site-identification

number1,2

1 Corporate owner’s site-identification number. 2 Only nesting neighborhoods having more than 15 full-term nests and more than 

75 assessed eggs were used in analysis.

Number of

full-term nests2 Effected hens3

(percent)

3 Hens clutch contains at least one inviable egg.

Number of 

assessed eggs2,4

4 Includes eggs from full-term nests and eggs from truncated-term nests whose 

embryo viability was determined prior to nest predation, flooding, or other cause of nest 

failure.

Effected

eggs5

(percent)

5 Inviable.

WF01-88 27 7.4 114 2.6

WF06-89 36 11.1 149 6.0

WF11-89 32 12.5 140 7.1

WF05-88 22 13.6 101 7.9

WF10-89 42 19.0 182 9.9

WF03-88 18 22.2 85 12.9

WF07-88 30 40.0 144 27.8

WF08-88 33 54.5 176 44.9

0 10 20 30 40 50 60 70 80

0

10

20

30

40

50

60

70

NUMBER OF SITES

AFFECTED HENS, IN PERCENT

Percent affected

hens at Kesterson

Reservoir
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 105

hens under background conditions would have one inviable egg 

per clutch of four, or an expected baseline ratio near 0.25 for 

percentage effected eggs to percentage effected hens.) However, 

under conditions of high exposure to selenium, almost 100 percent of the hens would be affected and 100 percent of the eggs 

would be inviable (thus a ratio of about 1.0 for percentage 

effected eggs to percentage effected hens). These expectations 

are matched reasonably well by the field data (fig. 45). A large, 

multispecies set of data from Kesterson Reservoir yielded rate 

estimates of 26 percent egg effects and 39 percent hen effects 

(Ohlendorf, 1989) for a ratio of percentages of 0.67. Even 

though the relation shown in figure 45 was based solely on data 

for stilts, the multispecies ratio for Kesterson appears to agree 

well with it (see K in fig. 45). This provides at least some degree 

of independent validation for the relation and for the assumption that data for a species of intermediate sensitivity, such as 

the stilt, is indeed appropriate for estimating the central-tendency response of avian-multispecies aggregations.

effects because the after-hatch observation period is truncated 

(only six days in the latter study). In addition captive hatchlings 

are not exposed to real-world stressors such as predators and 

weather that could interactively mediate the demographic 

effect of selenium-induced debility of hatchlings. At Kesterson 

Reservoir, the estimated ratio of after-hatch effects to embryonic effects was 7:1, meaning that 88 percent of all reproductive impairment occurred after hatching (Ohlendorf, 1989). 

Because most NIWQP sampling sites were not as contaminated as Kesterson, applying the estimated ratio of effects at 

Kesterson to NIWQP data may overestimate the average afterhatch effects if the ratio is exposure dependent. Thus, a ratio of 

3.5:1 for after-hatch to embryonic losses, intermediate between 

the captive-bird and Kesterson data, is used here. Therefore, 

the projected overall reproductive effects for the NIWQP study 

areas is 0.3-percent selenium-induced egg inviability among 

otherwise-viable eggs and an additional 1.05-percent selenium-caused mortality of hatchlings. Comparable projections 

for the effect sites would be 1.2-percent selenium-induced egg 

inviability among otherwise-viable eggs and an additional 4.2-

percent selenium-caused mortality of hatchlings. Because neither egg inviability nor early hatchling death is likely to represent compensatory mortality (Hill, 1984, 1988), and because 

embryonic and after-hatch losses would be additive, they were 

combined herein and, for the sake of interpretive simplicity, 

equated to about 1.4-percent and 5.4-percent selenium-induced 

depression in nest success for the overall NIWQP and effectsite data sets, respectively. 

As Terborgh (1989) reported with regard to the effects of 

cowbird parasites on clutches of prairie warblers (Dendroica 

discolor), an avian population’s ability to tolerate even low 

rates of reproductive impairment from evolutionarily novel 

sources (such as anthropogenically mobilized contaminants) is 

strongly contingent on the extent to which the population 

demographically has adjusted to prior causes of reproductive 

failure, such as nest predation. The results from a study of prairie warblers in Indiana (Nolan, 1978) indicate the normal 

annual rate of adult mortality was about 35 percent and about 

3.4 fledglings normally were produced per successful nest. 

Having normal nest predation of just less than 80 percent, this 

population just exceeded its demographic break-even point of 

about 20-percent nest success. Exposed to relatively high natural losses to nest predators, this population had adjusted demographically to produce a small surplus of young recruits. 

Human-caused modifications of the warblers’ habitat caused 

them to become newly susceptible to cowbird parasites (an 

evolutionarily novel source of reproductive impairment for the 

warblers). About 24 percent of the warbler nests were parasitized by the cowbirds and the parasitized nests produced only 

0.9 fledglings each compared to the normal 3.4 fledglings per 

nest. Thus, 24 percent of the 20 percent of nests not destroyed 

by nest predators had their productivity reduced by 73.5 percent (from 3.4 down to 0.9 fledglings), meaning that normal 

FIGURE 45. Relation between effected eggs (Ea) and effected hens (Ha) for 

black-necked stilts during 1983–85 at Kesterson Reservoir and during 1988–

89 in Tulare Lake Bed area in California.

Using the regression in figure 45, the projection of an overall 

1.9-percent hen-effects rate approximately corresponds to 0.3-

percent selenium-induced egg inviability. At effect sites, a 3.9-

percent hen-effects rate approximately corresponds to 1.2-percent selenium-induced egg inviability. To place these projections fully in demographic context, the inevitable after-hatch 

toxicity that would be associated with selenium-contaminated 

environments also must be taken into account. In a controlled 

experiment using game-farm mallards, the estimated ratio of 

after-hatch effects to embryonic effects was 1:1, meaning that 

about 50 percent of all reproductive impairment occurred after 

hatching (Heinz and others, 1987). However, ratios of embryonic to after-hatch effects from studies of captive birds are 

likely to underestimate the relative importance of after-hatch 

0 0 20 30 0 0 0 5 15 25 35 45 10 0 0 40 50 60 55 0.3

0.4

0.5

0.6

0.7

0.8

0.9

AFFECTED HENS, IN PERCENT

RATIO OF EFFECTED EGGS TO EFFECTED HENS

EXPLANATION

Tulare Lake Bed area

Kesterson Reservoir

Ea / Ha = 0.047 + 0.423 log

10 Ha ( r 2 = 0.83)

K

K
106 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

nest success was reduced about 3.5 percent by cowbird parasites’ effect on prairie warblers clutches (that is, 0.24 x 0.20 x

0.735 = 0.035). However, that relatively small effect was sufficient to push the population over the demographic break-even 

point (Terborgh, 1989).

