Slope Steepness 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.

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.

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.

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.

Optimization of land management measures for soil erosion risk using GIS in agricultural landscape of western Hararghe highlands, Ethiopia

Soil loss through water is one of the major environmental problems leading to land degradation worldwide. Though soil erosion is an inevitable natural phenomenon, it is possible to reverse and minimize the rate at a local level. This requires spatially unambiguous evidence on rate of soil loss so as to implement site specific land management decisions. The aim of this study was to estimate the mean annual soil loss and optimize land management measures in the Midhagdu watershed of western Hararghe highlands, Ethiopia. The study used five soil loss factors as input variables to estimate the soil loss. A thematic layer map was generated for each factor with spatial resolution of 30 m using Geographical Information System (GIS), and combined them with revised universal soil loss equation (RUSLE) model to estimate soil loss and identify erosion risky area in the watershed. According to the results, the distribution of annual soil loss varied between 0 and 179 t ha−1 yr−1, with a mean annual loss rate of 44.24 t ha−1 yr−1. The total annual soil loss in the entire watershed was 240,250 tons. Of this, 93.48% (224,593 tons) of the loss occurred in 49% (2,674 ha) of the catchment area,which was above the maximum tolerable soil loss limits. Most of the soil erosion affected areas are spatially situated in the upper and hillsides of the watershed. This is due to factors attributed to the steepness of the slope, dominance of Leptosols with shallow soil depth, coupled with absence of supporting practice in these parts of the watershed. Thus, slope length and slope steepness (LS) factor was the primary RUSLE parameters that controlled the soil loss in the studied watershed followed by soil erodibility (K) and support practice (P) factor. Hence, this study optimized land management options for future intervention based on the inherent limitation of the biophysical environment, socioeconomic and cultural conditions of the watershed community.

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 estimation of slope-length gradient (LS) factor for entire Afghanistan

Slope length gradient (LS) is one of the crucial factors in the Universal Soil Loss Equations (USLE, RUSLE). This study aimed at estimating the slope-length and slope-steepness (LS) factor for the entire watersheds of Afghanistan by using three different methods, namely; (1) LS-TOOLMFD (Method 1); (2) The Method of Equations (Method 2); and (3) The approach of Moore and Burch (Method 3). The first method uses the digital elevation model (DEM) in the ASCII format, and the other two methods use the DEM in the spatial domain. The results show that the LS-factor of the study area ranges from 0.01 to 44.31, with a mean of 5.24 and standard deviation of 6.95, according to Method 1; 0.03 to 163.49, with a mean of 9.6 and standard deviation of 13.58, according to Method 2; and 0 to 3985, with a mean of 7.16 and standard deviation of 29.7, according to Method 3. The study reveals that Methods 1 and 2 are more appropriate than Method 3 because Method 3 yields high LS-factor values close to or at streamlines located near mountainous regions. The highest LS values are found to be in the northeast, north, and central regions of Afghanistan, which is consistent with the high mountains and deep valley geomorphology, indicating that these regions are particularly vulnerable to soil erosion by rainfall-runoff processes. The sediment delivery ratio (SDR) for the Upper-Helmand River Basin (Upper-HRB) is also estimated by the RUSLE, employing the LS factors produced by the three methods. The results revealed that the average annual soil loss is found to be, respectively, 9.3, 18.2, and 11.1 (ton/ha/year) by using the three methods, corresponding to SDR of 23.5%, 12.1%, and 19.9%. Abbreviations: ABD: Asian Development Bank; ASCII: American Standard Code for Information Interchange; Bsh: cold semiarid steppes; Bsk: cold semiarid steppes; Bwh: warm and cold deserts; Bwk: warm and cold deserts; CMS: Convention on Migratory Species; Csa: humid subtropical; Csb: Mediterranean; D: humid continental; DEM: Digital Elevation Model; ET: extreme tundra; GCS: Geographical Coordinate System; GIS: Geographic Information System; GUI: Graphically User Interface; Ha: Hectare; HMRB: Harirod-Murghab River Basin; HRB: Helmand River Basin; KRB: Kabul River Basin; LS: Slope–Length and Slope–Steepness; Masl: meters above sea level; MFD: Multiple-Flow Direction; NRB: Northern River Basin; PARB: Panj-Amu Darya River Basin; RUSLE: Revised Universal Soil Loss Equations; SDR: Sediment Delivery Ratio; SFD: Single–Flow Direction; SRTM: Shuttle Radar Topography Mission; SY: Sediment Yield; UCA: Unit Contributing Area; USGS: United States Geological-Survey; USLE: Universal Soil Loss Equations

