Slope Length Research Articles

A GIS-RS Approach for RUSLE-Based Method of Mean Estimation of Mean Annual Soil Loss of the Tagoloan River Basin, Philippines

Abstract. Soil erosion is a serious environmental concern in Tagoloan River Basin (TRB), a major watershed in Northern Mindanao, Philippines. It leads to soil loss causing detrimental impacts such as decreased soil productivity, nutrient loss, siltation, and water quality degradation among others. These impacts are better understood by estimating the degree of soil loss in the watershed and visualized in a GIS-based and factor-based approach using the Revised Universal Soil Loss Equation (RUSLE) model. Thematic maps of soil loss were generated with factors for rainfall erosivity (R), soil erodibility (K), topographic (LS) consisting of slope length (L) and slope steepness (S), cover management (C), and support practice (P). Factors were calculated separately and multiplied together to develop combined soil loss maps. Results show that TRB has an actual mean annual soil loss of 153.20 tons/hectare/year with 47.15% of the study area having very high to very severe susceptibility to soil loss. Further, a comparison of the potential (RKLS) and actual (RKLSCP) soil loss maps indicates the significance of cover management and support practices to the resulting mean annual soil loss. The present characterized soil loss level maps and its understanding driving forces of soil erosion for the planning of management practices and mitigating environmental hazards in the watershed.

Estimation of Soil Erosion by RUSLE Model Using Geoinformatics Techniques: A Case Study of Mulshi Reservoir Catchment, Pune District, Western Maharashtra.

In the western part of Maharashtra, where the Western Ghat highlands are located, soil erosion is a serious and persistent environmental issue. To make matters worse, the majority of the dams in this region run the risk of sedimenting their reservoirs, which is a serious concern. This research was being conducted on the Mulshi reservoir catchment area, Pune district of western Maharashtra having area of 250.25 km2. It is fact that the surface runoff of seasonal rainfall is more in this region due to the presence of western ghats. In this study, high resolution satellite images were used to estimate average annual soil erosion based on the five factors defined in the Revised Universal Soil Loss Equation (RUSLE) with the help of Geoinformatics techniques. Overlay of five parameters, viz., rainfall erosivity factor (R), soil erodibility factor (K), slope length and steepness factor (LS), cover and management factor (C) and support & conservations practices factor (P) has been done in ArcGIS (version 10.8) software. Annual average soil erosion of the catchment is 0 to 433.43 tons/hector/year and has been classified into Six categories, defined as very high, high, moderate, low, and very low and negligible according to the severity of erosion. The high values of soil erosion (>20 tons/hector/year) found on the highlands due to the high slope and bare lands. however, low values (<10 tons/hector/year) are found on the area occupied by reservoir and dense vegetation.

Mapping of soil erosion risk in Ezeagu Local Government Area of Enugu State, Nigeria

This study centres on mapping soil erosion risk in Ezeagu Local Government Area of Enugu State, Nigeria. A multi-criteria research design was adopted in carrying out this study by using remote sensed images, topographic, soil and rainfall data. K Factor, R Factor, P Factor, K Factor and Digital Elevation Model were generated. SWAT model was used to generate surface runoff and sediment yield and AHP model were used to identify and map levels of soil erosion risk in the study area. Loamy sandy soil with a K Factor value of 0.23 – 0.34 (t h MJ-1 mm-1) and loam soil with a K factor Value of 0.08 – 0.14 (t h MJ-1 mm-1) dominates the study area. The organic matter contents for all the soils were rated low with an average measured organic matter of 0.97 (%). The rainfall erosivity factor (R-factor) results showed variations across the study area from 1029.295 to 1343.092, 1343.092 to 1589.713, 1589.713 to 1744.922, 1744.922 to 1965.777, and 1965.777 to 2060.864 MJ mm ha year-1, h-1 year-1. Areas with dense vegetation had lower P-factors indicating better protection against erosion, while areas under cultivation, especially those with poor management practices and without cover crops show higher P-factors. Almost all the area has a moderate (0.5-0.6) C factor which means moderate protection, areas with higher protection (0.6-1) C factor occupied a small portion towards the east while patches of low C factors are found scattered in the north, west northwest, south and eastern part of the study area. The eastern part of the study area are more susceptible to soil erosion with value ranging from 3.2-5.8, 5.9-9.9 and 10.0-19. Slope steepness and slope length is lower in the northern, western and south-west of the study area. Areas with annual soil loss between 0 to 10 grams, 10 to 20 grams and 30 to 40 grams dominates the northern, western and southern parts of the study area. The erosion risk map depicts that very low-risk area coverage of 24.06%, low risk covers 20.29%, moderate risk occupies 17.75% and high risk erosion risk covers 21.30% while very high risk occupies 16.61% of the total area. This study recommends terracing and gabions in areas identified at risk of erosion, promotion and adoption of agronomic soil conservation methods of mulching, cover cropping and planting plants to enhance soil stability and reduce erosion.