The demographic break-even point for North American populations of ducks has been estimated at 15-percent nest success 

for mallards and northern pintails (Klett and others, 1988; 

Greenwood and others, 1995) and at 20 percent for gadwalls, 

blue-winged teals, and northern shovelers. Additionally, breakeven rates of nest success 11.7 percent for mallards and 13.5 

percent for pintails, based on demographic models for productivity, were presented by Johnson and others (1987) and Carlson 

and others (1993). The overall rate (1.4 percent) of seleniuminduced depression in nest success projected for the NIWQP 

study areas would be demographically crucial only for duck 

populations that are within about 0.2 to 0.3 percent of their 

break-even points because only about 11.7 to 20 percent of the 

contaminant-induced losses would actually be expressed in 

populations that are near their break-even points [that is, 0.014 

x 0.117 = 0.0016, and 0.014 x 0.20 = 0.0028]. The remaining 

contaminant losses would be demographically masked in nests 

that were doomed to fail anyway from predation and other noncontaminant causes. Similarly, the median selenium-induced 

depression of nest success projected for NIWQP effect sites (5.4 

percent) would be demographically crucial only for duck populations that are within about 0.6 to 1.1 percent of the demographic break-even point. 

Many waterfowl populations apparently were existing close 

to or even below their demographic break-even points during 

the mid-1960’s through the mid-1980’s, according to broadbased regional surveys of nest success for waterfowl. Based on 

data presented by Klett and others (1988) for that 20-year 

period, the overall regional nest success for different species of 

ducks in the prairie pothole region of the United States (Minnesota, North Dakota, and South Dakota) was estimated to be 11.1 

percent for mallards, 18.9 percent for gadwalls, 17.9 percent for 

blue-winged teals, 21.1 percent for northern shovelers, and 11.5 

percent for northern pintails. None of those five estimates is 

within 0.2 to 0.3 percent of estimated break-even points, the 

amount that would be demographically crucial given the 1.4-

percent selenium-induced depression in nest success projected 

for all the NIWQP study areas. However, four of those five estimates are within 0.6 to 1.1 percent of an estimated break-even 

point, the amount that would be demographically crucial given 

the 5.4-percent selenium-induced depression in nest success 

projected for all the NIWQP effect sites. Greenwood and others 

(1995) pooled data for several species of ducks across many 

study sites in the prairie pothole region of Canada to estimate 

the regional rates of nest success for ducks: 17 percent in 1982, 

15 percent in 1983, 7 percent in 1984, and 14 percent in 1985. 

One of those four estimates is within 0.2 to 0.3 percent of a 

demographic break-even point, and two are within 0.6 to 1.1 

percent. Collectively, the mid-1960’s to the mid-1980’s was a 

period of declining regional populations of dabbling ducks in 

the prairie pothole regions in both Canada and the United 

States (Dickson, 1989). Thus, the overall rate of seleniuminduced depression of nest success projected for the NIWQP 

would have been demographically crucial in one out of nine 

cases just discussed, and at a minimum would have added to 

prior demographic deficits in several cases. The median selenium-induced depression in nest success projected for the 

NIWQP effect sites would have been demographically crucial 

(sufficient to push populations over the demographic breakeven point) in five out of the nine cases.

For North American ducks, low-quality nesting habitats—

due primarily to agricultural conversion of high-quality habitats (Gilmer and others, 1982; Malecki and Sullivan, 1987)—

and relatively dry climatic cycles during the mid-1960’s to 

mid-1980’s produced conditions that left ducks highly vulnerable to nest predators. Although noncontaminant factors bear 

the primary responsibility for depressing nesting success to 

near or below the demographic break-even point, even small 

contaminant effects can be demographically decisive under 

such conditions.

In summary, for ducks, the magnitudes of avian effects projected for the NIWQP were sufficient to be potentially crucial 

at a population level of analysis. This conclusion is especially 

relevant considering that ducks are substantially more sensitive 

to selenium exposure than stilts, yet herein the assessment of 

hen effects is based on a stilt standard of sensitivity. However, 

the NIWQP study areas can be assumed to represent a worsethan-average subset of all potential study areas in the Western 

United States.

Published reports on demographic modeling for shorebirds 

are scarce; however, Robinson and Oring (1997) provided 

enough information on demographic parameters for American 

avocets to estimate a demographic break-even point of about 

42 percent for nest success. This estimate also should apply to 

black-necked stilts because stilt and avocet life-history parameters are similar (Johnsgard, 1981; Robinson and others, 1997; 

Robinson and others, 1999). Thus, the overall 1.4-percent selenium-induced depression in nest success projected for the 

NIWQP study areas would be demographically crucial for 

regional populations of avocets and stilts that are within about 

0.6 percent of their demographic break-even point. Similarly, 

the median 5.4-percent selenium-induced depression for nest 

success projected for the NIWQP effect sites would be demographically crucial only for avocet and stilt populations that are 

within about 2.3 percent of the demographic break-even point. 