Spatial Assessment of Soil Erosion Risk Using RUSLE Embedded in GIS Environment: A Case Study of Jhelum River Watershed

The watershed area of the Mangla Reservoir spans across the Himalayan region of India and Pakistan, primarily consisting of the Jhelum River basin. The area is rugged with highly elevated, hilly terrain and relatively thin vegetation cover, which significantly increases the river’s sediment output, especially during the monsoon season, leading to a decline in the reservoir’s storage capacity. This work assesses the soil erosion risk in the Jhelum River watershed (Azad Jammu and Kashmir (AJ&K), Pakistan) using the Revised Universal Soil Loss Equation of (RUSLE). The RUSLE components, including the conservation support or erosion control practice factor (P), soil erodibility factor (K), slope length and slope steepness factor (LS), rainfall erosivity factor (R), and crop cover factor (C), were integrated to compute soil erosion. Soil erosion risk and intensity maps were generated by computing the RUSLE parameters, which were then integrated with physical factors such as terrain units, elevation, slope, and land uses/cover to examine how these factors affect the spatial patterns of soil erosion loss. The 2021 rainfall data were utilized to compute the rainfall erosivity factor (R), and the soil erodibility (K) map was created using the world surface soil map prepared by the Food and Agriculture Organization (FAO). The slope length and slope steepness factor (LS) were generated in the highly rough terrain using Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). The analysis revealed that the primary land use in the watershed was cultivated land, accounting for 27% of the area, and slopes of 30% or higher were present across two-thirds of the watershed. By multiplying the five variables, the study determined that the annual average soil loss was 23.47 t ha−1 yr−1. In areas with dense mixed forest cover, soil erosion rates ranged from 0.23 t ha−1 yr−1 to 25 t ha−1 yr−1. The findings indicated that 55.18% of the research area has a low erosion risk, 18.62% has a medium erosion risk, 13.66% has a high risk, and 11.6% has a very high erosion risk. The study’s findings will provide guidelines to policy/decision makers for better management of the Mangla watershed.

Stand and tree characteristics influence damage severity after a catastrophic hurricane disturbance

Catastrophic wind disturbances can greatly impact the economic and ecological values of working forests worldwide. For example, Hurricane Michael caused severe economic damage in 2018 to commercial loblolly (Pinus taeda L.) and slash (Pinus elliotti Engelm.) planted pine stands in the southeastern U.S. This project examined the relative importance of prior management activities, stand characteristics, and soil and terrain attributes in predicting stand damage from a catastrophic hurricane event. We assembled data from land managers in coastal Alabama, the Florida panhandle, and South Georgia, on percent loss of trees after Hurricane Michael in over 400 stands, along with stand management history, and stand characteristics. Digital elevation models were utilized to determine terrain and hydrological characteristics of the landscape including aspect, channel distance, elevation, LS factor (slope and length steepness factor), slope angle, slope position, terrain ruggedness index, topographic wetness index, and wind exposition index. Random forest models were used to determine the relative accuracy of windspeed, management history, stand characteristics, and soil and terrain attributes in predicting stand damage. From the variables evaluated, stand density (as described by trees per acre), and tree height (as described by the average height of dominant and co-dominant trees) were among the most influential characteristics in predicting damage severity along with windspeed and linear distance from the coast. Stands with dominant heights > 14 m, trees per acre < 600 (trees per hectare < 1,483), and within 50 km of the coast were the most likely to experience severe damage. Topographic wetness index was the most influential terrain characteristic, with sands in wetter soils more likely to experience more severe damage. Recently thinned stands were slightly more vulnerable to severe damage. Results from this study can be used to develop vulnerability profiles for commercial pine stands in the coastal plain region in the southern U.S. region.

