Slope Length Factor Research Articles

Evaluation of soil erosion rate using geospatial techniques for enhancing soil conservation efforts

According to reports, Ethiopia is one of the countries in sub-Saharan Africa with the worst affected by soil erosion. It has both on-site and off-site consequences on biophysical and socioeconomic settings in an area. The study area is heavily affected by soil erosion forming diverse erosion structures, particularly in the upper course of the watershed. Hence, this work seeks to estimate the geographically distributed annual soil loss rate and mapping of soil erosion hazard hotspot areas in the watershed using the Revised Universal Soil Loss Equation (RUSLE) adapted to Ethiopian conditions. The RUSLE parameters, such as rainfall erosion factor (R-factor), soil erodibility factor (K-factor), slope steepness and slope length factor (LS-factor), land cover factor (C-factor), and conservation practice factor (P-factor) were considered as data input for the analysis to quantify the soil loss rate in the study area. A digital elevation model (DEM) with a 12.5 × 12.5-meter resolution was employed for catchment delineation and determination of the LS factor.The mean yearly rainfall data from the surrounding rain gauge stations was used to analyze the R-factor. The results of the current conditions showed that the average typical soil loss rate from the entire watershed is 23.8 t ha-1 yr-1, and the quantity of soil loss from the study area ranged from 0 to 776.71tan /ha-1 yr-1. Nonetheless, Tiro Afeta experiences mean soil erosion at a rate of roughly 50.2 t ha-1 yr-1, exceeding the acceptable threshold of 11 t ha-1 yr-1. Determining the sustainability of soil production requires assessment, particularly in cases where significant yearly soil erosion occurs. Due to intensive agricultural activities in the Xiro Afeta watershed, significant soil erosion is predominantly occurring in thissteep upper region. Consequently, this area urgently requires appropriate soil protection measures.

Comparison of Several Methods for Analysis Slope Length Index Factor at A Watershed Scale

Slope length and steepness factor index (LS) is one of the parameters for the Universal Soil Loss Equation (USLE) to estimate soil erosion. Currently, several methods for LS analysis, i.e. Wischmeier-Smith, Moore-Nieber, and Desmet – Govers. This study aims to compare the Wischmeier-Smith method, Moore–Nieber method, and Desmet–Govers method to analyze LS in the watershed in Manokwari – West Papua. This research consists of 4 main stages, i.e. data inventory, watershed boundary delineation, LS analysis, and LS comparison. The research showed that the Wischmeier-Smith method gave a higher LS value than the Moore – Nieber method and the Desmet – Govers method. Meanwhile, the Desmet – Gover method gives a lower average LS value than the Wischmeier-Smith method and the Moore – Nieber method. Based on the T-test, the LS produced by the Wischmeier-Smith, Moore-Nieber, and Desmet–Govers methods has significant differences in analyzing LS in the watershed in Manokwari – West Papua. Keywords: Desmet – Govers, Moore – Nieber, Universal Soil Loss Equation, Watershed, Wischmeier-Smith

Quantifying the Relationship between Slope Spectrum Information Entropy and the Slope Length and Slope Steepness Factor in Different Types of Water-Erosion Areas in China

Topography critically affects the occurrence of soil erosion, and computing slope spectrum information entropy (SSIE) allows for the convenient mirroring of the patterns of macroscopic topographic variation. However, whether SSIE can be effectively utilized for the quantitative assessment of soil erosion across various types of water-erosion areas and the specific methodology for its application remain unclear. This study focused on the quantitative relationship between SSIE, the slope length and slope steepness (LS) factor within various types of water-erosion areas across different spatial scales in China using multi-source geographic information data and technical tools such as remote sensing and geographic information systems. The results revealed (1) clear consistency in the spatial patterns of SSIE and the LS factor, which both displayed a distinct three-step distribution pattern from south to north. (2) The power model (Y = A·X^B) demonstrated a superior capacity to explaining the relationship between SSIE and the LS factors compared to the linear or exponential models, as evidenced by a higher coefficient of determination (R2). R2 values of different evaluation units (second-grade water-erosion area, third-grade water-erosion area, 30 km × 30 km grid, and 15 km × 15 km grid) were 0.88, 0.88, 0.81, and 0.79, respectively. (3) Despite a range of variances across various spatial scale evaluation units and different types of water-erosion areas, no significant disparities were evident within the power model. These findings offer a new topographic factor that can be incorporated into models designed for the expedited evaluation of soil erosion rates across water-erosion areas. Information about the proximity of the SSIE to the LS factor is valuable for enhancing the practical utilization of SSIE in the quantitative evaluation of soil erosion.

