Agricultural and Environmental Input Parameters for the Biosphere Model REV 02, ICN 00

ANL-MGR-MD-00000

September 2004 


1. PURPOSE 
This analysis is one of 10 technical reports that support the Environmental Radiation Model for 
Yucca Mountain Nevada (ERMYN) (i.e., the biosphere model. It documents development of 
agricultural and environmental input parameters for the biosphere model, and supports the use of 
the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one 
of a series of process models supporting the total system performance assessment (TSPA) for the 
repository at Yucca Mountain. The ERMYN provides the TSPA with the capability to perform 
dose assessments. A graphical representation of the documentation hierarchy for the ERMYN is 
presented in Figure 1-1. This figure shows the interrelationships between the major activities 
and their products (the analysis and model reports) that were planned in Technical Work Plan for 
Biosphere Modeling and Expert Support (BSC 2004 [DIRS 169573]). The Biosphere Model 
Report (BSC 2004 [DIRS 169460]) describes the ERMYN and its input parameters. 
This analysis was conducted according to AP-SIII.9Q, Scientific Analyses, and the biosphere 
Technical Work Plan (BSC 2004 [DIRS 169573]). It is one of the five reports that develop input 
parameters for the biosphere model. This report defines and justifies values for twelve 
parameters required in the biosphere model that are related to the use of contaminated 
groundwater to irrigate crops. Values for the twelve parameters developed in this analysis are 
used for input to the calculations for the BDCFs for the biosphere groundwater exposure scenario 
(Nominal Performance Biosphere Dose Conversion Factor Analysis, BSC (2004 [DIRS 
169674]), and values for five of the parameters are used for input to the calculations for the 
BDCFs for the volcanic ash exposure scenario (Disruptive Event Biosphere Dose Conversion 
Factor Analysis, BSC 2004 [DIRS 167287]) as described in Figure 1-1. The parameters support 
development of BDCFs for the three climate states considered in TSPA. The parameter values 
recommended in this report are used in the soil, plant, and carbon-14 submodels of the ERMYN 
(Table 1-1). The twelve parameters addressed are: 
Dry Biomass (kg/m2), DBjTotal, aboveground standing dry biomass for each crop type j. 
Dry-to-Wet-Weight Ratio (kgdry-plant/kgwet-plant), DWjRatio of dry to wet biomass for edible 
parts of plants for each crop type j. 
Fraction of Overhead Irrigation (dimensionless), fojFraction of irrigation applied to crop type 
j using overhead (o) methods, such as sprinklers and spray. 
Growing Time (days), tg,jLength of growing season(s) for crop type j. 
Irrigation Rate-Annual Average (m/year), IRAverage amount of groundwater applied per 
year to irrigated lands, including cropland, gardens, and landscapes. 
Irrigation Intensity (cm/hour), IjRate at which groundwater is applied during sprinkler 
irrigation to crop type j. 
Irrigation Application (mm), IAjAmount of irrigation per application event for crop type j. 
Irrigation Rate-Daily (mm/day), IRDjDaily average irrigation rate for crop type j applied over 
all seasons. 

Overwatering Rate (m/year), OWAverage amount of precipitation or groundwater applied by 
irrigation that percolates beyond the root zone and leaches salts and radionuclides out of that 
zone, for all crop types. 
Rooting Depth (m), Zr  Mean maximum effective rooting depth for all crops. 
Tillage Depth (m), TdThe depth to which soil is tilled or plowed prior to planting. 
Yield (kgwet/m2), YjCrop biomass or wet yield per crop type j. 
The parameters developed in this report support treatment of twelve features, events, and 
processes (FEPs) addressed in the biosphere model (Table 1-1). Inclusion and treatment of FEPs 
in the biosphere model is described in the Biosphere Model Report (BSC 2004 [DIRS 169460], 
Section 6.2). Consideration of the LA FEPs List (DTN: MO0407SEPFEPLA.000 
[DIRS 170760] constitutes a deviation from the Technical Work Plan for Biosphere Modeling 
and Expert Support (TWP) (BSC 2004 [DIRS 169573]), which referred to an earlier revision of 
the FEPs list (DTN: MO0307SEPFEPS4.000 [DIRS 164527]). 

Figure 1-1. Documentation Hierarchy for the Environmental Radiation Model for Yucca Mountain Nevada 

Table 1-1. Parameters and Related FEPs 
Report Section 
Summarizing 
Parameter Related FEP 
LA FEP 
Number 
Biosphere 
Submodel 
Disposition in 
TSPAa 
Dry Biomass Plant uptake 3.3.02.01.0A Plant Section 6.1 
Dry-to-Wet-Weight 
Ratio 
Plant uptake 3.3.02.01.0A Plant Section 6.2 
Fraction of Overhead 
Irrigation 
Water management activities 1.4.07.01.0A 
Plant Section 6.3 Agricultural land use and irrigation 2.4.09.01.0B 
Plant uptake 3.3.02.01.0A 
Biosphere characteristics 2.3.13.01.0A 
Plant Section 6.4 Growing Time 
Agricultural land use and irrigation 2.4.09.01.0B 
Climate change 1.3.01.00.0A 
Plant uptake 3.3.02.01.0A 
Biosphere characteristics 2.3.13.01.0A 
Soil, 
Carbon-14 
Section 6.5 
Agricultural land use and irrigation 2.4.09.01.0B 
Irrigation Rate-Annual 
Average 
Climate change 1.3.01.00.0A 
Precipitation 2.3.11.01.0A 
Radionuclide accumulation in soils 2.3.02.02.0A 
Urban and industrial land/water use 2.4.10.00.0A 
Water management activities 1.4.07.01.0A 
Plant Section 6.6 
Biosphere characteristics 2.3.13.01.0A 
Irrigation Intensity Agricultural land use and irrigation 2.4.09.01.0B 
Plant uptake 3.3.02.01.0A 
Soil type 2.3.02.01.0A 
Biosphere characteristics 2.3.13.01.0A 
Plant Section 6.7 
Agricultural land use and irrigation 2.4.09.01.0B 
Irrigation Application Climate change 1.3.01.00.0A 
Precipitation 2.3.11.01.0A 
Plant uptake 3.3.02.01.0A 
Biosphere characteristics 2.3.13.01.0A 
Plant, 
Carbon-14 Section 6.8 
Agricultural land use and irrigation 2.4.09.01.0B 
Irrigation Rate-Daily Climate change 1.3.01.00.0A 
Plant uptake 3.3.02.01.0A 
Precipitation 2.3.11.01.0A 
Biosphere characteristics 2.3.13.01.0A 
Soil Section 6.9 
Agricultural land use and irrigation 2.4.09.01.0B 
Overwatering Rate Climate change 1.3.01.00.0A 
Precipitation 2.3.11.01.0A 
Radionuclide accumulation in soils 2.3.02.02.0A 

Table 1-1. Parameters and Related FEPs (Continued) 
Location of 
Summary of 
Parameter Related FEP 
LA FEP 
Number 
Biosphere 
Submodel 
Disposition in 
TSPAa 
Radionuclide accumulation in soils 2.3.02.02.0A 
Soil, Air, 
Carbon-14, 
External 
Exposure 
Section 6.10 
Soil type 2.3.02.01.0A 
Tillage Depth (surface 
soil depth) 
Soil and sediment transport in the 
biosphere 
2.3.02.03.0A 
Agricultural land use and irrigation 2.4.09.01.0B 
Atmospheric transport of 
contaminants 
3.2.10.00.0A 
Plant uptake 3.3.02.01.0A 
External exposure 3.3.04.03.0A 
Yield Plant uptake 3.3.02.01.0A Plant Section 6.11 
Radionuclide accumulation in soils 2.3.02.02.0A 
Soil, Air, 
Carbon-14, 
External 
Section 6.12 
Soil type 2.3.02.01.0A 
Rooting Depth (surface 
soil depth) 
Soil and sediment transport in the 
biosphere 
2.3.02.03.0A 
Atmospheric transport of 3.2.10.00.0A 
contaminants Exposure 
Plant uptake 3.3.02.01.0A 
External exposure 3.3.04.03.0A 
Source: FEPs are listed in MO0407SEPFEPLA.000 [DIRS 170760]. 
a 
The effects of the related FEPs are included in the Total System Performance Assessment through the biosphere 
dose conversion factors. See BSC (2004 [DIRS 169460], Section 6.2), for a complete description of the inclusion 
and treatment of FEPs in the biosphere model. The treatment of each parameter is described in the listed 
sections of this report and summarized in Section 7. 
BDCF=biosphere dose conversion factor; FEPs=features, events and processes; LA=License Application; 
TSPA=Total System Performance Assessment 

INTENTIONALLY LEFT BLANK 

2. QUALITY ASSURANCE 
Development of this report involves analysis of data to support performance assessment, as 
described in the TWP (BSC 2004 [DIRS 169573]), and thus is a quality affecting activity in 
accordance with AP-2.27Q, Planning for Science Activities. Approved quality assurance 
procedures identified in the TWP (BSC 2004 [DIRS 169573], Section 4) have been used to 
conduct and document the activities described in this report. Electronic data used in this analysis 
were controlled in accordance with the methods specified in the TWP (BSC 2004 
[DIRS 169573], Section 8). 
The natural barriers and items identified in the Q List (BSC 2004 [DIRS 168361]) are not 
pertinent to this analysis and a Safety Category per AP-2.22Q, Classification Analyses and 
Maintenance of the Q-List, is not applicable. 

INTENTIONALLY LEFT BLANK 

3. USE OF SOFTWARE 
The only software used to analyze data was the commercial off-the-shelf product Microsoft 
Excel 97 SR-2. Standard functions of that software were used to calculate means and standard 
deviations for distribution development in Section 6, to develop graphs used in sensitivity 
analyses (Figures 6.1-1 and 6.6-1), and to manipulate data for calculation of irrigation parameters 
(Appendices C through E). Uses of those functions, including formulas, algorithms, inputs, and 
outputs are described in the tables, figures, or associated text. 

INTENTIONALLY LEFT BLANK 

4. INPUTS 
4.1 DIRECT INPUTS 
The technical product inputs for each parameter are described with justification below and 
summarized in Table 4.1-1. See the document input reference system for the status of all inputs 
and references. 
Table 4.1-1. Direct Inputs Used in Analysis 
Input Data Source Parameter Presented In 
Water Content of Food USDA (2002 [DIRS 159272]) Dry-to-Wet-Weight Ratios Table 6.2-1 
Dry-to-Wet-Weight Till and Meyer (1983 [DIRS 101895] Dry-to-Wet-Weight Ratios Section 4.1.2, 
Ratios (alfalfa, corn Table 5.16, with oat hay=forage) Table 6.2-1 
silage, and oat hay) 
Harvest Indices Neitsch et al. (2002 [DIRS 163122], Dry Biomass Table 6.1-1 
Table A-8, pp. 381 to 384) 
Present-Day Climate 
Growing Seasons 
Mills et al. (no date [DIRS 124338]); 
Call (1999 [DIRS 158672]); 
Morris and Johnson (1991 [DIRS 103034], 
pp. 3 and 4); 
Irrigation Application 
Irrigation Rate - Annual 
Average 
Irrigation Rate - Daily 
Section 4.1.4, 
Appendix D, 
Section 2.1.1 
USDA (2002 [DIRS 159273], pp. 16 and 17); Overwatering Rate 
Allen et al. (1998 [DIRS 157311] Table 11, 
pp.104 to 108); 
TDMSa Parameter: 
Grow Time 
LeStrange (1997 [DIRS 125452] and 
[DIRS 125429]). 
Upper Bound Glacial 
Transition Climate 
Growing Seasons 
Washington State University Cooperative 
Extension (2002 [DIRS 159256], p. 2); 
Antonelli et al. (1998 [DIRS 158654], 
Table 2); 
Washington Agricultural Statistics Service 
(1999 [DIRS 152232]); 
Irrigation Application 
Irrigation Rate - Annual 
Average 
Irrigation Rate - Daily 
Overwatering Rate 
Table D-2 
Painter et al. (1995 [DIRS 158674], 
Tables A1 and A4); 
Schmierer et al. (1997 [DIRS 160479], 
pp. 9 to 18); 
Orloff and Marble (1997 [DIRS 158655], 
pp. 106 to 107). 
Weather - Present-Day 
Climate Conditions 
MO04019SUM9397.000 [DIRS 167054] Irrigation Application 
Irrigation Rate - Annual 
Average 
Table 4.1-2 
Irrigation Rate - Daily 
Overwatering Rate 
TDMSa Parameters: 
Precipitation quantity 
Relative humidity 
Solar Flux 
Temperature 
Wind Speed 
Weather - Upper Western Regional Climate Center (2003 Irrigation Rate - Annual Table 4.1-3 
Bound Monsoon [DIRS 162307]), (2003 [DIRS 162301]), (2003 Average 
Climate Conditions [DIRS 162302]) 

Table 4.1-1. Direct Inputs Used in Analysis (Continued) 
Input Data Source Parameter Presented In 
Weather - Lower National Weather Service (2003 Irrigation Rate - Annual Table 4.1-4 
Bound Glacial [DIRS 162299]); Average 
Transition Climate 
Conditions 
Western Regional Climate Center (2003 
[DIRS 162302]); 
Western Regional Climate Center (2003 
[DIRS 162300]). 
Weather - Upper Western Regional Climate Center (1997 Irrigation Application Table 4.1-5 
Bound Glacial 
Transition Climate 
Conditions 
[DIRS 152233]) Irrigation Rate - Annual 
Average 
Irrigation Rate - Daily 
Overwatering Rate 
Soil Infiltration Rate Dollarhide (1999 [DIRS 159253]) Irrigation Intensity Section 6.6 
Well Water Salinity LA0206AM831234.001 [DIRS 160051] Irrigation Application Section 4.1.7 
Irrigation Rate - Annual 
Average 
Irrigation Rate - Daily 
Overwatering Rate 
TDMSa Parameter: 
Electrical Conductivity 
Crop Yield - Leafy 
Vegetables 
USDA (1998 [DIRS 158648], Tables 4-14, 
4-15, 4-21, 4-22, 4-33, 4-35, and 4-54); 
Yield Table 6.11-1 
USDA (1999 [DIRS 158647], Tables 4-14, 
4-15, 4-21, 4-22, 4-33, 4-35, and 4-54); 
USDA (2000 [DIRS 158646], Tables 4-14, 
4-15, 4-21, 4-22, 4-33, 4-35, and 4-54); 
USDA (2001 [DIRS 158645], Tables 4-14, 
4-15, 4-21, 4-22, 4-34, 4-36, and 4-55). 
Crop Yield - Other 
Vegetables 
USDA (1998 [DIRS 158648], Tables 4-18, 
4-26, 4-40, 4-43, and 4-47); 
Yield Table 6.11-2 
USDA (1999 [DIRS 158647], Tables 4-18, 
4-26, 4-40, 4-43, and 4-47); 
USDA (2000 [DIRS 158646], Tables 4-18, 
4-26, 4-40, 4-43, and 4-47); 
USDA (2001 [DIRS 158645], Tables 4-18, 
4-26, 4-41, 4-44, and 4-48). 
Crop Yield - Fruits USDA (1998 [DIRS 158648], Tables 4-17, 
4-32, 4-61, 4-73, and 5-70); 
Yield Table 6.11-3 
USDA (1999 [DIRS 158647], Tables 4-17, 
4-32, 4-61, 4-72, and 5-72); 
USDA (2000 [DIRS 158646], Tables 4-17, 
4-32, 4-61, 4-72, and 5-72); 
USDA (2001 [DIRS 158645], Tables 4-17, 
4-33, 4-62, 4-71, and 5-76). 
Crop Yield - Apples 
and Grapes 
USDA (1998 [DIRS 158649], Tables Apples, 
Commercial: Bearing Acreage and Yield by 
State and United States, 1995-97 and 
Yield Table 6.11-6 
Grapes: Bearing Acreage and Yield by Type, 
State, and United States, 1995-97); 
USDA (1999 [DIRS 158650], Tables on pp. 8 
and 40); 

Table 4.1-1. Direct Inputs Used in Analysis (Continued) 
Input Data Source Parameter Presented In 
Crop Yield - Apples 
and Grapes 
(Continued) 
USDA 2000 (DIRS 158653), Tables on pp. 8 
and 40; 
USDA 2001 (DIRS 158651), Tables on pp. 10 
and 44; 
USDA 2002 (DIRS 158652), Tables on pp. 10 
and 46. For all years, grapes = all types. 
Crop Yield - Cattle 
Forage 
USDA 1998 (DIRS 158648), Tables 1-41, 6-3, 
and 6-4; 
USDA 1999 (DIRS 158647), Tables 1-41, 6-3, 
and 6-4; 
USDA 2000 (DIRS 158646), Tables 1-41, 6-3, 
and 6-4; 
USDA 2001 (DIRS 158645), Tables 1-39, 6-3, 
and 6-4. 
Yield Table 6.11-4 
Crop Yield - Grain 
USDA 1998 (DIRS 158648), Tables 1-8, 1-40, 
1-50, and 1-56; 
USDA 1999 (DIRS 158647), Tables 1-8, 1-40, 
1-51, and 1-57; 
USDA 2000 (DIRS 158646), Tables 1-8, 1-39, 
1-51, and 1-57; 
USDA 2001 (DIRS 158645) Tables 1-8, 1-37, 
1-49, and 1-55. 
Yield Table 6.11-5 
Tillage Depth Lang et al. 1999 (DIRS 160031), p. 3; 
Granberry et al. 2000 (DIRS 160033), p. 8; 
Johnson 1999 (DIRS 160029), Chapter 8, p. 1. 
Tillage Depth Section 6.10 
Irrigation Methods Martin et al. 1999 (DIRS 159383), 1999 
(DIRS 159384), 1999 (DIRS 159382); 
Mayberry 2000 (DIRS 159386), 2000 
(DIRS 159388), 2000 (DIRS 159389), 2000 
(DIRS 160005); 
Teegerstrom and Umeda 2001 (DIRS 159392); 
Teegerstrom et al. 2001 (DIRS 159391); 
Hinman et al. 1997 (DIRS 159376); 
Klonsky and De Moura 2001 (DIRS 159381); 
Uriu and Magness 1967 (DIRS 159169), 
pp. 697 to 698; 
Wolf and Johnson 1999 (DIRS 159393), p. 5; 
MO0208SPAMETHO.004 (DIRS 159565) 
Fraction of Overhead 
Irrigation 
TDMSa Parameter: 
Agricultural Statistics 
Section 6.3 
Table 4.1-6 
Rooting Depth Allen et al. 1998 (DIRS 157311), Table 22, 
pp. 163 to 165 
Rooting Depth 
Irrigation Application 
Irrigation Rate - Annual 
Average 
Irrigation Rate - Daily 
Overwatering Rate 
Table 4.1-7 
a 
TDMS=Technical Data Management System; USDA=U.S. Department of Agriculture. 

For some parameters, data and agricultural practices outside the Yucca Mountain region were 
selected for use. This is justified in Section 6 and Appendix A. To ensure that distributions 
developed from these data are consistent with conditions in the Yucca Mountain region, 
appropriate analogue sites were chosen or uncertainties were considered as described in Section 
6 and Appendix A. All references cited in this document and listed in Section 8, other than those 
identified as inputs in this section, were included to support or corroborate the methods and 
conclusion of the analyses. 
4.1.1 Water Content of Foods 
Information on water content of foodstuffs from the USDA Nutrient Database for Standard 
Reference, Release 14, prepared by the U.S. Department of Agriculture (USDA) Agricultural 
Research Service (ARS) (USDA 2002 [DIRS 159272]) was used to calculate dry biomass and 
dry-to-wet-weight ratios of vegetables, fruits, and grains, as described in Sections 6.1.2 and 
6.2.2, respectively. The USDA ARS is a federal government organization and considered a 
source of established fact data. The inputs from this established fact source are technically 
defensible and appropriate for this analysis for the following reasons: 
 
As the principal in-house research component of USDA, ARS provides the scientific 
expertise needed to support the work of most of the Departments action and regulatory 
agencies and other Federal agencies, such as the Food and Drug Administration, the 
U.S. Environmental Protection Agency, some components within the U.S. Department 
of Defense, and the U.S. Department of the Interior. For example, the Food and 
Nutrition Service, which administers the nutrition assistance programs of the USDA, 
uses data from the ARS Continuing Survey of Food Intakes by Individuals and Diet and 
Health Knowledge Survey to update the thrifty food plan that, in turn, is used to monitor 
the effectiveness of food assistance programs by measuring the dietary status of 
low-income Americans, analyze the nutrient content of foods commonly eaten by 
low-income individuals, and develop improved methods to assess the absorption and 
bioavailability of key nutrients in the diets of important population subgroups. 
 
The ARS information is documented and substantiated in electronic databases and 
publications and is considered factual and suitable for quality-affecting work. 
The information in USDA (2002 [DIRS 159272]) is appropriate for this analysis because it 
comes from a comprehensive dataset that summarizes percent water content of most 
representative crops, and these values can be used directly to calculate dry-to-wet-weight ratios. 
According to the USDA, this dataset is the major source of food composition data in the United 
States and provides the foundation for most food composition databases in the public and private 
sectors (USDA (2002 [DIRS 159272], p. 2). The data were compiled from numerous sources 
and the water content of most representative crops was derived from 10 to more than 200 data 
points or sources of information. The percent water contents used in parameter development 
are presented in Table 6.2-1. An additional external source was required for dry-to-wet-weight 
ratios of cattle forage because the USDA Nutrient Database for Standard Reference, Release 14 
(USDA 2002 [DIRS 159272]) does not include animal forage. 

4.1.2 Dry-to-Wet-Weight Ratios for Cattle Forage 
The reciprocal values of fresh to dry ratios for alfalfa (0.227), corn silage (0.238), and oat hay 
(0.182) were selected from NUREG/CR-3332, Radiological Assessment, A Textbook on 
Environmental Dose Analysis (Till and Meyer 1983 [DIRS 101895], Table 5.16, with oat 
hay = grass forage). This source was published by the Nuclear Regulatory Commission (NRC). 
The resulting dry-to-wet-weight ratios from this source were used to develop the distribution for 
dry-to-wet-weight ratios for cattle forage as described in Section 6.2.2. 
The NUREG/CR-3332 authored by Till and Meyer (1983 [DIRS 101895]) constitutes one of the 
premier textbooks used for environmental dose analysis for radionuclides. The fresh-to-dry 
weight ratios for cattle forage were compiled from an Agricultural Handbook from 
1963 published by the USDA ARS, which is considered an established fact source 
(see Section 4.1.1 for source justification). The fresh-to-dry weight data for cattle forage in Till 
and Meyer (1983 [DIRS 101895]) have been used routinely in other radiological assessments for 
dose analysis (e.g., IAEA 1994 [DIRS 100458], Table 5); Kennedy and Strenge (1992 
[DIRS 103776], Table 6.17); Napier et al. (1988 [DIRS 157927], Table 4.25). This additional 
source is necessary because the primary source for dry-to-wet-weight ratios (USDA 2002 
[DIRS 159272]) does not include values for cattle forage. Use of these values, and discussions 
of uncertainty associated with their use, is further described in Section 6.2.2. 
4.1.3 Harvest Indices 
Aboveground dry biomass for grains, other vegetables, and fruits cannot be determined directly 
from yield and dry-to-wet-weight ratios because not all of the aboveground plant parts are 
considered edible. The non-edible parts are not included in yield and dry-to-wet-weight ratio 
measurements. Therefore, harvest indices were used with yield and dry-to-wet-weight ratios to 
calculate total above ground dry biomass for these crop types. Harvest indices are a measure of 
the ratio of seed, fruit, or tuber dry biomass to total aboveground dry biomass. Dividing the 
product of yield and dry-to-wet-weight ratio by the harvest index gives the total above ground 
dry biomass (i.e., biomass of fruits, leaves, and stems) for a representative crop. 
Harvest index values for grains, other vegetables, and fruits reported in Neitsch et al. (2002 
[DIRS 163122], Table A-8, pp. 381 through 384) were used in Section 6.1.2 to calculate total 
aboveground dry biomass. The methods for determining harvest indices in this source resulted 
in values that were appropriate for calculation of dry biomass from USDA measurements of 
commercial crop yield. The document contains a comprehensive list of harvest indices for the 
representative crops within each crop type. It is a joint publication between the USDA ARS (see 
source justification in Section 4.1.1) and the Texas Agricultural Experimental Station, which are 
considered sources of established fact data. The Texas Agricultural Experimental Station is the 
research extension of the land-grant system in agriculture. It is committed to basic and applied 
research in the areas of agriculture, life sciences, and natural resources. The agency is a leader in 
agricultural research nationwide and is therefore an appropriate source for this analysis. The 
selected harvest indices are reported in Table 6.1-1. 
Harvest indices tend to be conservative unless crops are grown under extreme stress conditions, 
and have changed little in recent years (Prince et al. 2001 [DIRS 159323], pp. 1196 to 1197). 

Most published measurements of harvest indices for grains reviewed by Prince et al. (2001 
[DIRS 159323], p. 1197) varied by no more than  0.06 from the values selected for this 
analysis. Additionally, the distribution of dry biomass is more sensitive to variation in yield than 
variation in harvest indices (see Section 6.1.2). Therefore, changes in the accuracy of harvest 
indices has little influence on the bounds for the distributions of dry biomass for crop types. 
Therefore, the selected harvest indices are appropriate for this analysis. Use of these harvest 
indices, and discussions of uncertainty associated with their use, is further described in 
Section 6.1.2. 
4.1.4 Growing Season 
The following sources were used to determine the start of the growing season (i.e., planting time 
for annuals, initiation of growth and start of irrigation for perennials) and season length for 
representative crops. This information was used in Section 6.4 to determine growing time 
distributions. Planting period and season length were also used in the development of growth 
stages (initial, development, mid-season, and late-season), which define the period of time that a 
crop coefficient (Kc) is used in the calculation of crop evapotranspiration (ETc, see Appendix D). 
Mean monthly ETc is used to calculate the four irrigation parameters (Sections 6.5, 6.7, 6.8, and 
6.9). Growing season length is also used to identify total number of growing days per month and 
the thirty-day period prior to harvest for each crop. This information is required for calculation 
of annual average irrigation rate (Section 6.5 and Appendix E), irrigation application 
(Section 6.7), daily average irrigation rate (Section 6.8 and Appendix E), and overwatering rate 
(Section 6.9 and Appendix E). 
4.1.4.1 Growing Season - Present-Day Climate 
Garden Crops and TurfInformation on agricultural and horticultural practices compiled by 
state Cooperative Extension Services was used to establish planting periods, harvest periods, and 
growing seasons for garden crops and turf for present-day climate conditions. Use of data 
from Cooperative Extension Services is technically defensible and appropriate for this analysis 
for the following reasons: 
 
Cooperative Extension Services are partnerships between state land-grant colleges and 
the U.S. Department of Agriculture Cooperative State Research, Education, and 
Extension Service, and are considered sources of established fact data. They serve as 
the outreach branches of state universities and the Department of Agriculture. The 
mission of the Cooperative Extension Services is to develop and disseminate 
information on agriculture, horticulture, health, environment, economics, and other 
topics of importance developed by the USDA and universities. 
 
Cooperative Extension Services are one of the most comprehensive sources of 
agricultural and horticultural information. No other organization summarizes and 
presents a wide range of site-specific information on how to grow crops and garden 
plants. For many garden crops, Cooperative Extension Services are the only source of 
site-specific information. 

 
Information distributed by Cooperative Extension Services is widely used by farmers, 
gardeners, and homeowners. For example, in southern Nevada, pamphlets and 
publications are available from Nevada Cooperative Extension offices, over the internet, 
and from other outlets such as gardening supply stores. 
 
Personnel working for Cooperative Extension Services are recognized experts in 
agriculture, gardening, and horticulture. 
The planting dates of garden crops under present-day climatic conditions at Yucca Mountain 
were obtained from Beginning Gardening in the Desert (Mills et al., no date [DIRS 124338]), 
published by the Southern Nye County Cooperative Extension. The document includes ranges of 
suggested planting dates for garden crops in southern Nye County, which includes Amargosa 
Valley. This source is appropriate because it contains information for a large selection of crops 
and is specific to the present-day climatic conditions in southern Nye County. These, and all 
other data described in this subsection, are presented in Table D-1. 
Growing season lengths of garden crops for the present-day climate are derived from the Arizona 
Master Gardener Manual (Call 1999 [DIRS 158672]), published by the University of Arizona 
Cooperative Extension. Crop-specific information on pages 71 through 125 of Chapter 10 was 
used if available; otherwise, data from Table 10.10 was used. This source is appropriate because 
it contains a comprehensive list of season lengths for garden crops grown under arid to semi-arid 
conditions. There is no similar, comprehensive source of season lengths for garden crops in 
southern Nevada. This source does not include information on growing season lengths for 
apples, strawberries, or grapes. 
Duration of home irrigation (which is only used to calculate annual average irrigation rate in 
Section 6.5) is from Maintaining Hybrid Bermudagrass for Urban Mojave Desert Landscapes 
(Morris and Johnson 1991 [DIRS 103034], pp. 3 and 4), published by the University of Nevada 
Cooperative Extension. This site-specific pamphlet recommends irrigating bermudagrass 
year-round in southern Nevada. 
Feed Corn and Corn SilageGrowing seasons for feed corn and corn silage are from Nevada 
Agricultural Statistics 2000-2001 (USDA 2002 [DIRS 159273], pp. 16 and 17), a state office of 
the USDA National Agricultural Statistics Service (NASS). Use of data from this source is 
technically defensible, and appropriate for this analysis for the following reasons: 
 
The NASS is the statistical agency for the USDA, which is considered a source of 
established fact data. The mission of the NASS is to serve the United States, 
agriculture, and its rural communities by providing meaningful, accurate, and objective 
statistical information and services. They are responsible for conducting surveys of 
agricultural production and practices and reporting the results of those surveys. 
 
The NASS is the only organization in the United States that compiles nationwide 
information on commercial crop production and agricultural trends. Therefore, they are 
the most consistent and comprehensive, and for many topics the only source of this 
information. 

 
USDA quality-assurance processes developed specifically for the types of surveys 
conducted by NASS are followed to control the accuracy of released information. 
Information provided by this organization therefore is suitable for quality-affecting work 
related to characterization of agricultural production and practices. 
According to this source, corn is planted during May and June, silage is harvested in August 
through October, and grain corn is harvested in October and November. Because this source 
describes growing seasons for all of Nevada, much of which has later and longer planting times 
than southern Nevada, the first months listed for planting and harvest were chosen. 
Apples and StrawberriesPlanting date and growing season lengths for several crops, planting 
periods, and climatic regions are reported in Crop Evapotranspiration (Allen et al. 1998 
[DIRS 157311], Table 11, p.107), a publication by the Food and Agriculture Organization of the 
United Nations (FAO). The growing season information for fruit orchard trees and berries from 
this source was used in Appendix D to establish planting dates and season lengths for apples and 
strawberries, respectively. The information is presented in Table D-1. This source was required 
because the information for apples and strawberries was not included in the primary information 
sources for growing season lengths used in this analysis (i.e., state Cooperative Extension 
Services and NASS). Information from this source is technically defensible, and appropriate for 
this analysis for the following reasons: 
 
The FAO is one of the largest specialized agencies in the United Nations system and the 
lead agency for agriculture and rural development, and it is considered a source of 
established fact data. Included in its many functions are collection, analysis, 
interpretation, and dissemination of information relating to nutrition, food, agriculture, 
forestry, and fisheries. The Organization serves as a clearing-house, providing farmers, 
scientists, government planners, traders and non-governmental organizations with the 
information they need to make rational decisions on planning, investment, marketing, 
research, and training. 
 
A series of Irrigation and Drainage Papers were written by experts in the various 
related fields of study and published by the FAO. Crop Evapotranspiration (Allen et al. 
1998 [DIRS 157311], FAO Irrigation and Drainage Paper 56) describes comprehensive 
guidelines for determining crop water requirements. Planting dates and growing season 
lengths for several crops and several climatic zones are found in Table 11, pp. 104 to 
108. Information was selected for orchard fruit trees and berries in arid climatic zones 
that are consistent with the current arid conditions in Amargosa Valley. This 
information is appropriate because the growing season information includes the 
appropriate climate zones, and the growing season information is reasonable for an arid 
climate. 
Use of this growing season information and discussions of uncertainty associated with its use, is 
further described in Appendix D and Section 6.4. 

Grapes, Grains, and Cattle ForageInformation regarding planting and harvesting dates for 
grapes, barley, winter wheat, oat hay, and alfalfa in southern Nye County, Nevada are from 
interviews with farmers in southern Nye County (LeStrange 1997 [DIRS 125452] and 1997 
[DIRS 125429]). These data were used in Appendix D to establish growing season lengths for 
grapes, barley, winter wheat, oat hay, and alfalfa and are presented below and in Appendix D, 
Section 2.1.1. This information is appropriate for this analysis because it is specific to the Yucca 
Mountain region. The data were obtained from sources not associated with the Yucca Mountain 
Project (YMP) and require qualification for use in this analysis per AP-SIII.9Q, Scientific 
Analysis. The following information was considered to evaluate whether the data sources were 
reliable and to confirm through corroboration that the data are suitable for use in this analysis. 
 
Reliability of Data SourcesInformation on alfalfa, barley, winter wheat, and oat hay 
was obtained from a farmer in southern Nye County with forty years of farming 
experience (LeStrange 1997 [DIRS 125429]). Information on bloom and harvest dates 
for grapes were provided by the founder of the Pahrump Valley Vineyards in southern 
Nye County (LeStrange 1997 [DIRS 125452]). Grapes for several wine varieties have 
been successfully produced at this vineyard since 1990. The number of years that both 
of these sources have been farming in southern Nye County is long enough to have 
gained experience under a variety of conditions that might occur in the area, including 
drought, normal, and above average precipitation years. Their success in southern Nye 
County and experience are such that it is concluded that the data sources are reliable for 
use in this analysis. 
 
Availability of Corroborating DataBecause variation in planting and harvest periods 
among years is common for arid to semi-arid environments, ranges for average planting 
and harvest times are generally reported in 20 to 60 day intervals (see Table D-1 for 
examples). Therefore, planting and harvest dates from arid to semi-arid environments 
were considered to corroborate the planting and harvest dates from the local farmers if 
they differed by one month or less. 
AlfalfaAccording to the southern Nye County farmer, alfalfa irrigation begins in early 
February. The first cutting occurs around mid-April with six to seven cuttings per year. 
According to Schmierer et al. (1997 [DIRS 160479], pp. 9 through 18), and Allen et al. 
(1998 [DIRS 157311], Table 11, p. 107), initiation of spring growth or planting of alfalfa in 
semi-arid climates is recommended when temperatures are -3 C to -4 C. The last harvest of 
the growing season should occur four to six weeks before the first killing frost (Schmierer et 
al. 1997 [DIRS 160479], pp. 9 through 18). Using mean monthly temperature data from 
Yucca Mountain meteorological monitoring Site 9 (see Section 4.1.5.1) the lowest minimum 
temperatures occur in January (mean minimum temperature 1.1 C) and December 
(mean minimum temperature = 0.8). Because the mean minimum temperatures are not low 
enough for killing frosts to occur, the potential growing period for alfalfa in Amargosa 
Valley is January through December. Additionally, Allen et al. (1998 [DIRS 157311], 
Table 11, p. 107), lists January as the planting month for alfalfa in California, with an 
approximate time between cuttings of 60 days. This corresponds to about six cuttings per 
year. Therefore, the growing season information for alfalfa given by the local farmer in 
LeStrange (1997 [DIRS 125429]) is reasonable for an arid climate. 

Barley, winter wheat, and oatsAccording to the southern Nye County farmer, one crop of 
barley, winter wheat, and oats can be produced per year in southern Nye County. Winter 
wheat and barley are planted in October and harvested in June, and oats are planted in March 
or April and harvested in June. Usual planting and harvesting periods for field crops are 
provided for most states in USDA NASS (1997 [DIRS 169307]). Because the arid climates 
of Arizona and California are similar to that of Amargosa Valley, these two states are used as 
analogues to corroborate growing season information for barley, winter wheat, and oats from 
the local farmer. For barley, the beginning of the planting season is September 5, November 
10, and September 15 for Nevada, Arizona, and California, respectively (USDA NASS 1997 
[DIRS 169307], Table: Barley, Fall: Usual Planting and Harvest Dates, by State, no page). 
The beginning of the harvest period is July 10 for Nevada, and May 15 for Arizona and 
California. The dates are the same for winter wheat with the exception of California where 
the beginning of the planting and harvest seasons are October 20 and June 15, respectively 
(USDA NASS 1997 [DIRS 169307], Table: Wheat, Winter: Usual Planting and Harvest 
Dates, by State, no page). These planting and harvest periods are within the same month or 
within one month of the October planting and June harvest for barley and winter wheat 
provided by the local farmer. There was no information on oats for Nevada, Arizona, or 
California, so information provided for other hay is used. Only harvest information was 
provided for other hay. The beginning of the harvest period was June 15 for Nevada, 
February 15 for Arizona, and May 15 for California (USDA NASS 1997 [DIRS 169307], 
Table: Hay, Other: Usual Planting and Harvest Dates, by State, no page). Harvest periods 
beginning in June and May for Nevada and California corroborate the harvest period for oats 
provided by the local farmer. The harvest period beginning in February for Arizona might be 
due to a wider variety of other hay grown in that state. Based on the above information, the 
growing season data for barley, winter wheat, and oats provided by the local farmer are 
reasonable for an arid climate 
GrapesAccording to the founder of the winery, grapes bloom in March to early April and 
are harvested late August to early September in southern Nye County. This is corroborated 
by the planting period (March) and growing season length (205 days) suggested for grapes 
grown in California by Allen et al. (1998 [DIRS 157311], Table 11, p. 107). Therefore, the 
growing season information for grapes provided by the owner of the local vineyard is 
reasonable for an arid climate. 
The data sources have several years of experience farming under conditions in southern Nye 
County and are considered reliable, and the data are corroborated by published, reliable 
sources. Therefore, it is concluded that the data are considered suitable and qualified for the 
specific application in this analysis. 
4.1.4.2 Growing Season - Upper Bound Glacial Transition Climate 
Planting season of most garden crops for upper bound glacial transition climate conditions were 
obtained from Vegetable Gardening (Washington State University Cooperative Extension 2002 
[DIRS 159256], p. 2). This document was published by Washington State University 
Cooperative Extension in Spokane County, which is considered a source of established fact data 
(see source justification in Section 4.1.4.1). It lists ranges of suggested planting dates for garden 
crops in eastern Washington. This source is appropriate because it contains information for a 

large selection of garden crops and is specific to eastern Washington. These, and all other data 
described in this subsection, are presented in Table D-2. 
Growing season length of most garden crops for future climatic conditions are from Home 
Gardens (Antonelli et al. 1998 [DIRS 158654], Table 2), a guide to gardening in Washington 
published by Washington State University Cooperative Extension, which is considered a source 
of established fact data. This source was selected because it contains a comprehensive list of 
season lengths for most garden crops grown in Washington. 
Planting dates for apples, grapes, and strawberries are derived from the midpoint of the Usual 
Planting Dates in the 1999 Annual Bulletin: Usual Planting & Harvesting Dates, Washington 
(Washington Agricultural Statistics Service 1999 [DIRS 152232]). This document was 
published by the USDA NASS, which is considered a source of established fact data 
(see Section 4.1.4.1 for source justification). This information was selected because it is 
representative of agricultural practices in Washington. 
Growing season for winter wheat and spring barley are from the 1995 Crop Rotation Budgets for 
Eastern Whitman County, Washington (Painter et al. 1995 [DIRS 158674], Tables A1 and A4). 
This document was published by Washington State University Cooperative Extension, which is 
considered a source of established fact data. Season length was calculated as the length of time 
between the midpoints of planting and harvesting months. This source was selected because it 
provides crop- and site-specific information for the county where two future-climate analogue 
weather stations (Rosalia and St. Johns) are located. 
Growing season for apples, grapes, strawberries, feed corn, oats, and oat hay are from the 1999 
Annual Bulletin: Usual Planting & Harvesting Dates, Washington (Washington Agricultural 
Statistics Service 1999 [DIRS 152232]), published by the USDA NASS. The midpoints of 
Usual Planting Dates and Most Active Usual Harvesting Dates are used to define planting 
and harvest dates, respectively. These data were selected because they are representative of 
agricultural practices in Washington. 
AlfalfaGrowth initiation dates, final harvest dates, and cutting schedules that are typical for 
alfalfa grown in the Intermountain West are reported in Intermountain Alfalfa Management 
(Schmierer et al. 1997 [DIRS 160479], pp. 9 to 18; Orloff and Marble 1997 [DIRS 158655], 
pp. 106 to 107). This manual was published by the University of California Division of 
Agriculture and Natural Resources, which is considered a source of, established fact data. The 
intent of the manual was to provide a comprehensive guide to alfalfa production and 
management that could be used by growers, advisors, and consultants. The growing season 
information for alfalfa from this source was used in Appendix D to establish cut schedules and 
growing season lengths for alfalfa and in Section 6.4 to develop the growing time distribution for 
cattle forage. This source was required because information on alfalfa was not included in the 
primary data sources for growing season lengths used in this analysis (i.e., state Cooperative 
Extension Services and the USDA NASS). 
Initiation of growth and cutting schedules for alfalfa from Intermountain Alfalfa Management 
(Schmierer et al. 1997 [DIRS 160479], pp. 9 to 18; Orloff and Marble 1997 [DIRS 158655], 
pp.106 to 107) are for conditions that are similar to those in eastern Washington (upper bound 

glacial transition climate analogue). Recommendations for initiation of spring growth or 
planting, and the last harvest of the growing season are based on temperatures (Schmierer et al. 
1997 [DIRS 160479] pp. 9 to 18) and are corroborated by Allen et al. (1998 [DIRS 157311], 
Table 11, p. 107). This allows the use of future climate information (Section 4.1.5) to determine 
appropriate dates for initiation of spring growth and the date of the last harvest. This is an 
appropriate source because it provides information that can be used with site specific data to 
determine initiation of growth and harvest dates, and it is corroborated by another published and 
technically defensible source (Allen et al. 1998 [DIRS 157311]), see Section 4.1.4.1 for source 
justification). Use of this information, and discussions of uncertainty associated its use, is 
further described in Appendix D and Section 6.4. 
4.1.5 Climate Information 
The primary source for climate information for future climate states (see introduction to Section 
6 for descriptions of future climate analogues [upper bound monsoon, lower bound of the glacial 
transition, and upper bound of the glacial transition]) is the National Oceanic and Atmospheric 
Administration (NOAA) and its agencies (e.g., National Climatic Data Center and the Western 
Regional Climate Center). This source is appropriate because the National Climatic Data Center 
serves as the repository for all NOAA meteorological information collected routinely from 
governmental agencies (e.g., Department for Commerce and Department of Defense) and private 
sources (e.g., National Cooperative Observer Program) and is considered a source of established 
fact data. The meteorological information undergoes quality control processing before being 
made available for public, private, or commercial use. This organization is recognized as the 
best source of national meteorological data by all agencies of the United States Government, and 
the data are accepted in the United States courts as interpreted by qualified experts. Weather 
data are used in Appendices C and E. 
4.1.5.1 Present-Day Climate 
Climate data collected at Yucca Mountain meteorological monitoring Site 9, were used to 
calculate reference evapotranspiration (ETo, see Appendix C) for present-day climatic conditions 
(i.e., present-day) at Yucca Mountain. This site is at an elevation of 838 m (2,750 feet) 
(CRWMS M&O 1999 [DIRS 102877], Table 1-1 on p. 6), near the southwestern corner of the 
Nevada Test Site at the approximate boundary of the accessible environment defined in 10 CFR 
63.302 [DIRS 156605]. Measurements of annual precipitation used in the biosphere model for 
the present-day climate were lower than those considered in the modeling of infiltration rates at 
Yucca Mountain (e.g., BSC 2004 [DIRS 170007], Table 6-8) because the location of the 
reasonably maximally exposed individual (RMEI) required by 10 CFR 63.312(a) [DIRS 156605] 
is at a lower elevation than the area of water infiltration above the repository. The location of the 
RMEI was used as the point of measurement for precipitation data to satisfy 10 CFR 63.102(i) 
[DIRS 156605] which states in part that The environment inhabited by the RMEI, along with 
associated human exposure pathways and parameters, make up the reference biosphere, as 
described in section 63.305. The following data were used: mean, minimum, and maximum 
temperature; minimum and maximum relative humidity; solar radiation; mean precipitation; and 
mean wind speed (DTN: MO04019SUM9397.000 [DIRS 167054]). Average monthly values 
were based on five years (1993-1997) of data with the following exceptions: For June, mean 
temperatures, average minimum temperatures, average maximum relative humidity, and average 

wind speed were based on four years of data (1994 - 1997). For July, mean temperatures and 
average minimum temperatures were based on four years of data (1994 - 1997). The data 
collection and analysis methods are described in BSC (2004 [DIRS 167055]). These data are 
appropriate because they were collected at the southernmost Yucca Mountain meteorological 
site, located in the valley bottom in northern Amargosa Valley and therefore are consistent with 
the current arid conditions of the Yucca Mountain region. The data are presented in Table 4.1-2. 
Table 4.1-2. Average Monthly Weather Data for Present-Day Climate 
Month 
Temperaturea 
(C) 
Relative Humiditya 
(%) Solar 
Radiationa 
(MJ/m2/day) 
Wind 
Speeda 
(m/s) 
Precipitationa 
(mm)Mean Max Min Max Min 
January 7.0 13.5 1.1 62.2 39.1 9.6 3.9 23.4 
February 9.6 16.5 3.0 55.2 27.6 13.9 4.3 17.1 
March 13.6 21.5 5.8 48.3 19.9 19.5 4.4 11.7 
April 16.7 24.6 8.0 37.9 13.7 24.6 4.7 3.0 
May 22.1 30.1 12.9 38.7 14.1 27.5 4.6 5.6 
June 27.3 35.3 16.8 27.2 8.7 30.0 4.9 7.6 
July 31.2 39.2 21.1 23.9 7.3 29.6 4.5 0.5 
August 30.5 38.9 21.0 24.2 8.0 27.0 4.7 0.3 
September 25.4 33.8 16.8 30.5 11.3 22.8 4.4 9.1 
October 17.7 25.9 9.7 33.3 13.8 17.4 4.2 5.4 
November 10.6 18.3 3.7 47.6 23.4 11.9 4.0 7.1 
December 6.9 13.9 0.8 54.4 28.0 9.6 4.0 11.7 
Source DTN: MO04019SUM9397.000 [DIRS 167054]. Weather data collection and summary methods are in 
BSC (2004 [DIRS 167055]). 
a 
Data were collected at Yucca Mountain Meteorological Monitoring Site 9. 
4.1.5.2 Upper Bound Monsoon Climate 
Information from Nogales and Tucson, Arizona weather stations were used to calculate ETo for 
the upper bound monsoon climate. Average monthly values were based on eight (wind speed) 
and 29 (remaining variables) years of information for Nogales, and 48 years of information for 
Tucson. Mean, minimum, and maximum temperature, mean precipitation, and mean wind speed 
were used from Nogales. Mean sunshine duration was used from Tucson. This information is 
appropriate because it is from the upper bound monsoon climate analogue weather station having 
the longest and most complete record (BSC 2004 [DIRS 170002], Table 6-1 and Section 6.6.2) 
and from a nearby weather station. The information for both sites was obtained from the 
Western Regional Climate Center (2003 [DIRS 162307]), (2003 [DIRS 162301]), and (2003 
[DIRS 162302]), which is cooperatively run by the Desert Research Institute of the University of 
Nevada, Reno, and the National Climatic Data Center of the NOAA. The information is 
presented in Table 4.1-3. 

Table 4.1-3. Average Monthly Weather Data for Upper Bound Monsoon Climate 
Month 
Temperaturea 
(C) Percent of 
Possible 
Sunshine 
Wind Speedb 
(m/s) 
Precipitationc 
(mm)Mean Max Min 
January 7.5 17.7 -2.7 80 2.01 33.3 
February 9.2 19.5 -1.2 82 2.95 27.7 
March 11.5 21.8 1.1 86 3.00 25.4 
April 14.7 25.7 3.6 92 2.95 12.4 
May 18.7 30.1 7.3 93 3.04 8.1 
June 23.9 35.4 12.4 93 2.95 13.7 
July 26.1 34. 6 17.5 78 2.32 108.5 
August 25.3 33.4 17.2 80 2.06 107.7 
September 22.8 32.3 13.2 87 2.24 42. 7 
October 17.1 27.8 6.4 88 2.46 46.7 
November 11.2 22.0 0.3 85 1.92 19.8 
December 7.8 18.1 -2.4 79 2.24 37.3 
Sources: Western Regional Climate Center (2003 [DIRS 162307] [temperature and precipitation from Nogales, 
Arizona]), (2003 [DIRS 162301] [percent of possible sunshine from Tucson, Arizona]), and (2003 
[DIRS 162302] [wind speed from Nogales, Arizona]). 
a 
Temperature was converted from F to C (C = [F-32]/1.8). 
b Wind speed was converted from mph to m/s (m/s = 0.447mph). 
c 
Precipitation was converted from inches to millimeters (mm = inches x 2.54 x 10). 
4.1.5.3 Lower Bound Glacial Transition Climate 
Information from Delta, Utah and Milford, Utah weather stations was used to calculate ETo for 
the lower bound glacial transition climate. Average monthly values based on 30 years of 
information for Delta and eight years of information for Milford were used. Mean minimum, 
and maximum temperature, mean dewpoint temperature, and mean precipitation were used from 
Delta. Mean temperature was calculated from the mean minimum and mean maximum 
temperatures. Mean sunshine duration and mean wind speed were used from Milford. This 
information is appropriate because it is from the future-climate analogue weather station having 
the longest and most complete record (BSC 2004 [DIRS 170002], Table 6-1 and Section 6.6.2) 
and from a nearby weather station. The information for Delta was obtained from the National 
Weather Service (2003 [DIRS 162299]) and the Western Regional Climate Center (2003 
[DIRS 162302]). The information for Milford was obtained from the Western Regional Climate 
Center (2003 [DIRS 162300]). The information is presented in Table 4.1-4. 

Table 4.1-4. Average Monthly Weather Data for Lower Bound Glacial Transition Climate 
Month 
Temperaturea 
(C) 
Monthly Dew 
Point 
Temperaturea 
(C) 
Percent of 
Possible 
Sunshine 
Wind Speedc 
(m/s) 
Precipitationd 
(mm)Meanb Max Min 
January -3.1 3.9 -10.0 -8.3 58 4.87 15.7 
February -0.3 6.7 -7.2 -6.7 64 4.74 14.5 
March 4.2 11.7 -3.3 -5.6 63 5.10 20.8 
April 9.7 17.8 1.7 -1.7 69 5.05 20.1 
May 14.7 23.3 6.1 1.1 73 5.45 21.3 
June 19.4 28.9 10.0 1.7 82 5.54 12.7 
July 24.7 34.4 15.0 7.8 77 5.36 6.6 
August 23.6 33.3 13.9 5.6 79 4.92 10.7 
September 18.3 28.3 8.3 2.8 80 4.43 10.4 
October 11.1 20.0 2.2 -0.6 76 4.65 20.8 
November 2.8 10.6 -5.0 -3.9 62 4.20 11.4 
December -1.4 5.6 -8.3 -6.7 60 4.38 16.8 
Sources: National Weather Service (2003 [DIRS 162299] [temperatures and precipitation from Delta, Utah]); 
Western Regional Climate Center (2003 [DIRS 162302] [wind speed from Milford, Utah]); Western 
Regional Climate Center (2003 [DIRS 162300] [percent of possible sunshine from Milford, Utah]). 
a 
Temperature was converted from F to C (C = [F-32]/1.8). 
b Mean temperature was calculated from the maximum and minimum temperatures. 
c 
Wind speed was converted from mph to m/s (m/s = 0.447mph). 
d 
Precipitation was converted from inches to millimeters (mm = inches x 2.54 x 10). 
4.1.5.4 Upper Bound Glacial Transition Climate 
Average monthly values based on 36 to 48 years of weather information collected at the Spokane 
International Airport were used to calculate ETo for the upper bound of the glacial transition 
climate (see Appendix C). The following information was used: mean, minimum, and 
maximum temperature; mean, minimum, and maximum relative humidity; mean sunshine 
duration; mean wind speed; and mean precipitation. This information is appropriate because it is 
from the future-climate analogue weather station having the longest and most complete record 
(BSC 2004 [DIRS 170002], Table 6-1 and Section 6.6.2). The information was obtained from 
the Western Regional Climate Center (1997 [DIRS 152233]), which is cooperatively run by the 
Desert Research Institute of the University of Nevada, Reno, and the National Climatic Data 
Center of the NOAA. The weather information is presented in Table 4.1-5. 

Table 4.1-5. Average Monthly Weather Data for Upper Bound Glacial Transition Climate 
Month 
Temperaturea 
(C) 
Relative 
Humidity (%) Percent of 
Possible 
Sunshine 
Wind 
Speedb 
(m/s) 
Precipitationc 
(mm)Mean Max Min Max Min 
January -2.7 0.7 -6.2 86 79 28 3.93 50.3 
February 0.7 4.8 -3.4 85 69 41 4.11 37.8 
March 3.7 8.7 -1.3 81 54 55 4.29 37.8 
April 7.7 13.9 1.5 77 44 61 4.47 30.0 
May 12.2 18.8 5.5 77 40 65 4.11 35.8 
June 16.7 23.7 9.6 75 36 67 4.16 32.0 
July 20.4 28.4 12.4 65 28 80 3.84 17.0 
August 20.2 28.1 12.4 63 28 78 3.71 18.3 
September 14.9 22.2 7.7 71 34 72 3.66 18.5 
October 8.5 14.8 2.2 79 49 55 3.66 25.2 
November 1.7 5.2 -1.8 87 76 29 3.89 54.6 
December -2.3 1.0 -5.7 88 83 23 3.89 61.5 
Source: Western Regional Climate Center (1997 [DIRS 152233]). 
a 
Temperature was converted from F to C (C = [F-32]/1.8). 
b Wind speed was converted from mph to m/s (m/s = 0.447mph). 
c 
Precipitation was converted from inches to millimeters (mm = inches x 2.54 x 10). 
4.1.6 Soil Infiltration Rate 
Information from the USDA Natural Resources Conservation Service (NRCS) on permeability 
rate of soil surface layers (0 - 15 cm) of soils in Amargosa Valley was used in Section 6.6 to 
develop a distribution of irrigation intensity. Permeability rate of the soil surface layers 
(measured in cm per hour) was used as a measure of infiltration, which is defined as the 
downward entry of water into the soil (Brady and Weil 1999 [DIRS 160019], p. 844). The 
permeability rates are from an unpublished soil survey of Amargosa Valley and were obtained 
directly from the Nevada Office of the NRCS (Dollarhide 1999 [DIRS 159253]). The NRCS is 
considered a source of established fact data. The permeability rates are appropriate measures of 
infiltration rates because they are specific to surface soils in northern Amargosa Valley and 
because they were collected by the federal agency with expertise in evaluating and describing 
soils. These rates are used to determine the feasible range of sprinkler output rates for soils in 
Amargosa Valley and are presented in Section 6.6. 
4.1.7 Salinity of Irrigation Water 
Electrical conductivity (EC) is commonly used to estimate water and soil salinity. Wells in the 
Amargosa and Yucca Mountain areas were drilled and monitored for salinity levels (among other 
variables) for the Nye County Early Warning Drilling Program (DTN: LA0206AM831234.001 
[DIRS 160051]). Electrical conductivity was sampled on three dates from three well zones at 
Well number NC-EWDP-19D, located in the southwest corner of the Nevada Test Site (within 
the region being evaluated for the receptor population). Average well water salinity, as reflected 
by the mean measurement of EC from these samples (EC = 0.44 dS/m, 
DTN: LA0206AM831234.001 [DIRS 160051]) rounded up to the nearest tenth 

(EC = 0.50 dS/m) was used in the calculations of crop leaching requirements (Appendix E). The 
values of EC were converted from units of S/cm to dS/m by multiplying the values in S/cm 
by 10-3 (1 dS/m = 103 S/cm). The leaching requirement uses the salinity of irrigation water 
and crop tolerance to salts to calculate the amount of water needed to flush salts below the 
rooting zone. It is used as the overwatering rate when precipitation does not meet leaching 
requirements (Section 6.9 and Appendix E). It is also added to the annual average and daily 
average irrigation rates when precipitation does not meet leaching requirements (Sections 6.5, 
6.8, and Appendix E). 
The EC data from this source are corroborated by salinity measurements from 31 irrigation or 
domestic wells located in the town of Amargosa Valley (formerly Lathrop Wells) or west of 
State Route 373 and south of Highway 95 in Amargosa Valley (McKinley et al. 1991 
[DIRS 116222], pp. 9 to 17). Average well water salinity for these 31 wells was 0.51 dS/m 
(converted from S/m). Thus, the data from the Nye County Early Warning Drilling Program 
are representative of local conditions. Additionally, irrigation calculations are relatively 
insensitive to salinity values that are below the tolerance levels of the crops under consideration. 
Salinity tolerances for the crops used in this analysis ranged from 1.0 dS/m for carrots and 
strawberries to 8.0 dS/m for barley (Allen et al. 1998 [DIRS 157311], Table 23, pp. 178 to 180). 
Therefore, the mean well water salinity value was lower than the salinity tolerances for the crops 
under consideration, making leaching requirements minimal. 
4.1.8 Crop Yield 
Information from the USDA NASS (USDA 1998 [DIRS 158648], 1998 [DIRS 158649], 1999 
[DIRS 158650], 1999 [DIRS 158647], 2000 [DIRS 158646], 2000 [DIRS 158653], 2001 
[DIRS 158645], 2001 [DIRS 158651], 2002 [DIRS 158652] [see Section 4.1.4.1 for source 
justification]) was used to develop distributions of yield (wet edible biomass), as described in 
Section 6.11. The USDA NASS is considered a source of established fact data. Yields of 
commercially produced crops during five years (1995 - 1999) from up to four states (Arizona, 
California, Nevada, and Washington) with arid to semi-arid conditions were selected. These 
yield values are appropriate because they were developed from a large dataset of information on 
crop production (yield) over a wide range of semi-arid to arid conditions and therefore include 
variation due to changes in weather and agricultural practices. Information from Arizona and 
California were used in addition to that from Nevada and Washington because sufficient 
information for many crops was not available from Nevada and Washington. Information from 
gardens was not used because the methods used to develop the limited available information 
generally were not defined and the yield values therefore were of unknown quality. The yield 
values and the USDA sources are presented in Tables 6.11-1 through 6.11-6. 
4.1.9 Tillage Depth 
Information from the University of Georgia, the University of Ohio, and Washington State 
University Cooperative Extension Services was used to develop the distribution for tillage depth, 
as described in Section 6.10. University Cooperative Extension Services are considered sources 
of established fact data (see Section 4.1.4.1 for source justification). Conventional tillage depth 
is cited as 25 to 30 cm (Lang et al. 1999 [DIRS 160031], p. 3; Granberry et al. 2000 
[DIRS 160033], p. 8; Johnson 1999 [DIRS 160029], Chapter 8, p. 1). This information is 

appropriate because it shows that there is little variation in conventional tillage depths, and that 
common tillage or plowing implements are designed to mix the soil to depths of 25 to 30 cm. 
Additionally, information on tillage depths from these non-site specific sources is appropriate 
because the use of irrigation and fertilizer in the Amargosa Valley would tend to make the site 
less distinguishable from other, more temperate areas. 
4.1.10 Irrigation Methods 
Information from University Cooperative Extension Service State Extension Offices was used in 
Section 6.3 to determine methods commonly used to irrigate commercial and garden crops 
(see Section 4.1.4.1 for source justification). Information on irrigation methods for leafy 
vegetables and other vegetables was selected from Martin et al. 1999 ([DIRS 159383], 1999 
[DIRS 159384], 1999 [DIRS 159382]), Mayberry 2000 ([DIRS 159386], 2000 [DIRS 159388]), 
Teegerstrom and Umeda 2001 ([DIRS 159392]), Teegerstrom et al. 2001 ([DIRS 159391]), and 
Hinman et al. 1997 ([DIRS 159376]). Information on irrigation methods for fruits was selected 
from Klonsky and De Moura 2001 ([DIRS 159381]), Mayberry 2000 ([DIRS 159389], 
2000 [DIRS 160005]), Teegerstrom and Umeda 2001 ([DIRS 159392]), Teegerstrom et al. 2001 
([DIRS 159391]), Uriu and Magness 1967 ([DIRS 159169], pp. 697 to 698), and Wolf and 
Johnson 1999 ([DIRS 159393], p. 5). This information was selected because it comes from a 
variety of arid and semi-arid conditions, was prepared by agriculture professionals, and the 
Cooperative Extension Services are considered sources of established fact data. The irrigation 
methods are described in Section 6.3. 
Data collected on irrigation methods in Amargosa Valley during surveys conducted for the 
Radiological Monitoring Program (DTN: MO0208SPAMETHO.004 [DIRS 159565]) were used 
in Section 6.3 to determine methods commonly used to irrigate grains and cattle forage. 
This information was selected because it is site specific for the Amargosa farming community. 
The data are presented in Table 4.1-6. 
Table 4.1-6. Acres Irrigated in Amargosa Valley 
Irrigation Method 
Crop Type Sprinkler Drip Surface No Data Total 
Grains and Forage 1,697.5 225.5 43.1 1,966.1 
Fruits and Nuts 37.0 2.0 83.9 122.9 
Leafy and other Vegetables 0.3 0.3 
To be Planted 58.0 87.1 145.1 
Fallow 420.3 204.5 624.8 
Sod 126.2 69.2 195.4 
Total 2,302.0 37.0 314.6 401.0 3,054.5 
DTN: MO0208SPAMETHO.004 [DIRS 159565]. 

4.1.11 Rooting Depth 
Ranges for maximum effective rooting depths (m) for crops used in this analysis were taken 
from Allen et al. 1998 ([DIRS 157311], Table 22, pp. 163 to 165). The low end of each range 
was selected for each crop. The ranges are presented in Table 4.1-7. Maximum effective rooting 
depths were used to develop the distribution for rooting depth in Section 6.12, and in Appendix E 
to calculate effective precipitation, available water in the root zone, irrigation application, and 
overwatering rates. 
Table 4.1-7. Maximum Effective Rooting Depths (m) 
Crop Depth (Range) Crop Depth (Range) 
Alfalfa 1.0 - 2.0 Grapes 1.0 - 2.0 
Apples 1.0 - 2.0 Lettucea 0.3 - 0.5 
Barley 1.0 - 1.5 Melons 0.8 - 1.5 
Bell peppers 0.5 - 1.0 Oats 1.0 - 1.5 
Bermuda grass 0.5 - 1.0 Onions 0.3 - 0.6 
Broccoli 0.4 - 0.7 Potatoes 0.4 - 0.6 
Cabbage 0.5 - 0.8 Spinach 0.3 - 0.5 
Carrots 0.5 - 1.0 Squash 0.6 - 1.0 
Cauliflower 0.4 - 0.7 Strawberries 0.2 - 0.3 
Celery 0.3 - 0.5 Sweet corn 0.8 - 1.2 
Field corn 1.0 - 1.7 Tomatoes 0.7 - 1.5 
Corn silage 1.0 - 1.7 Winter wheat 1.5 - 1.8 
Cucumbers 0.7 - 1.2 
Fescue 0.5 - 1.0 
Source: Allen et al. 1998 ([DIRS 157311], Table 22, pp. 163 to 165). 
a 
Head lettuce or leaf lettuce not specified. 
Crop Evapotranspiration, Guidelines for Computing Crop Water Requirements (Allen et al. 
1998 [DIRS 157311], FAO Irrigation and Drainage Paper 56, see Section 4.1.4.1 for source 
justification) describes comprehensive guidelines for determining crop water requirements. 
Ranges of maximum effective rooting depths for most of the representative crops and turf used 
in this analysis are provided in Allen et al. (1998 [DIRS 157311], Table 22, pp. 163 to165). 
Allen et al. (1998 [DIRS 157311]) recommended using the smaller range values for irrigation 
scheduling because a large percentage of root biomass and activity occurs in the upper portion of 
the rooting zone. Therefore, the smaller values for rooting depth were selected for this analysis. 
This source is appropriate for use in this analysis because it is one of several Irrigation and 
Drainage Papers published by the FAO, a leading agency for agriculture in the United Nations 
system, and it is considered a source of established fact data. Additionally, similar rooting 
depths to those reported in Allen et al. (1998 [DIRS 157311]) are described as typical by the 
American Society of Civil Engineers (Jensen et al. 1990 [DIRS 160001], Table 2.7, pp. 22 to 
23), and are supported by Bishop and Beetham (1989 [DIRS 160038], Table 20, no page 
number), and Hagan et al. (1967 [DIRS 160037], various chapters). 

4.2 CRITERIA 
Table 4.2-1 lists the requirements from the Project Requirements Document (Canori and Leitner 
2003 [DIRS 166275]) that are applicable to this analysis. These requirements are for compliance 
with applicable portions of 10 CFR Part 63 [DIRS 156605]. 
Table 4.2-1. Requirements Applicable to this Analysis 
Requirement Number Requirement Title Related Regulation 
PRD-002/T-015 Requirements for Performance Assessment 10 CFR 63.114 
PRD-002/T-026 Required Characteristics of the Reference Biosphere 10 CFR 63.305 
PRD-002/T-028 Required Characteristics of the Reasonably Maximally 
Exposed Individual 
10 CFR 63.312 
Source: Canori and Leitner 2003 ([DIRS 166275], Table 2-3). 
In addition to the requirements listed in Table 4.2-1, definition of terms in 10 CFR 63.2 and 
description of concepts in 10 CFR 63.102 (DIRS 156605) that are relevant to biosphere 
modeling are also applicable to this analysis. 
The acceptance criteria from Sections 2.2.1.3.13 (Redistribution of Radionuclides in Soil) and 
2.2.1.3.14 (Biosphere Characteristics) of the Yucca Mountain Review Plan, Final Report 
(YMRP) (NRC 2003 [DIRS 163274]) are based on meeting the requirements of 10 CFR 63.114, 
63.305, and 63.312 [DIRS 156605] as they relate to biosphere characteristics modeling. These 
criteria are listed to further describe how the requirements referenced in Table 4.2-1 should be 
met. Only those bulleted items from Sections 2.2.1.3.13 and 2.2.1.3.14 of the YMRP (NRC 
2003 [DIRS 163274]) that apply to this analysis are included here. Where a subcriterion 
includes several components, only some of those components may be addressed. How these 
components are addressed is summarized in Section 7.2 of this report. Section 2.3.1.3.11 of the 
YMRP (NRC 2003 [DIRS 163274]) (Airborne Transport of Radionuclides) is interpreted to 
apply only to airborne transport of radionuclides to the biosphere following a volcanic eruption; 
airborne transport of radionuclides within the biosphere is evaluated in the context of the review 
criteria in Section 2.3.1.3.14. Only those acceptance criteria and related explanations that apply 
to this analysis are listed. 
Section 2.2.1.3.13.3, Redistribution of Radionuclides in Soil 
Acceptance Criterion 1 - System Description and Model Integration Are 
Adequate 
Subcriterion 2. The total system performance assessment model abstraction 
identifies and describes aspects of redistribution of radionuclides in soil that are 
important to repository performance, including the technical bases for these 
descriptions. For example the abstraction should include modeling of the 
deposition of contaminated material in the soil and determination of the depth 
distribution of the deposited radionuclides. 

Acceptance Criterion 2 - Data are Sufficient for Model Justification 
Subcriterion 1. Behavioral, hydrological, and geochemical values used in the 
license application are adequately justified (e.g., irrigation and precipitation rates, 
erosion rates, radionuclide solubility values, etc.). Adequate descriptions of how 
the data were used, interpreted, and appropriately synthesized into the parameters 
are provided. 
Subcriterion 2. Sufficient data (e.g., field, laboratory, and natural analogue data) 
are available to adequately define relevant parameters and conceptual models 
necessary for developing the abstraction of redistribution of radionuclides in soil 
in the total system performance assessment. 
Acceptance Criterion 3 - Data Uncertainty in Characterized and Propagated 
Through the Model Abstraction 
Subcriterion 1. Models use parameter values, assumed ranges, probability 
distributions, and bounding assumptions that are technically defensible, 
reasonably account for uncertainties and variabilities, do not result in an under-
representation of the risk estimate, and are consistent with the characteristics of 
the reasonably maximally exposed individual in 10 CFR Part 63. 
Subcriterion 2. The technical bases for the parameter values and ranges in the 
total system performance assessment abstraction are consistent with data from the 
Yucca Mountain region [e.g., Amargosa Valley survey (Cannon Center for 
Survey Research 1997), studies of surface processes in the Fortymile Wash 
drainage basin; applicable laboratory testings; natural analogues; or other valid 
sources of data. For example, soil types, crop types, plow depths, and irrigation 
rates should be consistent with current farming practices, and data on the airborne 
particulate concentration should be based on the resuspension of appropriate 
material in a climate and level of disturbance similar to that which is expected to 
be found at the location of the reasonably maximally exposed individual, during 
the compliance time period. 
Subcriterion 3. Uncertainty is adequately represented in parameters for 
conceptual models, process models, and alternative conceptual models considered 
in developing the total system performance assessment abstraction of 
redistribution of radionuclides in soil, either through sensitivity analyses, 
conservative limits, or bounding values supported by data, as necessary. 
Correlations between input values are appropriately established in the total system 
performance assessment. 

Section 2.2.1.3.14.3, Biosphere Characteristics 
Acceptance Criterion 1 - System Description and Model Integration are 
Adequate 
Subcriterion 3. Assumptions are consistent between the biosphere characteristics 
modeling and other abstractions. For example, the U.S. Department of Energy 
should ensure that the modeling of features, events, and processes, such as climate 
change, soil types, sorption coefficients, volcanic ash properties, and the physical 
and chemical properties of radionuclides are consistent with assumption in other 
total system performance assessment abstractions. 
Acceptance Criterion 2 - Data are Sufficient for Model Justification 
Subcriterion 1. The parameter values used in the license application are 
adequately justified (e.g., behaviors and characteristics of the residents of the 
Town of Amargosa Valley, Nevada, characteristics of the reference biosphere, 
etc.) and consistent with the definition of the reasonably maximally exposed 
individual in 10 CFR Part 63. Adequate descriptions of how the data were used, 
interpreted, and appropriately synthesized into the parameters are provided. 
Subcriterion 2. Data are sufficient to assess the degree to which features, events, 
and processes related to biosphere characteristics modeling have been 
characterized and incorporated in the abstraction. As specified in 10 CFR Part 63, 
the U.S. Department of Energy should demonstrate that features, events, and 
processes, which describe the biosphere, are consistent with present knowledge of 
conditions in the region, surrounding Yucca Mountain. As appropriate, the U.S. 
Department of Energy sensitivity and uncertainty analyses (including 
consideration of alternative conceptual models) are adequate for determining 
additional data needs, and evaluating whether additional data would provide new 
information that could invalidate prior modeling results and affect the sensitivity 
of the performance of the system to the parameter value or model. 
Acceptance Criterion 3 - Data Uncertainty Is Characterized and Propagated 
Through the Model Abstraction 
Subcriterion 1. Models use parameter values, assumed ranges, probability 
distributions, and bounding assumptions that are technically defensible, 
reasonably account for uncertainties and variabilities, do not result in an under-
representation of the risk estimate, and are consistent with the definition of the 
reasonably maximally exposed individual in 10 CFR Part 63. 
Subcriterion 2. The technical bases for the parameter values and ranges in the 
abstraction, such as consumption rates, plant and animal uptake factors, mass-
loading factors, and biosphere dose conversion factors, are consistent with site 
characterization data, and are technically defensible. 

Subcriterion 4. Uncertainty is adequately represented in parameter development 
for conceptual models and process-level models considered in developing the 
biosphere characteristics modeling, either through sensitivity analyses, 
conservative limits, or bounding values supported by data, as necessary. 
Correlations between input values are appropriately established in the total system 
performance assessment, and the implementation of the abstraction does not 
inappropriately bias results to a significant degree. 
4.3 CODES, STANDARDS, AND REGULATIONS 
No codes, standards, or regulations other than those identified in the Project Requirements 
Document (Canori and Leitner 2003 [DIRS 166275], Table 2-3) and determined to be applicable 
(Table 4-3) were used in this analysis. 

INTENTIONALLY LEFT BLANK 

5. ASSUMPTIONS 
No assumptions were made in the absence of direct confirming data or evidence to develop the 
distributions of parameter values in this analysis. Other scientific analysis assumptions are 
described in Section 6 and Appendices C through E. 