A dataset of slope length and slope steepness factors based on soil loess equation with 12.5 m spatial resolution in the Yellow River Basin

The Yellow River Basin is one of the areas worst affected by soil and water loss in China. The slope length and slope steepness factors are the basic data for the study on soil loss equation, and they are also important parameters in soil erosion models of USLE and CSLE. In this study, we obtained the dataset of four key factors of soil erosion equation, namely slope steepness factor, slope length factor, LS factor and slope length through regional LS factor calculation tool based on ALOS 12.5 m DEM data. This dataset can be used for regional soil erosion assessment, land use as well as the measures and planning of soil and water conservation.

The Significance of Digital Elevation Models in the Calculation of LS Factor and Soil Erosion

This study focuses on the role of topography in soil erosion modelling by examining the impact of topographic data from various sources on the calculation of the slope length and slope steepness factor (LS). For this purpose, the Pinios dam drainage basin in the Ilia Regional Unit, Western Greece, was selected as a pilot area of this study. Specifically, six Digital Elevation Models (DEM) from four different sources with various resolutions (5, 30, and 90 m) were compared with ground control point (GCP) values to assess their relative vertical accuracy. These DEM were acquired for the calculation of the LS factor by using two different equations. Then the calculated LS factors were implemented in the RUSLE model for the estimation of soil loss. The current study includes a comparative analysis of the elevation, the slopes, the LS factor, and the soil loss. The results showed that the 5 m resolution DEM had the best vertical accuracy, and thus it is considered to be the most suitable DEM for soil erosion modelling. Moreover, the comparison of the DEM elevation values showed high similarity, in contrast to the slope values. In addition, the comparative assessment of the LS and soil loss values calculated from each DEM with the two LS equations revealed a great divergence. It is noticeable that both LS and soil loss results presented higher values for slopes greater than 20°. It is concluded that the comparison of the LS values calculated with the two examined approaches and the use of different DEM with various resolutions and different sources does not change consistently with the increase of DEM grid size and accuracy. Thus, it is very significant in soil erosion modelling to use an LS equation that imports thresholds in its formula to avoid overestimation in soil loss calculations.

The nexus between land use, land cover dynamics, and soil erosion: a case study of the Temecha watershed, upper Blue Nile basin, Ethiopia.

At the current times, soil erosion is the major problem that affects land and water resources, especially in Ethiopia's highlands. Due to the dynamics of land use land cover change, land degradation, and soil erosion increase significantly and result in a loss of fertile soil every year and lead reduction in agricultural production. This study was therefore designed to explore the land use land cover (LULC) dynamics from 1986 to 2020, to estimate mean annual soil erosion rates and identify erosion hotspot areas from 1986 to 2020, and finally, to evaluate the impacts of land use land cover change on soil loss of 1986 to 2020. For this, Landsat imageries of 4 years from 1986 to 2020 were used. Maximum likelihood supervised classification methods were used to classify LULCs. The dynamics of LULC change were used as an input for measuring soil loss by employing the combination of geospatial technologies with the revised universal soil loss equation (RUSLE). The LULC maps of 1986, 1997, 2009, and 2020 were used for identifying crop management (C) factor and conservation practice (P) factor. Rainfall erosivity factor (R), soil erodibility factor (K), and slope length and steepness factor (LS) were also used as sources of data. Based on the five factors, soil erosion intensity maps were prepared for each year. Results showed that the annual soil loss in the watershed ranged from 0 to 3938.66 t/ha/year in 1986, 0 to 4550.94 t/ha/year in 1997, 0 to 5011.21 t/ha/year in 2009, and 0 to 6953.23 t/ha/year in 2020. The annual soil loss for the entire watershed was estimated at 36.889, 42.477, 47.805, and 48.048 t/ha/year in 1986, 1997, 2009, and 2020, respectively. The mean soil loss of 1986, 1997, 2009, and 2020 was higher in cultivated land followed by shrub land, grazing land, and forest land. Mean soil loss increased from 1986 to 1997, from 1997 to 2009, and from 2009 to 2020. This is because of the expansion of agricultural land at the expense of grazing land and shrub land. Therefore, urgent soil and water conservation practices should be made in hotspot areas.