Application of the Modified Universal Soil Loss Equation (MUSLE) for the prediction of sediment yield in Agewmariam experimental watershed, Tekeze River basin, Northern Ethiopia

The study utilized the Modified Universal Soil Loss Equation (MUSLE) to predict sediment loss and evaluate the model's performance in the Agewmariam experimental watershed in order to support the planning, management, and appropriate use of the soil and water resources in the watershed. The natural resources conservation service (NRCS) curve number method was used to model runoff energy factor. By overlaying maps of runoff energy, soil erodibility, slope length and steepness, cover management, and support practice factors with assigned values, the cumulative effect of these parameters for the suspended sediment yield was calculated using the ArcGIS raster calculator. The runoff energy factor was the most sensitive parameter, followed by slope length and steepness factor. To improve the model's fit to the local conditions, the initial abstraction to storage ratio (λ) of the runoff energy factor was reduced to 0.023, and the MUSLE model coefficient and exponent were adjusted to 1 and 0.59, respectively. During calibration, the mean observed and estimated suspended sediment yields were 0.2 and 0.23 ton/ha, respectively, while during validation, they were 0.7 and 0.53 ton/ha, respectively. The model evaluation showed that the MUSLE model, without calibration, was not appropriate for estimating runoff and sediment yield. However, with appropriate calibration, the model showed good performance with a coefficient of determination (R2), coefficient of efficiency (E), and index of agreement (d) of 0.85, 0.85, and 0.96 respectively, during calibration and 0.84, 0.65, and 0.83 respectively, during validation. Based on these findings, this study suggests that the calibrated MUSLE model can be used to prioritize soil and water conservation interventions within the watershed or can be extrapolated to neighboring similar watersheds. Further refinement of model input parameters using more data from the watershed is recommended to increase the prediction accuracy of the model.

Influence of Morphometric Relief Parameters on Soil Depth Changes and Humus Horizon Thickness in Relation to Erosion-Accumulation Processes: A Study in the Ipeľská Pahorkatina Hills, Slovakia

Abstract This study examines the spatial distribution of soil types and their susceptibility to erosion and accumulation processes in a study area in Slovakia. Field research involving 71 probes identified various soil types, with Regosols and Cutanic Luvisols being predominant. The study found that erosion-accumulation processes were detected in 69.97% of the probes, with changes observed in soil horizons. Soil analysis revealed different relations between soil depth, humus thickness, and terrain characteristics such as slope, slope length, and slope length and steepness factor (LS factor). Specifically, we confirmed a moderately strong positive correlation between soil depth and humus thickness (r = 0.597, n = 71, p < 0.001). Shallow soils (0–30 cm) exhibited a very strong positive correlation between soil depth and humus horizon thickness (r = 0.978, n = 33, p < 0.001). Conversely, no relationship was found in moderately deep soils (30–60 cm) (r = 0.018, n = 14, p < 0.948). For deep soils, we identified a moderately strong positive correlation (r = 0.345, n = 24, p = 0.098). While slope and slope length showed relationships with soil depth and humus thickness, the LS factor did not exhibit a clear correlation. These findings underscore the importance of understanding soil dynamics in informing land management practices, especially in areas susceptible to erosion. Recommendations include continued monitoring of eroded soils and implementing erosion control measures to maintain soil health and sustainability in agricultural production amidst climate change challenges.

