Soil erosion is widely predicted as a function of six input factors, including rainfall erosivity, soil erodibility, slope length, slope steepness, cover management, and support practice. Because of the multiple factors, their interactions, and their spatial and temporal variability, accurately mapping the factors and further soil loss is very difficult. This paper compares two geostatistical methods and a traditional stratification to map the factors and to estimate soil loss. Soil loss is estimated by integrating a sample ground data set, TM images, and a slope map. The geostatistical methods include collocated cokriging and a joint sequential co-simulation model. With both geostatistical methods, local estimates and variances at any location where the factors and soil loss are unknown can be computed. The results showed that the two geostatistical methods performed significantly better than traditional stratification in terms of overall and spatially explicit estimates. Furthermore, the cokriging led to higher accuracy of mean estimates than did the co-simulation, while the latter provided decision makers with reliable uncertainties of the local estimates as useful information to assess risk when making decisions based on the prediction maps.
ASJC Scopus subject areas
- Computers in Earth Sciences