Regional surveys of nest success among avocets and stilts that 

are comparable in scope to the scientific studies cited for ducks 

have not been done. The most extensive regional survey of nest 

success among avocets and stilts was completed during 1987–

89 in the Tulare Lake Bed area (U.S. Fish and Wildlife Service, 
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 107

unpublished data), where populations were monitored intensively at 16 sites across the 5,800-mi2 basin. Regional nest success for stilts was measured at 50.8 percent with 95-percent 

confidence boundaries at 48.1 and 53.7 percent (based on a 

sample of 21,797 nest-exposure days). Regional nest success 

for avocets was measured at 63.3 percent with 95-percent confidence boundaries at 60.7 and 65.9 percent (based on a sample 

of 26,865 nest-exposure days). In neither of these instances 

would the median rates of reproductive effects projected for the 

NIWQP be demographically crucial. In contrast, the projected 

local effects rate for the Tulare Lake Bed area (16.2 percent; 

table 33) would be demographically crucial for a population 

within 6.8 percent of the break-even point. The lower end of the 

confidence interval for stilt nest success is only 6.1 percent 

greater than the break-even point. Thus, at a local scale, the basinwide population (or subpopulation) of stilts in this area is, at 

best, at the brink of a contaminant-induced push over the demographic edge.

The NIWQP selected only 26 areas for reconnaissance investigations out of the hundreds of irrigation-drainage facilities and 

national wildlife refuges constructed or managed by DOI. The 

true demographic risks for biota associated with irrigationinduced water pollution in the Western United States cannot be 

assessed until a truly random sampling of irrigation projects 

(including Federal, State, and private projects) is completed. 

The areas selected for investigation by NIWQP were not randomly selected, instead they were those believed most likely to 

have irrigation-induced contamination effects. Because of this, 

the NIWQP areas more likely represents the worst-case and are 

not typical of the Western United States Accordingly, the worstcase scenario for the Western United States does include biotic 

effects at a demographically meaningful level for taxa such as 

ducks that already have been pushed close to their demographic 

break-even point by other factors such as the increased susceptibility to nest predators associated with degraded nesting habitat. Even the worst-case median levels of contaminant effects 

could be tolerated by populations of ducks existing just modestly above demographic break-even points. This suggests the 

biotic risk to ducks could be addressed by reducing irrigationinduced water pollution but more effectively by restoring highquality (more predator-safe) nesting habitat.

RELATION BETWEEN SELENIUM IN WATER, 

SEDIMENT, AND BIOTA

Determining the relation between selenium concentrations in 

water and biota is important for several reasons. It provides a 

way to evaluate whether a screening investigation is adequate or 

has missed evidence of contamination. In addition, it provides 

a way to assess whether water criteria are indeed protective of 

fish and wildlife.

The relation between selenium concentrations in water, bottom sediment, and crayfish tissue was examined. Sites where 

crayfish were collected were matched with sites where surfacewater and bottom-sediment samples were collected and analyzed for selenium. As discussed in the section “Sampling-Site 

Agreement,” p. 21, not all biological samples were collected at 

locations where water or sediment samples were collected. 

Samples of 123 crayfish representing 86 distinct populations 

were collected in the NIWQP study areas. At the 66 sites where 

both water and crayfish were sampled, only those sites that had 

water samples collected during April–July (51 of the 66 sites) 

were analyzed. Collection sites for 41 crayfish populations 

were matched with 37 bottom-sediment sites. The geometricmean selenium concentration for each crayfish population 

was computed and matched with selenium measurements of 

water or bottom-sediment samples collected during April–July 

(fig. 46).

FIGURE 46. Relations among selenium concentrations in surface water and 

bottom sediment (<0.062-millimeter fraction), and invertebrate tissue 

[background concentration for invertebrate tissue from U.S. Department of 

the Interior (1998)].

1 10 100 0.1

1

10

100

100

< 1

0.1

0.1

1 10 100

1,000

1

10

GEOMETRIC-MEAN SELENIUM CONCENTRATION IN CRAYFISH POPULATION, IN MICROGRAMS PER GRAM

SELENIUM IN FILTERED SURFACE WATER AT CRAYFISH COLLECTION SITE,

APRIL—JULY, IN MICROGRAMS PER LITER

SELENIUM IN FINE FRACTION OF BOTTOM SEDIMENT AT CRAYFISH COLLECTION SITE,

IN MICROGRAMS PER GRAM, DRY WEIGHT

Background concentration in invertebrate tissue

Background concentration in invertebrate tissue

A

B
108 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

Trends for increasing selenium concentration in crayfish tissue with increasing waterborne and bottom-sediment selenium 

are evident in figure 46. The large data scatter has several possible explanations: The amount of selenium absorbed may depend 

on the organic-carbon content of the bottom sediment, and data 

are insufficient to analyze this potential relation. Also, in an 

open system, crayfish can move into a contaminated area from 

a nearby uncontaminated area; the history of the organism collected is unknown. Also, because of survivor bias, in water bodies having high selenium concentrations, the most likely 

organisms to be collected are those having the lowest selenium 

burdens—the survivors.

Because selenium concentrations in water can vary rapidly 

from high to low in response to hydrologic factors (fig. 24), a 

single measurement of waterborne selenium could miss evidence of contamination. Most of the selenium in fish tissue 

results from uptake through diet rather than through water 

(Lemly, 1996c), however, one should expect a congruence 

between selenium concentrations in invertebrate and plant tissue and typical selenium concentrations in the water. If water 

samples do not show elevated selenium concentrations but tissue samples do, evidence of water contamination likely has been 

missed 

The relation between selenium concentration in water and 

avian eggs was investigated to determine if the selenium concentration in water at the nesting site could be related to selenium concentrations in bird eggs. Of 804 surface-water sites 

where selenium was analyzed, 78 were matched with nesting 

sites where bird eggs were collected. Water samples commonly 

were not collected at the same time the eggs were collected, and 

selenium concentrations in the water can change greatly during 

a year (fig. 26), as discussed in the section “Temporal Changes 

in Selenium Concentrations,” p. 58. Hence, only those nesting 

sites that had water samples collected during April–July (44 of 

the potential 78 matched sites) were used for this analysis 

because samples collected during this period more likely represent those to which breeding birds are exposed than would samples collected during August–March. Where multiple water 

samples were collected at a site, the earliest sample collected in 

the four-month period April–July was used to represent the selenium concentration in water at the nesting site.