Soil Erosion Risk Estimation by using Semi Empirical RUSLE model: A case study of Maniyari Basin, Chhattisgarh

Soil is the protective skin of our earth's surface, but today’s numerous population pressures on land, along with industrialization, climatic variability such as a vigorous increase in temperature, acid rain, and deforestation, definitely degrade the quality of land. It should have to evaluate the quality of the land and find out the nutrition status as well as the soil health. The present study is employed in a Geographic Information System (GIS) environment to predict erosion risk using the Semi-Empirical Revised Soil Loss Erosion Model (RUSLE). The physiographic soil map has been prepared by visual interpretation of the Sentinal 2 satellite image, from which the soil erodibility factor has been derived. The digital elevation model (DEM) has been prepared from a contour map and used as the base map for the topographic-related analysis. In this model, the slope length (LS) factor has been prepared from the DEM. The crop conservation and management factor (C) and support practice factor (P) factors have been derived from the LULC map. It has been found that 4.45% of the watershed comes under very high erosion, 3.50% under high erosion, 7.80% under moderate erosion, 11.37% under low erosion, and 51.36% under a very low erosion-prone zone.

Assessment of Soil Erosion within the Gayathripuzha River Basin Using RUSLE Model and GIS Tools

Soil erosion, a natural phenomenon accelerated by human activities, became a major problem for agricultural land use and for water quality concerns. Soil erosion by water is influenced by various factors like climate, nature of land surface, slope steepness, soil properties, land cover, and management practices. Estimation of soil erosion is essential to know the nature and quantity of soil loss and create specific strategies for soil conservation. This research paper thoroughly examines the susceptibility of soil erosion in the Gayathripuzha river basin in Kerala, India, which is encompassing a wide area of around 961 km². Employing the Revised Universal Soil Loss Equation (RUSLE), this investigation assesses the influential factors affecting soil erosion, which comprise rainfall (R Factor), soil erodibility (K Factor), topography (LS Factor), land usage (C Factor), and the impact of conservation measures (P Factor). The study was conducted using Geographic Information System (GIS) tools like ArcGIS 10.8 Desktop and integrating data on rainfall, soil properties, land topographic features, and land use. Rainfall erosivity is a major factor causing erosion in Gayathripuzha river basin and it ranges from 746.03 to 1222.35 MJ mm ha−1 h−1 y−1 in the river basin. Soil erodibility factor also varies widely within the region. Slope length and steepness factor showed that highland ecosystems are present only in a small area of the river basin. Variation in land use and land cover also influenced the soil erosion within the area. The estimated average annual soil erosion ranges from 0 to 133.18 t ha-1 yr-1 within the research site. The research found that soil erosion varied greatly across the study area. Regions with slopes over 50% had much higher erosion rates, while most of the study area had low erosion levels. Notably, the intensity of soil erosion demonstrated a positive correlation with slope percentage, with areas classified as bare ground demonstrating the highest erosion rates. These findings emphasize the necessity for targeted and comprehensive conservation strategies tailored to the diverse landscape of the region to effectively manage soil erosion and mitigate its adverse impacts.

Empirical modeling of soil erosion using unit plot data at Sparacia experimental area (southern Italy)

PurposeSoil loss estimation by the Universal Soil Loss Equation (USLE) – based approach is widely used to perform soil conservation interventions. The recently proposed USLE-MB model explicitly accounts for plot runoff coefficient in the erosivity factor. Both the USLE and the USLE-MB can be deduced using a reference condition given by the unit plot, which is characterized by fixed length, steepness and bare soil tilled along the steepest slope. There is little evidence about the existence of the unit plot among those used to develop the USLE model, and few investigations experimentally considered this condition later.MethodsIn the present investigation, the USLE and USLE-MB models were parameterized using measurements performed in the Sparacia unit plots, in Sicily.ResultsThe USLE soil erodibility factor differed significantly from the nomograph value and also from the estimates previously obtained by two methods applied to measurements collected in plots having different length and steepness compared to the unit plot. The experimentally determined soil erodibility factor of the USLE-MB also differed from those determined with these two methods. The slope steepness factor determined according to its definition was not consistent with that estimated with known literature relationships. The slope length factor was nearly constant for the USLE and assumed to be constant for the USLE-MB, in contrast with the increasing relationship with the plot length suggested by the USLE.ConclusionThis investigation elucidated the discrepancy between the single factors of the models obtained using measurements from the unit plot and those otherwise estimated.