INTENTIONALLY LEFT BLANK 

6. SCIENTIFIC ANALYSIS DISCUSSION 
This section describes the analyses conducted to develop average values and distributions for the 
twelve parameters considered in this report. The recommended parameter values are intended 
for use as inputs in the ERMYN biosphere model to support calculation of BDCFs for 
three climate states and for both the biosphere groundwater exposure scenario and volcanic ash 
exposure scenario. 
Seven of the parameters (dry biomass, dry-to-wet-weight ratios, fraction of overhead irrigation, 
growing time, irrigation application, daily irrigation rate, and yield) require separate distributions 
for the five crop types used in the biosphere model (leafy vegetables, root and other vegetables 
[hereafter called other vegetables], fruits, grain, and cattle forage). Five of the parameters 
(annual irrigation, irrigation intensity, overwatering rate, tillage depth, and rooting depth) are 
composite values with a single distribution representative of all crop types and turf. 
Much of the variation in these parameters is from differences among crops and much of the 
uncertainty is due to lack of knowledge about the specific crops a farmer or gardener will choose 
to grow (for example, see Section 6.5.2). To ensure that this variation and uncertainty is 
adequately addressed, the first step in this analysis was to select a set of crops for each crop type 
that is representative of the variation in types of plants likely to be grown under present-day and 
future climatic conditions. To ensure that parameters developed in this analysis are consistent 
with arid to semi-arid conditions of the present-day and predicted future climates, selection of 
these representative crops was based on an evaluation of crops grown in southern Nye County, 
Nevada and eastern Washington (upper bound glacial transition climate analogue), variation in 
the growing season in arid to semi-arid environments for commonly grown crops, and plant 
growth form. National patterns of food consumption were evaluated to support the selection. 
This analysis is described in Appendix A and the recommended crops are listed in Table 6-1. 
Average parameter values were calculated using these representative crops throughout the 
analysis. 
Development of the parameter distributions was based on values calculated for the representative 
crops, which resulted in the use of uniform, normal, and cumulative probability distributions, or 
fixed values. Minimum and maximum values were required for most of the parameter 
distributions to preserve biological meaning and avoid selection of nonsensical values. For 
example, minimum and maximum values were necessary for irrigation parameters so that values 
likely to result in yield reduction or crop mortality would not be selected. For irrigation 
parameters, minimum values tended to be closer to the distribution mean than maximum values 
because of crop sensitivity to water stress. Under these circumstances, non-symmetrical 
truncation of normal distributions and shifts in the calculated mean were avoided by using 
cumulative distribution functions to better represent the available data. Cumulative distribution 
functions were also used when it was suspected that the data did not meet the assumptions of the 
normal distribution. 
To calculate means and develop probabilities for cumulative distribution intervals for cattle 
forage, a 3 to 1 weighting process was used, where alfalfa was assigned a value of 3, and oat hay 
and corn silage were each assigned a value of 1. In Amargosa Valley, alfalfa totaled 67 to 
97 percent of the acreage planted in hay for 1996 through 1999 (Table A-1) and 2004 

(Appendix A). In Whitman and Spokane County, Washington (upper bound glacial transition 
climate analogues) alfalfa totaled 59 and 69 percent (respectively) of the acreage planted in hay, 
with very low percentages planted in oat hay and corn silage (Table A-3). Inclusion of values for 
oat hay and corn silage were necessary to account for uncertainties associated with crop selection 
and crop differences in parameter values. In some cases (e.g., yield), corn silage had very 
different parameter values compared to alfalfa. Because of this and the importance of alfalfa 
compared to corn silage and oat hay, weighting of means and probabilities was necessary to 
calculate averages and generate distributions for cattle forage. Weighting was not necessary for 
other crop types or for parameters developed from all 26 crops and turf because there was no 
information indicating that some crops were more common than others, or values within a crop 
type were similar, making weighting unnecessary. 
Table 6-1. Representative Crops 
Crop Type 
Representative Cropsa 
Crop Type 
Representative Crops 
Leafy Vegetables Fruits 
Broccoli Apples 
Cabbage Grapes 
Cauliflower Melons 
Celery Strawberries 
Head Lettuce Tomatoes 
Leaf Lettuce Grains 
Spinach Barley 
Other Vegetables Feed Corn 
Bell Peppers Oats 
Carrots Wheat 
Cucumbers Cattle Forage 
Onions Alfalfa 
Potatoes Corn silage 
Squash Oat hay 
Sweet Corn Home Irrigation 
Present-Day - Bermudagrass 
Future - Fescue 
a 
See Appendix A for information on selection of representative crops. 
Information from literature and field surveys was used to determine appropriate and reasonable 
values that are consistent with arid to semi-arid environments for each crop for dry biomass, 
dry-to-wet-weight ratios, growing time, tillage depth, rooting depth, and yield. The methods 
outlined in the FAO Irrigation and Drainage Papers 56 (Allen et al. 1998 [DIRS 157311]) and 
24 (Doorenbos and Pruitt 1977 [DIRS 103062]) were used to calculate crop water and irrigation 
supply requirements, respectively. Allen et al. 1998 ([DIRS 157311]) provides energy balance 
and mass transfer equations to calculate reference evapotranspiration (ETo) and lists crop 
coefficients (Kc), which are used to determine crop water requirements. These equations were 
recommended as the international standard for calculating ETo (Allen et al. 1998 
[DIRS 157311]) following an evaluation of several methods used to calculate evapotranspiration 
across a variety of climatic conditions (Jensen et al. 1990 [DIRS 160001]). Members of the 

International Commission for Irrigation and Drainage and the World Meteorological 
Organization were among the panel of experts that made the recommendations for revisions and 
improvements for calculation of ETo. The methods for calculating net irrigation and seasonal 
irrigation requirements in Doorenbos and Pruitt 1977 ([DIRS 103062]) are widely accepted and 
were used to complete the analysis to determine irrigation rates. Alternate technical methods and 
justification for use of the methods in Allen et al. 1998 ([DIRS 157311]) and Doorenbos and 
Pruitt 1977 ([DIRS 103062]) are in Appendix B. Variation and uncertainty associated with Kc 
are discussed in Section 6.5.2. Variation and uncertainty associated with ETo are discussed in 
Sections 6.5.2, 6.7.2, 6.8.2, and 6.9.2. 
Climate StatesTo ensure assumptions are consistent between biosphere modeling and other 
abstractions as described in the Yucca Mountain Review Plan, Final Report (NRC 2003 
[DIRS 163274], Section 2.2.1.3.14.3) parameters in this analysis were developed to support 
BDCF calculations for the three climate states used in TSPA (present-day interglacial, monsoon, 
and glacial transition (BSC 2003 [DIRS 166296], p. 79)). These climates and their predicted 
occurrence at Yucca Mountain in the future are described in Future Climate Analysis 
(BSC 2004 [DIRS 170002], Section 6.6.2). Analogue weather stations for the climates used in 
this analysis are identified in BSC 2004 ([DIRS 170002], Table 6-1 and Section 6.6.2). 
The present-day interglacial climate includes current conditions (BSC 2004 [DIRS 170002], 
Section 6.6.2) and is referred to as present-day climate in this report. Current conditions are 
characterized by hot, dry summers, warm winters, and have lower annual precipitation and 
higher annual temperatures than glacial transition climate states. Conditions for the present-day 
climate state were characterized using weather measurements taken at or near Yucca Mountain, 
and agricultural practices in southern Nevada and other arid southwestern regions that are 
consistent with the climate of Amargosa Valley (e.g., Imperial Valley California, Maricopa 
County Arizona). 
The lower bound monsoon climate state predicted to occur after the present-day interglacial 
climate state is also characterized by current conditions (BSC 2004 [DIRS 170002], Section 
6.6.2). Therefore, parameter distributions that are developed for present-day climate are also 
applicable to the lower bound monsoon climate. 
The upper bound monsoon climate is characterized by strong summer monsoons and warmer 
winter seasons with increased precipitation compared to the present-day climate (BSC 2004 
[DIRS 170002], Section 6.6.2). Recommended analogue weather stations for the upper bound 
monsoon climate are Nogales, Arizona and Hobbs, New Mexico. Temperature, precipitation, 
and wind speed data from the Nogales weather station were used in the analysis. Solar radiation 
data were not available from either the Nogales or Hobbs weather stations. Therefore, these data 
were obtained from the Tucson, Arizona weather station, which was the closest station to 
Nogales that had the required information. Agricultural practices in southern Nevada and other 
arid southwestern regions (e.g., Imperial Valley California, Maricopa County Arizona) that were 
used to characterize the conditions (i.e., crop selection and season lengths) for present-day 
climate were also used to characterize conditions for the upper bound monsoon climate. 
The lower bound glacial transition climate is semi-arid and characterized by predominantly 
winter precipitation. Precipitation for this climate state is higher and temperatures are cooler 

than for present-day climate (BSC 2004 [DIRS 170002], Section 6.6.2). The recommended 
weather stations for the lower bound glacial transition climate are Delta, Utah and Beowawe, 
Nevada. Temperature, precipitation, and dewpoint temperature from Delta were used in the 
analysis. Wind speed and solar radiation data were not available from either the Delta or 
Beowawe weather stations. Therefore, these data were obtained from the Milford, Utah weather 
station, which was the closest station to Delta that had the required information. Cold limiting 
temperatures (see Tables 4.1-4 and 4.1-5) that affect crop growth and season length occur during 
March through April in the spring and October in the fall for both Delta and Spokane (location of 
analogue weather station for the upper bound glacial transition climate, see below). Therefore, 
the agricultural practices (i.e., crop selection and season lengths) in east central Washington that 
were used in this analysis to characterize conditions for the upper bound glacial transition 
climate state were also used for the lower bound. 
The upper bound glacial transition climate is semi-arid and characterized by cool, wet winters, 
and warm to cool dry summers relative to present-day conditions (BSC 2004 [DIRS 170002], 
Section 6.6.2). Recommended analogue weather stations for the upper bound glacial transition 
climate (i.e., cooler and wetter) are Spokane, St. John, and Rosalia, Washington (BSC 2004 
[DIRS 170002], Table 6-1 and Section 6.6.2). Data from the Spokane weather station and 
agricultural practices in east central Washington were used in this analysis to characterize 
conditions for the upper bound glacial transition climate state. 
Biosphere dose conversion factors are developed for the three climate states used in TSPA 
(BSC 2004 [DIRS 169674], Section 6.1.3; BSC 2003 [DIRS 166296], p. 79). Distributions of 
parameters in this analysis that are affected by climate (growing time, irrigation application, 
annual irrigation rate, daily irrigation rate, and overwatering rate) were developed for the 
present-day climate and the upper bound of the glacial transition climate. In addition, means of 
annual average irrigation rate (which has a strong influence on BDCFs) were developed for the 
upper bound of the monsoon and lower bound of the glacial transition climates. These means, 
and the distributions for annual average irrigation rate for the present-day and upper bound 
glacial transition climates were used to develop BDCFs for the three climate states, as described 
in BSC 2004 ([DIRS 169674], Section 6.1.3). 
Biosphere Groundwater Exposure Scenario and Volcanic Ash Exposure ScenarioFive of 
the parameters in this analysis report (dry biomass, dry-to-wet-weight ratios, growing time, 
tillage depth, and yield) are used in both the biosphere groundwater exposure scenario and the 
biosphere volcanic ash exposure scenario (BSC 2004 [DIRS 169460], Section 6.4, note that 
tillage depth and rooting depth are treated as one parameter [surface soil depth] in the biosphere 
model). Ash depths 18 km downwind from Yucca Mountain were predicted to range from 
0.07 to 55 cm (based on 100 realizations of the ASHPLUME model). About 35 percent of 
predicted depths were less than 1 cm, 75 percent were less than 5 cm, and 90 percent were less 
than 15 cm (BSC 2004 [DIRS 170026], Table 6-4). Ash depths at the location of the RMEI 
(18 km south of Yucca Mountain) would be about 2 orders of magnitude or more lower under 
normal, variable wind conditions (CRWMS M&O 2000 [DIRS 153246], Section 3.10.5.1 and 
Figure 3.10-14) because the wind at Yucca Mountain blows to the south infrequently 
(BSC 2004 [DIRS 170026], Figure 8-1). The use of tillage, irrigation, and fertilizers with 
agricultural and garden crops would result in rapid mixing of the thin ash layer with little effect 
on soils or crop characteristics considered in this analysis (i.e., tillage depth or dry-to-wet-weight 

ratios). Therefore, separate distributions for five of the parameters in this analysis are not 
necessary for the two biosphere exposure scenarios. 
6.1 DRY BIOMASS 
6.1.1 Use in Biosphere Model 
Dry biomass (DBj, kg/m2) is a measure of the total, above-ground standing crop biomass per unit 
area, for each crop type. It is used in the plant submodel in the calculations of water and dust 
interception fractions. In both calculations, it represents the amount of plant material available to 
intercept contaminated water or dust. 
Water Interception FractionDry biomass is one of three parameter inputs to the calculation of 
the water interception fraction (Rwj) (Equation 6.1-1; BSC 2004 [DIRS 169460], Section 6.4.3), 
which is based on experiments of Beryllium-7 (7Be) and Iodine-131 (131I). This fraction, which 
can vary from zero to one, represents the percentage of radionuclides in irrigation water sprayed 
on plants that is intercepted and deposited on plant leaves. 
Rwj =DB K jK2 IAj 
K3 I jK4 (Eq. 6.1-1) 
1 
where 
Rwj = water interception fraction for crop type j (dimensionless) 
K1, K2, K3, and K4 = empirical constants that depend on the plant-type and contaminant 
form. K1 is in units of (kg/m2)-K2 (mm)-K3 (cm/hr)-K4 and K2, K3, and K4 are dimensionless. 
DBj = standing biomass of crop type j (kgdry weight/m2) 
IAj = amount of irrigation per application event for crop type j (mm) 
I = irrigation intensity (cm/hr) 
j = crop type 
Values for constants cited in the Biosphere Model Report (BSC 2004 [DIRS 169460], 
Section 6.4.3) are as follows: 
K1 = 2.29 for beryllium (Be+), 1.54 for iodine (I-), 
K2 = 0.695 for beryllium (Be+), 0.697 for iodine (I-), 
K3 = -0.29 for beryllium (Be+), -0.909 for iodine (I-), 
K4 = -0.341 for beryllium (Be+), -0.049 for iodine (I-). 
The interception fraction is obtained from a regression equation derived from experimental data 
with recommended values for the empirical constants which depend on contaminant form, and 
were developed based on given values for standing biomass (DBj), irrigation amount per 
application (IAj), and irrigation intensity (Ij) (BSC 2004 [DIRS 169460], Section 6.4.3). Because 
biomass is raised to the power of approximately 0.7 in this equation, there is a positive 
relationship between biomass and water interception. For example, for dry biomass values 
ranging from 0.1 to 1.5 kg/m2, the water interception fraction for Be+ changes from about 0.1 to 

about 0.7 (with IAj = 30 mm and Ij = 4 cm/hour). The interception values for I- only changes 
from about 0.01 to 0.08 over that range. Thus, interception for the cationic Be+ is sensitive to 
changes in biomass, but interception for the anionic I- is insensitive to those changes. 
Dust Interception FractionDry biomass is one of two variables in the calculation of dust 
interception, Raj (Equation 6.1-2; BSC 2004 [DIRS 169460], Section 6.4.3), which represents 
the percentage (expressed as a number from zero to one) of suspended dust that is intercepted by 
the leaves of a plant. 
j
.
Raj =0 1- e -DB a j 
(Eq. 6.1-2) 
where 
aj = an empirical factor in units of square meter per kilogram of dry plant biomass (2.9 for leafy 
vegetables, fresh forage feed and grain, 3.6 for other vegetables and fruit). 
Changes in biomass ranging from 0.15 to 0.7 kg/m2 result in changes in the dust interception 
fraction from about 0.4 to 0.9 (Figure 6.1-1). Values of dry biomass greater than about 0.8 cause 
little change in the interception fraction, as the fraction asymptotes toward 1.0 at high values of 
dry biomass. Thus, the dust interception fraction is sensitive to changes in dry biomass ranging 
from 0.1 to 0.8, but insensitive to higher values. 
1 
2) 
l0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.8 
0.9 
0.2 0.3 0.4 0.6 0.7 0.9 1.0 1.1 1.3 1.4 
Dry Biomass (kg/mDust Interception Fraction 
eafy vegetables other crops 
NOTE: Calculated as Raj = 1.0  e-ajDBj, where aj = 2.9 for leafy vegetables and 3.6 for other crops. 
Figure 6.1-1. Sensitivity of Dust Interception Fraction to Dry Biomass 

6.1.2 Parameter Development 
Dry biomass of leafy vegetables and cattle forage was calculated by multiplying yield of each 
representative crop (Section 6.11) by the dry-to-wet-weight ratio for that crop (Section 6.2). 
Because the total aboveground portions of leafy vegetables and cattle forage are edible and 
weighed fresh (i.e., wet weight) to determine yield, the resulting values are valid estimates of 
total aboveground dry biomass (Table 6.1-1). 
Dry biomass for grains, other vegetables, and fruits were calculated by multiplying edible yield 
of each representative crop (Section 6.11) by the dry-to-wet-weight ratio for that crop 
(Section 6.2) and dividing the resulting value by a harvest index (Table 6.1-1). 
A harvest index is a measure of the ratio of seed, fruit, or tuber dry biomass to total aboveground 
dry biomass. The harvest index was first used in plant breeding studies to identify cultivars and 
select for desirable traits that would improve crop yield (Hay 1995 [DIRS 160540], p. 198; 
Prince et al. 2001 [DIRS 159323], p. 1196). More recently it has been used to estimate net 
primary production for cropped land (Prince et al. 2001 [DIRS 159323]), assess dry matter 
partitioning responses of horticultural crops to fertilizer or irrigation treatments (Scholberg et al. 
2000 [DIRS 160434]; van Delden 2001 [DIRS 160433]), and estimate aboveground biomass 
from published yield values (Prince et al. 2001 [DIRS 159323], p. 1196). 
Harvest index inputs are described in Section 4.1.3 and presented in Table 6.1-1. Harvest indices 
for grains, other vegetables, and fruits were selected from Neitsch et al. 2002 ([DIRS 163122], 
Table A-8, pp. 381 to 384) because they are appropriate for use with USDA measurements of 
yield and dry-to-wet-weight ratios to estimate total aboveground dry biomass. The harvest 
indices from Neitsch et al. 2002 ([DIRS 163122], Table A-8, pp. 381 to 384) were established 
for non-stressed crops. Values for the optimal harvest index were selected from Table A-8 
(Neitsch et al. 2002 [DIRS 163122], Table A-8, pp. 381 to 384). For crops with aboveground 
yield (e.g., bell peppers and strawberries) the harvest index is less than 1.0 (Neitsch et al. 2002 
[DIRS 163122], p. 381). For crops with below ground yield (e.g., onions and carrots) the harvest 
index may be greater than 1.0 (Neitsch et al. 2002 [DIRS 163122], p. 381). 
Squash and corn were not included in the dry biomass distribution for other vegetables because 
yield data were not available (see Section 6.11). Apples were not included in the dry biomass 
distribution for fruits because trees are usually drip irrigated and so are not used in calculation of 
the Water Interception Fraction, and the equation for the Dust Interception Fraction has not been 
validated for trees (IAEA 1996 [DIRS 160402], pp. 7 to 13). 

Table 6.1-1. Dry Biomass (kg/m2) 
Crop Type 
Crop Yielda Ratiob HIc Biomassd 
Crop Type 
Crop Yielda Ratiob HIc Biomassd 
Leafy Vegetables Fruits 
Broccoli 1.46 0.093 -0.14 Grapese 1.51 0.194 0.45 0.65 
Cabbage 3.83 0.078 -0.30 Melons 2.92 0.102 0.50 0.60 
Cauliflower 2.01 0.081 -0.16 Strawberries 3.63 0.084 0.45 0.68 
Celery 7.79 0.054 -0.42 Tomatoes 3.0 0.062 0.33 0.56 
Head Lettuce 3.25 0.041 -0.13 Average 0.62 
Leaf Lettuce 2.98 0.060 -0.18 SDf 0.05 
Spinach 1.78 0.084 -0.15 
Average 0.21 Grains 
SDf 0.11 Barley 0.44 0.906 0.54 0.74 
Corn 1.10 0.896 0.50 1.97 
Other Vegetables Oats 0.28 0.918 0.42 0.61 
Bell Peppers 3.37 0.078 0.60 0.44 Winter wheat 0.54 0.891 0.40 1.20 
Carrots 3.64 0.122 1.12 0.40 Average 1.13 
Cucumbers 3.56 0.035 0.27 0.46 SDf 0.61 
Onions 4.92 0.103 1.25 0.41 
Potatoes 5.15 0.08 0.95 0.43 Cattle Forage 
Average 0.43 Alfalfa hay 1.02 0.227 -0.23 
SDf 0.02 Corn silage 5.78 0.238 -1.38 
Oat hay 1.87 0.182 -0.34 
Average 0.65 
SDf 0.63 
Source: USDA 2002 ([DIRS 159272]). 
a 
Wet yield (kg/m2), See Tables 6.11-1 to 6.11-6 . 
b Dry-wet-weight ratio, See Table 6.2-1. 
Harvest Index (ratio of edible dry biomass to total above ground dry biomass, see Section 4.1.3. A dash means no 
index was required, optimal harvest index values from Neitsch et al. 2002 ([DIRS 163122], Table A-8, pp. 381 to 384). 
d 
kg/m2, calculated as (yield x dry-to-wet-weight ratio)  harvest index. 
e 
HI for strawberries was used for grapes. 
f 
Standard deviation calculated using the STDEV function of Excel. 
The average for each crop type is the mean biomass of representative crops, with the exception 
of the weighted mean used for cattle forage (Table 6.1-2). 

Table 6.1-2. Averages and Cumulative Distributions for Dry Biomass (kg/m2) 
Crop Type Averagea 
Upper 
Limit of 
Intervalb 
Cumulative 
Probability Crop Type Averagea 
Upper Limit 
of Intervalb 
Cumulative 
Probability 
Leafy 0.21 0.10 0.00 Fruits0.62 0.10 0.00 
Vegetables 0.13 0.05 0.56 0.05 
0.14 0.20 0.60 0.35 
0.15 0.35 0.65 0.65 
0.16 0.50 0.68 0.95 
0.18 0.65 1.30 1.00 
0.30 0.80 
0.42 0.95 Grains 1.13 0.50 0.00 
0.50 1.00 0.61 0.05 
0.74 0.35 
Other 0.43 0.30 0.00 1.20 0.65 
Vegetables 0.40 0.05 1.97 0.95 
0.41 0.28 2.20 1.00 
0.43 0.51 
0.44 0.73 Cattle 
Foragec0.48 0.10 0.00 
0.46 0.95 0.23 0.05 
0.60 1.00 0.34 0.73 
1.38 0.95 
1.50 1.00 
a 
Mean dry biomass for a crop type from Table 6.1-1, with the exception of the weighted mean calculated for cattle 
forage. The weighted mean was calculated as (3 x 0.23 [dry biomass for alfalfa] + 1 x 1.38 [dry biomass for corn 
silage] + 1 x 0.34 [dry biomass for oat hay]) / 5 = 0.48. 
b Limits determined from crop specific biomass (see Table 6.1-1). 
For 90 percent of the distribution between the minimum and maximum crop biomass, the probabilities for the two 
cattle forage intervals were weighted 3:1 for the range between oat hay to alfalfa (p = 0.68) and alfalfa to corn silage 
(p = 0.22). 
A cumulative distribution function is recommended for each crop type (Table 6.1-2). 
Ninety percent of the probability distribution is between the minimum and maximum biomass of 
representative crops within a crop type (e.g., biomass for leafy vegetables ranges from 0.13 [head 
lettuce] to 0.42 kg/m2 [celery], Table 6.1-1). The distribution between the minimum and 
maximum crop dry biomass is divided into intervals of virtually equal probability (summing to 
90 percent), with the exception of cattle forage (see below). The number of intervals is one less 
than the number of representative crops considered and the upper limits are crop-specific values 
of biomass. The probabilities for the two intervals for cattle forage were weighted 3:1 for the 
range between oat hay and alfalfa (p = 0.675) versus the range between alfalfa and corn silage 
(p = 0.225) (see Section 6 for justification). This results in a higher probability for selection of 
values that are similar to alfalfa. To account for variation and uncertainty that could result in 
values beyond the range of crop specific values, intervals of five percent probability each were 
added to the lower and upper ends of the distribution. Yield (Section 6.11) was evaluated for 
crops within a crop type having low and high dry biomass values to determine appropriate 
bounds for the distributions. The lowest yield value reported for crops with relatively low dry 
biomass was used with dry-to-wet-weight ratios and harvest indices (when appropriate) to 
recalculate dry biomass. The resulting value was rounded down to the nearest tenth and used as 

the lower bound. For example, broccoli and head lettuce had the lowest reported dry biomass for 
leafy vegetables; however, broccoli had a lower yield value and was selected to calculate the 
lower bound. Using the minimum yield reported for broccoli (1.08 kg/m2, Table 6.11-1) and the 
dry-to-wet-weight ratio reported for broccoli (0.093, Table 6.2-1) the lower bound for leafy 
vegetables is 1.08 x 0.093 = 0.10. Carrots, strawberries, oats, and alfalfa were used to calculate 
the lower bounds for other vegetables, fruits, grains, and cattle forage, respectively. The upper 
bounds were determined with the same method using the highest yield value reported for crops 
with high biomass. The resulting values were rounded up to the nearest tenth (Table 6.1-2). 
Celery, onions, strawberries, corn, and corn silage were used to calculate the upper bounds for 
leafy vegetables, other vegetables, fruits, grains, and cattle forage, respectively. It should be 
noted that potatoes had a higher yield than onions for other vegetables (6.61 kg/m2 versus 
6.50 kg/m2). However, the low dry-to-wet-weight ratios for potatoes resulted in a lower dry 
biomass than that calculated for the highest yield of onions. Therefore, the yield for onions was 
more appropriate for calculating the upper bound. The wide range in yield values for 
strawberries made their use appropriate for calculation of both lower and upper bounds for fruits 
(see Table 6.11-3) 
Much of the variation in this parameter is due to variation in yield (i.e., wet biomass of harvest). 
As discussed in Section 6.11, the distributions of yield adequately incorporate variation and 
uncertainty due to climate or farming conditions, farming and gardening practices, and selection 
of crops and crop types. There is little uncertainty in the measurements of dry-to-wet-weight 
ratios (Section 6.2); thus, they contribute little to the uncertainty in dry biomass. Harvest indices 
tend to be conservative unless crops are grown under extreme stress conditions (Prince et al. 
2001 [DIRS 159323], p. 1196). Most published measurements of harvest indices reviewed by 
Prince et al. 2001 ([DIRS 159323], p. 1197) varied by no more than  0.06 from the values used 
in this analysis. Increasing or decreasing the values of harvest indices of grain by 0.06 would 
result in mean dry biomass values of about 1.0 and 1.3, respectively, well within the bounds of 
the recommended distribution (range = 0.50 to 2.20 kg/m2). Thus, there is little variation or 
uncertainty associated with harvest indices for grains. Uncertainty in harvest index values for 
other vegetables and fruits was accounted for by selection of four to five crops per crop type. 
The distributions for dry biomass are more sensitive to variation in yield than harvest indices 
(see selection of distribution bounding values above). Therefore, changes in the accuracy of 
harvest index for representative crops has little influence on the distribution bounds of dry 
biomass for each crop type. 
The same distributions are recommended for both climate scenarios and for use in both the 
biosphere groundwater and volcanic ash exposure scenarios (see introduction to Section 6). 
Distributions for yield and dry-to-wet-weight ratios were developed from a variety of crops and 
arid to semi-arid climate conditions representative of present-day and future climates (see 
Sections 6.2 and 6.11). Therefore, yield, dry-to-wet-weight ratios, or other physical 
characteristics of crops that influence dry biomass account for influences that climate change or 
a volcanic eruption at Yucca Mountain would have. Uncertainty associated with differences that 
might occur in dry biomass between crops grown in Amargosa Valley and other locations is 
accounted for through use of locally grown crops and incorporation of variation in the 
distributions. 

6.2 DRY-TO-WET-WEIGHT RATIOS 
6.2.1 Use in Biosphere Model 
The dry-to-wet-weight ratio (DWj, kg dry plant/kg wet plant) is a measure of the ratio of dry mass to 
wet mass of edible foodstuffs per crop type. It is used in the plant submodel in the calculation of 
radionuclide concentrations in plant foodstuffs contributed from plant root uptake (Cproot i,j, 
Bq/kgwet plant) (Equation 6.2-1; BSC 2004 [DIRS 169460], Section 6.4.3). The dry-to-wet-weight 
ratio is included in this equation because the transfer factors are based on the dry weight of food. 
,
Cp
i root , j =Csi mFs. i p , j DWj (Eq. 6.2-1) 
where 
Csm, i = activity concentration of radionuclide i in surface soil (Bq/kg dry soil), 
Fs.p i,j = soil-to-plant transfer factor for radionuclide i and crop type j (Bq/kg dry plant/ 
Bq/kg dry soil), 
DWj = dry-to-wet weight ratio for edible part of plant (kg dry plant/kg wet plant) 
In this equation, the dry-to-wet-weight ratio has a positive, linear effect on radionuclide 
concentrations. Thus, plant root uptake will be greater for drier foodstuffs within a crop type 
(i.e., those with a larger ratio) than it will be for wetter plants. 
6.2.2 Parameter Development 
Information on the water content of food products compiled by the USDA 2002 ([DIRS 159272]; 
see Section 4.1.1) and dry-to-wet-weight ratios for alfalfa, corn silage, and oat hay from Till and 
Meyer 1983 ([DIRS 101895], Table 5.16, p. 5-48; see Section 4.1.2) are in Table 6.2-1. These 
data were used to develop distributions of dry-to-wet-weight ratios (Table 6.2-2). 
The average for each crop type is the mean dry-to-wet-weight ratio of representative crops, with 
the exception of the weighted mean used for cattle forage (Table 6.2-2). 
A cumulative distribution function is recommended for each crop type (Table 6.2-2). 
The probability distribution ranges between the minimum and maximum dry-to-wet-weight 
ratios of representative crops within a crop type (e.g., dry-to-wet-weight ratios for leafy 
vegetables range from 0.041 [head lettuce] to 0.093 [broccoli], Table 6.2-1). The distribution 
between the minimum and maximum crop dry-to-wet-weight ratios is divided into intervals of 
virtually equal probability, with the exception of cattle forage. The number of intervals is one 
less than the number of representative crops and the upper bounds are crop-specific values of 
dry-to-wet-weight ratios (Table 6.2-2). The probabilities for the two cattle forage intervals were 
weighted 3:1 for the range between oat hay and alfalfa (p = 0.75) versus the range between 
alfalfa and corn silage (p = 0.25) (see introduction to Section 6 for justification). This results in a 
higher probability of selection of values that are similar to alfalfa. 

The number of samples used by USDA to calculate water content per crop generally is large 
(10 to more than 200 measurements for all but cucumbers [3 measurements], leaf lettuce 
[estimated], potatoes [9], and barley [7]). The standard errors of their estimates of average 
percent water content per crop are very small (range = 0.06 to 1.0 percent). Because 
dry-to-wet-weight ratios are a simple conversion of crop water content (1  [% water / 100]) the 
variation per crop in dry-to-wet-weight ratios is also very small. Thus, there is so little variation 
or uncertainty about the dry-to-wet-weight ratio per crop that it was not necessary to extend the 
distribution beyond crop specific values. Additionally, because very little within crop variation 
in moisture content occurred across climatic zones that were included in the USDA database, it is 
reasonable to expect that published values of dry-to-wet-weight ratios would be consistent with 
those of crops grown in Amargosa Valley. 
Table 6.2-1. Dry-to-Wet-Weight Ratios 
Crop Type 
Crop NDB No.b % Water 
Dry:Wet 
Ratioc 
Crop Type 
Crop NDB No.b % Water 
Dry:Wet 
Ratioc 
Leafy Vegetables Fruits 
Broccoli 11090 90.69 0.093 Apples 09004 84.46 0.155 
Cabbage 11109 92.15 0.078 Grapes 09132 80.56 0.194 
Cauliflower 11135 91.91 0.081 Melons 09181 89.78 0.102 
Celery 11143 94.64 0.054 Strawberries 09316 91.57 0.084 
Head Lettuce 11252 95.89 0.041 Tomatoes 11529 93.76 0.062 
Leaf Lettuce 11253 94.00 0.060 Average 0.120 
Spinach 11457 91.58 0.084 SDd 0.054 
Average 0.070 
SDd 0.019 Grains 
Barley 20004 9.44 0.906 
Other Vegetables Corn 20014 10.37 0.896 
Bell peppers 11333 92.19 0.078 Oats 20038 8.22 0.918 
Carrots 11124 87.79 0.122 Wheat flour 20076 10.94 0.891 
Cucumbers 11206 96.49 0.035 Average 0.903 
Onions 11282 89.68 0.103 SDd 0.012 
Potatoes 11352 92.02 0.080 
Squash 11641 93.68 0.063 Cattle Forage 
Corn 11167 75.96 0.240 Alfalfa haye 0.227 
Average 0.103 Corn silagee 0.238 
SDd 0.067 Oat haye 0.182 
Average 0.216 
SDd 0.030 
a 
Source for vegetables, fruits, and grains: USDA 2002 ([DIRS 159272]). 
b USDA 2002 ([DIRS 159272]) nutrient database number (NDB No.) for a foodstuff. 
Calculated as 1- (% water  100) for most vegetables, fruits, and grains. 
d 
Standard deviation calculated using the STDEV function of Excel. 
e 
Source: Till and Meyer 1983 ([DIRS 101895], Table 5.16 p. 5-48, with grass considered representative of oat hay). 

Table 6.2-2. Averages and Cumulative Distribution Functions for Dry-to-Wet-Weight Ratios 
Crop Type Averagea 
Upper 
Limit of 
Intervalb 
Cumulative 
Probability Crop Type Averagea 
Upper 
Limit of 
Intervalb 
Cumulative 
Probability 
Leafy 0.070 0.041 0.00 Fruits 0.120 0.062 0.00 
Vegetables 
0.054 0.17 0.084 0.25 
0.060 0.33 0.102 0.50 
0.078 0.50 0.155 0.75 
0.081 0.67 0.194 1.00 
0.084 0.83 
0.093 1.00 Grains 0.903 0.891 0.00 
0.896 0.33 
Other 
Vegetables 
0.103 0.035 0.00 0.906 0.67 
0.063 0.17 0.918 1.00 
0.078 0.33 
0.080 0.50 Cattle 
Foragec 
0.220 0.182 0.00 
0.103 0.67 0.227 0.75 
0.122 0.83 0.238 1.00 
0.240 1.00 
a 
Mean dry-to-wet-weight ratio for a crop type from Table 6.2-1, with the exception of the weighted mean 
calculated for cattle forage. The weighted mean was calculated as (3 x 0.227 [dry-to-wet-weight ratio for alfalfa] 
+ 1 x 0.238 [dry-to-wet-weight ratio for corn silage] + 1 x 0.182 [dry-to-wet-weight ratio for oat hay]) / 5 = 0.220. 
b Limits determined from crop specific dry-to-wet-weight ratios (see Table 6.2-1). 
The probabilities for the two cattle forage intervals were weighted 3:1 for the range between oat hay and alfalfa 
(p = 0.75) versus the range between alfalfa and corn silage (p = 0.25). 
The important sources of variation and uncertainty for dry-to-wet-weight ratios are related to 
variation among crops within a crop type and uncertainty in the types of locally grown crops 
planted and consumed. These are adequately accounted for through the use of three or more 
representative crops within a crop type. 
Values of the dry-to-wet-weight ratios for alfalfa (0.227), corn silage (0.238), and oat hay 
(0.182) were selected for this analysis (see Section 4.1.2). Comparable values used in 
radiological assessments include 0.19 for alfalfa (IAEA 1994 [DIRS 100458], Table 5), 0.22 for 
fresh forage (Kennedy and Strenge 1992 [DIRS 103776], Table 6.17), 0.20 for fresh forage 
(Napier et al. 1988 [DIRS 157927], Table 4.25 on p. 4.71), and 0.22 for beef cattle fresh forage 
(LaPlante and Poor 1997 [DIRS 101079], Table B-1 on p. B-9). In addition, Orloff 1997 
([DIRS 158788], p. 109) states that the moisture content of alfalfa is generally between 75 and 
80 percent, the midpoint of which equals a dry-to-wet-weight ratio of 0.225. These values are 
within the range of those selected, indicating that there is little uncertainty about the dry-to-
wet-weight ratios of forage. Therefore, the recommended distributions are adequate for each 
crop-type. 
The same distributions are recommended for both climate scenarios and for use in both the 
biosphere groundwater and volcanic ash exposure scenarios (see introduction to Section 6) 

because climate change and a volcanic eruption at Yucca Mountain will not result in a change in 
the moisture content of foods or forage. 
6.3 FRACTION OF OVERHEAD IRRIGATION 
6.3.1 Use in Biosphere Model 
The fraction of a crop type that is irrigated using sprinkler or spray irrigation (fo,j, dimensionless) 
is used in the plant submodel in the calculation of uptake into foodstuffs of radionuclides 
deposited on the plant surface via water (Equation 6.3-1; BSC 2004 [DIRS 169460], 
Section 6.4.3). This equation, without fo,j, is also used to calculate the interception of soil on the 
surface of plants. 
jw
Cp
i water , j =
Dwi, j foj T Rw j (1 - e -. tg , j ) (Eq. 6.3-1) 
. Yj
w 
where 
Cpwater i,j = activity concentration of radionuclide i in crop type j contributed from the 
direct deposition on crop leaves due to interception of contaminated 
irrigation water (Bq/kg wet). There are two deposition mechanisms, 
irrigation water (Cpwater i,j) and dust (Cpdust i,j). 
Dwi,j = the deposition rate of radionuclide i due to application of irrigation water 
(Dwi,j) or resuspended dust (Dai,j) onto crop type j (Bq/m2 d), 
foj = fraction of irrigation applied using overhead methods for crop type j 
(dimensionless); this parameter only applies to uptake from irrigation water 
and does not appear in the equation for deposition via dust, 
Rwj = the interception fraction for irrigation water for crop type j; or Raj 
interception fraction of resuspended dust for crop type j (dimensionless), 
Tj = the translocation factor for crop type j, (dimensionless), 
.w = the weathering constant (per d), which can be calculated from weathering 
half-life (Tw in units of day) by .w = ln(2)/Tw, 
Yj = crop yield or wet biomass for crop type j (kg wet weight/m2), 
tg,j = crop growing time for crop type j (d). 
The fraction of overhead irrigation is included in the model to account for the portion of crops 
that are not watered using overhead sprinklers and to propagate uncertainty in irrigation methods. 
A change in this fraction results in a proportional change in the numerator of Equation 6.3-1. 
6.3.2 Parameter Development 
There are three basic methods used to irrigate field crops, orchards, and gardens: surface 
irrigation, drip systems, and sprinkler systems. Surface irrigation includes ditch and furrow 
irrigation and other flood methods that saturate part, or all, of the soil surface. Drip irrigation 

includes the use of bubblers, drip emitters, drip tubing, micro sprays, or other methods that 
deliver water to the soil surface at or near the base of plants. Sprinkler systems include 
stationary and mobile sprinklers (e.g., center pivot, side roll sprinklers) that spray water over 
plants, and lawn-type sprinklers and garden hoses sprayed over gardens. Overhead spraying is 
the only method that will result in groundwater contaminated with radionuclides being applied to 
the leaf surfaces (i.e., uptake by foliar interception of irrigation water). Because the rate of 
removal of radionuclides from the surface of plants (i.e., the weathering factor) is relatively fast 
(see Section 6.4.1), the method of irrigation used during the month prior to harvesting is more 
important than that used during germination or early growth stages. 
Distributions of the probability of leafy vegetables, other vegetables, and fruits being irrigated 
with overhead spray or sprinkler irrigation were developed from descriptions of irrigation 
methods commonly used to grow the representative crops in arid and semi-arid environments 
from USDA Cooperative Extension Service State Extension Offices. Distributions of the 
probability of grains and cattle forage being irrigated with overhead spray or sprinkler irrigation 
were developed from observations of irrigation methods in Amargosa Valley. These 
observations were recorded in 1998 during surveys conducted for the Radiological Monitoring 
Program (DTN: MO0208SPAMETHO.004 [DIRS 159565], Section 4.1.10, Table 4.1-6). Most 
(86 percent of grains and forage and at least 75 percent of all acreage) agricultural fields in 
Amargosa Valley during 1998 were irrigated with overhead sprinklers (Table 4.1-6). Because 
few fruits and vegetables are commercially grown in Amargosa Valley, and because there is little 
irrigation of crops in eastern Washington (Table A-3), much of the following information on 
irrigation practices for fruits and vegetables comes from Arizona and California. Information 
from Arizona and California was chosen because the arid climates of these southwestern states 
are consistent with the current arid conditions in Amargosa Valley, and it is reasonable to expect 
that irrigation methods would be similar in Amargosa Valley. There is no information available 
on the prevalence of irrigation methods used in gardens, although recommended methods are 
described in publications such as Antonelli et al. (1998 [DIRS 158654], p. 11) and Call (1999 
[DIRS 158672], Chapter 18). 
Because there is much variation and uncertainty associated with this parameter for most crop 
types, recommended distributions, which are summarized in Table 6.3-1, have relatively large 
standard deviations. 
Table 6.3-1. Recommended Distributions for Fraction of Overhead Irrigation 
Crop Type 
Type of 
Distribution Average 
Standard 
Deviation Minimum Maximum 
Leafy Vegetables Normal 0.75 0.1 0.49 1.0 
Other Vegetables Normal 0.75 0.1 0.49 1.0 
Fruits Normal 0.50 0.1 0.24 1.0 
Grains Normal 0.90 0.05 0.77 1.0 
Cattle Forage Normal 0.90 0.05 0.77 1.0 
The same distributions are recommended for present-day and upper bound glacial transition 
climates because irrigation methods would not change appreciably due to changes in climate (in 
part because irrigation methods are substantially influenced by water availability, economics, 
ANL-MGR-MD-000006 REV 02 6-15 September 2004 

and crop selection) and because increases or decreases of human knowledge and technology over 
time are not to be considered in this analysis, per 10 CFR 63.305(b) [DIRS 156605]. This 
parameter is not used in the biosphere volcanic ash exposure scenario; therefore, changes 
resulting from a volcanic eruption at Yucca Mountain are not considered in this analysis. 
Leafy Vegetables and Other VegetablesSurface irrigation (flood, ditch, and furrow) is 
commonly used for commercial production of most leafy vegetables and other vegetables, such 
as lettuce, carrots, and onions in Arizona (Martin et al. 1999 [DIRS 159383], 1999 
[DIRS 159384], 1999 [DIRS 159382]; lettuce in California (Mayberry 2000 [DIRS 159386]); 
numerous vegetables in central and western Arizona (Teegerstrom and Umeda 2001 
[DIRS 159392]; Teegerstrom et al. 2001 [DIRS 159391]), although some vegetables, such as bell 
peppers in California, may be grown using drip irrigation (Mayberry 2000 [DIRS 159388]). Use 
of overhead sprinkler irrigation for vegetables is uncommon in the southwestern United States, 
but is used at least some in semiarid regions of the Pacific Northwest (e.g., potatoes and sweet 
corn in south central Washington [Hinman et al. 1997 (DIRS 159376)]. Surface, drip, or 
sprinkler irrigation may be used in gardens. 
To ensure that leaf interception of radionuclides is not underestimated, a normal distribution with 
a mean of 0.75 is recommended for leafy vegetables and other vegetables. To account for the 
large amount of uncertainty in this parameter, a standard deviation of 0.1 is recommended, with a 
minimum of 0.49 and a maximum of 1.0 (Table 6.3-1). The minimum value was based on the 
ninety-ninth percentile of the low end of the distribution (calculated as 0.75 - [2.58 x 0.1]). 
Fruits-Surface irrigation (melons in central and southwestern Arizona-Teegerstrom and Umeda 
2001 [DIRS 159392]; Teegerstrom et al. 2001 [DIRS 159391]; cantaloupe in 
California-Mayberry 2000 [DIRS 159389]) and drip irrigation (watermelons in 
California-Mayberry 2000 [DIRS 160005]; strawberries in California-Klonsky and De Moura 
2001 [DIRS 159381]) are commonly used for commercial production of melons and berries. 
Grapes are grown using drip or flood irrigation, in part because wetting leaves with overhead 
spraying causes leaf diseases (Wolf and Johnson 1999 [DIRS 159393], p. 5). Fruit and nut trees 
may be irrigated using stationary sprays on risers (Uriu and Magness 1967 [DIRS 159169], 
pp. 697 to 698); however, the water is sprayed under the canopy, and fruits do not get wet. 
Therefore, spray irrigation that would contaminate fruits is uncommon for commercial 
production of fruits. Surface, drip, or sprinkler irrigation may be used in gardens for melons, 
berries, and other low-growing fruits. 
A normal distribution with a mean of 0.5, a standard deviation of 0.1, a minimum of 0.24, and a 
maximum of 1.0 is recommended for fruits (Table 6.3-1). The minimum value was based on the 
ninety ninth percentile of the low end of the distribution (calculated as 0.5 - [2.58 x 0.1]). The 
mean of this distribution is lower than that recommended for vegetables because leaf interception 
is not an important process for fruit and nut trees and because spray irrigation is uncommon for 
commercial production of other fruits. 
Grains and Cattle ForageAt least 86 percent of 1,966 acres of grains and forage grown in 
Amargosa Valley during 1998 was irrigated using center pivot, side roll, or other types of 
sprinklers. About 12 percent was surface irrigated, and the method used to irrigate the remainder 
was not recorded (Table 4.1-6). Surface and sprinkler irrigation also are used elsewhere to 

irrigate grains and forage (Hinman et al. 1997 [DIRS 159376]; Orloff et al. 1997 [DIRS 158774], 
pp. 36 to 37; Teegerstrom and Clay 1999 [DIRS 159390]). 
Because overhead sprinkler irrigation is used most often, but not exclusively, in Amargosa 
Valley for commercial crops, a normal distribution with a mean of 0.9, a minimum of 0.77, and a 
maximum of 1.0 is recommended for grains and cattle forage. Because there is less uncertainty 
about the type of irrigation used for these crops in Amargosa Valley than for other crops, a 
smaller standard deviation of 0.05 is recommended. The minimum value was based on the 
ninety ninth percentile of the low end of the distribution (calculated as 0.9 - [2.58 x 0.05]). 
6.4 GROWING TIME 
6.4.1 Use in Biosphere Model 
Growing time for crop type j (tgj, days/growing season) is a measure of the amount of time crops 
are growing and exposed to contaminated water and dust. It is used in the biosphere model in 
the calculation of the uptake into foodstuffs of radionuclides deposited on the plant surface via 
water and dust interception (Equation 6.3-1). 
Growing time is part of a negative exponent in the last term of Equation 6.3-1 that accounts for 
the weathering of radionuclides on plant surfaces. This term approaches one (i.e., no effect on 
radionuclide concentration as weathering of radionuclides on the leaf approach equilibrium 
conditions) as growing time increases. For a weathering half life of 14 days (.w = 0.05, 
calculated as ln2/weathering half life), the weathering decay term equals 0.92 when tg,j = 50 days, 
0.98 when tg,j = 75 days, and 0.99 when tg,j = 90 days. Therefore, weathering approaches 
equilibrium at 50 to 100 days, and leaf uptake is not sensitive to values of growing time greater 
than about 50 to 75 days. 
6.4.2 Parameter Development 
Selection of values for growing season length of representative crops is described in 
Section D2.1 of Appendix D and summarized in Table 6.4-1. The data from which these values 
were derived are described in Section 4.1.4. 
Table 6.4-1. Growing Time (days) 
Crop Type 
Crop 
Present-
Day 
Climatea 
Upper 
Bound 
Glacial 
Transition 
Climatea 
Crop Type 
Crop 
Present-
Day 
Climatea 
Upper 
Bound 
Glacial 
Transition 
Climatea 
Leafy Vegetables Fruits 
Broccoli 80 83 Apples 240 166 
Cabbage 85 75 Grapes 183 105 
Cauliflower 80 63 Melons 100 103 
Celery 125 110 Strawberries 205 64 
Head Lettuce 60 78 Tomatoes 80 88 
Leaf Lettuce 60 58 Average 161 105 
Spinach 50 55 Recommendedb 160 105 

Table 6.4-1. Growing Time (days) (Continued) 
Average 77 75 
Recommendedb 75 75 Grains 
Barley 243 91 
Other Vegetables Corn 154 178 
Bell peppers 78 100 Oats 160 141 
Carrots 75 80 Winter wheat 243 334 
Cucumbers 60 68 Average 200 186 
Onions 110 155 Recommendedb 200 185 
Potatoes 110 115 
Squash 58 65 Cattle Forage 
Sweet corn 82 105 Alfalfa hay 56 70 
Average 82 98 Corn silage 93 137 
Recommendedb 80 100 Oat hay 75 57 
Average 75 88 
Recommendedb 75 90 
a 
Midpoint of season length, from Tables D-1 (present-day climate) and D-2 (upper bound glacial 
transition climate), except alfalfa, which is calculated as the average of all cutting. 
b 
Recommended values are the average per crop type, rounded to the nearest number divisible by five. 
Different values are presented for each climate because differences in temperature, planting 
season, and selection of varieties adapted to those climates result in differences in season length 
for some crops. For example, barley is always grown as a winter/spring crop in southern Nevada 
and therefore has a long growing season, but can be grown in a much shorter time during spring 
and summer in eastern Washington (Appendix A, Figures A-1 and A-2). 
The values in Table 6.4-1 represent the typical number of days that the representative crops must 
be irrigated (because they were selected primarily for calculations of irrigation rates). Thus, they 
are valid measurements of growing time for water interception calculations. They are also valid 
measurements of soil exposure time for annual crops (i.e., all representative vegetables and 
grains), cattle forage, and perennial fruits in southern Nevada, because these crops are irrigated 
throughout the entire growing season. Similarly, annual crops and cattle forage in eastern 
Washington are irrigated throughout the entire growing season making irrigation time equal to 
soil exposure time for the upper bound glacial transition climate analogue. However, irrigation 
time is not equal to soil exposure time for perennial fruits in eastern Washington (grapes, apples, 
and strawberries) because these crops are only irrigated until fruit harvest, but the leaves of these 
plants remain and are exposed to dust throughout the entire growing season. Because the 
average value of growing time for fruits for the upper bound glacial transition climate is greater 
than 100 days (and the model is insensitive to higher values, see Section 6.4.1) separate values 
for the soil interception calculation are not required. 
Because leaf uptake is insensitive to changes in growing times greater than about 75 days, fixed 
values are recommended for this parameter, and recommended values were selected by rounding 
the average growing time per crop type to the nearest value divisible by five (Table 6.4-1). 
Rounding to the nearest value divisible by five was done to simplify presentation of data and 
because a higher level of accuracy was not necessary. 

Deposition and redistribution of a thin layer of ash expected from a volcanic eruption at Yucca 
Mountain would not cause long-term changes in climate or soil that would result in substantial 
changes in crop growing time (see Section 6.); therefore, the same distribution is recommended 
for both the biosphere groundwater and volcanic ash exposure scenarios. 
6.5 IRRIGATION RATE-ANNUAL AVERAGE 
6.5.1 Use in Biosphere Model 
Irrigation rate (IR, m/year) is a measure of the average rate at which contaminated groundwater 
is applied to soils to irrigate plants. It is used in the soil submodel to calculate radionuclide 
concentrations in soil (Equation 6.5-1; BSC 2004 [DIRS 169460], Section 6.4.1), and in a very 
similar equation in the carbon-14 submodel. Changes in land use and crop rotation practices 
make it possible that a variety of plants, including garden crops, commercial crops, and 
horticultural plants could be grown on a plot of land over a long period. Because of this, the 
distribution for annual irrigation rate is based on all 26 representative crops and turf (Table 6.1). 
Using several plant types to develop the distribution for annual irrigation rate accounts for 
uncertainty in crop selection and rotation. 
The equation used to calculate radionuclide concentrations in soil is (BSC 2004 [DIRS 169460], 
Equation 6.4.1-4): 
IR Cw 
Csi =i (Eq. 6.5-1) 
,
.
i eff 
where 
Csi = activity concentration of radionuclide i in surface soil per unit area (Bq/m2), 
i = index of primary radionuclide, 
Cwi = activity concentration of radionuclide i in the groundwater (Bq/m3), 
IR = annual average irrigation rate on land (m/yr), 
.eff, i = .d,i + .l,i + .e 
.d, i = radioactive decay constant for radionuclide i (1/yr); this can be calculated from 
radionuclide half-life using the conversion ln(2)/Td,i, where Td,i is half-life of 
radionuclide i (yr), 
.l, i = average annual leaching removal constant for radionuclide i (1/yr), 
.e = annual average surface soil erosion removal constant (1/yr). 
Changes in annual average irrigation rate have a linear effect on soil concentrations and therefore 
may be an important parameter in calculating BDCFs. 
6.5.2 Parameter Development 
Methods in Allen et al. (1998 [DIRS 157311]) and Doorenbos and Pruitt (1977 [DIRS 103062]) 
published by the FAO were used to calculate IR and are justified in Appendix B (Section 2). 
Background information on plant water use is also included in Appendix B (Section 1). The 
methodology is based on determination of crop water requirements, which are calculated from 

evapotranspiration of a grass reference surface and adjusted with a crop-specific coefficient 
(Appendices C and D). 
Parameter inputs were growing season lengths (Section 4.1.4), average monthly weather data for 
present-day, upper bound monsoon, lower bound and upper bound glacial transition climates 
(Section 4.1.5), and salinity of irrigation water (Section 4.1.7). Growing season lengths were 
used in Appendices D and E to adjust growth stage lengths and calculate seasonal water 
requirements, respectively. Average monthly weather data were used in Appendix C to calculate 
reference evapotranspiration (ETo) and in Appendix E to calculate effective precipitation. 
Salinity of irrigation water (Section 4.1.7) was used in Appendix E to determine the leaching 
requirement used to calculate seasonal water requirements. 
Reference evapotranspiration was calculated for a grass reference surface and represents the 
effects of climate on crop evapotranspiration (ETc). The reference surface as defined by Allen et 
al. (1998 [DIRS 157311], p. 15) is a hypothetical grass reference crop with an assumed crop 
height of 0.12 m, a fixed surface resistance of 70 s m-1 and an albedo of 0.23. It is assumed to 
be of uniform height, actively growing, completely shading the ground, with an adequate water 
supply. Climatic variables that drive ETo include air temperature, humidity, radiation, and wind 
speed. The FAO Penman-Monteith equation (Allen et al. 1998 [DIRS 157311], Equation 6, 
p. 24) was used to calculate ETo (calculations and examples are in Appendix C). Mean monthly 
ETo was calculated for present-day, upper bound monsoon, lower bound future, and upper bound 
glacial transition climates (Appendix C, Table C-5). Variation and uncertainty in ETo that could 
affect irrigation parameter values are discussed in Sections 6.5.2, 6.7.2, 6.8.2, and 6.9.2. 
The crop coefficient (Kc) integrates the effects of four primary crop characteristics that differ 
from the reference grass surface (crop height, albedo, canopy resistance, and evaporation from 
soil). Changes in crop characteristics (i.e., leaf area, stomatal conductance, developmental 
stages) over the growing season also affect Kc; therefore, growth stage information was used to 
derive crop specific values (calculations and examples are in Appendix D). Locally determined 
values for Kc were not available for this analysis and so values published in Allen et al. (1998 
[DIRS 157311], Table 12, pp. 104 to 108) were used for the 26 representative crops and turf. To 
reduce uncertainty associated with published Kc, and to ensure consistency with present 
knowledge of the conditions in the Yucca Mountain region, these values were adjusted to local 
conditions using relative humidity and wind speed for the four climate states (Appendix D, 
Tables D-5 and D-6). A monthly mean Kc was calculated to correspond with monthly mean ETo 
(Appendix D). 
Variation in Kc is primarily influenced by differences in crop specific characteristics (Allen et al. 
1998 [DIRS 157311], p. 90). This allows standard Kc values to be used across geographical 
locations and different climates, which has resulted in general acceptance and usefulness of the 
Kc methodology. There is little variation in Kc values among crops within a crop type (Allen et 
al. 1998 [DIRS 157311], p. 109). Use of a variety of representative crops and crop types that are 
grown in Amargosa Valley and eastern Washington adequately accounts for variation and 
uncertainty in Kc for this analysis, and ensures consistency with present knowledge of the 
conditions in the Yucca Mountain region and future climate states. 

Crop evapotranspiration was used with information on timing of growing seasons to determine 
average monthly crop water requirements. Average daily ETc (ETc daily) for each month 
(Appendix D, Section 5, Tables D-7 and D-8) was calculated according to Allen et al. (1998 
[DIRS 157311], Equation 58 on p. 103): 
ET
daily c =K  ET (Eq. 6.5-2) 
co 
Where ETc daily and ETo are in units of mm/day, and Kc is dimensionless. The resulting value was 
multiplied by the number of growing season days per month to get mean monthly ETc ( ETc 
monthly, mm/month) needed to estimate seasonal water requirements. 
Seasonal crop water requirements (Ws, Appendix E, Section 2.4) were calculated using the 
following equation from Doorenbos and Pruitt (1977 [DIRS 103062], p. 79): 
S 
n 
i = 1(ETc monthly - Pe)
W = 
s 
1- LR (Eq. 6.5-3) 
where 
ETc monthly = monthly mean crop evapotranspiration (mm), 
Pe = monthly mean effective precipitation (mm [see Appendix E for calculation 
methods]), 
LR = leaching requirement (dimensionless), 
n = months in growing season. 
Seasonal irrigation requirements (In) were calculated from one of the following equations from 
Doorenbos and Pruitt (1977 [DIRS 103062], p. 70). The first equation was used if precipitation 
met the seasonal LR (Appendix E, Section 2.4). The second equation was used if irrigation was 
required to meet the seasonal LR (Appendix E, Section 2.4). 
n
n (
In S = i= 1ETmonthly c S - i= 1 Pe + Ge + Wb) (Eq. 6.5-4) 
In =W -( Ge + Wb) (Eq. 6.5-5) 
s 
where 
ETc monthly = monthly mean crop evapotranspiration (mm),
Pe = monthly mean effective precipitation (mm), 
Ge = groundwater contribution to the water requirement (mm [see Appendix E 
for calculation methods]), 
Wb = stored soil moisture in the root system (mm [see Appendix E for calculation 
methods]), 
Ws = seasonal crop water requirement (mm)
n = months in growing season. 

For each crop and turf, annual average irrigation rate (IR) was equal to In (Appendix E, 
Sections 1 though 2.4 and Table 6.5-1) and used to calculate means and develop distributions. 
For two season crops and alfalfa, average values of IR were summed across growing seasons to 
get a total for the year (Tables 6.5-1 and 6.5-2). 
Values of IR were determined for the 26 crops and turf to calculate average IR for the upper 
bound monsoon and lower bound glacial transition climates (Table 6.5-1). This was done to 
support development of BDCFs for the three climate states used in TSPA. See Section 6 
[Climate States] and BSC (2004 [DIRS 169674], Section 6.1.3) for description of use of these 
means in development of BDCFs for the three climate states used in TSPA. The averages for IR 
were 0.52 m/year and 0.88 m/year for upper bound monsoon and lower bound glacial transition 
climate conditions, respectively (Table 6.5-1). 
Table 6.5-1. Average Annual Irrigation Rates (IR, m/year) for 26 Crops and Turf Grass for Upper Bound 
Monsoon and Lower Bound Glacial Transition Climates 
Lower Bound Lower Bound 
Upper Bound 
Monsoon 
Glacial 
Transition 
Upper Bound 
Monsoon 
Glacial 
Transition 
Crop Climatea Climatea Crop Climatea Climatea 
Alfalfa 1.07 1.36 Head lettuce 0.36 1.09 
Apples 1.00 1.18 Lettuce 0.37 0.80 
Barley 0.54 0.56 Melons 0.29 0.76 
Bell Peppers 0.35 0.80 Oat feed 0.34 0.92 
Broccoli 0.48 1.11 Oat hay 0.26 0.39 
Cabbage 0.52 1.05 Onions 0.90 1.05 
Carrots 0.55 1.29 Potatoes 0.51 0.90 
Cauliflower 0.47 0.80 Spinach 0.27 0.66 
Celery 0.85 0.86 Squash 0.15 0.43 
Feed Corn 0.44 1.15 Strawberries 0.81 0.39 
Corn silage 0.31 1.08 Sweet corn 0.46 0.88 
Cucumbers 0.16 0.51 Tomatoes 0.32 0.74 
Grapes 0.52 0.58 Turf Grass 1.05 1.26 
Winter Wheat 0.57 1.22 
Average 0.52 0.88 
SDb 0.26 0.29 
CVc 0.51 0.32 
a 
Irrigation rates from Tables E-6 and E-7 for upper bound monsoon and lower bound glacial transition 
climates, respectively. See Appendix E for calculation methods and examples. 
b Standard deviation calculated using the STDEV function of Excel. 
Coefficient of variation (SD/mean). 
Averages and two types of distributions (cumulative and normal) were developed for IR using 
the 26 crops and turf for present-day and upper bound glacial transition climates. See Section 6 
[Climate States] and BSC (2004 [DIRS 169674], Section 6.1.3) for description of use of these 
distributions in development of BDCFs for the three climate states used in TSPA. 
The averages for IR for present-day and upper bound glacial transition climate conditions were 
0.95 m/year and 0.50 m/year, respectively (Table 6.5-2). Two distributions for IR are included in 

this analysis for present-day and upper bound glacial transition climates so that the more 
appropriate distribution can be selected for use in the biosphere model. IR differs from other 
parameters in this analysis because it is used for long-term radionuclide accumulation in soil. 
Because of this, the biosphere model could require IR values that are representative of long-term 
averages, which do not include the entire range of possible variation. In this case, normal 
distributions with the calculated means and standard errors are recommended for both climates 
(Table 6.5-3). To avoid extreme values that are not representative of long-term averages, the 
ninety-ninth percentiles are recommended as the minimum and maximum values for the 
distributions. For present-day climate, this results in a distribution with values that range from 
0.74 to 1.16 m/year (Table 6.5-3). For upper bound glacial transition climate, the distribution 
values range from 0.40 to 0.60 m/year (Table 6.5-3). 
Table 6.5-2. Average Annual Irrigation Rates (IR, m/year) for 26 Crops and Turf Grass for Present-Day 
and Upper Bound Glacial Transition Climates 
Upper Bound Upper Bound 
Glacial Glacial 
Crop 
Present-Day 
Climatea 
Transition 
Climatea Crop 
Present-Day 
Climatea 
Transition 
Climatea 
Alfalfa 1.94 0.83 Head lettuce 0.66 0.63 
Apples 1.82 0.73 Lettuce 0.66 0.46 
Barley 0.84 0.31 Melons 0.84 0.49 
Bell Peppers 0.72 0.42 Oat feed 0.57 0.55 
Broccoli 0.83 0.64 Oat hay 0.46 0.21 
Cabbage 0.91 0.58 Onions 1.34 0.54 
Carrots 1.00 0.71 Potatoes 0.84 0.47 
Cauliflower 0.83 0.44 Spinach 0.51 0.37 
Celery 1.50 0.46 Squash 0.40 0.18 
Feed Corn 1.18 0.73 Strawberries 1.44 0.16 
Corn silage 0.83 0.69 Sweet corn 0.74 0.52 
Cucumbers 0.50 0.21 Tomatoes 0.69 0.38 
Grapes 0.99 0.36 Turf Grass 1.62 0.83 
Winter Wheat 0.94 0.67 
Average 0.95 0.50 
SDb 0.41 0.19 
CVc 0.43 0.38 
Table 6.5-3. Averages and Normal Distributions for Long-Term Average Annual Irrigation Rates 
(IR m/year) 
a 
Irrigation rates from Tables E-5 and E-8 for present-day and upper bound glacial transition climates, 
respectively. See Appendix E for calculation methods and examples. 
b Standard deviation calculated using the STDEV function of Excel. 
Coefficient of variation (SD/mean). 
Averagea 
Standard 
Error Minimum Maximum 
Present-Day Climate 0.95 0.08 0.74 1.16 
Upper Bound Glacial 
Transition Climate 
0.50 0.04 0.40 0.60 
a 
Mean IR of 26 representative crops and turf calculated from Table 6.5-2. 

