Rwanda has experienced accelerated soil erosion as a result of unsustainable human activities and changes in land use. Therefore, this study aimed at applying the RUSLE (Revised Universal Soil Loss Equation) model using GIS (Geographical Information System) and remote sensing to assess water erosion in Rwanda, focusing on the erosion-prone lands for the time span 2000 to 2015. The estimated mean annual soil losses were 48.6 t ha⁻¹ y⁻¹ and 39.2 t ha⁻¹ y⁻¹ in 2000 and 2015, respectively, resulting in total nationwide losses of approximately 110 and 89 million tons. Over the 15 years, 34.6% of the total area of evaluated LULC (land use/land cover) types have undergone changes. The highest mean soil loss of 91.6 t ha⁻¹ y⁻¹ occurred in the area changing from grassland to forestland (0.5%) while a mean soil loss of 10.0 t ha⁻¹ y⁻¹ was observed for grassland converting to cropland (4.4%). An attempt has been made to identify the embedded driving forces of soil erosion in Rwanda. As a result, we found that mean soil loss for Rwanda’s districts in 2015 was significantly correlated with poverty (r = 0.45, p = 0.013), increased use of chemical fertilizers (r = 0.77, p = 0.005), and especially was related to extreme poverty (r = 0.77, p = 0.000). The soil conservation scenario analysis for Rwanda’s cropland in 2015 revealed that terracing could reduce the soil loss by 24.8% (from 14.6 t ha⁻¹ y⁻¹ to 11.7 t ha⁻¹ y⁻¹). Most importantly, the study suggests that (1) terracing integrated with mulching and cover crops could effectively control water erosion while ameliorating soil quality and fertility, and (2) reforestation schemes targeting the rapid-growing tree species are therefore recommended as an important feature for erosion control in the study area.

The stochasticity of soil erosion reflects the variability of soil hydrological response to precipitation in a complex environment. Assessing this stochasticity is important for the conservation of soil and water resources; however, the stochasticity of erosion event in restoration vegetation types in water-limited environment has been little investigated. In this study, we constructed an event-driven framework to quantify the stochasticity of runoff and sediment generation in three typical restoration vegetation types (Armeniaca sibirica (T1), Spiraea pubescens (T2) and Artemisia copria (T3)) in closed runoff plots over five rainy seasons in the Loess Plateau of China. The results indicate that, under the same rainfall condition, the average probabilities of runoff and sediment in T1 (3.8 and 1.6 %) and T3 (5.6 and 4.4 %) were lowest and highest, respectively. The binomial and Poisson probabilistic model are two effective ways to simulate the frequency distributions of times of erosion events occurring in all restoration vegetation types. The Bayes model indicated that relatively longer-duration and stronger-intensity rainfall events respectively become the main probabilistic contributors to the stochasticity of an erosion event occurring in T1 and T3. Logistic regression modelling highlighted that the higher-grade rainfall intensity and canopy structure were the two most important factors to respectively improve and restrain the probability of stochastic erosion generation in all restoration vegetation types. The Bayes, binomial, Poisson and logistic regression models constituted an integrated probabilistic assessment to systematically simulate and evaluate soil erosion stochasticity. This should prove to be an innovative and important complement in understanding soil erosion from the stochasticity viewpoint, and also provide an alternative to assess the efficacy of ecological restoration in conserving soil and water resources in a semi-arid environment.

The stochasticity of soil erosion reflects the variability of soil hydrological response to precipitation in a complex environment. Assessing this stochasticity is important for the conservation of soil and water resources; however, the stochasticity of erosion event in restoration vegetation types in water-limited environment has been little investigated. In this study, we constructed an event-driven framework to quantify the stochasticity of runoff and sediment generation in three typical restoration vegetation types (Armeniaca sibirica (T1), Spiraea pubescens (T2) and Artemisia copria (T3)) in closed runoff plots over five rainy seasons in the Loess Plateau of China. The results indicate that, under the same rainfall condition, the average probabilities of runoff and sediment in T1 (3.8 and 1.6 %) and T3 (5.6 and 4.4 %) were lowest and highest, respectively. The binomial and Poisson probabilistic model are two effective ways to simulate the frequency distributions of times of erosion events occurring in all restoration vegetation types. The Bayes model indicated that relatively longer-duration and stronger-intensity rainfall events respectively become the main probabilistic contributors to the stochasticity of an erosion event occurring in T1 and T3. Logistic regression modelling highlighted that the higher-grade rainfall intensity and canopy structure were the two most important factors to respectively improve and restrain the probability of stochastic erosion generation in all restoration vegetation types. The Bayes, binomial, Poisson and logistic regression models constituted an integrated probabilistic assessment to systematically simulate and evaluate soil erosion stochasticity. This should prove to be an innovative and important complement in understanding soil erosion from the stochasticity viewpoint, and also provide an alternative to assess the efficacy of ecological restoration in conserving soil and water resources in a semi-arid environment.