Quantifying Soil Erosion in Drought-Impacted Central Odisha, India, through Geospatial Mapping with RUSLE

Soil degradation in Odisha poses a significant conservation concern for the local environment. The present research focused on a region in central Odisha State, India, affected by drought conditions. Several models have emerged to assess soil loss, with the Revised Universal Soil Loss Equation (RUSLE) standing out as the most suitable option. The erosion computation process entails utilizing the digital elevation model (DEM), Landsat-9 imagery, and soil data from several sources accessible in different forms and scales. The present analysis took into account various elements, namely, crop management factor (C), practice management factor (P), slope length factor (LS), steepness factor of the slope (S), and rainfall factor (R). Multiplying these factors yielded the average rate of soil erosion. Areas with a high slope length factor, such as those in Kandhamal, Kalahandi, and Nuapada, have a high erosion rate. The study reveals that 57% of the land in the study area experiences very low to moderate soil erosion at a rate of 2–10 tons per hectare per year, while 43% faces moderately to very severe erosion at a rate of 10–25 tons per hectare per year. Erosion hotspots, covering 32,205 square kilometers, are mainly identified in agricultural and forested hilly areas where slopes exceed 10°, such as those in Kandhamal, Kalahandi, and Nuapada, which have a high erosion rate. These districts are especially vulnerable to soil loss and resulting climate action (Sustainable Development Goals-13) because of frequent and severe rainfall, shifting agricultural practices, a thin surface soil covering, natural erosion, and barren hills. The research emphasizes the urgent need for implementing conservation and management measures to protect high-risk areas from further degradation. In conclusion, the study underscores the effectiveness of the RUSLE-GIS model in conducting quantitative and spatial assessments of soil erosion on a river watershed scale. The model is deemed crucial in formulating conservation strategies to address the identified erosion issues in the tropical highlands of the area.

Mapping of Multiple Hazards in the Cilongkrang Sub-Watershed, Majalengka, Indonesia

The intensive agricultural activities in the Cilongkrang Sub-Watershed are causing environmental changes that can lead to various disasters. This study aims to map the spatial distribution of multiple hazards, including flash floods, erosion, and landslides. Three hazards selected because the critical condition of Cilongkrang Sub-Watershed as an upstream area caused the area to be prone to those hazards. The flash flood hazard model was created using the Flash Flood Potential Index (FFPI) method, which uses land use, slope, vegetation cover, and soil texture. The erosion hazard model was created using the Universal Soil Loss Equation (USLE) method with parameters such as erosivity, erodibility, slope length factor, crop management index, and soil conservation index. The landslide hazard model was created using the Spatial Multi-Criteria Evaluation (SMCE) method, which uses slope, landform, land use, and soil texture. The results showed that the dominant flash flood-prone areas were in the medium class of 1.556 hectares, the erosion tended to be very low, which was 2.699,5 tons/ha/year, and The Argapura Sub-District encompasses multiple regions that are highly susceptible to landslides.