If the biosphere model requires an IR distribution that includes yearly variation and a wider 
range of uncertainty, then cumulative distribution functions are recommended for present-day 
and upper bound glacial transition climates (Table 6.5-4). Ninety percent of the probability 
distribution is between the minimum and maximum IR of representative crops (e.g., range = 0.40 
[squash] to 1.94 [alfalfa] m/year for present-day climate). The distribution between the 
minimum and maximum crop IR is divided into five intervals of equal probability (summing to 
90 percent, Table 6.5-4), with 5 or 6 crops per interval. To account for variation and uncertainty 
beyond the range of crop specific values, intervals of five-percent probability each were added to 
the lower and upper ends of the distribution. To avoid IR values that could result in yield 
reduction or plant mortality due to water stress, recommended minimum bounds for present-day 
and upper bound glacial transition climate distributions are 0.33 m/year and 0.14 m/year, 
respectively (Table 6.5-4). To include variation and uncertainties associated with season length 
and excessive irrigation, maximum bounds of 2.29 m/year and 0.98 m/year are recommended for 
present-day and upper bound glacial transition climates, respectively (Table 6.5-4). Minimum 
and maximum bounds are justified in the analysis below. 
Table 6.5-4. Averages and Cumulative Distributions for Annual Average Irrigation Rates (IR m/year) for 
Present-Day and Upper Bound Glacial Transition Climates 
Present-Day Climate 
Upper Bound Glacial Transition 
Climate 
Upper 
Limit of Cumulative 
Upper 
Limit of Cumulative 
Averagea Interval Probability Averagea Interval Probability 
0.95 0.33 0.00 0.50 0.14 0.00 
0.40 0.05 0.16 0.05 
0.66 0.23 0.36 0.23 
0.83 0.41 0.46 0.41 
0.91 0.59 0.54 0.59 
1.34 0.77 0.69 0.77 
1.94 0.95 0.83 0.95 
2.29 1.00 0.98 1.00 
a Mean IR of 26 representative crops and turf calculated from Table 6.5-2. 
This parameter is not used in the biosphere volcanic ash exposure scenario; therefore, changes 
resulting from a volcanic eruption at Yucca Mountain are not considered in this analysis. 
Variation and UncertaintyVariation in IR is primarily from differences in water use among 
crops, variation in growing season length among crops, differences between minimum and 
maximum season lengths for each crop, and yearly variation in climate variables that drive ETo. 
Uncertainty in the distribution of IR is due to lack of knowledge about which crops a farmer will 
choose to grow and about water management practices (i.e., excessive watering or under 
watering during a growing season). 
Variation among crops and uncertainty about which crops a farmer will grow are adequately 
accounted for through use of 26 crops and turf. Selection was based on an evaluation of crops 
currently grown in southern Nye County, Nevada and eastern Washington, national patterns of 

food consumption, and variation in the growing season under arid to semi-arid climate conditions 
for commonly grown and consumed crops (see Appendix A). 
Within Crop Variation in Season LengthGenerally, the midpoint of minimum and maximum 
season lengths gathered from several sources was selected as a representative and reasonable 
growing season length for each crop (Appendix D, Tables D-1 and D-2). For the present-day 
climate, within crop differences between the minimum and maximum season lengths ranged 
from 10 to 60 days with a mean difference of 32 days (SD = 15.2, calculated from Table D-1). 
For the upper bound glacial transition climate, within crop differences between the minimum and 
maximum season lengths ranged from 5 to 70 days with a mean difference of 24 days 
(SD = 15.2, calculated from Table D-2). Even though the ranges in minimum and maximum 
season lengths appear to be considerable, in most cases, season lengths were constrained by 
mean monthly temperatures for the two climates (i.e., temperatures below crop tolerance levels) 
or by growing two-season crops. These constraints resulted in relatively little variation in IR due 
to within crop differences in potential growing season lengths compared to variation in IR among 
crops. Examples are illustrated below. 
Crops with the lowest IRs were evaluated to establish low bounds for the distributions that would 
encompass the potential variation caused by minimum season lengths for a crop. Squash and 
strawberries had the lowest IR for present-day and upper bound glacial transition climates, 
respectively (Table 6.5-2). However, there was no information on minimum and maximum 
ranges for season length for strawberries. Therefore, squash was used to evaluate the low end of 
the distributions for both climate conditions. Squash season length for present-day climate was 
58 days, and the minimum season length was 50 days (Appendix D, Table D-1). Re-calculation 
of IR based on the minimum season length showed a reduction from 0.40 m/year to 0.33 m/year. 
This value was selected as the minimum for the recommended distribution because it included 
variation in IR due to season length for a single-season, low water-use crop, and also 
encompassed uncertainties regarding under-watering (discussed below). 
Squash season length for upper bound glacial transition climate was 58 days, and the minimum 
season length was 50 days (Appendix D, Table D-2). Re-calculation of IR based on the 
minimum season length showed a reduction from 0.18 m/year to 0.14 m/year. This value was 
selected as the minimum for the recommended distribution because it included variation in IR 
due to season length for a low water-use crop, and also encompassed uncertainties regarding 
under-watering (discussed below). 
Bounds for the high end of the distributions were evaluated using crops with high water-use, or a 
wide range between minimum and maximum growing season lengths. Alfalfa, apples, 
bermudagrass, and celery were considered for present-day climate, and alfalfa, fescue, carrots, 
and feed corn were considered for upper bound glacial transition climate. 
Alfalfa had the highest IR for present-day climate (1.94 m/year, Table 6.5-2) compared to other 
crops. Additionally, six cuttings were used, making the total growing time 336 days. Because of 
the time required for each cutting, and the short winter dormant period, additional cuttings or a 
longer time between cuttings were not possible. Thus, 1.94 m/year is the maximum amount of 
water that alfalfa can use based on maximum growing season length. Apples also had relatively 
high water requirements (1.82 m/year); however, apples are usually drip irrigated making the IR 

less important to the distribution than alfalfa, which is sprinkler, irrigated. Bermudagrass (turf) 
also had a relatively high IR (1.62 m/year); however, its growing season was 365 days and could 
not be increased. The mean difference between minimum and maximum growing season lengths 
of 32 days for present-day climate was added to early celery because a range specific to celery 
was not available (Appendix D, Table D-1). A 32-day increase is similar to the ranges in 
growing season length for other leafy vegetables (Appendix D, Table D-1), and does not overlap 
the planting time for late season celery. Addition of days to the late season celery crop would do 
little to increase the irrigation requirement because of the very low evaporative demand in 
January and February. The resulting irrigation requirement for celery (early and late season) was 
1.82 m/year, an increase of 0.32 m/year. This is similar to IR for alfalfa (1.94 m/year). 
Therefore, variation due to season length for two-season and multi-season high water-use crops 
is accounted for in the range of crop specific IR for present-day climate conditions. 
Tall fescue (turf grass) and alfalfa had the highest IR (0.83 m/year) for upper bound glacial 
transition climate compared to the remaining 25 crops. Growing season length was constrained 
by dormancy and occurrence of killing frost from November through the end of March, making 
it unreasonable to extend the growing seasons. Based on maximum growing season lengths, it is 
reasonable to conclude that 0.83 m/year is the maximum amount of water that fescue or alfalfa 
would normally use. Maximum season lengths for carrots and feed corn were examined to 
determine whether variable growing season length or variable onset of growth (with no increase 
in season length) should be used to establish upper bounds for the upper bound glacial transition 
climate distribution of IR. These crops were chosen because they have relatively high IR 
(carrots = 0.71 m/year and feed corn = 0.73 m/year). 
Moving the planting date for carrots from April 23 to April 15 (Appendix D, Table D-2) resulted 
in an eight-day extension at the beginning of the growing season. Extension of the harvest date 
was not possible because of constraints imposed by the planting date for the late season crop 
(July 13 for carrots). Because of low evaporative demands in April, the 8-day extension of the 
growing season resulted in an IR increase of only 0.02 m/year for carrots. This illustrates that 
use of two season crops encompasses the variation that might occur from within crop variation in 
growing season length at the high end of the distribution. 
The planting and harvest dates for feed corn were moved forward 26 days to illustrate the impact 
of a change in timing of growth for a relatively high water-use crop (0.73 m/year). The change 
in timing changed several parameters, including precipitation and evaporative demands, and 
resulted in an IR of 0.85 m/year. This value is similar to those for fescue and alfalfa. Therefore, 
potential variation in IR due to within-crop variation in timing of growth is encompassed by 
variation in IR among crops for upper bound glacial transition climate conditions. 
Yearly Variation in ClimateVariation in IR from yearly variation in mean monthly climate 
variables that drive ETo was not directly calculated because the dataset that was used did not 
include this information. While hourly or daily variations in ETo can be large, ETo averaged for 
monthly time periods tend to be conservative from year to year (Tanner 1967 [DIRS 159950], 
Chapter 29, p. 557). Error in estimates of ETo using empirical formulas have been shown to 
decrease by two to three times as the period of estimation increased from one week to one month 
(Tanner 1967 [DIRS 159950], Chapter 29, p. 557). Variation from the expected monthly 
average in ETo was estimated using air temperature data as a meteorological index representing 

variation in evaporative demands. Monthly mean, average maximum, and average minimum air 
temperatures with standard deviations for 26 to 29 years in Amargosa Valley, Nevada 
(Table 6.5-5) were obtained from the Western Regional Climate Center (2002 [DIRS 161250], 
2002 [DIRS 161251], 2002 [DIRS 160537]). The coefficient of variation (CV = SD/mean) 
showed little yearly variation occurred in the three temperature parameters each month 
(Table 6.5-5). Yearly variation ranged from 3.0 percent to 10.0 percent depending on the 
temperature parameter and month (Table 6.5-5). It should be noted that the input data used to 
calculate IR for present-day climate were averaged over 5 years and the CV would be higher than 
that for data averaged over 26 to 29 years. More variation in IR occurred due to variation among 
crops (43 percent and 38 percent for present-day and upper bound glacial transition climates, 
respectively, Table 6-5.2) than would occur due to yearly variation in monthly mean ETo. 
Therefore, it is reasonable to conclude that the recommended distributions encompass the 
variation in IR that could be caused by yearly variation in mean monthly climate variables that 
drive ETo. 
Table 6.5-5. Monthly Mean Air Temperatures (F) for Amargosa, Nevada 
Jan Feb March April May June July Aug Sept Oct Nov Dec 
Mean Temperature 45.9 50.2 55.5 62.3 71.7 80.0 85.9 84.9 77.2 66.0 52.9 45.4 
SDa 2.6 3.3 3.0 3.9 3.4 3.0 2.3 2.2 2.7 3.6 3.1 3.0 
CVb 0.06 0.07 0.05 0.06 0.05 0.04 0.03 0.03 0.03 0.05 0.06 0.07 
Yearsc 27.0 29.0 29.0 29.0 28.0 28.0 28.0 27.0 27.0 26.0 26.0 28.0 
Maximum 
Temperature 
60.5 65.4 71.3 79.3 88.8 97.5 103.2 102.0 95.0 83.8 69.3 60.9 
SDa 3.9 4.8 4.8 4.0 4.8 4.0 3.2 2.6 2.9 3.4 4.4 4.4 
CVb 0.06 0.07 0.07 0.05 0.05 0.04 0.03 0.03 0.03 0.04 0.06 0.07 
Yearsc 27.0 29.0 29.0 29.0 29.0 28.0 28.0 28.0 27.0 27.0 26.0 28.0 
Minimum 
Temperature 
31.4 35.0 39.6 45.3 54.6 62.4 68.6 67.8 59.4 48.2 36.5 30.0 
SDa 2.8 3.1 2.7 3.5 3.4 3.2 2.6 2.5 3.2 3.6 3.0 2.9 
CVb 0.09 0.09 0.07 0.08 0.06 0.05 0.04 0.04 0.05 0.07 0.08 0.10 
Yearsc 27.0 29.0 29.0 29.0 28.0 28.0 28.0 27.0 27.0 26.0 26.0 28.0 
Source: Western Regional Climate Center 2002 ([DIRS 161250], 2002 [DIRS 161251], 2002 [DIRS 160537]). 
a 
Standard deviation. 
b Coefficient of variation = SD/mean). 
Number of years that data were collected. 
Uncertainty in Water Management PracticesUncertainty in the distribution of IR due to lack 
of knowledge about water management practices (i.e., excessive irrigation or under-watering 
during a growing season) can be bounded by assessing practices that would result in reductions 
in crop yield. 
Excessive irrigationWhen irrigation exceeds the storage capacity of soil in the root zone, water 
percolates past the root zone, or runs off site and is unavailable for use by the plant, or will 
accumulate and waterlog poorly drained soils (Viets 1967 [DIRS 159952], Chapter 24, p. 466). 
Such situations affect nutrient availability and therefore crop yield. Excessive percolation 

leaches nitrates and other mineral nutrients that are essential to plant growth 
(Viets 1967 [DIRS 159952], Chapter 24, p. 466). In poorly drained soils, or if water is ponded at 
the surface for an extended period, water will replace oxygen in soil pores resulting in oxygen 
deficiencies for root growth and microbial activity (Viets 1967 [DIRS 159952], Chapter 24, 
pp. 466 and 478). Microbes compete with plant roots for oxygen, and reduce available nitrate 
through denitrification (nitrate converted to unusable gaseous nitrogen) in wet soils 
(Viets 1967 [DIRS 159952], Chapter 24, p. 478). Excessive irrigation can also increase annual 
weeds and perennial grasses which causes reduction in yield for most crops. Overwatering 
increases pumping costs and is limited by water permits. A farmer or gardener would probably 
respond to signs of excessive irrigation and modify their water management practices to avoid 
losses in yield or increases in pumping costs. 
To determine reasonable upper bounds for IR distributions, impacts of overwatering alfalfa were 
evaluated. Overwatering alfalfa causes root and shoot diseases, nutrient losses through leaching, 
weed encroachment, and does not increase yield (Orloff et al. 1997 [DIRS 158774], p. 25). 
Environmental problems such as stream, river, or groundwater pollution can be a direct result of 
leaching fertilizers due to excessive irrigation of alfalfa (or other crops). Values for yield 
reduction caused by overwatering suggest that an approximate 18 percent increase in irrigation 
over crop requirements can result in yield reduction of about 0.4 tons per acre (Keller and 
Carlson 1967 [DIRS 159951], Figure 31-2, p. 612). Orloff et al. 1997 ([DIRS 158774]) suggest 
that alfalfa does not tolerate wet soils during periods of active growth, and wet soils have the 
potential to shorten stand life. Therefore, it is reasonable to suggest that farmers would not over 
irrigate alfalfa by more than about 18 percent of the crop water requirement. This percent 
increase results in IR values of 2.29 m/year and 0.98 m/year for alfalfa under present-day and 
upper bound glacial transition climate conditions, respectively. To include uncertainty 
associated with excessive watering practices, these values were used as the upper bounds for the 
distributions of IR for present-day and upper bound glacial transition climates. 
Under-wateringMoisture stress occurs if precipitation and/or irrigation do not meet crop water 
requirements (see Appendix B). The level of moisture stress can vary from minor, where crop 
leaves wilt during the day but recover at night, to severe, where recovery is not possible and leaf 
desiccation occurs. Reductions in crop yield and quality can occur at various levels of water 
stress depending on the sensitivity of the crop. For example, grain crops tend to be more tolerant 
of water stress than potatoes or leafy vegetables (Robins et al. 1967 [DIRS 159939], pp. 635 and 
636; Vittum and Flocker 1967 [DIRS 159941], pp. 676 and 677). As with excessive irrigation, it 
is likely that a farmer or gardener would respond to signs of under-watering by modifying their 
water management practices to avoid losses in quality and yield. 
To evaluate the impacts that under-watering might have on the distribution of IR, soil moisture 
storage capacity of the root zone and crop sensitivity to soil moisture stress were considered for 
crops at the low end of the recommended distributions (Appendix E, Section 2.6). The methods 
used to calculate irrigation application (Section 6.7 and Appendix E, Section 2.5) were modified 
to reflect under-watering conditions that would likely cause stress. Soil moisture was allowed to 
stay at a level that would impose stress for two days prior to each scheduled irrigation event over 
a 30-day time period (scheduling of irrigation events differed according to crop requirements). 
Two days was chosen as a likely time interval that would cause some level of crop stress without 
causing mortality. The resulting number of days the crop would experience stress and the 

percent reduction in applied water was determined (Appendix E, Section 2.6). The results of this 
exercise showed that small percent decreases in irrigation could cause several (non-consecutive) 
days of water stress in the 30 day time period (Appendix E, Table E-14). Based on this analysis, 
a 10 percent reduction in irrigation parameters was selected to determine whether under-watering 
should be used to determine lower bounds for the distributions, and to avoid selection of a lower 
bound that would cause yield reduction or crop mortality. Squash was chosen for the IR analysis 
for present-day climate and strawberries were chosen for upper bound glacial transition climate. 
These two crops had the lowest IR values for the two climate conditions (Table 6.5-1). 
Using a 10 percent reduction in IR for squash resulted in a decrease from 0.40 m/year to 
0.36 m/year. 0.36 m/year was only slightly higher than the recommended minimum value for the 
distribution based on variation in season length (0.33 m/year). Thus, the recommended 
distribution accounts for uncertainty in water management practices without including values 
that would result in crop mortality. 
For upper bound glacial transition climate conditions, a 10 percent reduction in IR for 
strawberries resulted in a decrease from 0.16 m/year to 0.14 m/year. This value was equal to the 
recommended minimum for the distribution of IR based on variation in season length. 
Therefore, the recommended distribution for upper bound glacial transition climate also accounts 
for uncertainty in water management practices without including values that would result in crop 
mortality. 
6.6 IRRIGATION INTENSITY 
6.6.1 Use in Biosphere Model 
Irrigation intensity (Ij, cm/hour) is a measure of the rate at which contaminated groundwater is 
applied to a crop type each time a crop is irrigated. It is used in the plant submodel in the 
calculation of the water interception fraction (Equation 6.1-1). In that equation, irrigation 
intensity is raised to the power of a negative fraction; thus, increasing the rate at which water is 
applied decreases the interception fraction (because the plant surface becomes saturated more 
rapidly and more contaminated water runs off of the leaves). Changing irrigation intensity from 
0.5 to 10 cm/hour results in a decrease in the interception fraction of beryllium from 0.57 to 
0.21 
(Figure 6.6-1, with biomass = 0.4 kg/m2 and irrigation application = 40 mm). The 
interception fraction is much more sensitive to values of irrigation intensity less than about 
4.0 
cm/hour because the fraction asymptotes at higher intensities (Figure 6.6-1). Irrigation 
intensity has no effect on the interception fraction for iodine because the parameter is raised to 
the power of -0.05 for that anion and so the outcome of this product is relatively insensitive to 
any significant change that may be made in the value of irrigation intensity. 

0 
1 2 3 5 6 7 8 9 
Iii (/hr) 
0.1 
0.2 
0.3 
0.4 
0.5 
0.6 
0.7 
0.5 1.5 2.5 3.5 3.95 4.5 5.5 6.5 7.5 8.5 9.5 10 
rrigaton intenstycmWater Interception Fraction 
NOTE: Calculated as fraction = 2.29 x dry biomass0.695 x irrigation application 0.29 x irrigation intensity-0.341, with dry 
biomass = 0.4 kg/m2 and irrigation application = 40 mm. 
Figure 6.6-1. Sensitivity of Water Interception Fraction to Irrigation Intensity 
6.6.2 Parameter Development 
Irrigation intensity can vary substantially depending on the method of irrigation used. High 
irrigation intensity is achieved in gardens using hoses and lawn sprinklers and in agricultural 
fields using large gun sprayers. Low rates can be achieved on garden and commercial sprinkler 
systems (e.g., center pivot, side roll, or stationary spray systems) by selecting nozzles with low 
flow rates and increasing the spacing between nozzles. Irrigation intensity for sprinkler types 
and typical spacing used in commercial agriculture can range from less than 0.5 cm per hour to 
more than 10 cm per hour (Doorenbos and Pruitt 1977 [DIRS 103062], Table 44, p. 77; 
Ley 1992 [DIRS 159380], Table 2; Hansen and Trimmer 1997 [DIRS 159373], Figures 1 and 2; 
Kranz 2002 [DIRS 159377], Figure 2). 
One of the most important factors considered when determining the rate at which water can be 
applied using agricultural sprinkler systems is the soil texture and associated infiltration rate 
(Christiansen and Davis 1967 [DIRS 159263], pp. 896 and 897; Hansen and Trimmer 1997 
[DIRS 159373]; Kranz 2002 [DIRS 159377]). On clay soils, which have infiltration rates of 
about 0.1 to 1.5 cm/hour (Doorenbos and Pruitt 1977 [DIRS 103062], Table 40, p. 91), water 
must be applied slowly to avoid ponding, runoff, and erosion. In contrast, sandy and sandy loam 
soils have high infiltration rates (1.5 to 7.5 cm/hour for sandy loam and 2.5 to 25 cm/hour for 
sandy soils (Doorenbos and Pruitt 1977 [DIRS 103062], Table 40, p. 91)) and can be irrigated at 
relatively high rates. 
Information on the permeability rates of soils in northern Amargosa Valley was obtained from 
the USDA NRCS (Dollarhide 1999 [DIRS 159253], Table: Physical Properties of Soils, 
Column: Permeability, see Section 4.1.6) and examined to determine the feasible range of 
Irrigation intensity for soils in that area. The common soils in northern Amargosa Valley 
(Arizo, Corbilt, Sanwell, Shamock, Yermo) are sandy to sandy loam, well drained, and have a 
moderate to rapid permeability (CRWMS M&O 1999 [DIRS 107736], Figure 1 and pp. C-1, 
C-2, C-25, C-27, C-39, and C-40). The infiltration rate of the upper layers of these soils is about 

5 to 15 cm per hour (Dollarhide 1999 [DIRS 159253], Table: Physical Properties of Soils, 
Column: Permeability). 
Because of the high infiltration rate of soils in northern Amargosa Valley, systems with low 
irrigation intensity would not be required and likely would be avoided because they are more 
expensive to operate. For example, evaporation is higher when using low spray rates, which 
decreases irrigation efficiency and increases costs, especially in hot, dry, windy conditions such 
as those experienced in Amargosa Valley during summer. Also, it takes a long time to deliver 
sufficient water at low application rates, which increases labor and pumping costs. At an 
irrigation efficiency of 70 percent, it takes almost 18 hours to apply 3 cm of water at an irrigation 
intensity of 0.25 cm per hour, and about 9 hours to apply that amount at an intensity of 0.5 cm 
per hour (Ley 1992 [DIRS 159380], Table 3). 
Based on this information, 4.3 cm per hour (midpoint of the distribution), and a uniform 
distribution of irrigation intensity with a minimum of 1.0 cm per hour and a maximum of 7.5 cm 
per hour are recommended. A minimum value less than the minimum infiltration rate of soils in 
Amargosa Valley (2 to15 cm/hour) is recommended to account for uncertainty about irrigation 
methods used and irrigation efficiency in Amargosa Valley. Although application rates higher 
than the maximum recommended value are possible for most soils in Amargosa Valley (and are 
quite likely for some irrigation methods such as gardens irrigated with a hose), a higher 
maximum is not recommended because higher values have little influence on the calculation of 
leaf interception fraction (see Section 6.6.1). A uniform distribution is recommend because there 
is no information to indicate which rates within this range are more likely. 
The same distribution is recommended for all crop types because sprinklers producing a wide 
range of application rates are available for both garden and commercial crops, and soil 
conditions would not differ substantially for garden and commercial crops. Likewise, the same 
distribution is recommended for present-day and upper bound glacial transition climates, because 
soil infiltration rates would not change and available sprinkler equipment do not differ between 
arid and semiarid regions. 
There is uncertainty in the development of a distribution for irrigation intensity because there is 
limited information on irrigation methods used in Amargosa Valley, there is a wide range of 
irrigation methods available, and irrigation systems can be modified easily to change irrigation 
intensity. The bounds of the distribution recommended in this analysis were selected to ensure 
that these uncertainties are propagated in the biosphere model. 
This parameter is not used in the biosphere volcanic ash exposure scenario; therefore, changes 
resulting from a volcanic eruption at Yucca Mountain are not considered in this analysis. 
6.7 IRRIGATION APPLICATION 
6.7.1 Use in Biosphere Model 
Irrigation application (IAj, mm) is a measure of the amount of contaminated water applied to a 
crop type each time crops are irrigated during the last 30 days of growth. It is used in the plant 
submodel in the calculation of the water interception fraction (Equation 6.1-1). In that equation, 
irrigation application is raised to the power of a negative fraction (see Section 6.1.1); thus, 

increasing the amount of water applied during each application decreases the interception 
fraction (because the plant surface becomes saturated and more contaminated water runs off of 
the leaves). Changing the irrigation amount from 15 to 65 mm results in a change in the 
interception fraction of beryllium from about 0.34 to 0.23 (with biomass = 0.4 kg/m2 and 
irrigation intensity = 4 cm/hour). The interception fraction for iodine changes from about 0.06 to 
about 0.02. Thus, the water interception fraction is relatively insensitive to changes in irrigation 
application. 
6.7.2 Parameter Development 
Methods for calculating crop water requirements that were published by FAO (Allen et al. 1998 
[DIRS 157311]) were used to calculate ETo (Appendix C), Kc (Appendix D), and ETc 
(Appendix D). Methods are justified in Appendix B (Section 2). Background information on 
plant water use is also in Appendix B (Section 1). Mean daily ETc averaged for one-month time 
periods and soil moisture balance over a 30 day period prior to harvest were used to calculate IA 
for each crop (see Appendix E, Section 2.5.2 for methods and example calculations). Several 
irrigation events were required during the last 30 days of growth for most crops. The average 
application amount for all irrigation events (per crop) was determined and these values were used 
to calculate the average IAj per crop type (Table 6.7-1). The average application amount for two 
seasons was used where applicable. Additionally, IAj values for alfalfa were averaged across 
cuttings. 
Parameter inputs were rooting depth (Section 6.12), growing season lengths (Section 4.1.4) and 
average monthly weather data (Section 4.1.5) for present-day and upper bound glacial transition 
climates. Rooting depth was used in soil water balance calculations in Appendix E. Growing 
season lengths were used in Appendix D to calculate mean monthly Kc and in Appendix E to 
determine when the last 30 days of growth occurred for each crop. Average monthly weather 
data were used in Appendix C to calculate ETo. Average monthly precipitation was used in 
Appendix E to calculate effective precipitation. Effective precipitation for 30 days prior to 
harvest was used in the calculations of IA (Appendix E, Section 2.5.2). 
The average for each crop type is the mean IAj of the representative crops, with the exception of 
the weighted mean used for cattle forage (Tables 6.7-2 and 6.7-3). 

Table 6.7-1. Irrigation Application 
Upper 
Bound 
Upper 
Bound 
Present-Glacial Present-Glacial 
Crop Type 
Crop 
Day 
Climate 
IAj (mm)a 
Transition 
Climate 
IAj (mm)a 
Crop Type 
Crop 
Day 
Climate 
IAj (mm)a 
Transition 
Climate 
IAj (mm)a 
Leafy Vegetables Fruits 
Broccoli 22.0 19.3 Apples 49.4 54.4 
Cabbage 23.5 26.1 Grapes 48.4 43.2 
Cauliflower 20.8 22.0 Melons 35.4 34.6 
Celery 8.4 8.0 Strawberries 6.0 7.3 
Head Lettuce 10.9 9.0 Tomatoes 30.3 31.4 
Leaf Lettuce 10.0 10.1 Average 33.9 34.2 
Spinach 7.5 7.8 SDb 17.6 17.5 
Average 14.7 14.6 CVc 0.52 0.51 
SDb 7.0 7.6 
CVc 0.48 0.5 Grains 
Barley 48.6 66.7 
Other Vegetables Corn 50.4 32.2 
Bell peppers 19.8 17.7 Oats 50.0 46.2 
Carrots 21.2 20.1 Winter wheat 77.9 59.9 
Cucumbers 34.8 37.2 Average 56.7 51.3 
Onions 9.1 11.3 SDb 14.1 15.3 
Potatoes 18.9 14.4 CVc 0.25 0.30 
Squash 33.3 34.1 
Sweet corn 44.7 40.3 Cattle Forage 
Average 26.0 25.0 Alfalfa hay 57.6 52.5 
SDb 12.1 11.8 Corn silage 60.0 61.9 
CVc 0.46 0.5 Oat hay 56.3 48.3 
Average 58.0 54.2 
SDb 1.9 7.0 
CVc 0.03 0.1 
a 
Irrigation application amounts from Tables E-13 and E-14 for present-day and upper bound glacial 
transition climates, respectively. See Appendix E for calculation methods and examples. 
b 
Standard deviation calculated using the STDEV function of Excel. 
Coefficient of variation (SD/mean). 

Table 6.7-2. Averages and Cumulative Distributions for Irrigation Application (IAj mm) for Present-Day 
Climate 
Crop Type Averagea 
Upper 
Limit of 
Intervalb 
Cumulative 
Probability Crop Type Averagea 
Upper 
Limit of 
Intervalb 
Cumulative 
Probability 
Leafy 14.7 6.0 0.00 Fruits 33.9 5.0 0.00Vegetables 7.5 0.05 6.0 0.05 
8.4 0.20 30.3 0.28 
10.0 0.35 35.4 0.51 
10.9 0.50 48.4 0.72 
20.8 0.65 49.4 0.95 
22.0 0.80 58.3 1.00 
23.5 0.95 
27.7 1.00 Grains 56.7 43.0 0.00 
Other 26.0 48.6 0.05Vegetables 8.0 0.00 50.1 0.35 
9.1 0.05 50.4 0.65 
18.9 0.20 77.9 0.95 
19.8 0.35 91.9 1.00 
21.2 0.50 
33.3 0.65 Cattle 
Foragec 
57.8 50.0 0.00 
34.8 0.80 56.3 0.05 
44.7 0.95 57.6 0.72 
52.7 1.00 60.0 0.95 
71.0 1.00 
a 
Mean IAj for a crop type from Table 6.7-1, with the exception of the weighted mean calculated for cattle forage. 
The weighted mean was calculated as (3 x 57.5 [IA for alfalfa] + 1 x 60.2 [IA for corn silage] + 1 x 56.5 [IA for 
b 
oat hay]) / 5 = 57.8. 
Limits determ
ined from crop specific IA (see Table 6.7-1). 
For 90 percent of the distribution between the minimum and maximum crop specific IAj, the probabilities for the 
two cattle forage intervals were weighted 3:1 for the range between oat hay to alfalfa (p = 0.68) and alfalfa to 
corn silage (p = 0.22). 
A cumulative distribution function is recommended for each crop type for both present-day and 
upper bound glacial transition climates (Tables 6.7-2 and 6.7-3). Ninety percent of the 
probability distribution is between the minimum and maximum IA of representative crops within 
a crop type (e.g., IA for leafy vegetables ranges from 7.5 [spinach] to 23.5 mm [cabbage] for 
present-day climate, Table 6.7-1). The distribution between the minimum and maximum crop 
IAj is divided into intervals of equal probability (summing to 90 percent), with the exception of 
cattle forage (see below). The number of intervals is one less than the number of representative 
crops and the upper limits are crop-specific values of IA (Table 6.7-2). The probabilities for the 
two intervals for the distribution of cattle forage were weighted 3:1 for the range between oat hay 
and alfalfa (p = 0.675) versus the range between alfalfa and corn silage (p = 0.225) (see 
Section 6 for justification). This results in a higher probability for selection of values that are 
similar to alfalfa. To account for variation and uncertainty that could result in values beyond the 
range of crop specific values, intervals of five percent probability each were added to the lower 
and upper ends of the distribution. The bounds are based on crop water stress calculations and 
overwatering potentials and are justified in the analysis below. 

This parameter is not used in the biosphere volcanic ash exposure scenario; therefore, changes 
resulting from a volcanic eruption at Yucca Mountain are not considered in this analysis. 
Table 6.7-3. Averages and Cumulative Distributions for Irrigation Application (IAj mm) for Upper 
Bound Glacial Transition Climate 
Crop Type Averagea 
Upper 
Limit of 
Intervalb 
Cumulative 
Probability Crop Type Averagea 
Upper 
Limit of 
Intervalb 
Cumulative 
Probability 
Leafy Vegetables 14.6 7.0 0.00 Fruits34.2 6.0 0.00 
7.8 0.05 7.3 0.05 
8.0 0.20 31.4 0.28 
9.0 0.35 34.6 0.51 
10.1 0.50 43.2 0.72 
19.3 0.65 54.4 0.95 
22.0 0.80 64.2 1.00 
26.1 0.95 
30.8 1.00 Grains 51.3 28.0 0.00 
32.2 0.05 
Other 25.0 10.0 0.00 46.2 0.35Vegetables 11.3 0.05 59.9 0.65 
14.4 0.20 66.7 0.95 
17.7 0.35 78.7 1.00 
20.1 0.50 
34.1 0.65 Cattle 
Foragec 
53.5 43.0 0.00 
37.2 0.80 48.3 0.05 
40.3 0.95 52.5 0.73 
47.6 1.00 61.9 0.95 
73.0 1.00 
a 
Mean IAj for a crop type from Table 6.7-1, with the exception of the weighted mean calculated for cattle forage. 
The weighted mean was calculated as (3 x 52.5 [IA for alfalfa] + 1 x 61.9 [IA for corn silage] + 1 x 48.3 [IA for oat 
b Limits determhay]) / 5 = 53.5. 
ined from crop specific IA (see Table 6.7-1). 
For 90 percent of the distribution between the minimum and maximum crop specific IA, the probabilities for the two 
cattle forage intervals were weighted 3:1 for the range between corn silage to oat hay (p = 0.22) and oat hay to 
alfalfa (p = 0.68). 
Variation and UncertaintyMuch of the variation and uncertainty associated with the 
distributions of IAj were accounted for through selection of three or more representative crops 
per crop type (see Section 6.5 and Appendix A). Variation that was not accounted for includes 
year to year variation in monthly climate variables. Uncertainty not accounted for is due to lack 
of knowledge about water management practices (i.e., excessive irrigation or under-watering 
during the 30 days prior to harvest). 
Yearly Variation in ClimateYear to year variation in mean monthly climatic variables affect 
IAj through calculation of ETo (Appendix C). Variation in IAj from yearly variation in mean 
monthly climate variables was not directly calculated because the data were not available. While 
daily or hourly fluctuations in ETo can be large, monthly means tend to be conservative from 
year to year (see Section 6.5.2.1). Variation from the expected monthly average ETo was 
calculated using air temperature data as a meteorological index representing variation in 

evaporative demands (Section 6.5.2). Monthly mean, monthly average maximum, and monthly 
average minimum air temperatures with standard deviations for 26 to 29 years in Amargosa 
Valley, Nevada (Table 6.5-5) were obtained from the Western Regional Climate Center 
2002 ([DIRS 161250], 2002 [DIRS 161251], 2002 [DIRS 160537]). Small coefficients of 
variation (CV = SD/mean) indicate little yearly variation in the average monthly temperature 
parameters (Table 6.5-5). Yearly variation ranged from 3.0 percent to 10.0 percent depending on 
the temperature parameter and month (Table 6.5-5). Variation in IAj among crops within a crop 
type ranged from 3 percent to 52 percent for present-day climate and from 10 percent to 
51 percent for upper bound glacial transition climate (Table 6.7-1). Therefore, variation in IAj 
among crops within a crop type is generally greater than the variation in IAj expected from yearly 
changes in mean monthly climate variables. It is reasonable to conclude that the recommended 
distributions sufficiently account for yearly variations in monthly mean climate. 
Uncertainty in Water Management PracticesUncertainty in the distribution of IAj due to lack 
of knowledge about water management practices (i.e., excessive irrigation or under watering 
during a growing season) can be bounded similarly to IR, by assessing practices that would result 
in crop stress. 
Excessive IrrigationProblems caused by excessive irrigation are discussed in Section 6.5.2. 
These include nutrient loss from the root zone through leaching, water loss through percolation 
below the root zone or runoff, oxygen deficiencies for root growth and microbial activity, 
denitrification, and infestation by weeds (Section 6.5.2). These processes can result in loss of 
yield, reduced crop quality, pollution, and increased water pumping costs. In Section 6.5.2 it 
was concluded that overwatering alfalfa by about 18 percent would result in yield reduction 
through a variety of mechanisms, including those mentioned above. It is reasonable to conclude 
that an 18 percent increase in IAj would have similar effects on other representative crops and 
pumping costs. To establish appropriate upper bounds for the distributions of IAj that would 
encompass uncertainties associated with excessive irrigation, maximum values (within a crop 
type) were increased by 18 percent (Tables 6.7-2 and 6.7-3). Cabbage, sweet corn, apples, 
winter wheat, and corn silage were used to establish upper bounds in the present-day climate 
analysis. Cabbage, sweet corn, apples, barley, and corn silage were used in the upper bound 
glacial transition climate analysis. 
Under wateringMoisture stress occurs if irrigation water does not meet crop water 
requirements (see Appendix B). The level of moisture stress can vary from minor, where crop 
leaves wilt during the day but recover at night, to severe where recovery is not possible and leaf 
desiccation occurs. Reductions in both crop yield and quality can occur at various levels of 
water stress depending on the sensitivity of the crop. For example, grain crops tend to be more 
tolerant of water stress than potatoes or leafy vegetables (Robins et al. 1967 [DIRS 159939], 
pp. 635 and 636; Vittum and Flocker 1967 [DIRS 159941], pp. 676 and 677). It is likely that a 
farmer or gardener would respond to signs of under watering by modifying their water 
management practices to avoid losses in crop quality and yield. 
The methods used to determine percent reduction in the total amount of irrigation applied for 
other irrigation parameters (see Appendix E for example) were not applicable to IAj. This is 
because stress was imposed by withholding water for 2 days after scheduled irrigation events, 
then enough water was added to bring the volume in the root zone to the level where stress 
would be alleviated. This often resulted in fewer applications but increased application amounts. 

However, the results of the exercise indicated that a 10 percent reduction in irrigation would 
likely cause water stress. Therefore, it is reasonable to use the 10 percent reduction 
recommended for other irrigation parameters to determine lower bounds for the distributions of 
IAj. 
Spinach, onions, strawberries, barley, and oat hay were chosen for the present-day climate 
analysis because these crops had the lowest IA within their crop type (Table 6.7-1). IA was 
reduced by 10 percent for each of these crops and used as minimum bounds for the distributions 
for crop types (Table 6.7-2). The recommended minimum values were rounded down to the 
nearest mm. 
Spinach, onions, strawberries, feed corn, and oat hay were chosen for the upper bound glacial 
transition climate analysis because these crops had the lowest IA within their crop type 
(Table 6.7-1). IA was reduced by 10 percent for each of these crops and used as minimum 
bounds for the distributions for crop types (Table 6.7-3). The recommended minimum values 
were rounded down to the nearest mm. 
6.8 IRRIGATION RATE - DAILY 
6.8.1 Use in Biosphere Model 
Daily irrigation rate (IRDj, mm day-1) is a measure of the average amount of contaminated 
groundwater applied per day over the growing season (over all growing seasons for multiple 
season crops) for a crop type. It is used in the plant uptake submodel to calculate the rate of 
deposition of radionuclides onto the surface of plants for crop type j (Dwij, BSC 2004 
[DIRS 169460], Section 6.4.3): 
Dw, i j =IRD Cw j (Eq. 6.8-1) 
i 
where Cwi is the concentration of radionuclide i in the groundwater (Bq/m2). The deposition rate 
is then used in the calculation of leaf uptake of radionuclides, as shown in Equation 6.3-1. 
Because values of daily irrigation rate directly influence the concentration of radionuclides in 
leaves, this parameter may have an important influence on BDCFs. It is also used in a similar 
equation in the carbon-14 submodel to calculate the concentration of carbon-14 (14C) in surface 
soils (by multiplying irrigation rate by 14C concentration in water and dividing by decay and 
removal constants). 
6.8.2 Parameter Development 
Distributions for IRDj were developed for each of the five crop types for present-day and upper 
bound glacial transition climates. Daily irrigation rates for each crop were determined by 
dividing IR by growing season days (Table 6.8-1). Therefore, IRDj is directly related to IR 
making inputs and calculation methods the same as those described in Section 6.5 and 
Appendix E. 
IRDj for each crop and averages per crop type are in Table 6.8-1 for present-day and upper 
bound glacial transition climates. The average for each crop type is the mean IRDj of the 
representative crops, with the exception of the weighted mean used for cattle forage (Tables 6.8-
2 and 6.8-3). 

Table 6.8-1. Daily Irrigation Rate 
Crop Type 
Crop 
Present-Day 
Climate 
IRDj, 
a (mm/ 
day) 
Upper Bound 
Glacial 
Transition 
Climate 
IRDj, 
a 
(mm/day) 
Crop Type 
Crop 
Present-Day 
Climate 
IRDj, 
a 
(mm/day) 
Upper Bound 
Glacial 
Transition 
Climate 
IRDj, 
a 
(mm/day) 
Leafy Vegetables Fruits 
Broccoli 5.19 3.86 Apples 7.59 4.38 
Cabbage 5.38 3.86 Grapes 5.40 3.48 
Cauliflower 5.21 3.51 Melons 8.38 4.79 
Celery 6.00 4.18 Strawberries 7.02 2.51 
Head Lettuce 5.48 4.02 Tomatoes 8.67 4.33 
Leaf Lettuce 5.48 3.92 Average 7.41 3.90 
Spinach 5.11 3.34 SDb 1.30 0.91 
Average 5.41 3.81 CVc 0.18 0.23 
SDb 0.30 0.29 
CVc 0.06 0.08 Grains 
Barley 3.44 3.42 
Other Vegetables Corn 7.69 4.11 
Bell peppers 9.26 4.16 Oats 3.58 3.93 
Carrots 6.65 4.43 Winter wheat 3.87 1.99 
Cucumbers 8.36 3.08 Average 4.64 3.36 
Onions 6.07 3.48 SDb 2.04 0.96 
Potatoes 7.67 4.08 CVc 0.44 0.28 
Squash 6.93 2.73 
Sweet corn 9.03 4.95 Cattle Forage 
Average 7.71 3.84 Alfalfa hay 5.85 4.01 
SDb 1.22 0.78 Corn silage 9.02 5.03 
CVc 0.16 0.20 Oat hay 6.18 3.64 
Average 7.02 4.23 
SDb 1.74 0.59 
CVc 0.25 0.14 
a 
Daily irrigation rates derived from seasonal net irrigation requirements (Tables E-5 and E-8) divided by the 
number of days in the growing season (mid season length in Tables D-1 and D-2). 
b Standard deviation. 
Coefficient of variation = SD/mean. 
A cumulative distribution function is recommended for each crop type for both climate 
conditions (Tables 6.8-2 and 6.8-3). Ninety percent of the probability distribution is between the 
minimum and maximum IRD of representative crops within a crop type (e.g., IRD for leafy 
vegetables ranges from 5.11 [spinach] to 6.00 mm/day [celery] for present-day climate 
Table 6.8-1). The distribution between the minimum and maximum crop IRD is divided into 
intervals of virtually equal probability (summing to 90 percent), with the exception of cattle 
forage (see below). The number of intervals is one less than the number of representative crops 
and the upper bound glacial transition climate have equal IRD values (Table 6.8-1). Therefore, 

probabilities were doubled for those intervals with upper limits of 5.48 and 3.86 for present-day 
and upper bound glacial transition climates, respectively. This resulted in two less intervals than 
the number of representative crops for leafy vegetables for both climates. The probabilities for 
the two intervals for the distribution of cattle forage were weighted 3:1 for the range between 
alfalfa and oat hay (p = 0.675) versus the range between oat hay and corn silage (p = 0.225) 
(see Section 6 for justification). This results in a higher probability for selection of values that 
are similar to alfalfa. To account for variation and uncertainty that could result in values beyond 
the range of crop specific values, intervals of five percent probability each were added to the 
lower and upper ends of the distributions (Tables 6.8-2 and 6.8-3). The bounds are based on 
crop water stress calculations and overwatering potentials and are justified in the analysis below. 
This parameter is not used in the biosphere volcanic ash exposure scenario; therefore, changes 
resulting from a volcanic eruption at Yucca Mountain are not considered in this analysis. 
Table 6.8-2. Recommended Cumulative Distributions for Daily Irrigation Rate (IRDj mm/day) for 
Present-Day Climate 
Crop Type Averagea 
Upper Limit 
of Intervalb 
Cumulative 
Probability Crop Type Averagea 
Upper 
Limit of 
Intervalb 
Cumulative 
Probability 
Leafy 5.41 4.00 0.00 Fruits7.41 4.00 0.00Vegetables 5.11 0.05 5.40 0.05 
5.19 0.20 7.02 0.28 
5.21 0.35 7.59 0.51 
5.38 0.50 8.38 0.72 
5.48 0.80 8.67 0.95 
6.00 0.95 10.23 1.00 
7.08 1.00 
Grains 4.64 3.00 0.00 
3.44 0.05 
Other 
Vegetables 
7.71 5.00 0.00 3.58 0.35 
6.07 0.05 3.87 0.65 
6.65 0.20 7.69 0.95 
6.93 0.35 9.07 1.00 
7.67 0.50 
8.36 0.65 Cattle Foragec 6.55 5.00 0.00 
9.03 0.80 5.85 0.05 
9.26 0.95 6.18 0.73 
10.93 1.00 9.02 0.95 
10.64 1.00 
a 
Mean IRDj for a crop type from Table 6.7-1, with the exception of the weighted mean calculated for cattle forage. 
The weighted mean was calculated as (3 x 5.84 [IRD for alfalfa] + 1 x 9.00 [IRD for corn silage] + 1 x 6.18 [IRD for 
b 
oat hay]) / 5 = 6.54. 
Limits determined from crop specific IRD (see Table 6.8-1). 
c 
For 90 percent of the distribution between the minimum and maximum crop specific IRDj values, the probabilities for 
the two intervals for the distribution of cattle forage were weighted 3:1 for the range between alfalfa and oat hay 
(p = 0.68) versus the range between oat hay and corn silage (p = 0.22). 

Table 6.8-3. Averages and Cumulative Distributions for Daily Irrigation Rate (IRDj mm/day) for Upper 
Bound Glacial Transition Climate 
Crop Type Averagea 
Upper 
Limit of 
Intervalb 
Cumulative 
Probability Crop Type Averagea 
Upper 
Limit of 
Intervalb 
Cumulative 
Probability 
Leafy 3.81 3.00 0.00 Fruits 3.90 2.00 0.00Vegetables 3.34 0.05 2.51 0.05 
3.51 0.20 3.48 0.28 
3.86 0.50 4.33 0.51 
3.92 0.65 4.38 0.72 
4.02 0.80 4.79 0.95 
4.18 0.95 5.65 1.00 
4.93 1.00 
Grains 3.36 1.00 0.00 
1.99 0.05 
3.42 0.35 
Other 3.84 2.00 0.00 3.93 0.65Vegetables 2.73 0.05 4.11 0.95 
3.08 0.20 4.85 1.00 
3.48 0.35 
4.08 0.50 Cattle 
Foragec 
4.14 3.00 0.00 
4.16 0.65 3.64 0.05 
4.43 0.80 4.01 0.73 
4.95 0.95 5.03 0.95 
5.84 1.00 5.94 1.00 
a 
Mean IRDj for a crop type from Table 6.7-1, with the exception of the weighted mean calculated for cattle forage. 
The weighted mean was calculated as (3 x 4.01 [IRD for alfalfa] + 1 x 5.03 [IRD for corn silage] + 1 x 3.64 [IRD for 
b 
oat hay]) / 5 = 4.14. 
Limits determined from crop specific IRD (see Table 6.8-1). 
c 
For 90 percent of the distribution between the minimum and maximum crop specific IRD values, the probabilities for 
the two intervals for the distribution of cattle forage were weighted 3:1 for the range between oat hay and alfalfa 
(p = 0.68) versus the range between corn silage and oat hay (p = 0.22). 
Uncertainty not accounted for is due to lack of knowledge about water management practices 
(i.e., excessive watering or under watering during a growing season). 
Variation and UncertaintyVariation and uncertainties associated with IRDj are the same as 
those associated with IR (see Section 6.5.2). Much of the variation and uncertainty in IR was 
accounted for through selection of several representative crops (see Appendix A and 
Section 6.5). Variation that was not accounted for includes differences between minimum and 
maximum season length for each crop, and year to year variation in monthly climate variables. 
Within Crop Variation in Season Length Daily irrigation rate changes little in response to 
changes in season length. This is because increases or decreases in IR caused by changing the 
season lengths are offset in IRDj through division by the number of days in the season. For 
example, in Section 6.5.2, IR was recalculated for celery based on a 32 day increase in the 
growing season for present-day climate. This resulted in an increase in IR from 1.50 m/year to 
1.82 
m/year. The concurrent increase in IRDj was only 0.5 mm/day (6.0 mm/day to 

6.5 
mm/day). Other crops showed similar or smaller changes in IRDj in response to changes in 
season length. Additionally, season lengths were constrained by mean monthly temperatures for 
the two climates (i.e., temperatures below crop tolerance levels) or by growing two season crops. 
It was shown in Section 6.5.2 that the use of two season crops accounted for about as much of 
the variation in IR as that produced by changing growing season lengths. Therefore, variation 
caused by changes in season lengths is included by the recommended distributions of IRDj. 
Yearly Variation in ClimateYear to year variation in mean monthly climate variables affect 
IRDj through ETo in the calculation of IR (see Section 6.5.2). While daily or hourly fluctuations 
in ETo can be large, monthly means tend to be conservative from year to year (Section 6.5.2). 
Variation in IR from yearly variation in mean monthly climate variables was not directly 
calculated because the data were not available. Variation from the expected monthly average 
ETo was calculated using air temperature data as a meteorological index representing variation in 
evaporative demands (Section 6.5.2). Monthly mean, monthly average maximum, and monthly 
average minimum air temperatures with standard deviations for 26 to 29 years in Amargosa 
Valley, Nevada (Table 6.5-5) were obtained from the Western Regional Climate Center 2002 
([DIRS 161250], 2002 [DIRS 161251], 2002 [DIRS 160537]). The coefficient of variation 
(CV = SD/mean) showed little yearly variation in the average monthly temperature parameters 
(Table 6.5-5). Yearly variation ranged from 3 percent to 10 percent depending on the 
temperature parameter and month (Table 6.5-5). Variation in IRDj among crops within a crop 
type ranged from 6 percent to 44 percent for present-day climate and from 8 percent to 
28 percent for upper bound glacial transition climate (Table 6.8-1). Therefore, variation in IRDj 
that could be caused by yearly variation in mean monthly climate variables is generally 
encompassed by variation among crops within a crop type. 
Uncertainty in Water Management PracticesUncertainty in the distribution of IRDj due to 
lack of knowledge about water management practices (i.e., excessive irrigation or 
under-watering during a growing season) can be bounded similarly to IR, by assessing practices 
that would result in crop yield reductions. 
Excessive IrrigationProblems caused by excessive irrigation are discussed in Section 6.5.2. 
These include nutrient loss from the root zone through leaching, water loss through percolation 
below the root zone or runoff, oxygen deficiencies for root growth and microbial activity, 
denitrification, and infestation by weeds. These processes can result in loss of yield, reduced 
crop quality, and increased water pumping costs. 
In Section 6.5.2 it was established that over watering alfalfa by about 18 percent would result in 
yield reduction through a variety of mechanisms, including those mentioned above. It is 
reasonable to conclude that an 18 percent increase in IRDj would have similar effects on other 
representative crops and pumping costs. To establish appropriate upper bounds for the 
distributions of IRDj that would include uncertainties associated with over irrigating, maximum 
values (within a crop type) were increased by 18 percent (Tables 6.8-2 and 6.8-3). Celery, bell 
peppers, tomatoes, corn, and corn silage were used for present-day climate. Celery, sweet corn, 
melons, corn, and corn silage were used for upper bound glacial transition climate. 
Under-wateringMoisture stress occurs if irrigation water does not meet crop water 
requirements (see Appendix B). The level of moisture stress can vary from minor, where crop 

leaves wilt during the day but recover at night, to severe where recovery at night is not possible 
and leaf desiccation occurs. Reductions in both crop yield and quality can occur at various levels 
of water stress depending on the sensitivity of the crop. For example, grain crops tend to be 
more tolerant of water stress than potatoes or leafy vegetables (Robins et al. 1967 
[DIRS 159939], pp. 635 and 636; Vittum and Flocker 1967 [DIRS 159941], pp. 676 and 677). It 
is likely that a farmer or gardener would respond to signs of under-watering by modifying their 
water management practices to avoid losses in quality and yield. 
Water storage capacity of the root zone and crop sensitivity to soil moisture stress were 
considered for crops with low IRD to establish reasonable lower bounds for the distributions (see 
Appendix E, Section 2.6). The methods used to calculate irrigation application (Section 6.7 and 
Appendix E, Section 2.5) were modified to reflect under-watering conditions that would likely 
cause stress. The resulting percent decrease that might be tolerated was calculated (Appendix E, 
Section 2.6). Soil moisture was allowed to stay at a level that would impose stress for two days 
following each scheduled irrigation event over a 30-day time period (scheduling of irrigation 
events differed according to crop requirements). Two days was chosen as a likely time interval 
that would cause some level of crop stress without causing mortality. The resulting number of 
days the crop would experience stress and the percent reduction in applied water was determined 
(Appendix E, Section 2.6). The results of this exercise showed that small percent decreases in 
irrigation could cause several (non-consecutive) days of water stress in the 30 day time period 
(Appendix E, Table E-14). Based on this analysis, a 10 percent reduction in IRDj was selected to 
establish lower bounds for the distributions. 
Spinach, onions, grapes, barley, and alfalfa were chosen for the present-day climate bounds 
because these crops had the lowest IRD within their crop type (Table 6.8-1). IRD for each crop 
was reduced by 10 percent and those values were used as the lower limits of the recommended 
distributions for each crop type (Table 6.8-2). The calculated minimum values were rounded 
down to the nearest mm. 
Spinach, squash, strawberries, winter wheat, and oats were chosen for the upper bound glacial 
transition climate analysis because these crops had the lowest IRD within their crop type 
(Table 6.8-1). IRD for each crop was reduced by 10 percent and those values were used as the 
lower limits of the recommended distributions for each crop type (Table 6.8-3). The calculated 
minimum values were rounded down to the nearest mm. 
6.9 OVERWATERING RATE 
6.9.1 Use in Biosphere Model 
The overwatering rate (OW, m/year) is the amount of irrigation water intentionally applied to soil 
to leach salts, and the amount of precipitation that percolates below the root zone. It is used in 
the surface soil submodel to calculate the radionuclide specific (i) leaching removal constant 
(.l i) (Equation 6.9-1; BSC 2004 [DIRS 169460], Section 6.4.1), which is used in the calculation 
of radionuclide concentration in soil (Equation 6.6-1). Because soil concentrations are calculated 
for long-term, equilibrium conditions, one overwatering rate representative of all irrigated plants 
is used, rather than crop-type specific rates. 

OW 
= 
. 
.
li d .(1+. Kd i ) (Eq. 6.9-1) 
where 
d = the depth of surface soil (m), 
. = the volumetric water content of soil, dimensionless, 
. = the soil bulk density for surface soil (kg/m3), 
Kdi = the surface soil solid/liquid partition coefficient for radionuclide i ([Bq/kgsolid/ 
Bq/m
3liquid] = m3 
liquid/kg solid). 
Depth of surface soil (d, m) is either tillage depth (Td, Section 6.10) or rooting depth (Zr, 
Section 6.12). 
6.9.2 Parameter Development 
The distribution for OW was either developed from the crop leaching fraction (LF) or from deep 
percolation (DP) of precipitation below the crop root zone, depending on which of the two 
values were greatest for a crop (Appendix E, Section 2.2). The leaching requirement (LR) is the 
fraction of infiltrated water that must pass through the root zone to remove excess salts. It is a 
function of the salinity of irrigation water and salt tolerance of individual crops (Doorenbos and 
Pruitt 1977 [DIRS 103062], p. 77). LF is the actual amount of water that must be applied in 
addition to crop water requirements to meet the LR. It is determined from ETc, LR, and effective 
precipitation (Appendix E, Sections 2.3 and 2.4). Deep percolation occurs when precipitation is 
great enough to cause the soil moisture to reach field capacity and drain below the root zone 
(Allen et al. 1998 [DIRS 157311], p. 156). It is calculated from storage capacity of the soil, 
monthly precipitation, and monthly ETc using soil water balance methods and information on 
growing season length (see Appendix E, section 2.4 for calculation methods). Deep percolation 
did not occur for any crops under present-day climate conditions because of low monthly 
precipitation and high evaporative demands. Thus, LR was used to develop the OW distribution 
for present-day climate. For upper bound glacial transition climate, enough precipitation 
occurred during the winter that deep percolation occurred for some two-season and short-season 
crops. This resulted in use of both deep percolation and LR to develop the distribution for OW 
for upper bound glacial transition climate conditions. If DP occurred, but did not meet the 
leaching requirement, it was accounted for by subtracting it from Ws (seasonal crop water 
requirement), and LF was used for OW. 
The distributions for OW were developed from 26 representative crops and a turf grass for 
present-day and upper bound glacial transition climates (Table 6.9-1). Seasonal crop water 
requirements were needed to calculate both LR and DP (calculation methods and examples are in 
Appendix E, Sections 2.2.1 to 2.3). Parameter inputs for Ws were average monthly weather data 
(Section 4.1.5) and growing season lengths for present-day and upper bound glacial transition 
climates (Section 4.1.4). Average monthly weather data was used to calculate ETo in 
Appendix C (see Section 6.5 for relationship of ETo to ETc and Ws). Growing season lengths 
were used in Appendices D and E to adjust growth stage lengths and calculate Ws, respectively. 
Salinity of irrigation water was derived from inputs described in Section 4.1.7. 

The OW means of representative crops and turf for present-day and upper bound glacial 
transition climate conditions were 0.079 and 0.067 m/year, respectively, (Table 6.9-1). 
A cumulative distribution function is recommended for present-day and upper bound glacial 
transition climate OW (Table 6.9-2). Ninety-five percent of the probability distribution is 
between the minimum and maximum OW of representative crops (e.g., range = 0.009 [oat hay] 
to 0.233 [strawberries] m/year for present-day climate). The distribution between the minimum 
and maximum crop OW is divided into five equal intervals of equal probability (summing to 
95 percent, Table 6.9-2). To account for variation and uncertainty beyond the range of crop 
specific values, an upper bound interval of five-percent probability was added to the upper end of 
the distributions for both climate conditions. The upper bounds are 0.275 m/year for present-day 
climate and 0.177 m/year for upper bound glacial transition climate (Table 6.9-2). These bounds 
are based on excessive irrigation and are justified in the analysis below. Because the low crop 
specific values for OW are for crops with relatively high salinity tolerance (and therefore low LR 
and OW), those values are used to bound the low ends of the distributions. Because there is little 
uncertainty regarding this low bound, addition of an interval of five-percent probability was not 
required for the lower ends of the OW distributions. 

Table 6.9-1. Average Overwatering Rates for 26 Crops and Turf Grass for Present-Day and Upper 
Bound Glacial Transition Climates 
Upper Bound 
Glacial 
Upper Bound 
Glacial 
Present-Day 
Climate 
Transition 
Climate 
Present-Day 
Climate 
Transition 
Climate 
OW a OW a OW a OW a 
Crop (m/year) (m/year) Crop (m/year) (m/year) 
Alfalfa 0.149 0.064 Head lettuce 0.080 0.150 
Apples 0.166 0.066 Lettuce 0.080 0.142 
Barley 0.015 0.006 Melons 0.058 0.034 
Bell Peppers 0.075 0.082 Oat feed 0.014 0.014 
Broccoli 0.045 0.098 Oat hay 0.009 0.004 
Cabbage 0.079 0.089 Onions 0.177 0.085 
Carrots 0.162 0.134 Potatoes 0.077 0.072 
Cauliflower 0.045 0.095 Spinach 0.039 0.121 
Celery 0.129 0.104 Squash 0.024 0.044 
Feed Corn 0.102 0.063 Strawberries 0.233 0.125 
Corn silage 0.071 0.059 Sweet corn 0.068 0.047 
Cucumbers 0.030 0.020 Tomatoes 0.042 0.030 
Grapes 0.103 0.038 Turf Grass 0.035 0.018 
Winter Wheat 0.023 0.016 
Average 0.079 0.067 
SDb 0.058 0.044 
CVc 0.734 0.647 
Table 6.9-2. Averages and Cumulative Distributions for Overwatering Rates (OW m/year) for Present-
Day and Upper Bound Glacial Transition Climates 
a 
Overwatering rates from Tables E-5 and E-8 for present-day and upper bound glacial transition climates, 
respectively. See Appendix E for calculation methods and examples. 
b Standard Deviation. 
Coefficient of variation = SD/mean. 
Present-Day Climate 
Upper Bound Glacial Transition 
Climate 
Upper 
Averagea 
Upper Limit 
of Interval 
Cumulative 
Probability Averagea 
Limit of 
Interval 
Cumulative 
Probability 
0.079 0.009 0.00 0.067 0.004 0.00 
0.030 0.19 0.020 0.19 
0.045 0.38 0.047 0.38 
0.077 0.57 0.072 0.57 
0.129 0.76 0.104 0.76 
0.233 0.95 0.150 0.95 
0.275 1.00 0.177 1.00 
a Mean OW of 26 representative crops and turf calculated from Table 6.9-1. 

This parameter is not used in the biosphere volcanic ash exposure scenario; therefore, changes 
resulting from a volcanic eruption at Yucca Mountain are not considered in this analysis. 
Variation and UncertaintyVariation in OW that could arise due to differences in salinity 
tolerance, water requirements, or growing season lengths among crops is adequately accounted 
for through use of 26 crops and turf. Selection was based on an evaluation of crops currently 
grown in southern Nye County, Nevada and eastern Washington, national patterns of food 
consumption, and variation in the growing season for commonly grown and consumed crops (see 
Appendix A). 
Overwatering rates that are based on LR are dependent on seasonal crop water requirements (Ws, 
Appendix E, Equations E-5 and E-6). Therefore, variation in Ws caused by differences in 
minimum and maximum growing season lengths for a crop and yearly variation in climate 
variables that drive ETo could potentially influence the distribution of OW. 
Uncertainty in the distribution of OW is from lack of knowledge about which crops a farmer will 
choose to grow and about water management practices (i.e., excessive irrigation during a 
growing season). Uncertainty regarding crops is accounted for through selection of 26 crops and 
turf. 
Variation in Minimum and Maximum Season LengthsRanges in minimum and maximum 
season lengths for crops within a crop type appear to be considerable, suggesting a potentially 
large influence on the distribution parameters for irrigation (Section 6.5.2). However, it was 
shown in Section 6.5.2 that season lengths were constrained by mean monthly temperatures for 
the two climates (i.e., temperatures below crop tolerance levels) or by growing two-season crops. 
Thus, potential variation in IR due to minimum and maximum growing season lengths was 
encompassed by the use of two-season crops and constraints on growing season length caused by 
temperatures (Section 6.5.2). Therefore, any influence of this variation on the distribution of OW 
would also be adequately accounted for through the use of two-season crops and constraints on 
growing season length caused by temperatures. 
Yearly Variation in Climate VariablesClimate variables affect OW through the effects of ETo 
on IR. Variation caused by mean monthly climate variables was not directly calculated because 
the information was not available. However, it was shown in Section 6.5.2 that variation in IR 
caused by differences in crop water-use was greater than that caused by potential yearly variation 
in climate variables. It is reasonable to suggest that the influence of variation in monthly mean 
climate variables on the distribution of OW would be accounted for in the variation among crops 
in IR and salt tolerance. Additionally, for both climates the CV for OW was greater than the CV 
for temperature data used in Section 6.5.2 (73 percent and 65 percent for present-day and upper 
bound glacial transition climate, respectively, versus 10 percent for yearly variation in monthly 
mean temperature). This suggests that the recommended distribution encompasses the variation 
in OW that could be caused by yearly variation in mean monthly climate variables. 
Uncertainty in Water Management PracticesUncertainty in the distributions of OW due to 
lack of knowledge about water management practices can be bounded similarly to the 
distributions for IR (Section 6.5.2) by assessing practices that could result in reductions in crop 

yield or water waste. Minimum values that were selected for the distributions of OW would not 
be affected by under watering so only excessive irrigation practices are considered. 
Excessive IrrigationProblems caused by excessive irrigation are discussed in Section 6.5.2. 
These include loss of nutrients from the root zone through leaching, water loss through 
percolation below the root zone or runoff, oxygen deficiencies for root growth and microbial 
activity, denitrification, and infestation by weeds. These processes can result in loss of yield, 
reduced crop quality, and increased water pumping costs. 
In Section 6.5.2 it was established that over watering alfalfa by about 18 percent would result in 
yield reduction through a variety of mechanisms, including those mentioned above. To establish 
reasonable upper bounds associated with excessive irrigation, maximum crop specific OW values 
were increased by 18 percent for both climate conditions. Strawberries and head lettuce were 
used for present-day and upper bound glacial transition climates, respectively. An 18 percent 
increase in OW for strawberries (0.233 m/year) resulted in an upper bound of 0.275 m/year for 
present-day climate. An 18 percent increase in OW for head lettuce (0.150 m/year) resulted in an 
upper bound of 0.177 m/year for upper bound glacial transition climate (Table 6.9-2). 
6.10 TILLAGE DEPTH 
6.10.1 Use in Biosphere Model 
Tillage depth (Td, in units of m) is the depth of the soil layer where mechanical plowing or tilling 
occurs. Either tillage depth or rooting depth (see Section 6.12) will be used as soil surface depth 
in the biosphere model. In the soil submodel, soil surface depth is used to calculate the 
radionuclide leaching removal constant (.l i) (Equation 6.9-1) and to estimate surface soil density 
(when multiplied by soil bulk density) in the calculation of the saturation activity concentration 
of radionuclides in surface soil per unit mass (Equation 6.10-1; BSC 2004 [DIRS 169460], 
Section 6.4.1): 
Csi
=
Cs
i m ,. d (Eq. 6.10-1) 
where 
Csm,i = activity concentration of radionuclide i in surface soil per unit mass (Bq/kg), 
Csi = saturation activity concentration of radionuclide i in surface soil per unit area 
(Bq/m2), 
. = bulk density for surface soil (kg/m3), 
d = depth of surface soil (m). 
Soil surface depth is used in a similar manner in the air, carbon-14, and external exposure 
biosphere submodels. In the biosphere model for the groundwater exposure scenario, 
radionuclide concentration in the soil is assumed to be at equilibrium (i.e., at saturated conditions 
that do not change over time or within the surface soil, for a given concentration of radionuclides 
in irrigation water). Therefore, surface soil depth has no influence on radionuclide concentration 

at equilibrium for the biosphere groundwater exposure scenario, but it does influence the time it 
would take to reach equilibrium conditions. For the biosphere volcanic ash exposure scenario, it 
is assumed that contaminated ash on agricultural fields and gardens is evenly mixed (i.e., diluted) 
throughout the surface soil. Therefore, deeper tillage depths will result in a decrease in 
radionuclide concentrations in the surface soil (and in resuspended soil deposited on plants) for 
the biosphere volcanic ash exposure scenario. 
6.10.2 Parameter Development 
Tillage is any mechanical manipulation of soil that is performed to prepare seed beds, control 
weeds, incorporate fertilizers, or mix organic residues into the soil. Conventional tillage for 
forage crops and vegetables is accomplished using a moldboard plow or chisel plow and is often 
supplemented by a disc plow. These plows are designed to till to a depth of approximately 25 to 
30 cm, which is the recommended range for conventional tillage depths (Lang et al. 1999 
[DIRS 160031], p. 3; Granberry et al. 2000 [DIRS 160033], p. 8; Johnson 1999 [DIRS 160029], 
Chapter 8, p. 1; see Section 4.1.9 for input information). Additionally, moldboard, chisel, and 
disc plows that till to depths of 25 to 30 cm would be available to farmers in Amargosa Valley 
through companies such as John Deere (a common producer of agricultural equipment). 
However, conventional tillage practices can result in high rates of soil erosion, soil compaction, 
and water runoff. Conservation tillage causes less compaction and protects the soil surface from 
erosion. Conservation tillage methods are designed to till to depths of approximately 5 to 10 cm 
and leave at least 30 percent residue on the soil surface (Brady and Weil 1999 [DIRS 160019], 
pp. 579 to 587). A growing percentage of farmed area uses conservation tillage methods 
(Brady and Weil 1999 [DIRS 160019], pp. 579 to 587). 
Based on this information, a reasonable estimate of 25 cm, and a uniform distribution of tillage 
depth with a minimum of 5 cm and a maximum of 30 cm is recommended. Tillage depth is 
constrained at the low end by seeding depth and seedbed preparation requirements. It is unlikely 
that depths greater than 30 cm would be used due to environmental concerns such as soil loss 
through erosion and use of herbicides to control weeds. While recent general trends suggest an 
increase in conservation tillage (Brady and Weil 1999 [DIRS 160019], pp. 579 to 587) there is 
no information regarding central tendencies. Therefore, the recommended distribution is 
reasonable and encompasses uncertainty regarding use of different tillage methods. 
The distribution of tillage depths is based on current agricultural technology. Additionally, it is 
reasonable to expect that farmers in Amargosa Valley would utilize tillage practices or plows 
that are commonly used and available. Therefore, this parameter is consistent with present 
knowledge of the conditions in the region surrounding Yucca Mountain. The same distribution 
is recommended for both present-day and upper bound glacial transition climates because 
climate change will not influence tillage depth. 
Deposition and redistribution of a thin layer of ash on agricultural fields expected from a 
volcanic eruption at Yucca Mountain (see Section 6.) would not cause long-term changes in the 
methods used to till agricultural fields and gardens; therefore, the distribution described above is 
recommended for both the biosphere groundwater and volcanic ash exposure scenarios. 

6.11 YIELD 
6.11.1 Use in Biosphere Model 
Yield (Yj, kg/m2) is a measure of the wet mass per unit area of the edible portion of each crop 
type j. It is used in the calculation of the uptake into foodstuffs of radionuclides deposited on the 
plant surface via water and dust interception (Equation 6.3-1). It represents the amount of 
foodstuffs into which the radionuclides are concentrated. Yield has a negative, linear effect on 
leaf uptake values, as any increase in yield results in a dilution, or decrease, in the concentration 
in foodstuffs contributed from leaf uptake. 
6.11.2 Parameter Development 
Distributions of yield were developed from measurements of commercial crops. The data used 
were gathered and compiled by the USDA NASS (Section 4.1.8). Data from five years 
(1995-1999) and up to four states (Arizona, California, Nevada, and Washington) were used 
(Tables 6.11-1 to 6.11-6). Data from Arizona and California were included because some 
representative crops are not grown commercially in Nevada and Washington in sufficient 
quantities to be reported. Arizona was selected because the arid climate of this state is consistent 
with that of the Yucca Mountain region. California was selected because it is representative of 
both arid (southern California) and semi-arid climate zones. 
Yield is reported per growing season for vegetables, so if more than one crop is grown in a year 
on the same acreage, production for both crops are reported (USDA 1999 [DIRS 158643], 
p. D-4). For example, if a spring and fall crop of carrots are grown on 25 acres, production for 
carrots would be reported for 50 acres for that year. Information on crop yields from gardens 
was not used because little such information is available, much of the information that is 
available is presented in units that are useful for home gardeners (e.g., heads of lettuce per 
10-foot row (Antonelli et al. 1998 [DIRS 158654], Table 2)) but not for this analysis, and the 
methods used to develop the data are not defined. 
Yields for leafy vegetables (Table 6.11-1), fruits (Table 6.11-3), grain (Table 6.11-5), and cattle 
forage (Table 6.11-4) were based on all representative crops per group. Yield of corn was not 
included in the calculations for other vegetables (Table 6.11-2) because commercial yield of corn 
is measured with the husk on. Squash also was not included because production per state is not 
reported by the NASS. For cattle forage, yield of oat hay was represented by other hay 
because oat hay was not presented separately by NASS and because many other hays are grown 
in southern Nevada and eastern Washington (Tables A-2 and A-3). Yield of other hay and corn 
silage was based on one cutting. For alfalfa yield, if multiple cuttings are made they are included 
in the total production that is reported. However, NASS does not include information on how 
many cuttings occur per year. For this analysis, yield of alfalfa was calculated based on four 
cuttings per year. The number of cuttings used here for alfalfa differs from those used in the 
calculations of irrigation rates because alfalfa yield data comes from four states with a large 
variation in growing conditions. Four cuttings was selected to represent the average for all areas, 
based on information in Orloff and Marble 1997 ([DIRS 158655]). This is a reasonable average 
for present-day and upper bound glacial transition climate conditions. 

The average for each crop type is the mean yield of representative crops, with the exception of 
the weighted mean used for cattle forage (Table 6.11-7). 
A cumulative distribution function is recommended for each crop type (Table 6.11-7). 
Ninety percent of the probability distribution is between the average minimum and maximum 
yield of representative crops within a crop type (e.g., yield for leafy vegetables ranges from 
1.46 [broccoli] to 7.79 kg/m2 [celery], Table 6.11-1). The distribution between the average 
minimum and maximum yield is divided into intervals of virtually equal probability (summing to 
90 percent), with the exception of cattle forage. The number of intervals is one less than the 
number of representative crops considered and the upper limits are average crop-specific values 
of yield (Table 6.11-7). The probabilities for the two intervals for the distribution of cattle 
forage were weighted 3:1 for the range between alfalfa and oat hay (p = 0.675) versus the range 
between oat hay and corn silage (p = 0.225) (see Section 6 for justification). This results in a 
higher probability for selection of values that are similar to alfalfa. To account for variation and 
uncertainty that could result in values beyond the range of mean crop specific values, intervals of 
five percent probability each were added to the lower and upper ends of the distribution. The 
lowest and highest yield values reported within a crop type over a five year period were selected 
as the lower and upper bounds of the distributions (Tables 6.11-1 to 6.11-6). 
The distributions presented in Table 6.11-7 are recommended for the present-day and future 
climates for the following reasons. Most of the uncertainty in yield per crop type is due to crop 
and variety selection and farming or gardening practices. Farmers and gardeners are likely to 
select crops and varieties of crops that are most productive for their growing conditions, and 
distributions of yield for a crop type therefore should not vary much due to climate change. In 
addition, the distributions were developed using data from a variety of crops and climatic 
conditions, including arid conditions representative of present-day climate (e.g., data from 
Arizona and parts of Nevada and California) and cooler conditions representative of future 
climates (e.g., data from Washington and parts of Nevada and California). 
The distributions for some vegetables and fruits matched well with the limited data available for 
production from gardens. For example, production of broccoli (10-12 lb per 10-ft by 2-ft row, 
or 2.7 kg/m2, calculated as production per [10-ft row length x 2-ft row spacing] x 1 kg/2.2 lb  
10.76 ft2/m2), cabbage (10-15 lb per 10-ft by 2-ft row, 3.1 kg/m2), and potatoes (20 lb per 10-ft 
by 2-ft row, 4.1 kg/m2) in eastern Washington gardens (Antonelli et al. 1998 [DIRS 158654], 
Table 2) match the distributions based on commercial production (Tables 6.9-1 to 6.9-3). 
However, values for carrots (12 lb per 10-ft by 1-ft row, 5.9 kg/m2), tomatoes (30-50 lb per 10-ft 
by 3-ft row, 6.5 kg/m2), and onions (10 lb per 10-ft by 0.5-ft row, 43.5 kg/m2) were higher than 
commercial yields. Because methods of calculation are not presented, these high garden values 
are less reliable than those based on yields reported for thousands of acres of commercial farms. 
Deposition and redistribution of a thin layer of ash expected from a volcanic eruption at Yucca 
Mountain (see Section 6) onto agricultural fields and gardens in northern Amargosa Valley 
would not cause a long-term change in the yield of crops. Therefore, the distributions of yield in 
Table 6.11-7 are intended for both the biosphere groundwater and volcanic ash exposure 
scenarios. 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 6-51 September 2004 
Table 6.11-1. Commercial Yield of Leafy Vegetables 
Leafy 
Area Harvested (1,000 Acres) Production (1,000 Cwt) Yield (kg/m2)a 
5-Yr 
Average 
Per 
Vegetable State 1995 1996 1997 1998 1999 1995 1996 1997 1998 1999 1995 1996 1997 1998 1999 Avg Crop 
Broccoli AZ 8.6 10.0 11.0 12.3 17.1 946 960 1,357 1,538 3,420 1.23 1.08 1.38 1.40 2.24 1.47 
CA 115.0 122.0 119.0 121.0 130.0 14,375 14,640 15,470 15,730 18,200 1.40 1.34 1.46 1.46 1.57 1.45 1.46 
Cabbage CA 10.0 10.0 13.8 14.0 12.5 3,700 3,500 4,692 4,620 4,000 4.15 3.92 3.81 3.70 3.59 3.83 3.83 
Cauliflower AZ 4.5 4.4 3.9 3.4 3.9 788 770 780 714 1,170 1.96 1.96 2.24 2.35 3.36 2.38 
CA 40.7 41.5 37.5 39.0 41.0 5,088 6,310 5,790 5,850 6,355 1.40 1.70 1.73 1.68 1.74 1.65 2.01 
Celery CA 24.5 24.5 24.0 24.5 25.0 17,150 17,150 16,680 16,666 17,500 7.85 7.85 7.79 7.62 7.85 7.79 7.79 
Head Lettuce AZ 44.1 59.4 54.8 55.0 46.6 17,661 16,713 17,345 18,658 15,546 4.49 3.15 3.55 3.80 3.74 3.75 
CA 144.0 150.5 141.0 135.0 140.0 42,480 43,645 49,350 42,525 53,200 3.31 3.25 3.92 3.53 4.26 3.65 
WA 1.3 1.0 0.9 0.9 0.8 273 220 180 189 168 2.35 2.47 2.24 2.35 2.35 2.35 3.25 
Leaf Lettuce AZ 3.2 4.3 5.7 7.30 5.60 1,440 1,441 1,568 1,971 1,512 5.04 3.76 3.08 3.03 3.03 3.59 
CA 35.0 36.0 42.0 38.00 43.00 7,350 7,560 8,660 8,170 9,460 2.35 2.35 2.31 2.41 2.47 2.38 2.98 
Spinach CA 6.5 9.0 15.2 15.0 17.0 1,040 1,350 2,660 2,400 2,550 1.79 1.68 1.96 1.79 1.68 1.78 1.78 
Average for all crops 3.30 
Standard Deviationb 2.16 
Source: 1995 data from USDA 1998 ([DIRS 158648], Tables 4-14, 4-15, 4-21, 4-22, 4-33, 4-35, and 4-54), 1996 data from USDA 1999 ([DIRS 158647], 
Tables 4-14, 4-15, 4-21, 4-22, 4-33, 4-35, and 4-54), 1997 data from USDA 2000 ([DIRS 158646], Tables 4-14, 4-15, 4-21, 4-22, 4-33, 4-35, and 4-54), 1998 and 
1999 data from USDA 2001 ([DIRS 158645], Tables 4-14, 4-15, 4-21, 4-22, 4-34, 4-36, and 4-55). 
a 
Calculated as [1,000 Cwt Produced x 100 lbs/Cwt x 0.4536 kg/lb] / [1,000 Acres Harvested x 4,047 m2/acre] = kg/m2. Cwt is a United States unit of weight that is 
b 
equivalent to 100 pounds. 
Calculated using the STDEV function of Excel. 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 6-52 September 2004 
Table 6.11-2. Commercial Yield of Other Vegetables 
Area Harvested (1,000 Acres) Production (1,000 Cwt) Yield (kg/m2)a 
5-Yr 
Average 
Per 
Vegetable State 1995 1996 1997 1998 1999 1995 1996 1997 1998 1999 1995 1996 1997 1998 1999 Avg Crop 
Carrots -AZ 1.9 2.4 2.5 2.5 2.5 523 624 663 625 625 3.09 2.91 2.97 2.80 2.80 2.92 
Fresh CA 55.0 85.7 83.2 86.5 87.0 15,950 25,710 29,998 28,545 25,665 3.25 3.36 4.04 3.70 3.31 3.53 
WA 1.9 2.5 2.8 3.0 2.6 760 1,050 1,120 1,140 1,040 4.48 4.71 4.48 4.26 4.48 4.48 3.64 
Cucumber CA 5.2 6.0 6.3 6.0 6.5 1,638 1,980 1,985 1,920 2,015 3.53 3.70 3.53 3.59 3.47 3.56 3.56 
Onions - NV 1.9 1.9 1.7 2.1 2.8 874 1,102 918 924 1,568 5.16 6.50 6.05 4.93 6.28 5.78 
Summer WA 1.0 0.7 0.9 0.85 0.8 400 266 333 255 288 4.48 4.26 4.15 3.36 4.03 4.06 4.92 
Bell Peppers CA 24.0 25.5 21.0 22.0 22.5 6,960 7,650 6,300 6,270 7,425 3.25 3.36 3.36 3.19 3.70 3.37 3.37 
Potatoes -CA 13.0 11.5 10.5 10.3 9.0 5,330 4,600 4,200 3,708 4,005 4.60 4.48 4.48 4.03 4.99 4.52 
Fall NV 7.6 7.9 6.9 7.0 6.5 2,774 3,160 2,967 2,800 2,860 4.09 4.48 4.82 4.48 4.93 4.56 
WA 147.0 161.0 152.0 165.0 170.0 80,850 94,990 88,160 93,225 95,200 6.16 6.61 6.50 6.33 6.28 6.38 5.15 
Average for all crops 4.13 
Standard Deviationb 0.84 
Source: 1995 data from USDA 1998 ([DIRS 158648], Tables 4-18, 4-26, 4-40, 4-43, and 4-47), 1996 data from USDA 1999 ([DIRS 158647], Tables 4-18, 4-26, 440, 
4-43, and 4-47), 1997 data from USDA 2000 ([DIRS 158646], Tables 4-18, 4-26, 4-40, 4-43, and 4-47), 1998 and 1999 data from USDA 2001 ([DIRS 158645], 
Tables 4-18, 4-26, 4-41, 4-44, and 4-48). For all years, onions = summer non-storage. 
a 
Calculated as [1,000 Cwt Produced x 100 lbs/Cwt x 0.4536 kg/lb] / [1,000 acres Harvested x 4,047 m2/acre] = kg/m2. Cwt is a United States unit of weight that is 
b 
equivalent to 100 pounds. 
Calculated using the STDEV function of Excel. 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 6-53 September 2004 
Table 6.11-3. Commercial Yield of Fruit 
Area Harvested (1,000 Acres) Production (1,000 Cwt) Yield (kg/m2)a 
5-Yr 
Average 
Per 
Fruits State 1995 1996 1997 1998 1999 1995 1996 1997 1998 1999 1995 1996 1997 1998 1999 Avg Crop 
Cantaloupes AZ 16.0 17.7 17.7 18.5 19.7 3,040 4,071 4,514 4,625 5,319 2.13 2.58 2.86 2.80 3.03 2.68 
CA 59.3 59.0 62.3 58.0 61.0 11,860 12,980 13,083 12,760 12,810 2.24 2.47 2.35 2.47 2.35 2.38 
Honeydew AZ 3.6 3.8 4.1 4.2 4.2 576 646 718 840 1,029 1.79 1.91 1.96 2.24 2.75 2.13 
CA 18.1 20.3 20.5 19.0 20.5 2,896 3,451 3,690 3,610 3,690 1.79 1.91 2.02 2.13 2.02 1.97 
Watermelon AZ 7.2 7.3 7.2 7.6 7.1 1,800 2,154 2,232 2,280 3,025 2.80 3.31 3.47 3.36 4.78 3.54 
CA 17.2 17.1 17.0 15.0 14.7 6,364 7,524 7,820 6,750 6,321 4.15 4.93 5.16 5.04 4.82 4.82 2.92b 
Strawberries CA 23.6 25.2 22.6 24.2 24.6 12,980 13,608 13,334 13,552 15,129 6.16 6.05 6.61 6.28 6.89 6.40 
WA 1.3 1.3 1.4 1.5 1.5 104 105 91 120 120 0.90 0.91 0.73 0.90 0.90 0.86 3.63 
Tomatoes CA 38.0 37.4 34.0 41.0 44.0 10,260 10,472 9,860 9,840 11,440 3.03 3.14 3.25 2.69 2.91 3.00 3.00 
Applesc 2.67 
Grapesc 1.51 
Average for all crops 2.75 
Standard deviationd 0.78 
Source: for melons, strawberries, and tomatoes: 1995 data from USDA 1998 ([DIRS 158648], Tables 4-17, 4-32, 4-61, 4-73, and 5-70), 1996 data from USDA 1999 
([DIRS 158647], Tables 4-17, 4-32, 4-61, 4-72, and 5-72), 1997 data from USDA 2000 ([DIRS 158646], Tables 4-17, 4-32, 4-61, 4-72, and 5-72), 1998 and 1999 
data from USDA 2001 ([DIRS 158645], Tables 4-17, 4-33, 4-62, 4-71, and 5-76). 
a 
Calculated as [1,000 Cwt Produced x 100 lbs/Cwt x 0.4536 kg/lb] / [1,000 acres Harvested/ x 4,047 m2/acre] = kg/m2. Cwt is a United States unit of weight that is 
b 
equivalent to 100 pounds. 
Average of all melons. 
See Table 6.11-6. 
d Calculated using the STDEV function of Excel. 

