Abstract

The Revised Universal Soil Loss Equation (RUSLE) is a model to predict longtime average annual soil loss, related to rainfall-runoff, soil erodibility, slope length and steepness, cover management, and support practice. The soil erodibility factor K accounts for the influence of soil properties on soil loss during storm events in upland areas.

In this paper, ordinary kriging, sequential Gaussian and indicator simulation methods were used and compared for spatial prediction and uncertainty analysis of soil erodibility based on a data set from a very intensive soil survey (524 observations, 10 m by 10 m grid). Half the data was used for calibration, the other half used for validation. The results show that the three methods produce similar spatial distributions for predicted values. The method yielding the smallest mean square error was Gaussian simulation, followed by ordinary kriging and indicator simulation. However, the variance estimates obtained using indicator simulation consistent with the spatial variation, while those obtained by Gaussian simulation and ordinary kriging were overly smoothed.

Keywords

assessment, prediction, soil erodibility, spatial statistical methods

Creative Commons License


This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Share

COinS
 
Apr 30th, 9:30 AM

APPLICATION AND COMPARISON OF THREE SPATIAL STATISTICAL METHODS FOR MAPPING AND ANALYZING SOIL ERODIBILITY

The Revised Universal Soil Loss Equation (RUSLE) is a model to predict longtime average annual soil loss, related to rainfall-runoff, soil erodibility, slope length and steepness, cover management, and support practice. The soil erodibility factor K accounts for the influence of soil properties on soil loss during storm events in upland areas.

In this paper, ordinary kriging, sequential Gaussian and indicator simulation methods were used and compared for spatial prediction and uncertainty analysis of soil erodibility based on a data set from a very intensive soil survey (524 observations, 10 m by 10 m grid). Half the data was used for calibration, the other half used for validation. The results show that the three methods produce similar spatial distributions for predicted values. The method yielding the smallest mean square error was Gaussian simulation, followed by ordinary kriging and indicator simulation. However, the variance estimates obtained using indicator simulation consistent with the spatial variation, while those obtained by Gaussian simulation and ordinary kriging were overly smoothed.