Assessment of soil erosion in the Irga watershed on the eastern edge of the Chota Nagpur Plateau, India

Human activities to improve the quality of life have accelerated the natural rate of soil erosion. In turn, these natural disasters have taken a great impact on humans. Human activities, particularly the conversion of vegetated land into agricultural land and built-up area, stand out as primary contributors to soil erosion. The present study investigated the risk of soil erosion in the Irga watershed located on the eastern fringe of the Chota Nagpur Plateau in Jharkhand, India, which is dominated by sandy loam and sandy clay loam soil with low soil organic carbon (SOC) content. The study used the Revised Universal Soil Loss Equation (RUSLE) and Geographical Information System (GIS) technique to determine the rate of soil erosion. The five parameters (rainfall-runoff erosivity (R) factor, soil erodibility (K) factor, slope length and steepness (LS) factor, cover-management (C) factor, and support practice (P) factor) of the RUSLE were applied to present a more accurate distribution characteristic of soil erosion in the Irga watershed. The result shows that the R factor is positively correlated with rainfall and follows the same distribution pattern as the rainfall. The K factor values in the northern part of the study area are relatively low, while they are relatively high in the southern part. The mean value of the LS factor is 2.74, which is low due to the flat terrain of the Irga watershed. There is a negative linear correlation between Normalized Difference Vegetation Index (NDVI) and the C factor, and the high values of the C factor are observed in places with low NDVI. The mean value of the P factor is 0.210, with a range from 0.000 to 1.000. After calculating all parameters, we obtained the average soil erosion rate of 1.43 t/(hm2•a), with the highest rate reaching as high as 32.71 t/(hm2•a). Therefore, the study area faces a low risk of soil erosion. However, preventative measures are essential to avoid future damage to productive and constructive activities caused by soil erosion. This study also identifies the spatial distribution of soil erosion rate, which will help policy-makers to implement targeted soil erosion control measures.

Assessment of Soil Loss Using RUSLE Method in Mrica Reservoir Catchment, Central Java, Indonesia

The Indonesian government has identified the Serayu Watershed as a priority area for restoration within the National Mid-Term Development Plan. One of the significant challenges in this region is the high level of soil erosion, which threatens the overall ecosystem. This study aims to estimate the amount of soil loss in the Mrica Catchment using the Revised Universal Soil Loss Equation (RUSLE) Method. Various data sources were utilized, including soil type, rainfall, land cover, Digital Elevation Model, and conservation data. Geographic Information System (GIS) techniques were employed to calculate the critical factors required by the RUSLE Method, including soil erodibility (K), rainfall erosivity (Ri), slope length and steepness factor (LS), and cover management and conservation factor (CP). This research provides critical information for land management in Mrica Catchment. These factors were used to estimate soil loss in the Mrica Catchment, revealing a range between 62,553 tons per year (t/y) and 21,323,311 t/y, with an average value of 443.90 ton per hectare per year (t/ha/y). These results indicate high erosion potential based on the Classification of Erosion Hazard (HER). This study provides critical information for land management and offers suggestions for devising effective strategies to mitigate sedimentation impact in the Serayu Watershed. The highest soil loss values according to the RUSLE Method, both under the Environmental and Management Variable, are observed in the same location, namely, in the north of Mrica Catchment. The findings emphasize the urgent need for erosion control measures and sustainable land management practices in this priority restoration area.

Combining RUSLE model and the vegetation health index to unravel the relationship between soil erosion and droughts in southeastern Tunisia

Droughts and soil erosion are among the most prominent climatic driven hazards in drylands, leading to detrimental environmental impacts, such as degraded lands, deteriorated ecosystem services and biodiversity, and increased greenhouse gas emissions. In response to the current lack of studies combining drought conditions and soil erosion processes, in this study, we developed a comprehensive Geographic Information System (GIS)-based approach to assess soil erosion and droughts, thereby revealing the relationship between soil erosion and droughts under an arid climate. The vegetation condition index (VCI) and temperature condition index (TCI) derived respectively from the enhanced vegetation index (EVI) MOD13A2 and land surface temperature (LST) MOD11A2 products were combined to generate the vegetation health index (VHI). The VHI has been conceived as an efficient tool to monitor droughts in the Negueb watershed, southeastern Tunisia. The revised universal soil loss equation (RUSLE) model was applied to quantitatively estimate soil erosion. The relationship between soil erosion and droughts was investigated through Pearson correlation. Results exhibited that the Negueb watershed experienced recurrent mild to extreme drought during 2000–2016. The average soil erosion rate was determined to be 1.8 t/(hm2·a). The mountainous western part of the watershed was the most vulnerable not only to soil erosion but also to droughts. The slope length and steepness factor was shown to be the most significant controlling parameter driving soil erosion. The relationship between droughts and soil erosion had a positive correlation (r=0.3); however, the correlation was highly varied spatially across the watershed. Drought was linked to soil erosion in the Negueb watershed. The current study provides insight for natural disaster risk assessment, land managers, and stake-holders to apply appropriate management measures to promote sustainable development goals in fragile environments.

