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Distribution of Slope Steepness in the Palouse Region of Washington
Author(s) -
Mulla David J.
Publication year - 1986
Publication title -
soil science society of america journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.836
H-Index - 168
eISSN - 1435-0661
pISSN - 0361-5995
DOI - 10.2136/sssaj1986.03615995005000060006x
Subject(s) - quadrangle , geology , surface runoff , gaussian , distribution (mathematics) , transformation (genetics) , spatial distribution , geomorphology , hydrology (agriculture) , mathematics , remote sensing , geotechnical engineering , ecology , mathematical analysis , biochemistry , chemistry , physics , quantum mechanics , gene , biology
Slope steepness on eight slope aspects in four 930‐ha study areas of the Palouse region of southeastern Washington were obtained from elevations digitized from the Garfield, La Crosse, Thornton, and Wilcox 7.5‐min topographic quadrangle maps. A statistical analysis showed that slope steepness in these study areas could be described in terms of Gaussian frequency distributions after applying either a log‐normal or square root transformation to the data. In the Garfield and La Crosse study areas, slope steepness was best described using a log‐normal transformation and the Gaussian frequency distribution. The mean slope steepnesses in the Garfield and La Crosse study areas were 8.8 and 9.2%, respectively. In the Thornton and Wilcox study areas, a square root transformation of slope steepness was fit to a Gaussian frequency distribution. Mean slope steepnesses in the Thornton and Wilcox study areas were 12.5 and 12.5%, respectively. To characterize slope steepness distributions for the eight slope aspects studied within each area, from two to five Gaussian models having statistically distinct parameters were required. The quantitative analysis of topography conducted in this paper shows that considerable spatial variability of slope steepness occurs between differing slope aspects of large areas in the Palouse. This information could be used in developing improved runoff and erosion prediction models that account for spatial variability of topography over large areas.