Journal Paper

Paper Information

video

sound

Persian Version

View:

151

Download:

82

Cites:

Information Journal Paper

Title

COMPARISON OF GEOSTATISTICS, PTFS, SSPFS METHODS AND THEIR COMBINATION FOR ESTIMATING SOIL SURFACE SHEAR STRENGTH

Pages

 Start Page 127 | End Page 138

Abstract

 Surface SOIL SHEAR STRENGTH (SSS) is among the most important and needed parameters that is required in soil erosion simulation and prediction. Lack of the SSS is often a problem in erosion simulation at watersheds. Because of SSS high variability and too sampling, SSS are too difficult, too time consuming, and/or too expensive to measure directly. Therefore it is necessary to measure indirectly. This study was conducted to predict this parameter using kriging, PTFs, SSPFs methods and their combination. In this reason, the study was conducted in central Zagros region on water erosion susceptibility with an area of 23562 ha (rangeland and degraded-rangeland landuse). Based on the maps of geology, topography, landuse and soil capability, 14 Land Unit Tracts (LUT) was created. A total of 90 samples were collected in triplicates in order to determine sample variability of each LUT. Soil samples were collected from the 0-10 cm of soil depthes. Routinely measured (available) parameters for surface shear strength PTFs and SSPFs included soil surface and subsurface attributes in addition to topographic and vegetation attributes: particle size distribution, soil organic carbon, gravel, slope, aspect, elevation and normalized difference vegetation index (NDVI) were used. Three MLR PTFs (pedotransfer functions) and SSPFs (soil spatial prediction functions) were tested and investigated in this study. Spatial variability of parameters was investigated using semivariograms and the ratio of nugget to total semivariance. Krigin method was used to create map of the needed data. Interpolated easily-obtained parameters by kriging maps are subsequently input into PTFs and SSPFs to predict surface SOIL SHEAR STRENGTH. The performances of the models were evaluated using mean absolute error (MAE) between the observed and the estimated values, root mean square error (RMSE), Geometric standard deviation of the error ratio (GSDER), and Geometric mean of error ratio (GMER). Results showed that the performance of PTFs and SSPFs as compared to combining kriging-PTFs and SSPFs was similar. All of the parameters were moderately spatially dependent.

Cites

  • No record.
  • References

    Related Journal Papers

    Related Seminar Papers

  • No record.
  • Related Plans

  • No record.