Modeling Dynamics of Soil Erosion by Water Due to Soil Organic Matter Change (1980–2020) in the Steppe Zone of Russia

This research aims to evaluate the dynamics of soil loss through soil erosion by water in agricultural lands in steppe areas using a modification of the RUSLE2 model from the 1980s to the 2010s. The calculation was performed using a raster model of data that included a model of the slope angle, slope length, soil erodibility, rainfall and snowmelt erosivity factors, types of land use, and cover management factor. All data were taken from open sources. The average soil erosion in the territory studied amounted to 1.48 t ha−1 year−1 in the 1980s and 1.72 t ha−1 year−1 in the 2010s. The discrepancy with other studies was 12% for the level of the 1980s and 2–7% for the level of the 2010s. The main factor leading to an increase in soil loss was soil erodibility due to the loss of soil organic matter, which affected about 52% of the studied lands. The increase in the amount of soil loss occurred against a background of compensating processes: reduction in precipitation and climate change (getting drier), as well as the overgrowth of agricultural lands with natural steppe vegetation. Modified model RUSLE2 has shown good results correlated with other studies for the research area.

Adaptability analysis and model development of various LS-factor formulas in RUSLE model: A case study of Fengyu River Watershed, China

The slope length and slope steepness factor (LS-factor) formula in the Revised Universal Soil Loss Equation (RUSLE) has a considerable level of uncertainty due to the existence of multiple methods. In this study, four commonly used formulas for the slope length factor and two formulas for the slope gradient factor were chosen and combined based on their applicability to the specific research context. Based on the ModelBuilder in ArcGIS Pro 3.0, a RUSLE calculation model (RUSLE-Cal model) was constructed in the study, which can automatically calculate the soil erosion modulus using four commonly used RUSLE formulas and one combination formula. Taking the Fengyu River watershed in China as a case study, this research analyzes the uncertainty of different LS-factor formulas and validates the accuracy of RUSLE simulation results using measured sediment data. The optimal combination of LS-factor formulas is selected, and an in-depth analysis is conducted on the origins and suitability of each formula. The accuracy validation results indicate that, for the Fengyu River watershed, the optimal combination of L-factor and S-factor formulas were determined based on the slope gradient. Specifically, L1 formula was used when slope ≤ 10°, and L3 formula was used when slope greater than 10°. Similarly, S1 formula was used when slope ≤ 18°, and S2 formula was used when slope greater than 18°. The RUSLE model achieved the best simulation results with a relative error of 5.55%. The results of the uncertainty analysis indicate that the four formulas have a significant impact on the simulated soil erosion, with a RE ranging from −99.18% to 31.49%. Therefore, based on literature review and formula analysis, a suitability selection table for L-factor and S-factor formulas is provided, which can provide formula basis for the improvement of soil erosion in watershed models.