Table 6.11-4. Commercial Yield of Cattle Forage 
a 
b 
c
Annual Yield (tons/acre)
Annual Yield (wet kg/m2)
Yield per Cutting (kg/m2)
5-Yr 
Avg Per 
State 
1995 
1996 
1997 
1998 
1999 
1995 
1996 
1997 
1998 
1999 
1995 
1996 
1997 
1998 
1999 
Avg 
Crop 
AZ 
7.8 
8.0 
8.2 
8.0 
7.9 
5.25 
5.38 
5.51 
5.38 
5.31 
1.31 
1.34 
1.38 
1.34 
1.33 
1.34 
CA 
6.9 
7.0 
7.2 
6.6 
6.9 
4.64 
4.71 
4.84 
4.44 
4.64 
1.16 
1.18 
1.21 
1.11 
1.16 
1.16 
NV 
4.5 
4.5 
4.2 
4.6 
4.1 
3.03 
3.03 
2.82 
3.09 
2.76 
0.76 
0.76 
0.71 
0.77 
0.69 
0.74 
WA 
5.1 
4.7 
4.8 
5.0 
4.9 
3.43 
3.16 
3.23 
3.36 
3.30 
0.86 
0.79 
0.81 
0.84 
0.82 
0.82 
1.02 
26.0 
27.0 
25.5 
26.5 
23.0 
5.83 
6.05 
5.72 
5.94 
5.16 
5.83 
6.05 
5.72 
5.94 
5.16 
5.74 
25.0 
25.0 
26.0 
25.0 
26.0 
5.60 
5.60 
5.83 
5.60 
5.83 
5.60 
5.60 
5.83 
5.60 
5.83 
5.69 
27.0 
26.0 
28.0 
25.0 
26.0 
6.05 
5.83 
6.28 
5.60 
5.83 
6.05 
5.83 
6.28 
5.60 
5.83 
5.92 
5.78 
3.5 
3.5 
3.7 
3.5 
4.3 
2.35 
2.35 
2.49 
2.35 
2.89 
2.35 
2.35 
2.49 
2.35 
2.89 
2.49 
3.5 
2.8 
2.8 
2.8 
2.9 
2.35 
1.88 
1.88 
1.88 
1.95 
2.35 
1.88 
1.88 
1.88 
1.95 
1.99 
1.7 
1.7 
1.8 
1.6 
1.8 
1.14 
1.14 
1.21 
1.08 
1.21 
1.14 
1.14 
1.21 
1.08 
1.21 
1.16 
2.8 
2.7 
2.6 
2.8 
2.8 
1.88 
1.82 
1.75 
1.88 
1.88 
1.88 
1.82 
1.75 
1.88 
1.88 
1.84 
1.87 
Average for all states 
2.89 
Alfalfa 
Corn Silage AZ 
CA 
WA 
Other Hay 
AZ 
CA 
NV 
WA 
ANL-MGR-MD-000006 REV 02 6-54 September 2004 
Standard deviationd 
2.54 
Source: 1995 data from USDA 1998 ([DIRS 158648], Tables 1-41, 6-3, and 6-4), 1996 data from USDA 1999 ([DIRS 158647], Tables 1-41, 6-3, and 6-4), 1997 
data from USDA 2000 ([DIRS 158646], Tables 1-41, 6-3, and 6-4), 1998 and 1999 data from USDA 2001 ([DIRS 158645], Tables 1-39, 6-3, and 6-4). 
a 
For alfalfa and other hay, data are the sum of all cuttings per year (USDA 1999 [DIRS 158643], p. D-3), reported as dry weight equivalent with a conversion 
factor of 3 tons green weight to 1 ton dry weight (USDA 1999 [DIRS 158643], pp. A-7 and A-8). 
b For alfalfa and other hay, calculated as [dry tons/acre x 3 tons wet/1 ton dry x 907.2 kg/ton] / 4,047 m2/acre = wet kg/m2; For corn silage calculated as [dry 
tons/acre x 907.2 kg/ton] / 4,047 m2/acre = wet kg/m2. 
For alfalfa, calculated as annual yield divided by 4 cuttings per year; for corn silage and other hay calculated as annual yield divided by 1 cutting per year. 
d Calculated using the STDEV function of Excel. 
Agricultural and Environmental Input Parameters for the Biosphere Model 

Table 6.11-5. Commercial Yield of Grain 
Yield (bushels/acre)a, Yield (kg/m2)b 
Grain State 1995 1996 1997 1998 1999 1995 1996 1997 1998 1999 
5-Yr 
Avg 
Average 
per Crop 
Barley AZ 90.0 105.0 102.0 110.0 114.0 0.48 0.57 0.55 0.59 0.61 0.56 
CA 70.0 60.0 57.0 60.0 64.0 0.38 0.32 0.31 0.32 0.34 0.34 
NV 80.0 95.0 100.0 100.0 90.0 0.43 0.51 0.54 0.54 0.48 0.50 
WA 72.0 62.0 74.0 65.0 59.0 0.39 0.33 0.40 0.35 0.32 0.36 0.44 
Corn AZ 170.0 175.0 165.0 175.0 195.0 1.07 1.10 1.04 1.10 1.22 1.10 
CA 160.0 160.0 170.0 160.0 170.0 1.00 1.00 1.07 1.00 1.07 1.03 
WA 190.0 185.0 190.0 190.0 180.0 1.19 1.16 1.19 1.19 1.13 1.17 1.10 
Oats CA 85.0 75.0 80.0 75.0 85.0 0.30 0.27 0.29 0.27 0.30 0.29 
WA 80.0 80.0 80.0 75.0 75.0 0.29 0.29 0.29 0.27 0.27 0.28 0.28 
Winter AZ 80.0 95.0 85.0 90.0 105.0 0.54 0.64 0.57 0.60 0.71 0.61 
Wheat CA 61.0 69.0 70.0 60.0 78.0 0.41 0.46 0.47 0.40 0.52 0.45 
NV 100.0 100.0 100.0 100.0 95.0 0.67 0.67 0.67 0.67 0.64 0.67 
WA 62.0 70.0 66.0 65.0 58.0 0.42 0.47 0.44 0.44 0.39 0.43 0.54 
Average for all crops 0.59 
Standard Deviationc 0.36 
Source: 1995 data from USDA 1998 ([DIRS 158648], Tables 1-8, 1-40, 1-50, and 1-56), 1996 data from USDA 1999 
([DIRS 158647], Tables 1-8, 1-40, 1-51, and 1-57), 1997 data from USDA 2000 ([DIRS 158646], Tables 1-8, 1-39, 
1-51, and 1-57), 1998 and 1999 data from USDA 2001 ([DIRS 158645], Tables 1-8, 1-37, 1-49, and 1-55). 
a 
Approximate net weight of a bushel of barley = 21.8 kg; shelled corn = 25.4 kg; oats = 14.5 kg, and wheat = 27.2 kg 
b 
(USDA 2001 [DIRS 158645], pp. v to vii). 
Calculated as bushels/acre x kg/bushel  4,047 m2/acre. 
Calculated using the STDEV function of Excel. 
Table 6.11-6. Commercial Yield of Apples and Grapes 
Fruit State 
Yield for Bearing Acreage 
(apples = lbs/acre, grapes = tons/acre) Yield (kg/m2)a 
1995 1996 1997 1998 1999 1995 1996 1997 1998 1999 
5-Yr 
Avg 
Average 
per 
Crop 
Apples AZ 2,620 25,000 12,200 12,100 8,790 -b 2.80 1.37 1.36 0.99 1.63 
CA 24,300 25,000 25,000 23,200 25,600 2.72 2.80 2.80 2.60 2.87 2.76 
WA 31,700 33,500 29,400 38,400 29,100 3.55 3.75 3.30 4.30 3.26 3.63 2.67 
Grapes AZ 5.78 5.81 5.81 5.35 5.12 1.30 1.30 1.30 1.20 1.15 1.25 
CA 8.42 7.16 9.17 7.12 7.02 1.89 1.61 2.06 1.60 1.57 1.74 
WA 9.59 4.11 8.62 5.69 6.46 2.15 0.92 1.93 1.28 1.45 1.55 1.51 
Source: 1995 data from USDA 1998 ([DIRS 158649], Tables Apples, Commercial: Bearing Acreage and Yield by State 
and United States, 1995-97 and Grapes: Bearing Acreage and Yield by Type, State, and United States, 1995-97), 1996 
data from USDA 1999 ([DIRS 158650], Tables on pp. 8 and 40), 1997 data from USDA 2000 ([DIRS 158653], Tables on 
pp. 8 and 40), 1998 data from USDA 2001 ([DIRS 158651], Tables on pp. 10 and 44), 1999 data from USDA 2002 
([DIRS 158652], Tables on pp. 10 and 46). For all years, grapes = all types. 
a 
Calculated as apples: lbs/acre x 0.4536 kg/lb  4,047 m2/acre; grapes: tons/acre x 907.2 kg/ton  4,047 m2/acre.
b Value for this year (0.29 kg/m2) was omitted from the analysis because it was extremely low. 

Table 6.11-7. Averages and Cumulative Distributions for Yield (kg/m2) 
Crop Type Averagea 
Upper 
Limit of 
Intervalb 
Cumulative 
Probability Crop Type Averagea 
Upper 
Limit of 
Intervalb 
Cumulative 
Probability 
Leafy Vegetables 3.30 1.08 0.00 Fruits2.75 0.73 0.00 
1.46 0.05 1.51 0.05 
1.78 0.20 2.67 0.28 
2.01 0.35 2.92 0.51 
2.98 0.50 3.00 0.72 
3.25 0.65 3.63 0.95 
3.83 0.80 6.89 1.0 
7.79 0.95 
7.85 1.00 Grains 0.59 0.27 0.00 
0.28 0.05 
Other Vegetables 4.13 2.80 0.00 0.44 0.35 
3.37 0.05 0.54 0.65 
3.56 0.28 1.10 0.95 
3.64 0.51 1.22 1.00 
4.92 0.72 
5.15 0.95 Cattle 
Foragec 
2.14 0.69 0.00 
6.61 1.00 1.02 0.05 
1.87 0.73 
5.78 0.95 
6.28 1.00 
a 
Mean yield for a crop type from Tables 6.11-1 to 6.11-6, with the exception of the weighted mean calculated for cattle 
forage. The weighted mean was calculated as (3 x 1.02 [yield for alfalfa] + 1 x 5.78 [yield for corn silage] + 1 x 1.87 
[yield for oat hay]) / 5 = 2.14. 
b Limits determined from crop specific yield (see Tables 6.11-1 to 6.11-6). 
The probabilities for the two intervals between the minimum and maximum crop specific values were weighted 3:1 for 
the range between alfalfa and oat hay (p = 0.68) versus the range between oat hay and corn silage (p = 0.22). 
6.12 ROOTING DEPTH 
6.12.1 Use in Biosphere Model 
Mean maximum effective rooting depth (Zr) is the proportion of maximum rooting depth where 
approximately 80 to 90 percent of a plants feeder roots occur. Either tillage depth or rooting 
depth will be used as soil surface depth in the biosphere model (see Section 6.10). Soil surface 
depth is used to calculate the radionuclide leaching removal constant (.li) (Equation 6.9-1) and to 
estimate surface soil density (when multiplied by soil bulk density) in the calculation of the 
saturation activity concentration of radionuclides in surface soil per unit mass (Equation 6.10-1). 
In the biosphere model for the groundwater exposure scenario, radionuclide concentration in the 
soil is assumed to be at equilibrium (i.e., at saturated conditions that do not change over time or 
within the surface soil, for a given concentration of radionuclides in irrigation water). Therefore, 
surface soil depth has no influence on radionuclide concentration at equilibrium for the biosphere 
groundwater exposure scenario, but it does influence the time it would take to reach equilibrium 
conditions. For the biosphere volcanic ash exposure scenario, it is assumed that contaminated 

ash on agricultural fields and gardens is evenly mixed (i.e., diluted) throughout the surface soil. 
Therefore, deeper rooting depths will result in a decrease in radionuclide concentrations in the 
surface soil (and in resuspended soil deposited on plants) for the volcanic ash exposure scenario. 
6.12.2 Parameter Development 
Information on rooting depth from Allen et al. 1998 ([DIRS 157311], Table 22, pp. 163 to 165) 
(see Section 4.1.11 for input information) for turf and 23 of the 26 representative crops was used 
to develop the distribution for rooting depth. Allen et al. 1998 ([DIRS 157311]) provided one 
rooting depth for lettuce (did not distinguish between leaf and head lettuce), one rooting depth 
for field corn (did not distinguish between feed corn and corn silage), and one rooting depth for 
oats (did not distinguish between oat feed and oat hay). This resulted in 24 values (including 
turf) for development of the rooting depth distribution (see Table 6-1 for complete list of 
representative crops). 
Allen et al. 1998 ([DIRS 157311], Table 22, pp. 163 to 165) present ranges for maximum 
effective rooting depths (Table 4.1-7). The smaller values for each range are recommended for 
irrigation scheduling because a large percentage of root biomass and activity occurs in the upper 
portion of the rooting zone. Effective rooting depth is less than the absolute maximum rooting 
depth of a mature plant because root density typically decreases near the lower part of the root 
zone (Jensen et al. 1990 [DIRS 160001], p. 22; Bishop and Beetham 1989 [DIRS 160038], 
p. 51). Generally, 80 to 90 percent of a plants roots occur in the upper 60 to 75 percent of the 
root zone (Jensen et al. 1990 [DIRS 160001], p. 22). For example, even though maximum 
rooting depth of sweet corn could be as great as 1.2 m, most of the root activity will occur within 
0.8 to 0.9 m. Therefore, the smaller values for rooting depth recommended by Allen et al. 1998 
([DIRS 157311], Table 22, pp. 163 to 165) represent general rooting depths that are adequate for 
this analysis. 
The mean Zr of representative crops was 0.65 (Table 6.12-1). 
A cumulative distribution function is recommended for Zr (Table 6.12-2). Ninety percent of the 
probability distribution is between the minimum and maximum Zr of representative crops 
(range = 0.2 m [strawberries] to 1.5 m [winter wheat]). The distribution between the minimum 
and maximum crop Zr is divided into five equal intervals of equal probability (summing to 
90 percent, Table 6.12-2). To account for variation and uncertainty beyond the range of crop 
specific values, intervals of five-percent probability each were added to the lower and upper ends 
of the distribution. To avoid unreasonably low values that would not likely support a healthy 
plant, a minimum bound of 0.15 m is recommended (Table 6.12-2). The high value of 2.0 m for 
the range of Zr reported for alfalfa in Allen et al. 1998 ([DIRS 157311], Table 22, p. 165) is 
recommended as the maximum bound for the distribution (Table 6.12-2). The same distribution 
is recommended for present-day and future climates. 
Genetic and environmental controls regulate root growth of agricultural crops. Maximum 
rooting depths can differ among varieties within a species, and among different crop species 
(Bishop and Beetham 1989 [DIRS 160038], Table 20). Important soil properties that influence 
root growth include texture, structure, and bulk density (Bishop and Beetham 1989 
[DIRS 160038], pp. 14 to 17; Jensen et al. 1990 [DIRS 160001], p. 22). Soil moisture 

availability, temperature, aeration, and nutrient supply also regulate root growth. Use of several 
crops and turf accounts for uncertainties associated with choice of crop, and variation associated 
with differences in rooting depths among crops. 
Deposition and redistribution of a thin layer of ash expected from a volcanic eruption at Yucca 
Mountain (see Section 6) onto agricultural fields and gardens in northern Amargosa Valley 
would not cause a change in growth characteristics of crops (i.e., root to shoot ratios). Therefore, 
the recommended distribution of rooting depth is intended for both the biosphere groundwater 
and volcanic ash exposure scenarios. 
Table 6.12-1. Maximum Effective Rooting Depths 
Crop Rooting Depth (m) Crop Rooting Depth (m) 
Alfalfa hay 1.0 Grapes 1.0 
Apples 1.0 Lettuce 0.3 
Barley 1.0 Melons 0.8 
Bell peppers 0.5 Oats 1.0 
Broccoli 0.4 Onions 0.3 
Cabbage 0.5 Potatoes 0.4 
Carrots 0.5 Spinach 0.3 
Cauliflower 0.4 Squash 0.6 
Celery 0.3 Strawberries 0.2 
Field corn 1.0 Sweet corn 0.8 
Cucumbers 0.7 Tomatoes 0.7 
Turf 0.5 
Winter wheat 1.5 
Mean 0.65 
SDa 0.33 
CVb 0.50 
Source: Allen et al. 1998 ([DIRS 157311], Table 22, pp. 163 to 165). 
a 
b 
Standard deviation calculated using the STDEV function of Excel. 
Coefficient of variation (SD/mean). 
Table 6.12-2. Average and Cumulative Distribution for Rooting Depth (Zr, m) 
Averagea 
Upper Limit of 
Interval Cumulative Probability 
0.65 0.15 0.00 
0.20 0.05 
0.30 0.23 
0.50 0.41 
0.70 0.59 
1.00 0.77 
1.50 0.95 
2.00 1.00 
a 
Mean Zr of 23 representative crops and turf calculated from Table 6.12-1. 
ANL-MGR-MD-000006 REV 02 6-58 September 2004 

7. CONCLUSIONS 
7.1 PARAMETER DISTRIBUTIONS 
This analysis report documents the development of reasonable distributions and averages for 
twelve agricultural parameters that are representative of environmental conditions expected 
under present-day and future climates. This information is summarized in Table 7.1-1 and 
contained in output DTN: MO0403SPAAEIBM.002. The same distributions for present-day 
and upper bound glacial transition climates are recommended for dry biomass, dry-to-wet-weight 
ratios, fraction of overhead irrigation, irrigation intensity, tillage depth, yield, and rooting depth. 
Separate distributions for present-day and upper bound glacial transition climates are 
recommended for growing time, irrigation rate - annual average, irrigation application, irrigation 
rate - daily, and overwatering rate. Average values are provided for irrigation rate - annual 
average for upper bound monsoon and lower bound glacial transition climates (Table 7.1-1). 
Fraction of overhead irrigation, irrigation intensity, overwatering rate, irrigation rate - annual 
average, irrigation application, and irrigation rate - daily are not used in the biosphere volcanic 
ash exposure scenario, and therefore are only intended for the groundwater exposure scenario. 
The remaining distributions are intended for both the biosphere groundwater and volcanic ash 
exposure scenarios. 
Uncertainties associated with the recommended parameter distributions are described in 
Sections 6., 6.1.2, 6.2.2, 6.3.2, 6.4.2, 6.5.2, 6.6.2, 6.7.2, 6.8.2, 6.9.2, 6.10.2, 6.11.2 and 6.12.2. 
One restriction for subsequent use of the recommended parameter distributions is that they are 
intended for use in the biosphere model equations presented in Section 6. If the equations used 
in the completed biosphere model differ from those presented here, use of these distributions 
must be justified or new parameter values must be developed. The distributions for irrigation 
parameters (irrigation rate - annual average, irrigation application, irrigation rate - daily, and 
overwatering rate) are restricted for use under the climate conditions described in Tables 4.1-2 
and 4.1-5. The averages for irrigation rate - annual average for upper bound monsoon and lower 
bound glacial transition climates are restricted for use under the climate conditions in 
Tables 4.1-3 and 4.1-4. The remaining parameter distributions are restricted for use under more 
general conditions described for present-day and upper bound glacial transition climates. 

Table 7.1-1. Recommended Distributions and Averages for Agricultural and Environmental Parameters 
for the Biosphere Model 
Parameter - Type of 
Distribution 
Crop Type Averagea Distribution Characteristicsb 
Dry Biomass (kg/m2) - Cumulative Distribution 
Leafy Vegetables 0.21 (0.10; 0%), (0.13; 5%), (0.14; 20%), (0.15; 35%), (0.16; 50%), (0.18; 
65%), (0.30; 80%), (0.42; 95%), (0.50; 100%) 
Other Vegetables 0.43 (0.30; 0%), (0.40; 5%), (0.41; 28%), (0.43; 51%), (0.44; 73%), (0.46; 
95%), (0.60; 100%) 
Fruits 0.62 (0.10; 0%), (0.56; 5%), (0.60; 35%), (0.65; 65%), (0.68; 95%), (1.30; 
100%) 
Grains 1.13 (0.50; 0%), (0.61; 5%), (0.74; 35%), (1.20; 65%), (1.97; 95%), (2.20; 
100%) 
Cattle Forage 0.48 (0.10; 0%), (0.23; 5%), (0.34; 73%), (1.38; 95%), (1.50; 100%) 
Dry-to-Wet-Weight Ratio (unitless) - Cumulative Distribution 
Leafy Vegetables 0.070 (0.041; 0%), (0.054; 17%), (0.060; 33%), (0.078; 50%), (0.081; 67%), 
(0.084; 83%), (0.093; 100%) 
Other Vegetables 0.103 (0.035; 0%), (0.063; 17%), (0.078; 33%), (0.080; 50%), (0.103; 67%), 
(0.122; 83%), (0.240; 100%) 
Fruits 0.120 (0.062; 0%), (0.084; 25%), (0.102; 50%), (0.155; 75%), (0.194; 100%) 
Grains 0.903 (0.891; 0%), (0.896; 33%), (0.906; 67%), (0.918; 100%) 
Cattle Forage 0.220 (0.182; 0%), (0.227; 75%), (0.238; 100%) 
Fraction of Overhead Irrigation (unitless) - Normal Distribution 
Leafy Vegetables 0.75 Mean = 0.75, Standard Deviation = 0.1, Minimum = 0.49, Maximum = 1.0 
Other Vegetables 0.75 Mean = 0.75, Standard Deviation = 0.1, Minimum = 0.49, Maximum = 1.0 
Fruits 0.50 Mean = 0.50, Standard Deviation = 0.1, Minimum = 0.24, Maximum = 1.0 
Grains 0.90 Mean = 0.90, Standard Deviation = 0.05, Minimum = 0.77, Maximum = 
1.0 
Cattle Forage 0.90 Mean = 0.90, Standard Deviation = 0.05, Minimum = 0.77, Maximum = 
1.0 
Growing Time (days) - Present-Day Climate - Fixed Value 
Leafy Vegetables 75 NA 
Other Vegetables 80 NA 
Fruits 160 NA 
Grains 200 NA 
Cattle Forage 75 NA 
Growing Time (days) - Upper Bound Glacial Transition Climate - Fixed Value 
Leafy Vegetables 75 NA 
Other Vegetables 100 NA 
Fruits 105 NA 
Grains 185 NA 
Cattle Forage 90 NA 

Table 7.1-1. Recommended Distributions and Averages for Agricultural and Environmental 
Parameters for the Biosphere Model (Continued) 
Parameter - Type of 
Distribution 
Crop Type Averagea Distribution Characteristicsb 
Average Annual Irrigation Rate (m/year)c - Present-Day Climate - Cumulative Distribution 
All 0.95 (0.33; 0%), (0.40; 5%), (0.66; 23%), (0.83; 41%), (0.91; 59%), (1.34; 
77%), (1.94; 95%), (2.29; 100%) 
Average Annual Irrigation Rate (m/year)c - Upper Bound Glacial Transition Climate - Cumulative 
Distribution 
All 0.50 (0.14; 0%), (0.16; 5%), (0.36; 23%), (0.46; 41%), (0.54; 59%), (0.69; 
77%), (0.83; 95%), 0.98; 100%) 
Average Annual Irrigation Rate (m/year)d - Present-Day Climate - Normal Distribution 
All 0.95 Mean = 0.95, Standard Error = 0.08, Minimum = 0.74, Maximum = 1.16 
Average Annual Irrigation Rate (m/year)d - Upper Bound Glacial Transition Climate - Normal Distribution 
All 0.50 Mean = 0.50, Standard Error = 0.04, Minimum = 0.40, Maximum = 0.60 
Average Annual Irrigation Rate (m/year) Upper Bound Monsoon Climate 
All 0.52 
Average Annual Irrigation Rate (m/year) Lower Bound Glacial Transition Climate 
All 0.88 
Irrigation Intensity (cm/hour) - Uniform Distributione 
All 4.3 Minimum = 1.0, Maximum = 7.5 
Irrigation Application (mm) - Present-Day Climate - Cumulative Distribution 
Leafy Vegetables 14.7 (6.0; 0%), (7.5; 5%), (8.4; 20%), (10.0; 35%), (10.9; 50%), (20.8; 65%), 
(22.0; 80%), (23.5; 95%), (27.7; 100%) 
Other Vegetables 26.0 (8.0; 0%), (9.1; 5%), (18.9; 20%), (19.8; 35%), (21.2; 50%), (33.3; 
65%), (34.8; 80%), (44.7; 95%), (52.7; 100%) 
Fruits 33.9 (5.0; 0%), (6.0; 5%), (30.3; 28%), (35.4; 51%), (48.4; 72%), (49.4; 
95%), (58.3; 100%) 
Grains 56.7 (43.0; 0%), (48.6; 5%), (50.1; 35%), (50.4; 65%), (77.9; 95%), (91.9; 
100%) 
Cattle Forage 57.8 (50.0; 0%), (56.3; 5%), (57.6; 72%), (60.0; 95%), (71.0; 100%) 
Irrigation Application (mm) - Upper Bound Glacial Transition Climate - Cumulative Distribution 
Leafy Vegetables 14.6 (7.0; 0%), (7.8; 5%), (8.0; 20%), (9.0; 35%), (10.1; 50%), (19.3; 65%), 
(22.0; 80%), (26.1; 95%), (30.8; 100%) 
Other Vegetables 25.0 (10.0; 0%), (11.3; 5%), (14.4; 20%), (17.7; 35%), (20.1; 50%), (34.1; 
65%), (37.2; 80%), (40.3; 95%), (47.6; 100%) 
Fruits 34.2 (6.0; 0%), (7.3; 5%), (31.4; 28%), (34.6; 51%), (43.2; 72%), (54.4; 
95%), (64.2; 100%) 
Grains 51.3 (28.0; 0%), (32.2; 5%), (46.2; 35%), (59.9; 65%), (66.7; 95%), (78.7; 
100%) 
Cattle Forage 53.5 (43.0; 0%), (48.3; 5%), (52.5; 73%), (61.9; 95%), (73.0; 100%) 
Daily Average Irrigation Rate (mm/day) - Present-Day Climate - Cumulative Distribution 
Leafy Vegetables 5.41 (4.00; 0%), (5.11; 5%), (5.19; 20%), (5.21; 35%), (5.38; 50%), (5.48; 
80%), (6.00; 95%), (7.08; 100%) 
Other Vegetables 7.71 (5.00; 0%), (6.07; 5%), (6.65; 20%), (6.93; 35%), (7.67; 50%), (8.36; 
65%), (9.03; 80%), (9.26; 95%), (10.93; 100%) 
Fruits 7.41 (4.00; 0%), (5.40; 5%), (7.02; 28%), (7.59; 51%), (8.38; 72%), (8.67; 
95%), (10.23; 100%) 

Table 7.1-1. Recommended Distributions and Averages for Agricultural and Environmental 
Parameters for the Biosphere Model (Continued) 
Parameter - Type of 
Distribution 
Crop Type Averagea Distribution Characteristicsb 
Grains 4.64 (3.00; 0%), (3.44; 5%), (3.58; 35%), (3.87; 65%), (7.69; 95%), (9.07; 
100%) 
Cattle Forage 6.55 (5.00; 0%), (5.85; 5%), (6.18; 73%), (9.02; 95%), (10.64; 100%) 
Daily Average Irrigation Rate (mm/day) - Upper Bound Glacial Transition Climate - Cumulative 
Distribution 
Leafy Vegetables 3.81 (3.00; 0%), (3.34; 5%), (3.51; 20%), (3.86; 50%), (3.92; 65%), (4.02; 
80%), (4.18; 95%), (4.93; 100%) 
Other Vegetables 3.84 (2.0; 0%), (2.73; 5%), (3.08; 20%), (3.48; 35%), (4.08; 50%), (4.16; 
65%), (4.43; 80%), (4.95; 95%), (5.84; 100%) 
Fruits 3.90 (2.00; 0%), (2.51; 5%), (3.48; 28%), (4.33; 51%), (4.38; 72%), (4.79; 
95%), (5.65; 100%) 
Grains 3.36 (1.00; 0%), (1.99; 5%), (3.42; 35%), (3.93; 65%), (4.11; 95%), (4.85; 
100%) 
Cattle Forage 4.14 (3.00; 0%), (3.64; 5%), (4.01; 73%), (5.03; 95%), (5.94; 100%) 
Overwatering Rate (m/year) - Present-Day Climate - Cumulative Distribution 
All 0.079 (0.009; 0%), (0.030; 19%), (0.045; 38%), (0.077; 57%), (0.129; 76%), 
(0.233; 0.95), (0.275; 100%) 
Overwatering Rate (m/year) - Upper Bound Glacial Transition Climate - Cumulative Distribution 
All 0.067 (0.004; 0%), (0.020; 19%), (0.047; 38%), (0.072; 57%), (0.104; 76%), 
(0.150; 0.95), (0.177; 100%) 
Tillage Depth (cm) - Uniform Distributionf 
All 25 Minimum = 5, Maximum = 30 
Yield (kg/m2) - Cumulative Distribution 
Leafy Vegetables 3.30 (1.08; 0%), (1.46; 5%), (1.78; 20%), (2.01; 35%), (2.98; 50%), (3.25; 
65%), (3.83; 80%), (7.79; 95%), (7.85; 100%) 
Other Vegetables 4.13 (2.80; 0%), (3.37; 5%), (3.56; 28%), (3.64; 51%), (4.92; 72%), (5.15; 
95%), (6.61; 100%) 
Fruits 2.75 (0.73; 0%), (1.51; 5%), (2.67; 28%), (2.92; 51%), (3.00; 72%), (3.63; 
95%), (6.89; 100%) 
Grains 0.59 (0.27; 0%), (0.28; 5%), (0.44; 35%), (0.54; 65%), (1.10; 95%), (1.22; 
100%) 
Cattle Forage 2.14 (0.69; 0%), (1.02; 5%), (1.87; 73%), (5.78; 95%), (6.28; 100%) 
Rooting Depth (m) - Cumulative Distribution 
All 0.65 (0.15; 0%), (0.20; 5%), (0.30; 23%), (0.50; 41%), (0.70; 59%), (1.00; 
77%), (1.50; 95%), (2.00; 100%) 
Output DTN: MO0403SPAAEIBM.002. 
a 
Averages are calculated per crop type (i.e., Leafy Vegetables) or for 26 representative crops and turf (All) 
unless otherwise indicated (see notes e and f). 
b Characteristics of the cumulative distribution are the upper bound of each interval and the cumulative 
probability associated with each interval. 
A cumulative distribution for IR is recommended for the Biosphere model if yearly variation and a wider range 
of uncertainty is required. 
d A normal distribution is recommended for IR if values that are representative of the long-term average are 
required for the Biosphere model. 
e 
The midpoint of the uniform distribution is presented instead of the average. 
f 
The most common conventional tillage depth is presented instead of the average. 

7.2 HOW THE APPLICABLE ACCEPTANCE CRITERIA ARE ADDRESSED 
The following information describes how this analysis addresses the acceptance criteria in the 
Yucca Mountain Review Plan, Final Report (NRC 2003 [DIRS 163274], Sections 2.2.1.3.13 and 
2.2.1.3.14). Only those acceptance criteria that are applicable to this report (see Section 4.2) are 
discussed. 
This analysis report is one of ten reports (Figure 1-1) that support biosphere modeling and 
describe how the acceptance criteria have been addressed by the biosphere model. 
A consideration of all ten reports is required to understand how all applicable acceptance criteria 
are satisfied by the biosphere model. 
Acceptance Criteria From Section 2.2.1.3.13.3, Redistribution of Radionuclides in Soil 
Three parameters developed in this analysis: annual average irrigation rate, overwatering rate, 
and tillage depth (soil surface depth), support modeling of the redistribution of radionuclides in 
soil. 
Acceptance Criterion 1, System Description and Model Integration are Adequate: 
 Subcriterion (2): Annual average irrigation rate, overwatering rate, and tillage depth are 
used to model the deposition and redistribution of contaminated material in soil. Annual 
average irrigation rate is used in the calculation of the primary radionuclide addition and 
removal process in the surface soil (BSC 2004 [DIRS 169460], Sections 6.4.1 and 
6.5.1). Overwatering rate and tillage depth (soil surface depth) are used in the 
calculation of radionuclide leaching from the soil surface. Tillage depth is also used in 
the calculation of surface soil erosion (BSC 2004 [DIRS 169460], Sections 6.4.1 and 
6.5.1). Distributions for these parameters are developed in Sections 6.5, 6.9, and 6.10. 
Other important aspects of redistribution of radionuclides in soil are considered in the 
Biosphere Model Report (BSC 2004 [DIRS 169460], Sections 6.4.1 and 6.5.1) and 
Soil-Related Input Parameters for the Biosphere Model (BSC 2004 [DIRS 169459]). 
Acceptance Criterion 2, Data are Sufficient for Model Justification: 
 
Subcriterion (1): Data required to calculate annual average irrigation rates and 
overwatering rates included information on growing season lengths for selected crops, 
average monthly weather data (including precipitation), and salinity of irrigation water. 
These data are described and adequately justified in Sections 4.1.4, 4.1.5, and 4.1.7, 
respectively. Additional justification for use of analogue weather stations for future 
climate states is in Section 6 (Climate States), and development of growing season 
length from the data in Section 4.1.4 is described in Appendix D (Section 2.1). The data 
were used in multiple calculations to derive annual average irrigation rate and 
overwatering rate parameter distributions. These calculations are adequately described 
in Appendices C, D, and E. Synthesis of these calculations into parameters are 
described in Sections 6.5 and 6.9. Data used to calculate tillage depth (used in the 
biosphere model to determine soil surface depth) are described and adequately justified 

in Section 4.1.9. Use of plow depths to develop the tillage depth parameter is 
adequately described in Section 6.10. 
 
Subcriterion (2): Growing season data, weather data, and well water salinity data were 
taken from appropriate, officially recognized sources, YMP sources operating under QA 
programs, or reliable local Nye County sources (Sections 4.1.4, 4.1.5, and 4.1.7). The 
number of measurements and the number of crops considered for these data are 
sufficient to define the annual average irrigation rate and overwatering rate parameters 
as demonstrated by evaluation of uncertainties associated with those parameters 
(Sections 6.5.2 and 6.9.2). 
Acceptance Criterion 3, Data Uncertainty is Characterized and Propagated Through the Model 
Abstraction: 
 
Subcriterion (1): Probability distributions were developed for the annual average 
irrigation rate, overwatering rate, and tillage depth parameters. These distributions are 
technically defensible and account for variation and uncertainties associated with each 
parameter (Sections 6.5.2, 6.9.2, and 6.10.2). The parameter distributions are adequately 
justified (Sections 6.5.2, 6.9.2, and 6.10.2) and parameter values are consistent with the 
characteristics of the RMEI (Section 4.1.5.1 and Appendix A). 
 
Subcriterion (2): Annual average irrigation rate and overwatering rate for the present-
day climate were derived from climate data collected from the location of the RMEI 
(Section 4.1.5.1) for crops that can be grown in Amargosa Valley (Appendix A). 
Growing season information for these parameters was derived from southern Nye county 
and appropriate arid climate analogues for the location of the RMEI, including Arizona 
and southern California (Section 4.1 and Appendix D [Section 2.1.1]). Data on soil 
characteristics were from northern Amargosa Valley (Sections 4.1.6, 6.6.2, and 
Appendix E, Section 2.1.1). Tillage depths were from non-local sources but were 
commonly recommended, and typical plows are available to Amargosa Valley farmers 
(Section 6.10). 
 
Subcriterion (3): Factors that contribute to uncertainty in each parameter are identified in 
Sections 6.5.2, 6.9.2, and 6.10.2. For those factors having the greatest influence on each 
parameter, site specific or analogue data were used to evaluate the sensitivity of the 
parameters to uncertainty in those factors, and to select parameter bounds that represent 
reasonable uncertainty, as described in Section 6. No correlations among biosphere 
model input parameters are identified in this analysis. 
Acceptance Criteria From Section 2.2.1.3.14.3, Biosphere Characteristics 
The Acceptance Criteria from Section 2.2.1.3.14.3 apply to all parameters developed in this 
analysis: dry biomass, dry-to-wet-weight ratios, fraction of overhead irrigation, irrigation 
intensity, tillage depth, yield, and rooting depth, growing time, annual average irrigation rate, 
irrigation application, daily irrigation rate, and overwatering rate. 

Acceptance Criterion 1, System Description and Model Integration are Adequate: 
 Subcriterion (3): Assumptions regarding climate change for parameters developed in this 
analysis were consistent with those used in other abstractions (Section 6.). Climate 
dependent parameters (growing time, annual average irrigation rate, irrigation 
application, daily irrigation rate, and overwatering rate) were developed in Section 6 for 
the three climate states modeled in other TSPA abstractions (present-day interglacial, 
monsoon, and glacial transition (BSC 2003 [DIRS 166296], p. 79)). These climates and 
their predicted occurrence at Yucca Mountain in the future are described in Future 
Climate Analysis (BSC 2004 [DIRS 170002], Section 6.6.2). 
Acceptance Criterion 2, Data are Sufficient for Model Justification: 
 
Subcriterion (1): The parameters developed in this analysis are used in the plant and soil 
submodels of the biosphere model (BSC 2004 [DIRS 169460], Sections 6.4.1, 6.4.3, 
6.5.1, and 6.5.3). The parameters are adequately described and justified in Section 6. 
The parameter values for the present-day climate were developed using climate data 
collected from the location of the RMEI (Section 4.1.5.1), and diet and living style of the 
RMEI were considered by selecting crops that can be grown in Amargosa Valley 
(Appendix A, Sections 1.1, 2., and 4.). Adequate descriptions of how the data were used 
and interpreted are in Section 6 and Appendices C, D, and E. Adequate descriptions of 
how the data were synthesized into parameters are in Section 6. 
 
Subcriterion (2): The sufficiency of data used to develop parameter distributions is 
described in Sections 4.1, 6, and Appendix A. Demonstration that the parameter 
distributions are consistent with present knowledge of the conditions in the Yucca 
Mountain region is in Section 6 and Appendix A. The relationship between the 
parameters developed in this report and the FEPs related to biosphere characteristics 
modeling is shown in Table 1-1. Because the FEPs are comprised of several parameters, 
the determination that the parameters discussed in this report are consistent with present 
knowledge of conditions in the region surrounding Yucca Mountain supports a 
determination that the corresponding FEPs also are consistent with present knowledge of 
conditions in that region. However, a final determination of whether a FEP is consistent 
with present knowledge of conditions in the region surrounding Yucca Mountain can be 
made only after all of the parameters which contribute to that FEP have been evaluated 
for consistency with present knowledge of conditions in the region surrounding Yucca 
Mountain. Sensitivity and uncertainty analyses are addressed in other biosphere 
modeling reports listed in Figure 1-1. 
Acceptance Criterion 3, Data Uncertainty is Characterized and Propagated Through the Model 
Abstraction: 
 
Subcriterion (1): The distributions and fixed values recommended in this analysis are 
technically defensible and adequately account for variation and uncertainties associated 
with each parameter (Section 6). The identification of uncertainties and variabilities, 
and how those uncertainties and variabilities were accounted for in the development of 

parameter bounds is described in Section 6. The consideration in this analysis of the 
definition of the RMEI is in Section 4.1.5.1 and in selection of crops in Appendix A. 
 
Subcriterion (2): The technical bases for the parameters developed in this analysis for 
use in the plant and soil submodels are consistent with site characterization data through 
use of site specific or appropriate analogue data inputs (Section 4 and Appendix A). The 
technical bases for the parameter values and ranges are technically defensible and 
provided in Section 6 and Appendices A - E. 
 
Subcriterion (4): Factors that contribute to uncertainty in each parameter are identified 
in Section 6. For those factors having the greatest influence on each parameter, site 
specific or analogue data were used to evaluate the sensitivity of the parameters to 
uncertainty in those factors, and to select parameter bounds that represent reasonable 
uncertainty, as described in Section 6. No correlations among biosphere model input 
parameters are identified in this analysis. 

8. INPUTS AND REFERENCES 
8.1 DOCUMENTS CITED 
Allen, R.G.; Pereira, L.S.; Raes, D.; and Smith, M. 1998. Crop Evapotranspiration, 157311 
Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage 
Paper 56. Rome, Italy: Food and Agriculture Organization of the United Nations. 
TIC: 245062. 
Antonelli, A.L.; Byther, R.S.; Collman, S.J.; Thornton, R.E.; and Van Denburgh, R. 158654 
1998. Home Gardens. EB0422. Pullman, Washington: Washington State 
University, Cooperative Extension, College of Agriculture & Home Economics. 
TIC: 251823. 
Bishop, G.P. and Beetham, C.J. 1989. Biotic Transport of Radionuclides in Soils as a 160038 
Result of the Action of Deep-Rooted Plant Species. ANS Report No. 780-5. Epsom,
Surrey, England: Associated Nuclear Services. TIC: 253399. 
Brady, N.C. and Weil, R.R. 1999. The Nature and Properties of Soils. 12th Edition. 160019 
Upper Saddle River, New Jersey: Prentice-Hall. TIC: 242178. 
Brouwer, C. and Heibloem, M. 1986. Chapter 3: Effective Rainfall. Irrigation 159869 
Water Management: Irrigation Water Needs. Rome, Italy: Food and Agriculture 
Organization of the United Nations. Accessed August 2, 2002. TIC: 252924. 
http://www.fao.org/docrep/s2022e/s2022e00.htm 
BSC (Bechtel SAIC Company) 2003. Total System Performance Assessment-License 166296 
Application Methods and Approach. TDR-WIS-PA-000006 REV 00 ICN 01. 
Las Vegas, Nevada: Bechtel SAIC Company. ACC: DOC.20031215.0001. 
BSC 2004. Analysis of 1993-1997 Meteorological Data from Yucca Mountain Site 9. 167055 
ANL-MGR-MM-000001 REV 00. Las Vegas, Nevada: Bechtel SAIC Company. 
ACC: DOC.20040126.0002. 
BSC 2004. Atmospheric Dispersal and Deposition of Tephra from a Potential 170026 
Volcanic Eruption at Yucca Mountain, Nevada. MDL-MGR-GS-000002 REV 01. 
Las Vegas, Nevada: Bechtel SAIC Company. 
BSC 2004. Biosphere Model Report. MDL-MGR-MD-000001 REV 01. Las Vegas, 169460 
Nevada: Bechtel SAIC Company. 
BSC 2004. Disruptive Event Biosphere Dose Conversion Factor Analysis. 167287 
ANL-MGR-MD-000003 REV 03. Las Vegas, Nevada: Bechtel SAIC Company. 
BSC 2004. Future Climate Analysis. ANL-NBS-GS-000008 REV 01. Las Vegas, 170002 
Nevada: Bechtel SAIC Company. 

BSC 2004. Nominal Performance Biosphere Dose Conversion Factor Analysis. 169674 
ANL-MGR-MD-000009 REV 03. Las Vegas, Nevada: Bechtel SAIC Company. 
BSC 2004. Q-List. 000-30R-MGR0-00500-000-000 REV 00. Las Vegas, Nevada: 168361 
Bechtel SAIC Company. ACC: ENG.20040721.0007. 
BSC 2004. Simulation of Net Infiltration for Present-Day and Potential Future 170007 
Climates. MDL-NBS-HS-000023, Rev. 00. Las Vegas, Nevada: Bechtel SAIC 
Company. 
BSC 2004. Soil-Related Input Parameters for the Biosphere Model. 169459 
ANL-NBS-MD-000009 REV 02. Las Vegas, Nevada: Bechtel SAIC Company. 
BSC 2004. Technical Work Plan for Biosphere Modeling and Expert Support. 169573 
TWP-NBS-MD-000004 REV 03. Las Vegas, Nevada: Bechtel SAIC Company. 
ACC: DOC.20040527.0004. 
Call, R.E. 1999. Arizona Master Gardener Manual. Tucson, Arizona: University of 158672 
Arizona, College of Agriculture, Cooperative Extension. TIC: 252617. 
Canori, G.F. and Leitner, M.M. 2003. Project Requirements Document. 166275 
TER-MGR-MD-000001 REV 02. Las Vegas, Nevada: Bechtel SAIC Company. 
ACC: DOC.20031222.0006. 
Caprile, J.L.; Grant, J.A.; Holtz, B.A.; Kelley, K.M.; Mitcham, E.J.; Klonsky, K.M.; 159938 
and De Moura, R.L. 2001. Sample Costs to Establish an Apple Orchard and Produce 
Apples, Granny Smith Variety, San Joaquin Valley-North 2001. Davis, California: 
University of California Cooperative Extension. TIC: 253202. 
Christiansen, J.E. and Davis, J.R. 1967. Sprinkler Irrigation Systems. In 159263 
Agronomy, Chapter 44 of Irrigation of Agricultural Lands. Madison, Wisconsin: 
American Society of Agronomy. TIC: 223826. 
CRWMS (Civilian Radioactive Waste Management System) M&O (Management and 101090 
Operating Contractor) 1997. Yucca Mountain Site Characterization Project Summary 
of Socioeconomic Data Analyses Conducted in Support of the Radiological 
Monitoring Program First Quarter 1996 to First Quarter 1997. Las Vegas, Nevada: 
CRWMS M&O. ACC: MOL.19971117.0460. 
CRWMS M&O 1999. Environmental Baseline File for Meteorology and Air Quality. 102877 
B00000000-01717-5705-00126 REV 00. Las Vegas, Nevada: CRWMS M&O. 
ACC: MOL.19990302.0186. 

CRWMS M&O 1999. Evaluation of Soils in the Northern Amargosa Valley. 107736 
B00000000-01717-5705-00084 REV 00. Las Vegas, Nevada: CRWMS M&O. 
ACC: MOL.19990224.0268. 
CRWMS M&O 2000. Total System Performance Assessment for the Site 
153246 
Recommendation. TDR-WIS-PA-000001 REV 00 ICN 01. Las Vegas, Nevada: 
CRWMS M&O. ACC: MOL.20001220.0045. 
Dastane, N.G. 1978. Chapter II. Measurement of Effective Rainfall. Effective 159870 
Rainfall in Irrigated Agriculture. Rome, Italy: Food and Agriculture Organization of 
the United Nations. Accessed August 19, 2002. TIC: 253117. 
http://www.fao.org/docrep/x5560e/x5560e00.htm 
DOE (U.S. Department of Energy) 1997. The 1997 Biosphere Food Consumption 100332 
Survey Summary Findings and Technical Documentation. Las Vegas, Nevada: U.S. 
Department of Energy, Office of Civilian Radioactive Waste Management. 
ACC: MOL.19981021.0301. 
Dollarhide, W.E. 1999. United States Department of Agriculture Soil Survey 159253 
Data  Lathrop Wells Area. Letter from W.E. Dollarhide (USDA) to R. Aguilar 
(SNL), December 5, 1999, with attachments. ACC: MOL.19991217.0513. 
Doorenbos, J. and Pruitt, W.O. 1977. Crop Water Requirements. FAO Irrigation and 103062 
Drainage Paper 24. Rome, Italy: Food and Agriculture Organization of the United 
Nations. TIC: 245199. 
Granberry, D.M.; Kelley, W.T.; and Boyhan, G.E. 2000. Soil and Fertilizer 160033 
Management. Commercial Production and Management of Cabbage and Leafy 
Greens. Bulletin 1181. Pages 8-12. Athens, Georgia: University of Georgia, 
College of Agricultural and Environmental Services. TIC: 253187. 
Hagan, R.M.; Haise, H.R.; and Edminster, T.W. 1967. Irrigation of Agricultural 160037 
Lands. Number 11 in the Series, Agronomy. Madison, Wisconsin: American 
Society of Agronomy. TIC: 223826. 
Hansen, H. and Trimmer, W. 1997. Irrigation Runoff Control Strategies. PNW 287. 159373 
Corvallis, Oregon: Pacific Northwest Extension Publication. TIC: 252843. 
Hatfield, J.L. and Allen, R.G. 1996. Evapotranspiration Estimates Under Deficient 159872 
Water Supplies. Journal of Irrigation and Drainage Engineering, 122, (5), 301-308. 
New York, New York: American Society of Civil Engineers. TIC: 252935. 

Hay, R.K.M. 1995. Harvest Index: A Review of its Use in Plant Breeding and Crop 160540 
Physiology. Annals of Applied Biology, 126, (1), 197-216. Wellesbourne, 
England: Association of Applied Biologists. TIC: 252882. 
Hinman, H.; Pelter, G.; Kulp, E.; Gillespie, R.; and Sorensen, E. 1997. 1997 159376 
Enterprise Budgets, Potatoes, Winter Wheat, Alfalfa Hay, Grain Corn, Silage Corn 
and Sweet Corn Under Center Pivot Irrigation, Columbia Basin, Washington. 
EB1667. Pullman, Washington: Washington State University, Bulletin Office. 
TIC: 252852. 
Horak, C. and Carns, D. 1997. Amargosa Focus Group Report. Biosphere Study. 124149 
Las Vegas, Nevada: University of Nevada, Las Vegas. TIC: 241712. 
IAEA (International Atomic Energy Agency) 1994. Handbook of Parameter Values 100458 
for the Prediction of Radionuclide Transfer in Temperate Environments. Technical 
Reports Series No. 364. Vienna, Austria: International Atomic Energy Agency. 
TIC: 232035. 
IAEA (International Atomic Energy Agency) 1996. Modelling of Radionuclide 160402 
Interception and Loss Processes in Vegetation and of Transfer in Semi-Natural 
Ecosystems, Second Report of the VAMP Terrestrial Working Group. 
IAEA-TECDOC-857. Vienna, Austria: International Atomic Energy Agency. 
TIC: 253017. 
Jensen, M.E.; Burman, R.D.; and Allen, R.G., eds. 1990. Evapotranspiration and 160001 
Irrigation Water Requirements. ASCE Manuals and Reports on Engineering Practice 
No. 70. New York, New York: American Society of Civil Engineers. TIC: 246697. 
Johnson, J.W. 1999. Chapter 8: Tillage. Most Asked Agronomic Questions. 160029 
Johnson, J.W. and Hudak, C., eds. Bulletin 760-88. Columbus, Ohio: Ohio State 
University, Department of Horticulture and Crop Sciences, OSU Extension. Accessed 
September 16, 2002. TIC: 253186. http://ohioline.osu.edu/b760/b760_8.html
Keller, W. and Carlson, C.W. 1967. Forage Crops. In Agronomy, Chapter 31 of 159951 
Irrigation of Agricultural Lands . Madison, Wisconsin: American Society of 
Agronomy. TIC: 223826. 
Kennedy, W.E., Jr. and Strenge, D.L. 1992. Technical Basis for Translating 103776 
Contamination Levels to Annual Total Effective Dose Equivalent. Volume 1 of 
Residual Radioactive Contamination from Decommissioning. NUREG/CR-5512. 
Washington, D.C.: U.S. Nuclear Regulatory Commission. ACC: 
MOL.20010721.0030. 

Klonsky, K.M. and De Moura, R.L. 2001. Sample Costs to Produce Fresh Market 159381 
Strawberries, Central Coast Region, Monterey & Santa Cruz Counties, 2001. 
ST-CC-01. Davis, California: University of California Cooperative Extension. 
TIC: 252850. 
Kranz, W. 2002. Selecting Sprinkler Packages for Center Pivots. G88-870-A. 159377 
Lincoln, Nebraska: University of Nebraska Cooperative Extension. Accessed 
June18, 2002. TIC: 252844. http://www.ianr.unl.edu/pubs/farmmgt/g870.htm
Lang, N.S.; Stevens, R.G.; Thornton, R.E.; Pan, W.L.; and Victory, S. 1999. Potato 160031 
Nutrient Management for Central Washington. EB1871. Pullman, Washington: 
Washington State University Cooperative Extension. TIC: 253185. 
LaPlante, P.A. and Poor, K. 1997. Information and Analyses to Support Selection of 101079 
Critical Groups and Reference Biospheres for Yucca Mountain Exposure Scenarios. 
CNWRA 97-009. San Antonio, Texas: Center for Nuclear Waste Regulatory 
Analyses. ACC: MOL.20010721.0035. 
LeStrange, S.L. Water Needs for Growing Grapes in Pahrump, Nevada. Call from 125452 
LeStrange, S.L. to Sanders, J., July 28, 1997. ACC: MOL.19990331.0129. 
LeStrange, S.L. Water Required for Crop Management Practices in Pahrump. Call 125429 
from LeStrange, S.L. to Hafen, T., July 28, 1997. ACC: MOL.19990323.0176. 
Ley, T.W. 1992. Sprinkler IrrigationApplication Rates and Depths. EB1305. 159380 
Pullman, Washington: Washington State University, Cooperative Extension. 
TIC: 252841. 
Locke, G.L. 2001. Selected Ground-Water Data for Yucca Mountain Region, 159957 
Southern Nevada and Eastern California, Through December 1999. Open-File 
Report 00-479. Carson City, Nevada: U.S. Geological Survey. 
ACC: MOL.20020206.0233. 
Martin, D.L.; Gilley, J.R.; and Skaggs, R.W. 1991. Appendix II: Evapotranspiration 101081 
Equations. Managing Nitrogen for Groundwater Quality and Farm Profitability, 
Proceedings of a Symposium Sponsored By Division A-5 of the American Society of 
Agronomy, Anaheim, California, 1988 Follett, R. F.; Keeney, D. R.; Cruse, R. M., 
eds. Pages 333-338. Madison, Wisconsin: Soil Science Society of America. 
TIC: 238376. 
Martin, E.C.; Slack, D.C.; and Pegelow, E.J. 1999. Water Use in 159383 
Vegetables: Carrots. Arizona Water Series No. 28. AZ1134. Tucson, Arizona: 
University of Arizona Cooperative Extension. TIC: 252847. 
Martin, E.C.; Slack, D.C.; and Pegelow, E.J. 1999. Water Use in Vegetables: Dry 159384 
Bulb Onions. Arizona Water Series No. 25. AZ1131. Tucson, Arizona: University 
of Arizona Cooperative Extension. TIC: 252848. 

Martin, E.C.; Slack, D.C.; and Pegelow, E.J. 1999. Water Use in 
159382 
Vegetables Western Head Lettuce. Arizona Water Series No. 26. AZ1132. Tucson, 
Arizona: University of Arizona Cooperative Extension. TIC: 252849. 
Mayberry, K.S. 2000. Sample Cost to Establish and Produce Bell Peppers, Imperial 159388 
County  2000. Circular 104-V. Davis, California: University of California 
Cooperative Extension. TIC: 252862. 
Mayberry, K.S. 2000. Sample Cost to Establish and Produce Cantaloupes, Mid-Bed 159389 
Trenched, Imperial County  2000. Circular 104-V. Davis, California: University of 
California Cooperative Extension. TIC: 252861. 
Mayberry, K.S. 2000. Sample Cost to Establish and Produce Iceberg Lettuce, 159386 
Imperial County  2000. Circular 104-V. Davis, California: University of California 
Cooperative Extension. TIC: 252863. 
Mayberry, K.S. 2000. Sample Cost To Establish and Produce Watermelon, Imperial 160005 
County  2000. Circular 104-V. Davis, California: University of California 
Cooperative Extension. TIC: 252860. 
McKinley, P.W.; Long, M.P.; and Benson, L.V. 1991. Chemical Analyses of Water 116222 
from Selected Wells and Springs in the Yucca Mountain Area, Nevada and 
Southeastern California. Open-File Report 90-355. Denver, Colorado: U.S. 
Geological Survey. ACC: NNA.19901031.0004. 
Mills, L.; Morris, B.; Roberts, A.; Robinson, M. L.; Hammond, B.; and Mandekic, J. 124338 
n.d. Beginning Gardening in the Desert. Pahrump, Nevada: Southern Nye County 
Cooperative Extension. TIC: 243450. 
Morris, B. and Johnson, W. 1986. Grass Selection for the Urban Mojave Desert 103033 
Landscape. Fact Sheet 86-72. Reno, Nevada: University of Nevada, Cooperative 
Extension. TIC: 244377. 
Morris, B. and Johnson, W. 1991. Maintaining Hybrid Bermudagrass for Urban 103034 
Mojave Desert Landscapes. Fact Sheet 91-24. Reno, Nevada: University of Nevada, 
Cooperative Extension. TIC: 244378. 
Napier, B.A.; Peloquin, R.A.; Strenge, D.L.; and Ramsdell, J.V. 1988. Conceptual 157927 
Representation. Volume 1 of GENII - The Hanford Environmental Radiation 
Dosimetry Software System. PNL-6584. Richland, Washington: Pacific Northwest 
Laboratory. TIC: 252237. 

National Weather Service. 2003. Delta Climate, International Station 
162299 
Meteorological Climate Summary. Climate Data - Utah Historical Weather 
Information. Salt Lake City, Utah: National Weather Service. Accessed March 4, 
2003. TIC: 253916. http://www.wrh.noaa.gov/Saltlake/climate/ismcs.u24.html 
Neitsch, S.L.; Arnold, J.G.; Kiniry, J.R.; Srinivasan, R.; and Williams, J.R. 2002. 163122 
Land Cover/Plant Growth Database. Soil and Water Assessment Tool User's 
Manual, Version 2000. Pages 356-385. College Station, Texas: Texas Water 
Resources Institute. TIC: 254059. 
Nobel, P.S. 1983. Plants and Fluxes. Biophysical Plant Physiology and Ecology. 160500 
Pages 461-523. New York, New York: W.H. Freeman and Company. TIC: 252952. 
Nobel, P.S. 1983. TemperatureEnergy Budgets. Biophysical Plant Physiology 159953 
and Ecology. Pages 339-386. New York, New York: W.H. Freeman and Company. 
TIC: 253519. 
NRC (U.S. Nuclear Regulatory Commission) 2003. Yucca Mountain Review Plan, 163274 
Final Report. NUREG-1804, Rev. 2. Washington, D.C.: U.S. Nuclear Regulatory 
Commission, Office of Nuclear Material Safety and Safeguards. TIC: 254568. 
Orloff, S.B. 1997. Hay Curing, Baling, and Storage. Chapter 12 of Intermountain 158788 
Alfalfa Management. Orloff, S.B.; Carlson, H.L.; and Teuber, L.R., eds. Publication 
3366. Oakland, California: University of California, Division of Agriculture and 
Natural Resources. TIC: 251826. 
Orloff, S.B. and Marble, V.L. 1997. Harvest Management. Chapter 11 of 158655 
Intermountain Alfalfa Management. Orloff, S.B.; Carlson, H.L.; and Teuber, L.R., 
eds. Publication 3366. Oakland, California: University of California, Division of 
Agriculture and Natural Resources. TIC: 251826. 
Orloff, S.B.; Carlson, H.L.; and Hanson, B.R. 1997. Irrigation. Chapter 4 of 158774 
Intermountain Alfalfa Management. Orloff, S.B.; Carlson, H.L.; and Teuber, L.R., 
eds. Publication 3366. Oakland, California: University of California, Division of 
Agriculture and Natural Resources. TIC: 251826. 
Painter, K.; Hinman, H.R.; and Burns, J. 1995. 1995 Crop Rotation Budgets for 158674 
Eastern Whitman County, Washington. EB1437. Pullman, Washington: Washington 
State University, Cooperative Extension. TIC: 251820. 
Prince, S.D.; Haskett, J.; Steininger, M.; Strand, H.; and Wright, R. 2001. Net 159323 
Primary Production of U.S. Midwest Croplands from Agricultural Harvest Yield 
Data. Ecological Applications, 11, (4), 1194-1205. Washington, D.C.: Ecological 
Society of America. TIC: 252845. 

Putnam, J.J. and Allshouse, J.E. 1999. Food Consumption, Prices, and Expenditures, 158676 
1970-97. Statistical Bulletin No. 965. Washington, D.C.: U.S. Department of 
Agriculture, Economic Research Service. TIC: 252630. 
Rasmuson, K.E. 2004. Summary of 2004 Agricultural Activities, Businesses, and 169506 
Community Services and Organizations in Amargosa Valley. Interoffice 
memorandum from K.E. Rasmuson (BSC) to K.R. Rautenstrauch, May 20, 2004, 
0520041667, with enclosures. ACC: MOL.20040519.0033. 
Robins, J.S.; Musick, J.T.; Finfrock, D.C.; and Rhoades, H.F. 1967. Grain and Field 159939 
Crops. In Agronomy, Chapter 32 of Irrigation of Agricultural Lands. Madison, 
Wisconsin: American Society of Agronomy. TIC: 223826. 
Schmierer, J.L.; Orloff, S.B.; and Benton, R.W. 1997. Stand Establishment. 160479 
Chapter 2 of Intermountain Alfalfa Management. Orloff, S.B.; Carlson, H.L.; and 
Teuber, L.R.; eds. Publication 3366. Oakland, California: University of California, 
Division of Agriculture and Natural Resources. TIC: 251826. 
Scholberg, J.; McNeal, B.L.; Jones, J.W.; Boote, K.J.; Stanley, C.D.; and Obreza, 160434 
T.A. 2000. Field-Grown Tomato, Growth and Canopy Characteristics of 
Field-Grown Tomato. Agronomy Journal, 92, ([1]), 159-168. 
[Madison, Wisconsin]: American Society of Agronomy. TIC: 252818. 
Stahnke, G.K.; Brauen, S.E.; Byther, R.S.; Antonelli, A.L.; and Chastagner, G. 2001. 158675 
Home Lawns. EB0482. Pullman, Washington: Washington State University, 
Cooperative Extension, College of Agriculture and Home Economics. TIC: 251822. 
Tanner, C.B. 1967. Measurement of Evapotranspiration. In Agronomy, Chapter 29 159950 
of Irrigation of Agricultural Lands. Madison, Wisconsin: American Society of 
Agronomy. TIC: 223826. 
Teegerstrom, T. and Clay, P. 1999. Arizona Field Crop Budgets 1999-2000, 159390 
Maricopa County. Extension Bulletin #AZ1118. Tucson, Arizona: University of 
Arizona, College of Agriculture and Life Sciences. TIC: 252872. 
Teegerstrom, T. and Umeda, K. 2001. 2001-2002 Arizona Vegetable Crop Budgets, 159392 
Central Arizona, Maricopa County. Publication AZ1261. Tucson, Arizona: 
University of Arizona, College of Agriculture and Life Sciences. TIC: 252874. 
Teegerstrom, T.; Palumbo, J.; and Zerkoune, M. 2001. 2001-2002 Arizona Vegetable 159391 
Crop Budgets, Western Arizona, Yuma County, La Paz County. Publication AZ1263. 
Tucson, Arizona: University of Arizona, Department of Agricultural and Resource 
Economics. TIC: 252873. 
Till, J.E. and Meyer, H.R. 1983. Radiological Assessment, A Textbook on 101895 
Environmental Dose Analysis. NUREG/CR-3332. Washington, D.C.: U.S. Nuclear 
Regulatory Commission. TIC: 223809. 

Uriu, K. and Magness, J.R. 1967. Deciduous Tree Fruits and Nuts. In Agronomy, 159169 
Chapter 35 of Irrigation of Agricultural Lands. Madison, Wisconsin: American 
Society of Agronomy. TIC: 223826. 
USDA (U.S. Department of Agriculture) 1997. Usual Planting and Harvesting Dates 169307 
for U.S. Field Crops. Agricultural Handbook Number 628. Washington, D.C.: U.S. 
Department of Agriculture. ACC: MOL.20040511.0080. 
USDA 1998. Noncitrus Fruits and Nuts, 1997 Preliminary Summary. Fr Nt 1-3 158649 
(98)a. Washington, D.C.: U.S. Department of Agriculture, National Agricultural 
Statistics Service. Accessed May 9, 2002. TIC: 252536. 
http://usda.mannlib.cornell.edu/reports/nassr/fruit/pnf-bb/ncit0198.txt 
USDA 1998. Agricultural Statistics 1998 . Washington, D.C.: U.S. Government 158648 
Printing Office. TIC: 252541. 
USDA 1999. 1997 Census of Agriculture, Nevada State and County Data. 158643 
Volume 1, Geographic Area Series Part 28. AC97-A-28. Washington, D.C.: U.S. 
Department of Agriculture, National Agricultural Statistics Service. TIC: 246010. 
USDA 1999. 1997 Census of Agriculture, Washington State and County Data. 159271 
Volume 1, Geographic Area Series Part 47. AC97-A-47. Washington, D.C.: U.S. 
Department of Agriculture, National Agricultural Statistics Service. TIC: 252668. 
USDA 1999. Agricultural Statistics 1999. Washington, D.C.: U.S. Government 158647 
Printing Office. TIC: 252542. 
USDA 1999. Noncitrus Fruits and Nuts, 1998 Preliminary Summary. Fr Nt 1-3 (99)a. 158650 
Washington, D.C.: U.S. Department of Agriculture, National Agricultural Statistics 
Service. TIC: 252537. 
USDA 2000. Agricultural Statistics 2000. Washington, D.C.: U.S. Government 158646 
Printing Office. TIC: 252543. 
USDA 2000. Noncitrus Fruits and Nuts, 1999 Preliminary Summary. Fr Nt 1-3 158653 
(00)a. Washington, D.C.: U.S. Department of Agriculture, National Agricultural 
Statistics Service. TIC: 252538. 
USDA 2001. Agricultural Statistics 2001. Washington, D.C.: U.S. Government 158645 
Printing Office. TIC: 252544. 
USDA 2001. Noncitrus Fruits and Nuts, 2000 Preliminary Summary. Fr Nt 1-3 158651 
(01)a. Washington, D.C.: U.S. Department of Agriculture, National Agricultural 
Statistics Service. TIC: 252539. 
USDA 2002. Composition of Foods Raw, Processed, Prepared, USDA Nutrient 159272 
Database for Standard Reference, Release 14. Washington, D.C.: U.S. Department 
of Agriculture, Agricultural Research Service, Nutrient Data Laboratory. TIC: 

252893. 
USDA 2002. Nevada Agricultural Statistics 2000-2001. Nevada Agricultural 159273 
Statistics Publications. Washington, D.C.: U.S. Department of Agriculture, Nevada 
Agricultural Statistics Service. Accessed July 24, 2002. TIC: 252859. 
http://www.nass.usda.gov/nv/rlsetoc.htm
USDA 2002. Noncitrus Fruits and Nuts, 2001 Preliminary Summary. Fr Nt 1-3 (02). 158652 
Washington, D.C.: U.S. Department of Agriculture, National Agricultural Statistics 
Service. TIC: 252540. 
van Delden, A. 2001. Cropping Systems, Yield and Growth Components of Potato 160433 
and Wheat Under Organic Nitrogen Management. Agronomy Journal, 93, 
1370-1385. Madison, Wisconsin: Academic Press. TIC: 252816. 
Ventura, F.; Spano, D.; Duce, P.; and Snyder, R.L. 1999. An Evaluation of Common 159871 
Evapotranspiration Equations. Irrigation Science, 18, (4), 163-170. New York, 
New York: Springer-Verlag. TIC: 252932. 
Viets, F.G., Jr. 1967. Nutrient Availability in Relation to Soil Water. In Agronomy, 159952 
Chapter 24 of Irrigation of Agricultural Lands. Madison, Wisconsin: American 
Society of Agronomy. TIC: 223826. 
Vittum, M.T. and Flocker, W.J. 1967. Vegetable Crops. In Agronomy, Chapter 34 159941 
of Irrigation of Agricultural Lands. Madison, Wisconsin: American Society of 
Agronomy. TIC: 223826. 
Washington Agricultural Statistics Service. 1999. Usual Planting & Harvesting 152232 
Dates, Washington. 1999 Annual Bulletin. Washington, D.C.: National 
Agricultural Statistics Service, U.S. Department of Agriculture. Accessed February 
23, 2000. TIC: 248851. http://www.nass.usda.gov/wa/annual99/plant99.htm
Washington State University Cooperative Extension. 2002. Vegetable Gardening. 159256 
C146. Spokane, Washington: Washington State University Cooperative Extension. 
TIC: 251733. 
Western Regional Climate Center. 1997. Spokane, Washington: Normals, Means, 152233 
and Extremes. Reno, Nevada: Western Regional Climate Center, Desert Research 
Institute. Accessed August 23, 2000. TIC: 248857. 
http://www.wrcc.dri.edu/cgi-bin/clilcd.pl?wa24157 
Western Regional Climate Center. 2002. Monthly Average Maximum Temperature 161250 
(Degrees Fahrenheit) from 1965-2002. Amargosa Farms Garey, Nevada. Reno, 
Nevada: Western Regional Climate Center. Accessed December 18, 2002. TIC: 
252920. http://www.wrcc.dri.edu/cgi-bin/cliMONtavt.pl?nvamar 
Western Regional Climate Center. 2002. Monthly Average Minimum Temperature 161251 
(Degrees Fahrenheit) from 1965-2002. Amargosa Farms Garey, Nevada. Reno, 

Nevada: Western Regional Climate Center. Accessed December 18, 2002. 
TIC: 252920. http://www.wrcc.dri.edu/cgi-bin/cliMONtavt.pl?nvamar 
Western Regional Climate Center. 2002. Monthly Average Temperature (Degrees 160537 
Fahrenheit) from 1965-2002. Amargosa Farms Garey, Nevada. Reno, Nevada: 
Western Regional Climate Center. Accessed December 18, 2002. TIC: 252920. 
http://www.wrcc.dri.edu/cgi-bin/cliMONtavt.pl?nvamar 
Western Regional Climate Center 2003. Average Wind Speed - MPH (1996-2002) 162302 
Data, Alaska, Arizona, California, Colorado, Idaho, Montana, New Mexico, Nevada, 
Oregon, Utah, Washington, and Wyoming. Reno, Nevada: Western Regional 
Climate Center. Accessed February 26, 2003. TIC: 253914. 
http://www.wrcc.dri.edu/htmlfiles/westwind 
Western Regional Climate Center 2003. Mean Monthly and Annual Percent of 162300 
Possible Sunshine Mean Percent of Possible Sunshine - Western Regional Climate 
Center (WRCC). Reno, Nevada: Western Regional Climate Center. Accessed March
4, 2003. TIC: 253912. http://www.wrcc.dri.edu/htmlfiles/westcomp.sun.html
Western Regional Climate Center 2003. Nogales 6N, Arizona, NCDC 1971-2000 162307 
Monthly Normals. Reno, Nevada: Western Regional Climate Center. Accessed 
March 4, 2003. TIC: 253915. 
http://www.wrcc.dri.edu/cgi-bin/cliNORMNCDC2000.pl?aznoga 
Western Regional Climate Center 2003. Tucson, Arizona, Normals, Means, and 162301 
Extremes Climate Data Summary Reno, Nevada: Western Regional Climate Center. 
Accessed March 4, 2003. TIC: 253913. 
http://www.wrcc.dri.edu/cgi-bin/clilcd.pl?az23160 
Wolf, F. and Johnson, W.S. 1999. Growing Seedless Grapes in Northwestern 159393 
Nevada. Fact Sheet 99-42. Reno, Nevada: University of Nevada Cooperative 
Extension. Accessed June 19, 2002. TIC: 252851. 
www.extension.unr.edu/Plants/grapes.html 
YMP (Yucca Mountain Site Characterization Project) 1999. Yucca Mountain Site 158212 
Characterization Project: Summary of Socioeconomic Data Analyses Conducted in 
Support of the Radiological Monitoring Program, April 1998 to April 1999. 
North Las Vegas, Nevada: Yucca Mountain Site Characterization Office. 
ACC: MOL.19991021.0188. 
8.2 CODES, STANDARDS, REGULATIONS, AND PROCEDURES 
10 CFR 63. Energy: Disposal of High-Level Radioactive Wastes in a Geologic 156605 
Repository at Yucca Mountain, Nevada. Readily available. 
AP-2.22Q, Rev. 1, ICN 1. Classification Analyses and Maintenance of the Q-List.
Washington, D.C.: U.S. Department of Energy, Office of Civilian Radioactive 
Waste Management. ACC: DOC.20040714.0002. 

