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

• Land use and land cover classes homogenize within-class differences in vegetation density, type and other characteristics that influence surface erosion. • When these classes are used to estimate current and projected future RUSLE C-factor for native vegetation, this introduces significant error. • Remote sensing methods can be used to assess intra-class variation in C-factor, improving the soil loss estimate. • A strong, predictive relationship exists between remotely-sensed C-factor values and climate. • Current and projected changes in climate can be used to directly estimate C-factor, improving the estimate of current and future soil loss rates. This new method provides a low-cost, efficient and reliable estimate of future C. 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 R2 = 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. [ABSTRACT FROM AUTHOR]