The eggs collected by the NIWQP investigators were grouped 

into sets that represent distinct breeding populations of birds. A 

total of 937 eggs from 31 species of birds were collected from 

nesting areas that could be matched with the 44 sites where 

water samples were collected during April–July. Analytical 

results for these 937 eggs were grouped into 158 sets, a set being 

a group of egg samples that represents a distinct breeding population of birds. For a set of eggs, a geometric-mean selenium 

concentration of 1 to 3 μg/g is considered normal. A geometricmean selenium concentration exceeding 6 μg/g is considered 

embryotoxic because it is the threshold for embryotoxicity in 

black-necked stilts (Skorupa, 1998). 

The relation between the selenium concentration in water at 

nesting sites and the geometric-mean selenium concentration 

in sets of eggs from NIWQP study areas is shown in figure 47. 

The graph shows that as the selenium concentration in water at 

a nesting site increases, the average selenium concentration in 

sets of bird eggs also increases. Of 65 sets of bird eggs collected from sites where the selenium concentration in the water 

equaled or exceeded 5 μg/L, the USEPA chronic criterion for 

selenium, 55 (85 percent of the sets) contained embryotoxic 

concentrations of selenium. 

Only four of 54 sets of bird eggs had embryotoxic concentrations of selenium when selenium in the water was less than 1 

μg/L. Nineteen of the 93 sets of bird eggs (20 percent) collected 

from sites where the selenium concentration in the water was 

less than 5 μg/L contained embryotoxic concentrations of selenium. The nineteen sets of eggs having embryotoxic selenium 

concentrations were from uncontaminated areas within a few 

miles of contaminated areas in the Kendrick Reclamation 

Project (H), middle Green River Basin (N), and Sun River area 

(X). Thus, even though eggs were from uncontaminated ponds 

and lakes, the hens may have been feeding in nearby contaminated areas.

Rankings of the study areas were used to answer two questions: (1) Do food organisms in areas where 25% of the water 

samples contain more than 5 μg/L selenium contain harmful 

amounts of selenium? (2) Are the organisms themselves from 

these areas harmed by the selenium? Rankings of the data, 

rather than the data itself, were used to reduce the effects of 

scale, sample bias, and extreme values in the data. The data and 

the rankings for the study areas are presented in table 35. 

Twelve study areas were classified as selenium contaminated because the 75th percentile selenium concentration in 

surface water exceeded the USEPA chronic criterion for the 

protection of aquatic life, 5 μg/L (tables 15 and 35). Except for 

plants, in most cases median selenium concentrations in tissue 

samples exceeded 3 μg/g in those 12 areas. Three μg/g in tissue 

is used as a guideline because it is a toxic threshold for selenium in aquatic food-chain organisms consumed by fish and 

wildlife (Lemly, 1996c). A plot of rankings (fig. 48A) indicates 

that the median selenium concentration in plant tissue 

exceeded 3 μg/g in only 4 of the 12 study areas classified as 

contaminated. The median selenium concentration in tissue 

from aquatic invertebrates and fish exceeded 3 μg/g in most of 

the 12 areas (fig. 48B, C). In most cases the median selenium 

concentration in tissue was less than 3 μg/g in areas where the 

75th percentile selenium concentration in surface water was 

less than 5 μg/L (fig. 48B, C). 
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 109

FIGURE 47. Relation between selenium concentrations in surface water at nesting site and in sets of avian eggs, during April–July 1987–92. 

19 55

74 10

D(y)=67.4, 1 df

P<10-9

GEOMETRIC-MEAN SELENIUM CONCENTRATION IN EGG SETS, 

IN MICROGRAMS PER GRAM, DRY WEIGHT

CLASSIFICATION OF SELENIUM CONCENTRATION

NORMAL ELEVATED EMBRYOTOXIC

0.1

1

10

100

200

<1 1 10 100 1,000

SELENIUM CONCENTRATION IN WATER AT NESTING SITE, IN MICROGRAMS PER LITER

EXPLANATION

Data points for egg set

Matrix of egg setsÑShowing number of egg sets per quadrant (as defined by U. S. 

 Environmental Protection Agency chronic criterion and embryotoxic concentration)

Egg selenium

concentration

Less than

5 μg/L

Greater than

or equal 5 μg/L

Greater than

or equal 6 μg/g

Less than

6 μg/g

Water selenium

concentration

U.S. Environmental Protection Agency (1987) chronic criterion for selenium in water 
TABLE 35. Study-area rankings, summary statistics for selenium concentrations in water, plants, aquatic invertebrates, and fish, and reprodu

[Abbreviations and symbols: μg/L, microgram per liter; μg/g, microgram per gram (dry weight); <, less than; -, not determined or not applicable

Identifier1

1 Used in figure 2 to show locations of study areas.

Name

Selenium in

surface water2

2 Concentrations and ranking from table 15.

Selenium in

plants3

3 Concentrations and rankings from data presented in figure 28.

Selenium

in aquatic

invertebrates3

Selenium 

fish3 

4 Percentages and rankings from table 33.

Study

area

rank

75th

percentile

concentration

(μg/L)

Study

area

rank

Medianconcentration

(μg/g)

Study

area

rank

Medianconcentration

(μg/g)

Study

area

rank

Mc

ABCD

E

American Falls Reservoir, IdahoAngostura Reclamation Unit, South DakotaBelle Fourche Reclamation Project, South DakotaColumbia River Basin, WashingtonDolores–Ute Mountain area, Colorado

18.51311.523

8

1.04.55

<1

7.0

1521132515

0.850.50.9

<1.2

0.85

15

9.5

121617

2.83.73.22.22.0

24

9

11.52215

F

G

H

I

J

Gunnison River Basin–Grand Valley Project, ColoradoHumboldt River area, NevadaKendrick Reclamation Project, WyomingKlamath Basin Refuge Complex, California–OregonLower Colorado River valley, California–Arizona