AP-2.27Q, Rev. 1, ICN 4. Planning for Science Activities. Washington, D.C.: U.S. 
Department of Energy, Office of Civilian Radioactive Waste Management. ACC: 
DOC.20040610.0006. 
AP-SIII.9Q, Rev. 1, ICN 6. Scientific Analyses. Washington, D.C.: U.S. 
Department of Energy, Office of Civilian Radioactive Waste Management. ACC: 
DOC.20040805.0003. 
8.3 SOURCE DATA, LISTED BY DATA TRACKING NUMBER 
LA0206AM831234.001. Eh-pH Field Measurements on Nye County EWDP Wells. 160051 
Submittal date: 06/21/2002. 
MO0010SPANYE00.001. Cleaned Nye County Food Consumption Frequency 154976 
Survey. Submittal date: 10/10/2000. 
MO0208SPAMETHO.004. Methods of Irrigation in Amargosa Valley. Submittal 159565 
date: 08/28/2002. 
MO0307SEPFEPS4.000. LA FEP List. Submittal date: 07/31/2003. 164527 
MO04019SUM9397.000. Summary of 1993-1997 Site 9 Meteorological Data. 167054 
Submittal date: 01/20/2004. 
MO0407SEPFEPLA.000. LA FEP List. Submittal date: 07/20/2004. 170760 
8.4 OUTPUT DATA, LISTED BY DATA TRACKING NUMBER 
MO0403SPAAEIBM.002. Agricultural and Environmental Parameters for the 
Biosphere Model. Submittal date: 3/22/04. 

APPENDIX A 
SELECTION OF REPRESENTATIVE PLANTS 


A. SELECTION OF REPRESENTATIVE PLANTS 
The first step in development of parameter distributions was selection of plants that are 
representative of the central tendency and variation within each of the five crop types and turf for 
the present-day climate (as represented by current conditions in Amargosa Valley [see Section 
6. Climate States]) and upper bound glacial transition climate conditions (as represented by 
conditions in eastern Washington [see Section 6. Climate States]). The parameter values 
calculated for the representative plants were then used in Section 6 of this report to develop 
averages and distributions that incorporate variation and uncertainty due to differences among 
plants within crop types. The following sections summarize information on plants grown in 
Amargosa Valley and eastern Washington, national food consumption patterns, and other factors 
considered in selection of representative plants. As described in Section A1, plants selected for 
present-day climate conditions are also used for the upper bound monsoon climate, and those 
selected for the glacial transition climate are used for both upper and lower bounds. 
A1. COMMONLY GROWN PLANTS 
A1.1 AMARGOSA VALLEY 
Field surveys and aerial photographs were used to measure acreage of crops grown in Amargosa 
Valley during 1996 through 1999 (CRWMS M&O 1997 [DIRS 101090]; YMP 1999 
[DIRS 158212]). Those surveys did not include gardens. Hay accounted for 91 to 93 percent of 
the total acreage planted per year; most (67 to 97 percent) hay was alfalfa (Table A-1). 
Pistachios were the next most common crop (4-5 percent of total acreage). Barley and oats were 
the only grains documented, and garlic and onions were the only vegetables. Fruit trees (listed as 
peaches, nectarines, and pomegranates, and so forth (CRWMS M&O 1997 [DIRS 101090], 
Table 3-12])) were also recorded. In 2004, about 2,000 acres were planted in commercial 
agriculture, with alfalfa and other hay accounting for more than 95 percent of the total acreage 
(Rasmuson 2004 [DIRS 169506]). Additionally, about 1,000 acres were planted with evergreen 
trees. 
Table A-1. Acres Planted in Amargosa Valley 
Cropa 1996b 1997b 1998c 1999c 
Alfalfa Hay 1,747 1,822 1,278 1,360 
Other Hay 51 68 634 313 
Barley 17 32 34 
Oats 45 
Pistachios 92 80 98 98 
Fruit Trees 2 8 18 16 
Grapes 8 10 10 11 
Garlic 5 5 0.3 0.3 
Onions 5 
a 
Commercial agricultural production during spring in Radiological 
Monitoring Program Grid cells 408, 409, 508, and 509. 
b Source: CRWMS M&O 1997 ([DIRS 101090], Tables 3-12 and 3-13). 
c 
Source: YMP 1999 ([DIRS 158212], Tables 10 and 11). 
ANL-MGR-MD-000006 REV 02 A-1 September 2004 

The 1997 U.S. Census of Agriculture (USDA 1999 [DIRS 158643]) also lists alfalfa as the most 
important crop in all of Nye County during 1997 (Table A-2). Other hay was second-most 
important, and pistachios were third. Tomatoes and numerous types of fruit trees were grown on 
a few farms. There were 97 farms and 10,221 acres planted in the county in 1997; all crop land 
was irrigated. 
Table A-2. Crops Grown in Nye County, 1997 
Crop 
Number of 
Farms Acres 
Harvested Cropland 97 10,221 
Irrigated Cropland 97 10,221 
Alfalfa Hay 51 5,703 
Small Grain Hay 8 178 
Tame Hay 8 379 
Wild Hay 15 2,820 
Vegetables, Total 5 9 
Tomatoes 4 4 
Orchards, Total 22 254 
Apples 4 11 
Apricots 3 3 
Cherries 3 1 
Grapes 7 16 
Peaches 8 16 
Pears 3 5 
Pomegranates 3 Da 
Pecans 3 Da 
Pistachios 9 181 
Source: USDA 1999 ([DIRS 158643], Chapter 2, 
Tables 13, 28, 29, 30, and 31). 
a 
D = Data not disclosed. 
Thirteen residents of Amargosa Valley (representing nine households) actively involved in 
agriculture filled out questionnaires and were interviewed in 1997 to determine, among other 
things, the garden and commercial crops they grew and the reasons for growing them (Horak and 
Carns 1997 [DIRS 124149]). Although the results of this focus group study may not be 
representative of all agricultural practices in the valley, the study provides a valid list of crops 
commonly grown there (Horak and Carns 1997 [DIRS 124149], Tables 1 and 2 on pp. 26 and 27, 
respectively). Alfalfa was the most common cattle feed grown, and oats and other hays were 
also mentioned. Grains grown by those interviewed were barley, oats, red wheat, and corn 
(Horak and Carns 1997 [DIRS 124149], Table 1 and p. 15). A large variety of vegetables were 
listed, including commercial production of garlic, onions, and pumpkins, and garden production 
of potatoes, carrots, tomatoes, squash, lettuce, broccoli, cabbage, and many others. Fruit trees 
(type not specified), grapes, and melons were grown commercially and for personal 
consumption. Three participants also had pistachio trees. 

A guide for planting vegetables in Nye County lists 50 vegetables and fruits that can be grown 
there (Mills et al. no date [DIRS 124338]). Although this list is not comprehensive, it likely 
includes the easiest to grow and most commonly grown garden plants in the area. 
Warm and cool season grasses can be grown in the Mojave Desert. Bermudagrass is a 
commonly used, drought-adapted turfgrass in southern Nevada (Morris and Johnson 1991 
[DIRS 103034], p. 1) and tall fescue is the recommended cool season grass for this region 
(Morris and Johnson 1986 [DIRS 103033], p. 3). 
A1.2 EASTERN WASHINGTON 
Agriculture is an important industry in eastern Washington. There were about 280,000 acres of 
farmland in Spokane County and 800,000 in Whitman County (where Rosalia and St. John are 
located) in 1997 (Table A-3). Only about four percent and one percent of the agricultural land in 
each county, respectively, was irrigated (USDA 1999 [DIRS 159271]). The most important crop 
was winter wheat, comprising 46 percent of the total acreage planted in the two counties. Other 
common crops were barley (19 percent of total acreage), dry peas (10 percent), spring wheat 
(9 percent), lentils (8 percent), alfalfa (4 percent), and grass seed crops (3 percent). Numerous 
fruits and vegetables were grown on a smaller scale, especially in Spokane County. The only 
crops commonly irrigated were some vegetables (e.g., dry beans, sweet corn, pumpkins, 
tomatoes, peppers) and orchards. About 14 percent of acreage planted in alfalfa was irrigated 
(Table A-3). 
The types of garden crops that can be grown in eastern Washington is quite varied and includes 
many of the same crops suggested for Nye County (Antonelli et al. 1998 [DIRS 158654]; 
Washington State University Cooperative Extension 2002 [DIRS 159256]). 
Cool season grasses recommended for eastern Washington include tall fescue and Kentucky 
bluegrass. Most warm season grasses are not recommended for that region (Stahnke et al. 2001 
[DIRS 158675], p. 6). 

Table A-3. Crops grown in Spokane and Whitman Counties, Washington, 1997 
Selected Crops 
Spokane Countya,b Whitman Countya 
Number 
of Farms Acres 
Irrigated 
Acres 
Number of 
Farms Acres 
Irrigated 
Acres 
Total farms 1,133 280,969 10,044 852 801,501 4,805 
Feed corn 3 D D(1) 4 101 D(1) 
Spring wheat 145 21,485 520 358 78,603 D(1) 
Winter wheat 303 93,839 882 747 399,495 D(2) 
Barley 246 43,927 837 566 160,110 268 
Canola 8 1,584 8 1,498 
Oats 51 2,435 D(2) 12 203 
Dry edible beans 10 1,283 1,283 
Dry edible peas 81 19,596 276 84,356 D(1) 
Lentils 80 25,373 155 57,544 
Field/grass seed 82 22,657 D(2) 45 4,251 D(2) 
Alfalfa hay 633 35,493 4,606 134 6,644 1,438 
Small grain hay 110 3,495 138 42 D D(2) 
Tame hay 184 8,390 538 102 2981 D(1) 
Wild hay 109 4,183 47 1,552 
Corn Silage 4 128 128 
Vegetables-Total 37 449 408 33 5,792 
Carrots 6 34 D(3) 
Green peas 4 D D(3) 31 5,589 
Lettuce 3 1 1 
Sweet peppers 3 7 7 
Pumpkins 17 139 119 
Squash 10 58 D(6) 
Sweet corn 15 152 150 
Tomatoes 11 5 5 
Orchards - Total 48 367 192 9 25 19 
Apples 44 227 -9 19 -
Apricots 14 11 --
Cherries 29 50 
4 D -
Peaches 17 42 
Pears 16 24 -4 2 -
Nursery Crops 69 378 -14 980 -
Source: USDA 1999 [DIRS 159271], Tables 13, 26, 27, 28, 29, 30, 31, and 33. 
a 
Blanks indicate not grown or irrigated, dash indicates not reported, D indicates data withheld to avoid 
reporting for individual farms (number in parentheses is the number of farms irrigating a crop). 
b 
Other crops listed for Spokane County include snap beans, cucumbers and pickles, garlic, herbs, dry 
onions, potatoes, grapes, plums, blackberries, raspberries, strawberries, and floriculture and nursery 
products. 

A2. FOOD CONSUMPTION 
A U.S. Department of Agriculture report (Putnam and Allshouse 1999 [DIRS 158676]) on 
United States food consumption patterns was examined to identify plants commonly eaten 
(Table A-4). Consumption estimates were derived from measurements of national food 
production (minus non-consumptive uses such as exports, farm use, industrial use) divided by 
population size; therefore, they are estimates of the upper bounds of national rates of 
consumption of commercially produced foods. However, because the same methods were used 
for all products within a food type, they are valid for general comparisons of nationwide 
consumption patterns within food types (Putnam and Allshouse 1999 [DIRS 158676], pp. 2 to 4). 
Crops not grown in southern Nevada or eastern Washington (e.g., bananas, citrus, rice) were 
omitted from this analysis (Table A-4). 
Table A-4. Per-Capita Food Consumption 
Crop Type 
Cropa, b 
Consumption 
(lb/person) 
Crop Type 
Cropa, b 
Consumption 
(lb/person) 
Leafy Vegetables Fruits and Nuts 
Lettuce-Head 24.3 Melons 30.4 
Cabbage 10.2 Tomatoes 18.9 
Lettuce-Leaf 6.1 Apples 18.5 
Celery 6.0 Grapes 8.0 
Broccoli 5.2 Peaches 5.7 
Cauliflower 1.6 Strawberries 4.2 
Asparagus 0.7 Pears 3.5 
Spinach 0.6 Plums & Prunes 1.5 
Other Vegetables Tree Nuts 2.2 
Potatoes 47.9 Grains 
Onions 17.9 Wheat Flour 149.7 
Carrots 12.5 Corn Products 23.1 
Sweet Corn 8.1 Oat Products 6.5 
Bell Peppers 7.2 Barley Products 0.7 
Cucumbers 6.3 
Garlic 2.1 
Snap Beans 1.4 
Source: Putnam and Allshouse 1999 ([DIRS 158676], 1997 data from Tables 2, 16, 17, 
27, 32, and 34). 
a 
Only crops with >0.5 pounds consumed are listed. 
b Crops not likely to be grown in southern Nevada or eastern Washington are not listed, 
including citrus, avocados, bananas, mangoes, pineapples, papayas, rice. 

Per capita consumption of head lettuce during 1997 was more than twice that of other leafy 
vegetables. Consumption of potatoes far exceeded consumption of other vegetables, including 
other root vegetables, corn, and other vegetables. Melons were consumed more than other fruits, 
followed by tomatoes, apples, and grapes. Wheat consumption was much greater than corn 
products and other grains (Table A-4). 
Food consumption information was examined only to identify commonly eaten plants in the 
United States, not to predict food consumption patterns of the population in the town of 
Amargosa Valley. Food consumption patterns used in the biosphere model were 
developed from a survey of people in Amargosa Valley (DOE 1997 [DIRS 100332]; 
DTN: MO0010SPANYE00.001 [DIRS 154976]). There is only limited information from that 
survey that can be used to identify commonly eaten, locally grown plants in Amargosa Valley 
because people surveyed were asked how often they ate any of a group of plants, but were not 
asked to identify specific plants. The only exception was a question asked toward the end of the 
survey requesting respondents to list any other locally-produced food, such as tomatoes, or 
anything I did not already mention (DOE 1997 [DIRS 100332], p. B-10). Previously mentioned 
fruits and vegetables included leafy vegetables (such as cabbage, asparagus, lettuce, spinach, 
broccoli, or herbs), root vegetables (such as potatoes, garlic, beets, turnips, carrots, or 
onions), grains, and fruits (such as grapes, raisins, berries, plums, melons, or peaches) 
(DOE 1997 [DIRS 100332], pp. B-3 to B-6). Therefore, responses to the question are not valid 
for identifying commonly eaten locally produced leafy vegetables, root vegetables, or fruits. The 
most common responses to the question by Nye County residents were squash, tomatoes, 
peppers, cucumbers, zucchini, corn, and radishes (DTN: MO0010SPANYE00.001 
[DIRS 154976]). 
A3. REPRESENTATIVE CROP VALUE PARAMETERS 
Of the parameters required for each crop type, irrigation rate probably is the most important 
contributor to variation in BDCFs because it appears in the numerator for calculations of soil 
concentrations (which is used in pathways for root uptake, external exposure, and inhalation 
exposure) and water-to-plant deposition rates. Irrigation rates among garden and agricultural 
crops for a specified location are influenced primarily by planting date and growing season, 
because those two parameters control how long and during what time of year plants must be 
irrigated. To evaluate variation in irrigation rate among plants within a crop type, growing 
seasons for commonly grown and consumed plants were plotted (Figures A-1 and A-2). Data on 
growing season are discussed in Appendix D, and presented in Tables D-1 and D-2. Plant 
growth form (i.e., morphology differences within a crop type) also was considered in selection of 
representative crops to ensure that the range in biomass and dry-to-wet-weight ratios within crop 
types was represented by crops selected. 
Lettuce and most other commonly consumed leafy vegetables are small annuals that are planted 
in the spring. In southern Nevada, many leafy vegetables can also be grown in the fall 
(Figure A-1), but the growing season for celery is too long for spring and fall production in 
eastern Washington (Figure A-2). Asparagus is the only perennial leafy vegetable and has a very 
different growth form from other plants in this category. 

Most root and other vegetables are planted in mid to late spring and have only one growing 
season per year. The exceptions are onions (two seasons in southern Nevada) and carrots 
(two seasons in both areas). Growth form varies substantially within this group. 
Figure A-1. Growing Season Lengths for Representative Crops under Present-Day Climate Conditions 
ANL-MGR-MD-000006 REV 02 A-7 September 2004 

Figure A-2. Growing Season Lengths for Representative Crops under Upper Bound Glacial Transition 
Climate Conditions 
ANL-MGR-MD-000006 REV 02 A-8 September 2004 

Fruits are a very diverse group. Melons and tomatoes are late spring or summer annuals. Other 
commonly consumed fruits are perennials, including orchard fruits (e.g., apples), vining grapes, 
and prostrate strawberries. 
Wheat and barley are grown during winter in Nye County, but barley often is grown as a spring 
crop in Washington. Feed corn and oats are spring-summer crops in both locations. 
Alfalfa hay is a perennial plant, and the common annual hays in Nye County (e.g., oats) 
generally are spring crops. Corn silage is planted in the spring. 
A4. PLANT SELECTION 
Based on the above information, three to seven plants were selected per crop type. The primary 
selection criterion was whether crops are grown in Nye County and eastern Washington. Once 
this was determined, the potential range of variation in crop type was considered. Information 
on crops commonly eaten in the United States was used to support the selection. Therefore, 
representative crops selected are those likely to be grown in the two regions of interest; 
representative of the range of variation in the crop type, but without having extreme values, and 
commonly eaten in the United States. Because the same crops can be grown in both climates 
considered, the same representative crops were selected for both conditions. However, different 
grasses were selected to represent home irrigation rates. Evergreen tree farms were observed in 
Amargosa Valley in 2004 (Rasmuson 2004 [DIRS 169506]) but were not included in the 
selection process because irrigation rates for evergreens are within the range established by low 
water-use vegetables (squash) and high water-use field crops (alfalfa). Additionally, the trees 
were drip irrigated, making them unimportant with respect to radionuclide deposition from 
irrigation water on leaves. 
Leafy vegetablesSeven locally grown leafy vegetables (Mills et al. no date [DIRS 124338]; 
Antonelli et al. 1998 [DIRS 158654]; (Horak and Carns 1997 [DIRS 124149], p. 5, Table 2 on 
p. 27) that were also commonly consumed (Table A-4) were selected: broccoli, cabbage, 
cauliflower, celery, head lettuce, leaf lettuce, and spinach. Asparagus was not selected because 
its growth form is not typical of leafy vegetables, its growing season length is extreme compared 
to other leafy vegetables, and it is not frequently consumed. 
Other vegetablesSeven locally grown other vegetables were selected: bell peppers, carrots, 
cucumbers, onions, potatoes, squash, and sweet corn (Horak and Carns 1997 [DIRS 124149], 
p. 5, Table 2 on p. 27; Mills et al. no date [DIRS 124338]). Six of these are the most commonly 
eaten other vegetables (Table A-4). The seventh, squash, was chosen instead of other commonly 
eaten vegetables (garlic and snap beans) because it was commonly mentioned in the food 
consumption survey for Amargosa Valley (DTN: MO0010SPANYE00.001 [DIRS 154976]). 
Peppers, cucumbers, and sweet corn were also commonly mentioned in the food consumption 
survey for Amargosa Valley (DTN: MO0010SPANYE00.001 [DIRS 154976]). 
FruitsFive locally grown fruits were selected: melons, tomatoes, apples, grapes, and 
strawberries (Horak and Carns 1997 [DIRS 124149], p. 5, Table 2 on p. 27; Mills et al. no date 
[DIRS 124338]). Peaches, plums, and pears, were not selected because they are similar to 
apples. Pistachios and other nuts were not selected because another tree (apples) was selected 

that has higher water use requirements (Allen et al. 1998 [DIRS 157311], Tables 11 and 12, 
pp. 104 to 108 and pp. 110 to 114). Tomatoes were commonly mentioned in the food 
consumption survey for Amargosa Valley (DTN: MO0010SPANYE00.001 [DIRS 154976]). 
GrainsWheat and barley were selected because they are the two most commonly grown grains 
in eastern Washington and were also grown in Nye County. Oats and feed corn were also 
selected because they are grown in both locations, although in small amounts. This selection 
includes both winter and spring/summer grains (Figures A-1 and A-2). 
Cattle forageAlfalfa was selected because it is the dominant crop in Amargosa Valley 
(Tables A-1 and A-2) and is the most common feed crop in eastern Washington (Table A-3). 
Oats and corn silage were also selected to include spring/summer hay and silage. 
To account for irrigation around homes and for landscapes, two turf grasses were selected. The 
recommended warm season grass, bermudagrass, was selected as representative for calculation 
of turf irrigation rates in southern Nevada (Morris and Johnson 1986 [DIRS 103033], 1991 
[DIRS 103034]). The cool season grass, tall fescue, was selected for eastern Washington, 
because warm season grasses generally are not grown there (Stahnke et al. 2001 
[DIRS 158675]). 

APPENDIX B 
JUSTIFICATION OF METHODS USED FOR 
CALCULATING IRRIGATION PARAMETERS 


B. JUSTIFICATION OF METHODS USED FOR CALCULATING 
IRRIGATION PARAMETERS 
This appendix contains a description of the relationship between photosynthesis and transpiration 
in terrestrial plants and how that relationship influences plant water use. Factors affecting water 
balance of a vegetated field are also discussed. This appendix also contains justification for use 
of FAO methodologies in Allen et al. (1998 [DIRS 157311]) for calculating evapotranspiration 
(ET) and irrigation parameters. Six commonly used methods for calculating ET are evaluated. 
B1. BACKGROUND INFORMATION 
Plant water usePhotosynthesis is the process by which light energy is used to drive the 
synthesis of organic compounds in plants. The photosynthetic process requires atmospheric 
carbon dioxide (CO2). To gain CO2 for photosynthesis, plants must lose water to the atmosphere. 
Carbon dioxide diffuses through small pores in the leaf surface (stomata) to intercellular spaces 
of the leaf, and to the photosynthetic cells (Figure B-1). Concurrently, water moves in the 
opposite direction, from wet cell membranes inside the leaf through open stomata to the 
atmosphere, a process called transpiration (Figure B-1). Because water and CO2 share the same 
diffusional pathway through the stomata, there is an inevitable cost of water for CO2 gain. 
Figure B-1. Leaf Cross Section Showing Diffusional Pathway for Carbon Dioxide (CO2) and Water (H2O) 

Water moves from the soil, through the plant, to the atmosphere down an increasingly negative 
water potential gradient (Figure B-2). Water potential is a thermodynamic parameter that 
describes the energy status of water in the soil-plant-atmosphere system (Brady and Weil 1999 
[DIRS 160019], pp. 178 and 179). The soil acts as a water reservoir with texture determining the 
water holding capacity. Soils with high clay and silt content hold water more tightly than sandy 
soils. Water enters the plant through the roots and moves in a column of high tensile water 
through specialized cells called xylem, and into the atmosphere through open stomata. Water 
flow through the soil-plant-atmosphere system represents important processes in the overall 
hydrologic cycle. 
NOTE: Water moves through the system along a gradient of increasingly negative water potentials. 
Figure B-2. Water Potential (MPa) In Various Components of the Soil-Plant-Atmosphere System 
When soil moisture is limiting, plants can reduce water loss through stomatal closure. However, 
stomatal closure also results in reducing the supply of CO2, which ultimately reduces plant 
productivity. In arid regions, approximately 400 to 700 units of water are lost for every unit of 
dry matter produced by a plant (Brady and Weil 1999 [DIRS 160019], pp. 227 to 228). This is 
because the diffusion gradient for water from inside the leaf to the atmosphere is orders of 
magnitude steeper than that for CO2 diffusion into the leaf. Water is required for photosynthesis 
and other metabolic processes; however, 95 - 99 percent of the water that passes through a plant 
is lost through transpiration (Nobel 1983 [DIRS 160500], p. 506). Thus, transpiration is an 
accurate estimate of water uptake by plant roots (Nobel 1983 [DIRS 160500], p. 506). Water is 

also lost from the soil and other surfaces (i.e., plant litter), through the process of direct 
evaporation. Direct evaporation from the soil generally occurs in the upper 0.15 to 0.20 m of the 
soil profile (Figure B-3). Evapotranspiration (ET) is the combined water loss through plant 
transpiration and direct evaporation. 
Figure B-3. Water Balance of a Cropped Field 
Plant water availability depends on soil texture, soil water potential, soil hydraulic conductivity, 
rooting depth, and species specific ability to extract moisture from the soil. When the rate of 
water absorption through the roots equals or exceeds ET, internal plant water balance is 
maintained, and carbon gain is not affected. If ET exceeds water absorption for a period of time, 
internal water deficits occur and plants wilt. Short-term water deficits can occur under periods of 
high air temperatures and low humidity. However, if soil moisture is available, plants can 
recover. As soil moisture is depleted, it becomes more difficult for plants to extract water, 
resulting in lower plant water potentials and reduced carbon gain. Without additional water, 
plants will permanently wilt. Therefore, in agricultural situations, irrigation water must be 
applied to a crop in time to prevent water stress from occurring if reduction in crop yield is to be 
avoided. 
Water balance of a cropped fieldTo prevent crop water stress, water entering a plot of 
vegetated land must equal that leaving. Water enters the system through precipitation, irrigation, 
surface and subsurface flow in, and capillary rise from the water table (Allen et al. 1998 
[DIRS 157311], p. 12) (Figure B-3). Water leaves the system through ET, runoff, subsurface 
flow out, and percolation below the root zone (Figure B-3). 

Fluxes such as subsurface flow on or off a vegetated plot of land, or capillary rise from a water 
table are difficult to measure and are usually ignored. Thus, methods for assessing the 
appropriate amount of irrigation water required to avoid crop water stress rely on estimates of 
crop evapotranspiration (ETc), precipitation, and the storage capacity of the soil within the crop 
rooting zone. 
Commonly, ET of a grass or alfalfa reference surface (ETo) is calculated and used with a crop 
specific coefficient (Kc) to estimate ETc (Doorenbos and Pruitt 1977 [DIRS 103062], p. 37; 
Allen et al. 1998 [DIRS 157311], p. 89; Jensen et al. 1990 [DIRS 160001], p.114). Climatic 
influences on ET are incorporated into ETo and crop specific influences on ET are reflected in Kc 
values. The FAO first published a procedure using the Kc ETo approach for calculating ETc 
(Doorenbos and Pruitt 1977 [DIRS 103062]). Four alternative methods for calculating ETo were 
suggested. Since this publication, advances in research and understanding of crop water 
requirements revealed the need to revise and update the calculation procedures (Allen et al. 1998 
[DIRS 157311], pp. 15 to 18). Improvements were identified and incorporated in the FAO 
Irrigation and Drainage Paper 56, Crop Evapotranspiration (Allen et al. 1998 [DIRS 157311], 
pp. 15 to 18). The methods for calculating crop water requirements and irrigation supply 
requirements presented in Allen et al. (1998 [DIRS 157311]) and Doorenbos and Pruitt (1977 
[DIRS 103062]) were used in this analysis report. 
B2. JUSTIFICATION FOR USE OF FAO METHODS 
There is a long history of the study of ET that dates back to the late 1800s (Jensen et al. 1990 
[DIRS 160001], p. 4). One of the advancements in estimating ET occurred when Penman 
developed an equation to estimate evaporation from a free water surface using energy balance 
and mass transfer concepts (Allen et al. 1998 [DIRS 157311], pp. 18 and 19). This free water 
surface was originally proposed as a reference surface. However, differences in aerodynamics, 
water vapor diffusion, and radiation characteristics between open water and a vegetated surface 
made relating ET to free water evaporation difficult (Allen et al. 1998 [DIRS 157311], p. 23). 
The approach was later modified by Penman to apply to leaf surfaces, and then by Monteith to 
apply to whole plant canopies (Equation B-1). The Penman-Monteith equation (Allen et al. 1998 
[DIRS 157311], Equation 3, p. 19) used net radiation balance, ambient temperature, vapor 
pressure deficit, conductance at the soil or canopy surface, and leaf or canopy conductance to 
characterize the rate of water loss from a vegetated surface: 
sa
.( R - G)+.cp 
( e - e ) 
na 
a
ET = 
r 
(Eq. B-1) 
.. 
s
... 
+ . .. 1 + 
r ..
.
.. 
.. ra .. 
where 
ET = evapotranspiration (mm/day), 
Rn = net radiation energy (MJ m-2 day-1), 
G = soil heat flux (MJ m-2 day-1), 
. a = density of air (kg m-3), 

c
p = specific heat of air (MJ kg-1 C-1), 
es - ea = vapor pressure deficit (kPa), 
. = slope of saturation vapor pressure curve (kPa C-1), 
. = latent heat of vaporization (MJ kg-1), 
. = psychrometric constant (kPa C-1), 
r
s and ra = (bulk) surface and aerodynamic resistances (s m-1). 
A variety of modifications to the Penman-Monteith equation have been developed to provide 
ease of calculation, or to provide methods for ET calculation when the data required for the 
Penman-Monteith are not available. Several of these methods for calculating ETo can provide 
reasonable predictions of ET for specific environmental circumstances. Several methods for 
estimating ETo have been evaluated in various comparative studies (see Jensen et al. 1990 
[DIRS 160001], pp. 164 to 265; Martin et al. 1991 [DIRS 101081]; Ventura et al. 1999 
[DIRS 159871] for examples). In a comprehensive evaluation of 20 different methods for 
estimating ETo, Jensen et al. (1990 [DIRS 160001], pp. 164 to 265) compared calculated ETo 
values to measured ET in 11 variable climate locations. 
Published evaluations of six commonly used methods are described below to show that the 
selected FAO methods (presented in Appendices C, D, and E) lessen the uncertainties in 
irrigation parameters compared to the alternatives. 
B2.1 THORNTHWAITE FORMULA 
The Thornthwaite formula, based on air temperature, is one of the simplest approaches for 
calculating potential ET (Martin et al. 1991 [DIRS 101081]): 
a 
PE =6.1 
(10T ) 
(Eq. B-2) 
I 
where 
PE = potential evapotranspiration (mm), 
T = mean monthly temperature (C), 
I = heat index, constant for a site, function of long term mean temperatures, 
a = an empirical derived value that is function of I. 
However, it has limited applicability and its recommended use is restricted to climates similar to 
that of the east-central region of the United States (Martin et al. 1991 [DIRS 101081]; Jensen 
et al. 1990 [DIRS 160001], pp. 225 to 235). Jensen et al. 1990 ([DIRS 160001], pp. 225 to 235) 
showed that the Thornthwaite formula consistently underestimated ET at arid locations and was 
one of the poorest methods in estimating ETo when compared to measured ET. Therefore, the 
Thornthwaite formula was considered inadequate for this analysis. 

B2.2 BLANEY-CRIDDLE 
The Blaney-Criddle method (Equation B-3), also based on air temperature, was modified in 
Doorenbos and Pruitt 1977 ([DIRS 103062], p. 3) to develop a grass reference method for 
estimating ETo: 
ET=k1 + k 2 ( pT /100) (Eq. B-3) 
o 
where 
ETo = daily ET for a grass reference crop (mm/day), 
T = average air temperature (C), 
p = percent of annual sunlight, 
k1 and k2 = adjustment coefficients for the FAO method (dimensionless). 
Jensen et al. 1990 ([DIRS 160001], p. 235) showed that this method tended to overestimate ETo 
by 15 to 25 percent in humid climates, but provided good estimates in arid climates when 
compared to measured data. Martin et al. 1991 ([DIRS 101081], p. 333) suggested that the 
modified Blaney-Criddle (Equation B-3) should only be considered an approximate method for 
determining ETo for irrigation scheduling, and that other methods were preferable if appropriate 
atmospheric data were available. Therefore, the Blaney-Criddle method was not selected for this 
analysis. 
B2.3 JENSEN-HAISE 
The Jensen-Haise equation is an energy balance approach used to predict ETo for an alfalfa 
reference surface (Martin et al. 1991 [DIRS 101081], Equation 2, p. 334; Jensen et al. 1990 
[DIRS 160001], p. 166): 
ET =CT (T - T )R /1486 (Eq. B-4) 
o xs 
where 
e
CT = 1/(C1 + C2CH), 
C1 = 68 - 3.6(elevation in feet)/1,000 
C2 = 13, F (a constant), 
CH = 50/(e2 - e1), mbars 
Tx = 27.5 - 0.25(e2 - e1) - elevation/1,000, 
2 = saturated vapor pressure (mbars) at the mean maximum air temperature for the 
e
hottest month, 
1 = Saturated vapor pressure (mbars) at the mean minimum air temperature for the 
hottest month, 
Rs = Incoming solar radiation, langleys/day 
T = Average monthly air temperature, F. 

This equation uses air temperature, incoming solar radiation, and air humidity to calculate ETo. 
Elevation and latitude are used to correct for local conditions. It is more reliable for arid 
climates than Blaney-Criddle because of the inclusion of solar radiation and adjustments for 
local conditions (Martin et al. 1991 [DIRS 101081]). It was less reliable in semiarid to subhumid 
climates where it tended to underestimate ETo when compared to measured data (Jensen et al. 
1990 [DIRS 160001], p. 235). 
Use of a grass reference surface as opposed to the alfalfa reference surface used in Jensen-Haise 
was preferred in this analysis because published Kc values for a grass reference were available 
for all of the representative crops. Additionally, because the Jensen-Haise method tended to 
underestimate ETo in semi-arid and sub-humid climates, it would likely underestimate ETo for 
the future climates (upper bound monsoon, lower bound glacial transition and upper bound 
glacial transition climates) required for this analysis. 
B2.4 PRIESTLEY-TAYLOR 
The Priestley-Taylor method is a simplification of the Penman-Montieth equation with the 
absence of an advection term for sensible heat energy (Jensen et al. 1990 [DIRS 160001], 
Equation 6.35, p. 100): 
ET0=a. ( R - G) (Eq. B-5) 
n
+ . . 
where 
a = constant ranging from 1.08 to 1.34 depending on the crop and location, 
. = slope of the saturation vapor pressure-temperature curve (kPa  C-1), 
. = psychrometric constant (kPa  C-1), 
Rn = net radiation (MJ m-2 day-1), 
G = soil heat flux (MJ m-2 day-1). 
The equation was developed to predict ETo for a grass reference under humid conditions with a 
wet grass surface (Jensen et al. 1990 [DIRS 160001], p. 100). Hatfield and Allen (1996 
[DIRS 159872]) compared the results of the Priestley-Taylor method and the Penman-Montieth 
equation (Equation B-1) with measured ET under arid conditions. They found the 
Penman-Montieth model tracked actual ET for cotton, grain sorghum, and grass forage better and 
more consistently throughout the growing season than the Priestley-Taylor method. When 
compared to measured ET, the Priestley-Taylor method produced reasonably good estimates in 
humid locations; however, it substantially underestimated seasonal ET in arid climates (Jensen et 
al. 1990 [DIRS 160001], p. 235) making it inappropriate for this analysis. 
B2.5 FAO CORRECTED PENMAN 
The FAO corrected Penman equation (Equation B-6) was modified from the original 
Penman-Montieth equation (Equation B-1) by Doorenbos and Pruitt (1977 [DIRS 103062]) to 
estimate ETo for a grass reference surface. A more sensitive wind function, an adjustment factor 

for local weather conditions (c), and an assumption that soil heat flux (G) equals 0 for daily time 
frames were added to the original Penman: 
. o .
ETo = c 
. .( R - G)+ 7.2 Wf (e- ez ).
. n z
. + ...+. . (Eq. B-6) 
where 
c = adjustment factor to compensate for local climate conditions 
(dimensionless), 
. = slope of the saturation vapor pressure-temperature curve (kPa  C-1), 
. = psychrometric constant (kPa  C-1), 
Rn = net radiation (MJ m-2 day-1), 
G = soil heat flux (MJ m-2 day-1), 
Wf = temperature related weighting factor (dimensionless), 
o
ez -ez = difference between the saturation vapor pressure at mean air temperature 
and the mean actual vapor pressure of the air (kPa). 
The Penman-Monteith and FAO corrected Penman equations (Equations B-1 and B-6) were 
fairly well correlated with measured ET data in 10 of 11 sites studied by Jensen et al. (1990 
[DIRS 160001], p. 234). However, the FAO corrected Penman equation consistently 
overestimated ETo under both humid and arid conditions (Jensen et al. (1990 [DIRS 160001], 
p. 234; Allen et al. 1998 [DIRS 157311], p. 17). 
The variable results of these and other validation studies prompted the FAO to elicit scientists 
and specialists to establish recommendations for an ETo formula that was generally applicable 
under a wide variety of conditions without the need for extensive local calibrations (see 
Allen et al. 1998 [DIRS 157311], pp. v, 17, and 18). The consultations and recommendations 
resulted in revised methodologies that are published by the FAO in Allen et al. (1998 
[DIRS 157311]). The FAO Penman-Monteith method is currently recommended as the standard 
for calculating ETo (Allen et al. 1998 [DIRS 157311]). Based on this recommendation and the 
results of studies by Jensen et al. (1990 [DIRS 160001]), Martin et al. (1991 [DIRS 101081]), 
and Hatfield and Allen (1996 [DIRS 159872]), it was determined that the FAO methodologies in 
Allen et al. (1998 [DIRS 157311]) would reduce the uncertainties in irrigation parameters 
compared to other methods, and produce valid, reasonable parameter values. The FAO Penman-
Monteith equation (Equation C-1) and FAO methodologies are presented in Appendices C, D, 
and E. 

APPENDIX C 
METHODS FOR CALCULATING REFERENCE EVAPOTRANSPIRATION 


C. METHODS FOR CALCULATING REFERENCE EVAPOTRANSPIRATION 
C1. INTRODUCTION 
Reference evapotranspiration (ETo) was calculated for a grass reference surface and represents 
the effects of climate on crop ET. The reference surface as defined by Allen et al. (1998 
[DIRS 157311], p. 15) is a hypothetical grass reference crop with an assumed crop height of 
0.12 m, a fixed surface resistance of 70 s m-1 and an albedo of 0.23. It is considered to be of 
uniform height, actively growing, completely shading the ground, with an adequate water supply. 
Meteorological factors that drive evapotranspiration include solar radiation, air temperature, air 
humidity, and wind speed. Climatological and physical parameters required to derive monthly 
mean ETo were either measured directly or derived from standard meteorological data. Weather 
data inputs are described in Section 4.1.5 (Tables 4.1-2, 4.1-3, 4.1-4, and 4.1-5). Monthly mean 
ETo was calculated for present-day, upper bound monsoon, lower bound glacial transition, and 
upper bound glacial transition climate conditions. Altitude and latitude of the YMP 
meteorological monitoring Site 9 were used in calculations for all climate conditions. 
The FAO Penman-Monteith equation was used to calculate ETo (Allen et al. 1998 
[DIRS 157311], Equation 6, p. 24): 
900 
ET = 
408.0 . (R - G) +.T + 273 
u2(e - e ) 
(Eq. C-1) 
n sa 
o 
+ . . (1+ 34.0 u )
2 
where 
ETo = reference evapotranspiration (mm/day), 
Rn = net radiation at the crop surface (MJ m-2 day-1), 
G = soil heat flux density (MJ m-2 day-1), 
T = mean daily air temperature at 2 m height ( C), 
u
2 = wind speed at 2 m height (m s-1), 
e
s = saturation vapor pressure (kPa), 
e
a = actual vapor pressure (kPa), 
es -ea = saturation vapor pressure deficit (kPa), 
. = slope vapor pressure curve (kPa  C-1), 
. = psychrometric constant (kPa  C-1). 
Justification for use of this equation is in Appendix B. The step-by-step methods to calculate 
ETo are described and example calculations are provided below. 
C2. CALCULATIONS 
Several calculations related to humidity and radiation parameters are required to generate the 
variables used in Equation C-1. 

C2.1 HUMIDITY 
Atmospheric humidity is an important driver of transpiration from plant leaves. The air in the 
intercellular spaces of a leaf (Appendix B, Figure B-1) is nearly saturated with water vapor. As 
the air outside the leaf dries, the leaf to air water vapor gradient increases, increasing the rate of 
water loss through the stomata (Appendix B, Figure B-2). With increasing evaporative demands 
the plant will begin to close stomata to prevent water loss. However, stomatal closure also 
results in reduced concentrations of CO2 for use in photosynthesis (see Appendix B for 
additional background). Similarly, when atmospheric humidity is high, the leaf-to-air water 
vapor gradient decreases. This results in lower evaporative demand, allowing stomates to remain 
open without high rates of water loss. 
Three atmospheric parameters were generated from meteorological data and used directly in the 
calculation of ETo. These include the slope of the saturation vapor pressure curve (. ),the 
psychrometric constant (. ), and the vapor pressure deficit (es - ea). 
C2.1.1 Slope of Saturation Vapor Pressure Curve (. ) 
. is the slope of the relationship between the saturation vapor pressure of the air and air 
temperature. Vapor pressure is the component of total atmospheric pressure exerted by the 
motion of water vapor molecules. Saturation vapor pressure is the vapor pressure the air would 
have if it were saturated with water vapor molecules at a given temperature. As temperature 
increases, the storage capacity of the air increases, which results in higher saturation vapor 
pressure. . is calculated from mean monthly air temperature ( C) according to the following 
equation (Allen et al. 1998 [DIRS 157311], Equation 13, p. 37): 
. 
. exp 6108.0 .. 
27.17 T..
.. 
= . 4098 .. T + 3.237 .. (Eq. C-2) 
.( T + 3.237 ) 2 . 
..
.. 
where 
exp(x) = 2.7183 (base of natural logarithm) raised to the power (x), 
T = mean monthly air temperature ( C). 
Example: 
For January present-day climate conditions, T = 7.0  C (see Table 4.1-2). 
. exp 6108.0 .. 
27.17  0.7 ..
.
. 0.7 + 3.237 .
.
.. 
= 4098.. 
6108.0  6379.1 .
= . 4098 
.. 
( 0.7 + 3.237 ) 2 .. 49.682,59 .. 
= 0.069 kPa  C-1 (Eq. C-2) 
..
.. 

Monthly mean . values for present-day, upper bound monsoon, lower bound glacial transition, 
and upper bound glacial transition climate conditions are in Tables C-1, C-2, C-3, and C-4 
respectively. 
Table C-1. Atmospheric Parameters for Present-Day Climate Conditions 
Month Tdew (oC) eo 
Tmax (kPa) eo 
Tmin (kPa) es (kPa) . (kPa oC-1) ea (kPa) es - ea (kPa) 
January 1.1 1.547 0.661 1.104 0.069 0.661 0.443 
February 2.0 1.877 0.758 1.317 0.080 0.706 0.612 
March 2.8 2.564 0.922 1.743 0.101 0.747 0.996 
April 5.0 3.093 1.073 2.083 0.121 0.872 1.211 
May 9.9 4.268 1.488 2.878 0.162 1.220 1.658 
June 13.8 5.717 1.913 3.815 0.212 1.578 2.237 
July 18.1 7.067 2.502 4.785 0.258 2.077 2.708 
August 18.0 6.954 2.487 4.721 0.249 2.064 2.656 
September 13.8 5.260 1.913 3.587 0.193 1.578 2.009 
October 6.7 3.342 1.203 2.273 0.128 0.981 1.291 
November 2.7 2.103 0.796 1.450 0.085 0.742 0.708 
December 0.8 1.588 0.647 1.118 0.068 0.647 0.470 
Tdew = dewpoint temperature (Equation C-5). 
o
eTmax = saturation vapor pressure at the maximum monthly air temperature (Equation C-3). 
o
eTmin = saturation vapor pressure at the maximum monthly air temperature (Equation C-3). 
es = saturation vapor pressure (Equation C-4). 
. = .
the slope of the saturation vapor pressure curve (Equation C-2). 
ea = actual vapor pressure (Equation C-6). 
es - ea = vapor pressure deficit (Equation C-8). 
Table C-2. Atmospheric Parameters for Upper Bound Monsoon Climate Conditions 
Month Tdew (oC) eo 
Tmax (kPa) eo 
Tmin (kPa) es (kPa) . (kPa oC-1) ea (kPa) es - ea (kPa) 
January -3.7 2.028 0.500 1.264 0.071 0.464 0.800 
February -2.2 2.267 0.559 1.413 0.078 0.519 0.894 
March 0.1 2.617 0.662 1.640 0.090 0.616 1.024 
April 2.6 3.307 0.788 2.047 0.108 0.734 1.313 
May 6.3 4.270 1.025 2.648 0.135 0.957 1.691 
June 11.4 5.745 1.439 3.592 0.178 1.347 2.245 
July 16.5 5.486 2.000 3.743 0.199 1.877 1.866 
August 16.2 5.157 1.958 3.558 0.192 1.838 1.720 
September 12.2 4.845 1.514 3.180 0.168 1.418 1.762 
October 5.4 3.743 0.961 2.352 0.124 0.896 1.456 
November -0.7 2.644 0.626 1.635 0.088 0.582 1.053 
December -3.4 2.071 0.512 1.292 0.072 0.476 0.816 
Tdew = dewpoint temperature (Equation C-5). 
o
eTmax = saturation vapor pressure at the maximum monthly air temperature (Equation C-3). 
o
eTmin = saturation vapor pressure at the maximum monthly air temperature (Equation C-3). 
es = saturation vapor pressure (Equation C-4). 
. = .
the slope of the saturation vapor pressure curve (Equation C-2). 
ea = actual vapor pressure (Equation C-6). 
es - ea = vapor pressure deficit (Equation C-8). 
ANL-MGR-MD-000006 REV 02 C-3 September 2004 

Table C-3. Atmospheric Parameters for Lower Bound Glacial Transition Climate Conditions 
Month eo 
Tmax (kPa) eo 
Tmin (kPa) es (kPa) . (kPa oC-1) ea (kPa) es - ea (kPa) 
January 0.807 0.286 0.546 0.036 0.326 0.221 
February 0.979 0.355 0.667 0.044 0.371 0.296 
March 1.372 0.478 0.925 0.058 0.404 0.521 
April 2.035 0.689 1.362 0.081 0.541 0.822 
May 2.867 0.942 1.904 0.108 0.662 1.242 
June 3.980 1.228 2.604 0.140 0.689 1.915 
July 5.452 1.705 3.579 0.186 1.057 2.522 
August 5.125 1.587 3.356 0.175 0.907 2.449 
Sept 3.854 1.097 2.476 0.132 0.746 1.730 
October 2.338 0.717 1.528 0.088 0.587 0.941 
November 1.274 0.421 0.848 0.053 0.458 0.390 
December 0.907 0.326 0.616 0.041 0.371 0.245 
o
eTmax = saturation vapor pressure at the maximum monthly air temperature (Equation C-3). 
o
eTmin = saturation vapor pressure at the maximum monthly air temperature (Equation C-3). 
es = saturation vapor pressure (Equation C-4). 
. = .
the slope of the saturation vapor pressure curve (Equation C-2). 
ea = actual vapor pressure (Equation C-6). 
es - ea = vapor pressure deficit (Equation C-8). 
Table C-4. Atmospheric Parameters for Upper Bound Glacial Transition Climate Conditions 
Month eo 
Tmax (kPa) eo 
Tmin (kPa) es (kPa) . (kPa oC-1) ea (kPa) es - ea (kPa) 
January 0.641 0.384 0.512 0.037 0.418 0.094 
February 0.859 0.476 0.667 0.047 0.498 0.169 
March 1.127 0.554 0.840 0.056 0.529 0.312 
April 1.587 0.681 1.134 0.072 0.611 0.523 
May 2.167 0.903 1.535 0.093 0.781 0.754 
June 2.934 1.192 2.063 0.120 0.975 1.088 
July 3.867 1.444 2.655 0.148 1.011 1.645 
August 3.793 1.439 2.616 0.146 0.984 1.632 
Sept 2.680 1.049 1.864 0.109 0.828 1.036 
October 1.681 0.717 1.199 0.075 0.695 0.504 
November 0.886 0.536 0.711 0.050 0.570 0.141 
December 0.657 0.399 0.528 0.038 0.448 0.080 
o
eTmax = saturation vapor pressure at the maximum monthly air temperature (Equation C-3). 
o
eTmin = saturation vapor pressure at the maximum monthly air temperature (Equation C-3). 
es = saturation vapor pressure (Equation C-4). 
. = .
the slope of the saturation vapor pressure curve (Equation C-2). 
ea = actual vapor pressure (Equation C-7). 
es - ea = vapor pressure deficit (Equation C-8). 

C2.1.2 Psychrometric Constant (. ) 
The psychrometric constant represents a balance between the heat required to evaporate water 
into an air stream from the wick of a wet bulb thermometer (wet wick with thermometer beneath 
it) and the airs potential to absorb the water and carry it away. The constant is dependent on 
atmospheric pressure, latent heat of vaporization (energy required for evaporation), the specific 
heat of air at a constant pressure (quantity of energy required to raise the temperature of a given 
amount of air by one degree at constant pressure), and the ratio of molecular weight of water 
vapor to dry air. Values for . at different altitudes are provided in Allen et al. (1998 
[DIRS 157311], Table 2.2, p. 214). The weather station altitude of 838 m for the Yucca 
Mountain meteorological monitoring Site 9 (data for present-day climatic conditions) 
corresponds to a table value for . of 0.061 kPa  C-1. This value was used in the calculations of 
ETo for present-day, upper bound monsoon, lower bound glacial transition, and upper bound 
glacial transition climates. 
C2.1.3 Vapor Pressure Deficit (es - ea) 
The vapor pressure deficit (es -ea) is the difference between the saturation vapor pressure (es) 
and the actual vapor pressure (ea) of the air. Essentially, it is the amount of water vapor that the 
air could still hold before becoming saturated and represents the evaporative power of the air. 
The air becomes dryer as the vapor pressure deficit increases. 
Mean es is calculated from mean monthly maximum (Tmax) and minimum (Tmin) air temperatures 
(see Tables 4.1-2, 4.1-3, 4.1-4, and 4.1-5 for temperature data). The relationship of es to 
temperature is given by the following (Allen et al. 1998 [DIRS 157311], Equations 11 and 12, 
p. 36): 
)(Teo= .. 
. 
.. 
. 
+ 3.237 
27.17 exp 6108.0 
T 
T (Eq. C-3) 
where 
o
e(T) = saturation vapor pressure at temperature T (kPa), 
T = air temperature ( C), 
exp(x) = 2.7183 (base of natural logarithm) raised to the power (x), 
and 
oo 
max min
e =e ( T )+ e ( T ) 
(Eq. C-4) 
s 
2 
Example: For January present-day climate conditions, Tmax = 13.5  C and Tmin = 1.1  C 
(Table 4.1-2). 
(
eo(T ) = exp 6108.0 . 5.13 27.17 ). = 0.6108 exp 0.929 = 1.547 kPa (Eq. C-3)
max
.. 
5.13 + 3.237 .. 

(). = 0.6108 exp 0.0797 = 0.661 kPa (Eq. C-3)eo(Tmin) = exp 6108.0 . 1.1 27.17 
.. 
1.1 + 3.237 .. 
547.1 + 661.0 
e= = 1.104 kPa (Eq. C-4)
s 
2 
oo
Monthly e(Tmax), e(Tmin), and es for present-day, upper bound monsoon, lower bound glacial 
transition, and upper bound glacial transition climate conditions are in Tables C-1, C-2, C-3, and 
C-4, respectively. 
Actual vapor pressure (ea) can be calculated from relative humidity (RH), the dewpoint 
temperature (Tdew), or psychrometric data. No air humidity data were available for the upper 
bound monsoon climate and so Tdew was calculated from Tmin using equation C-5 (see below). 
Monthly mean Tdew was available from the Delta, Utah weather station (lower bound glacial 
transition climate analogue). Monthly mean Tdew was used in equation C-6 to calculate ea for the 
upper bound monsoon and lower bound glacial transition climates (see below). Monthly mean 
maximum and minimum RH values were available for both present-day and upper bound glacial 
transition climates. However, examination of RH values for the present-day climate indicated 
that these values were not representative of the expected conditions of the reference area for 
which ETo was calculated (see Allen et al. 1998 [DIRS 157311], Annex 6, pp. 257 to 262). 
Under reference area conditions, RHmax is expected to approach 90 - 100 percent. For the 
present-day climate, mean RHmax ranged from a low of 23.9 percent in July to a high of 62.2 
percent in January (Sections 4.1.3.1, Table 4.1-2). Use of such low RH values would result in 
overestimation of ETo, which would translate into overestimation of crop irrigation requirements. 
Allen et al. (1998 [DIRS 157311], p. 36 and Annex 6, pp. 257 to 262) recommended use of Tdew 
calculated from daily minimum temperature rather than using unreliable or unrepresentative RH 
values, or when no humidity data is available. Therefore, instead of using RHmax to calculate ea 
for the present-day climate and in the absence of humidity data for the upper bound monsoon 
climate, Tdew was estimated from Tmin (Allen et al. 1998 [DIRS 157311], Equation 6-6, p. 261): 
T
dew = Tmin- K (Eq. C-5) 
o 
Where Ko is a correction factor ( C). Values selected for Ko were 0  C for January, 1  C for 
February, 3  C for March through October, 1  C for November, and 0  C for December for 
present-day climate, and Ko = 1  C for all months for upper bound monsoon climate. Different 
values for Ko were used for present-day climate because the extreme aridity could cause the 
minimum temperature to be significantly greater than the dewpoint temperature in spring through 
fall months. Smaller differences between minimum temperature and dewpoint were expected 
during the same time period for the moister monsoon climate. The monsoon climate has warmer 
(more evaporative) winter seasons than present-day climate making the 1  C adjustment 
appropriate for December and January. These adjustments increased values of Tdew to reflect the 
higher humidity anticipated under reference conditions. The adjusted Tdew was used in the 
following equation to calculate ea for the present-day and upper bound monsoon climates (Allen 
et al. 1998 [DIRS 157311], Equation 14, p. 37): 

o
e= e ( T )= exp 6108.0 
.. 
27.17 Tdew . 
(Eq. C-6) 
a dew . 
. Tdew + 3.237 . 
Example: For January present-day climate Tmin = 1.1  C (Section 4.1.5.1, Table 4.1-2) and 
Tdew = 1.1  C (Equation C-5). 
e=exp 6108.0 . 27.17  1.1 .= 0797.0 exp 6108.0 )= 661.0 kPa (Eq. C-6) 
a 
.. 
1.1 + 3.237 .. 
( 
Because RHmax from the upper bound glacial transition climate data set approached 90 percent 
for most months (Section 4.1.5.4, Table 4.1-5) no correction was needed. Therefore, RHmin, 
RHmax, Tmin, and Tmax were used to calculate ea (Allen et al. 1998 [DIRS 157311], Equation 17, 
p. 38): 
) RH 
e
o max
( T + eo ( T ) RH min 
min max 
e =100 100 (Eq. C-7) 
a 
2 
Example: For January upper bound glacial transition climate RHmin = 79 percent and RHmax = 
oo86 percent (Section 4.1.5.4, Table 4.1-5), e(Tmin) = 0.384 and e(Tmax) = 0.641 (from Table C-4). 
330.0 + 506.0 
e=86(.384.0 )+ 79(.641.0 )== 418.0 kPa (Eq. C-7) 
a 
22 
Monthly ea values for present-day, upper bound monsoon, lower bound glacial transition, and 
upper bound glacial transition climates are in Tables C-1,C-2, C-3, and C-4 respectively. 
Using mean es and ea calculated for January present-day climate conditions, the vapor pressure 
deficit is: 
(es -ea) = 1.104 - 0.661 = 0.443 kPa (Eq. C-8) 
Monthly (es - ea) values for present-day, upper bound monsoon, lower bound glacial transition, 
and upper bound glacial transition climates are in Tables C-1, C-2, C-3, and C-4, respectively. 
C2.2 RADIATION 
Net radiant energy is one of the main factors controlling the energy balance of a vegetated soil 
surface. Heat energy for ET is principally supplied by solar radiation, which can reach the plant 
canopy as direct sunlight, or sunlight scattered by molecules and particles in the atmosphere. 
Both direct and scattered sunlight can be reflected by surroundings to the plant canopy. Net 
radiation (Rn) represents the balance between energy absorbed, reflected, and emitted by the 
earths surface and is used directly in the calculation of ETo. Extraterrestrial radiation (Ra), solar 
radiation (Rs), relative sunshine duration (n/N), clear sky radiation (Rso), net shortwave radiation 
(Rns), and net longwave radiation (Rnl) are required either directly or indirectly in calculating Rn. 

C2.2.1 Extraterrestrial Radiation (Ra) 
Extraterrestrial radiation is the solar radiation received at the top of the earths atmosphere on a 
horizontal surface. It is a function of latitude, date, and time of day. Allen et al. (1998 
[DIRS 157311], Annex 2, Table 2.6, p. 219) provide Ra values for the 15th day of each month 
for different latitudes. These values provide an estimate of Ra that deviates from the monthly 
average by less than 1 percent. Because the latitude will not change among climate conditions, 
latitude for the weather station representing present-day climate was used and Ra was the same 
for all climate conditions. 
Example: Weather station latitude for the present-day climate was 36 40 38 (Table 4.1-2). 
From Table 2.6 (Allen et al. 1998 [DIRS 157311], p. 219), Ra for January at the station latitude is 
17.5 MJ m-2 day-1. 
Monthly Ra averages are in Tables C-5, C-6, C7, and C8. 
Table C-5. Radiation Parameters, Soil Heat Flux, and Wind Speed for Present-Day Climate Conditions 
Month 
Ra 
(MJ m-2 
day-1) 
Rs 
(MJ m-2 
day-1) 
Rso 
(MJ m-2 
day-1) 
Rns 
(MJ m-2 
day-1) 
Rnl 
(MJ m-2 
day-1) 
Rn 
(MJ m-2 
day-1) 
G 
(MJ m-2 
day-1) 
u2 
(m s-1) 
January 17.5 9.6 13.4 7.4 4.2 3.2 0.19 2.9 
February 22.6 13.9 17.3 10.7 5.1 5.6 0.46 3.2 
March 29.0 19.5 22.2 15.0 6.1 8.9 0.50 3.3 
April 35.7 24.6 27.4 18.9 6.2 12.7 0.60 3.5 
May 40.0 27.5 30.7 21.2 5.9 15.2 0.74 3.4 
June 41.7 30.0 32.0 23.1 5.9 17.2 0.64 3.7 
July 40.8 29.6 31.3 22.8 5.3 17.4 0.22 3.4 
August 37.4 27.0 28.7 20.8 5.3 15.5 -0.41 3.5 
Sept 31.5 22.8 24.2 17.6 5.9 11.6 -0.90 3.3 
October 24.6 17.4 18.9 13.4 6.4 7.0 -1.04 3.1 
November 18.7 11.9 14.3 9.2 5.4 3.7 -0.76 3.0 
December 16.1 9.6 12.3 7.4 4.8 2.5 -0.25 3.0 
Ra = extraterrestrial radiation (from Allen et al. [1998 DIRS 157311], Annex 2, Table 2.6, p. 219). 
Rs = solar radiation (from Table 4.1-2). 
Rso = clear sky radiation (Equation C-10). 
Rns = net solar radiation (Equation C-11). 
Rnl = net longwave radiation (Equation C-12). 
Rn = net radiation (Equation C-13). 
G = solar heat flux (Equation C-14). 
u2 = wind speed (from Table 4.1-2) corrected for height according to Equation C-15. 

Table C-6. Radiation Parameters, Soil Heat Flux, and Wind Speed for Upper Bound Monsoon Climate 
Conditions 
Month 
Ra 
(MJ m-2 
day-1) n/N 
Rs 
(MJ m-2 
day-1) 
Rso 
(MJ m-2 
day-1) 
Rns 
(MJ m-2 
day-1) 
Rnl 
(MJ m-2 
day-1) 
Rn 
(MJ m-2 
day-1) 
G 
(MJ m-2 
day-1) 
u2 
(m s-1) 
January 17.5 0.80 11.4 13.4 8.8 6.0 2.8 0.09 1.5 
February 22.6 0.82 14.9 17.3 11.5 6.1 5.4 0.28 2.2 
March 29.0 0.86 19.7 22.2 15.2 6.3 8.9 0.38 2.2 
April 35.7 0.92 25.3 27.4 19.5 6.7 12.8 0.51 2.2 
May 40.0 0.93 28.6 30.7 22.0 6.6 15.4 0.65 2.3 
June 41.7 0.93 29.8 32.0 23.0 6.2 16.7 0.51 2.2 
July 40.8 0.78 26.1 31.3 20.1 4.5 15.6 0.10 1.7 
August 37.4 0.80 24.3 28.7 18.7 4.7 14.1 -0.23 1.5 
Sept 31.5 0.87 21.6 24.2 16.6 5.6 11.0 -0.58 1.7 
October 24.6 0.88 17.0 18.9 13.1 6.3 6.8 -0.81 1.8 
November 18.7 0.85 12.6 14.3 9.7 6.3 3.4 -0.65 1.4 
December 16.1 0.79 10.4 12.3 8.0 5.9 2.1 -0.26 1.7 
Ra = extraterrestrial radiation (from Allen et al. [1998 DIRS 157311], Annex 2, Table 2.6, p. 219). 
n/N = percent of possible sunshine converted to decimal value (from Table 4.1-3). 
Rs = solar radiation (from Equation C-9). 
Rso = clear sky radiation (Equation C-10). 
Rns = net solar radiation (Equation C-11). 
Rnl = net longwave radiation (Equation C-12). 
Rn = net radiation (Equation C-13). 
G = solar heat flux (Equation C-14). 
u2 = wind speed (from Table 4.1-3) corrected for height according to Equation C-15. 

Table C-7. Radiation Parameters, Soil Heat Flux, and Wind Speed for Lower Bound Glacial Transition 
Climate Conditions 
Month 
Ra 
(MJ m-2 
day-1) n/N 
Rs 
(MJ m-2 
day-1) 
Rso 
(MJ m-2 
day-1) 
Rns 
(MJ m-2 
day-1) 
Rnl 
(MJ m-2 
day-1) 
Rn 
(MJ m-2 
day-1) 
G 
(MJ m-2 
day-1) 
u2 
(m s-1) 
January 17.5 0.58 9.45 13.4 7.3 4.1 3.2 0.08 3.6 
February 22.6 0.64 12.88 17.3 9.9 4.5 5.4 0.51 3.5 
March 29.0 0.63 16.39 22.2 12.6 4.7 7.9 0.70 3.8 
April 35.7 0.69 21.24 27.4 16.4 5.2 11.1 0.74 3.8 
May 40.0 0.73 24.6 30.7 18.9 5.6 13.3 0.68 4.1 
June 41.7 0.82 27.52 32.0 21.2 6.6 14.6 0.70 4.1 
July 40.8 0.77 25.91 31.3 19.9 5.9 14.1 0.29 4.0 
August 37.4 0.79 24.12 28.7 18.6 6.2 12.4 -0.45 3.7 
Sept 31.5 0.80 20.48 24.2 15.8 6.2 9.6 -0.88 3.3 
October 24.6 0.76 15.50 18.9 11.9 5.7 6.2 -1.09 3.5 
November 18.7 0.62 10.47 14.3 8.1 4.5 3.6 -0.88 3.1 
December 16.1 0.60 8.86 12.3 6.8 4.2 2.6 -0.41 3.3 
Ra = extraterrestrial radiation (from Allen et al. 1998 [DIRS 157311], Annex 2, Table 2.6, p. 219). 
n/N = percent of possible sunshine converted to decimal value (from Table 4.1-4). 
Rs = solar radiation (from Equation C-9). 
Rso = clear sky radiation (Equation C-10). 
Rns = net solar radiation (Equation C-11). 
Rnl = net longwave radiation (Equation C-12). 
Rn = net radiation (Equation C-13). 
G = solar heat flux (Equation C-14). 
u2 = wind speed (from Table 4.1-4), with corrections for measurement height using Equation C-15. 

Table C-8. Radiation Parameters, Soil Heat Flux, and Wind Speed for Upper Bound Glacial Transition 
Climate Conditions 
Month 
Ra 
(MJ m-2 
day-1) n/N 
Rs 
(MJ m-2 
day-1) 
Rso 
(MJ m-2 
day-1) 
Rns 
(MJ m-2 
day-1) 
Rnl 
(MJ m-2 
day-1) 
Rn 
(MJ m-2 
day-1) 
G 
(MJ m-2 
day-1) 
u2 
(m s-1) 
January 17.5 0.28 6.8 13.4 5.3 2.2 3.1 0.21 2.9 
February 22.6 0.41 10.3 17.3 7.9 3.0 4.9 0.45 3.1 
March 29.0 0.55 15.2 22.2 11.7 4.0 7.8 0.49 3.2 
April 35.7 0.61 19.8 27.4 15.3 4.4 10.8 0.59 3.4 
May 40.0 0.65 23.0 30.7 17.7 4.7 13.0 0.63 3.1 
June 41.7 0.67 24.4 32.0 18.8 4.8 14.0 0.58 3.1 
July 40.8 0.80 26.5 31.3 20.4 5.8 14.6 0.25 2.8 
August 37.4 0.78 23.9 28.7 18.4 5.7 12.7 -0.39 2.8 
Sept 31.5 0.72 19.2 24.2 14.8 5.2 9.6 -0.82 2.8 
October 24.6 0.55 12.9 18.9 9.9 4.0 6.0 -0.93 2.8 
November 18.7 0.29 7.4 14.3 5.7 2.3 3.4 -0.76 2.9 
December 16.1 0.23 5.9 12.3 4.5 1.9 2.6 -0.31 2.9 
Ra = extraterrestrial radiation (from Allen et al. 1998 [DIRS 157311], Annex 2, Table 2.6, p. 219). 
n/N = percent of possible sunshine (from Table 4.1-5). 
Rs = solar radiation (from Equation C-9). 
Rso = clear sky radiation (Equation C-10). 
Rns = net solar radiation (Equation C-11). 
Rnl = net longwave radiation (Equation C-12). 
Rn = net radiation (Equation C-13). 
G = solar heat flux (Equation C-14). 
u2 = wind speed (from Table 4.1-5), with corrections for measurement height using Equation C-15. 
C2.2.2 Solar Radiation (Rs) 
Solar radiation (Rs) was measured at the YMP meteorological monitoring Site 9 for the present-
day climate (Section 4.1.5, Table 4.1-2). However, it was not measured at the Nogales, Delta, or 
Spokane weather stations, (analogues for upper bound monsoon, lower bound glacial transition, 
and upper bound glacial transition climates), and was therefore calculated according to Allen et 
al. (1998 [DIRS 157311], Equation 35, p. 50). This equation uses the Angstrom formula to 
relate Rs to relative sunshine duration and Ra: 
R
s 
=
...
a
s
+
b
s 
n 
N
...
R (Eq. C-9) 
a 
where 
n/N = relative sunshine duration (percent of possible sunshine), 
as = fraction of extraterrestrial radiation reaching the earth on overcast days (n = 0), 
as + bs 
Ra 
= 
= 
fraction of extraterrestrial radiation reaching the earth on clear days (n = N), 
extraterrestrial radiation (MJ m-2 day-1). 
The Angstrom values as and bs vary with atmospheric conditions such as dust and humidity, and 
with solar declination. However, no site specific calibration for these variables were available. 

