## Appendix 3

- A: Description of Scenario Inputs Used in SAM

The SAM model described in Section 2 of the SAP document Development of a Spatial Aquatic Model (SAM) for Pesticide Assessments uses scenarios that are created by intersecting soil map units from USDA’s Soil Survey Geographic (SSURGO) database, general land cover/crop groups of the Cropland Data Layer, and weather station grids generated from NOAA data. The primary sources for mod el inputs are described in Section 3.1. These represent what USEPA OPP has determined to be the best nationally

- available data for model inputs for SAM. The data and model frameworks are designed so that, as newer and/or improved datasets become available, these can be used in place of existing datasets.

The table below summarizes the input data extracted from these sources and compiled for each soil /land cover/weather station combination.

Table 1

– Scenario inputs used in SAM.

Para

meter Parameter Name / Description Units Source Extraction/ Derivation Major Factor in Model Sensitivity? Relationships/ correlation

Soil factors affecting runoff and pesticide movement from fields into water hydgrp Hydrologic soil

group (HSG)

, used to determine curve number A, B, C, D SSURGO for Soil Map Unit: HYDGRPDCD (dominant HSG) from muaggatt Taken directly from muaggatt table. A/D, B/D, and C/D HSG

groups were assigned HSG D for curve number determination. Major : Used in combination with land cover to determine curve number Determined by hydrologic conductivity, depth to drainage restrictive layers, which are influenced by soil texture, structure, organic matter content, bulk density, mineralogy

cn_ag cn_ fallow Runoff curve numbers of antecedent moisture condition for fallow, cropping, and residue. Number between 1 and 100 Hydrologic soil group from SSURGO, land use from land cover data groups & crop

use. Curve number guidance from USDA NRCS (2008b). Using HSG, general land

cover g roups, follow curve number guidance in Appendix 7

. Major : Determines the portion of rainfall that runs off the land, potentially carrying pesticides with it. Determined by h ydrologic soil group, land cover type, and management practicesPara

meter Parameter Name / Description Units Source Extraction/ Derivation Major Factor in Model Sensitivity? Relationships/ correlation

organic Percent soil organic carbon (OC) for the surface layer % SSURGO chorizon table (linked to major component and map unit): OM_R (chorizon) for top horizon (hzdept_r=0) Convert from percent organic matter (om_r) to organic carbon (OC) using the equation: OC=(om_r)/1. 724 Major : In combination with the pesticide sorption coefficient, determines how much pesticide is held (sorbed) to soil and how much is dissolved in water ( available for runoff) Impacts (directly or indirectly) density, water holding properties, erodibi lity; in combination with chemical Koc, impacts the amount of pesticide held on soil

bd1 bd2 Bulk density for the surface (1) and subsurface (2) horizon s g/cm 3 SSURGO chorizon table (linked to major component , map unit): DBTHIRDBAR_R (chorizon) for each horizon, along with horizon depths (hzdept_r) bd1 = dbthirdbar_r for hzdept_r=0 (surface horizon) bd2 = sum of (dbthirdbar_r for all horizons below 10 cm x thickness of the horizon ) divided by the total thickness Secondary Related to soil texture (sand, silt, clay), structure, organic content. Influences soil permeability/ drainage, which indirectly influences hydrologic soil

groups fc1 fc2 Field capacity for the surface (1) and subsurface (2) horizon s, roughly reflective o f the amount of water retained after large pores have drained ( represented by the 1/3 bar

water content ) cm 3 /cm 3 SSURGO chorizon table (linked to major component , map unit): W THIRDBAR_R (chorizon) for each horizon, along with horizon depths (hzdept_r) fc1 = w thirdbar_r for hzdept_r=0 (surface horizon) fc2 = sum of (wthirdbar_r for all horizons below 10 cm x thickness of the horizon ) divided by the total thickness Secondary Combined with wilting point, influences water holding capacity of soil, which inf luences plant growth, irrigation needs, how much water soil can hold before runoff. Related to soil texture (sand, silt, clay), structure, bulk density, organic content.Para

meter Parameter Name / Description Units Source Extraction/ Derivation Major Factor in Model Sensitivity? Relationships/ correlation

wp1 wp2 Wilting point for the surface (1) and subsurface (2) horizon s, roughly refle ctive of the minimum

water content in which plants cannot draw water from the soil ( represented by the 15 bar

water content ) cm 3 /cm 3 SSURGO chorizon table (linked to major component , map unit): WFIFTEEN BAR_ R (chorizon) for each horizon, along with horizon depths (hzdept_r) wp1 = wfifteen bar_r for hzdept_r=0 (surface horizon) wp2 = sum of (wfifteen bar_r for all horizons below 10 cm x thickness of the horizon ) divided by the total thickness Secondary Combined with field capacity

, influences water holding capacity of soil, which influences plant growth, irrigation needs, how much water soil can hold before runoff. Similar to field capacity, although soil texture (sand, silt, clay) and organic content has a greater influence than

structure on wilting point.