Soil Erosion Estimation of Kunthippuzha Watershed Using GIS and RUSLE Model

Soil erosion is an environmental crisis in the world today that threatens the natural environment and also agriculture. Erosion removes the top fertile soil, degrades soil fertility, water quality and soil productivity. The soil erosion risk assessment is helpful for land evaluation in the regions where soil erosion is a major threat for sustainable agriculture. Modelling can provide a quantitative and consistent approach to estimate soil erosion and sediment yield under a wide range of conditions. The soil erosion and erosion prone areas in the Kunthippuzha sub-watershed of Bharathapuzha river basin, Kerala, India was estimated using RUSLE. The study area is having a drainage area of 950 km2 up to the gauging station. The estimation was done for 1990 to 2021. The estimated rainfall erosivity, soil erodibility, Slope length and slope steepness factor and crop management factors range from 929.36 and 980.38 MJ mm ha-1 h-1 yr-1, 0.01387 to 0.0385 t ha h ha−1 MJ−1 mm−1, 0 to 9.77 and 0.057796 to 1.0999. The results indicate that the estimated total annual potential soil loss of about 842175 t/y is comparable with the measured sediment of 845500 t/yr during the years 1990-2021. The soil erosion rate categorized into six classes based on the erosion severity, the major portion (60%) of the study area comes under very slight erosion zone and only a small portion (14%) comes under severe and very severe erosion zone. Result suggests the area of the north-eastern part suffers from a high soil erosion risk due to steep slope. The results can certainly aid in implementation of soil management and conservation practices to reduce the soil erosion in the Kunthippuzha sub-watershed.

Modification of the RUSLE slope length factor based on a multiple flow algorithm considering vertical leakage at karst landscapes

Heterogeneous karst surfaces exerted scaling effects whereby specific runoff decrease with increasing area. The existing RUSLE-L equations are limited by the default implicit assumption that the surface-runoff intensity is constant at any slope length. The objective of this study was to modify the L-equation by establishing the functional relationship between surface-runoff intensity and karst slope length, and to evaluate its predictive capability at different resolution DEMs. Transfer grid layers were generated based on the area rate of surface karstification and considered the runoff transmission percentage at the exposed karst fractures or conduits to be zero. Using the multiple flow direction algorithm united with the transfer grid (MFDTG), the flow accumulation of each grid cell was simulated to estimate the average surface-runoff intensity over different slope lengths. The effectiveness of MFDTG algorithm was validated by runoff plot data in Southwestern China. The simulated results in a typical peak-cluster depression basin with an area rate of surface karstification of 6.5% showed that the relationship between surface-runoff intensity and slope length was a negative power function. Estimated by the proposed modified L-equation ((alx(b+1)/22.13)m), the L-factor averages of the study basin ranged from 0.35 to 0.41 at 1, 5, 25 and 90 m resolutions respectively. This study indicated that the modified L-equation enables an improved prediction of the much smaller L-factor and the use of any resolution DEMs on karst landscapes. Particular attention should be given to the variation of surface-runoff intensity with slope length when predicting L-factor on hillslopes with runoff scale effect.

Assessment of soil erosion hazard and its relation to land use land cover changes: Case study from alage watershed, central Rift Valley of Ethiopia

Soil erosion by water and wind is among the most crucial land degradation processes in Ethiopia. This is also the case for Alage watershed located in the cental Rift Valley system. This study aimed at assessment of soil erosion hazard and its relation to land use land cover change in the watershed during the period from 1984 to 2016 for a better land management. The study is based on application of Remote Sensing (RS) and Geographical Information System (GIS) to extract inputs factor values for the Revised Universal Soil Loss Equation (RUSLE). Time-series satellite imageries of Landsat TM 1984, ETM+ 2000 and OLI 2016 were used for land use land cover change detection and determination of cover management (C) factor of the RUSLE. Biophysical data such as rainfall, soil properties, land management practices including soil and water conservation measures within the watershed were collected using field survey and secondary data sources. Slope steepness and slope length factors were derived using Digital Elevaition Model (DEM). Long-term average annual soil loss rates were estimated by the RUSLE integrated with GIS for 1984, 2000 and 2016. Using satellite imageries, the land use land cover and changes within the watershed during the three periods were obtained through a supervised classification with maximum likelihood algorithim. The results of land use land cover change indicated that the proportion of rain-fed cropland, bare land and built up areas increased by 17.4%, 5.9% and 2.9% respectively over the three study period. In contrast the proportion of bush/shrub land, irrigated cropland, grass land, forested areas and waterbodies decresaed by 15.5%, 4.7%, 3.4%, 2.3% and 0.3% respectively during the same period. Estimated average annual soil loss rates showed an increasing trend from 24.3 ton ha−1 yr−1 in 1984 to 38 ton ha−1 yr−1 in 2016. Increasing trends of average annual soil loss rate is attributed to increased proportion of cropland, bare land and built up areas during those periods leading to decreased protective vegetation cover. Hotspot areas within the watershed require implementation of land management practices to prevent further degradation and expansion of gullies. This study is relevant to demonstrate environmental implication of land use land cover change for future land management practices and land use policy in the Rift Valley of central Ethiopia.