4

16

3

2316

35

2.0

64

<1

2.0

5

18

1

15

9.5

1.70.77.30.851.2

7

11

2

24

--

4.83.5

28

0.48

--

8

15

2

26

7

K

L

M

N

O

Lower Rio Grande valley, TexasMalheur National Wildlife Refuge, OregonMiddle Arkansas River Basin, Colorado–KansasMiddle Green River Basin, UtahMiddle Rio Grande, New Mexico

18.523

52

23

1.0

<1

1073

<1

2319

34

25

0.40.65.43.9

<0.4

2221

43

25

0.901.469.00.45

1917

13.5

25

P

Q

R

S

T

Milk River Basin, MontanaOwyhee–Vale Reclamation Project areas, Oregon–IdahoPine River area, ColoradoRiverton Reclamation Project, WyomingSacramento Refuge Complex, California

2316

9.5

11.523

<1

2.06.05.0

<1

1217

67.5

25

0.950.81.61.3

<1.2

19.519.514

5

23

1.61.62.95.80.68

151910

6

23

U

V

W

XY

Z

Salton Sea area, CaliforniaSan Juan River area, New MexicoStillwater Wildlife Management Area, NevadaSun River area, MontanaTulare Lake Bed area, CaliforniaVermejo Project area, New Mexico

6

1423

719.5

8.03.0

<1

7.5

265

6.0

20

9.5

22

7.52

11

0.581.20.421.36.01.0

8

1318

619.5

4.63.11.74.9

41

3.7

5

1319

3.5

2111.5
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 111

FIGURE 48. Relation between study-area rankings for selenium in water and plants, water and aquatic invertebrates, water and fish, and water 

and reproductive effects on birds.

Study area ranking by 75th percentile

selenium concentration in surface water

0 2 4 6 8 10 12 14 16 18 20 22 24 26

Study area ranking by percent of hens 

predicted to lose eggs to selenium poisoning

0

2

4

6

8

10

12

14

16

18

20

22

24

26

Study area ranking by 75th percentile 

selenium concentration in surface water

0 2 4 6 8 10 12 14 16 18 20 22 24 26

Study area ranking by median selenium

concentration in aquatic invertebrates

0

2

4

6

8

10

12

14

16

18

20

22

24

26

Y

N

H

V

B

U

C

C

S

E

G,Q

W

D,I,L,O,P

T

A

X

R

M

F

Study sites where 75th 

percentile exceeds 5 μg/L

Study sites where 75th 

percentile exceeds 5 μg/L

Study areas where medianselenium concentration is less than 3 μg/g

Study sites where percent 

of hens predicted to lose

eggs is greater than 0

Study area ranking by 75th percentile

selenium concentration in surface water

0 2 4 6 8 10 12 14 16 18 20 22 24 26

Study area ranking by median selenium

concentration in plant material

0

2

4

6

8

10

12

14

16

18

20

22

24

26

Study area ranking by 75th percentile

selenium concentration in surface water

0 2 4 6 8 10 12 14 16 18 20 22 24 26

Study area ranking by median selenium

concentration in fish 

0

2

4

6

8

10

12

14

16

18

20

22

24

26

Study sites where 75th

percentile exceeds 5 μg/L

Study sites where 75th 

percentile exceeds 5 μg/L

Study areas where medianselenium concentration is less than 3 μg/g

Y

N

H

V

U

C

S

E

Q K

D

X

R

M

F

G

J

L

T

A

I

P

O

Y

N

H

V

B

U

C

S

E

Q

W

K

X

R

M

F

G

J

L

D,O,T

A I

P

B

Z

Y

N

H

V

U

S

E

Q

W

K

X

R

M

F

G

C

L

D

A

I

P

Z B

O

T

W

Study areas where medianselenium concentration is less than 3 

Z

μg/g

A Plants B Aquatic invertebrates

C Fish D Reproductive effects on birds

Data point for sampling site National

 Irrigation Water Quality Program 

 study areaÑLetter indicates study 

 area

EXPLANATION

D(y)=5.15, l df

P<0.033

D(y)=8.96, l df

P<0.005

D(y)=7.28, l df

P<0.014

D(y)=14.3, l df

P<0.0003
112 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

In the 23 study-areas where bird eggs were collected, selenium contamination of surface water is significantly associated 

with hens losing eggs to selenium poisoning (fig. 48D). For 11 

areas the 75th-percentile of selenium concentrations in surface 

water exceeded 5 μg/L and in all of those areas some hens were 

predicted to lose eggs to selenium poisoning. For 12 areas the 

75th-percentile of selenium concentrations in surface water 

were less than 5 μg/L and in only 3 of those areas hens were predicted to lose eggs to selenium poisoning. 

In some cases selenium concentrations in water were not congruent with selenium concentrations in tissue or the presence of 

biological effects. This can be the result of sample bias or locations of sampling sites for water and tissue samples not matching. For example, all of the fish collected during the Tulare Lake 

Bed area (Y) reconnaissance investigation were from irrigation 

canals and creeks, but most of the water samples and all of the 

plant, invertebrate, and bird samples were collected from selenium-contaminated evaporation ponds. In the Stillwater Wildlife Management Area (W), the eggs with the highest selenium 

concentrations are from a small pond distant from the main 

study area. Eggs from this pond comprise one-third of the bird 

eggs collected from the study area; however, less than 4 percent 

of the water samples were collected near that pond.

The rankings analysis indicates that most food organisms, 

particularly aquatic invertebrates and fish, contain potentially 

harmful amounts of selenium in study areas where selenium 

concentrations in more than 25 percent of the water samples 

exceed 5 μg/L. The rankings analysis also indicates that hens 

are losing eggs to selenium poisoning in all those study areas 

where selenium concentrations in more than 25 percent of the 

water samples exceed 5 μg/L. These results suggest that areas 

where selenium contamination of the food chain and loss of 

eggs to selenium poisoning is occurring may be identified using 

the same methods developed to identify areas where selenium 

contamination of water is likely to occur. The map of areas susceptible to irrigation-induced selenium contamination (fig. 34), 

which is based solely on physical data, identifies as susceptible 

11 of 13 areas where the median selenium concentration in 

aquatic-invertebrates tissue exceeds 3 micrograms per gram and 

7 of 10 areas for fish. Of the 14 areas where hens were predicted 

to lose eggs to selenium poisoning, 11 were identified as being 

susceptible to irrigation-induced selenium contamination.

