Soil erosion and sediment yield assessment using RUSLE and GIS-based approach in Anjeb watershed, Northwest Ethiopia

Evaluation of future land use change impacts on soil erosion for holota watershed, Ethiopia

Soil erosion is a critical global challenge that degrades land and water resources, leading to reduced soil fertility, pollution of water bodies, and sedimentation in hydraulic structures and reservoirs. In Ethiopia, where agriculture forms the backbone of the economy, unplanned LULC changes have intensified soil erosion, posing a significant threat to food security and sustainable development. In the Holota watershed of Ethiopia, rapid population growth and urbanization have accelerated unplanned land use and land cover (LULC) changes, significantly affecting soil erosion patterns. This study aims to assess the spatiotemporal changes in LULC and their impact on soil erosion from 2000 to 2050. Using Landsat imagery from 2000, 2010, and 2020, supervised classification with the maximum likelihood algorithm was applied in Google Earth Engine (GEE) to map five LULC classes: forest, cropland, built-up areas, shrubland, and grassland. The future LULC for 2050 was predicted using the CA-Markov chain model. Soil erosion for 2020 and 2050 LULC maps was estimated using the Revised Universal Soil Loss Equation (RUSLE). Results indicate that annual soil loss in the watershed was 13.3 t ha - 1 yr - 1 in 2020, increasing to 15.9 t ha - 1 yr - 1 by 2050. Cropland, built-up areas, and grassland are expected to be the major contributors to future soil erosion, while forest and shrubland are likely to play a mitigating role. The novelty of this research lies in its integration of cutting-edge remote sensing technologies, such as GEE and the CA-Markov model, to predict the combined impact of LULC changes on soil erosion in a data-scarce region, providing actionable insights for conservation planning in Ethiopian highlands. These findings offer essential guidance for conservation planners to implement sustainable land management practices aimed at reducing soil erosion, including promoting forest restoration, adopting contour farming, and enforcing land use regulations to limit the expansion of cropland and built-up areas in erosion-prone zones.

Comparative analysis of RUSLE and SWPT for sub-watershed conservation prioritization in the Ayu watershed, Abay basin, Ethiopia

Ethiopia is currently facing a major environmental problem caused by soil erosion. In order to tackle this problem, it is essential to implement a comprehensive watershed management approach and give priority to conservation efforts depending on the level of severity. Therefore, the objective of this research is to evaluate the mean annual soil erosion and rank the sub-watersheds for conservations in the Ayu watershed, utilizing the Revised Universal Soil Loss Equation (RUSLE) model and the Sub-Watershed Prioritization Tool (SWPT). RUSLE was utilized to predict the annual average soil erosion rate, while SWPT was applied to conduct Weighted Sum Analysis (WSA) for ranking sub-watersheds. Support Vector Machine (SVM) was employed for classifying land use and land cover. The Relative importance of morphometric and topo-hydrologic features in the SWPT was analyzed using a Random Forest model. The Bland-Altman plot and Wilcoxon Signed Rank Test were employed to assess the agreement in prioritizing watersheds between RUSLE results and the SWPT. Furthermore, field observations were conducted to validate the land use classification by collecting ground data. In addition, the study was enhanced with local viewpoints by conducting focus group discussions with agricultural experts and farmers to obtain qualitative insights and validation of resuts. The findings showed that soil loss varied from 0 to 110 t/ha/yr, with an average of 8.95 t/ha/yr, resulting in a total loss of 384365.3 tons annually. The comparison of RUSLE and SWPT showed a moderate positive relationship (r = 0.59). The results of the Bland-Altman plot indicate a consistent agreement between the two methods. However, there is inconsistency among the five sub watersheds. This study enhances the knowledge of soil erosion patterns and offers useful guidance for watershed conservation techniques. It can be also used as a beneficial framework for managing watersheds, with possible uses outside of the Ayu watershed.