Assessment of land degradation susceptibility within the Shaqlawa subregion of Northern Iraq-Kurdistan Region via synergistic application of remotely acquired datasets and advanced predictive models.

Land degradation (LD) is the decline in a land's functional capacity and productive potential, which includes various anthropogenic and natural drivers. This study focuses on three primary manifestations of LD including soil erosion, landslides, and rockfalls, which are the most prevalent in the Shaqlawa district. A set of 22 LD conditioning factors, encompassing curvature, lithology, aspect, river density, soil type, lineament density, river distance, elevation, road distance, length slope (LS), land use land cover (LULC), stream power index (SPI), valley depth, profile curvature, slope, solar radiation, road density, lineament distance, rainfall, topographic wetness index (TWI), plan curvature, and normalized difference vegetation index (NDVI), were integrated into the analysis. Variance inflation factors (VIF) and tolerance (TOL) values from linear regression indicate that most LD factors have acceptable levels of multicollinearity. The Information Gain Ratio (IGR) identified key variables TWI, NDVI, and lithology-as pivotal factors for predicting LD. Additionally, the study evaluated degradation factors using various machine learning (ML) algorithms, including random forest (RF), Naive Bayes, logistic regression, rotation forest, forest penalized attributes (FPA), and Fisher's Linear discriminant analysis (FLDA). This facilitated categorizing the study area into five susceptibility categories. The FLDA model categorized the highest area under very high degradation risk at 26.72%, emphasizing the varied insights each algorithm brought to characterizing the degradation risk. Additionally, the receiver operating characteristic curves (ROC) were employed for model validation, identifying RF as the most successful model in the training dataset with an area under the curve (AUC) of 0.882, while FLDA outperformed in the testing dataset with an AUC of 0.883. The identified LD-prone areas will help land-use planners and emergency management officials apply effective mitigation strategies for similar terrains.

Effect of shoaling length on rogue wave occurrence

The impact of shoaling on linear water waves is well known, but it has only been recently found to significantly amplify both the intensity and frequency of rogue waves in nonlinear irregular wave trains atop coastal shoals. At least qualitatively, this effect has been partially attributed to the ‘rapid’ nature of the shoaling process, i.e. shoaling occurs over a distance far shorter than that required for waves to modulate themselves and adapt to the reduced water depth. Through a theoretical model and highly accurate nonlinear simulations, we disentangle the respective effects of the length and angle of a shoal's slope. We investigate the effects of the shoaling process rapidness on the evolution of key statistical and spectral sea-state parameters. We let the wave field evolve over a slope with constant angle in all cases while we vary the slope length. Our results indicate that the non-equilibrium dynamics is not affected by the slope length, because further extending the slope length does not influence the magnitude of the statistical and spectral measures as long as the non-equilibrium dynamics dominates the wave evolution. Thus, the shoaling effect on rogue waves is deduced to be mainly driven by the slope magnitude rather than the slope length.