F actors affecting soil erosion and transport of pesticide s sorbed to soil particles into water kwfact Universal soil loss equation (K) of soil erodibility factor Dimen

sionless fraction SSURGO Soil Map Unit, joined to chorizon table through muaggatt, component tables: KWFACT for topmost horizon (hzdept_r = 0) Extract kwfact (K factor, whole soil) for surface horizon of major component in chorizon table Varies

, depending on the sorption coefficient of the pesticide; greater impact expected with pesticid es with high sorption coefficient. Degree of erodibility depends on soil texture (silt

- sized particles are most prone), organic matter content, soil structure (how well soil particles aggregate), permeability (which is related to bulk density and soil stru cture)Para

meter Parameter Name / Description Units Source Extraction/ Derivation Major Factor in Model Sensitivity? Relationships/ correlation

slope Land slope

, the average slope gradient for the soil mapping unit. % SSURGO for Soil Map Unit: SLOPEGRADD (wtd. avg. slope) from muaggatt Weighted average % slope for the soil map unit (slopegradd) from SSURGO muaggatt table Secondary Used to determine the USLE LS (slope/length) factor, which, in combination with other RUSLE parameters, estimates the amount of sediment moving from the field, along with any pesticide mass retained on soil. Impacts soil erosion potential in combination with the slope length. The combined impact is characterized in the USLE LS factor below.

slp_ usle Slope length used to calculate the USLE LS factor. Distance from point of origin of overland flow to the point where gradient decreases and deposition begins feet SSURGO component table: SLOPELENUS_R (linked to mapunit) SLOPELENUS_R in component table Secondary Used t o determine the USLE LS (slope/length) factor, which, in combination with other RUSLE parameters, estimates the amount of sediment moving from the field, along with any pesticide mass retained on soil. Some correlation with slope gradient, but weak. Impa cts soil erosion potential in combination with the slope length. The combined impact is characterized in the USLE LS factor below.Para

meter Parameter Name / Description Units Source Extraction/ Derivation Major Factor in Model Sensitivity? Relationships/ correlation

uslels Universal soil loss equation ( LS) length

slope topographic factor Dimen

sionless fraction Table 5.5 in PRZM manual (C arousel et al, 2005) provides value based on % slope [SLOPEGRADD from SSURGO], slope length [SLOPELENUS_R from SSURGO] Derive from SLOPEGRADD from SSURGO muaggatt table; length from flowpath (upslope and downslope) lengths for the basic map units or from S LOPELENUS_R in component table. Cross

- referenc e with Table 5.5 of PRZM Manual (Carousel et al, 2005) Uncertain

. Sensitivity analysis on soil erosion (USLE/RUSLE) model inputs is needed. Expected to have greater impact on high

Koc chemicals, but the extent has not been tested. Derived from slope gradient, slope length parameters above.

cfact_1 cfact_3 Universal soil loss cover management factors for fallow, cropping, and residue (C value) Dimen

sionless fraction Table 5.7 of the PRZM manual (Carousel et al, 2005) , based on

crop, management, rotation practices; Ag Handbook 703; or RUSLE data tables. Other

sources include existing PRZM scenarios Link crop

- related data to major crops in CDL general land cover classes Uncertain

. Sensitivity analysis on soil erosion (USLE/RUSLE) model inputs is needed. Expected to have greater impact on high

Koc chemicals, but the ext ent has not been tested. Unknown; related to crop type, cultivation practice.

