Spatial Prediction and Uncertainty Analysis of Topographic Factors for the Revised Universal Soil Loss Equation (RUSLE)

• Published in: Journal of soil and water conservation Vol. 55; no. 3; pp. 374 - 384
• Main Authors: WANG, G, GERTNER, G, PARYSOW, P, ANDERSON, A. B
• Format: Journal Article
• Language: English
• Published: Ankeny, IA Soil and Water Conservation Society 01.07.2000; Soil & Water Conservation Society
• Subjects: Agronomy. Soil science and plant productions; Biological and medical sciences; Conservation of natural resources; Fundamental and applied biological sciences. Psychology; Land; Mathematical models; Measurement; Natural resource conservation; Predictions; Soil conservation; Soil erosion; Soil erosion, conservation, land management and development; Soil science; Topographical surveying; United States; United States; North America; Texas; America; Spatial variability; Soil erosion; Universal equation; Geostatistics; Land slope; Uncertainty relation; Forecast model; Statistical simulation; Topographic form; Kriging
• ISSN: 0022-4561; 1941-3300

Spatial prediction and uncertainty assessment of ecological modeling and simulation systems are a difficult task because of system complexities that include multi components, their interaction and variability over space and time. Developing a general methodology and framework of uncertainty assessment for the systems' users has become very important. As the first part of a large study addressing these issues, the focus of this paper is on spatial prediction and uncertainty assessment of topographic factors involved in the Revised Universal Soil Loss Equation (RUSLE). The spatial variability of these topographic factors including slope steepness factor S, slope length factor L, and their combined LS factor were modeled with semivariogram models. Three geostatistical methods, including ordinary kriging, indicator kriging, and sequential indicator simulation, were applied and compared. The predicted value maps of these factors, their error variance or conditional variance maps, and probability maps for the predicted values larger than a given threshold value were derived. The comparison of the geostatistical methods suggests that sequential indicator simulation better than ordinary and indicator kriging.