Quantitative Evaluation of Soil Erosion in Loess Hilly Area of Western Henan Based on Sampling Approach

The terrain in the loess hilly area of western Henan is fragmented, with steep slopes and weak soil erosion resistance. The substantial soil erosion in this region results in plenty of problems, including decreased soil productivity and ecological degradation. These problems significantly hinder the social and economic development in the region. Soil conservation planning and ecological development require accurate soil erosion surveys. However, the studies of spatio-temporal patterns, evolution, and the driving force of soil erosion in this region are insufficient. Therefore, based on a multi-stage, unequal probability, systematic area sampling method and field investigation, the soil erosion of the loess hilly area of western Henan was quantitatively evaluated by the Chinese Soil Loss Equation (CSLE) in 2022. The impact forces of soil erosion were analyzed by means of a geographic detector and multiple linear regression analysis, and the key driving factors of the spatio-temporal evolution of soil erosion in this region were revealed. The results were as follows. (1) The average soil erosion rate of the loess hilly area in western Henan in 2022 was 5.94 t⋅ha−1⋅a−1, with a percentage of soil erosion area of 29.10%. (2) High soil erosion rates mainly appeared in the west of Shangjie, Xingyang, and Jiyuan, which are related to the development of production and construction projects in these areas. The areas with a high percentage of soil erosion area were in the north (Xinan and Yima), west (Lushi), and southeast (Songxian and Ruyang) of the study area. Moreover, areas with the most erosion were found in forest land, cultivated land, and areas with a slope above 25°. (3) At the landscape level, the number and density of patches of all land types, except orchard land, increased significantly, and the boundary perimeter, landscape pattern segmentation, and degree of fragmentation increased. (4) The geographical detector and multiple linear regression analysis indicated that the driving forces of soil erosion are mainly topographic and climatic (slope length, elevation, precipitation, and temperature). Soil erosion was significantly influenced by the density of landscape patches. These maps and factors influencing soil erosion can serve as valuable sources of information for regional soil conservation plans and ecological environment improvements.

Piezoelectric energy harvesting from walking motion of a passive biped robot model with flexible legs

Passive dynamic walking biped robots utilize the inherent dynamics of their structure to achieve walking motion without continuous actuation, enhancing energy efficiency and stability. Replacing conventional rigid legs with flexible elastic beams in the simplest passive walker will be associated with undesirable vibrations. Despite the positive effect of the damper in reducing vibrations, the current research has used the piezoelectric energy harvesting with the aim of reducing the wasted energy of the gait cycle and improving its stability range. For this purpose, the electromechanical Lagrange equations are determined by applying Hamilton's principle and the assumed mode method and then, the new definition of the step function is obtained through numerical methods, solving a boundary value problem to establish proper initial conditions for stable walking. Numerical simulations indicate that the piezoelectric energy harvester can create a stable period-one gait cycle by optimizing the length of the piezo layers attached to the undamped elastic legs despite the presence of small vibrations. Additional results are presented in bifurcation diagrams, investigating the effect of important physical and structural parameters such as slope angle, length and thickness of the piezo layer.

A reverse tracing of the water flow path algorithm for slope length extraction based on triangulated irregular network

Terrain factor is an important factor affecting soil erosion, in which slope length is an important indicator of terrain factor. In this paper, we model the regional topographic relief with triangulated irregular network TIN, use the slope aspect of the TIN triangular surface to determine the flow direction, and propose an algorithm (RT-WFP) for extracting the slope length by tracing the water flow trajectory in reverse direction. The slope cutoff point rule is set in the algorithm to improve the accuracy of the slope length extraction results. We calculated the slope length of the experimental area of the small watershed of Golden Hook-shaped collapsing hill in Bailu Township, Ganxian District, and the rationality of the algorithm proposed in this paper is verified through the comparison and analysis with the traditional slope length extraction algorithm. The experimental results show that, compared with the traditional D8 algorithm, the water flow path extracted by this algorithm in the experimental area more closely matches the water flow path based on contour mapping, and the slope length extracted by this algorithm has a lower sensitivity to the resolution of the data, and the percentage of cells with a slope length value of no more than 300 m (the limit standard of the RUSLE model) at a resolution of 30 m reaches 94.19%.