SUMMARY AND CONCLUSIONS

NIWQP was created out of concern by the U.S. Congress and 

environmental groups that irrigation-induced contamination, 

which resulted in the collapse of the warm-water fishery and 

death and deformities of birds at Kesterson Reservoir in California in the 1980’s, could happen elsewhere in irrigated areas of 

the United States. From 1986 through 1993, 26 areas for which 

the U.S. Department of the Interior is responsible were investigated for irrigation-induced contamination. In 1992, a NIWQP 

data-synthesis project was begun to identify factors common to 

contaminated areas. The main approach was to create a data 

base of information collected during investigations of the 26 

NIWQP areas and to analyze the compiled data.

Selenium was the contaminant most often associated with 

irrigation in the areas investigated by NIWQP. Selenium concentrations in more than 40 percent of the surface-water samples exceeded 5 micrograms per liter, the USEPA chronic 

criterion for the protection of aquatic life. The study areas were 

not randomly selected. Many of the study areas were selected 

because of known or suspected selenium contamination and, 

thus, it is not surprising that a large percentage of the selenium 

samples exceeded the criterion.

Selenium, however, was not the only contaminant of concern. In some areas, concentrations of boron and molybdenum 

in surface water greatly exceeded criteria for the protection of 

aquatic life. Arsenic concentrations in surface water rarely, if 

ever, exceeded the chronic criterion for the protection of 

aquatic life; however, in four areas, the median arsenic concentration exceeded the current drinking-water MCL of 10 micrograms per liter and in 7 areas 25 percent or more of the samples 

exceeded the MCL. Except for DDT, pesticides in water rarely 

exceeded the aquatic-life criteria. Of the samples analyzed for 

total DDT, 21 percent exceeded the aquatic-life criterion; however, almost all the samples that exceeded the criterion were 

from a single study area.

Degradation of ground-water quality as a result of irrigation 

practices is a common occurrence. In some study areas, shallow domestic wells are the principal source of drinking water 

for individual households; however, most of the ground-water 

sites sampled by the NIWQP are not used as sources of drinking water. In 3 of the 13 study areas where samples were collected, selenium concentrations exceeded the MCL in more 

than 50 percent of the ground-water samples. Arsenic concentrations in 22 percent of the ground-water samples equalled or 

exceeded the current drinking-water MCL; however, almost all 

these samples were from a single area in Nevada. The median 

uranium concentration in ground water exceeded the MCL in 

two of ten areas, and individual samples exceeded the criterion 

in four of the areas.

Except for molybdenum, selenium and uranium, trace elements in bottom sediment generally did not exceed the upper 

limit of the qualitative sediment guideline for the Western 

United States. Selenium is the only trace element for which 

ecological sediment guidelines are used and selenium concentrations commonly exceeded that guideline of 2 micrograms 

per gram. DDE, a degradation product of DDT, was found in 

81 percent of the samples of bottom sediment and in all 21 

study areas where sediment was analyzed for pesticides.
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 113

In the NIWQP studies, certain contaminants were typically 

found associated. Such association indicates that they had similar sources or processes controlling their concentrations. Selenium was associated with sulfate, indicative of derivation from 

sulfide-containing sediments. Selenium contamination in an 

area was not correlated with contamination by arsenic, boron, or 

molybdenum. However, elevated selenium concentrations commonly were associated with elevated uranium concentrations. 

Boron and molybdenum commonly were found together. High 

concentrations of these elements were associated with high 

chloride concentrations, which indicate that evaporative processes were occurring. Contamination by arsenic is not associated with contamination by any other trace element.

Contaminant concentrations can have a wide range within a 

study area. Concentration ranges of arsenic, boron, molybdenum, selenium, and uranium all exceeded two orders of magnitude in some areas. Within the Kendrick Reclamation Project in 

Wyoming and the middle Green River Basin in Utah, selenium 

concentrations in surface water ranged from less than 1 to more 

than 5,000 micrograms per liter. Such a wide range means that 

chance combinations of flow and ground-water movement, 

local geology, and nearness to irrigated fields may result in 

uncontaminated samples being found even within contaminated 

areas.

Criteria for the protection of aquatic life have not been developed for boron and molybdenum by USEPA but may be needed 

to protect wildlife. Data collected by the NIWQP indicate that 

concentrations of these elements commonly exceeded levels at 

which adverse effects on wildlife may be expected. The State of 

California has developed aquatic-life criteria for boron and 

molybdenum, but such criteria are not used in all States. If the 

criteria developed for use in California are sound, then wildlife 

in other States likely are being exposed to toxic concentrations 

of boron and molybdenum as well.

Although water-quality standards and biological criteria generally were developed by using concentrations in whole (unfiltered) water, concentrations in filtered-water samples are nearly 

the same as concentrations in unfiltered samples for arsenic, 

boron, molybdenum, and selenium for concentrations greater 

than about 10 micrograms per liter. In the range of 1 to 10 

micrograms per liter there may be a tendency for unfiltered 

arsenic concentrations to be greater than filtered concentrations. 

For selenium, however, the data suggest differences from equality in that range result from analytical imprecision and not a 

general tendency for unfiltered concentrations to be greater than 

filtered concentrations. This similarity suggests that contaminant concentrations measured in filtered samples can be compared to criteria developed by using whole-water samples. The 

equality of total-and filtered-selenium concentrations may not 

hold in lentic, nutrient-rich waters because in such settings 

algae can bioaccumulate large amounts of selenium.

Selenium was the trace element that most commonly 

exceeded USEPA criteria for the protection of aquatic life and 

was chosen to be the major focus of this report. Of the 26 areas 

investigated by the NIWQP, 12 were classified as selenium 

contaminated because selenium concentrations exceeded 5 

micrograms per liter (the USEPA chronic criterion for the protection of aquatic life) in more than 25 percent of the surfacewater samples. 