Therefore the values of as = 0.25 and bs = 0.50 recommended by Allen et al. (1998 
[DIRS 157311], p. 50) were used in the calculations of Rs for upper bound monsoon, lower 
bound glacial transition, and upper bound glacial transition climates. Values for percent of 
possible sunshine (n/N) from Tables 4.1-3, 4.1-4, and 4.1-5 were converted to decimal values for 
calculations of Rs. 
Example: January Ra for upper bound glacial transition climate = 17.5 (from Table C-8), and 
n/N = 0.28 (converted from percent to decimal for calculation, Table 4.1-5). 
R =( 25.0 + 50.0 ( 28.0 )) 5.17 = 8.6 MJ m-2 day-1 (Eq. C-9)
s 
Monthly Rs values for present-day and future climates are in Tables C-5, C-6, C-7, and C-8. 
C 2.2.3 Clear Sky Radiation (Rso) 
Clear sky radiation (Rso) is the radiation that would hit a flat surface under cloudless conditions 
(Allen et al. 1998 [DIRS 157311], Equation 37, p. 51): 
-) a
R =( 75.0 + 2  10 5 R Z (Eq.C-10)
so 
where 
Z = station elevation above sea level (m, note that this is the same for all climates). 
Example: Station elevation for the present-day climate = 838 m and Ra for January = 17.5 (from 
Table C-5). 
- 5 )
R=( 75.0 + 2  10  5.17 838 = 4.13 MJ m-2 day-1 (Eq.C-10)
so 
Clear sky radiation is required to calculate net longwave radiation (Rnl), which is used directly in 
the calculation of Rn. Mean monthly Rso values are in Tables C-5, C-6, C-7, and C-8. 
C2.2.4 Net Solar (shortwave) Radiation (Rns) 
Net solar radiation incorporates albedo (shortwave radiation reflected from the canopy of the 
grass reference crop) into incoming solar radiation and is used directly in the calculation of Rn 
(Allen et al. 1998 [DIRS 157311], Equation 38, p. 51): 
R =( 1-a) R (Eq. C-11) 
ns s 
where: 
a = albedo of grass reference crop = 0.23 (Allen et al. 1998 [DIRS 157311], p. 51). 
Example: For January present-day climate Rs = 9.6 MJ m-2 day-1 (from Table C-5). 
)
R =( 1 - 6.9 23.0 = 4.7 MJ m-2 day-1 (Eq. C-11)
ns 

Mean monthly Rns values are in Tables C-5, C-6, C-7, and C-8. 
C2.2.5 Net Longwave Radiation (Rnl) 
Net loss of radiant energy (Rnl) occurs primarily through thermal or longwave radiation. The 
Stefan-Boltzmann law predicts that black body radiation emission (radiation emitted by a perfect 
radiator) is proportional to surface temperature raised to the fourth power (Nobel 1983 
[DIRS 159953], p. 347). Plants are virtually black body absorbers and emitters to longwave 
radiation. However, radiant energy is also absorbed and emitted by water vapor, carbon dioxide, 
ozone and clouds, which affects the outgoing energy flux. Because of this, the 
Stefan-Boltzmann law is corrected for humidity and cloudiness in the calculation of net outgoing 
longwave radiation (Rnl, Allen et al. 1998 [DIRS 157311], Equation 39, p. 52): 
4 . 
. R .
s
R
nl =s.. 
Tmax, K 4 + Tmin, K ( 34.0 - 14.0 e ). 35.1 - 35.0 . (Eq. C-12) 
. a 
. 2 . . Rso . 
where 
e
s = Stefan-Boltzmann constant (4.903 10-9 MJ K-4 m-2 day-1), 
Tmax, K = maximum absolute temperature during the 24-hour period (K = oC + 273.16),
Tmin, K = minimum absolute temperature during the 24-hour period (K = oC + 273.16), 
a = actual vapor pressure (kPa), 
Rs/Rso = relative shortwave radiation (limited to = 1.0), 
Rs = measured (present-day climate) or calculated (future climate) solar radiation 
(MJ m-2 day-1), 
Rso = calculated clear-sky radiation (MJ m-2 day-1). 
Example: For January present-day climate Tmax = 13.5 oC and Tmin = 1.1 oC (Section 4.1.5, 
Table 4.1-2), ea = 0.661 (Table C-1), Rs = 9.6 MJ m-2 day-1, Rso = 13.4 MJ m-2 day-1 (Table C-5). 
Allen et al. (1998 [DIRS 157311], Table 2.8, p. 221) provides values for s Tmax, K4 based on air 
temperatures (oC). For Tmax = 13.5 oC the value for s Tmax, K4 = 33.11. For Tmin = 1.1 oC the 
value for s Tmin, K4 = 27.70. 
. 11.33 + 70.27 . 
.. 6.9 ..
).
R
nl =( 34.0 - 14.0 35.1 661. 
.. 
4.13 .. 
- 35.0 . (Eq. C-12) 
. 
. 2 .. 
.. 
= 30.42 x 0.226 x 0.616 = 4.2 MJ m-2 day-1 
Mean monthly Rnl values are in Tables C-5, C-6, C-7, and C-8. 

C2.2.6 Net Radiation (Rn) 
Net Radiation is the balance between net shortwave radiation (both incoming and reflected) and 
net loss of longwave radiation (Allen et al. 1998 [DIRS 157311], Equation 40, p. 53): 
R =R - Rnl (Eq. C-13) 
n ns 
Example: For January present-day climate Rns = 7.4 and Rnl = 4.2 (from Table C-5). 
R= 4.7 - 2.4 = 2.3 MJ m-2 day-1 (Eq. C-13)
n 
Mean monthly Rn values are in Tables C-5, C-6, C-7, and C-8. 
C2.3 SOIL HEAT FLUX 
Soil heat flux (G) can be derived for monthly periods assuming a constant soil heat capacity of 
2.1 MJ m3 oC-1 and that, over long time periods, soil temperature at a depth of 2 m changes 
approximately with average air temperature (Allen et al. 1998 [DIRS 157311], Equation 43, 
p. 54) 
, ,, 
G
i month = 07.0 (Ti month +1 - Ti month -1 ) (Eq. C-14) 
Example: For January present-day climate, Tmonth, i+1 = 9.6 oC and Tmonth, i-1 = 6.9 oC (Table 4.1-
2). 
(
G
january = 6.9 07.0 - 9.6 )= 19.0 (Eq. C-14) 
Monthly values for G are in Tables C-5, C-6, C-7, and C-8, respectively. 
C2.4 WIND SPEED (u2) 
Wind speed (u2) data were collected at the weather stations for present-day, upper bound 
monsoon, and upper bound glacial transition climates. Wind speed for lower bound glacial 
transition climate was taken from Milford, Utah, the closest weather station to Delta, Utah (lower 
bound glacial transition climate analogue). Standard anemometer height in agrometeorology is 2 
m above the ground surface (Allen et al. 1998 [DIRS 157311], p. 55). Anemometer height at the 
weather stations used in this analysis was 10 m. Because wind speed increases with height 
above the soil surface, a logarithmic wind profile function is required to adjust wind speeds 
placed at heights 
other than the standard 2 m. Therefore, the following correction was made for wind speed (Allen 
et al. 1998 [DIRS 157311], Equation 47, p. 56): 
87 . 4 
u 2 = uz
(
8 . 67 ln z - 24 . 5 ) (Eq. C-15) 
where 

uz = wind speed measured at z m above ground surface (m s-1), 
z = height of measurement above ground surface (m). 
Example: For January upper bound glacial transition climate, u10 = 3.9 m s-1 (Table 4.1-5). 
()2 -
u24 . 5 10 * 8 . 67 ln 
9 . 3 = 87 . 4 = 9 . 2 s m 1(
Eq. C-15) 
Mean monthly values for u2 are in Tables C-5, C-6, C-7, and C-8. 
C2.5 REFERENCE EVAPOTRANSPIRATION 
Using the humidity, radiation, soil heat flux, and wind speed values generated in this appendix 
for January present-day climate, mean monthly ETo for January can be calculated using 
Equation C-1. 
Example: For January present-day climate conditions, 
. = 0.069 
Rn = 3.2 
G = 0.19 
. = 0.061 
T=7.0 
u2 = 2.9 
(es -ea) = 0.443 
900
069.0 * 408.0 (2.3- 19.0 )+ 061.0 9.2 (443.0 )
ET = 0.7 + 273 =1.77 mm/day (Eq. C-16)
o
069.0 + 061.0 (1+ 9.2 * 34.0 )
Mean monthly ETo for present-day, upper bound monsoon, lower bound glacial transition, and 
upper bound glacial transition climates are in Table C-9. 

Table C-9. Mean Monthly Reference Evapotranspiration for Present-Day, Upper Bound Monsoon, Lower 
Bound Glacial Transition, and Upper Bound Glacial Transition Climate Conditions 
Month 
Present-Day 
Climate ETo 
a 
(mm/day) 
Upper Bound 
Monsoon Climate ETo 
a 
(mm/day) 
Lower Bound Glacial 
Transition Climate 
ETo 
a (mm/day) 
Upper Bound 
Glacial 
Transition 
Climate ETo 
a 
(mm/day) 
January 1.77 1.92 1.21 0.62 
February 2.64 2.96 1.67 1.10 
March 4.24 3.82 2.85 1.99 
April 5.51 5.10 4.30 3.18 
May 6.86 6.32 6.01 4.21 
June 8.38 7.36 7.96 5.31 
July 8.89 6.25 8.82 6.51 
August 8.62 5.64 8.26 6.15 
September 6.84 5.08 6.27 4.44 
October 4.67 3.99 4.05 2.50 
November 2.72 2.45 1.90 0.97 
December 1.85 2.00 1.25 0.58 
a 
Mean reference evapotranspiration (ETo) calculated according to Equation C-1. Climate data used in the 
calculations are from Section 4, Tables 4.1-2 to 4.1-5. 

APPENDIX D 
METHODS FOR DERIVING CROP COEFFICIENTS 
AND CROP EVAPOTRANSPIRATION 


D. METHODS FOR DERIVING CROP COEFFICIENTS AND 
CROP EVAPOTRANSPIRATION 
The single crop coefficient (Kc) approach described by Allen et al. (1998 [DIRS 157311], 
pp. 103 to 134) was used to calculate crop evapotranspiration (ETc), which is required for 
calculation of the irrigation parameters. This appendix describes methods for deriving growing 
season lengths for the crops used in this analysis and the methods from Allen et al. 1(998 
[DIRS 157311], pp. 103 to 134) used to calculate Kc and ETc. 
D1. INTRODUCTION 
A grass reference evapotranspiration (ETo, Appendix C) and crop specific coefficients are used 
to calculate ETc (Allen et al. 1998 [DIRS 157311], Equation 58, p. 103): 
ETc = ETo * Kc (Eq. D-1) 
ETo incorporates the effects of local climatic conditions on ETc, and Kc integrates the effects of 
four primary crop characteristics that differ from the reference grass (crop height, albedo, canopy 
resistance, and evaporation from soil). Changes in crop characteristics (i.e., leaf area, stomatal 
conductance, phenological stages) over the growing season also affect Kc, and so growth stage 
information is used in deriving crop specific values. Locally determined values for Kc were not 
available for this analysis so values published in Allen et al. (1998 [DIRS 157311], Table 12, 
pp. 110 to 114) were used for the 26 representative crops and turf. Relative humidity and wind 
speed were used to correct the published Kc values to correspond with local conditions according 
to Allen et al. (1998 [DIRS 157311], pp. 121 to 127). Methods and example calculations are 
provided below. 
D2. METHODS 
Crop coefficients were derived for the 26 representative crops and turf grass by: 1) using the 
growth stage lengths and Kc values from Allen et al. (1998 [DIRS 157311], Table 11, pp. 104 to 
108, and Table 12, pp. 110 to 114), 2) developing season lengths from appropriate local or 
regional data sources to correct growth stage lengths, and 3) correcting Kc values under 
non-standard climatic conditions. Once Kc values were corrected, average monthly values were 
calculated to correspond with average monthly ETo. 
D2.1 GROWING SEASON LENGTHS 
D2.1.1 Present-Day Climate 
The season lengths developed in this section apply to both present-day climate and upper bound 
monsoon climate states. 
Fruits and vegetablesAs described and justified in Section 4.1.4, information from the 
University of Nevada and University of Arizona Cooperative Extensions were used to determine 
planting dates (Mills et al. no date [DIRS 124338]) and season lengths (Call 1999 
[DIRS 158672]) for most representative fruits and vegetables. Use of data from these sources 
resulted in growing seasons that are consistent with current knowledge of the conditions in the 

region surrounding Yucca Mountain. Planting dates, which can vary across arid climate zones, 
were selected for southern Nevada. Season lengths are constrained by crop specific 
developmental processes that result in approximate times to maturity and are general for most 
climate zones; therefore, use of Arizona as a natural analogue for season length is appropriate. 
Planting dates and season lengths for each crop were calculated as the midpoint of ranges 
(Table D-1). Harvest date was calculated by adding the number of days in the season to the 
selected planting date (Table D-1). Except for sweet corn, two planting seasons were included 
for all vegetables that Mills et al. no (date [DIRS 124338]) show can be planted in spring and fall 
in southern Nye County. Mills et al. (no date [DIRS 124338]) show that sweet corn can be 
planted from mid-April through mid-May and from mid through late June. Because the season 
length of sweet corn is 63 to 100 days (Table D-1), an April-May crop could not be harvested 
before the second crop is planted in June. 
Apples and strawberriesAs described in Section 4.1.4, information from Allen et al. (1998 
[DIRS 157311], Table 11, p. 107) was used to determine the onset of growth and season lengths 
for apples and strawberries. Allen et al. (1998 [DIRS 157311], Table 11, p. 107) lists a planting 
time of March and a total growing season of 240 days for orchard fruit trees in California. This 
is corroborated by Caprile et al. (2001 [DIRS 159938]) with reference to irrigating apples from 
April through September in the San Joaquin Valley, California. In the category of Grapes and 
Berries, a planting time of March and growing season of 205 days is suggested (Allen et al. 
1998 [DIRS 157311], Table 11, p. 107). Based on this information, March 1 was selected for 
onset of growth for both apples and strawberries (Table D-1). A growing season length of 
240 days and harvest date of October 27 were selected for apples. A growing season length of 
205 days and harvest date of September 22 were selected for strawberries (Table D-1). 
GrapesAs described in Section 4.1.4, information from the founder of the Pahrump Valley 
Vineyards was used for initiation of growth and season length for grapes. Grapes in southern 
Nye County bloom in late March to early April and are harvested late August to early September 
(LeStrange 1997 [DIRS 125452]). This is corroborated by the planting period (March) and 
growing season length (205 days) suggested by Allen et al. (1998 [DIRS 157311], Table 11, 
p. 107,) for grapes grown in California. Based on this information, a growth initiation date of 
March 1 and a harvest date of August 31 were selected for grapes (Table D-1). 
Barley, winter wheat, oat hay, and alfalfaAs described in Section 4.1.4, information from a 
local farmer was used to determine planting and harvest dates for barley, winter wheat, oat hay, 
and alfalfa. According to this source, one crop of winter wheat, barley, and oats can be produced 
per year in Amargosa Valley (LeStrange 1997 [DIRS 125429]). Winter wheat and barley are 
planted in October and harvested in June, and oats are planted in March or April and harvested in 
June (LeStrange 1997 [DIRS 125429]). According to Allen et al. (1998 [DIRS 157311], 
Table 11, p. 106), cereal oats in desert climates are planted in December and have a growing 
season of 160 days. Based on this information, a planting date of October 16 (mid-month) and a 
harvest date of June 16 (mid-month) were chosen for winter wheat and barley. A planting date 
of March 31 (mid-point between March 1 and April 30) and harvest date of June 14 (mid-month) 
were chosen for oat hay (Table D-1). A planting date of December 16 (mid-month) and a 
harvest date of May 25 were selected for oats (Table D-1). 

In southern Nye County, approximately six to seven alfalfa cuttings can occur per year with the 
first cutting around mid to late April and the last cutting mid to late November (LeStrange 1997 
[DIRS 125429]). Irrigation generally begins in early February and ends in December when 
alfalfa goes dormant (LeStrange 1997 [DIRS 125429]). Allen et al. 1998 [DIRS 157311] 
suggested a planting month of January with a 60-day growing period for the first cutting of 
alfalfa in California. Given this, the suggested six cuttings per year with dormancy beginning in 
December (LeStrange 1997 [DIRS 125429]) can be achieved with initial growth starting 
January 1, the first cutting 60 days later on March 2, and the second through sixth cuttings 
occurring 55 days apart. Based on this information, January 1 was chosen for growth initiation, 
with cuttings on March 2, April 26, June 20, August 14, October 8, and December 2 (Table D-1). 
The average time between cuttings is 56 days. 
Table D-1. Growing Season - Present-Day and Upper Bound Monsoon Climates 
Crop 
Start of Seasona Season Length (days)b 
Start End Midc Juliand Min Max Mid Harveste Julian 
Broccoli early 09-Feb 20-Mar 1-Mar 60 60 100 80 20-May 140 
Broccoli late 01-Aug 20-Sep 26-Aug 238 60 100 80 14-Nov 318 
Cabbage early 09-Feb 20-Mar 1-Mar 60 70 100 85 25-May 145 
Cabbage late 01-Aug 20-Sep 26-Aug 238 70 100 85 19-Nov 323 
Cauliflower early 09-Feb 20-Mar 1-Mar 60 70 90 80 20-May 140 
Cauliflower late 01-Aug 20-Sep 26-Aug 238 70 90 80 14-Nov 318 
Celery early 01-Apr 20-Apr 11-Apr 101 125 125 125 14-Aug 226 
Celery late 01-Sep 30-Sep 16-Sep 259 125 125 125 19-Jan 19 
Head lettuce early 09-Feb 31-Mar 6-Mar 65 40 80 60 5-May 125 
Head lettuce late 10-Aug 20-Sep 31-Aug 243 40 80 60 30-Oct 303 
Leaf lettuce early 09-Feb 31-Mar 6-Mar 65 40 80 60 5-May 125 
Leaf lettuce late 10-Aug 20-Sep 31-Aug 243 40 80 60 30-Oct 303 
Spinach early 09-Feb 20-Apr 16-Mar 75 40 60 50 5-May 125 
Spinach late 01-Sep 30-Sep 16-Sep 259 40 60 50 5-Nov 309 
Bell peppers 10-Apr 31-May 6-May 126 70 85 78 23-Jul 204 
Carrots early 09-Feb 20-Apr 16-Mar 75 70 80 75 30-May 150 
Carrots late 01-Aug 20-Sep 26-Aug 238 70 80 75 9-Nov 313 
Cucumbers 01-Apr 31-Jul 1-Jun 152 50 70 60 31-Jul 212 
Onions early 01-Mar 20-Apr 26-Mar 85 100 120 110 14-Jul 195 
Onions late 01-Sep 30-Sep 16-Sep 259 100 120 110 4-Jan 4 
Potatoes 01-Mar 20-Apr 26-Mar 85 100 120 110 14-Jul 195 
Squash 10-Apr 20-Jun 16-May 136 50 65 58 13-Jul 194 
Sweet corn 10-Apr 20-May 30-Apr 120 63 100 82 21-Jul 202 
Apples 1-Mar 60 240 27-Oct 300 
Grapes 1-Mar 60 183 31-Aug 243 
Melons 10-Apr 20-Jun 16-May 136 70 130 100 24-Aug 236 
Strawberries 1-Mar 60 205 22-Sept 265 
Tomatoes 10-Apr 31-May 6-May 126 55 105 80 25-Jul 206 

Table D-1. Growing Season - Present-Day and Upper Bound Monsoon Climates (Continued) 
Crop 
Start of Seasona Season Length (days)b 
Start End Midc Juliand Min Max Mid Harveste Julian 
Barley 01-Oct 31-Oct 16-Oct 289 213 272 243 16-Jun 167 
Feed Corn 01-May 30-May 16-May 136 154 17-Oct 290 
Oats 01-Dec 31-Dec 16-Dec 350 160 25-May 145 
Winter wheat 01-Oct 31-Oct 16-Oct 289 213 272 243 16-Jun 167 
Corn silage 01-May 30-May 16-May 136 93 17-Aug 229 
Oat hay 01-Mar 30-Apr 31-Mar 90 75 75 75 14-Jun 165 
a 
Sources: Mills et al. no date [DIRS 124338], except corn and corn silage (USDA 2002 [DIRS 159273], pp. 16 and 
17) apples and strawberries (Allen et al. 1998 [DIRS 157311], Table 11, pp. 104-108), grapes, barley, oats, winter 
b 
wheat, alfalfa, and oat hay (LeStrange 1997 [DIRS 125452], 1997 [DIRS 125429]). 
Sources: 
Call (1999 [DIRS 158672], Table 10.10 for celery, spinach, and carrots and crop-specific information on 
pp. 71-125 of Chapter 10 for others), except corn and corn silage (USDA 2002 [DIRS 159273], pp. 16 and 17) 
apples and strawberries (Allen et al. 1998 [DIRS 157311], Table 11, pp. 104-108) grapes, barley, oats, winter 
wheat, alfalfa, and oat hay (LeStrange 1997 [DIRS 125452], 1997 [DIRS 125429]) (see Section 4.1.4). 
c 
d 
Midpoint of start of season. 
Date is expressed in Julian format, excluding year, and represents the midpoint of the start of the season. 
e 
Calculated as midpoint of start of season plus median season length, except apples, grapes, strawberries, winter 
wheat, barley, grain corn, oats, winter wheat, corn silage and oat hay. 
Feed corn and corn silageAs described in Section 4.1.4, information from Nevada 
Agricultural Statistics 2000-2001 (USDA 2002 [DIRS 159273], pp. 16 and 17) was used to 
determine planting and harvest dates for feed corn and corn silage. According to this source, 
corn is planted during May and June, silage is harvested in August through October, and feed 
corn is harvested in October and November. Because this source describes growing seasons for 
all of Nevada, the first months listed for planting and harvest were chosen for this analysis to 
conform with the likely planting and harvesting times in southern Nye County, Nevada. May 16 
(mid-month) was selected as the planting date for corn silage and feed corn (Table D-1). August 
17 (mid-month) and October 17 (mid-month) were selected as harvest dates for corn silage and 
feed corn, respectively (Table D-1). 
D2.1.2 Upper Bound Glacial Transition Climate 
The season lengths developed in this section apply to both lower bound future and upper bound 
glacial transition climate states. 
As described and justified in Section 4.1.4, information from the Washington State University 
Cooperative Extension and Washington Agricultural Statistics Service were used to determine 
planting dates (Washington State University Cooperative Extension 2002 [DIRS 159256]; 
Painter et al. 1995 [DIRS 158674]; Washington Agricultural Statistics Service 
1999 [DIRS 152232]) and season lengths (Antonelli et al. 1998 [DIRS 158654]; Painter et al. 
1995 [DIRS 158674]; Washington Agricultural Statistics Service 1999 [DIRS 152232]) for most 
representative fruits, vegetables, and field crops. Planting date and season length for early 
season and single season crops were calculated as the midpoint of ranges, and harvest date was 
calculated by adding the number of days in the season to the selected planting date (Table D-2). 
Two seasons were included for all vegetables having a season length less than the number of 
days between first harvest and October 1, the expected date that temperatures become too cold 
for vegetable growth in eastern Washington (Antonelli 1998 [DIRS 158654], Figure 3, p. 4). For 

example, a second season for celery was not included because the time between the first harvest 
(August 11) and October 1 is less than the 110-day season length for celery. An exception to this 
method was made for broccoli, because the early and late seasons overlapped by only five days. 
To accommodate, the late-season length was moved back by six days, which is within the range 
of season length for this crop (Table D-2). 
Table D-2. Growing Season - Upper and Lower Bound Glacial Transition Climates 
Crop 
Start of Seasona Season Length (days)b 
Start End Midc Juliand Min Max Mid Harveste Julian 
Broccoli early 15-Apr 01-May 23-Apr 113 65 100 83 15-Jul 196 
Broccoli late 16-Jul 197 65 100 83 7-Oct 280 
Cabbage early 15-Apr 01-May 23-Apr 113 60 90 75 7-Jul 188 
Cabbage late 18-Jul 199 75 1-Oct 274 
Cauliflower early 15-Apr 01-May 23-Apr 113 50 75 63 25-Jun 176 
Cauliflower late 30-Jul 211 50 75 63 1-Oct 274 
Celery 15-Apr 01-May 23-Apr 113 100 120 110 11-Aug 223 
Head lettuce early 15-Apr 01-May 23-Apr 113 75 80 78 10-Jul 191 
Head lettuce late 15-Jul 196 75 80 78 1-Oct 274 
Leaf lettuce early 15-Apr 01-May 23-Apr 113 55 60 58 20-Jun 171 
Leaf lettuce late 4-Aug 196 55 60 58 11-Sep 254 
Spinach early 15-Apr 01-May 23-Apr 113 50 60 55 17-Jun 168 
Spinach late 7-Aug 219 50 60 55 1-Oct 274 
Bell peppers 01-Jun 15-Jun 8-Jun 159 90 110 100 16-Sep 259 
Carrots early 15-Apr 01-May 23-Apr 113 70 90 80 12-Jul 193 
Carrots late 13-Jul 194 70 90 80 1-Oct 274 
Cucumbers 15-May 01-Jun 24-May 144 60 75 68 31-Jul 212 
Onions 01-Mar 01-Mar 1-Mar 60 130 180 155 3-Aug 215 
Potatoes 15-Apr 01-May 23-Apr 113 90 140 115 16-Aug 228 
Squash 15-May 01-Jun 24-May 144 60 70 65 28-Jul 209 
Sweet corn 15-May 01-Jun 24-May 144 70 140 105 6-Sep 249 
Apples 05-Apr 10-May 22-Apr 112 166 5-Oct 278 
Grapes 25-May 10-Jul 17-Jun 168 105 30-Sep 273 
Melons 15-May 01-Jun 24-May 144 90 115 103 4-Sep 247 
Strawberries 10-Apr 15-May 27-Apr 117 64 30-Jun 181 
Tomatoes 01-Jun 15-Jun 8-Jun 159 65 110 88 4-Sep 247 
Spring barley 01-Apr 30-Apr 16-Apr 106 91 16-Jul 197 
Feed Corn 15-Apr 5-Jun 11-May 131 178 5-Nov 309 
Oats 5-Mar 20-Apr 28-Mar 87 141 16-Aug 228 
Winter wheat 01-Sep 30-Sep 16-Sep 259 334 16-Aug 228 

Table D-2. Growing Season - Upper and Lower Bound Glacial Transition Climates (Continued) 
Crop 
Start of Seasona Season Length (days)b 
Start End Midc Juliand Min Max Mid Harveste Julian 
Alfalfa hay 1-Mar 60 211 28-Sept 271 
Corn silage 15 Apr 5 Jun 11 May 131 137 25-Sep 268 
Oat hay 15-May 135 57 11-Jul 192 
a 
Sources: Early season and single season annual vegetables and fruits-Washington State University Cooperative 
Extension (2002 [DIRS 159256], p. 2) with celery assigned the same dates as other leafy vegetables; late season 
annual vegetables-calculated as October 1, which is about expected date of first killing frost (Antonelli et al. 1998 
[DIRS 158654], Figure 3) minus median season length; winter wheat-Painter et al. (1995 [DIRS 158674], Table 
A1); spring barley-Painter et al. (1995 [DIRS 158674], Table A4); apples, grapes, strawberries, grain corn, oats, 
corn silage, and oat hay-Washington Agricultural Statistics Service (1999 [DIRS 152232], with oat hay = other 
hay); alfalfa - Schmierer et al. (1997 [DIRS 160479], pp. 9 to 18), Orloff and Marble (1997 [DIRS 158655], pp. 106 
b 
to 107). 
Sources: Antonelli et al. (1998 [DIRS 158654], Table 2), except apples, grapes, strawberries, wheat, barley, and 
oat hay, which were calculated as days from midpoint of season start to harveste. 
d 
Midpoint of start of season. 
Date is expressed in Julian format, excluding year, and represents the midpoint of start of season. 
e 
Calculated as midpoint of season start plus median season length, except winter wheat-Painter et al. (1995 
[DIRS 158674], Table A1); spring barley-Painter et al. (1995 [DIRS 158674], Table A4); apples, grapes, 
strawberries, grain corn, oats, corn silage, and oat hay-Washington Agricultural Statistics Service (1999 
[DIRS 152232]) midpoint of most active harvest dates, with oat hay = other hay. 
As described and justified in Section 4.1.4, information from Intermountain Alfalfa Management 
(Orloff and Marble 1997 [DIRS 158655], pp. 106 to 107; Schmierer et al. 1997 [DIRS 160479], 
pp. 9 to 18) was used to determine dates for growth initiation, the last harvest, and cutting 
schedules for alfalfa. For conditions similar to those in eastern Washington, three- to four-cut 
schedules are common for alfalfa (Orloff and Marble 1997 [DIRS 158655], pp. 106 to 107) with 
a three-cut schedule recommended if at least one cutting is used for beef cattle or horses. A 
three-cut schedule was chosen for this analysis. Initiation of spring growth or planting is 
recommended when temperatures are -3 C to -4 C (Schmierer et al. 1997 [DIRS 160479], 
pp. 9 to 18; Allen et al. 1998 [DIRS 157311], Table 11, p. 107). The last harvest of the growing 
season should occur four to six weeks before the first killing frost (Schmierer et al. 1997 
[DIRS 160479], pp. 9 to 18). Using mean monthly temperature data for Spokane (Table 4.1-5) 
this corresponds approximately to a growing period of March 1 (mean minimum temperatures -
1.33 C) through September 28 (assuming first killing frost occurs the first week in November). 
Allen et al. (1998 DIRS 157311], Table 11, p. 107) recommended 75 days for the first cutting 
cycle in Idaho (similar climate to Spokane, but drier). The recommended interval between the 
first and second cuttings, or second and third cuttings, was 30-50 days in Schmierer et al. (1997 
[DIRS 160479], pp. 9 to 18). Based on this information, March 1 and September 28 were chosen 
for growth initiation and the last cutting date, respectively (Table D-2). Using a three-cut 
schedule with the initial cut after 75 days of growth, the remaining intervals between the second 
and third cuts are 68 days (Table D-2). A three-cut schedule was chosen with the first cutting on 
May 15 (75 days from growth initiation), the second cutting on July 22 (68 days), and the third 
cutting on September 28 (Table D-2). 

D2.2 GROWTH STAGE LENGTHS 
Allen et al. (1998 [DIRS 157311], pp. 103 to 108) divided crop development into four growth 
stages (initial, development, mid-season, and late season) that are related to leaf area index 
(ground area covered by crop canopy) and developmental stages. The initial stage begins at the 
planting date and ends when the crop has reached approximately 10 percent ground cover. The 
development stage runs from 10 percent cover to effective full cover, which, for many crops, 
occurs when flowering is initiated. The mid-season stage begins when the crop has reached 
effective full cover and ends at the start of maturity. The late season stage runs from maturity to 
harvest or senescence. The stages are crop specific and the lengths are affected by local climatic 
factors. 
Growth stages and total growing season lengths from Allen et al. (1998 [DIRS 157311], 
Table 11. pp. 104 to 108) were selected for present-day and future climates based on regional 
information. Growth stage information selected for present-day climate was also used for upper 
bound monsoon climate. Growth stage information selected for the glacial transition climate was 
used for both lower and upper bounds. For the present-day climate, growth stages for California, 
California Desert, Semi Arid, or Arid Region were selected depending upon availability 
(Table D-3). When both California Desert and Arid Region were options, the region with 
planting dates and season lengths most similar to those identified for the local conditions were 
selected (see Table D-1). For the glacial transition climate, growth stage lengths for the 
California Desert, Mediterranean, Idaho, 35 - 45 L, high latitudes or Europe were selected 
depending on which more accurately reflected data for Spokane conditions (Table D-4). 
Growing season lengths developed for each crop for all climates (Tables D-1 and D-2) were used 
to adjust growth stage lengths from Allen et al. (1998 [DIRS 157311], Table 11, pp. 104 to 108) 
to local conditions. This was done by determining the ratio of the published stage length to the 
total growing time. This ratio was multiplied by the length of the growing season determined for 
local conditions and rounded to the nearest whole day. Occasionally, rounding resulted in stage 
lengths that were either a day too long or too short to sum to the total growing season length. If 
the sum of the days of stage lengths did not equal the total number of days in the growing season, 
the stage length days were adjusted to sum to the growing season length. Adjusted stages for 
present-day climate were also used for upper bound monsoon, and those for glacial transition 
climate were used for both lower and upper bounds. 
Example: For the present-day climate early lettuce crop the published growth stage lengths and 
total growing time were (Allen et al. 1998 [DIRS 157311], Table 11, pp. 104 to 108): 
Initial stage = 25 days 
Development stage = 35 days 
Mid-season stage = 30 days 
Late stage = 10 days 
Total = 100 days 

The season length for the early lettuce crop for present-day climate conditions was 60 days (from 
Table D-1). The adjusted stage lengths were: 
Initial stage = 25/100  60 = 15 days 
Development stage = 34/100  60 = 21 days 
Mid-season stage = 30/100  60 = 18 days 
Late stage = 10/100  60 = 6 days 
Published and adjusted crop growth stage lengths are in Tables D-3 and D-4, respectively. 
Table D-3. Length (days) of Four Crop Growth Stages and Total Growing Season for Present-Day and 
Upper Bound Monsoon Climate Conditions 
Crop Initial Dev Mid Late 
Season 
Length Regiona 
Leaf Lettuce early 
Stage Lengthb 25 35 30 10 100 Arid Region 
Adjusted Stage Lengthc 15 21 18 6 60 
Leaf Lettuce late 
Stage Length 25 35 30 10 100 Arid Region 
Adjusted Stage Length 15 21 18 6 60 
Head Lettuce early 
Stage Length 25 35 30 10 100 Arid Region 
Adjusted Stage Length 15 21 18 6 60 
Head Lettuce late 
Stage Length 25 35 30 10 100 Arid Region 
Adjusted Stage Length 15 21 18 6 60 
Cabbage early 
Stage Length 40 60 50 15 165 California Desert 
Adjusted Stage Length 20 31 26 8 85 
Cabbage late 
Stage Length 40 60 50 15 165 California Desert 
Adjusted Stage Length 20 31 26 8 85 
Celery early 
Stage Length 30 55 105 20 210 Semi Arid 
Adjusted Stage Length 18 33 62 12 125 
Celery late 
Stage Length 25 40 95 20 180 Semi Arid 
Adjusted Stage Length 17 28 66 14 125 
Broccoli early 
Stage Length 35 45 40 15 135 California Desert 
Adjusted Stage Length 21 26 24 9 80 

Table D-3. Length (Days) of Four Crop Growth Stages and Total Growing Season for Present-Day 
and Upper Bound Monsoon Climate Conditions (Continued) 
Crop Initial Dev Mid Late 
Season 
Length Regiona 
Broccoli late 
Stage Length 35 45 40 15 135 California Desert 
Adjusted Stage Length 21 26 24 9 80 
Cauliflower early 
Stage Length 35 50 40 15 140 California Desert 
Adjusted Stage Length 20 29 23 8 80 
Cauliflower late 
Stage Length 35 50 40 15 140 California Desert 
Adjusted Stage Length 20 29 23 8 80 
Spinach early 
Stage Length 20 30 40 10 100 Arid Region 
Adjusted Stage Length 10 15 20 5 50 
Spinach late 
Stage Length 20 30 40 10 100 Arid Region 
Adjusted Stage Length 10 15 20 5 50 
Potatoes 
Stage Length 30 35 50 25 140 California Desert 
Adjusted Stage Length 24 27 39 20 110 
Onions early 
Stage Length 20 35 110 45 210 Arid Region; CA 
Adjusted Stage Length 10 18 58 24 110 
Onions late 
Stage Length 20 35 110 45 210 Arid Region; CA 
Adjusted Stage Length 10 18 58 24 110 
Carrots early 
Stage Length 30 50 90 30 200 California Desert 
Adjusted Stage Length 11 19 34 11 75 
Carrots late 
Stage Length 30 50 90 30 200 California Desert 
Adjusted Stage Length 11 19 34 11 75 
Sweet corn 
Stage Length 20 40 70 10 140 California Desert 
Adjusted Stage Length 12 23 41 6 82 
Bell peppers 
Stage Length 30 40 110 30 210 Arid Region 
Adjusted Stage Length 11 15 41 11 78 
Cucumbers 
Stage Length 20 30 40 15 105 Arid Region 
Adjusted Stage Length 11 17 23 9 60 
Zucchini Squash 
Stage Length 25 35 25 15 100 Arid Region 
Adjusted Stage Length 15 20 14 9 58 

Table D-3. Length (Days) of Four Crop Growth Stages and Total Growing Season for Present-Day 
and Upper Bound Monsoon Climate Conditions (Continued) 
Crop Initial Dev Mid Late 
Season 
Length Regiona 
Melons 
Stage Length 15 40 65 15 135 California Desert 
Adjusted Stage Length 11 30 48 11 100 
Tomatoes 
Stage Length 35 40 50 30 155 California 
Adjusted Stage Length 18 21 26 15 80 
Apples 
Stage Length 30 50 130 30 240 California 
Adjusted Stage Length 30 50 130 30 240 
Wine Grapes 
Stage Length 20 50 75 60 205 California 
Adjusted Stage Length 18 45 67 53 183 
Strawberries 
Stage Length 20 50 75 60 205 California 
Adjusted Stage Length 20 50 75 60 205 
Winter Wheat 
Stage Length 20 60 70 30 180 California 
Adjusted Stage Length 27 81 94 41 243 
Barley 
Stage Length 20 50 60 30 160 California Desert 
Adjusted Stage Length 30 76 91 46 243 
Corn-feed 
Stage Length 25 40 45 30 140 Arid Region 
Adjusted Stage Length 27 44 50 33 154 
Oats 
Stage Length 20 50 60 30 160 California 
Adjusted Stage Length 20 50 60 30 160 
Alfalfa hay (1st cutting) 
Stage Length 10 20 20 10 60 California 
Adjusted Stage Length 10 20 20 10 60 
Alfalfa hay (2nd cutting) 
Stage Length 5 10 10 5 30 California 
Adjusted Stage Length 9 18 18 10 55 
Alfalfa hay (3rd cutting) 
Stage Length 5 10 10 5 30 California 
Adjusted Stage Length 9 18 18 10 55 
Alfalfa hay (4th cutting) 
Stage Length 5 10 10 5 30 California 
Adjusted Stage Length 9 18 18 10 55 
Alfalfa hay (5th cutting) 
Stage Length 5 10 10 5 30 California 
Adjusted Stage Length 9 18 18 10 55 

Table D-3. Length (Days) of Four Crop Growth Stages and Total Growing Season for Present-Day 
and Upper Bound Monsoon Climate Conditions (Continued) 
Crop Initial Dev Mid Late 
Season 
Length Regiona 
Alfalfa hay (6th cutting) 
Stage Length 5 10 10 5 30 California 
Adjusted Stage Length 9 18 18 10 55 
Corn silage 
Stage Length 25 40 45 30 140 Arid Region 
Adjusted Stage Length 17 26 30 20 93 
Oat hay 
Stage Length 20 50 60 30 160 California Desert 
Adjusted Stage Length 9 24 28 14 75 
a 
Geographic region from which growth stage and season lengths were determined (Source: Allen et al. 1998 
b 
[DIRS 157311], Table 11, pp. 104 to108). 
C 
Source: Allen et al. (1998 [DIRS 157311], Table 11, pp. 104 to 108). 
Stage lengths adjusted from Allen et al. (1998 ([DIRS 157311]) according to Appendix D, Section 2.2). 
Note: Initial = initial crop growth stage, Dev = development stage, Mid = mid-season stage, and Late = late season 
stage. 
Table D-4. Length (Days) of Crop Growth Stages and Total Growing Season for Lower and Upper 
Bound Glacial Transition Climate Conditions 
Crop Initial Dev Mid Late 
Season 
Length Regiona 
Leaf Lettuce early 
Stage Lengthb 20 30 15 10 75 Mediterranean 
Adjusted Stage Lengthc 15 23 12 8 58 
Leaf Lettuce late 
Stage Length 20 30 15 10 75 Mediterranean 
Adjusted Stage Length 15 23 12 8 58 
Head Lettuce early 
Stage Length 20 30 15 10 75 Mediterranean 
Adjusted Stage Length 21 31 16 10 78 
Head Lettuce late 
Stage Length 20 30 15 10 75 Mediterranean 
Adjusted Stage Length 21 31 16 10 78 
Cabbage early 
Stage Length 40 60 50 15 165 California Desert 
Adjusted Stage Length 18 27 23 7 75 
Cabbage late 
Stage Length 40 60 50 15 165 California Desert 
Adjusted Stage Length 18 27 23 7 75 
Celery 
Stage Length 25 40 45 15 125 Mediterranean 
Adjusted Stage Length 22 35 40 13 110 

Table D-4. Length (Days) of Crop Growth Stages and Total Growing Season for Lower and Upper 
Bound Glacial Transition Climate Conditions (Continued) 
Crop Initial Dev Mid Late 
Season 
Length Regiona 
Broccoli early 
Stage Length 35 45 40 15 135 California Desert 
Adjusted Stage Length 21 28 25 9 83 
Broccoli late 
Stage Length 35 45 40 15 135 California Desert 
Adjusted Stage Length 21 28 25 9 83 
Cauliflower early 
Stage Length 35 50 40 15 140 California Desert 
Adjusted Stage Length 16 22 18 7 63 
Cauliflower late 
Stage Length 35 50 40 15 140 California Desert 
Adjusted Stage Length 16 22 18 7 63 
Spinach early 
Stage Length 20 20 20 5 65 Mediterranean 
Adjusted Stage Length 17 17 17 4 55 
Spinach late 
Stage Length 20 20 20 5 65 Mediterranean 
Adjusted Stage Length 17 17 17 4 55 
Potatoes 
Stage Length 45 30 70 20 165 Idaho 
Adjusted Stage Length 31 21 49 14 115 
Onions 
Stage Length 15 25 70 40 150 Mediterranean 
Adjusted Stage Length 16 26 72 41 155 
Carrots early 
Stage Length 30 40 60 20 150 Mediterranean 
Adjusted Stage Length 16 21 32 11 80 
Carrots late 
Stage Length 30 40 60 20 150 Mediterranean 
Adjusted Stage Length 16 21 32 11 80 
Sweet corn 
Stage Length 20 25 25 10 80 Mediterranean 
Adjusted Stage Length 26 33 33 13 105 
Bell peppers 
Stage Length 30 35 40 20 125 Europe and Med. 
Adjusted Stage Length 24 28 32 16 100 
Cucumbers 
Stage Length 20 30 40 15 105 Arid Region 
Adjusted Stage Length 13 19 26 10 68 
Squash 
Stage Length 20 30 25 15 90 Med; Europe 
Adjusted Stage Length 14 22 18 11 65 

Table D-4. Length (Days) of Crop Growth Stages and Total Growing Season for Lower and Upper 
Bound Glacial Transition Climate Conditions (Continued) 
Crop Initial Dev Mid Late 
Season 
Length Regiona 
Melons 
Stage Length 25 35 40 20 120 Mediterranean 
Adjusted Stage Length 22 30 34 17 103 
Tomatoes 
Stage Length 30 40 45 30 145 Mediterranean 
Adjusted Stage Length 18 24 28 18 88 
Apples 
Stage Length 20 70 90 30 210 High Latitudes 
Adjusted Stage Length 16 55 71 24 166 
Wine Grapes 
Stage Length 20 50 90 20 180 High Latitudes 
Adjusted Stage Length 12 30 52 11 105 
Strawberries 
Stage Length 20 50 90 20 180 High Latitudes 
Adjusted Stage Length 7 18 32 7 64 
Winter wheat 
Stage Length 30 140 40 30 240 Mediterranean 
Adjusted Stage Length 42 195 55 42 334 
Barley 
Stage Length 20 25 60 30 135 35-45 L 
Adjusted Stage Length 14 17 40 20 91 
Feed corn 
Stage Length 30 40 50 50 170 Idaho 
Adjusted Stage Length 32 42 52 52 178 
Oat Feed 
Stage Length 20 25 60 30 135 35-45 L 
Adjusted Stage Length 21 26 63 31 141 
Alfalfa hay (1st cutting) 
Stage Length 10 30 25 10 75 Idaho 
Adjusted Stage Length 10 30 25 10 75 
Alfalfa hay (2nd cutting) 
Stage Length 5 20 10 10 45 Idaho 
Adjusted Stage Length 8 30 15 15 68 
Alfalfa hay (3rd cutting) 
Stage Length 5 20 10 10 45 Idaho 
Adjusted Stage Length 8 30 15 15 68 
Corn silage 
Stage Length 30 40 50 50 170 Idaho 
Adjusted Stage Length 24 32 40 41 137 

Table D-4. Length (Days) of Crop Growth Stages and Total Growing Season for Lower and Upper 
Bound Glacial Transition Climate Conditions (Continued) 
Crop Initial Dev Mid Late 
Season 
Length Regiona 
Oat hay 
Stage Length 20 25 60 30 135 35-45 L 
Adjusted Stage Length 9 10 26 12 57 
a 
Geographic region from which growth stage and season lengths were determined (Source: Allen et al. 1998 
b 
[DIRS 157311], Table 11, pp. 104 to 108). 
C 
Source: Allen et al. (1998 [DIRS 157311], Table 11, pp 104 to 108). 
Stage lengths adjusted from Allen et al. (1998 [DIRS 157311]) according to Appendix D, Section 2.2. 
NOTE: Initial = initial crop growth stage, Dev = development stage, Mid = mid-season stage, and Late = late 
season stage. 
D3. KC CORRECTIONS 
Allen et al. (1998 [DIRS 157311], Table 12, pp. 110 to 114) published Kc values for initial 
(Kc ini), mid-season (Kc mid), and end of the late season (Kc end) growth stages for several crops. 
The values were developed for non-stressed, well-managed crops in subhumid climates 
(minimum relative humidity, [RHmin]  45 percent, wind speed [u2]  2 m/s). When RHmin and u2 
(2 m above the surface) were different from the assumptions, the following recommended 
corrections for Kc mid and Kc end were used (Allen et al. 1998 [DIRS 157311], Equations 62 and 
65, pp. 121 and 125, respectively): 
3.0 
... 
h
)]45 
3 
...
K 
K 
RH
[04.0 (u
-
2)
-
004.0 (
- 
(Eq. D-2) 
+
= 
mid c (Tab) 
2 
min
cmid 
3.0 
... 
h
)]45 
3 
...
K 
K 
RH
[04.0 (u
-
2)
-
004.0 (
- 
(Eq. D-3) 
+
= 
end c (Tab) 
2 
min
cend 
where: 
Kc mid (Tab) or Kc end (Tab) = Kc mid value (Equation D-2) or Kc end value (Equation D-3) from 
Table 12 (Allen et al. 1998 [DIRS 157311], pp. 110 to 114), 
RHmin = mean minimum RH (%) during the mid-season (Equation D-2) or late season 
(Equation D-3) growth stages, 
u2 = mean daily wind speed (m s-1) at 2 m during the mid-season (Equation D-2) or late season 
(Equation D-3) growth stages, 
h = mean plant height (m) during the mid-season (Equation D-2) or late season (Equation D-3) 
growth stages. 
The following limitations to RH, u2, and h apply to Equations D-2 and D-3: 
20 percent = RHmin = 80 percent, 

1 m s-1 = u2 = 6 m s-1, and 
0.1 m= h = 10 m. 
Additionally, Equation D-3 is only applied when Kc end values from Table 12 (Allen et al. 1998 
[DIRS 157311], pp. 110 to 114) exceed 0.45. This is because a Kc end value of 0.45 or less 
indicates that the crop is allowed to senesce and dry in the field. In this case, RHmin and u2 have 
little effect on Kc end and no adjustment is necessary. Because the Kc adjustments are based on 
site specific relative humidity and wind speed, adjustments were required for all climate states 
considered for annual average irrigation rate calculations. 
For the present-day climate mean daily u2 was greater than 2 m s-1 and less than 6 m s-1 for all 
months (Appendix C, Table C-5). RHmin was less than 45 percent for all months and less than 
20 percent March through October (Section 4.15, Table 4.1-2). To meet the requirements for 
Equations D-2 and D-3 for March through October and still adjust approximately for local 
conditions, 20 percent was substituted for the recorded RHmin. Adjustments for Kc mid and Kc end 
were required for all crops except grapes, oats, oat hay, feed corn, barley, and winter wheat 
which had Kc end values less than 0.45 (Allen et al. 1998 [DIRS 157311], Table 12, pp. 110 to 
114). 
Minimum relative humidity was not available for the upper bound monsoon or lower bound 
glacial transition climates. To estimate RHmin the following equation was used from Allen et al. 
(1998 [DIRS 157311], Equation 10, p. 35): 
e
a
RH =100 
o
e (T ) (Eq. D-4) 
where 
T = Tmax 
Estimated RHmin values for upper bound monsoon and lower bound glacial transition climates are 
in Table D-5. 
Table D-5. Estimated Minimum Relative Humidity for Upper Bound Monsoon and Lower Bound Glacial 
Transition Climates 
Month 
Upper Bound Monsoon Climate 
RHmin (%)a 
Lower Bound Glacial 
Transition Climate 
RHmin (%)a 
January 22.9 40.4 
February 22.9 37.9 
March 23.5 29.4 
April 22.2 26.6 
May 22.4 23.1 
June 23.4 17.1 
July 34.2 19.4 

Table D-5. Estimated Minimum Relative Humidity for Upper Bound Monsoon 
and Lower Bound Glacial Transition Climates (Continued) 
Month 
Upper Bound Monsoon Climate 
RHmin (%)a 
Lower Bound Glacial 
Transition Climate 
RHmin (%)a 
August 35.6 17.7 
September 29.3 19.4 
October 23.9 25.1 
November 22.0 35.9 
December 23.0 40.9 
a 
Minimum Relative humidity was calculated from maximum temperatures in 
Tables 4.1-3 (upper bound monsoon) and 4.1-4 (lower bound future) and Allen et al. 
(1998 [DIRS 157311], Equation 10, p. 35). 
For the upper bound monsoon climate, mean daily u2 was greater than 2 m s-1 February through 
June and less than 6 m s-1 for all months (Table C-6). RHmin was less than 45 percent and greater 
than 20 percent for all months (Table D-5). Therefore, the requirements of Equations D-2 and 
D-3 were met for all months. Adjustments for Kc mid and Kc end were required for all crops except 
grapes, oat hay, oat feed, feed corn, barley, and winter wheat which had Kc end values less than 
0.45 (Allen et al. 1998 [DIRS 157311], Table 12, pp. 110 to 114). 
For the lower bound glacial transition climate, mean daily u2 was greater than 2 m s-1 and less 
than 6 m s-1 for all months (Section 4.1.5, Table 4.1-4, and Appendix C Table C-7). RHmin was 
less than 45 percent for all months and less than 20 percent June through September (Table D-5). 
To meet the requirements for Equations D-2 and D-3 for June through September and still adjust 
approximately for local conditions, 20 percent was substituted for the recorded RHmin. 
Adjustments for Kc mid and Kc end were required for all crops except grapes, oats, oat hay, feed 
corn, barley, and winter wheat which had Kc end values less than 0.45 (Allen et al. 1998 
[DIRS 157311], Table 12, pp. 110 to 114). 
For the upper bound glacial transition climate, mean daily u2 was greater than 2 m s-1 and less 
than 6 m s-1 for all months (Table C-8). RHmin was less than 45 percent May through September 
and was 20 percent or greater for all months (Section 4.1.5, Table 4.1-5). Therefore, the 
requirements of Equations D-2 and D-3 were met for all months. Adjustments for Kc mid and Kc 
end were required for all crops except grapes, oat hay, oat feed, feed corn, barley, and winter 
wheat which had Kc end values less than 0.45 (Allen et al. 1998 [DIRS 157311], Table 12, 
pp. 110 to 114). 
The adjustments to Kc mid and Kc end required mean plant height during the mid- and late season 
growing stages. Because the mid-season stage begins at effective full cover, it was reasonable to 
assume that plants had reached their maximum height at this time. No local data exists for crop 
heights so the maximum crop heights published in Allen et al. (1998 [DIRS 157311], Table 12, 
pp. 110 to 114) were used in the calculations, with the exception of wine grapes (see Tables D-6 
through D-9). Those heights are listed in Tables D-6 through D-9. 

Example: The Kc mid calculation for early leaf lettuce (present-day climate) requires the 
following: 
Kc mid = 1.0 (Allen et al. 1998 [DIRS 157311], Table 12, p. 110), 
Stage length = 18 days (Table D-3), 
Stage month(s) = April = 18 days (Table D-1), 
RHmin = 13.7 percent (Table 4.1-2, required adjustment = 20 percent), 
u2 = 3.5 m s-1 (Table C-5), 
h = 0.3 m (Allen et al. 1998 [DIRS 157311], Table 12, p. 110, and Table D-6). 
. 3.0 . 3. 0 
= 0.1 + [04.0 (5.3- 2)- 004.0 (20 - )]45 . .= 1.1 (Eq. D-2) 
K
c mid 
. 3 . 
The following inputs are required for Kc end. The late stage growing period occurs in April and 
May. Values for both months must be considered. 
Kc end = 0.95 (Allen et al. 1998 [DIRS 157311], Table 12, p. 110), 
Stage length = 6 days (Table D-3), 
Stage month(s) = April = 1 day; May = 5 days (Table D-1), 
RHmin = April = 13.7 percent; May = 14.1 percent (Table 4.1-2, required adjustment = 20 
percent), 
u2 = April = 3.5 m s-1; May = 3.4 m s-1 (Table C-5), 
h = 0.3 m (Allen et al. 1998 [DIRS 157311], Table 12, p. 110, and Table D-6). 
Mean RHmin = 20 percent 
.u Mean 2 = .. 
1  5.3 . + .. 
5  4.3 .. 
= 4.3 s m - 1 
. 6 .. 6 . 
. 3.0 . 3. 0 
= 95.0 + [04.0 (4.3- 2)- 004.0 (20 - )]45 . .= 08.1 (Eq. D-3) 
K
cend 
. 3 . 
Adjusted Kc values and maximum crop heights are in Tables D-6 through D-9. 

Table D-6. Adjusted Crop Coefficients (Kc) and Maximum Crop Height for Early and Late Season Crops 
for Present-Day Climate Conditions 
Crop Kc ini 
a Kc mid 
b Kc end 
c Maximum Crop Height (m)d 
Leaf Lettuce early 0.70 1.08 1.03 0.3 
Leaf Lettuce late 0.70 1.07 1.02 0.3 
Head Lettuce early 0.70 1.08 1.03 0.3 
Head Lettuce late 0.70 1.07 1.02 0.3 
Cabbage early 0.70 1.14 1.04 0.4 
Cabbage late 0.70 1.12 1.02 0.4 
Celery early 0.70 1.15 1.10 0.6 
Celery late 0.70 1.12 1.04 0.6 
Broccoli early 0.70 1.13 1.03 0.3 
Broccoli late 0.70 1.12 1.01 0.3 
Cauliflower early 0.70 1.14 1.04 0.4 
Cauliflower late 0.70 1.13 1.02 0.4 
Spinach early 0.70 1.08 1.03 0.3 
Spinach late 0.70 1.07 1.01 0.3 
Potatoes 0.50 1.25 0.85 0.6 
Onions early 0.70 1.14 0.84 0.4 
Onions late 0.70 1.12 0.80 0.4 
Carrots early 0.70 1.13 1.03 0.3 
Carrots late 0.70 1.12 1.01 0.3 
Sweet corn 0.30 1.28 1.18 1.5 
Bell peppers 0.60 1.16 1.00 0.7 
Cucumbers 0.60 1.08 0.83 0.3 
Squash 0.50 1.03 0.83 0.3 
Melons 0.50 1.14 0.84 0.4 
Tomatoese 0.60 1.25 0.90 0.6 
Apples 0.60 1.12 0.91 4.0 
Wine Grapes 0.30 0.83 0.45 1.5 
Strawberries 0.40 0.92 0.82 0.2 
Winter wheat 0.70 1.25 0.25 1.0 
Barley 0.30 1.25 0.25 1.0 
Feed Cornf 0.30 1.34 0.35 2.0 
Oats 0.30 1.26 0.25 1.0 
Alfalfa hay (1st cutting) 0.40 1.28 1.23 0.7 
Alfalfa hay (2nd cutting) 0.40 1.30 1.25 0.7 
Alfalfa hay (3rd cutting) 0.40 1.30 1.26 0.7 
Alfalfa hay (4th cutting) 0.40 1.30 1.25 0.7 
Alfalfa hay (5th cutting) 0.40 1.30 1.24 0.7 

Table D-6. Adjusted Crop Coefficients (Kc) and Maximum Crop Height for Early and Late 
Season Crops for Present-Day Climate Conditions (Continued) 
Crop Kc ini 
a Kc mid 
b Kc end 
c Maximum Crop Height (m)d 
Alfalfa hay (6th cutting) 0.40 1.28 1.23 0.7 
Oat hay 0.30 1.26 0.25 1.0 
Corn silageg 0.30 1.34 0.74 2.0 
a 
Kc values for the initial growth stage. Source: Allen et al. (1998 [DIRS 157311], Table 12, pp. 110 to 
b 
114). 
Kc values for the mid-season growth stage adjusted from Allen et al. (1998 [DIRS 157311], Equation 
62, p. 121) according to Equation D-2. 
c 
Kc values for the end of the late season growth stage adjusted from Allen et al. (1998 [DIRS 157311], 
d 
Equation 65, p. 125) according to Equation D-3. 
Source: Allen et al. (1998 [DIRS 157311], Table 12, pp. 110 to 114). 
e 
Midpoint of the range for Kc end (0.8) was used. 
f 
Kc end for dry harvest was used. 
g Kc end for wet harvest was used. 
Table D-7. Adjusted Crop Coefficients (Kc) and Maximum Crop Height for Early and Late 
Season Crops for Upper Bound Monsoon Climate Conditions 
Crop Kc ini 
a Kc mid 
b Kc end 
c Maximum Crop Height (m)d 
Leaf Lettuce early 0.70 1.05 1.00 0.3 
Leaf Lettuce late 0.70 1.04 0.99 0.3 
Head Lettuce early 0.70 1.05 1.00 0.3 
Head Lettuce late 0.70 1.04 0.99 0.3 
Cabbage early 0.70 1.11 1.01 0.4 
Cabbage late 0.70 1.09 0.99 0.4 
Celery early 0.70 1.09 1.01 0.6 
Celery late 0.70 1.09 1.04 0.6 
Broccoli early 0.70 1.10 1.00 0.3 
Broccoli late 0.70 1.09 0.98 0.3 
Cauliflower early 0.70 1.05 1.01 0.4 
Cauliflower late 0.70 1.09 0.99 0.4 
Spinach early 0.70 1.05 1.00 0.3 
Spinach late 0.70 1.04 0.98 0.3 
Potatoes 0.50 1.21 0.78 0.6 
Onions early 0.70 1.10 0.78 0.4 
Onions late 0.70 1.09 0.79 0.4 
Carrots early 0.70 1.10 1.00 0.3 
Carrots late 0.70 1.09 0.99 0.3 
Sweet corn 0.30 1.21 1.08 1.5 
Bell peppers 0.60 1.10 0.92 0.7 
Cucumbers 0.60 1.02 0.77 0.3 
Squash 0.50 0.99 0.77 0.3 
Melons 0.50 1.07 0.76 0.4 
Tomatoese 0.60 1.19 0.82 0.6 

Table D-7. Adjusted Crop Coefficients (Kc) and Maximum Crop Height for Early and Late 
Season Crops for Upper Bound Monsoon Climate Conditions (Continued) 
Crop Kc ini 
a Kc mid 
b Kc end 
c Maximum Crop Height (m)d 
Apples 0.60 1.01 0.83 4.0 
Wine Grapes 0.30 0.77 0.45 1.5 
Strawberries 0.40 0.88 0.76 0.2 
Winter wheat 0.70 1.22 0.25 1.0 
Barley 0.30 1.22 0.25 1.0 
Feed Cornf 0.30 1.23 0.35 2.0 
Oats 0.30 1.22 0.25 1.0 
Alfalfa hay (1st cutting) 0.40 1.26 1.21 0.7 
Alfalfa hay (2nd cutting) 0.40 1.26 1.21 0.7 
Alfalfa hay (3rd cutting) 0.40 1.26 1.18 0.7 
Alfalfa hay (4th cutting) 0.40 1.22 1.16 0.7 
Alfalfa hay (5th cutting) 0.40 1.23 1.20 0.7 
Alfalfa hay (6th cutting) 0.40 1.24 1.20 0.7 
Oat hay 0.30 1.22 0.25 1.0 
Corn silageg 0.30 1.24 0.62 2.0 
a 
Kc values for the initial growth stage. Source: Allen et al. (1998 [DIRS 157311], Table 12, pp. 110 
b 
to 114). 
Kc values for the mid-season growth stage adjusted from Allen et al. 1998 ([DIRS 157311], 
Equation 62, p. 121) according to Equation D-2. 
c 
Kc values for the end of the late season growth stage adjusted from Allen et al. (1998 
[DIRS 157311], Equation 65, p. 125) according to Equation D-3. 
d Source: Allen et al. 1998 ([DIRS 157311], Table 12, pp. 110 to 114). 
e 
Midpoint of the range for Kc end (0.8) was used. 
f 
Kc end for dry harvest was used. 
g Kc end for wet harvest was used. 
Table D-8. Adjusted Crop Coefficients (Kc) and Maximum Crop Height for Early and Late 
Season Crops for Lower Bound Glacial Transition Climate Conditions 
Crop Kc ini 
a Kc mid 
b Kc end 
c Maximum Crop Height (m)d 
Leaf Lettuce early 0.70 1.09 1.04 0.3 
Leaf Lettuce late 0.70 1.08 1.03 0.3 
Head Lettuce early 0.70 1.09 1.04 0.3 
Head Lettuce late 0.70 1.08 1.03 0.3 
Cabbage early 0.70 1.15 1.05 0.4 
Cabbage late 0.70 1.13 1.03 0.4 
Spinach early 0.70 1.09 1.04 0.3 
Spinach late 0.70 1.08 1.02 0.3 
Celery 0.70 1.16 1.10 0.6 
Broccoli early 0.70 1.14 1.04 0.3 
Broccoli late 0.70 1.13 1.02 0.3 
Cauliflower early 0.70 1.15 1.05 0.4 
Cauliflower late 0.70 1.13 1.03 0.4 
Potatoes 0.50 1.26 0.85 0.6 
Onions 0.70 1.14 0.85 0.4 
Carrots early 0.70 1.14 1.04 0.3 

Table D-8. Adjusted Crop Coefficients (Kc) and Maximum Crop Height for Early and 
Late Season Crops for Lower Bound Glacial Transition Climate 
Conditions (Continued) 
Crop Kc ini 
a Kc mid 
b Kc end 
c Maximum Crop Height (m)d 
Carrots late 0.70 1.13 1.03 0.3 
Sweet Corn 0.30 1.29 1.18 1.5 
Bell peppers 0.60 1.16 1.00 0.7 
Cucumbers 0.60 1.09 0.84 0.3 
Squash 0.50 1.04 0.84 0.3 
Melons 0.50 1.14 0.84 0.4 
Tomatoese 0.60 1.26 0.90 0.6 
Alfalfa hay (1st cutting) 0.40 1.30 1.26 0.7 
Alfalfa hay (2nd cutting) 0.40 1.32 1.27 0.7 
Alfalfa hay (3rd cutting) 0.40 1.30 1.25 0.7 
Apples 0.45 1.14 0.86 4.0 
Wine Grapes 0.30 0.84 0.45 1.75 
Strawberries 0.40 0.93 0.83 0.2 
Winter wheat 0.40 1.28 0.25 1.0 
Barley 0.30 1.28 0.25 1.0 
Oats 0.30 1.28 0.25 1.0 
Feed Cornf 0.30 1.35 0.35 2.0 
Corn silageg 0.30 1.36 0.74 2.0 
Oat hay 0.30 1.28 0.25 1.0 
a 
Kc values for the initial growth stage. Source: Allen et al. (1998 [DIRS 157311], Table 12, 
b 
pp. 110 to 114). 
Kc values for the mid-season growth stage adjusted from Allen et al. (1998 [DIRS 157311], 
Equation 62, p. 121) according to Equation D-2. 
c 
Kc values for the end of the late season growth stage adjusted from Allen et al. (1998 
[DIRS 157311], Equation 65, p. 125) according to Equation D-3. 
d Source: Allen et al. (1998 [DIRS 157311], Table 12, pp. 110 to 114). 
e 
Midpoint of the range for Kc end was used. 
f 
Kc end for dry harvest was used. 
g Kc end for wet harvest was used. 
Table D-9. Adjusted Crop Coefficients (Kc) and Maximum Crop Height for Early and Late Season Crops 
for Upper Bound Glacial Transition Climate Conditions 
Crop Kc ini 
a Kc mid 
b Kc end 
c Maximum Crop Height (m)d 
Leaf Lettuce early 0.70 1.04 0.99 0.3 
Leaf Lettuce late 0.70 1.05 0.99 0.3 
Head Lettuce early 0.70 1.04 1.00 0.3 
Head Lettuce late 0.70 1.04 0.99 0.3 
Cabbage early 0.70 1.09 1.00 0.4 
Cabbage late 0.70 1.09 0.99 0.4 
Spinach early 0.70 1.04 0.99 0.3 
Spinach late 0.70 1.04 0.98 0.3 
Celery 0.70 1.11 1.06 0.6 
Broccoli early 0.70 1.09 1.00 0.3 
Broccoli late 0.70 1.09 0.96 0.3 

Table D-9. Adjusted Crop Coefficients (Kc) and Maximum Crop Height for Early and Late 
Season Crops for Upper Bound Glacial Transition Climate Conditions 
(Continued) 
Crop Kc ini 
a Kc mid 
b Kc end 
c Maximum Crop Height (m)d 
Cauliflower early 0.70 1.09 0.99 0.4 
Cauliflower late 0.70 1.09 0.99 0.4 
Potatoes 0.50 1.21 0.81 0.6 
Onions 0.70 1.09 0.80 0.4 
Carrots early 0.70 1.09 1.00 0.3 
Carrots late 0.70 1.09 0.99 0.3 
Sweet Corn 0.30 1.24 1.13 1.5 
Bell peppers 0.60 1.11 0.95 0.7 
Cucumbers 0.60 1.05 0.80 0.3 
Squash 0.50 1.00 0.80 0.3 
Melons 0.50 1.10 0.80 0.4 
Tomatoese 0.60 1.21 0.86 0.6 
Alfalfa hay (1st cutting) 0.40 1.24 1.19 0.7 
Alfalfa hay (2nd cutting) 0.40 1.26 1.21 0.7 
Alfalfa hay (3rd cutting) 0.40 1.25 1.20 0.7 
Apples 0.45 1.05 0.77 4.0 
Wine Grapes 0.30 0.78 0.45 1.75 
Strawberries 0.40 0.89 0.80 0.2 
Winter wheat 0.40 1.20 0.25 1.0 
Barley 0.30 1.20 0.25 1.0 
Oats 0.30 1.21 0.25 1.0 
Feed Cornf 0.30 1.28 0.35 2.0 
Corn silageg 0.30 1.29 0.67 2.0 
Oat hay 0.30 1.21 0.25 1.0 
a 
Kc values for the initial growth stage. Source: Allen et al. (1998 [DIRS 157311], Table 12, pp. 110 
b 
to 114). 
Kc values for the mid-season growth stage adjusted from Allen et al. (1998 [DIRS 157311], 
Equation 62, p. 121) according to Equation D-2. 
c 
Kc values for the end of the late season growth stage adjusted from Allen et al. (1998 
[DIRS 157311], Equation 65, p. 125) according to Equation D-3. 
d Source: Allen et al. (1998 [DIRS 157311], Table 12, pp. 110 to 114). 
e 
Midpoint of the range for Kc end was used. 
f 
Kc end for dry harvest was used. 
g Kc end for wet harvest was used. 

D4. AVERAGE MONTHLY KC VALUES 
For Kc values for different growth stages to correspond with mean monthly ETo, it was necessary 
to calculate mean monthly Kc for each crop. This was done by first identifying the months in 
which the four growth stages occurred using planting dates in Tables D-1 and D-2 and growth 
stage lengths in Tables D-3 and D-4. Months with overlapping growth stages were noted and the 
number of days in the month for each growth stage was recorded. Kc curves were constructed 
according to Allen et al. (1998 [DIRS 157311], Figure 36, p. 132) to aide in determination of 
mean monthly values (Figure D-1). If the development or late stages were split across a month, 
the following equation was used to calculate Kc for the day of the growth stage that ended the 
month: 
)
i
-
.
L 
L
stage 
(
.
.
prev 
K
(
next c 
-
K 
prev c 
) 
(Eq. D-5) 
K
=
K
..
. 
+
..
. 
ci 
prev c 
where 
i = day within the growing season, 
Kci = Kc crop coefficient on day i, 
Kc prev = Kc for the previous growth stage, 
Lstage = length of stage under consideration (days), 
S (Lprev) = sum of the lengths of all previous stages (days). 
Example: The crop coefficient curve for lettuce (present-day climate) is shown in Figure D-1. 
The following inputs were required to calculate the monthly Kc values. 
Planting date = March 6 (Julian Day 65, Table D-1). 
Stage length (days): Initial = 15, Developmental = 21, Mid = 18, Late = 6, Total = 60. 
End of stage (Julian day): Initial = 80, Developmental = 101, Mid = 119, Late = 125. 
Kc for stages: Initial = 0.7, Mid = 1.08, End = 1.03. 
Growing days per stage in March: Initial = 15, Developmental = 10, Total = 25. 
Growing days per stage in April: Developmental = 11, Mid = 18, Late = 1, Total = 30. 
Growing days per stage in May: Late = 5, Total = 5 
Kci (Equation D-5) was required for March 31 (Julian Day 90) because the development stage 
overlapped March and April, and for April 30 (Julian Day 120) because the late stage overlapped 
April and May. 
.
(
.-
15 
08.1 
K
c(90) 
=
70.0
+
..
-
(9065)
21
..
-
70.0
)
=
88.0 (Eq. D-5) 

(120
-
65)
-
(15
+
21
+
18)
03.1 
(
.
.
-
08.1
)
=
07.1 (Eq. D-5) 
K
=
08.1
+
..
..
c(120) 
6 
The mean Kc month values were calculated by multiplying the number of days in the growth stage 
by the corresponding Kc to get a weighted average: 
15 
10
70.0 
88.0 
...
...
= 
+ 
K 
March c 
70.0
+
74.0
=
25 25 
2 
=
11
30
...
88.0 
08.1 
18 
1 
08.1 
07.1 
...
...
...
=
05.1
+
+
K 
cApril 
08.1 
+
+
2 
30 30
2 
5 
07.1 
03.1
.
.
+ 
K 
cMay 
05.1
=
.. 
.. 
= 
5 
2 
Mean monthly Kc values are in Tables D-10, D-11, D-12, and D-13. 
NOTE: Circled numbers are crop coefficients. Numbers are dates expressed in Julian format, excluding year 
(JD). Initial stage = JD 65 through 80, development stage = JD 81 through 101, midseason stage = JD 
102 through 119, and the late stage = JD 120 through 125. 90* and 120* are the last Julian Days of March 
and April for which calculation of Kci was required in order to calculate monthly means (see text). 
Figure D-1. Crop Coefficient Curve for Early Lettuce under Present-Day Climate Conditions 

D5. CALCULATION OF CROP EVAPOTRANSPIRATION 
Mean daily ETo for each month (Appendix C, Table C-9) and Kc (Tables D-10 through D-13) 
were used in Equation D-1 to generate mean monthly crop evapotranspiration (ETc) for the 
26 crops and turf grass for all climates (Tables D-10 through D-13). Mean daily ETc values for 
each month were multiplied by the number of growing days per month to get total mean monthly 
ETc (Tables D-10 through D-13). These monthly values were used to generate seasonal 
irrigation requirements (Appendix E), which were used to calculate annual average irrigation rate 
(IR), daily average irrigation rate (IRDj), irrigation application (IAj), and overwatering rate (OW) 
(see Section 6 and Appendix E). 
Example: Monthly mean ETo, monthly mean Kc, and number of growing season days in each 
month were required for calculating mean daily and monthly ETc. ETc daily is Kc multiplied by 
ETo (Equation D-1). ETc monthly is ETc daily multiplied by the number of days in the month. 
Example calculation: 
March April May 
Kc 0.74 1.05 1.05 
ETo 4.24 5.51 6.86 
Days 25 30 5 
ETcdaily = Kc * ETo 0.74*4.24=3.14 1.05*5.51=5.78 1.05*6.86=7.20 
ETcmonthly = ETcdaily * Days 3.14*25=78.5 5.78*30=173.40 7.20*5=36.00 
It should be noted that the values presented in the appendix tables are rounded. Calculations 
were done using more precise values from the original data sources, resulting in small 
differences between the examples and the data reported in the tables in some cases. 