A Study on Flood Hazard Zonation Mapping Based on GIS-driven Approach using Remote Sensing Data and Weighted Overlay Analysis (Woa) Model

Flood hazard mapping is crucial for identifying flood-prone areas and developing effective mitigation strategies. This study utilized a Weighted Overlay Analysis, which is one of the subjective model derived from Multi-Criteria Decision Support System (MCDA), to assess flood hazards in the Auranga watershed, India. Six key flood-inducing factors were selected, including topographic data such as elevation, slope, river distance, and flow length derived from remote sensing data of Digital Elevation Model and classified Land use/Land cover data, along with hydro-meteorological data of precipitation. Each dataset was standardized to a common scale to facilitate comparison and integration. Weights were assigned to the layers like elevation, slope, river distance, flow length, Land use/Land cover, and precipitation according to their significance in contributing to flood risk. These weighted layers were then combined using GIS tools like ArcGIS, resulting in a composite map that delineates areas with varying flood hazard zones. The final flood hazard map identified that approximately 914 sq. km (60.0%) of the study area was at high to very high flood risk, particularly in regions close to rivers, while around 97 sq. km (6.4%) exhibited very low flood hazard. The GIS-Weighted Overlay Analysis method proved effective in flood hazard zone mapping, especially when incorporating a greater number of parameters. This approach is widely recognized in environmental planning and risk assessment, especially in areas vulnerable to natural disasters like floods.

Prediction of Landslide Susceptibility in the Karakorum under the Context of Climate Change

Climate change has recently increased the frequency of landslides in alpine areas. Susceptibility mapping is crucial for anticipating and assessing landslide risk. However, traditional methods focus on static environmental variables to emphasize the spatial distribution of landslides, ignoring temporal dynamics in landslide development in the context of climate change. In this work, we focused on static and dynamic environment factors and utilized the certainty factor-logistic regression (CF-LR) model to assess and predict landslide susceptibility in Taxkorgan County, located in the Karakorum. The assessment and prediction were based on a catalog of climate change-related landslides over the past 20 years, the causative factors, and predicted climatic variables for the Shared Socioeconomic Pathways (SSP1-2.6) scenario. The results indicated that elevation, slope, groundwater, slope length gradient (LS) factor, Topographic Wetness Index (TWI), valley depth, and maximum precipitation were the key causes of slides below the snow line. The key factors causing debris flow above the snow line were elevation, slope, topographic relief, aspect, LS factor, distance to the river, and maximum temperature. The accuracy of slide and debris flow susceptibility was 0.92 and 0.89, respectively. The area of slides with medium, high, and very high susceptibility is 25.5% of the Taxkorgan. In addition, 82.6% of the slides happened in this region, and 49.5% of the entire area is covered by debris flows with medium, high, and very high susceptibility. Moreover, this area accounts for 91.8% of all debris flows. Until 2060, the region’s climate is anticipated to become warmer and wetter. Slides below the snow line will gradually decrease and shift eastward, and debris flows above the snow line will expand. Our findings will contribute to the management of landslide risks at the regional scale.

Harmonizing models and measurements: Assessing soil erosion through RUSLE model.

Soil erosion poses significant ecological and socioeconomic challenges, driven by factors such as inappropriate land use, extreme rainfall events, deforestation, farming methods, and climate change. This study focuses on the Kozhikode district in Kerala, South India, which has seen increased vulnerability to soil erosion due to its unique geographical characteristics, increase in extreme events, and recent land use trends. The research employs RUSLE (Revised Universal Soil Loss Equation), considering multiple contributing factors such as rainfall erosivity (R), slope length and steepness (LS), cover management (C), conservation practices (P), and soil erodibility (K). The study is unique and novel, since it integrates extensive field data collected from agricultural plots across Kozhikode with the RUSLE model predictions, providing a more accurate and context-specific understanding of soil erosion processes and also suggesting management strategies based on risk priority. The study found that Kozhikode experiences an average annual soil loss of 28.7 tons per hectare. A spatial analysis revealed varying erosion risk levels across the district. 52.0% of the area experiences very slight erosion, 10.31% has slight erosion, 6.18% undergoes moderate erosion, 3.88% is moderately severe, 7.34% is at severe erosion risk, 5.6% has very severe erosion, and 14.65% faces extremely severe erosion. Field data collected from agricultural plots across Kozhikode were compared with RUSLE-predicted values, revealing a low root mean square error, indicating a strong correlation between observed and simulated data. Based on these findings, the district was categorized into low, medium, and high-priority regions, with tailored recommendations proposed for each. Implementing these measures could mitigate erosion, preserve soil fertility, and support the long-term sustainability of natural and agricultural ecosystems in Kozhikode. Given the practical challenges in estimating RUSLE factors in Southern India, where data scarcity is a common issue, this preliminary study underscores the need for expanded, long-term field observations to enhance understanding of soil erosion processes at the watershed level.