The impact of vegetation reconstruction on soil erosion in the Loess plateau

Vegetation restoration not only extensively reshapes spatial land use patterns but also profoundly affects the dynamics of runoff and sediment loss. However, the influence of vegetation restoration on runoff and sediment yield from a regional perspective are scarce. This study therefore focused on 85 sites within the "Grain for Green" Project (GGP) region on the Loess Plateau, to investigate the impacts of the GGP on soil erosion. The results revealed a notable reduction in sediment loss and runoff due to vegetation restoration. Since the inception of the GGP in 1999, approximately 4.1 × 106 ha of degraded lands have been converted into forestlands, shrublands, and grasslands, resulting in an average annual reduction of 1.4 × 109 m3 in runoff and a decrease of 3.6 × 108 t in annual sediment loss on the whole Loess Plateau, with the GGP contributing approximately 26.7% of the sediment reduction in the Yellow River basin. The reduced soil erosion has mainly been regulated by vegetation cover, soil properties (clay, silt, and sand), slope, and precipitation on the Loess Plateau. The insights gained offer valuable contributions to large-scale assessments of changes in soil erosion in response to vegetation reconstruction and enhance our understanding of the spatial configurations associated with soil erosion control measures.

Quantification of soil erosion and sediment yield using the RUSLE model in Boyo watershed, central Rift Valley Basin of Ethiopia

Changes in land use and land cover (LULC) are becoming recognized as critical to sustainability research, particularly in the context of changing landscapes. Soil erosion is one of the most important environmental challenges today, particularly in developing countries like Ethiopia. The objective of this study was evaluating the dynamics of soil loss, quantifying sediment yield, and detecting soil erosion hotspot fields in the Boyo watershed. To quantify the soil erosion risks, the Revised Universal Soil Loss Equation (RUSLE) model was used combined with remote sensing (RS) and geographic information system (GIS) technology, with land use/land cover, rainfall, soil, and management approaches as input variables. The sediment yield was estimated using the sediment delivery ratio (SDR) method. In contrast to a loss in forest land (1.7 %), water bodies (3.0 %), wetlands (1.5 %), and grassland (1.7 %), the analysis of LULC change (1991-2020) showed a yearly increase in the area of cultivated land (1.4 %), built-up land (0.8 %), and bare land (3.5 %). In 1991, 2000, and 2020, respectively, the watershed's mean annual soil loss increases by 15.5, 35.9, and 38.3 t/ha/y. Approximately 36 cm of the watershed's economically productive topsoil was lost throughout the study's twenty-nine-year period (1991-2020). According to the degree of erosion, 16 % of the watershed was deemed seriously damaged, while 70 % was deemed slightly degraded. Additionally, it is estimated for the year 2020 that 74,147.25 t/y of sediment (8.52 % of the total annual soil loss of 870,763.12 t) reach the Boyo watershed outlet. SW4 and SW5 were the two sub-watersheds with the highest erosion rates, requiring immediate conservation intervention to restore the ecology of the Boyo watershed.

Multi-objective Allocation Optimization of Soil Conservation Measures Under Data Uncertainty

Many regions worldwide face soil loss rates that endanger future food supply. Constructing soil and water conservation measures reduces soil loss but comes with high labor costs. Multi-objective optimization allows considering both soil loss rates and labor costs, however, required spatial data contain uncertainties. Spatial data uncertainty has not been considered for allocating soil and water conservation measures. We propose a multi-objective genetic algorithm with stochastic objective functions considering uncertain soil and precipitation variables to overcome this gap. We conducted the study in three rural areas in Ethiopia. Uncertain precipitation and soil properties propagate to uncertain soil loss rates with values that range up to 14%. Uncertain soil properties complicate the classification into stable or unstable soil, which affects estimating labor requirements. The obtained labor requirement estimates range up to 15 labor days per hectare. Upon further analysis of common patterns in optimal solutions, we conclude that the results can help determine optimal final and intermediate construction stages and that the modeling and the consideration of spatial data uncertainty play a crucial role in identifying optimal solutions.