Topographic factor of water erosion – analysis of watershed morphometry and RUSLE LS factor in GIS environment

Soil erosion is the most serious soil degradation factor on the territory of Bulgaria. Nearly 66 % of its area have slopes in a range greater than 3°. The predominant hilly-mountainous relief and high slope degrees are prerequisites for development of water erosion. The properties of the topographic surface as a conditioning factor of erosion processes are evaluated in the current study on the example of the Dzhebelska River watershed (Southern Bulgaria). The following morphometric parameters are considered: basin relief, Melton index, slope and stream power index (SPI). Slope length and steepness factor (LS factor) is calculated using the slope degree and specific contributing area. The values vary between 0 and 43.36. The results of the analysis of the morphometric parameters indicate high erosion susceptibility in the upper and the middle part of the watershed but active erosion can be observed too in the low part of the watershed, where first order streams (Strahler’s method) flows directly to the river of fifth order. The current study is done in GIS environment on the base of 12 m digital elevation model.

Can slope spectrum information entropy replace slope length and steepness factor: A case study of the rocky mountain area in northern China

Slope spectrum information entropy (SSIE) can reflect the overall fluctuation characteristics of the topography with convenient calculation. In order to explore whether the complex slope length and steepness factor (LS factor) can be replaced by SSIE in the evaluation of soil erosion in the rocky mountain area, this study took the rocky mountain area in northern China (the first zone of soil and water conservation) as the research object. The soil texture of study area is the most typical “soil-rock dual structure”. The SSIE, soil erosion intensity (SEI), and LS factor were calculated using ASTER GDEM and Landsat8 image data (30 m × 30 m). The correlation between the SSIE and SEI was revealed, and a functional relationship model of SSIE and LS factor was built. This study clarified whether SSIE could replace the LS factor to evaluate soil erosion in the “soil-rock dual structure” area. The results showed that: (1) The slope spectrum curves of the six secondary zones of soil and water conservation in the study area presented “L” shape, and the SSIE was between 0.59 and 2.79 nat. SEI grades were mainly micro-erosion, with strong erosion and above accounting for 1.7%. The spatial distribution difference was significant and strong erosion and above was mostly distributed in mountainous and hilly areas. The LS factor of the whole region was between 0 and 55.03, with an average value of 1.37. The regions with an average LS factor < 5 accounted for more than 82% in each secondary zone. (2) The significant logarithmic function relationship of SSIE and SEI was displayed on the secondary zone (R2 = 0.9372, P < 0.01), tertiary zone (R2 = 0.7908, P < 0.01), and basin scales (R2 = 0.3534, P < 0.01). (3) The relationship of SSIE and LS factor also was significant logarithmic function on the secondary zone (R2 = 0.7866, P < 0.05), tertiary zone (R2 = 0.8269, P < 0.01), and basin scales (R2 = 0.8958, P < 0.01). It was indicated that in the typical hydraulic erosion areas of “soil-rock dual structure”, SSIE can replace the LS factor to evaluate SEI and soil erosion modulus.