Abstract
Abstract. The Universal Soil Loss Equation (USLE) is the most commonly used model to assess soil erosion by water. The model equation quantifies long-term average annual soil loss as a product of the rainfall erosivity R, soil erodibility K, slope length and steepness LS, soil cover C, and support measures P. A large variety of methods exist to derive these model inputs from readily available data. However, the estimated values of a respective model input can strongly differ when employing different methods and can eventually introduce large uncertainties in the estimated soil loss. The potential to evaluate soil loss estimates at a large scale is very limited due to scarce in-field observations and their comparability to long-term soil estimates. In this work we addressed (i) the uncertainties in the soil loss estimates that can potentially be introduced by different representations of the USLE input factors and (ii) challenges that can arise in the evaluation of uncertain soil loss estimates with observed data. In a systematic analysis we developed different representations of USLE inputs for the study domain of Kenya and Uganda. All combinations of the generated USLE inputs resulted in 972 USLE model setups. We assessed the resulting distributions in soil loss, both spatially distributed and on the administrative level for Kenya and Uganda. In a sensitivity analysis we analyzed the contributions of the USLE model inputs to the ranges in soil loss and analyzed their spatial patterns. We compared the calculated USLE ensemble soil estimates to available in-field data and other study results and addressed possibilities and limitations of the USLE model evaluation. The USLE model ensemble resulted in wide ranges of estimated soil loss, exceeding the mean soil loss by over an order of magnitude, particularly in hilly topographies. The study implies that a soil loss assessment with the USLE is highly uncertain and strongly depends on the realizations of the model input factors. The employed sensitivity analysis enabled us to identify spatial patterns in the importance of the USLE input factors. The C and K factors showed large-scale patterns of importance in the densely vegetated part of Uganda and the dry north of Kenya, respectively, while LS was relevant in small-scale heterogeneous patterns. Major challenges for the evaluation of the estimated soil losses with in-field data were due to spatial and temporal limitations of the observation data but also due to measured soil losses describing processes that are different to the ones that are represented by the USLE.
Highlights
The Universal Soil Loss Equation (USLE, Wischmeier and Smith, 1965, 1987) formulates the most commonly applied concept to assess soil loss by water erosion (Alewell et al, 2019; Borrelli et al, 2017; Panagos et al, 2015e; Kinnell, 2010)
On a national level we compared the USLE model ensemble results to the results presented in Fenta et al
4.1 Analysis of the soil loss simulated with the USLE model ensemble
Summary
The Universal Soil Loss Equation (USLE, Wischmeier and Smith, 1965, 1987) formulates the most commonly applied concept to assess soil loss by water erosion (Alewell et al, 2019; Borrelli et al, 2017; Panagos et al, 2015e; Kinnell, 2010). More than 10 000 plot years of field data were analyzed with reference to a “unit plot” to formulate a generally applicable approach for soil loss estimation in the USA (Wischmeier and Smith, 1965; Kinnell, 2010; Renard et al, 2011). Further data were collected over the following decades and the methods to calculate the USLE input factors were substantially revised (Renard et al, 1991, 1997; Govers, 2011). This resulted in an update of the iso-erodent maps, the consideration of seasonality and rock fragments in the K factor, or a consideration of additional subfactors, such as prior land use, for the computation of the C factor (Renard et al, 1997).
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