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Charge Questions for SAP Discussion

The specific charge questions on the Spatial Aquatic Model (SAM) for this SAP are organized by 

section.

Section 1: SAM Conceptual model

Q1. The conceptual watershed model for SAM accounts for spatial and temporal variability in 

soil, land cover, weather, and crop/management inputs and integrates outputs at 

watershed pour points by area-weighting. 

a. Please discuss the strengths and limitations of the conceptual watershed model for 

representing spatial and temporal variability in pesticide concentrations in water. 

b. Please comment on how clearly this conceptual approach is explained. What 

additional documentation, description, and/or characterization is necessary to ensure 

clarity and transparency?

Section 2: SAM Model Components and Approach

Q2. Please comment on the model organization and improvements to model code.

a. SAM is organized into three components (scenario generator, hydrology, and calculator) 

to reduce redundant calculations, increase model efficiency, and make use of preprocessing for creating standalone scenarios and hydrology (Section 2.1). USEPA OPP

found this to be the best approach for handling the large quantity of the spatial and 

temporal inputs, while preserving the user’s ability to run unique simulations every 

time. Please comment on the separation of independent processes (e.g., hydrology, 

pesticide transport) to maximize computational efficiency and minimize user run-time.

b. USEPA OPP has improved the PRZM model code to eliminate inefficiencies and 

excessive calculations. For example, simplifying the soil surface into a single layer, as 

described in Section 2.1.1, increased computational time by 10-fold with negligible 

change in results. Please comment on this general approach to improve the model’s

speed and efficiency without sacrificing accuracy and provide any additional 

recommendations for improving model efficiency. 

Q3. Section 2.3 and 2.4 describe initial model evaluation steps for SAM to evaluate model 

uncertainty, sensitivity, and performance in comparison to measured (monitoring) data. 

Additional model evaluations occur in Sections 4.3 and 5.4. 

a. What additional sensitivity analyses would the SAP recommend for model evaluation?

b. Current model evaluation compared SAM estimates to available atrazine monitoring 

data collected at daily to weekly intervals. Given the importance of robust, frequentlysampled monitoring data for evaluation, what additional monitoring or other types of 

data are currently available to test and evaluate how well SAM meets EPA objectives 
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of transparent processes and clear, consistent, and reasonable products for risk 

assessments and risk characterization?

Section 3: Input Data

Q4. To substantially lessen the number of scenarios and improve computational speed of the 

model, USEPA OPP evaluated the option of grouping soil map units into classes based on 

factors that have the greatest impact on pesticide loss due to runoff and erosion (USDA 

water quality index, described in Section 3.3). A comparison of runoff volume, pesticide 

mass, and pesticide concentration outputs showed little difference between the two 

approaches. Please comment on any implications for using soil grouping classes for 

watershed-scale modeling.

Q5. In order to generate the soil-land cover-weather station scenarios needed for modeling, 

USEPA OPP took spatial data at different scales and re-gridded them to the same scale for 

aggregation (Section 3.2). Based on comparative testing of the model (Sections 2.4, 4.3, and 

5.4), this appears to be a feasible approach. Please comment on the implications for 

aggregating spatial inputs across varying scales. 

Section 4: Accounting for Time-of-Travel Effects

Q6. As described in Sections 4.1 and 4.2, USEPA OPP has evaluated an approach for 

representing concentrations at the pour points of drainage networks that involves 

aggregating upstream drainage areas into integer-day stream travel-time zones, and the use 

of mathematical convolution to represent in-stream dispersive spreading of both influent 

runoff volumes and pesticide masses. Please comment on the use of this approach, and on 

any modifications or alternative approaches that USEPA OPP might consider for 

accomplishing the same ends.

Q7. USEPA OPP has not yet investigated possible adaptations of the approach referred to in 

Question 6 to simulate reactive and/or sorbing chemicals. Please comment on the potential

for modifying this approach to simulate such chemicals. 

a. Given the risk assessment purpose of SAM, please comment on the applicability of the 

described approach in dual-compartment (aqueous and benthos) systems for 

representing chemical decay and sorption during transport in surface waters across a

range of spatial scales.

b. Please recommend any watershed-scale monitoring datasets that may be suitable for 

use in evaluating estimated concentrations of pesticides that sorb non-negligibly to 

sediment, and any possible sources of data for representing the benthic sediment layer 

in surface waters throughout the country.
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Section 5: Defining the Likely Pesticide Application Window based on Crops and Weather

Q8. Pesticide applications often depend on planting dates, crop growth, and harvest dates, 

which vary with weather. To improve upon the initial approach of stratifying planting and 

harvesting dates within states by using a Plant Hardiness Zone map (Section 3.1.4), USEPA 

OPP tested the potential for using empirical data (USDA weekly crop progress reports) and, 

where such data are incomplete, crop growth models (Sections 5.2 and 5.3). 

a. Please comment on the use of crop planting dates and growth stages to provide 

reference points for pesticide application windows. How applicable is this approach for 

predicting the application window for all types of conventional pesticides (e.g., 

herbicides, growth regulators, fungicides, insecticides, etc.). For pesticide or pest types 

for which this approach may not work, what alternative methods are available? 

b. As noted in Section 5.2, empirical crop progress data are not available for all crops, all 

areas, or all years. Please recommend any additional data sources that could provide 

useful information on spatial and temporal (year-to-year) variability in crop planting, 

growth, and harvesting dates for use in modeling.

c. Where empirical data are missing, USEPA OPP explored the possibility of using crop 

growth/phenology models such as growing degree days (GDD) to fill in missing data. 

Please comment on the number of crops with available GDD models and availability of 

alternative models/data for other crops or crop groupings.

Q9. The test version of SAM provides the user with options for defining the extent of the 

pesticide application window and the distribution of pesticide applications across that 

window (e.g., uniform distribution, triangular distribution). Crop progress reports or, in their 

absence, crop growth models, offer an option for defining the application window and 

shape of the distribution (Section 5.1). Please comment on the use of empirical data or 

models to define the distribution of pesticide applications within an application window.