The Weather Generator (WGEN) and Chemical Movement through Layered Soils (CMLS) computer models were modified and combined with two sets of soil and climate inputs to evaluate the impact of input data map resolution on model predictions. The basic soil and climate inputs required by WGEN and CMLS were acquired from either : (i) the USDA-NRCS State Soil Geographic Database (STATSGO) database ; (ii) the USDA-NRCS (County) Soil Survey Geographic (SSURGO) database ; (iii) the Montana Agricultural Potential System (MAPS) database (which divides Montana into approximately 18 000 twenty square kilometer cells and stores more than 200 different land and climate characteristics for each of these cells) ; and (iv) a series of fine-scale monthly climate surfaces developed by the authors (0.55 km 2 cell size) using thin-plate splines, published climate station records, and USGS Digital Elevation Models (DEMs). Fifteen years of daily precipitation and evapotranspiration (ET) values were generated and combined with soil and pesticide inputs in CMLS to estimate the depth of picloram 4-amino-3,5,6-trichloro-2-pyridinecarboxylic acid) movement at the end of the growing season for every unique combination (polygon) of soil and climate in a 320 km 2 area in Teton County, Montana. Results indicate that : (i) the mean depths of picloram movement predicted for the study area with the SSURGO (county) soils and MAPS (coarse-scale) climate information and the two model runs using the fine-scale climate data were significantly different from the values predicted with the STATSGO (state) soils and MAPS climate data (based on a new variable containing the differences between the depths of leaching predicted with the different input data by soil/climate map unit and testing whether the mean difference was significantly different from zero at the 0.01 significance level) ; and (ii) CMLS identified numerous (small) areas where the mean center of the picloram solute front was likely to leach beyond the root zone when the county soils information was used. This last measure may help to identify areas where potential chemical applications are likely to contaminate groundwaters.
[1]
T. Moorman,et al.
Aerobic and Anaerobic Degradation of Alachlor in Samples from a Surface‐to‐Groundwater Profile
,
1990
.
[2]
G. A. Nielsen,et al.
Climate, soil and crop yield relationships in Cascade County, Montana
,
1992
.
[3]
John P. Wilson,et al.
Coupling Geographic Information Systems and Models for Weed Control and Groundwater Protection
,
1993,
Weed Technology.
[4]
Stephen D. DeGloria,et al.
Regional water flow and pesticide leaching using simulations with spatially distributed data
,
1991
.
[5]
William U. Reybold,et al.
Soil geographic data bases
,
1989
.
[6]
Richard E. Macur,et al.
Input parameter and model resolution effects on predictions of solute transport
,
1996
.
[7]
J. Merchant.
GIS-based groundwater pollution hazard assessment: a critical review of the DRASTIC model
,
1994
.
[8]
J. Hutson.
Applying one-dimensional deterministic chemical fate models on a regional scale
,
1993
.
[9]
D. L. Nofziger,et al.
A microcomputer-based management tool for chemical movement in soil
,
1986
.
[10]
Robert D. Snyder,et al.
A comparison of hand- and spline-drawn precipitation maps for mountainous Montana
,
1996
.