A new method for calculating C factor when projecting future soil loss using the Revised Universal soil loss equation (RUSLE) in semi-arid environments

Understanding how reservoir sedimentation rates may evolve due to climate change is essential for projecting future changes in reservoir water storage capacity. The Revised Universal Soil Loss Equation (RUSLE) is commonly used to assess regional soil loss rates because of its suitability for working with the coarse temporal and spatial scale of climate model outputs. Application of the RUSLE for projecting future erosion rates is constrained by the relatively limited number of classes used in projected changes of native vegetation: this typically reduces a wide range of variation in RUSLE cover (C) factors to a single value per class, and these classes may be insensitive to changes in species composition and canopy density with time and space. This paper develops a low-cost, efficient, and objective approach to projecting future C values directly based on widely available Landsat data, downscaled climate model data, and a limited number of terrain variables. Observed C is estimated from Landsat-derived Normalized Difference Vegetation Index (NDVI) and Modified Soil-Adjusted Vegetation Index (MSAVI) values using standard methods. A linear relationship is established between the observed C values and average annual antecedent temperature, average annual antecedent precipitation, latitude, and percent sand (adjusted R-2 = 0.75604, 0.7543, and RMSE = 0.09448, 0.07043, respectively). A proof of principal study demonstrates that, unlike when land cover classes are used to estimates C, a RUSLE model driven by the regression-based C provides a correct order-of-magnitude estimate of observed long-term erosion and sediment yield rates (e.g., an estimate of 2.9-3.2 t ha(-1) y(-1) is obtained where observed rates range from 1.2 to 3.9 t ha(-1) y(-1) for the same time period, and up to 8 t ha(-1) y(-1) historically, while standard formulations of RUSLE yield 16.1 and 0.3 t ha(-1) y(-1)). Using climate model data to estimate both precipitation intensity (R factor) and C in the RUSLE model results in basin wide projected end 21st century soil erosion rates for relative concentration pathway 8.5 of 5.5-7.5 t ha(-1) y(-1), consistent with expectations for accelerated soil loss in the study area as climate change reduces soil moisture while increasing precipitation intensity. The same model driven by land cover class estimates of C produces soil loss rates an order of magnitude smaller (0.3-0.4 t ha(-1) y(-1)), consistent with a much more heavily vegetated landscape than could be supported under the increasingly arid projected climate.

Automated summary

This study develops a low-cost, efficient, and objective method for projecting future Revised Universal Soil Loss Equation (RUSLE) cover values using Landsat data, downscaled climate model data, and terrain variables. Regression-based cover values provide more accurate estimates of soil erosion and sediment yield rates compared to land cover class estimates. Using climate model data, projected end 21st century soil erosion rates for the study area are 5.5-7.5 t ha(-1) y(-1), indicating accelerated soil loss due to climate change.