## Abstract

The Revised Universal Soil Loss Equation (RUSLE) is a model widely used to predict soil loss. An important component of RUSLE is the combined topographical factor (LS), which is the product of the slope length factor (L) and the slope steepness factor (S). It is important to identify the main sources of uncertainty in the LS factor and reduce the uncertainty where practical. Moreover, the uncertainty of the LS factor may vary across space, and this spatial uncertainty may require error management. For this reason, the spatial effects of slope steepness and slope length should be quantified, and their uncertainty propagation should be modeled. This article presents a general methodology for spatial uncertainty assessment of the RUSLE and its application results to the uncertainty analysis of LS as an example. A sequential indicator simulation was used to develop spatial prediction maps of slope steepness and slope length based on collected field data. The uncertainty due to slope steepness, slope length, and model parameters were propagated through topographical factor LS using the Fourier Amplitude Sensitivity Test (FAST). Spatial variance partitioning was performed to generate error budgets, and uncertainty sources were identified. Slope steepness contributed the largest variance in predicting topographical factor LS, followed by slope length. The variance contributions from the model parameters and measurement errors were relatively small. The results provide modelers and decision-makers with spatial uncertainty information for the purpose of error management.

Original language | English (US) |
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Pages (from-to) | 109-118 |

Number of pages | 10 |

Journal | Transactions of the American Society of Agricultural Engineers |

Volume | 45 |

Issue number | 1 |

State | Published - 2002 |

## Keywords

- Error budgets
- Sequential indicator simulation
- Soil erosion
- Topographical factor
- Uncertainty propagation

## ASJC Scopus subject areas

- Agricultural and Biological Sciences (miscellaneous)