Comparative Analysis of Tree-Based Ensemble Learning Algorithms for Landslide Susceptibility Mapping: A Case Study in Rize, Turkey

The Eastern Black Sea Region is regarded as the most prone to landslides in Turkey due to its geological, geographical, and climatic characteristics. Landslides in this region inflict both fatalities and significant economic damage. The main objective of this study was to create landslide susceptibility maps (LSMs) using tree-based ensemble learning algorithms for the Ardeşen and Fındıklı districts of Rize Province, which is the second-most-prone province in terms of landslides within the Eastern Black Sea Region, after Trabzon. In the study, Random Forest (RF), Gradient Boosting Machine (GBM), CatBoost, and Extreme Gradient Boosting (XGBoost) were used as tree-based machine learning algorithms. Thus, comparing the prediction performances of these algorithms was established as the second aim of the study. For this purpose, 14 conditioning factors were used to create LMSs. The conditioning factors are: lithology, altitude, land cover, aspect, slope, slope length and steepness factor (LS-factor), plan and profile curvatures, tree cover density, topographic position index, topographic wetness index, distance to drainage, distance to roads, and distance to faults. The total data set, which includes landslide and non-landslide pixels, was split into two parts: training data set (70%) and validation data set (30%). The area under the receiver operating characteristic curve (AUC-ROC) method was used to evaluate the prediction performances of the models. The AUC values showed that the CatBoost (AUC = 0.988) had the highest prediction performance, followed by XGBoost (AUC = 0.987), RF (AUC = 0.985), and GBM (ACU = 0.975) algorithms. Although the AUC values of the models were close to each other, the CatBoost performed slightly better than the other models. These results showed that especially CatBoost and XGBoost models can be used to reduce landslide damages in the study area.

Application of Geo-informatics for Soil Erosion Mapping

Soil erosion is a most severe environmental problem in hilly area. The study is carried out on Upper Bagmati River basin, North of Kathmandu valley having an area of 61 Sq.km. (approx). Universal Soil Loss Equation (USLE) model, with Geographic Information System (GIS) has been used to quantify the soil loss. Erosion modelling requires huge amount of information and data, usually coming from different sources and available in different formats and scales and for management of these data, GIS was used, which helped considerably in organizing the spatial data representing the effects of each factor affecting soil erosion. Five essential parameters of USLE Rainfall erosivity factor (R), Soil erodibility Factor (K), Slope length and steepness (LS) factor, Cropping management factor (C) and Support practice factor (P) have been used to estimate soil loss amount in the study area. All of these layers have been prepared in Arc GIS using various data sources and data preparation methods. DEM was prepared from the contour data with the interval of 20m which was used to generate LS factor. The monthly rainfall data (2010) of 17 rain gauge stations within the catchment area have been used to predict the R factor. K, C and P factors in basin area are adopted from the literature. The spatial distribution map of soil loss of the basin has been generated and classified into six categories depending on the calculated soil erosion amount. The annual predicted soil loss ranges between 0 and 292.878 t/ha/y. Low soil loss (mean 9.7 t/ha/y) have been recorded under forested areas. The high rate (mean 40.4 t/ha/y) of soil erosion was found in the cultivation area.