Assessing soil erosion risk in a peri-urban catchment of the Lake Victoria basin

Soil erosion and sedimentation contribute to deteriorating water quality, adverse alterations in basin hydrology and overall ecosystem biogeochemistry. Thus, understanding soil erosion patterns in catchments is critical for conservation planning. This study was conducted in a peri-urban Inner Murchison Bay (IMB) catchment on the northern shores of Lake Victoria since most soil erosion studies in Sub-Saharan Africa have been focused on rural landscapes. The study sought to identify sediment sources by mapping erosion hotspots using the revised universal soil loss equation (RUSLE) model in appendage with field walks. RUSLE model was built in ArcGIS 10.5 software with factors including: rainfall erosivity, soil erodibility, slope length and steepness, land cover and support practices. The model was run, producing an erosion risk map and field assessments conducted to ground-truth findings and identify other hotspots. The percentage areas for RUSLE modelled erosion rates were: 66.8% for 0–2 t ha⁻¹ year⁻¹; 10.8% for 2–5 t ha⁻¹ year⁻¹; 10.1% for 5–10 t ha⁻¹ year⁻¹; 9% for 10–50 t ha⁻¹ year⁻¹ and 3.3% for 50–100 t ha⁻¹ year⁻¹. Average erosion risk was 7 t ha⁻¹ year⁻¹ and the total watershed erosion risk was 197,400 t year⁻¹, with croplands and steep areas (slope factor > 20) as the major hotspots (> 5 t ha⁻¹ year⁻¹). Field walks revealed exposed soils, marrum (gravel) roads and unlined drainage channels as other sediment sources. This study provided the first assessment of erosion risk in this peri-urban catchment, to serve as a basis for identifying mitigation priorities. It is recommended that tailored soil and water conservation measures be integrated into physical planning, focusing on identified non-conventional hotspots to ameliorate sediment pollution in Lake Victoria.

Analyzing the Benefit-Cost Ratio of Sediment Resources by Remote Sensing Data in the Ping River Basin, Thailand

Sediment is one of the important natural resources on the Earth. Information on sediment resources is key to making decisions regarding soil resources management and mitigation during sediment hazard events. Thus, this research analyzed and mapped the benefit-cost ratio (BCR) of sediment in the Ping River using a Geographic Information System (GIS). Furthermore, the benefit of sediment was analyzed using a new application of the Revised Universal Soil Loss Equation (RUSLE) with a spatial resolution of 1 km2. The results reveal that the potential of annual soil loss and sediment deposition in the Ping River Basin (PRB) were approximately 825 and 530 m3/km2·y, respectively. In addition, the results indicated that there was a higher BCR in the upstream area of the PRB where there was greater sediment deposition. The average benefit of sediment in the PRB is USD 4280/km2·y. It is expected that the BCR of the sediment resources map analyzed in this research will help policy-makers for decision-making on the benefits of sediment resources in Thailand.

Assessment of soil erosion extent using RUSLE model integrated with GIS and RS: the case of Megech-Dirma watershed, Northwest Ethiopia

Soil erosion valuation at a spatial scale is crucial for assessing natural resource quality in a farming country like Ethiopia. The study's goal was to determine the rate of soil erosion in the Megech-Dirma catchment in Northwest Ethiopia using the Revised Universal Soil Loss Equation model aggregation with Geographic Information System and Remote Sensing. Sediment yield and transport were also estimated using sediment delivery ratio. Revised Universal Soil Loss Equation model data inputs included precipitation data for the R value, soil data for the K value, land cover data from satellite images for the C and P value, and topographical data from a Digital Elevation Model for the LS component. It was completed using the ArcGIS 10.4 software. The mean annual soil loss is 110.60 t ha^-1 yr^-1. Each year, a total of 8499.74 t ha^-1 yr^-1 of soil eroded and on average resulting in 1605.30 t/km²/yr, sediment material has been transported to the stream channels and deposited with a sediment delivery ratio of 1.87. The strength of soil erosion in the area is divided into six categories. The erosion rate classes were 46.38 percent (0-12 t ha^-1 yr^-1) low, 13.63 percent (12-20 ha^-1 yr^-1) moderate, 9.22 percent (20-35 ha^-1 yr^-1) high, 12.30 percent (35-50 ha^-1 yr^-1) very high, 7.20 percent (50 up to 100 ha^-1 yr^-1) severe, and 11.27 percent (>100 ha^-1 yr^-1) very severe erosion. According to erosion severity, 46.38 percent of the watershed is at risk of low erosion, while 11.27 percent is at risk of extremely severe erosion. The north and northeastern sections of the watershed have a moderate to extremely severe erosion risk due to steep slopes, high rainfall, and weak conservation measures. The severely eroded parts of the plateau and steep portions are proposed to be covered by plantation, stone bund, and check dam constructions.