Table D-10. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for Present-Day 
Climate Conditions 
Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-26 September 2004 
Crop Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 
Leaf Lettuce early - Kc 
a 0.74 1.04 1.05 
ETo (mm/day)b 4.24 5.51 6.86 
ETc (mm/day)c 3.12 5.75 7.21 
Monthly ETc (mm/month)d 77.95 172.47 36.05 
Leaf Lettuce late - Kc 0.77 1.06 
Eto (mm/day) 6.84 4.67 
Etc (mm/day) 5.24 4.94 
Etc (mm/month) 157.21 148.27 
Head Lettuce early -Kc 0.74 1.04 1.05 
ETo (mm/day) 4.24 5.51 6.86 
ETc (mm/day) 3.12 5.75 7.21 
Monthly ETc (mm/month) 77.95 172.47 36.05 
Head Lettuce late - Kc 0.77 1.06 
Eto (mm/day) 6.84 4.67 
Etc (mm/day) 5.24 4.94 
Etc (mm/month) 157.21 148.27 
Cabbage early - Kc 0.72 1.03 1.12 
Eto (mm/day) 4.24 5.51 6.86 
Etc (mm/day) 3.06 5.69 7.69 
Etc (mm/month) 91.92 170.70 192.26 
Cabbage late - Kc 0.70 0.75 1.07 1.10 
Eto (mm/day) 8.62 6.84 4.67 2.72 
Etc (mm/day) 6.03 5.14 4.96 3.00 
Etc (mm/month) 30.17 154.09 154.82 56.96 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-27 September 2004 
Table D-10. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for Present-
Day Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 
Celery early - Kc 0.70 0.92 1.15 1.15 1.13 
Eto (mm/day) 5.51 6.86 8.38 8.89 8.62 
Etc (mm/day) 3.86 6.34 9.63 10.22 9.72 
Etc (mm/month) 73.29 196.68 289.00 316.67 136.09 
Celery late - Kc 1.09 0.70 0.89 1.12 1.12 
Eto (mm/day) 1.77 6.84 4.67 2.72 1.85 
Etc (mm/day) 1.93 4.78 4.16 3.04 2.07 
Etc (mm/month) 36.60 66.99 128.88 91.30 64.20 
Broccoli early - Kc 0.72 1.05 1.11 
Eto (mm/day) 4.24 5.51 6.86 
Etc (mm/day) 3.06 5.78 7.60 
Etc (mm/month) 91.78 173.52 151.96 
Broccoli late - Kc 0.70 0.75 1.08 1.09 
Eto (mm/day) 8.62 6.84 4.67 2.72 
Etc (mm/day) 6.03 5.15 5.06 2.95 
Etc (mm/month) 30.17 154.40 156.98 41.35 
Cauliflower early - Kc 0.73 1.05 1.11 
Eto (mm/day) 4.24 5.51 6.86 
Etc (mm/day) 3.07 5.80 7.65 
Etc (mm/month) 92.24 173.93 152.94 
Cauliflower late - Kc 0.70 0.76 1.08 1.09 
Eto (mm/day) 8.62 6.84 4.67 2.72 
Etc (mm/day) 6.03 5.18 5.07 2.97 
Etc (mm/month) 30.17 155.28 157.32 41.58 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-28 September 2004 
Table D-10. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for Present-
Day Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 
Spinach early - Kc 0.72 1.04 1.05 
Eto (mm/day) 4.24 5.51 6.86 
Etc (mm/day) 3.05 5.72 7.24 
Etc (mm/month) 45.81 171.54 36.19 
Spinach late - Kc 0.71 1.02 1.04 
Eto (mm/day) 6.84 4.67 2.72 
Etc (mm/day) 4.88 4.79 2.84 
Etc (mm/month) 68.35 148.45 14.18 
Potatoes - Kc 0.50 0.56 1.14 1.24 0.94 
Eto (mm/day) 4.23 5.51 6.86 8.38 8.89 
Etc (mm/day) 2.12 3.06 7.80 10.39 8.36 
Etc (mm/month) 10.59 91.88 241.65 311.60 116.99 
Onions early - Kc 0.70 0.93 1.14 1.12 0.92 
Eto (mm/day) 4.24 5.51 6.86 8.38 8.89 
Etc (mm/day) 2.96 5.14 7.81 9.37 8.22 
Etc (mm/month) 14.82 154.26 242.10 281.04 115.13 
Onions late - Kc 0.83 0.71 1.05 1.12 1.04 
Eto (mm/day) 1.77 6.84 4.67 2.72 1.85 
Etc (mm/day) 1.47 4.88 4.89 3.04 1.92 
Etc (mm/month) 5.88 68.27 151.62 91.36 59.37 
Carrots early - Kc 0.71 1.04 1.11 
Eto (mm/day) 4.24 5.51 6.86 
Etc (mm/day) 3.02 5.75 7.62 
Etc (mm/month) 45.24 172.64 288.78 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-29 September 2004 
Table D-10. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for Present-
Day Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 
Carrots late - Kc 0.70 0.90 1.12 1.06 
Eto (mm/day) 8.62 6.84 4.67 2.72 
Etc (mm/day) 6.03 6.18 5.25 2.88 
Etc (mm/month) 30.17 185.52 162.70 25.92 
Sweet Corn - Kc 0.55 1.27 1.27 
Eto (mm/day) 6.86 8.38 8.89 
Etc (mm/day) 3.76 10.66 11.26 
Etc (mm/month) 116.70 319.60 236.48 
Bell Peppers - Kc 0.75 1.15 1.12 
Eto (mm/day) 6.86 8.38 8.89 
Etc (mm/day) 5.12 9.68 9.94 
Etc (mm/month) 127.84 290.52 228.66 
Cucumbers - Kc 0.76 1.04 
Eto (mm/day) 8.39 8.89 
Etc (mm/day) 6.34 9.26 
Etc (mm/month) 183.98 287.03 
Squash - Kc 0.50 0.85 0.96 
Eto (mm/day) 6.86 8.38 8.89 
Etc (mm/day) 3.43 7.16 8.54 
Etc (mm/month) 51.47 214.98 111.08 
Melons - Kc 0.51 0.75 1.15 1.07 
Eto (mm/day) 6.86 8.38 8.89 8.62 
Etc (mm/day) 3.51 6.29 10.18 9.27 
Etc (mm/month) 52.65 188.74 315.65 222.37 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-30 September 2004 
Table D-10. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for Present-
Day Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 
Tomatoes - Kc 0.63 1.15 1.14 
Eto (mm/day) 6.86 8.38 8.89 
Etc (mm/day) 4.32 9.63 10.17 
Etc (mm/month) 108.14 289.02 254.18 
Apples - Kc 0.60 0.76 1.06 1.12 1.12 1.12 1.12 1.00 
Eto (mm/day) 4.24 5.51 6.86 8.38 8.89 8.62 6.84 4.67 
Etc (mm/day) 2.54 4.17 7.24 9.42 9.98 9.68 7.67 4.70 
Etc (mm/month) 76.24 125.05 224.50 282.45 309.44 300.04 230.02 126.89 
Grapes - Kc 0.33 0.62 0.83 0.83 0.77 0.56 
Eto (mm/day) 4.24 5.51 6.86 8.38 8.89 8.62 
Etc (mm/day) 1.39 3.41 5.69 6.97 6.89 4.84 
Etc (mm/month) 41.72 102.21 176.42 209.05 213.56 150.03 
Strawberries - Kc 0.57 0.66 0.90 0.92 0.92 0.88 0.84 
Eto (mm/day) 4.24 5.51 6.86 8.38 8.89 8.62 6.84 
Etc (mm/day) 2.41 3.64 6.21 7.72 8.14 7.61 5.73 
Etc (mm/month) 72.18 109.14 192.39 231.73 252.24 235.98 126.04 
Winter wheat - Kc 1.14 1.25 1.25 1.25 1.01 0.45 0.70 0.74 0.93 
Eto (mm/day) 1.77 2.64 4.24 5.51 6.86 8.38 4.67 2.72 1.85 
Etc (mm/day) 2.02 3.31 5.31 6.91 6.91 3.74 3.27 2.00 1.72 
Etc (mm/month) 62.60 92.78 164.72 207.29 214.36 59.84 49.08 60.11 53.30 
Barley - Kc 1.07 1.25 1.25 1.25 0.94 0.42 0.30 0.35 0.68 
Eto (mm/day) 1.77 2.64 4.24 5.51 6.86 8.38 4.67 2.72 1.85 
Etc (mm/day) 1.89 3.31 5.31 6.90 6.43 3.56 1.40 0.94 1.26 
Etc (mm/month) 58.74 92.69 164.54 207.07 199.26 56.95 21.03 28.30 39.14 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-31 September 2004 
Table D-10. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for Present-
Day Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 
Feed Corn - Kc 0.30 0.43 1.08 1.34 1.21 0.60 
Eto (mm/day) 6.86 8.38 8.89 8.62 6.84 4.67 
Etc (mm/day) 2.06 3.59 9.62 11.55 8.28 2.83 
Etc (mm/month) 30.88 107.57 298.18 357.96 248.45 48.06 
Oat Feed - Kc 0.51 1.06 1.26 1.25 0.67 0.30 
Eto (mm/day) 1.76 2.64 4.24 5.51 6.86 1.85 
Etc (mm/day) 0.90 2.81 5.34 6.86 4.60 0.56 
Etc (mm/month) 27.93 78.60 165.47 205.92 115.11 8.32 
Alfalfa hay (1st) - Kc 0.69 1.26 1.20 
Eto (mm/day) 1.77 2.64 4.24 
Etc (mm/day) 1.22 3.33 5.10 
Etc (mm/month) 36.73 93.34 10.19 
Alfalfa hay (2nd) -Kc 0.74 1.29 
Eto (mm/day) 4.24 5.51 
Etc (mm/day) 3.14 7.12 
Etc (mm/month) 91.21 185.25 
Alfalfa hay (3rd) - Kc 0.40 0.90 1.29 
Eto (mm/day) 5.51 6.86 8.38 
Etc (mm/day) 2.20 6.15 10.84 
Etc (mm/month) 8.81 190.69 216.90 
Alfalfa hay (4th) - Kc 0.40 1.07 1.28 
Eto (mm/day) 8.38 8.99 8.62 
Etc (mm/day) 3.38 9.49 11.07 
Etc (mm/month) 33.75 294.22 154.96 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-32 September 2004 
Table D-10. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for Present-
Day Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 
Alfalfa hay (5th) - Kc 0.49 1.21 1.26 
Eto (mm/day) 8.62 6.84 4.67 
Etc (mm/day) 4.26 8.30 5.87 
Etc (mm/month) 72.38 248.93 46.99 
Alfalfa hay (6th) - Kc 0.61 1.26 1.23 
Eto (mm/day) 4.67 2.72 1.85 
Etc (mm/day) 2.84 3.43 2.28 
Etc (mm/month) 65.44 103.00 4.57 
Corn silage - Kc 0.30 0.85 1.33 0.99 
Eto (mm/day) 6.86 8.28 8.89 8.62 
Etc (mm/day) 2.06 7.16 11.85 8.58 
Etc (mm/month) 30.88 214.70 367.47 145.79 
Oat hay - Kc 0.60 1.26 0.76 
Eto (mm/day) 5.51 6.86 8.38 
Etc (mm/day) 3.28 8.63 6.34 
Etc (mm/month) 98.32 267.53 88.84 
Bermudae - Kc 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 
Eto (mm/day) 1.77 2.64 4.24 5.51 6.86 8.38 8.89 8.62 6.84 4.67 2.72 1.85 
Etc (mm/day) 1.50 2.24 3.60 4.68 5.83 7.13 7.56 7.33 5.81 3.97 2.31 1.57 
Etc (mm/month) 46.61 62.87 111.60 140.44 180.83 213.84 234.27 227.15 174.31 123.17 69.33 48.75 
a 
Mean monthly Kc values calculated according to Appendix D, Section 4. 
b 
Mean monthly ETo values from Appendix C, Table C-9. 
c 
Mean daily ETc calculated using Equation D-1. 
d Mean monthly ETc calculated from mean daily ETc and the number of growing days per month (Appendix D, Section 5). 
e 
Bermudagrass was selected for present-day and upper bound monsoon climates because it is a warm season grass. 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-33 September 2004 
Table D-11. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for 
Upper Bound Monsoon Climate Conditions 
Crop Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 
Leaf Lettuce early - Kc 
a 0.73 1.02 1.02 
ETo (mm/day)b 3.82 5.10 6.32 
ETc (mm/day)c 2.80 5.18 6.45 
Monthly ETc (mm/month)d 69.99 155.43 32.27 
Leaf Lettuce late - Kc 0.76 1.02 
Eto (mm/day) 5.08 3.99 
Etc (mm/day) 3.87 4.09 
Etc (mm/month) 115.97 122.66 
Head Lettuce early - Kc 0.73 1.02 1.02 
ETo (mm/day) 3.82 5.10 6.32 
ETc (mm/day) 2.80 5.18 6.45 
Monthly ETc (mm/month) 69.99 155.30 32.27 
Head Lettuce late - Kc 0.76 1.02 
Eto (mm/day) 5.08 3.99 
Etc (mm/day) 3.87 4.09 
Etc (mm/month) 115.97 122.66 
Cabbage early - Kc 0.72 1.01 1.09 
Eto (mm/day) 3.82 5.10 6.32 
Etc (mm/day) 2.76 5.15 6.88 
Etc (mm/month) 82.67 154.37 172.08 
Cabbage late - Kc 0.70 0.75 1.04 1.07 
Eto (mm/day) 5.64 5.08 3.99 2.45 
Etc (mm/day) 3.95 3.80 4.15 2.62 
Etc (mm/month) 19.73 113.95 128.52 49.72 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-34 September 2004 
Table D-11. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for Upper 
Bound Monsoon Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 
Celery early - Kc 0.70 0.89 1.09 1.09 1.06 
Eto (mm/day) 5.10 6.32 7.36 6.25 5.64 
Etc (mm/day) 3.57 5.65 8.01 6.80 5.95 
Etc (mm/month) 67.82 175.09 240.16 210.95 83.26 
Celery late - Kc 1.08 0.70 0.88 1.09 1.09 
Eto (mm/day) 1.92 5.08 3.99 2.45 2.00 
Etc (mm/day) 2.07 3.56 3.51 2.68 2.19 
Etc (mm/month) 39.31 49.82 108.67 80.38 67.93 
Broccoli early - Kc 0.72 1.03 1.08 
Eto (mm/day) 3.82 5.10 6.32 
Etc (mm/day) 2.75 5.23 6.81 
Etc (mm/month) 82.55 156.97 136.24 
Broccoli late - Kc 0.70 0.75 1.05 1.05 
Eto (mm/day) 5.64 5.08 3.99 2.45 
Etc (mm/day) 3.95 3.81 4.21 2.58 
Etc (mm/month) 19.73 114.18 130.36 36.15 
Cauliflower early - Kc 0.72 1.03 1.08 
Eto (mm/day) 3.82 5.10 6.32 
Etc (mm/day) 2.76 5.24 6.84 
Etc (mm/month) 82.94 157.08 136.83 
Cauliflower late - Kc 0.70 0.75 1.05 1.06 
Eto (mm/day) 5.64 5.08 3.99 2.45 
Etc (mm/day) 3.95 3.82 4.20 2.59 
Etc (mm/month) 19.73 114.74 130.35 36.27 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-35 September 2004 
Table D-11. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for Upper 
Bound Monsoon Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 
Spinach early - Kc 0.72 1.01 1.03 
Eto (mm/day) 3.82 5.10 6.32 
Etc (mm/day) 2.75 5.15 6.48 
Etc (mm/month) 41.20 154.64 32.40 
Spinach late - Kc 0.71 0.99 1.01 
Eto (mm/day) 5.08 3.99 2.45 
Etc (mm/day) 3.62 3.97 2.48 
Etc (mm/month) 50.73 123.11 12.39 
Potatoes - Kc 0.50 0.55 1.10 1.20 0.92 
Eto (mm/day) 3.82 5.10 6.32 7.36 6.25 
Etc (mm/day) 1.91 2.82 6.96 8.81 5.73 
Etc (mm/month) 9.54 84.57 215.82 264.39 80.19 
Onions early - Kc 0.70 0.92 1.10 1.08 0.88 
Eto (mm/day) 3.82 5.10 6.32 7.36 6.25 
Etc (mm/day) 2.67 4.67 6.98 7.96 5.48 
Etc (mm/month) 13.36 140.05 216.32 238.86 76.72 
Onions late - Kc .82 0.71 1.55 1.09 1.01 
Eto (mm/day) 1.92 5.08 3.99 2.45 2.00 
Etc (mm/day) 1.57 3.62 6.20 2.67 2.02 
Etc (mm/month) 6.28 50.70 192.07 80.04 62.65 
Carrots early - Kc 0.71 1.02 1.08 
Eto (mm/day) 3.82 5.10 6.32 
Etc (mm/day) 2.72 5.21 6.84 
Etc (mm/month) 40.73 156.25 205.17 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-36 September 2004 
Table D-11. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for Upper 
Bound Monsoon Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 
Carrots late - Kc 0.70 0.89 1.09 1.03 
Eto (mm/day) 5.64 5.08 3.99 2.45 
Etc (mm/day) 3.95 4.51 4.34 2.51 
Etc (mm/month) 19.73 135.26 134.44 22.63 
Sweet Corn - Kc 0.53 1.20 1.19 
Eto (mm/day) 6.32 7.36 6.25 
Etc (mm/day) 6.01 7.96 8.82 
Etc (mm/month) 186.31 238.8 185.22 
Bell Peppers - Kc 0.73 1.10 1.06 
Eto (mm/day) 6.32 7.36 6.25 
Etc (mm/day) 4.62 8.09 6.61 
Etc (mm/month) 115.39 242.69 152.07 
Cucumbers - Kc 0.74 0.98 
Eto (mm/day) 7.36 6.25 
Etc (mm/day) 5.42 6.14 
Etc (mm/month) 157.21 190.25 
Squash - Kc 0.50 0.83 0.91 
Eto (mm/day) 6.32 7.36 6.25 
Etc (mm/day) 3.16 6.08 5.70 
Etc (mm/month) 47.37 182.32 74.14 
Melons - Kc 0.51 0.71 1.07 1.00 
Eto (mm/day) 6.32 7.36 6.25 5.64 
Etc (mm/day) 3.22 5.25 6.69 5.63 
Etc (mm/month) 48.33 157.46 207.47 135.17 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-37 September 2004 
Table D-11. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for Upper 
Bound Monsoon Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 
Tomatoes - Kc 0.63 1.10 1.08 
Eto (mm/day) 6.32 7.36 6.25 
Etc (mm/day) 3.96 8.11 6.77 
Etc (mm/month) 99.12 243.19 169.16 
Apples - Kc 0.60 0.72 0.96 1.01 1.01 1.01 1.01 0.91 
Eto (mm/day) 3.82 5.10 6.32 7.36 6.25 5.64 5.08 3.99 
Etc (mm/day) 2.29 3.68 6.04 7.42 6.30 5.68 5.12 3.64 
Etc (mm/month) 68.72 110.48 187.14 222.55 195.45 176.21 153.66 98.22 
Grapes - Kc 0.33 0.58 0.77 0.77 0.73 0.54 
Eto (mm/day) 3.82 5.10 6.32 7.36 6.25 5.64 
Etc (mm/day) 1.24 2.98 4.88 5.69 4.54 3.07 
Etc (mm/month) 37.25 89.32 151.17 170.72 140.69 95.16 
Strawberries - Kc 0.57 0.64 0.87 0.88 0.68 0.84 0.79 
Eto (mm/day) 3.82 5.10 6.32 7.36 6.25 5.64 5.08 
Etc (mm/day) 2.16 3.27 5.49 6.51 4.25 4.73 4.00 
Etc (mm/month) 64.78 98.22 170.15 195.22 131.67 146.67 87.91 
Winter wheat - Kc 1.11 1.22 1.22 1.22 0.98 0.44 0.70 0.73 0.91 
Eto (mm/day) 1.92 2.96 3.82 5.10 6.32 7.36 3.99 2.45 2.00 
Etc (mm/day) 2.14 3.61 4.66 6.22 6.20 3.23 2.79 1.80 1.83 
Etc (mm/month) 66.46 100.94 144.36 186.52 192.15 51.69 41.92 53.96 56.80 
Barley - Kc 1.04 1.22 1.22 1.22 0.91 0.42 0.30 0.35 0.67 
Eto (mm/day) 1.92 2.96 3.82 5.10 6.32 7.36 3.99 2.45 2.00 
Etc (mm/day) 2.01 3.60 4.66 6.21 5.77 3.08 1.20 0.85 1.34 
Etc (mm/month) 62.27 100.91 144.31 186.45 178.86 49.26 17.96 25.36 41.52 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-38 September 2004 
Table D-11. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for Upper 
Bound Monsoon Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 
Feed Corn - Kc 0.30 0.41 1.00 1.23 1.11 0.58 
Eto (mm/day) 6.32 7.36 6.25 5.64 5.08 3.99 
Etc (mm/day) 1.89 3.04 6.23 6.91 5.65 2.30 
Etc (mm/month) 28.42 91.26 193.01 214.07 169.58 39.05 
Oat Feed - Kc 0.50 1.03 1.22 1.21 0.65 0.30 
Eto (mm/day) 1.92 2.96 3.82 5.10 6.32 2.00 
Etc (mm/day) 0.96 3.05 4.66 6.15 4.13 0.60 
Etc (mm/month) 29.86 85.27 144.32 184.40 103.26 9.01 
Alfalfa hay (1st) - Kc 0.69 1.25 1.19 
Eto (mm/day) 1.92 2.96 3.82 
Etc (mm/day) 1.32 3.69 4.55 
Etc (mm/month) 39.69 103.40 9.10 
Alfalfa hay (2nd)-Kc 0.71 1.26 
Eto (mm/day) 3.82 5.10 
Etc (mm/day) 2.71 6.40 
Etc (mm/month) 75.76 172.76 
Alfalfa hay (3rd) - Kc 0.40 0.87 1.24 
Eto (mm/day) 5.01 6.32 7.36 
Etc (mm/day) 2.04 5.52 9.15 
Etc (mm/month) 8.16 171.16 183.04 
Alfalfa hay (4th) - Kc 0.47 1.39 1.20 
Eto (mm/day) 7.36 6.25 5.64 
Etc (mm/day) 3.48 8.72 6.76 
Etc (mm/month) 34.78 270.18 94.58 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-39 September 2004 
Table D-11. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for Upper 
Bound Monsoon Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec 
Alfalfa hay (5th) - Kc 0.49 1.01 1.21 
Eto (mm/day) 5.64 5.08 3.99 
Etc (mm/day) 2.76 5.15 4.84 
Etc (mm/month) 46.84 51.54 96.85 
Alfalfa hay (6th) - Kc 0.60 1.23 1.20 
Eto (mm/day) 3.99 2.45 2.00 
Etc (mm/day) 2.40 3.00 2.40 
Etc (mm/month) 55.09 90.13 4.81 
Corn silage - Kc 0.30 0.85 1.23 0.87 
Eto (mm/day) 6.32 7.36 6.25 5.64 
Etc (mm/day) 1.89 6.22 7.67 4.88 
Etc (mm/month) 28.42 186.59 237.85 78.02 
Oat Hay - Kc 0.58 1.22 0.74 
Eto (mm/day) 5.10 6.32 7.36 
Etc (mm/day) 2.97 7.70 5.42 
Etc (mm/month) 89.14 238.57 75.90 
Bermudae - Kc 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 0.85 
Eto (mm/day) 1.92 2.96 3.82 5.10 6.32 7.36 6.25 5.64 5.08 3.99 2.45 2.00 
Etc (mm/day) 1.64 2.51 3.25 4.33 5.37 6.25 5.31 4.79 4.32 3.39 2.08 1.70 
Etc (mm/month) 50.71 70.35 100.60 129.97 166.43 187.57 164.73 148.52 129.62 105.19 62.43 52.76 
a 
Mean monthly Kc values calculated according to Appendix D, Section 4. 
b 
Mean monthly ETo values from Appendix C, Table C-9. 
Mean daily ETc calculated using Equation D-1. 
d Mean monthly ETc calculated from mean daily ETc and the number of growing days per month (Appendix D, Section 5). 
e 
Bermudagrass was selected for upper bound monsoon climate because it is a warm season grass. 

Table D-12. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for Lower Bound 
Glacial Transition Climate Conditions 
Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-40 September 2004 
Crop Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 
Leaf Lettuce early - Kc 
a 0.70 0.85 1.08 
ETo (mm/day)b 4.30 6.01 7.96 
ETc (mm/day)c 3.01 5.08 8.62 
Monthly ETc (mm/month)d 21.07 157.59 172.44 
Leaf Lettuce late - Kc 0.70 0.95 1.06 
ETo (mm/day) 8.82 8.26 6.27 
ETc (mm/day) 6.18 7.87 6.66 
Monthly ETc (mm/month) 98.86 243.84 73.23 
Head Lettuce early - Kc 0.70 0.76 1.05 1.07 
ETo (mm/day) 4.30 6.01 7.96 8.82 
ETc (mm/day) 3.01 4.56 8.37 9.41 
Monthly ETc (mm/month) 21.07 141.43 251.15 94.09 
Head Lettuce late - Kc 0.70 0.83 1.06 1.03 
ETo (mm/day) 8.82 8.26 6.27 4.05 
ETc (mm/day) 6.17 6.88 6.67 4.16 
Monthly ETc (mm/month) 98.78 213.14 200.23 4.16 
Cabbage early - Kc 0.70 0.81 1.14 1.10 
ETo (mm/day) 4.30 6.01 7.96 8.82 
ETc (mm/day) 3.01 4.86 9.06 9.70 
Monthly ETc (mm/month) 21.07 150.52 271.73 67.92 
Cabbage late - Kc 0.70 0.87 1.12 1.04 
ETo (mm/day) 8.82 8.26 6.27 4.05 
ETc (mm/day) 6.17 7.23 7.05 4.21 
Monthly ETc (mm/month) 80.26 224.04 211.49 4.21 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-41 September 2004 
Table D-12. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for 
Lower Bound Glacial Transition Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 
Celery - Kc 0.70 0.75 1.08 1.16 1.13 
ETo (mm/day) 4.30 6.01 7.96 8.82 8.26 
ETc (mm/day) 3.01 4.53 8.62 10.25 9.32 
Monthly ETc (mm/month) 21.07 140.58 258.58 317.71 102.57 
Broccoli early - Kc 0.70 0.77 1.11 1.11 
ETo (mm/day) 4.30 6.01 7.96 8.82 
ETc (mm/day) 3.01 4.65 8.84 9.81 
Monthly ETc (mm/month) 21.07 144.14 265.22 147.10 
Broccoli late - Kc 0.70 0.85 1.12 1.06 
ETo (mm/day) 8.82 8.26 6.27 4.05 
ETc (mm/day) 6.17 7.05 7.04 4.30 
Monthly ETc (mm/month) 92.61 218.55 211.30 30.11 
Cauliflower early - Kc 0.70 0.86 1.14 
ETo (mm/day) 4.30 6.01 7.96 
ETc (mm/day) 3.01 5.17 9.05 
Monthly ETc (mm/month) 21.07 160.27 226.37 
Cauliflower late - Kc 0.70 0.78 1.10 1.04 
ETo (mm/day) 8.82 8.26 6.27 4.05 
ETc (mm/day) 6.17 6.45 6.91 4.21 
Monthly ETc (mm/month) 6.17 200.06 207.42 4.21 
Spinach early - Kc 0.70 0.86 1.09 
ETo (mm/day) 4.30 6.01 7.96 
ETc (mm/day) 3.01 5.16 8.64 
Monthly ETc (mm/month) 21.07 159.82 146.92 
Spinach late - Kc 0.72 1.04 1.03 
ETo (mm/day) 8.26 6.27 4.05 
ETc (mm/day) 5.97 6.51 4.18 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-42 September 2004 
Table D-12. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for 
Lower Bound Glacial Transition Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 
Monthly ETc (mm/month) 143.24 195.16 4.18 
Potatoes - Kc 0.50 0.53 1.14 1.26 1.08 
ETo (mm/day) 4.30 6.01 7.96 8.82 8.26 
ETc (mm/day) 2.15 3.18 9.10 11.13 8.95 
Monthly ETc (mm/month) 15.05 98.50 273.08 345.09 143.15 
Onions - Kc 0.76 1.10 1.14 1.14 0.98 0.86 
ETo (mm/day) 2.85 4.30 6.01 7.96 8.82 8.26 
ETc (mm/day) 2.16 4.75 6.86 9.05 8.65 7.10 
Monthly ETc (mm/month) 64.94 142.52 212.81 271.37 268.25 21.29 
Carrots early - Kc 0.70 0.86 1.14 1.10 
ETo (mm/day) 4.30 6.01 7.96 8.82 
ETc (mm/day) 3.01 5.19 9.10 9.67 
Monthly ETc (mm/month) 21.07 161.02 272.91 116.00 
Carrots late - Kc 0.70 1.01 1.11 1.03 
ETo (mm/day) 8.82 8.26 6.27 4.05 
ETc (mm/day) 6.19 8.34 6.98 4.17 
Monthly ETc (mm/month) 111.49 258.66 209.45 4.17 
Sweet corn - Kc 0.30 0.36 1.05 1.28 1.21 
ETo (mm/day) 5.39 6.96 7.83 7.39 5.77 
ETc (mm/day) 6.01 7.96 8.82 8.26 6.27 
Monthly ETc (mm/month) 42.07 238.80 273.42 256.06 37.62 
Bell peppers - Kc 0.60 0.87 1.16 1.08 
ETo (mm/day) 7.96 8.82 8.26 6.27 
ETc (mm/day) 4.78 7.67 9.57 6.76 
Monthly ETc (mm/month) 105.07 237.88 296.52 108.17 
Cucumbers - Kc 0.60 0.84 1.05 
ETo (mm/day) 6.01 7.96 8.82 
ETc (mm/day) 3.61 6.66 9.27 
Monthly ETc (mm/month) 25.24 199.95 287.25 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-43 September 2004 
Table D-12. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for 
Lower Bound Glacial Transition Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 
Squash - Kc 0.50 0.72 1.00 
ETo (mm/day) 6.01 7.96 8.82 
ETc (mm/day) 3.00 5.70 8.83 
Monthly ETc (mm/month) 21.04 171.04 247.28 
Melons - Kc 0.50 0.58 1.07 1.10 0.88 
ETo (mm/day) 6.01 7.96 8.82 8.26 6.27 
ETc (mm/day) 3.00 4.62 9.41 9.05 5.50 
Monthly ETc (mm/month) 21.04 138.65 291.67 280.67 27.00 
Tomatoes - Kc 0.61 1.08 1.19 0.94 
ETo (mm/day) 7.96 8.82 8.26 6.27 
ETc (mm/day) 4.86 9.52 9.87 5.91 
Monthly ETc (mm/month) 106.81 295.27 305.82 23.63 
Corn silage - Kc 0.30 0.67 1.34 1.29 0.93 
ETo (mm/day) 6.01 7.96 8.82 8.26 6.27 
ETc (mm/day) 1.80 5.35 11.79 10.68 5.82 
Monthly ETc (mm/month) 36.06 160.37 365.35 331.19 145.41 
Feed Corn - Kc 0.30 0.43 1.11 1.35 1.26 0.74 0.40 
ETo (mm/day) 6.01 7.96 8.82 8.26 6.27 4.05 1.90 
ETc (mm/day) 1.80 3.46 9.83 11.12 7.93 3.01 0.76 
Monthly ETc (mm/month) 36.06 103.76 304.78 344.68 237.82 93.24 3.78 
Oat Feed - Kc 0.30 0.39 1.16 1.28 1.16 0.52 
ETo (mm/day) 2.85 4.30 6.01 7.96 8.82 8.26 
ETc (mm/day) 0.86 1.68 6.98 10.19 10.22 4.26 
Monthly ETc (mm/month) 2.66 50.37 216.25 305.63 316.97 68.16 
Alfalfa (1st cutting) - Kc 0.60 1.25 1.28 
ETo (mm/day) 2.85 4.30 6.01 
ETc (mm/day) 1.71 5.36 7.72 
Monthly ETc (mm/month) 51.23 160.78 115.78 
Alfalfa (2nd cutting) - Kc 0.46 1.07 1.30 
ETo (mm/day) 6.01 7.96 8.82 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-44 September 2004 
Table D-12. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for 
Lower Bound Glacial Transition Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 
ETc (mm/day) 2.77 8.53 11.47 
Monthly ETc (mm/month) 44.35 255.88 252.41 
Alfalfa (3rd cutting) - Kc 0.40 0.89 1.29 
ETo (mm/day) 8.82 8.26 6.27 
ETc (mm/day) 3.54 7.38 8.06 
Monthly ETc (mm/month) 31.88 228.64 225.78 
Apples - Kc 0.45 0.56 0.92 1.13 1.14 1.07 0.89 
ETo (mm/day) 4.30 6.01 7.96 8.82 8.26 6.27 4.05 
ETc (mm/day) 1.94 3.34 7.35 10.01 9.38 6.69 3.61 
Monthly ETc (mm/month) 15.48 103.66 220.59 310.29 290.80 200.77 18.05 
Grapes - Kc 0.30 0.60 0.84 0.77 
ETo (mm/day) 7.96 8.82 8.26 6.27 
ETc (mm/day) 2.39 5.25 6.92 4.81 
Monthly ETc (mm/month) 31.12 162.70 214.59 144.25 
Strawberries - Kc 0.40 0.71 0.92 
ETo (mm/day) 4.30 6.01 7.96 
ETc (mm/day) 1.72 4.26 7.32 
Monthly ETc (mm/month) 5.16 131.92 219.49 
Winter wheat - Kc 0.76 0.89 1.02 1.16 1.27 1.28 1.01 0.45 0.40 0.40 0.48 0.62 
ETo (mm/day) 1.21 1.67 2.85 4.30 6.01 7.96 8.82 8.26 6.27 4.05 1.90 1.25 
ETc (mm/day) 0.92 1.45 2.92 5.00 7.64 10.18 8.93 3.68 2.51 1.62 0.91 0.77 
Monthly ETc (mm/month) 28.45 41.69 90.53 149.93 236.74 305.54 276.76 58.96 35.11 50.30 27.43 23.98 
Barley - Kc 0.30 1.01 1.27 0.66 
ETo (mm/day) 4.30 6.01 7.96 8.82 
ETc (mm/day) 1.29 6.08 10.08 5.84 
Monthly ETc (mm/month) 18.06 188.41 302.38 93.42 
Oat Hay - Kc 0.45 1.27 0.72 
ETo (mm/day) 6.01 7.96 8.82 
ETc (mm/day) 2.71 10.09 6.38 
Monthly ETc (mm/month) 43.33 302.67 70.22 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-45 September 2004 
Table D-12. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for 
Lower Bound Glacial Transition Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 
Fescuee - Kc 0.95 0.95 0.95 0.95 0.95 0.95 0.95 
ETo (mm/day) 4.30 6.01 7.96 8.82 8.26 6.27 4.05 
ETc (mm/day) 4.08 5.71 7.56 8.38 7.85 5.96 3.85 
Monthly ETc (mm/month) 122.55 176.99 226.86 259.75 243.26 178.70 119.27 
a 
Mean monthly Kc values calculated according to Appendix D, Section 4. 
b Mean monthly ETo values from Appendix C, Table C-9. 
c 
Mean daily ETc calculated using Equation D-1. 
d 
Mean monthly ETc calculated from mean daily ETc and the number of growing days per month (Appendix D, Section 5). 
e 
Fescue was selected for lower bound glacial transition climate because it is a cool season grass. 

Table D-13. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for Upper Bound 
Glacial Transition Climate Conditions 
Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-46 September 2004 
Crop Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 
Leaf Lettuce early - Kc 
a 0.70 0.83 1.03 
ETo (mm/day)b 3.18 4.21 5.31 
ETc (mm/day)c 2.23 3.48 5.47 
Monthly ETc (mm/month)d 15.58 107.82 109.40 
Leaf Lettuce late - Kc 0.70 0.93 1.03 
ETo (mm/day) 6.50 6.15 4.44 
ETc (mm/day) 4.55 5.72 4.55 
Monthly ETc (mm/month) 72.80 177.17 50.09 
Head Lettuce early - Kc 0.70 0.75 1.00 1.02 
ETo (mm/day) 3.18 4.21 5.31 6.50 
ETc (mm/day) 2.23 3.16 5.33 6.63 
Monthly ETc (mm/month) 15.58 98.03 159.98 66.31 
Head Lettuce late - Kc 0.70 0.82 1.03 0.99 
ETo (mm/day) 6.50 6.15 4.44 2.50 
ETc (mm/day) 4.55 5.04 4.56 2.47 
Monthly ETc (mm/month) 72.80 156.13 136.72 2.47 
Cabbage early - Kc 0.70 0.79 1.08 1.05 
ETo (mm/day) 3.18 4.21 5.31 6.50 
ETc (mm/day) 2.23 3.34 5.74 6.82 
Monthly ETc (mm/month) 15.58 103.63 172.33 47.74 
Cabbage late - Kc 0.70 0.86 1.08 0.99 
ETo (mm/day) 6.50 6.15 4.44 2.50 
ETc (mm/day) 4.55 5.28 4.81 2.49 
Monthly ETc (mm/month) 59.15 163.60 144.16 2.49 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-47 September 2004 
Table D-13. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for 
Upper Bound Glacial Transition Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 
Celery - Kc 0.70 0.75 1.04 1.11 1.08 
ETo (mm/day) 3.18 4.21 5.31 6.50 6.15 
ETc (mm/day) 2.23 3.15 5.51 7.20 6.65 
Monthly ETc (mm/month) 15.58 97.64 165.37 223.28 73.16 
Broccoli early - Kc 0.70 0.77 1.06 1.06 
ETo (mm/day) 3.18 4.21 5.31 6.50 
ETc (mm/day) 2.23 3.22 5.65 6.92 
Monthly ETc (mm/month) 15.58 99.88 169.53 103.82 
Broccoli late - Kc 0.70 0.84 1.09 1.01 
ETo (mm/day) 6.50 6.15 4.44 2.50 
ETc (mm/day) 4.55 5.16 4.82 2.53 
Monthly ETc (mm/month) 68.25 160.09 144.53 17.72 
Cauliflower early - Kc 0.70 0.85 1.08 
ETo (mm/day) 3.18 4.21 5.31 
ETc (mm/day) 2.23 3.54 5.73 
Monthly ETc (mm/month) 15.58 109.59 143.33 
Cauliflower late - Kc 0.70 0.77 1.06 0.99 
ETo (mm/day) 6.50 6.15 4.44 2.50 
ETc (mm/day) 4.55 4.76 4.71 2.49 
Monthly ETc (mm/month) 4.55 147.46 141.38 2.49 
Spinach early - Kc 0.70 0.84 1.03 
ETo (mm/day) 3.18 4.21 5.31 
ETc (mm/day) 2.23 3.52 5.48 
Monthly ETc (mm/month) 15.58 109.16 93.21 
Spinach late - Kc 0.72 1.00 0.99 
ETo (mm/day) 6.15 4.44 2.50 
ETc (mm/day) 4.43 4.45 2.47 
Monthly ETc (mm/month) 106.32 133.57 2.47 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-48 September 2004 
Table D-13. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for 
Upper Bound Glacial Transition Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 
Potatoes - Kc 0.50 0.53 1.10 1.21 1.03 
ETo (mm/day) 3.18 4.21 5.31 6.50 6.15 
ETc (mm/day) 1.59 2.22 5.83 7.85 6.36 
Monthly ETc (mm/month) 11.13 68.73 174.85 243.35 101.79 
Onions - Kc 0.75 1.05 1.09 1.08 0.93 0.81 
ETo (mm/day) 1.99 3.18 4.21 5.31 6.50 6.15 
ETc (mm/day) 1.50 3.35 4.58 5.74 6.05 5.00 
Monthly ETc (mm/month) 44.90 100.59 141.89 172.29 187.62 15.00 
Carrots early - Kc 0.70 0.84 1.09 1.05 
ETo (mm/day) 3.18 4.21 5.31 6.50 
ETc (mm/day) 2.23 3.56 5.79 6.82 
Monthly ETc (mm/month) 15.58 110.25 173.66 81.81 
Carrots late - Kc 0.70 0.98 1.08 0.99 
ETo (mm/day) 6.50 6.15 4.44 2.50 
ETc (mm/day) 4.56 6.05 4.78 2.48 
Monthly ETc (mm/month) 82.14 187.55 143.37 2.48 
Sweet corn - Kc 0.30 0.36 1.02 1.23 1.15 
ETo (mm/day) 4.21 5.31 6.50 6.15 4.44 
ETc (mm/day) 1.26 1.90 6.61 7.58 5.12 
Monthly ETc (mm/month) 8.84 56.93 204.83 234.86 30.74 
Bell peppers - Kc 0.60 0.85 1.11 1.03 
ETo (mm/day) 5.31 6.50 6.15 4.44 
ETc (mm/day) 3.19 5.52 6.85 4.58 
Monthly ETc (mm/month) 70.09 171.08 212.50 73.30 
Cucumbers - Kc 0.60 0.82 1.01 
ETo (mm/day) 4.21 5.31 6.50 
ETc (mm/day) 2.53 4.34 6.55 
Monthly ETc (mm/month) 17.68 130.09 203.15 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-49 September 2004 
Table D-13. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for 
Upper Bound Glacial Transition Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 
Squash - Kc 0.50 0.70 0.96 
ETo (mm/day) 4.21 5.31 6.50 
ETc (mm/day) 2.10 3.72 6.24 
Monthly ETc (mm/month) 14.74 111.48 174.79 
Melons - Kc 0.50 0.58 1.03 1.06 0.84 
ETo (mm/day) 4.21 5.31 6.50 6.15 4.44 
ETc (mm/day) 2.10 3.06 6.70 6.49 3.71 
Monthly ETc (mm/month) 14.74 91.69 207.85 201.33 14.87 
Tomatoes - Kc 0.61 1.05 1.15 0.90 
ETo (mm/day) 5.31 6.50 6.15 4.44 
ETc (mm/day) 3.24 6.81 7.07 3.99 
Monthly ETc (mm/month) 71.17 211.02 219.18 15.94 
Corn silage - Kc 0.30 0.65 1.27 1.23 0.86 
ETo (mm/day) 4.21 5.31 6.50 6.15 4.44 
ETc (mm/day) 1.26 3.44 8.26 7.54 3.83 
Monthly ETc (mm/month) 25.26 103.23 256.03 233.89 95.73 
Feed Corn - Kc 0.30 0.43 1.07 1.28 1.21 0.72 0.39 
ETo (mm/day) 4.21 5.31 6.50 6.15 4.44 2.50 0.98 
ETc (mm/day) 1.26 2.26 6.93 7.90 5.36 1.79 0.39 
Monthly ETc (mm/month) 25.26 67.92 214.68 244.57 160.68 55.63 1.93 
Oat Feed - Kc 0.30 0.38 1.10 1.21 1.10 0.50 
ETo (mm/day) 1.99 3.18 4.21 5.31 6.50 6.15 
ETc (mm/day) 0.60 1.22 4.62 6.41 7.12 3.06 
Monthly ETc (mm/month) 1.79 36.62 143.26 192.45 220.83 48.93 
Alfalfa (1st cutting) - Kc 0.59 1.19 1.22 
ETo (mm/day) 1.99 3.18 4.21 
ETc (mm/day) 1.17 3.79 5.15 
Monthly ETc (mm/month) 35.01 113.78 77.25 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-50 September 2004 
Table D-13. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for 
Upper Bound Glacial Transition Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 
Alfalfa (2nd cutting) - Kc 0.46 1.03 1.24 
ETo (mm/day) 4.21 5.31 6.50 
ETc (mm/day) 1.92 5.45 8.08 
Monthly ETc (mm/month) 30.80 163.62 177.75 
Alfalfa (3rd cutting) - Kc 0.40 0.87 1.24 
ETo (mm/day) 6.50 6.15 4.44 
ETc (mm/day) 2.61 5.33 5.49 
Monthly ETc (mm/month) 23.49 165.16 153.81 
Apples - Kc 0.45 0.54 0.87 1.05 1.05 0.98 0.80 
ETo (mm/day) 3.18 4.21 5.31 6.50 6.15 4.44 2.50 
ETc (mm/day) 1.43 2.29 4.61 6.85 6.49 4.37 2.00 
Monthly ETc (mm/month) 11.45 71.00 138.27 212.43 201.13 130.98 9.99 
Grapes - Kc 0.30 0.56 0.78 0.72 
ETo (mm/day) 5.31 6.50 6.15 4.44 
ETc (mm/day) 1.60 3.65 4.78 3.19 
Monthly ETc (mm/month) 20.75 113.22 148.25 95.58 
Strawberries - Kc 0.40 0.69 0.88 
ETo (mm/day) 3.18 4.21 5.31 
ETc (mm/day) 1.27 2.89 4.69 
Monthly ETc (mm/month) 3.82 89.65 140.62 
Winter wheat - Kc 0.73 0.85 0.97 1.10 1.20 1.20 0.96 0.43 0.40 0.40 0.47 0.60 
ETo (mm/day) 0.62 1.10 1.99 3.18 4.21 5.31 6.50 6.15 4.44 2.50 0.98 0.58 
ETc (mm/day) 0.45 0.93 1.93 3.49 5.04 6.40 6.22 2.66 1.78 1.00 0.46 0.35 
Monthly ETc (mm/month) 14.00 26.17 59.93 104.69 156.17 191.90 192.80 42.49 24.86 31.05 13.94 10.79 
Barley - Kc 0.30 0.96 1.19 0.63 
ETo (mm/day) 3.18 4.21 5.31 6.50 
ETc (mm/day) 0.95 4.02 6.32 4.10 
Monthly ETc (mm/month) 13.36 124.74 189.69 65.67 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 D-51 September 2004 
Table D-13. Mean Monthly Crop Coefficients (Kc), Reference Evapotranspiration (ETo), and Crop Evapotranspiration (ETc) for 
Upper Bound Glacial Transition Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 
Oat Hay - Kc 0.44 1.19 0.69 
ETo (mm/day) 4.21 5.31 6.50 
ETc (mm/day) 1.85 6.33 4.48 
Monthly ETc (mm/month) 29.57 189.98 49.25 
Fescuee - Kc 0.95 0.95 0.95 0.95 0.95 0.95 0.95 
ETo (mm/day) 3.18 4.21 5.31 6.50 6.15 4.44 2.50 
ETc (mm/day) 3.02 4.00 5.04 6.18 5.84 4.22 2.38 
Monthly ETc (mm/month) 90.63 123.99 151.34 191.43 181.12 126.54 73.63 
a 
Mean monthly Kc values calculated according to Appendix D, Section 4. 
b 
Mean monthly ETo values from Appendix C, Table C-9. 
c 
Mean daily ETc calculated using Equation D-1. 
d Mean monthly ETc calculated from mean daily ETc and the number of growing days per month (Appendix D, Section 5). 
e 
Fescue was selected for upper bound glacial transition climate because it is a cool season grass. 

INTENTIONALLY LEFT BLANK 

APPENDIX E 
METHODS FOR DERIVING SEASONAL WATER REQUIREMENTS, 
LEACHING REQUIREMENTS, DEEP PERCOLATION, AND IRRIGATION 
APPLICATION AMOUNTS 


E. METHODS FOR DERIVING SEASONAL WATER REQUIREMENTS, 
LEACHING REQUIREMENTS, DEEP PERCOLATION, AND 
IRRIGATION APPLICATION AMOUNTS 
E1. INTRODUCTION 
Seasonal water requirements (Ws) and net irrigation requirements (In) are related variables and 
are used to determine several parameters including annual average irrigation rate (IR), daily 
average irrigation rate (IRDj), irrigation application (IAj) and overwatering rate (OW). Seasonal 
water requirements were derived from water lost from the soil-plant system (monthly ETc), water 
added to the system (monthly effective precipitation [Pe], and leaching requirements [LR]). In 
addition to ETc, Pe, and LR, determination of In requires information on stored soil moisture at 
the beginning of the growing season (Wb), and groundwater contribution to the water 
requirement (Ge). 
A Leaching Fraction (LF, the actual amount of water that must be added to leach salts below the 
crop root zone in addition to water needed to balance ETc) was determined from Ws for each 
crop. This was compared to deep percolation of precipitation below the root zone (DP) and the 
greater of the two values was used as the overwatering rate (OW). Depending on whether LR 
was met by precipitation or irrigation, either Ws or In was used to calculate annual average 
irrigation rate (IR), and daily average irrigation rate (IRDj). Seasonal crop evapotranspiration 
(ETc), deep percolation below the root zone (DP), stored soil moisture in the rooting zone (Wb), 
and leaching requirements (LR) were needed to estimate Ws, In, irrigation application (IAj), and 
overwatering rate (OW) (see Section 6). 
Methods for deriving ETc are in Appendix D. Methods for deriving Ws, In, IAj, IR, IRDj, and OW 
are described below. 
E2. METHODS 
E2.1 EFFECTIVE PRECIPITATION 
Not all precipitation is available for plant use. Precipitation that collects on the soil surface can 
be lost to evaporation and surface flow can be lost as runoff. Some of the rainfall that percolates 
through the soil can be lost below the root zone (Doorenbos and Pruitt 1977 [DIRS 103062]). 
The portion of rainfall that percolates through the soil and remains in the root zone is available 
for plant use (Pe). Thus, Pe is the total rainfall minus the losses that occur from the system. 
There are several methods for direct measurement of Pe (Brouwer and Heibloem 1986 
[DIRS 159869], Section 4.2) however none of those methods were employed under the climate 
conditions in this analysis. Empirically developed formulae also exist, but are specific to the 
conditions under which they were developed, and in most cases their use elsewhere is not 
recommended (Dastane 1978 [DIRS 159870], Sections 2.2 and 2.3). Doorenbos and Pruitt (1977 
[DIRS 103062], pp. 74 and 75) use the evaporation/precipitation ratio method developed by the 
USDA 1969, from Doorenbos and Pruitt (1977 [DIRS 103062], p. 74) to estimate Pe. This 
method was included in an evaluation of 12 direct and empirical methods for estimating Pe 

(Dastane 1978 [DIRS 159870], Section 2.4). It was rated as satisfactory for preliminary 
planning purposes with medium accuracy and low relative cost. This compared to four other 
empirical methods, two of which were rated low for accuracy, one medium, and one low to high. 
Only direct measurement methods were given high and very high accuracy ratings. Based on 
this information and the lack of direct measurements of Pe, the evaporation/ precipitation ratio 
method was selected for use in this analysis. 
The evaporation/precipitation ratio method requires mean monthly rainfall measurements 
(Section 4, Tables 4.1-2, 4.1-3, 4.1-4, and 4.1-5), mean monthly ETc (Appendix D, 
Tables D-10 through D-13), and the net depth of water that could be effectively stored over the 
root zone (Doorenbos and Pruitt 1977 [DIRS 103062], p. 74). 
E2.1.1 Soil Water Availability (TAW) 
The concept of total available water in the root zone (TAW) discussed by Allen et al. (1998 
[DIRS 157311], pp. 161 and 162) was used to estimate the net depth of water that could be 
effectively stored over the root zone for the 26 crops and turf in a sandy loam soil. A sandy loam 
soil was chosen because the common soils in northern Amargosa Valley (Arizo, Corbilt, 
Sanwell, Shamock, Yermo) are sandy to sandy loam, well drained, and have a moderate to rapid 
permeability (CRWMS M&O 1999 [DIRS 107736], Figure 1 and pp. C-1, C-2, C-25, C-27, 
C-39, and C40). After rainfall or irrigation, the hydraulic gradient in the soil causes some of the 
water to rapidly drain downward until field capacity is reached. Field capacity is the amount of 
water left in the soil after this downward movement becomes negligible (Jensen et al. 1990 
[DIRS 160001], p. 20; Allen et al. 1998 [DIRS 157311], p. 161). As crops deplete water from 
the rooting zone, soil water potentials become more negative, making it increasingly difficult for 
crops to extract soil moisture. With no additional water input, the soil will continue to dry out 
and plants will begin to wilt to conserve moisture during the day (Jensen et al. 1990 
[DIRS 160001], p. 21). When soil water potentials become so negative that water can no longer 
be extracted by plants, the permanent wilting point is reached (Jensen et al. 1990 [DIRS 160001], 
p. 21; Allen et al. 1998 [DIRS 157311], p. 161). At the permanent wilting point, water remains 
in the soil, but is held too tightly by matric and osmotic forces to allow absorption by plant roots. 
TAW can be estimated from the difference between field capacity and the permanent wilting 
point (Allen et al. 1998 [DIRS 157311], Equation 82, p. 162): 
TAW = 1000(. -.WP )Z (Eq. E-1) 
FC r 
where 
.FC = soil water content at field capacity (m3 m-3), 
.WP = soil water content at the wilting point (m3 m-3), 
Zr = rooting depth (m). 

m
For a sandy loam soil, typical ranges for .FC and .WP are 0.18 - 0.28 m3 m-3 and 0.06 - 0.16 m3 
-3, respectively (Allen et al. 1998 [DIRS 157311], Table 19, p. 144). The midpoint of each 
range was chosen for this analysis (.FC = 0.23 and .WP =0.11). 
Mean monthly rooting depths required for TAW were calculated by taking the maximum 
effective rooting depths (Zr max, Table E-1) for the 26 crops and turf from Allen et al. (1998 
[DIRS 157311], Table 22, pp. 163 through 165), and using minimum root depths (Zr min) of 0.15 
for plants with Zr max of 0.3 to 0.5 m and 0.20 m for plants with Zr max > 0.5 m in the following 
equation (Allen et al. 1998 [DIRS 157311], Equation 8-3, p. 279): 
J - J start 
for Jstart = J = Jmax (Eq. E-2)
Z 
ri = Zr min+(Zr max - Zr min ) 
J max - J start 
where 
Zri = effective depth of the root zone on day i (m), 
Jstart = Day of year that Zri increases beyond Zr min, 
Jmax = Day of year that maximum rooting depth is attained. 
For annual plants, Zri was set equal to Zr min for days 1 through 5 of the initial growth stage (see 
Tables D-1 and D-2 for timing of growth seasons and Tables D-3 and D-4 for stage lengths). Zri 
was calculated according to Equation E-2 for day 6 through the last day of the development 
stage. Zri was set equal to Zr max for the mid-season and late growth stages. Monthly means were 
calculated from the daily values generated from Equation E-2 for each annual crop and used in 
Equation E-1 to calculate mean monthly TAW. 
Example: Using early lettuce and present-day climate conditions: 
planting day = 65 
Jstart = 71 
Jmax = 101 
Zr min = 0.15 
Zr max = 0.30 
Using equation E-1: 
TAW for Zr min = 1000*(0.23-0.11)*0.15=18 mm 
TAW for Zr max =1000*(0.23-0.11)*0.30=36.0 mm 

Example calculations for Zri using Equation E-2: 
Julian daya Rooting depth (Zri) Monthly Mean 
65 - 70 0.15 
71 0.15+(0.3-0.15)*(71-71)/(101-71)=0.15 
72 0.15+(0.3-0.15)*(72-71)/(101-71)=0.155 
73 0.15+(0.3-0.15)*(73-71)/(101-71)=0.16 
74 0.15+(0.3-0.15)*(74-71)/(101-71)=0.165 
75 0.15+(0.3-0.15)*(75-71)/(101-71)=0.17 
76 0.15+(0.3-0.15)*(76-71)/(101-71)=0.175 
77 0.15+(0.3-0.15)*(77-71)/(101-71)=0.18 
78 0.15+(0.3-0.15)*(78-71)/(101-71)=0.185 
79 0.15+(0.3-0.15)*(79-71)/(101-71)=0.19 
80 0.15+(0.3-0.15)*(80-71)/(101-71)=0.195 
81 0.15+(0.3-0.15)*(81-71)/(101-71)=0.2 
82 0.15+(0.3-0.15)*(82-71)/(101-71)=0.205 
83 0.15+(0.3-0.15)*(83-71)/(101-71)=0.21 
84 0.15+(0.3-0.15)*(84-71)/(101-71)=0.215 
85 0.15+(0.3-0.15)*(85-71)/(101-71)=0.22 
86 0.15+(0.3-0.15)*(86-71)/(101-71)=0.225 
87 0.15+(0.3-0.15)*(87-71)/(101-71)=0.23 
88 0.15+(0.3-0.15)*(88-71)/(101-71)=0.235 
89 0.15+(0.3-0.15)*(89-71)/(101-71)=0.24 
90 0.15+(0.3-0.15)*(90-71)/(101-71)=0.245 0.188 
91 0.15+(0.3-0.15)*(91-71)/(101-71)=0.25 
92 0.15+(0.3-0.15)*(92-71)/(101-71)=0.255 
93 0.15+(0.3-0.15)*(93-71)/(101-71)=0.26 
94 0.15+(0.3-0.15)*(94-71)/(101-71)=0.265 
95 0.15+(0.3-0.15)*(95-71)/(101-71)=0.27 
96 0.15+(0.3-0.15)*(96-71)/(101-71)=0.275 
97 0.15+(0.3-0.15)*(97-71)/(101-71)=0.28 
98 0.15+(0.3-0.15)*(98-71)/(101-71)=0.285 
99 0.15+(0.3-0.15)*(99-71)/(101-71)=0.29 
100 0.15+(0.3-0.15)*(100-71)/(101-71)=0.295 
101 0.15+(0.3-0.15)*(101-71)/(101-71)=0.3 0.291 
102 - 120 0.3 0.3 
aDate is expressed in Julian format, excluding year. 
It was assumed that perennial crops were established for this analysis, and so maximum rooting 
depths were used for each month to calculate TAW. Equation E-2 was used to calculate monthly 
rooting depths for the first cutting of alfalfa. Zr max was used for subsequent cuttings. Mean 
monthly rooting depths and effective storage (TAW) for each crop are listed in Tables E-2 and 
E-3. 

Table E-1. Minimum and Maximum Rooting Depths for 26 Crops and Turf 
Crop Minimum Rooting 
Depth (m)a 
Maximum Rooting 
Depth (m)b 
Lettuce 0.15 0.3 
Cabbage 0.15 0.5 
Celery 0.15 0.3 
Broccoli 0.15 0.4 
Cauliflower 0.15 0.4 
Spinach 0.15 0.3 
Potatoes 0.15 0.4 
Onions 0.15 0.3 
Carrots 0.15 0.5 
Sweet corn 0.20 0.8 
Bell peppers 0.15 0.5 
Cucumbers 0.20 0.7 
Squash 0.20 0.6 
Melons 0.20 0.8 
Tomatoes 0.20 0.7 
Alfalfa hay 0.20 1.0 
Oat hay 0.20 1.0 
Apples 0.20 1.0 
Grapes 0.20 1.0 
Strawberries 0.15 0.2 
Winter wheat 0.20 1.5 
Barley 0.20 1.0 
Feed Corn 0.20 1.0 
Corn silage 0.20 1.0 
Oats 0.20 1.0 
Bermuda 0.15 0.5 
Fescue 0.15 0.5 
a 
Source for minimum rooting depth: Allen et al. (1998 
b 
[DIRS 157311], p. 279). 
Source for maximum rooting depth: Allen et al. (1998 
[DIRS 157311], Table 22, pp. 163-165). 

Table E-2. Mean Monthly Rooting Depths (m), Effective Storage Depths (mm), and Effective 
Precipitation (mm) for 26 Crops and Turf for Present-Day and Upper Bound Monsoon Climate 
Conditions 
Crop Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec 
Leaf Lettuce early 
Rooting deptha 0.19 0.29 0.30 
Effective storageb 22.6 34.9 36.0 
Effective precip. c 6.9 0 0 
Effective precip. d 13.9 8.6 6.9 
Leaf Lettuce late 
Rooting depth 0.20 0.30 
Effective storage 24.0 36.0 
Effective precip. 7.7 0 
Effective precip. 23.1 31.8 
Head Lettuce early 
Rooting depth 0.19 0.29 0.30 
Effective storage 22.6 34.9 36.0 
Effective precip. 6.9 0 0 
Effective precip. 13.9 8.6 6.9 
Head Lettuce late 
Rooting depth 0.20 0.30 
Effective storage 24.0 36.0 
Effective precip. 7.70 0 
Effective precip. 23.1 31.8 
Cabbage early 
Rooting depth 0.23 0.44 0.50 
Effective storage 27.3 53.5 60.0 
Effective precip. 6.9 0 0 
Effective precip. 13.9 9.3 8.1 
Cabbage late 
Rooting depth 0.15 0.26 0.47 0.50 
Effective storage 18.0 31.5 56.7 60.0 
Effective precip. 0 7.7 0 0 
Effective precip. 19.7 25.8 34.4 16.5 
Celery early 
Rooting depth 0.17 0.25 0.30 0.30 0.30 
Effective storage 19.9 29.6 36.0 36.0 36.0 
Effective precip. 0 0 0 0 0 
Effective precip. 6.6 8.1 11.2 78.3 59.3 

Table E-2. Mean Monthly Rooting Depths (m), Effective Storage Depths (mm), and Effective 
Precipitation (mm) for 26 Crops and Turf for Present-Day and Upper Bound Monsoon 
Climate Conditions (Continued) 
Crop Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec 
Celery late 
Rooting depth 0.30 0.16 0.24 0.30 0.30 
Effective storage 36.0 19.2 29.1 36.0 36.0 
Effective precip. 13.8 6.6 0 0 7.7 
Effective precip. 21.5 18.2 27.0 15.5 23.2 
Broccoli early 
Rooting depth 0.21 0.37 0.40 
Effective storage 25.3 44.7 48.0 
Effective precip. 6.9 0 0 
Effective precip. d 13.9 9.3 8.1 
Broccoli late 
Rooting depth 0.15 0.24 0.39 0.40 
Effective storage 18.0 28.3 46.6 48.0 
Effective precip. 0 7.7 0 0 
Effective precip. 19.7 23.1 34.4 14.9 
Cauliflower early 
Rooting depth 0.21 0.37 0.40 
Effective storage 25.1 44.4 48.0 
Effective precip. 6.9 0 0 
Effective precip. 13.9 10.2 8.1 
Cauliflower late 
Rooting depth 0.15 0.23 0.39 0.40 
Effective storage 18.0 28.0 46.2 48.0 
Effective precip. 0 7.7 0 0 
Effective precip. 19.7 23.1 39.1 15.9 
Spinach early 
Rooting depth 0.17 0.28 0.30 
Effective storage 20.8 34.3 36.0 
Effective precip. 5.8 0 0 
Effective precip. 12.4 8.6 6.9 
Spinach late 
Rooting depth 0.17 0.28 0.30 
Effective storage 20.4 34.3 36.0 
Effective precip. 6.6 0 0 
Effective precip. 18.2 31.8 13.8 

Table E-2. Mean Monthly Rooting Depths (m), Effective Storage Depths (mm), and Effective 
Precipitation (mm) for 26 Crops and Turf for Present-Day and Upper Bound Monsoon 
Climate Conditions (Continued) 
Crop Jan Feb Mar April May June July Aug Sept Oct Nov Dec 
Potatoes 
Rooting depth 0.15 0.23 0.38 0.40 0.40 
Effective storage 18.0 27.4 45.7 48.0 48.0 
Effective precip. 5.8 0 0 0 0 
Effective precip. 9.5 6.9 8.1 12.1 64.2 
Onions early 
Rooting depth 0.15 0.24 0.30 0.30 0.30 
Effective storage 18.0 29.1 36.0 36.0 36.0 
Effective precip. 5.8 0 0 0 0 
Effective precip. 11.7 7.7 8.1 11.2 59.3 
Onions late 
Rooting depth 0.30 0.17 0.28 0.30 0.30 
Effective storage 36.0 20.1 33.9 36.0 36.0 
Effective precip. 5.8 6.6 0 0 6.9 
Effective precip. 6.3 18.2 37.8 15.5 23.2 
Carrots early 
Rooting depth 0.19 0.45 0.50 
Effective storage 23.2 53.9 60.0 
Effective precip. 6.2 0 0 
Effective precip. 13.1 9.3 8.1 
Carrots late 
Rooting depth 0.15 0.35 0.50 0.50 
Effective storage 18.0 42.5 60.0 60.0 
Effective precip. 0 9.46 0 0 
Effective precip. 19.7 25.8 35.9 15.5 
Sweet corn 
Rooting depth 0.42 0.80 0.80 
Effective storage 50.4 96.0 96.0 
Effective precip. 0 0 0 
Effective precip. 8.1 13.3 87.7 
Bell Peppers 
Rooting depth 0.28 0.50 0.50 
Effective storage 34.0 59.9 60.0 
Effective precip. 0 0 0 
Effective precip. 8.1 12.6 78.6 

Table E-2. Mean Monthly Rooting Depths (m), Effective Storage Depths (mm), and Effective 
Precipitation (mm) for 26 Crops and Turf for Present-Day and Upper Bound Monsoon 
Climate Conditions (Continued) 
Crop Jan Feb Mar April May Jun July Aug Sept Oct Nov Dec 
Cucumbers 
Rooting depth 0.42 0.70 
Effective storage 49.9 84.0 
Effective precip. 0 0 
Effective precip. 9.3 91.0 
Squash 
Rooting depth 0.24 0.51 0.60 
Effective storage 29.0 61.5 72.0 
Effective precip. 0 0 0 
Effective precip. 6.2 10.7 69.0 
Melons 
Rooting depth 0.25 0.60 0.80 0.80 
Effective storage 30.2 72.7 96.0 96.0 
Effective precip. 0 0 0 0 
Effective precip. 6.2 10.0 92.8 77.5 
Tomatoes 
Rooting depth 0.32 0.70 0.70 
Effective storage 37.8 84.0 84.0 
Effective precip. 0 0 0 
Effective precip. 7.7 13.0 86.0 
Apples 
Rooting depth 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 
Effective storage 120.0 120.0 120.0 120.0 120.0 120.0 120.0 120.0 
Effective precip. 9.4 0 0 0 0 0 9.1 0 
Effective precip. 18.7 9.4 8.1 12.5 94.6 89.4 32.2 36.4 
Grapes 
Rooting depth 1.00 1.00 1.00 1.00 1.00 1.00 
Effective storage 120.0 120.0 120.0 120.0 120.0 120.0 
Effective precip. 8.3 0 0 0 0 0 
Effective precip. 18.7 10.4 8.1 13.5 94.6 75.9 
Strawberries 
Rooting depth 0.20 0.20 0.20 0.20 0.20 0.20 0.20 
Effective storage 24.0 24.0 24.0 24.0 24.0 24.0 24.0 
Effective precip. 6.9 0 0 0 0 0 7.7 
Effective precip. 13.1 6.9 8.1 10.0 66.2 66.2 21.6 

Table E-2. Mean Monthly Rooting Depths (m), Effective Storage Depths (mm), and Effective 
Precipitation (mm) for 26 Crops and Turf for Present-Day and Upper Bound Monsoon 
Climate Conditions (Continued) 
Crop Jan Feb Mar April May Jun July Aug Sept Oct Nov Dec 
Winter Wheat 
Rooting depth 1.30 1.50 1.50 1.50 1.50 1.50 0.24 0.51 0.90 
Effective storage 155.5 180.0 180.0 180.0 180.0 180.0 28.6 61.5 108.1 
Effective precip. 18.0 9.6 11.7 0 0 0 0 0 8.2 
Effective precip. 28.9 20.3 22.5 11.8 8.1 8.6 24.6 16.5 25.5 
Barley 
Rooting depth 0.89 1.00 1.00 1.00 1.00 1.00 0.22 0.39 0.64 
Effective storage 106.5 120.0 120.0 120.0 120.0 120.0 26.9 47.0 76.8 
Effective precip. 17.3 9.4 11.4 0 0 0 0 0 8.0 
Effective precip. 26.0 19.8 21.8 11.4 8.1 8.3 18.0 16.3 26.0 
Alfalfa 1st cutting 
Rooting depth 0.53 1.00 1.00 
Effective storage 64.0 120.0 120.0 
Effective precip. 15.5 9.4 8.3 
Effective precip. 24.2 19.8 9.1 
Alfalfa 2nd cutting 
Rooting depth 1.00 1.00 
Effective storage 120.0 120.0 
Effective precip. 9.4 0 
Effective precip. 18.7 11.4 
Alfalfa 3rd cutting 
Rooting depth 1.00 1.00 1.00 
Effective storage 120.0 120.0 120.0 
Effective precip. 0 0 0 
Effective precip. 4.1 8.1 11.4 
Alfalfa 4th cutting 
Rooting depth 1.00 1.00 1.00 
Effective storage 120.0 120.0 120.0 
Effective precip. 0 0 0 
Effective precip. 20.6 89.4 31.0 
Alfalfa 5th cutting 
Rooting depth 1.00 1.00 1.00 
Effective storage 120.0 120.0 120.0 
Effective precip. 0 9.1 0 
Effective precip. 31.4 89.4 26.0 

Table E-2. Mean Monthly Rooting Depths (m), Effective Storage Depths (mm), and Effective 
Precipitation (mm) for 26 Crops and Turf for Present-Day and Upper Bound Monsoon 
Climate Conditions (Continued) 
Crop Jan Feb Mar April May Jun July Aug Sept Oct Nov Dec 
Alfalfa 6th cutting 
Rooting depth 1.00 1.00 1.00 
Effective storage 120.0 120.0 120.0 
Effective precip. 0 0 4.6 
Effective precip. 33.3 18.7 25.0 
Oat hay 
Rooting depth 0.50 1.00 1.00 
Effective storage 59.6 120.0 120.0 
Effective precip. 0 0 0 
Effective precip. 8.7 8.1 9.4 
Bermudagrass 
Rooting depth 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 0.50 
Effective storage 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 60.0 
Effective precip. 16.5 8.7 8.7 0 0 0 0 0 10.7 0 0 7.8 
Effective precip. 23.2 15.5 16.3 7.7 8.1 8.5 82.7 78.4 27.9 34.0 15.5 23.2 
Feed Corn 
Rooting depth 0.24 0.50 0.87 1.00 1.00 1.00 
Effective storage 28.4 60.2 104.5 120.0 120.0 120.0 
Effective precip. 0 0 0 0 9.1 0 
Effective precip. 6.9 10.7 92.8 99.8 33.3 33.3 
Corn silage 
Rooting depth 0.26 0.73 1.00 1.00 
Effective storage 31.8 87.3 120.0 120.0 
Effective precip. 0 0 0 0 
Effective precip. 6.9 11.2 106.1 71.8 
Oat feed 
Rooting depth 0.51 0.80 1.00 1.00 1.00 0.24 
Effective storage 61.5 96.1 120.0120.0 120.0 28.8 
Effective precip. 15.5 9.2 11.4 0 0 6.2 
Effective precip. 23.3 18.4 21.8 11.4 8.1 9.0 
a 
b 
Mean monthly rooting depth calculated according to Equation E-2. 
Mean monthly effective storage depth for sandy loam soil calculated from Equation E-1. 
d 
Mean monthly effective precipitation for present-day climate calculated according to Appendix E, Section 2.1.2. 
Mean monthly effective precipitation for upper bound monsoon climate calculated according to Appendix E, 
Section 2.1.2 

Table E-3. Mean Monthly Rooting Depths, Effective Storage Depths, and Effective Precipitation for 
26 Crops and Turf for Upper and Lower Bound Glacial Transition Climate Conditions 
Crop Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec 
Rooting deptha 0.15 0.23 0.30 
Effective storageb 18.2 27.8 36.0 
Effective precip. c 11.7 21. 6 24.1 
Effective precip. d 11.7 16.2 9.5 
Leaf Lettuce late 
Rooting depth 0.17 0.27 0.30 
Effective storage 19.9 31.8 36.0 
Effective precip. 6.6 9.5 6.9 
Effective precip. 0 10.7 7.7 
Head Lettuce early 
Rooting depth 0.15 0.21 0.29 0.30 
Effective storage 18.1 24.7 34.8 36.0 
Effective precip. 11.7 21. 6 26.7 7.7 
Effective precip. 11.7 16.2 11.2 0 
Head Lettuce late 
Rooting depth 0.16 0.23 0.30 0.30 
Effective storage 19.3 28.2 36.0 36.0 
Effective precip. 6.6 7.7 8.6 2.5 
Effective precip. 0 9.4 9.5 4.2 
Cabbage early 
Rooting depth 0.15 0.30 0.49 0.50 
Effective storage 18.2 36.3 59.2 60.0 
Effective precip. 11.7 24.1 31.0 7.8 
Effective precip. 11.7 18.1 12.6 0 
Cabbage late 
Rooting depth 0.17 0.36 0.50 0.50 
Effective storage 20.3 42.8 60.0 60.0 
Effective precip. 5.8 9.5 9.7 2.5 
Effective precip. 0 10.3 10.4 4.2 
Celery 
Rooting depth 0.15 0.20 0.28 0.30 0.30 
Effective storage 18.0 24.0 34.0 36.0 36.0 
Effective precip. 11.7 21.6 27.5 10.3 7.7 
Effective precip. 11.7 16.2 11.2 0 7.7 
Broccoli early 
Rooting depth 0.15 0.25 0.39 0.40 
Effective storage 18.1 29.9 46.7 48.0 
Effective precip. 11.7 21.6 29.8 8.4 
Effective precip. 11.7 16.2 12.1 0 

Table E-3. Mean Monthly Rooting Depths, Effective Storage Depths, and Effective Precipitation for 26 
Crops and Turf for Upper and Lower Bound Glacial Transition Climate Conditions 
(Continued) 
Crop Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec 
Broccoli late 
Rooting depth 0.17 0.30 0.40 0.40 
Effective storage 20.1 35.4 48.0 48.0 
Effective precip. 6.6 8.6 9.3 14.9 
Effective precip. 0 10.3 10.2 14.9 
Cauliflower late 
Rooting depth 0.15 0.24 0.40 0.40 
Effective storage 18.0 28.3 47.5 48.0 
Effective precip. 4.6 7.7 9.3 2.5 
Effective precip. 0 8.5 10.2 4.2 
Spinach early 
Rooting depth 0.15 0.24 0.30 
Effective storage 18.1 28.7 36.0 
Effective precip. 11.7 21. 6 24.1 
Effective precip. 11.7 16.2 8.6 
Spinach late 
Rooting depth 0.19 0.29 0.30 
Effective storage 22.6 35.0 36.0 
Effective precip. 6.9 8.6 2.5 
Effective precip. 7.7 9.5 4.2 
Potatoes 
Rooting depth 0.15 0.24 0.38 0.40 0.40 
Effective storage 18.0 29.1 46.0 48.0 48.0 
Effective precip. 11.1 20.8 29.8 12.1 8.4 
Effective precip. 11.7 14.6 12.1 0 9.3 
Onions 
Rooting depth 0.19 0.29 0.30 0.30 0.30 0.30 
Effective storage 23.0 34.9 36.0 36.0 36.0 36.0 
Effective precip. 19.2 16.3 26.7 27.5 9.5 6.9 
Effective precip. 13.9 18.1 21.3 11.2 0 6.9 
Carrots early 
Rooting depth 0.15 0.34 0.50 0.50 
Effective storage 18.2 41.0 60.0 60.0 
Effective precip. 11.7 24.1 31.0 8.7 
Effective precip. 11.7 18.1 12.6 0 
Carrots late 
Rooting depth 0.20 0.43 0.50 0.50 
Effective storage 23.9 52.0 60.0 60.0 
Effective precip. 6.9 10.2 9.7 2.5 
Effective precip. 0 10.7 10.4 4.2 

Table E-3. Mean Monthly Rooting Depths, Effective Storage Depths, and Effective Precipitation for 26 
Crops and Turf for Upper and Lower Bound Glacial Transition Climate Conditions 
(Continued) 
Crop Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec 
Sweet corn 
Rooting depth 0.20 0.39 0.72 0.80 0.80 
Effective storage 24.0 46.8 86.4 96.0 96.0 
Effective precip. 8.8 23.2 11.0 12.2 8.2 
Effective precip. 13.1 12.1 0 10.7 8.2 
Bell Peppers 
Rooting depth 0.20 0.39 0.50 0.50 
Effective storage 23.6 47.2 60.0 60.0 
Effective precip. 20.8 10.2 11.6 8.7 
Effective precip. 6.9 0 10.7 8.7 
Cucumbers 
Rooting depth 0.20 0.51 0.70 
Effective storage 24.3 60.9 84.0 
Effective precip. 17.7 29.1 11.0 
Effective precip. 12.3 10.7 0 
Squash 
Rooting depth 0.20 0.42 0.60 
Effective storage 24.2 50.3 72.0 
Effective precip. 14.7 26.0 11.0 
Effective precip. 12.3 10.2 0 
Melons 
Rooting depth 0.20 0.42 0.76 0.80 0.80 
Effective storage 24.2 49.8 90.7 96.0 96.0 
Effective precip. 14.7 26.0 11.2 11.2 8.2 
Effective precip. 12.3 9.3 0 10.7 8.2 
Tomatoes 
Rooting depth 0.29 0.61 0.70 0.70 
Effective storage 34.3 73.8 84.0 84.0 
Effective precip. 23.2 11.0 12.0 8.0 
Effective precip. 7.7 0 10.7 8.0 
Apples 
Rooting depth 1.00 1.00 1.00 1.00 1.00 1.00 1.00 
Effective storage 120.0 120.0 120.0 120.0 120.0 120.0 120.0 
Effective precip. 11.4 28.1 32.2 11.4 11.4 10.4 10.0 
Effective precip. 16.6 19.8 12.5 0 10.7 10.4 16.6 
Grapes 
Rooting depth 1.00 1.00 1.00 1.00 
Effective storage 120.0 120.0 120.0 120.0 
Effective precip. 20.8 10.4 10.4 9.4 
Effective precip. 8.3 0 10.7 10.4 

Table E-3. Mean Monthly Rooting Depths, Effective Storage Depths, and Effective Precipitation for 26 
Crops and Turf for Upper and Lower Bound Glacial Transition Climate Conditions 
(Continued) 
Crop Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec 
Strawberries 
Rooting depth 0.20 0.20 0.20 
Effective storage 24.0 24.0 24.0 
Effective precip. 3.8 21.6 23.9 
Effective precip. 5.2 15.4 9.2 
Winter Wheat 
Rooting depth 0.85 1.02 1.18 1.36 1.49 1.50 1.50 1.50 0.21 0.34 0.51 0.68 
Effective storage 102.3 122.3 142.2 162.8 178.8 180.0 180.0 180.0 25.7 40.2 60.8 81.4 
Effective precip. 14.0 25.0 26.5 20.3 33.2 35.3 11.8 8.6 6.2 13.8 13.9 10.8 
Effective precip. 8.2 8.3 20.1 20.1 21.3 12.7 0 8.6 6.2 14.6 7.8 8.0 
Barley 
Rooting depth 0.28 0.86 1.00 1.00 
Effective storage 33.9 103.2 120.0 120.0 
Effective precip. 13.4 30.6 34.3 9.4 
Effective precip. 13.8 21.3 12.7 0 
Alfalfa 1st cutting 
Rooting depth 0.44 0.96 1.00 
Effective storage 52.2 115.8 120.0 
Effective precip. 22.3 20.8 28.1 
Effective precip. 15.8 20.1 20.8 
Alfalfa 2nd cutting 
Rooting depth 1.00 1.00 1.00 
Effective storage 120.0 120.0 120.0 
Effective precip. 25.0 33.3 11.4 
Effective precip. 17.7 12.7 0 
Alfalfa 3rd cutting 
Rooting depth 1.00 1.00 1.00 
Effective storage 120.0 120.0 120.0 
Effective precip. 8.3 11.4 10.4 
Effective precip. 0 10.7 10.4 
Oat hay 
Rooting depth 0.41 1.00 1.00 
Effective storage 49.4 120.0 120.0 
Effective precip. 22.3 34.3 8.3 
Effective precip. 15.8 12.7 0 
Fescue 
Rooting depth 0.50 0.50 0.50 0.50 0.50 0.50 0.50 
Effective storage 60.0 60.0 60.0 60.0 60.0 60.0 60.0 

Table E-3. Mean Monthly Rooting Depths, Effective Storage Depths, and Effective Precipitation for 
26 Crops and Turf for Upper and Lower Bound Glacial Transition Climate Conditions 
(Continued) 
Crop Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec 
Effective precip. 18.4 29.1 30.1 10.7 10.7 9.7 17.5 
Effective precip. 19.4 21.3 12.7 0 10.7 10.4 19.4 
Feed Corn 
Rooting depth 0.26 0.54 0.90 1.00 1.00 1.00 1.00 
Effective storage 31.4 65.6 107.4 120.0 120.0 120.0 120.0 
Effective precip. 20.6 26.2 12.2 13.5 10.4 17.7 1.9 
Effective precip. 13.8 8.7 0 10.7 10.4 19.8 3.8 
Corn silage 
Rooting depth 0.28 0.67 0.99 1.00 1.00 
Effective storage 34.1 80.6 2119.1 120.0 120.0 
Effective precip. 20.6 28.0 13.5 12.5 9.4 
Effective precip. 13.8 10.0 0 10.7 10.4 
Oat feed 
Rooting depth 0.20 0.42 0.94 1.00 1.00 1.00 
Effective storage 24.0 50.8 113.1 120.0 120.0 120.0 
Effective precip. 1.8 14.9 32.2 34.3 12.5 8.3 
Effective precip. 2.6 15.8 21.3 12.7 0 9.4 
a 
b 
Mean monthly rooting depth calculated according to Equation E-2. 
Mean monthly effective storage depth for sandy loam soil calculated from Equation E-1. 
c 
Mean monthly effective precipitation for upper bound glacial transition climate calculated according to Appendix 
E, Section 2.1.2. 
d Mean monthly effective precipitation for lower bound glacial transition climate calculated according to Appendix
E, Section 2.1.2. 
E2.1.2 Evapotranspiration/Precipitation Ratio Method for Estimating Effective 
Precipitation 
Pe was derived using methods from Doorenbos and Pruitt (1977 [DIRS 103062], Table 34, 
p. 
75) using mean monthly precipitation (Section 4, Tables 4.1-2 through 4.1-5), mean monthly 
ETc (Tables D-10 through D-13), and effective storage in the root zone (TAW, Tables E-2 and 
E-3). For direct use of Table 34 (reproduced below in Table E-4), Doorenbos and Pruitt assumed 
an effective root zone storage of 75 mm. This was rarely the case for the representative crops 
and so the correction factors for storage were used (Doorenbos and Pruitt 1977 [DIRS 103062], 
p. 
75, see example below). The mean monthly precipitation values in Table 34 (Doorenbos and 
Pruitt 1977 [DIRS 103062], p. 75) were listed in 12.5 mm increments, and mean monthly ETc in 
25 mm increments. The closest table values to the calculated mean monthly precipitation and 
ETc were used. If mean monthly precipitation was less than 8 mm, Pe was set equal to zero 
(recommended by Dastane 1978 [DIRS 159870], Section 2.1). If the adjusted table Pe values 
were greater than the monthly mean ETc, effective precipitation was set equal to mean monthly 
ETc because precipitation in excess of what is used by the crop cannot be considered effective. 