Confirmed by data from the NIWQP, the association of selenium and sulfide minerals in geologic formations of Cretaceous age has been known since the 1930’s. Median sulfate and 

selenium concentrations in NIWQP study areas showed positive correlations. Cretaceous geologic units were found to be 

associated with all 12 areas where selenium concentrations 

exceeded 5 micrograms per liter in more than 25 percent of the 

surface-water samples. Rocks of Cretaceous age are commonly 

seleniferous and are regionally the most important geologic 

source of selenium even though they are not the only seleniferous rocks. These rocks form the bedrock in more than 17 percent of the land area of the Western United States. Of the 26 

study areas, 8 had no direct or indirect association with Upper 

Cretaceous sedimentary rocks and were not classified as selenium contaminated.

Local geologic sources of selenium are not the only important sources of selenium. Selenium can be imported into an 

area in irrigation water. An example of this occurs in the Imperial Valley/Salton Sea area in California. Colorado River water 

used for irrigation in the Imperial Valley contains concentrations of selenium only slightly less than Federal water-quality 

criteria because of irrigation drainage and natural runoff from 

seleniferous rock units in Colorado, Utah, and New Mexico. In 

other areas, discharges of selenium from oil-field and mine-pitdewatering operations may result in selenium contamination 

when the effluent from such operations mixes with water that 

is used for irrigation downstream.

The climatic setting is important because once selenium is 

mobilized by application of irrigation water, the aridity of the 

area largely determines whether toxic concentrations of selenium result. Selenium concentrations in ground water in arid 

areas can become elevated because there is less infiltration of 

precipitation to dilute the selenium and because there are 

higher evapotranspiration rates. Evapotranspiration consumes 

water which increases selenium concentrations in the remaining soil and ground water. Two indices of aridity were compared—precipitation and the ratio of evaporation to 

precipitation. The relation between selenium concentrations 

and the ratio of evaporation to precipitation was more significant than the relation between selenium and precipitation 

alone. In those NIWQP study areas where irrigated lands overlie Upper Cretaceous sedimentary bedrock, and where the ratio 

of evaporation to precipitation exceeded about 3.0, selenium 

concentrations exceeded the USEPA chronic criterion in more 

than 25 percent of surface-water samples.
114 SYNTHESIS OF DATA FROM THE NATIONAL IRRIGATION WATER QUALITY PROGRAM

Whether water bodies are terminal or flow-through systems 

is important because selenium is not removed by flushing from 

terminal lakes. Although the presence of terminal water bodies 

of itself does not cause selenium problems to develop, if the 

geologic and climatic setting of an area are conducive to selenium contamination, then the presence of terminal water bodies 

is likely to make the selenium problem worse. As selenium 

loads are transported into a terminal water body, evaporation 

gradually increases the selenium concentration. In flow-through 

systems, the selenium load is moved through either continuously or episodically, thereby ameliorating existing selenium 

problems or decreasing the potential for selenium problems.

Geologic and climatic data for the Western United States 

were incorporated into a geographic information system to produce a map identifying areas susceptible to irrigation-induced 

selenium contamination. Areas are considered susceptible 

where marine sedimentary rocks form the bedrock in and near 

the area and where the evaporation rate is more than 2.5 times 

the precipitation rate. The map, which is based solely on physical data, may be useful in identifying areas where selenium contamination of the food chain and loss of eggs to selenium 

poisoning are occurring. The map identifies as susceptible 11 of 

13 areas where the median selenium concentration in aquaticinvertebrates tissue exceeds 3 μg/g. In addition, the map identifies as susceptible 7 of 10 areas where the median selenium concentration for fish exceeds 3 μg/g. Of the 14 areas where hens 

were predicted to lose eggs to selenium poisoning, 11 were 

identified as being susceptible to irrigation-induced selenium 

contamination.

The potential for selenium contamination can change

depending on climatic conditions. Some areas may not have 

selenium-contamination problems under normal conditions, but 

contamination may occur during drought years. Selenium 

becomes concentrated in ground and drain water because less

water is available for dilution and evapotranspiration rates are 

higher. In addition, results of investigations to determine 

whether contamination actually is occurring may be misleading 

if sampling is done during wet periods when the selenium has 

been diluted temporarily.

In biological tissues, arsenic, boron, cadmium, copper, mercury, molybdenum, selenium, zinc, and DDE were identified as 

the contaminants of greatest concern by reconnaissance and 

detailed investigations. For avian eggs, tabulated toxic exceedance rates for each contaminant of greatest concern clearly 

showed that selenium was the most hazardous constituent associated with irrigation drainage in NIWQP study areas. 

Selenium concentrations in biota were compared to concentrations that have been demonstrated to have adverse effects on 

similar species (the effect level) or to have adverse effects on 

another species if the contaminated biota are consumed (the 

dietary effect level). Twenty-five percent of the plant samples 

had selenium concentrations exceeding the dietary effect level, 

whereas 57 percent of the invertebrate samples and 61 percent 

of the fish samples exceeded that level. Of the more than 2,000 

bird eggs collected, 44 percent had selenium concentrations 

exceeding 6 μg/g, a threshold value for reproductive effects. In 

14 of the 26 NIWQP study areas, selenium concentrations in 

eggs from some populations of birds contained sufficient selenium to cause reduced hatchability of the eggs. Seleniumcaused deformities of bird embryos were found in 4 of the 26 

study areas.

Eggs from 54 populations were collected from nesting sites 

where the selenium in the water during April–July was less 

than 1 μg/L and only four populations contained embryotoxic 

concentrations. Eggs from 93 populations of birds were collected from nesting sites where the selenium concentration in 

the water during April–July was less than 5 μg/L, which is the 

USEPA chronic criterion for the protection of freshwater 

aquatic life. Nineteen of the 93 populations (20 percent) contained embryotoxic concentrations of selenium in the eggs. 