Manni ngsN Manning's N

, roughness coefficient for field. Dimen

sionless fraction Use default of 0.17 for typical

row crop tillage; Table 5.46 in PRZM manual (Carousel et al, 2005) provides values for other conditions. Link crop

- related data to major crops in CDL general land cover classes Uncertain , expected small

. Sensitivity analysis on soil erosion (USLE/RUSLE) model inputs is needed. Expected to have grea ter impact on high

Koc chemicals, but the extent has not been tested. Unknown; related to crop type, cultivation practice, but varies little.Para

meter Parameter Name / Description Units Source Extraction/ Derivation Major Factor in Model Sensitivity? Relationships/ correlation

Crop/Plant Factors that will likely have a moderating influence on pesticide movement offsite plant Julian day of crop planting day number Derived from USDA Usual Planting and Harvesting Dates ( 2006, 2007, 2010), linked to USDA Plant Hardiness Zone map (see Appendix 4a)

. Convert day/month to Julian day. Link

crop

- related data to major crops in cdlclass; adjust by plant hardiness

zone. Use 1 for noncultivated

covers. Expected Major as it dictates the timing of pesticide application. Impact may be lessened since we are using an application window rather than single date. harvest Julian day of crop harvest month number Derived from USDA Usual Planting and Harvesting Dates (2010), linked to USDA Plant Hardiness Zone map (see Appendix 4a)

. Convert day/month to Julian day. Link

crop

- related data to major crops in cdlclass; adjust by plant hardiness

zone. Use 365 for noncultivated

covers. Uncertain, but expected small in relation to planting date. Dictated by planting date, time to maturity for plant

cintcp Maximum rainfall interception storage of crop (cm) cm Table 5.4 i n PRZM manual (Carousel et al, 2005) provides i ntercept storage for major crops (see also Appendix 4a)

. Link crop

- related data to major crops in CDL general land cover classes Uncertain , expected small

. Has not been evaluated, but expected small in relation to curve number impacts. Related to crop type.

covmax Maximum areal coverage of canopy (%) % Based on existing PRZM scenarios (see Appendix 4a)

. Link crop

- related data to major crops in CDL general land cover classes Uncertain , expected small

. Has not been evaluated, but expected small in relati on to curve number impacts. Related to crop type. For most crops, 80

- 100% maximum.Para

meter Parameter Name / Description Units Source Extraction/ Derivation Major Factor in Model Sensitivity? Relationships/ correlation

amxdr Maximum active rooting depth of crop (cm) cm Table 5.9 in PRZM manual (Carousel et al, 2005) provides average rooting depth for major crops (columns are off). Other sources (USDA) are available for regional variations (see Appendix 4a)

. Link crop

- related data to major crops in cdlclass. Adjust amxdr so that it does not exceed the total soil depth. Varies , impact is on activating irrigation. Minimal except where irrigation

occurs. Related to crop, soil depth.

Weather Factors beyond rainfall sfac Snowmelt factor (cm/C), used to calculate snowmelt rates in relation to temperature. cm/C Table 5.1 in PRZM manual (Carousel et al, 2005) gives range in SFAC related to forest covers. Maximum value of the minimum range of values for cover type reported in Table 5.1 (0.36 for

crops, non

forested land covers; 0.16 for forest covers) Uncertain,

ex pected minimal . Has not been evaluated. Currently 2 default values based on land cover, weather.

pfac Pan factor (dimensionless ), was used to convert daily

pan evaporation to daily potential ET. Current weather input files define daily potential ET directly, so

no conversion is necessary. Dimen

sionless fraction Figure 5.9 in PRZM manual (Carousel et al, 2005)

. Spatial pfac layer (Farnsworth and Peck, 1982). Digitized pfac layer. Spatial join with weather station grids (see Appendix 3b). Uncertain , expected minimal . Has not been evaluated. Related to weather.

rainfall Rainfall distribution region used to calculation time of concentration of peak flow Figure 5.8 in PRZM manual (Carousel et al, 2005)

. IREG = 3, based

on location in Type II from rainfall distribution map (Fig 5.8 in PRZM manual) Uncertain , expected minimal . Broad regions across country.Para

meter Parameter Name / Description Units Source Extraction/ Derivation Major Factor in Model Sensitivity? Relationships/ correlation

anetd Min. depth from which evaporation is extracted during fallow period (cm); 10 cm = soil with limited drainage cm 10 cm for soil w/ limited drainage. Figure 5.2 in PRZM manual (Carousel et al, 2005) for free

drainage soils. Digitized Figure 5.2, using midpoint of range of values. Default of 17.5 used for hydro

- region 5. Uncertain, expected minimal . Has not been evaluated. Used single default value