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This article in JEQ

  1. Vol. 25 No. 3, p. 445-453
    Received: Apr 28, 1995

    * Corresponding author(s): uesjw@msu.oscs.montana.edu
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GIS-Based Solute Transport Modeling Applications: Scale Effects of Soil and Climate Data Input

  1. John P. Wilson *,
  2. William P. Inskeep,
  3. Jon M. Wraith and
  4. Robert D. Snyder
  1. Dep. of Earth Sciences, Montana State Univ., Bozeman, MT 59717;
    Geographic Information and Analysis Center, Montana State Univ., Bozeman, MT 59717.



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 km2 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 km2 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.

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