The specifics оf тhe development оf soil flushing during spring snowmelt in the arable land of the northern zone of the Krasnoyarsk forest-steppe

The results of studies of soil flushing and accumulation in the period 2009–2017 in the northern part of the Krasnoyarsk forest-steppe in areas with high intensity of erosive and accumulative processes are presented. A wide range of erosion values from meltwater has been established – from 0.4 to 25 mm for the spring period. The results of the application of a refined Regional model for calculating potential flushing for the northern part of the Krasnoyarsk forest-steppe are presented. The analysis of meteorological factors and conditions of the underlying surface affecting the distribution of values of melt runoff, flushing and accumulation of soils on arable massifs located on inclined watershed surfaces is carried out: the specificity of the distribution of meltwater flows determines the formation of zones of extreme erosion along the perimeter of arable land in the lower parts of the slope – up to 15–20 mm of soil layer. In conditions of low slope steepness, the effect of relief on erosion is largely determined by the high slope lengths and melt water flow lines. The distribution of flushing values over the area of arable land shows a close correlation with the values of snow accumulation and soil moisture. The spatial and temporal patterns of the influence of the previous autumn soil moisture on the intensity of erosion have been established: the values of flushing increase with increasing soil moisture, the average values of erosion correlate with the values of autumn soil moisture in dynamics over the years of field observations. Special soil protection measures have been proposed for sites with the most intensive flushing in the research area.

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.

Using USLE, GIS and remote sensing for the soil loss assessment in the National Park of Theniet El Had, Algeria

Soil water erosion is one of the problems that affect the environment, agriculture and social life by threatening several land surfaces. The objective of this study is to use the USLE model, GIS and remote sensing (RS) to estimate the annual rate of soil loss by water erosion in the Theniet El Had National Park (THNP) which belongs to the mountainous ecosystem of Djebel El Meddad, located in the northwest of Algeria. The use of the USLE model takes into account the five factors controlling water erosion, namely: the rain erosivity (R) determined from the annual rainfall data, the soil erodibility (K) developed from soil survey data, the slope lengths (LS) generated by using DEM, the vegetation cover (C) by the use of RS data and erosion control management practices (P) by field trips. The integration of these factors made it possible to establish the quantitative map of the annual rate of soil loss varying between 0.02 and 55.10 (t/ha.year), with an average of around 6.64 (t/ha.year). Five erosion aggressiveness classes are used; very weak, weak, moderate, strong and very strong which represent a rate respectively of 23.70, 44.65, 22.72, 4.41 and 4.52 % of the study area surface. The areas with high and very high erosion rates are located in the north having a very rugged relief and low vegetation cover. This study can be used in the mountainous ecosystems and it will make it possible to set up priority intervention zones to combat the risk of water erosion.

Genome wide detection of CNV and their association with body size in Danzhou chickens

Copy number variation (CNV) is a crucial component of genetic diversity in the genome, serving as the foundation for the genetic architecture and phenotypic variability of complex traits. In this study, we examined CNVs in the Danzhou (DZ) chicken, an indigenous breed exclusive to Hainan Province, China. By employing whole-genome resequencing data from 200 DZ chickens, we conducted a comprehensive genome-wide analysis of CNVs using CNVpytor and performed CNV-based genome-wide association studies (GWAS) on six body size traits, including body slope length (BSL), keel length (KeL), tibial length (TiL), tibial circumference (TiC), chest width (ChW), and chest depth (ChD) utilizing linear mixed model methods considering a genomic relationship matrix. We identified a total of 144,265 autosomal CNVs among the 200 individuals, comprising 67,818 deletions and 76,447 duplications. After merging these variants together, we obtained 4,824 distinct copy number variant regions, which accounted for approximately 20% of the chicken autosomal genome. Furthermore, we discovered several significantly associated CNV segments with body size traits located proximal to genes such as IHH, WNT6, WNT10A, LPR4, FZD2, WNT7B, and GNAS that have been extensively implicated in skeletal development and growth processes. These findings enhance our understanding of CNVs in chickens and their potential impact on body size traits by revealing candidate genes involved in the regulation of these traits. This establishes a solid framework for future studies and may prove particularly beneficial for exploring genetic structural variation in chickens.

