Land susceptibility to water and wind erosion risks in the East Africa region

Fenta, Ayele Almaw; Tsunekawa, Atsushi; Haregeweyn, Nigussie; Poesen, Jean; Tsubo, Mitsuru; Borrelli, Pasquale; Panagos, Panos; Vanmaercke, Matthias; Broeckx, Jente; Yasuda, Hiroshi; Kawai, Tadashi; Kurosaki, Yasunori

doi:10.1016/j.scitotenv.2019.135016

Cited by 174 publications

(176 citation statements)

References 106 publications

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“…The mean estimates of 39.2 t ha −1 y −1 of soil eroded in 2015 ( Figure 6b) and 41.4 t ha −1 y −1 from Buberuka highlands AEZ (Table A1) are in accordance with the work of Kagabo et al [29], Kabirigi et al [36] and Karamage et al [37], but far less from the results reported by Karamage et al [9,26] ( Table 5). In addition, our findings are relatively comparable with a recent study conducted over the entire East Africa region with a mean soil loss of 34.2 t ha −1 y −1 for Rwanda [83], and in line with mean soil erosion rates ranging between 35 t ha −1 y −1 and 75 t ha −1 y −1 predicted in Sub-Saharan Africa [84]. Comparing the results of Karamage et al [9,26] (Table 4) with the findings of this study and other studies carried out in Rwanda and in the region, suggests that their results may be overestimates, an issue previously highlighted by Kabirigi et al [36].…”

Section: Perspective Of Soil Erosion By Water In Rwandasupporting

confidence: 89%

Land Use Change Impacts on Water Erosion in Rwanda

Nambajimana

Zhou

et al. 2019

Sustainability

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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.

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Section: Perspective Of Soil Erosion By Water In Rwandasupporting

confidence: 89%

Land Use Change Impacts on Water Erosion in Rwanda

Nambajimana

Zhou

et al. 2019

Sustainability

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“…Understanding the pattern of rainfall variability is essential because negative rainfall trends mostly result in a higher probability of droughts that have historically affected millions of people in Ethiopia [56,63,64]. It has been reported that the interannual variability of rainfall in the region could be under the influence of El Niño-Southern Oscillation (ENSO), and the Indian Ocean Sea Surface Temperature (SST) [43,52,65,66]. ENSO phenomena periodicities ranging from seasonal to about 8 years have been reported on East African region [67,68].…”

Section: Discussionmentioning

confidence: 99%

Evaluation and Application of Multi-Source Satellite Rainfall Product CHIRPS to Assess Spatio-Temporal Rainfall Variability on Data-Sparse Western Margins of Ethiopian Highlands

et al. 2019

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The spatio-temporal characteristic of rainfall in the Beles Basin of Ethiopia is poorly understood, mainly due to lack of data. With recent advances in remote sensing, satellite derived rainfall products have become alternative sources of rainfall data for such poorly gauged areas. The objectives of this study were: (i) to evaluate a multi-source rainfall product (Climate Hazards Group Infrared Precipitation with Stations: CHIRPS) for the Beles Basin using gauge measurements and (ii) to assess the spatial and temporal variability of rainfall across the basin using validated CHIRPS data for the period 1981–2017. Categorical and continuous validation statistics were used to evaluate the performance, and time-space variability of rainfall was analyzed using GIS operations and statistical methods. Results showed a slight overestimation of rainfall occurrence by CHIRPS for the lowland region and underestimation for the highland region. CHIRPS underestimated the proportion of light daily rainfall events and overestimated the proportion of high intensity daily rainfall events. CHIRPS rainfall amount estimates were better in highland regions than in lowland regions, and became more accurate as the duration of the integration time increases from days to months. The annual spatio-temporal analysis result using CHIRPS revealed: a mean annual rainfall of the basin is 1490 mm (1050–2090 mm), a 50 mm increase of mean annual rainfall per 100 m elevation rise, periodical and persistent drought occurrence every 8 to 10 years, a significant increasing trend of rainfall (~5 mm year−1), high rainfall variability observed at the lowland and drier parts of the basin and high coefficient of variation of monthly rainfall in March and April (revealing occurrence of bimodal rainfall characteristics). This study shows that the performance of CHIRPS product can vary spatially within a small basin level, and CHIRPS can help for better decision making in poorly gauged areas by giving an option to understand the space-time variability of rainfall characteristics.

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“…The support practice factor P was excluded from the analysis, as large-scale data to derive estimates for P are very limited. Previous large-scale studies, for example, inferred the P factor from relationships with the land use (e.g., Yang et al, 2003), the land cover, and slope (Fenta et al, 2020), implemented a global estimate of P for the entire study region (e.g., Karamage et al, 2017), or did not consider the P factor (e.g., Borrelli et al, 2017). The rainfall erosivity factor R relates the intensity of rainfall events to the kinetic energy that is available to erode soil particles (Wischmeier and Smith, 1987;Panagos et al, 2015a).…”

Section: Estimation Of Usle Model Inputsmentioning

confidence: 99%

“…Several authors question the applicability of the plot-scalebased USLE to the landscape scale (e.g., Boardman, 2006;Evans, 1995;Govers, 2011), particularly as in large domains other processes such as gully erosion, bank erosion, or sediment deposition can dominate the erosion response (Govers, 2011). Multiple approaches are available from the literature that account, for instance, for the deposition of eroded material by employing concepts such as the sediment delivery ratio (e.g., Rajbanshi and Bhattacharya, 2020;Ferro and Minacapilli, 1995;Graham, 1975). While the USLE principally only accounts for the soil removal and does not consider soil deposition, Evans (2013) concludes that the USLE can be helpful in identifying the erosion potential or erosion hotspots but fails to predict the exact magnitude of soil that is eroded.…”

Section: Introductionmentioning

confidence: 99%

A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda

Schürz

Mehdi

Kiesel

et al. 2020

Hydrol. Earth Syst. Sci.

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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.

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Land susceptibility to water and wind erosion risks in the East Africa region

Cited by 174 publications

References 106 publications

Land Use Change Impacts on Water Erosion in Rwanda

Land Use Change Impacts on Water Erosion in Rwanda

Evaluation and Application of Multi-Source Satellite Rainfall Product CHIRPS to Assess Spatio-Temporal Rainfall Variability on Data-Sparse Western Margins of Ethiopian Highlands

A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda

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