Eggs from 65 populations of birds were collected from nesting 

sites where the selenium concentration in the water during 

April–July was 5 μg/L or more, and 55 of those 65 populations 

(85 percent) had eggs that contained embryotoxic concentrations of selenium.

Eggs were sampled from 34 species of birds belonging to 10 

orders. Nearly all the eggs collected come from aquatic species 

of birds, with American coots, mallards, and American avocets 

being the three species most frequently collected. Of the 34 

species, at least one set of eggs from 16 species had a geometric-mean selenium concentration of at least 12.5 μg/g, a highrisk threshold. All three species of grebes yielded at least one 

set of high risk eggs, as did four of five species of shorebirds 

and five of eleven species of waterfowl. Egg-set data were 

examined to determine if some feeding guilds are more at risk 

to selenium poisoning than others. Analysis of data for waterbird eggs from study areas where the 75th percentile selenium 

concentration in surface water exceeded 5 μg/L suggests that 

herbivorous birds bioaccumulate less selenium than insect- and 

fish-eating birds. Selenium concentrations for 39 percent of the 

egg sets from herbivorous birds fell in the normal range (less 

than 3 μg/g) while only 7 and 0 percent, respectively, of egg 

sets from insect-and fish-eating birds fall in the normal range. 

Although herbivorous birds may be at less risk, it does not 

appear that any waterbird feeding guilds are particularly well 

buffered from exposure to selenium contamination.

For a quantitative risk assessment, avian and fish eggs are 

optimal as risk metrics. NIWQP biologists rarely sampled fish 

eggs; however, avian eggs were sampled extensively. Unlike 

the other extensively sampled biotic tissues (whole body fish or 

avian livers), avian eggs are not compromised by survivor bias. 

Examination of multielement-response data for the San 

Joaquin Valley in California led to the conclusion that teratogenic response to in-egg selenium exposure was free of significant confounding interaction with other trace elements. Thus, 

the response curves for that valley should be applicable to 

most, if not all, the NIWQP study areas.
IRRIGATION-INDUCED CONTAMINATION OF WATER, SEDIMENT, AND BIOTA IN THE WESTERN UNITED STATES 115

There was no evidence for site-specific teratogenic-response 

functions. However, these response functions were strongly 

taxon-specific: Stilts were two times and ducks four times as 

sensitive as avocets. Regardless of which avian sensitivity standard or response curve—duck (sensitive), stilt (intermediate), or 

avocet (tolerant)—was applied to the NIWQP data base, at least 

75 percent of the NIWQP study areas were predicted to have 

insufficient avian exposure to selenium to induce embryo teratogenesis. The reliability of teratogenesis predictions were 

tested by power analysis and found to match the observed 

results for 13 of 14 NIWQP study areas that reported embryo 

assessment data.

On the basis of embryo inviability, which is a more sensitive 

response variable than teratogenesis, 14 of 23 NIWQP study 

areas were projected to be subject to at least some degree of 

selenium-induced reproductive depression among waterbirds. 

Overall, 19 hens per 1,000 (1.9 percent) were projected to be 

affected by selenium-induced embryo inviability. At projected 

effect sites (79 of the 161 individual sample sites), 39 hens per 

1,000 (3.9 percent) were predicted to be affected by seleniuminduced embryo inviability. About 10 percent of effect sites 

were projected to suffer effects of a magnitude equal to or 

greater than that observed at Kesterson Reservoir in California.

After discounting hen effect for only partial clutch loss and 

for the masking of contaminant losses by other sources of nest 

failure such as nest predation, and after adding projections for 

after-hatch effects of selenium exposure, the overall seleniuminduced reproductive depression in NIWQP study areas was 

estimated to be equivalent to a 1.4-percent reduction from normal nesting success. A comparable estimate for NIWQP effect 

sites would be a 5.4-percent reduction from normal nesting success.

Regional surveys of nesting success among ducks in the prairie pothole regions of the United States and Canada revealed 

that duck populations commonly were existing near their demographic break-even points. Consequently, even the overall projection of only 1.4-percent selenium-induced depression in nest 

success derived from NIWQP data was large enough to be 

demographically crucial in one of nine comparisons with survey 

data. Thus, one out of nine regionally surveyed duck populations was close enough to the demographic break-even point 

that an additional 1.4-percent reproductive depression in surviving nests (or the equivalent 0.1- to 0.3-percent depression in nest 

success) would push the population past the break-even point. 

The effect-sites projection of 5.4-percent selenium-induced 

reproductive depression in surviving nests (or the equivalent 

0.6- to 1.1-percent depression in nest success) was large enough 

to be demographically crucial in five of nine comparisons with 

the regional demographic status of duck populations.

The true demographic risks for biota associated with irrigation-induced water pollution within the Western United States 

cannot be assessed until a truly random sampling of irrigation 

projects (including Federal, State, and private projects) is completed. The areas selected for investigation by NIWQP were 

not randomly selected, instead they were those believed most 

likely to have irrigation-induced contamination effects. 

Because of this, the NIWQP areas more likely represents the 

worst-case and are not typical of the Western United States. 

Accordingly, the worst-case scenario for the Western United 

States clearly includes biotic effects at a demographically 

meaningful level for taxa such as ducks whose regional populations commonly appeared to be existing close to their demographic break-even point.

It is important to consider why duck populations in the Western United States are so near the demographic break-even point 

that even relatively small depressions in nesting success caused 

by selenium can be demographically crucial. For North American ducks, relatively dry climatic cycles and low quality nesting habitats—due primarily to agricultural conversion of highquality habitats—have produced conditions that left ducks 

highly vulnerable to nest predators. Non-contaminant factors 

bear the primary responsibility for depressing nesting success 

to near or below the break-even point, nonetheless, under such 

circumstances, even small effects from contaminants can be 

crucial. However, even the worst-case median levels of contaminant effects could be tolerated by populations of ducks 

existing just modestly above demographic break-even points. 

This suggests the biotic risk to ducks could be addressed by 

reducing irrigation-induced water pollution but more effectively by restoring high-quality (more predator-safe) nesting 

habitat.

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