Example: Using early lettuce in March for present-day climate conditions, mean monthly 
precipitation = 11.7 mm (Section 4, Table 4.1-2), mean monthly ETc = 77.95 mm (Table D-10), 
and effective storage = 22.6 mm (Table E-2). 
From Table E-4 below, the closest mean monthly precipitation and ETc values were 12.5 mm and 
75 mm, respectively. These values correspond to an average monthly Pe of 9 mm (Table E-4). 
The correction factor for effective storage of 22.6 mm was 0.77 (Table E-4). Thus, effective 
precipitation for lettuce in March was 9  0.77 = 6.93 mm. 
Table E-4. Average Monthly Effective Precipitation Determined From Mean Monthly Precipitation and 
Average Monthly Crop Evapotranspiration 
Monthly mean precipitation (mm) 
12.5 25.0 37.5 50 62.5 75.0 87.5 100.0 112.5 
Average monthly effective precipitation (mm) 
Average 
monthly 
ETc 
(mm) 
25 8 16 24 
50 8 17 25 32 39 46 
75 9 18 27 34 41 48 56 62 69 
100 9 19 28 35 43 52 59 66 73 
125 10 20 30 37 46 54 62 70 76 
150 10 21 31 39 49 57 66 74 81 
175 11 23 32 42 52 61 69 78 86 
200 11 24 33 44 54 64 73 82 91 
225 12 25 35 47 57 68 78 87 96 
250 13 25 38 50 61 72 84 92 102 
Correction factors for soil water storage depths that are not equal to 75 mm. 
Effective 
storage 20 25 37.5 50 62.5 75 100 125 150 175 
Storage 
factor .73 .77 .86 .93 .97 1.00 1.02 1.04 1.06 1.07 
NOTE: Partial Table Redrawn from Doorenbos and Pruitt (1977 [DIRS 103062], Table 34, p. 75). 
Mean monthly Pe for the present-day, lower bound monsoon, lower bound glacial transition, and 
upper bound glacial transition climate conditions are in Tables E-2 and E-3. Seasonal totals for 
Pe are in Tables E-5, E-6, E-7, and E-8. 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 E-18 September 2004 
Table E-5. Seasonal Water Inputs and Requirements for 26 Crops and Turf for Present-Day Climate Conditions 
Crop Pe (mm)a Wb (mm)b DP (mm)c OW (mm) LRd LF (mm)e ETc (mm)f Ws (mm)g In (mm)h 
Leaf Lettuce early 6.9 0 0 38.71 0.12 38.71 286.47 318.25 318.25 
Leaf Lettuce late 7.7 0 0 41.23 0.12 41.23 305.48 339.01 339.01 
Head Lettuce early 6.9 0 0 38.71 0.12 38.71 286.47 318.25 318.25 
Head Lettuce late 7.7 0 0 41.23 0.12 41.23 305.48 339.01 339.01 
Cabbage early 6.9 0 0 42.05 0.09 42.05 454.88 490.00 490.00 
Cabbage late 7.7 0 0 36.45 0.09 36.45 396.04 424.80 424.80 
Celery early 0 0 0 94.97 0.09 94.97 1011.73 1106.71 1106.71 
Celery late 28.1 0 0 33.78 0.09 33.78 388.00 393.68 393.68 
Broccoli early 6.9 0 0 23.43 0.05 23.43 417.25 433.75 433.75 
Broccoli late 7.7 0 0 21.42 0.05 21.42 383.00 396.62 396.62 
Cauliflower early 6.9 0 0 23.53 0.05 23.53 419.12 435.72 435.72 
Cauliflower late 7.7 0 0 21.50 0.05 21.50 384.35 398.16 398.16 
Spinach early 5.8 0 0 20.60 0.08 20.60 253.55 268.31 268.31 
Spinach late 6.6 0 0 18.66 0.08 18.66 231.00 243.07 243.07 
Potatoes 5.8 0 0 76.94 0.09 76.94 772.70 843.80 843.80 
Onions early 5.8 0 0 122.64 0.13 122.64 807.35 924.16 924.16 
Onions late 19.2 0 0 54.65 0.13 54.65 376.49 411.82 411.82 
Carrots early 6.2 0 0 85.32 0.16 85.32 446.65 525.81 525.81 
Carrots late 9.5 0 0 76.48 0.16 76.48 404.31 471.33 471.33 
Sweet corn 0 0 0 67.50 0.09 67.50 672.78 740.28 740.28 
Bell Peppers 0 0 0 75.29 0.10 75.29 647.02 722.30 722.30 
Cucumbers 0 0 0 30.47 0.06 30.47 471.01 501.48 501.48 
Squash 0 0 0 24.42 0.06 24.42 377.53 401.95 401.95 
Melons 0 0 0 58.17 0.07 58.17 779.41 837.58 837.58 
Tomatoes 0 0 0 42.14 0.06 42.14 651.35 693.49 693.49 
Apples 18.5 0 0 166.17 0.09 166.17 1674.63 1822.34 1822.34 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 E-19 September 2004 
Table E-5. Seasonal Water Inputs and Requirements for 26 Crops and Turf for Present-Day Climate Conditions (Continued) 
Crop Pe (mm)a Wb (mm)b DP (mm)c OW (mm) LRd LF (mm)e ETc (mm)f Ws (mm)g In (mm)h 
Grapes 8.3 0 0 102.94 0.10 102.94 892.98 987.60 987.60 
Strawberries 14.6 0 0 233.42 0.16 233.42 1219.70 1438.48 1438.48 
Winter Wheat 47.5 0 0 23.22 0.02 23.22 964.1 939.80 939.80 
Barley 46.1 0 0 15.44 0.02 15.44 867.74 837.04 837.04 
Alfalfa 1st 33.2 0 0 8.90 0.08 8.90 140.26 115.96 115.96 
Alfalfa 2nd 9.4 0 0 22.21 0.08 22.21 276.46 289.30 289.30 
Alfalfa 3rd 0 0 0 34.63 0.08 34.63 416.40 451.02 451.02 
Alfalfa 4th 0 0 0 39.40 0.08 39.40 482.94 513.24 513.24 
Alfalfa 5th 9.1 0 0 29.87 0.08 29.87 368.30 389.07 389.07 
Alfalfa 6th 4.6 0 0 14.01 0.08 14.01 173.00 182.44 182.44 
Oats feed 42.3 0 0 14.16 0.02 14.16 601.37 573.23 573.23 
Bermuda 50.8 0 0 34.66 0.02 34.66 1633.18 1617.03 1617.03 
Feed Corn 9.1 0 0 101.57 0.09 101.57 1091.10 1183.57 1183.57 
Corn silage 0 0 0 71.23 0.09 71.23 758.84 830.08 830.08 
Oats hay 0 0 0 8.55 0.02 8.55 454.68 463.23 463.23 
a 
Effective precipitation calculated according to Appendix E, Section 2.1.2. 
b Water stored in the root zone prior to planting (annuals) or onset of growth (perennials) calculated according to methods in Appendix E, Section 2.2.1. 
Deep percolation of soil moisture below the root zone calculated according to methods in Appendix E, Section 2.2.1. 
d 
Leaching requirement calculated according to Equation E-4. 
e 
Leaching fraction calculated according to Appendix E, Section 2.4, Equation E-6. 
f 
Crop evapotranspiration. Values from Appendix D, Table D-10, were summed over the growing season. 
g 
h 
Seasonal water requirement calculated from Equation E-5. 
Net irrigation requirement calculated from Equation E-8. 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 E-20 September 2004 
Table E-6. Seasonal Water Inputs and Requirements for 26 Crops and Turf for Upper Bound Monsoon Climate Conditions 
Crop Pe (mm)a Wb (mm)b DP (mm)c OW (mm) LRd LF (mm)e ETc (mm)f Ws (mm)g In (mm)h 
Leaf Lettuce early 29.3 36.0 0 31.62 0.12 31.62 257.69 259.97 223.97 
Leaf Lettuce late 54.9 36.0 112.15 112.15 0.12 25.44 238.64 209.15 147.72 
Head Lettuce early 29.3 47.3 0 31.60 0.12 31.60 257.56 259.82 212.50 
Head Lettuce late 54.9 36.0 112.15 112.15 0.12 25.44 238.64 209.15 147.72 
Cabbage early 31.2 46.6 0 35.47 0.09 35.47 409.12 413.30 366.65 
Cabbage late 96.4 60.0 51.61 51.61 0.09 20.23 311.92 235.72 155.49 
Celery early 163.5 22.3 0 57.62 0.09 57.62 777.27 671.41 649.08 
Celery late 105.4 36.0 125.58 125.58 0.09 22.60 346.11 263.30 204.71 
Broccoli early 31.3 47.0 0 19.67 0.05 19.67 375.77 364.15 317.12 
Broccoli late 92.1 48.0 71.70 71.70 0.05 11.89 300.42 220.20 160.31 
Cauliflower early 32.2 47.0 0 19.68 0.05 19.68 376.84 364.30 317.27 
Cauliflower late 97.7 48.0 74.61 74.61 0.05 11.61 301.09 215.00 155.39 
Spinach early 27. 9 36.0 0 16.66 0.08 16.66 228.24 217.01 181.01 
Spinach late 63.8 36.0 125.58 125.58 0.08 10.18 186.23 132.58 86.40 
Potatoes 100.9 48.0 148.05 148.05 0.09 55.55 654.52 609.21 505.66 
Onions early 98.0 31.3 0 89.86 0.13 89.86 685.31 677.15 645.83 
Onions late 101.1 36.0 120.12 120.12 0.13 44.48 391.73 335.14 254.67 
Carrots early 30.5 51.5 0 71.98 0.16 71.98 402.15 443.61 392.12 
Carrots late 96.9 60.0 48.70 48.70 0.16 41.67 312.06 256.79 155.12 
Sweet corn 109.1 96.0 40.90 50.29 0.09 50.29 610.33 551.51 455.51 
Bell Peppers 99.3 60.0 102.22 102.22 0.10 47.81 510.15 458.65 350.85 
Cucumbers 100.3 84.0 200.19 200.19 0.06 15.99 347.46 263.15 163.16 
Squash 85.83 72.0 91.17 91.17 0.06 14.10 303.83 232.10 146.00 
Melons 186.5 96.0 0 27.01 0.07 27.01 548.43 388.94 292.94 
Tomatoes 106.7 84.0 100.36 100.36 0.06 26.18 511.47 430.92 320.73 
Apples 301.4 0 0 91.41 0.09 91.41 1212.43 1002.43 1002.43 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 E-21 September 2004 
Table E-6. Seasonal Water Inputs and Requirements for 26 Crops and Turf for Upper Bound Monsoon Climate Conditions (Continued) 
Crop Pe (mm)a Wb (mm)b DP (mm)c OW (mm) LRd LF (mm)e ETc (mm)f Ws (mm)g In (mm)h 
Grapes 221.3 0 0 53.87 0.10 53.87 684.32 516.86 516.86 
Strawberries 192.2 24.0 0 136.06 0.16 136.06 894.62 838.53 814.53 
Winter Wheat 166.8 180.0 0 18.45 0.02 18.45 894.81 746.75 566.75 
Barley 155.8 120.0 0 12.24 0.02 12.24 806.92 663.39 543.39 
Alfalfa 1st 53.1 15.3 0 8.24 0.08 8.24 152.19 107.32 91.96 
Alfalfa 2nd 30.2 0 0 18.16 0.08 18.16 248.52 236.52 236.52 
Alfalfa 3rd 23.6 0 0 27.83 0.08 27.83 362.35 362.45 362.45 
Alfalfa 4th 141.2 0 0 20.30 0.08 20.30 399.54 264.42 264.42 
Alfalfa 5th 146.8 0 0 5.63 0.08 5.63 195.24 73.38 73.38 
Alfalfa 6th 77.0 0 0 7.75 0.08 7.75 150.03 100.97 100.97 
Oat hay 26.2 120.0 61.57 61.57 0.02 7.09 403.61 384.49 257.39 
Bermuda 341.1 0 0 22.51 0.02 22.51 1368.87 1050.32 1050.32 
Feed Corn 276.8 59.3 0 43.05 0.09 43.05 735.40 501.63 442.30 
Corn silage 195.9 59.3 0 31.44 0.09 31.44 530.88 366.38 307.06 
Oat feed 92.0 120.0 27.71 27.71 0.02 11.76 556.12 475.82 344.06 
a 
Effective precipitation calculated according to Appendix E, Section 2.1.2. 
b Water stored in the root zone prior to planting (annuals) or onset of growth (perennials) calculated according to methods in Appendix E, Section 2.2.1. 
Deep percolation of soil moisture below the root zone calculated according to methods in Appendix E, Section 2.2.1. 
d 
Leaching requirement calculated according to Equation E-4. 
e 
Leaching fraction calculated according to Appendix E, Section 2.4, Equation E-6. 
f 
Crop evapotranspiration. Values from Appendix D, Table D-11, were summed over the growing season. 
g 
h 
Seasonal water requirement calculated from Equation E-5. 
Net irrigation requirement calculated from Equations E-7 and E-8. 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 E-22 September 2004 
Table E-7. Seasonal Water Inputs and Requirements for 26 Crops and Turf for Lower Bound Glacial Transition Climate Conditions 
Crop Pe (mm)a Wb (mm)b DP (mm)c OW (mm) LRd LF (mm)e ETc (mm)f Ws (mm)g In (mm)h 
Leaf Lettuce early 37.3 5.3 0 43.45 0.12 43.45 351.10 357.24 351.94 
Leaf Lettuce late 18.4 0 0 55.04 0.12 55.04 415.93 452.53 452.53 
Head Lettuce early 39.0 5.3 0 64.90 0.12 64.90 507.74 533.62 528.33 
Head Lettuce late 23.0 0 0 68.31 0.12 68.31 516.32 561.58 561.58 
Cabbage early 42.4 5.3 0 44.01 0.09 44.01 511.24 512.90 507.61 
Cabbage late 24.9 0 0 46.47 0.09 46.47 520.01 541.55 541.55 
Celery 46.8 5.3 0 74.51 0.09 74.51 840.51 868.25 862.95 
Broccoli early 39.9 5.3 0 30.69 0.05 30.69 577.53 568.29 562.99 
Broccoli late 35.4 0 0 29.52 0.05 29.52 552.57 546.66 546.66 
Cauliflower early 40.9 5.3 0 20.94 0.05 20.94 407.71 387.75 382.46 
Cauliflower late 22.9 0 0 22.55 0.05 22.55 417.86 417.50 417.5 
Spinach early 36.4 5.3 0 24.23 0.08 24.23 327.81 315.59 310.30 
Spinach late 21.3 0 0 26.71 0.08 26.71 342.59 347.97 347.97 
Potatoes 47.7 5.3 0 82.99 0.09 82.99 874.87 910.17 904.88 
Onions 71.3 0 0 139.23 0.13 139.23 981.20 1049.15 1049.15 
Carrots early 42.4 5.3 0 102.40 0.16 102.40 570.99 631.04 625.75 
Carrots late 25.3 0 0 108.18 0.16 108.18 583.78 666.68 666.68 
Sweet corn 44.0 6.9 0 80.66 0.09 80.66 847.97 884.59 877.71 
Bell Peppers 26.4 8.4 0 83.93 0.10 83.93 747.64 805.20 796.78 
Cucumbers 23.0 8.8 0 31.67 0.06 31.67 512.44 521.14 512.29 
Squash 22.6 8.8 0 26.97 0.06 26.97 439.36 443.77 434.93 
Melons 40.5 8.8 0 53.26 0.06 53.26 754.02 766.79 757.95 
Tomatoes 26.4 8.4 0 45.62 0.06 45.62 731.54 750.72 742.30 
Apples 86.6 0 0 107.66 0.09 107.66 1159.64 1180.68 1180.68 
Grapes 29.4 0 0 60.88 0.10 60.88 552.66 584.12 584.12 
Strawberries 29.8 0 0 63.30 0.16 63.30 356.58 390.07 390.07 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 E-23 September 2004 
Table E-7. Seasonal Water Inputs and Requirements for 26 Crops and Turf for Lower Bound Glacial Transition Climate Conditions (Continued) 
Crop Pe (mm)a Wb (mm)b DP (mm)c OW (mm) LRd LF (mm)e ETc (mm)f Ws (mm)g In (mm)h 
Winter Wheat 135.8 0 0 30.13 0.02 30.13 1325.42 1219.73 1219.73 
Barley 47.8 4.7 0 10.42 0.02 10.42 602.27 564.93 560.25 
Alfalfa 1st cutting 56.7 0 0 22.54 0.08 22.54 327.78 293.62 293.62 
Alfalfa 2nd cutting 30.4 0 0 43.43 0.08 43.43 552.64 565.69 565.69 
Alfalfa 3rd cutting 21.1 0 0 38.68 0.08 38.68 486.31 503.89 503.89 
Oat Hay 28.5 7.9 0 7.29 0.02 7.29 416.23 395.00 387.14 
Fescue 93.9 0 0 27.02 0.02 27.02 1327.38 1260.50 1260.50 
Feed Corn 67.1 7.1 0 99.22 0.09 99.22 1124.12 1156.21 1149.08 
Corn silage 44.9 7.1 0 93.26 0.09 93.26 1038.39 1086.80 1079.70 
Oat Feed 61.7 5.9 0 22.76 0.02 22.76 959.95 920.97 915.07 
a 
Effective precipitation calculated according to Appendix E, Section 2.1.2. 
b Water stored in the root zone prior to planting (annuals) or onset of growth (perennials) calculated according to methods in Appendix E, Section 2.2.1. 
Deep percolation of soil moisture below the root zone calculated according to methods in Appendix E, Section 2.2.1. 
d 
Leaching requirement calculated according to Equation E-4. 
e 
Leaching fraction calculated according to Appendix E, Section 2.4, Equation E-6. 
f 
Crop evapotranspiration. Values from Appendix D, Table D-12, were summed over the growing season. 
g 
h 
Seasonal water requirement calculated from Equation E-5. 
Net irrigation requirement calculated from Equations E-7 and E-8. 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 E-24 September 2004 
Table E-8. Seasonal Water Inputs and Requirements for 26 Crops and Turf for Upper Bound Glacial Transition Climate Conditions 
Crop Pe (mm)a Wb (mm)b DP (mm)c OW (mm) LRd LF (mm)e ETc (mm)f Ws (mm)g In (mm)h 
Leaf Lettuce early 57.3 36.0 103.47 103.47 0.12 24.30 232.80 199.78 139.48 
Leaf Lettuce late 22.9 0.0 0.0 38.39 0.12 38.39 300.11 315.59 315.59 
Head Lettuce early 67.6 36.0 102.81 102.81 0.12 37.70 339.90 309.96 236.26 
Head Lettuce late 25.3 0.0 0.0 47.46 0.12 47.46 368.11 390.24 390.24 
Cabbage early 74.6 60.0 56.81 56.81 0.09 24.85 339.29 289.58 204.73 
Cabbage late 27.5 0.0 0.0 32.10 0.09 32.10 369.40 374.01 374.01 
Celery 78.8 36.0 104.37 104.37 0.09 46.58 575.03 542.79 460.21 
Broccoli early 71.4 48.0 78.08 78.08 0.05 18.12 388.82 335.57 269.45 
Broccoli late 39.4 0.0 0.0 20.05 0.05 20.05 390.60 371.30 371.30 
Cauliflower early 64.6 48.0 79.81 79.81 0.05 11.64 268.50 215.55 155.91 
Cauliflower late 24.0 0.0 0.0 15.52 0.05 15.52 295.88 287.36 287.36 
Spinach early 57.3 36.0 102.81 102.81 0.08 13.36 217.97 173.98 124.63 
Spinach late 18.0 0.0 0.0 18.66 0.08 18.66 242.36 243.01 243.01 
Potatoes 82.1 48.0 72.16 72.16 0.09 51.94 599.84 569.65 469.70 
Onions 106.1 36.0 65.3 85.11 0.13 85.11 662.30 641.30 540.05 
Carrots early 75.5 60.0 56.81 59.23 0.16 59.23 381.30 365.00 248.19 
Carrots late 29.3 0.0 0.0 74.81 0.16 74.81 415.54 461.01 461.01 
Sweet corn 63.5 0.0 0.0 47.43 0.09 47.43 536.20 520.14 520.14 
Bell Peppers 50.4 60.0 82.39 82.39 0.10 55.34 526.97 530.92 415.58 
Cucumbers 57.8 84.0 20.25 20.25 0.06 18.96 350.92 312.10 209.14 
Squash 51.8 72.0 43.72 43.72 0.06 16.12 301.01 265.36 177.23 
Melons 71.4 0.0 0 34.27 0.07 34.27 530.47 493.36 493.36 
Tomatoes 54.2 84.0 28.26 29.96 0.06 29.96 517.32 493.06 380.81 
Apples 115.0 0.0 0 66.24 0.09 66.24 775.22 726.42 726.42 
Grapes 50.9 0.0 0 38.04 0.10 38.04 377.81 364.93 364.93 
Strawberries 49.2 24.0 125.09 125.09 0.16 35.80 234.08 220.64 160.84 

Agricultural and Environmental Input Parameters for the Biosphere Model 
ANL-MGR-MD-000006 REV 02 E-25 September 2004 
Table E-8. Seasonal Water Inputs and Requirements for 26 Crops and Turf for Upper Bound Glacial Transition Climate Conditions (Continued) 
Crop Pe (mm)a Wb (mm)b DP (mm)c OW (mm) LRd LF (mm)e ETc (mm)f Ws (mm)g In (mm)h 
Winter Wheat 219.3 0.0 0 16.45 0.02 16.45 868.80 666.01 666.01 
Barley 87.6 0.0 0 5.75 0.02 5.75 393.45 311.57 311.57 
Alfalfa 1st cutting 71.2 0.0 0 12.88 0.08 12.88 226.05 167.72 167.72 
Alfalfa 2nd cutting 69.7 0.0 0 25.15 0.08 25.15 372.16 327.64 327.64 
Alfalfa 3rd cutting 30.2 0.0 0 25.97 0.08 25.97 342.47 338.28 338.28 
Oat hay 65.0 0.0 0 3.83 0.02 3.83 268.80 207.67 207.67 
Fescue 126.1 0.0 0 17.80 0.02 17.80 938.66 830.36 830.36 
Feed Corn 102.6 0.0 0 62.71 0.09 62.71 770.67 730.78 730.78 
Corn silage 84.0 0.0 0 59.15 0.13 59.15 714.15 689.30 689.30 
Oat feed 104.0 0.0 0 13.67 0.02 13.67 643.87 553.52 553.52 
a 
Effective precipitation calculated according to Appendix E, Section 2.1.2. 
b Water stored in the root zone prior to planting (annuals) or onset of growth (perennials) calculated according to methods in Appendix E, Section 2.2.1. 
Deep percolation of soil moisture below the root zone calculated according to methods in Appendix E, Section 2.2.1. 
d 
Leaching requirement calculated according to Equation E-4. 
e 
Leaching fraction calculated according to Appendix E, Section 2.4, Equation E-6. 
f 
Crop evapotranspiration. Values from Appendix D, Table D-13, were summed over the growing season. 
g 
h 
Seasonal water requirement calculated from Equation E-5. 
Net irrigation requirement calculated from Equations E-7 and E-8. 

E2.2 STORED SOIL MOISTURE (WB), LEACHING REQUIREMENT (LR), AND 
DEEP PERCOLATION (DP) 
Soil moisture can be stored in the root zone if precipitation is greater than ETc when a crop is 
actively growing, or if precipitation is greater than evaporation from the soil surface when no 
crop is present. It is dependant on the water holding capacity of the soil and depth of the root 
zone. Deep percolation (DP) occurs after a rain or irrigation event that causes soil moisture in 
the root zone to reach and exceed field capacity. Field capacity is the amount of water held 
against gravitational forces when downward drainage following a rain or irrigation event has 
markedly decreased. Soil moisture stored in the root zone and DP were derived using soil water 
balance calculations across months. The soil water balance is based on water holding capacity of 
the soil in the root zone and the portion of that water that can be used by the crop (TAW), total 
evaporable water from the soil surface (TEW, Allen et al. 1998 [DIRS 157311], p. 144), Pe, and 
ETc. 
E2.2.1 Stored Soil Moisture and Deep Percolation 
TEW is the amount of water (mm) that can be depleted from the upper soil layers through direct 
evaporation during a complete drying cycle (Allen et al. 1998 [DIRS 157311], Equation 73, 
p. 144): 
TEW =1000(. - 5.0 .WP )Z (Eq. E-3) 
FC e 
where 
.FC = soil water content at field capacity (m3 m-3), 
.WP = soil water content at wilting point (m3 m-3), 
Ze is the depth of the soil surface layer (m) that is subject to drying through the process of 
evaporation. 
In the absence of site specific data, Allen et al. (1998 [DIRS 157311]) recommended a range of 
0.10 to 0.50 m for Ze, and provided typical values for TEW for a sandy loam soil which ranged 
from 15 - 20 mm (Allen et al. 1998 [DIRS 157311], Table 19, p. 144). The midpoint of this 
range (17.5 mm) was selected for the analysis. 
Moisture can be stored in the soil when precipitation is greater than ETc from a cropped surface 
or when precipitation is greater than TEW from a fallow field. Percolation below the root zone 
can only occur when the soil water content in the root zone exceeds .FC. 
Annual CropsThe following guidelines were used for annual crops: 
 
Early and late season crops were planted on the same land 
 
The land was fallow outside of the growing season 

 
Of the monthly precipitation that entered the system outside of the growing season, TEW 
was evaporated, the rest percolated into the soil 
 
Of the monthly precipitation that entered the system during the growing season, ETc was 
evaporated, the rest percolated into the soil 
 
TAW was the maximum amount of water that could be stored in the root zone and used 
by plants 
 
Deep percolation occurred when precipitation outside of the growing season exceeded 
.FC -TEW or when precipitation during the growing season exceeded .FC -ETc. 
Example: For upper bound glacial transition climate conditions, the growing seasons for early 
and late head lettuce were April 23 - July 10 and July 15 - Oct 1, respectively (Appendix D, 
Table D-2). During this time monthly ETc (see Table D-13) exceeded precipitation so no 
moisture was stored over the growing seasons. The soil plot was fallow Oct. 2 - April 23. The 
maximum amount of plant available water that can be stored in the lettuce root zone (TAW) was 
36 mm (Table E-3), TEW was 17.5 mm, and .FC of the root zone was 69 mm (.FC of the root 
zone = 1000 x .FC x Zr, from equation E-1). Table E-9 shows monthly precipitation inputs, water 
available for percolation into the soil, water stored in the root zone, percolation below the root 
zone, and stored soil water at the time of planting. The calculations start in October with the 
assumption that there is no soil moisture left the month of harvest: October water balance = 
precipitation (25.2 mm) - TEW (17.5 mm) = 7.7 mm. The .FC of the soil in the root zone was 
69 mm, therefore, 7.7 mm of water was stored in the root zone, but no percolation below the root 
zone occurred in October. 
Novemberwater balance = precipitation (54.6 mm) + water stored in October (7.7 mm) - TEW 
(17.5 
mm) = 44.8 mm. 44.8 mm of water was stored in the root zone; no water percolated below 
the root zone. 
Decemberwater balance = precipitation (61.5 mm) + water stored in November (44.8 mm) - TEW 
(17.5 
mm) = 88.8 mm. Of this amount, 69 mm was stored in the root zone and 19.8 mm 
percolated below the root zone. 
Calculations for January, February, and March were the same as those for November and 
December (Table E-9). Cumulative DP for October through March was 93.2 mm. 
In April, there were 22 days before planting the early season crop. Therefore, TEW = (17.5/30) x 
23 = 13.4 mm and precipitation = (30.0/30) x 23 = 23.0 mm. The water balance equals 
precipitation (23.0 mm) + water stored in March (69 mm) -TEW (13.4 mm) = 78.6 mm. Of this 
amount, 69 mm was stored in the root zone and 9.6 percolated below the root zone. Not all of 
the water at field capacity can be used by the crop (see Section 2.1.1 above). For upper bound 
glacial transition climate conditions when soil water in the rooting zone reached .FC it was 
necessary to use Equation 3 to estimate TAW to determine Wb. For this example, TAW = Wb = 
36 mm and cumulative DP = 103.0 mm. 

Table E-9. Monthly Stored Water and Deep Percolation Totals (mm) 
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 
Lettuce 
Monthly Precip (mm) 50.3 37.8 37.8 23.0 25.2 54.6 61.5 
Monthly Precipitation + 
stored moisture from 
previous month - 
Evaporation 
(i.e., Percolation) 
101.8 89.3 89.3 78.6 7.7 44.8 88.8 
Water Stored in Root 
Zone 
69.0 69.0 69.0 7.7 44.8 69.0 
Cumulative Percolation 
Below Root Zone (DP) 
52.6 72.9 93.2 102.8 0 0 19.8 
Stored Water at the 
time of Planting (Wb) 
36 
For present-day and lower bound glacial transition climate conditions, water was rarely stored in 
the root zone at the beginning of the planting season. Because .FC was never reached, Wb was 
simply the water stored in the root zone prior to planting (it was not necessary to calculate TAW 
to determine Wb). 
Water stored in the root zone and cumulative DP are in Tables E-5, E-6, E-7, and E-8. 
Perennial Crops -The same methods used to calculate Wb and DP for annual crops were used 
for perennial crops with the following exception: 
During low growth and dormant periods, perennials were assumed to continue to loose water at 
low rates. To reflect this, initial Kc values were used to calculate monthly mean ETc for 
non-growing periods (see Appendix D for information regarding Kc and ETc calculations). This 
ETc was used instead of TEW to calculate the water balance for perennials during non-growing 
periods. 
Water stored in the in the root zone at the onset of active growth and cumulative DP for 
perennials are in Tables E-5, E-6, E-7, and E-8. 
E2.3 LEACHING REQUIREMENT (LR) 
Salt build up in agricultural soils can occur when crops are irrigated with water containing 
significant quantities of soluble salts. In a well-drained soil, addition of enough water to cause 
drainage below the root zone can eliminate salt build up that can be harmful to plants. If 
downward drainage is insufficient, salts that are left in the soil can precipitate in the root zone 
and increase soil salinity as evaporation occurs. Soil salinity is measured by the electrical 
conductivity (EC) of the saturated soil solution. The leaching requirement (LR) is the fraction of 
infiltrated water that must pass through the root zone to remove excess salts. It is a function of 
the salinity of the irrigation water, and crop tolerance to salts. For a sandy loam to clay loam soil 

in arid to semi-arid environments, LR can be calculated according the following equation 
(Doorenbos and Pruitt 1977 [DIRS 103062], pp. 77 and 78 ): 
EC 1
LR =w  (Eq. E-4) 
5EC - EC LE
ew 
where 
ECw = electrical conductivity of the irrigation water (mmhos/cm), 
ECe = crop salt tolerance under acceptable yield reduction (mmhos/cm), 
LE = leaching efficiency which varies with soil type (dimensionless). 
As described in Section 4.1.7, an ECw of 0.50 dS/m was selected for this analysis. 
Crop salt tolerance values with no reduction in yield were taken from Doorenbos and Pruitt 
(1977 [DIRS 103062], Table 36, p. 78) for ECe (Table E-10). There were no ECe values 
available for celery, cauliflower, squash, oats, or fescue so values for similar crops in the same 
crop type were chosen for each. The value for cabbage was used for celery, broccoli was used 
for cauliflower, cucumber was used for squash, winter wheat for oats, and bermudagrass for tall 
fescue (Table E-10). 
Table E-10. Crop Salt Tolerance Levels (ECe, mmhos/cm) that Result in No Yield 
Reduction for 26 Crops and Turf 
Crop ECe 
a Crop ECe 
a 
Lettuce 1.3 Melons 2.2 
Cabbage 1.8 Tomatoes 2.5 
Celery 1.8 Alfalfa hay 2.0 
Broccoli 2.8 Oats 6.0 
Cauliflower 2.8 Barley 8.0 
Spinach 2.0 Apples 1.7 
Potatoes 1.7 Grapes 1.5 
Onions 1.2 Strawberries 1.0 
Carrots 1.0 Winter wheat 6.0 
Sweet corn 1.7 Feed Corn 1.8 
Bell peppers 1.5 Corn silage 1.8 
Cucumbers 2.5 Oat hay 6.0 
Squash 2.5 Bermuda 6.9 
Fescue 6.9 
Source: Doorenbos and Pruitt (1977 [DIRS 103062], Table 36, p. 78). 
a 
Electrical conductivity. 

The common soils in northern Amargosa Valley (Arizo, Corbilt, Sanwell, Shamock, Yermo) are 
sandy to sandy loam so an LE of 0.7 for a medium textured soil (Doorenbos and Pruitt 1977 
[DIRS 103062], p. 79) was used for LR calculations. 
Example: Using early lettuce, ECe = 1.3 mmhos/cm (Table E-10), ECw = 0.50 mmhos/cm, and 
LE = 0.7. 
50.0 1
LR= = 12.0 (Eq. E-4) 
5  3.1 - 50.0 7.0 
For the lettuce crop, about 12 percent of the total water entering the soil must pass through and 
out of the root zone. 
The LRs for the 26 crops and turf grass are in Tables E-5, E-6, E-7, and E-8. 
E2.4 SEASONAL WATER REQUIREMENTS (WS), NET IRRIGATION 
REQUIREMENTS (IN), LEACHING FRACTION (LF), AND OVERWATERING 
RATE (OW) 
Seasonal water requirements (Ws) were estimated using the following equation from Doorenbos 
and Pruitt (1977 [DIRS 103062], p. 79): 
ET - Pe
c
W = (Eq. E-5) 
s 
1- LR 
where 
ETc = monthly mean crop evapotranspiration summed over the growing season (mm), 
Pe = monthly mean effective precipitation summed over the growing season (mm), 
LR = leaching requirement (unitless). 
Example: For early lettuce and present-day climate conditions, seasonal ETc = 286.5 mm 
(Table E-5), seasonal Pe = 6.9 mm (Table E-5), and LR = 0.12 (Table E-5). 
. 5.286 - 9.6 .
W
s =. .= 3.318 mm (Eq. E-5) 
. 1 - 1216.0 . 
The Ws values for the 26 crops and turf are in Tables E-5, E-6, E-7, and E-8. 
Once LR and Ws were determined, it was necessary to evaluate whether seasonal precipitation 
resulted in cumulative DP that was greater than LR. This directly impacts the net irrigation 
requirement (In) and the overwatering rate (OW). If precipitation results in cumulative DP that 
equals or exceeds LR, then additional irrigation water for leaching calculated into Ws is not 
required. The overwatering rate (OW), which is defined as the average amount of groundwater 
or precipitation that percolates through the root zone and leaches salts and radionuclides out of 

that zone (Section 1), is equal to DP or the Leaching Fraction (LF), depending on which is 
greater. 
Leaching Fraction, which is the amount of water that percolates below the root zone, can be 
determined from: 
LF =W -(ET - Pe) (Eq. E-6) 
sc 
Example: From the previous example for lettuce and present-day climate conditions, Ws = 
318.3 mm, ETc = 286.5, and Pe = 6.9. 
LF = 318.3 mm - (286.5 mm - 6.9 mm) = 38.7 mm. 
The cumulative DP for early lettuce was 0. Therefore, LF was the value selected for lettuce that 
would be included in development of the distribution for OW and was included in the net 
irrigation requirement (In). Because of the aridity of present-day and lower bound glacial 
transition climate conditions, DP never occurred, making LR a necessary inclusion to all net 
irrigation calculations. Additionally, for present-day climate, LF for each crop and turf was used 
to generate the distribution of OW (see Section 6.9). 
For upper bound monsoon and upper bound glacial transition climate conditions, DP often 
exceeded LF (Tables E-6 and E-8, respectively). Under these circumstances, LR was not needed 
to meet the net irrigation requirements and Equation E-7 was used to calculate In. For crops that 
didnt require additional water to meet LR, DP was used to generate the distribution of OW. 
In a few cases, DP occurred but did not meet the crop LR. Under these circumstances, LR was 
included in the calculation for In (Equation E-8) and DP was subtracted from the total to 
compensate for the extra water in the system. Leaching Fraction was used to generate the 
distribution of OW. 
One of the following equations from Doorenbos and Pruitt (1977 [DIRS 103062], p. 70) were 
used to calculate net irrigation requirements (In) depending on whether DP was greater or less 
than LF: 
n
n (For DP > LF: In S = i =1ETmonthly c S - i =1Pe + Ge + Wb) (Eq. E-7) 
For DP < LF: In =W -(Ge + Wb) (Eq. E-8) 
s 
where 
ETc = monthly mean crop evapotranspiration summed over the growing season (mm), 
Pe = monthly mean effective precipitation summed over the growing season (mm), 
Ge = groundwater contribution to the water requirement (mm, direct plant use), 
Wb = stored soil moisture in the root system (mm). 

Groundwater contribution (Ge) to the water requirement was set to zero for all calculations. 
Data collected by the USGS from Well AD-2 located in the Amargosa Valley showed that depth 
to groundwater was about 99 m (325 feet, Locke 2001 [DIRS 159957], Figure 1, p. 3 and 
Figure 4, p. 35). This depth remained relatively constant from 1987 through 1999 (Locke 2001 
[DIRS 159957], Figure 4, p. 35). Groundwater contribution from a water table that is more than 
about 1 m below the bottom of a crop rooting zone is generally considered negligible (Allen et 
al. 1998 [DIRS 157311], p. 171). Therefore a water table as deep as 99 m would not contribute 
to crop water requirements. 
Net seasonal irrigation requirements (Tables E-5, E-6, E-7, and E-8) were used to calculate 
annual average irrigation rates (IR, Section 6.5) for present-day, upper bound monsoon, lower 
bound glacial transition, and upper bound glacial transition climates. Net seasonal irrigation 
requirements for present-day and upper bound glacial transition climates (Tables E-5 and E-8) 
were used to calculate daily average irrigation rates (IRDj, Section 6.8). 
E2.5 IRRIGATION APPLICATION (IAJ) 
Average amount of water applied per irrigation event during the 30 days prior to harvest for each 
crop type was needed to develop the distribution for irrigation application (IAj, Section 6.7) for 
present-day and upper bound glacial transition climates. Irrigation application rates for each 
crop were calculated using a soil water balance approach and the soil moisture threshold at 
which crop stress was expected to occur. 
E2.5.1 Readily Available Water (RAW) 
As described in Section 2.1.1, field capacity, permanent wilting point, and crop specific rooting 
depth were used to estimate the total available water (TAW) in the rooting zone. In theory, water 
is available to plants until the wilting point is reached. However, decreases in hydraulic 
conductivity as the soil dries decreases the rate at which plant roots can extract water from the 
soil. Thus, crop water uptake is reduced long before soil moisture is extracted to the wilting 
point (Allen et al. 1998 [DIRS 157311], p. 162). Crops will begin to experience stress at the soil 
moisture threshold at which root absorption and transport of water are less than transpiration 
demands. Readily available soil water (RAW) is expressed as the fraction of TAW that can be 
extracted from the root zone before crop water stress occurs (Allen et al. 1998 [DIRS 157311], 
Equation 83, p. 162): 
RAW =pTAW (Eq. E-9) 
where 
p is a crop specific average fraction of TAW that can be depleted from the root zone before 
moisture stress occurs. 
Values for p from Allen et al. (1998 [DIRS 157311], Table 22, pp. 163 to 165), TAW at 
maximum root depth, and RAW for each crop are in Table E-11. 

Table E-11. Maximum Rooting Depths and Soil Moisture Parameters for 26 Crops and Turf 
Crop 
Maximum Rooting 
Depth (m)a TAW (mm)b pc RAW (mm)d 
Lettuce early 0.3 36 0.30 10.8 
Lettuce late 0.3 36 0.30 10.8 
Cabbage early 0.5 60 0.45 27.0 
Cabbage late 0.5 60 0.45 27.0 
Celery early 0.3 36 0.20 7.2 
Celery late 0.3 36 0.20 7.2 
Broccoli early 0.4 48 0.45 21.6 
Broccoli late 0.4 48 0.45 21.6 
Cauliflower early 0.4 48 0.45 21.6 
Cauliflower late 0.4 48 0.45 21.6 
Spinach early 0.3 36 0.20 7.2 
Spinach late 0.3 36 0.20 7.2 
Potatoes 0.4 48 0.35 16.8 
Onions early 0.3 36 0.30 10.8 
Onions late 0.3 36 0.30 10.8 
Carrots early 0.5 60 0.35 21.0 
Carrots late 0.5 60 0.35 21.0 
Sweet corn 0.8 96 0.50 48.0 
Bell peppers 0.5 60 0.30 18.0 
Cucumbers 0.7 84 0.50 42.0 
Squash 0.6 72 0.50 36.0 
Melons 0.8 96 0.40 38.4 
Tomatoes 0.7 84 0.40 33.6 
Alfalfa hay 1.0 120 0.55 66.0 
Oats 1.0 120 0.55 66.0 
Feed Corn 1.0 120 0.55 66.0 
Corn silage 1.0 120 0.55 66.0 
Apples 1.0 120 0.50 60.0 
Grapes 1.0 120 0.45 54.0 
Strawberries 0.2 24 0.20 4.8 
Winter wheat 1.5 180 0.55 99.0 
Barley 1.0 120 0.55 66.0 
Fescue 0.5 60 0.40 24.0 
Bermudagrass 0.5 60 0.50 30.0 
a 
Source: Allen et al. (1998 [DIRS157311], Table 22, pp. 163 to 165, minimum range 
b 
values). 
Total available soil moisture in the root zone calculated from Equation E-1. 
c 
Soil water depletion fraction, Source: Allen et al. (1998 [DIRS 157311], Table 22, pp. 163 
d 
to 165). 
Readily available soil moisture in the root zone calculated from Equation E-9. 

E2.5.2 Soil Water Balance Approach 
Irrigation application for each crop was determined by calculating a simplified soil water balance 
over the 30 days prior to harvest using the following parameters: 
 
TAW at maximum root depth (Table E-11) 
 
RAW (Table E-11) 
 
Average daily ETc per month for 30 days prior to harvest (calculated from Tables D-10 
and D-13) 
 
Irrigation requirement (In) for 30 days prior to harvest (calculated from Equations E-7 
and E-8, and Tables E-5 and E-8) 
 
Average daily effective precipitation (Pd) for 30 days prior to harvest (calculated from 
Tables E-2 and E-3). 
RAW is the fraction of TAW that can be extracted from the soil before moisture stress occurs. 
Therefore, irrigation water should be applied when RAW is depleted. TAW -RAW was used as 
the threshold at which irrigation water should be applied to avoid onset of crop stress. If the 
amount of irrigation exceeds field capacity of the soil in the root zone, percolation below the root 
zone will occur. Excessive watering could cause nutrient leaching, changes in nutrient 
availability due to microbial responses to wet soil conditions, and water waste. Therefore, to 
avoid exceeding field capacity, irrigation was calculated such that TAW would not be exceeded 
in the root zone. Average daily ETc was used to estimate daily water loss from the soil system, 
and average daily effective precipitation was used as water input to the soil system. 
The following guidelines were observed: 
 
Soil moisture at the beginning of the 30 day period was set equal to TAW for a given 
crop 
 
When RAW was depleted (within +/- 4 mm), irrigation water was applied the following 
morning to increase soil moisture to TAW 
 
Daily ETc was subtracted at the end of the day 
 
Average daily precipitation was added to the balance at the end of each day. 
Thus, the soil water balance at the end of each day (SWBd) was calculated as: 
SWBd = Irr + Pd - ET , with SWBd = TAW (Eq. E-10)
c 

where 
Irr = the irrigation water applied in the morning (when applicable) (mm), 
Pd = the average daily effective precipitation input (mm), 
ETc = the average daily crop evapotranspiration calculated for 30 days prior to 
harvest (mm/day). 
Equation E-10 was derived from Allen et al. (1998 [DIRS 157311], Equation 85, p. 170). 
Example: Using early lettuce and present-day climate conditions the following parameters were
determined from the appropriate Tables: 
The last Julian day of the growing season = 125 = May 5 (Table D-1). Therefore, for the last 
30 days of the growing season, 25 days were in April and 5 days were in May. 
Effective precipitation for April and May = 0 (Table E-2). Therefore, mean daily effective 
precipitation for the last 30 days of the growing season was equal to 0. 
Mean daily ETc for April = 5.75 mm/day (Table D-10). 
Mean daily ETc for May = 7.21 mm/day (Table D-10). 
Irrigation requirement for 25 days in April = 143.72 mm (5.75  25). 
Irrigation requirement for 5 days in May = 36.05 mm (7.21  5). 
TAW at maximum root depth = 36 (Table E-11). 
RAW = 10.8 (Table E-11). 
The water balance calculations (Equation E-10) for the 30 days prior to harvest for early lettuce 
are illustrated in Table E-12 using the values above. Irrigation water was added when RAW was 
depleted. This occurred on days 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 26, 27, 28, 29, and 
30. Irrigation events occurred on the days following RAW depletion for days 1 through 25. 
Because of high daily ETc on days 25 through 30, irrigation was applied prior to RAW depletion 
so that soil moisture would not go below RAW on a daily basis. Enough water was added so that 
soil moisture equaled, but did not exceed TAW. 
Table E-12. Water Balance Calculations Over the Thirty-Day Time Period Prior to Harvest for Early 
Season Lettuce and Present-Day Climate Conditions 
Day TAWa ETc 
b Pd 
c Irrigation Balance 
1 36 5.75 0 36.0-25.2=10.8 25.2+10.8-5.75=30.25 
2 36 5.75 0 30.25-5.75=24.5 
3 36 5.75 0 36-24.5=11.5 24.5+11.5-5.75=30.25 
4 36 5.75 0 30.25-5.75=24.5 
5 36 5.75 0 36-24.5=11.5 24.5+11.5-5.75=30.25 
6 36 5.75 0 30.25-5.75=24.5 
7 36 5.75 0 36-24.5=11.5 24.5+11.5-5.75=30.25 
8 36 5.75 0 30.25-5.75=24.5 
9 36 5.75 0 36-24.5=11.5 24.5+11.5-5.75=30.25 

Table E-12. Water Balance Calculations Over the Thirty-Day Time Period Prior to Harvest for 
Early Season Lettuce and Present-Day Climate Conditions (Continued) 
Day TAWa ETc 
b Pd 
c Irrigation Balance 
10 36 5.75 0 30.25-5.75=24.5 
11 36 5.75 0 36-24.5=11.5 24.5+11.5-5.75=30.25 
12 36 5.75 0 30.25-5.75=24.5 
13 36 5.75 0 36-24.5=11.5 24.5+11.5-5.75=30.25 
14 36 5.75 0 30.25-5.75=24.5 
15 36 5.75 0 36-24.5=11.5 24.5+11.5-5.75=30.2 
16 36 5.75 0 30.25-5.75=24.5 
17 36 5.75 0 36-24.5=11.5 24.5+11.5-5.75=30.2 
18 36 5.75 0 30.25-5.75=24.5 
19 36 5.75 0 36-24.5=11.5 24.5+11.5-5.75=30.2 
20 36 5.75 0 30.25-5.75=24.5 
21 36 5.75 0 36-24.5=11.5 24.5+11.5-5.75=30.2 
22 36 5.75 0 30.25-5.75=24.5 
23 36 5.75 0 36-24.5=11.5 24.5+11.5-5.75=30.2 
24 36 5.75 0 30.25-5.75=24.5 
25 36 5.75 0 36-24.5=11.5 24.5+11.5-5.75=30.2 
26 36 7.21 0 36- 30.2=5.8 30.2+5.8-7.21=28.8 
27 36 7.21 0 36-28.8=7.2 28.8+7.2-7.21=28.8 
28 36 7.21 0 36-28.8=7.2 28.8+7.2-7.21=28.8 
29 36 7.21 0 36-28.8=7.2 28.8+7.2-7.21=28.8 
30 36 7.21 0 36-28.8=7.2 28.8+7.2-7.21=28.8 
a 
TAW (total available water) at maximum root depth for early season lettuce under present-day climate 
conditions (Table E-11). 
b 
Mean daily ETc (crop evapotranspiration) for the last 30 days of the growing season for early season
lettuce under present-day climate conditions (Table D-10). 
Mean daily effective precipitation (Table E-2). 
Mean irrigation application amounts for each crop for present-day and upper bound glacial 
transition climate conditions are in Tables E-13 and E-14, respectively. 
E2.6 CROP MOISTURE STRESS 
In order to quantify uncertainty in irrigation management practices that could affect the 
distributions of irrigation parameters, the soil moisture balance method described above in 
Section 2.5 was used to determine the percent reduction in irrigation water that would be 
required to cause crop water stress. It was concluded that under-watering would likely result in 
visible crop stress that would signal the farmer or gardener to make adjustments in order to avoid 
loss in yield (Sections 6.5, 6.7, 6.8, and 6.9). 
E2.6.1 Under-Watering 
To avoid crop water stress, irrigation water should be applied when RAW is depleted (Allen et al. 
1998 [DIRS 157311], p. 171). Because most crop species are fairly sensitive to stress caused 
from lack of water, it was concluded that withholding water for 2 days after depletion of RAW at 
each irrigation event would cause visible signs of stress. The water balance calculations for the 

last 30 days prior to harvest were altered by withholding irrigation for 2 days after depletion of
RAW at each irrigation event. After 2 days, enough irrigation water was added to bring soil 
moisture to TAW. The resulting percent decrease in irrigation water and the number of days the
crop experienced water stress were calculated. The percent decrease per crop was determined 
for present-day and upper bound glacial transition climate conditions. The results of this
analysis indicated that small percent decreases in irrigation resulted in several (nonconsecutive) 
days of water stress (Tables E-16 and E-17). Based on this analysis, a 10 percent reduction was 
used to assess the lower ends of the distributions for the irrigation parameters to determine 
whether adjustments were necessary (Sections 6.5, 6.7, 6.8, and 6.9). 
Example: Using early lettuce and present-day climate conditions the following parameters were
determined from the appropriate Tables: 
The last Julian day of the growing season = 125 = May 5 (Table D-1). Therefore, for the last 
30 days of the growing season, 25 days were in April and 5 days were in May. 
Effective precipitation for April and May = 0 (Table E-2). Therefore, mean daily effective 
precipitation for the last 30 days of the growing season was equal to 0. 
Mean daily ETc for April = 5.75 mm/day (Table D-10). 
Mean daily ETc for May = 7.21 mm/day (Table D-10). 
Irrigation requirement for 25 days in April = 143.75 mm (5.75  25). 
Irrigation requirement for 5 days in May = 36.10 mm (7.21  5). 
TAW at maximum root depth = 36 (Table E-11). 
RAW = 10.8 (Table E-11). 
The water balance calculations with water withheld to cause water stress for the 30 days prior to
harvest for early lettuce are illustrated in Table E-15 using the above values. Irrigation water 
was added two days after depletion of RAW (i.e., when soil water balance was at or just below 
36 mm - 10.8 mm = 25.2 mm at the end of the day). Enough water was added so that soil 
moisture was equal to but did not exceed TAW. Irrigation events occurred on days 1, 5, 9, 13, 
17, 21, 25, and 29. The total amount of irrigation for the 30-day period decreased from 183 mm 
to 176 mm (a 3.9 percent decrease) and the crop experienced some degree of water-stress for 
15 days. 
Table E-13. Irrigation Application for Crops under Present-Day Climate Conditions 
Crop 
Irrigation 
Applicationa 
(mm) 
Number of 
Applicationsb 
30-day Totalc 
(mm) 
Leaf Lettuce early 10.19 18 183.36 
Leaf Lettuce late 9.87 15 148.10 
Head Lettuce early 12.00 15 179.95 
Head Lettuce late 9.87 15 148.10 
Cabbage early 24.56 9 221.03 
Cabbage late 22.47 5 112.36 
Celery early 9.98 30 299.53 
Celery late 6.84 7 47.89 

Table E-13. Irrigation Application for Crops under Present-Day Climate Conditions 
(Continued) 
Crop 
Irrigation 
Applicationa 
(mm) 
Number of 
Applicationsb 
30-day Totalc 
(mm) 
Broccoli early 23.35 9 210.11 
Broccoli late 20.61 7 123.64 
Cauliflower early 21.12 10 211.20 
Cauliflower late 20.55 6 123.30 
Spinach early 5.97 30 179.23 
Spinach late 8.94 15 134.04 
Potatoes 18.89 15 283.29 
Onions early 8.84 30 265.08 
Onions late 9.26 6 55.55 
Carrots early 22.90 10 229.05 
Carrots late 19.49 7 136.42 
Sweet corn 44.68 7 312.79 
Bell Peppers 19.77 15 296.56 
Cucumbers 34.76 8 278.07 
Squash 33.30 7 233.1 
Melons 35.43 8 283.48 
Tomatoes 30.26 10 302.57 
Apples 49.39 3 148.18 
Grapes 48.38 3 145.15 
Strawberries 6.04 30 181.22 
Winter Wheat 77.93 2 155.86 
Barley 48.62 3 145.87 
Alfalfa hay 1st cutting 46.53 2 93.06 
Alfalfa hay 2nd cutting 65.84 3 197.52 
Alfalfa hay 3rd cutting 66.36 4 265.43 
Alfalfa hay 4th cutting 60.56 5 302.78 
Alfalfa hay 5th cutting 55.56 4 222.23 
Alfalfa hay 6th cutting 50.88 2 101.75 
Oat Feed 49.96 3 149.88 
Feed Corn 50.39 3 151.18 
Corn silage 60.01 5 300.03 
Oat hay 56.32 4 225.29 
a 
b 
Average amount of water applied per irrigation event for 30 days prior to harvest. 
Number of irrigation events for 30 days prior to harvest. 
c 
Total irrigation requirement for 30 days prior to harvest. 

Table E-14. Irrigation Application for Crops under Upper Bound Glacial Transition 
Climate Conditions 
Crop 
Irrigation Applicationa 
(mm) 
Number of 
Applicationsb 
30-day Totalc 
(mm) 
Leaf Lettuce early 10.1 12 121.2 
Leaf Lettuce late 10.0 15 150.3 
Head Lettuce early 9.5 16 152.7 
Head Lettuce late 8.4 15 126.2 
Cabbage early 25.7 6 154.5 
Cabbage late 26.5 5 132.4 
Celery 8.0 25 200.9 
Broccoli early 18.9 9 169.7 
Broccoli late 19.7 6 118.1 
Cauliflower early 22.2 6 133.1 
Cauliflower late 21.7 6 130.1 
Spinach early 7.3 16 116.2 
Spinach late 8.2 15 123.0 
Potatoes 14.4 14 201.9 
Onions 11.3 15 169.3 
Carrots early 18.2 9 164.2 
Carrots late 22.0 6 131.7 
Sweet corn 40.3 5 201.6 
Bell Peppers 17.7 9 159.2 
Cucumbers 37.2 5 186.0 
Squash 34.1 5 170.6 
Melons 34.6 5 173.1 
Tomatoes 31.4 6 188.6 
Apples 54.4 2 108.9 
Grapes 43.2 2 86.4 
Strawberries 7.3 16 116.7 
Winter Wheat 59.9 2 119.8 
Barley 66.7 2 133.3 
Alfalfa 1st cutting 55.0 2 110.0 
Alfalfa 2nd cutting 51.1 4 204.4 
Alfalfa 3rd cutting 51.3 3 153.9 
Oat Feed 48.3 3 144.9 
Feed Corn 32.2 1 32.2 
Corn silage 61.9 2 123.7 
Oat hay 46.2 3 138.7 
a 
b 
Average amount of water applied per irrigation event for 30 days prior to harvest. 
Number of irrigation events for 30 days prior to harvest. 
c 
Total irrigation requirement for 30 days prior to harvest. 

Table E-15. Water Balance Calculations Over the Thirty-Day Time Period Prior to Harvest 
with Water Withheld to Cause Crop Water-Stress 
Day TAW ETc Pe Irrigation current balance 
1 36 5.75 0 36-25.2=10.8 25.2+10.8-5.75=30.2 
2 36 5.75 0 30.2-5.75=24.5 
3 36 5.75 0 24.5-5.75=18.6 
4 36 5.75 0 18.6-5.75=12.9 
5 36 5.75 0 36-12.9=23.1 12.9+23.1-5.75=30.2 
6 36 5.75 0 30.2-5.75=24.4 
7 36 5.75 0 24.4-5.75=18.6 
8 36 5.75 0 18.6-5.75=12.9 
9 36 5.75 0 36-12.9=23.1 12.9+23.1-5.75=30.2 
10 36 5.75 0 30.2-5.75=24.4 
11 36 5.75 0 24.4-5.75=18.6 
12 36 5.75 0 18.6-5.75=12.9 
13 36 5.75 0 36-12.9=23.1 12.9+23.1-5.75=30.2 
14 36 5.75 0 30.2-5.75=24.4 
15 36 5.75 0 24.4-5.75=18.6 
16 36 5.75 0 18.6-5.75=12.9 
17 36 5.75 0 36-12.9=23.1 12.9+23.1-5.75=30.2 
18 36 5.75 0 30.2-5.75=24.4 
19 36 5.75 0 24.4-5.75=18.6 
20 36 5.75 0 18.6-5.75=12.9 
21 36 5.75 0 36-12.9=23.1 12.9+23.1-5.75=30.2 
22 36 5.75 0 30.2-5.75=24.4 
23 36 5.75 0 24.4-5.75=18.6 
24 36 5.75 0 18.6-5.75=12.9 
25 36 5.75 0 36-12.9=23.1 12.9+23.1-5.75=30.2 
26 36 7.21 0 30.2-7.21=23.0 
27 36 7.21 0 23.0-7.21=15.8 
28 36 7.21 0 15.8-7.21=8.6 
29 36 7.21 0 36-8.6=27.4 8.6+27.4-7.21=28.8 
30 36 7.21 0 28.8-7.21=21.6 
Mean percent decreases in irrigation application amounts for each crop under present-day and 
upper bound glacial transition climates are in Tables E-16 and E-17, respectively. 
Table E-16. Reduction in Irrigation Requirement with Crop Stress for Present-Day Climate Conditions 
Crop 
30-day Irrigation 
Requirementa (mm) 
30-day Reduced 
Irrigationb (mm) % Reductionc 
Number of 
days stressed 
Alfalfa 1st cutting 93.1 86.0 7.6 4 
Alfalfa 2nd cutting 197.5 186.5 5.6 6 
Alfalfa 3rd cutting 265.4 256.0 3.6 7 
Alfalfa 4th cutting 302.8 289.4 4.4 8 

Table E-16. Reduction in Irrigation Requirement with Crop Stress for Present-Day Climate 
Conditions (Continued) 
Crop 
30-day Irrigation 
Requirementa (mm) 
30-day Reduced 
Irrigationb (mm) % Reductionc 
Number of 
days stressed 
Alfalfa 5th cutting 222.2 210.9 5.1 6 
Alfalfa 6th cutting 101.8 97.0 4.7 4 
Bell Peppers 296.6 289.8 2.3 15 
Bermuda 226.9 212.9 6.2 12 
Broccoli early 210.1 200.6 4.5 12 
Broccoli late 123.6 118.5 4.1 10 
Cabbage early 221.0 211.2 4.5 12 
Cabbage late 112.4 108.9 3.1 8 
Carrots early 229.1 211.6 7.6 12 
Carrots late 136.4 130.5 4.4 10 
Cauliflower early 211.2 201.9 4.4 14 
Cauliflower late 123.3 118.7 3.7 10 
Celery early 299.5 277.6 7.3 20 
Celery late 47.9 45.1 5.8 10 
Feed Corn 151.2 146.9 2.8 6 
Corn-silage 300.0 288.7 3.8 8 
Cucumbers 278.1 264.2 5.0 10 
Head Lettuce early 180.0 168.9 6.1 16 
Head Lettuce late 148.1 144.4 2.5 15 
Leaf Lettuce early 183.4 176.2 3.9 15 
Leaf Lettuce late 148.1 144.4 2.5 15 
Melons 283.5 266.3 6.1 10 
Oat Feed 149.9 141.8 5.4 4 
Oat hay 225.3 219.7 2.9 6 
Onions early 265.1 251.1 5.3 20 
Onions late 55.6 53.4 3.8 8 
Potatoes 283.3 266.6 5.9 18 
Spinach early 179.2 164.6 8.2 20 
Spinach late 134.0 132.1 1.4 15 
Tomatoes 302.6 285.1 5.8 12 
Squash 233.1 223.4 4.1 10 
Apples 148.2 139.1 6.1 5 
Grapes 145.2 136.9 5.7 6 
Strawberries 181.2 169.7 6.4 20 
Barley 145.9 139.0 4.7 4 
Winter Wheat 155.9 148.8 4.5 4 
a 
Irrigation requirement with no moisture stress calculated according to Appendix E, Section 2.6.1. 
b 
Irrigation reduced by withholding irrigation for two days after RAW is depleted (calculated according 
to Appendix E, Section 2.6.1). 
c 
Percent reduction from column 2 to column 3. 
ANL-MGR-MD-000006 REV 02 E-41 September 2004 

Table E-17. Reduction in Irrigation Requirement with Crop Stress for Upper Bound Glacial 
Transition Climate Conditions 
Crop 
30-day Irrigation 
Requirement (mm)a 
30-day Reduced Irrigation 
Requirement (mm)b 
% Reductionc Number of days 
stressed 
Alfalfa 1st cutting 110.0 94.1 14.4 3 
Alfalfa 2nd cutting 204.4 170.1 16.8 8 
Alfalfa 3rd cutting 153.9 142.7 7.2 4 
Apples 108.9 104.2 4.3 2 
Bell Peppers 159.2 142.9 10.2 11 
Fescue 174.9 164.0 6.3 10 
Broccoli early 169.7 164.7 2.9 8 
Broccoli late 118.1 116.3 1.5 6 
Cabbage early 154.3 141.9 8.0 7 
Cabbage late 132.4 125.8 5.2 8 
Carrots early 164.2 159.0 3.1 7 
Carrots late 131.7 128.0 2.8 6 
Cauliflower early 133.1 121.3 8.9 8 
Cauliflower late 130.1 127.2 2.2 10 
Celery 200.9 169.7 15.5 16 
Feed Corn 32.2 31.0 3.8 4 
Corn silage 123.7 119.3 3.6 1 
Cucumbers 186.0 166.0 10.8 9 
Head Lettuce early 152.7 125.1 18.0 14 
Head Lettuce late 126.2 117.5 6.9 12 
Leaf Lettuce early 121.2 118.0 2.6 10 
Leaf Lettuce late 150.4 139.5 7.2 14 
Melons 173.1 161.0 7.0 9 
Oat Feed 144.9 138.7 4.3 2 
Oat hay 138.7 133.2 4.0 41 
Onions 169.3 165.8 2.1 13 
Potatoes 201.9 188.2 6.7 12 
Spinach early 116.2 109.4 5.9 13 
Spinach late 123.0 119.5 2.9 14 
Sweet Corn 201.6 191.8 4.9 8 
Tomatoes 188.6 180.6 4.2 6 
Squash 170.6 147.7 13.4 8 
Grapes 86.2 82.8 4.2 1 
Strawberries 116.8 113.7 2.6 14 
Barley 133.3 128.2 3.9 1 
Winter Wheat 119.8 99.0 17.4 8 
a 
Irrigation requirement with no moisture stress calculated according to Appendix E, Section 2.6.1. 
b 
Irrigation reduced by withholding irrigation for two days after RAW is depleted (calculated according to 
Appendix E, Section 2.6.1). 
Percent reduction from column 2 to column 3.
ANL-MGR-MD-000006 REV 02 E-42 September 2004