Sacroiliac pain after total hip arthroplasty: a combined analysis of clinical data and three-dimensional imaging in standing and sitting positions.

Patients frequently complain of low back pain and sacroiliac joint pain (SIP) following total hip arthroplasty (THA). We hypothesized that patients with SIP would display different pelvic incidence (PI) values between standing and relaxed sitting positions, indicative of increased motion in the sacroiliac joints. In this retrospective case-control study, 94 patients who underwent unilateral THA and experienced SIP were compared with 94 control patients without SIP. SIP was confirmed through clinical tests and investigated using biplanar imaging in both standing and sitting positions. The key parameters analyzed included PI, sacral slope (SS), lumbar lordosis (LL), and limb length discrepancy (LLD). Patients without SIP showed a mean difference in PI of -1.5° (-8°-5°) between standing-to-sitting positions, whereas those with SIP showed a difference of -3.3° (-12°-0°)(P < 0.0001), indicating more motion in the sacroiliac joint during daily activities in the latter group. Patients with SIP showed smaller change in LL between standing-to-sitting positions (mean:6.3°; range:-8°-27°) compared with those without SIP (mean:9.5°; range:-12°-28°)(P = 0.006). No significant differences were noted in functional leg length between patients with (mean:7mm; range:0-12mm) and without SIP (mean:7mm; range:0-11mm)(P = 0.973). This study revealed significant sacroiliac joint motion in patients with SIP post-THA, as indicated by PI changes, increased posterior pelvic tilt, and reduced change in the LL. Contrary to common belief, SIP did not correlate with LLD.

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

Spatial analysis and assessment of soil erosion in the southern Western Ghats region in India.

Soil erosion is expected to worsen in the future as a result of climate change, growing population demands, improper land use, and excessive exploitation of natural resources in India. Due to the growing population and changes in land use, it has become increasingly crucial to map and quantitatively assess soil for the purpose of sustainable agricultural usage and planning conservation efforts. The problem of soil erosion is mainly on steeper slopes with intense rainfall in parts of Western Ghats. The 20.17% of geographical area have been converted into wasteland due to soil erosion. The Revised Universal Soil Loss Equation (RUSLE) is a highly prevalent and effective technique utilized for estimating soil loss in order to facilitate the planning of erosion control measures. Despite the fact that RUSLE is accurately estimate sediment yields from gully erosion, it is an effective tool in estimating sheet and rill erosions losses from diverse land uses like agricultural to construction sites. The current study is mainly about combining the RUSLE model with GIS (Geographic Information System) to find out how much soil is being lost, particularly in Noyyal and Sanganur watersheds which is located in Coimbatore district of Tamil Nadu, India. This analysis is based on the soil order, with a significant proportion of alfisols and inceptisols being considered. The obtained outcome is contrasted with the established soil loss tolerance threshold, leading to the identification of the areas with the highest susceptibility to erosion. Within the narrower and more inclined section of the watershed, yearly soil loss scales from 0 to 5455 tonnes/ha/year, with an average annual loss of soil of 2.44 tonnes/ha. The severe soil erosion of 100 to 5455 tonnes/ha/year is found along the steep and greater slope length. The generated soil map was classified into six categories: very slight, slight, moderate, high, severe, and very severe. These classifications, respectively, occupied 6.23%, 14.88%, 10.56%, 15.70%, 7.73%, and 6.63% of the basin area. Based on the results of cross-validation, the estimated result of the present study was found to be very high compared to past studies conducted 0 to 368.12 tonnes/ha/year especially in very severe erosion zones. But very slight to severe erosion zones nearly matched with same level of soil loss. To protect the soil in the study area from erosion, more specific actions should be taken. These include micro-catchment, broad bed furrows, up-and-down farming, soil amendment with coconut coir pith composition, streambank stabilization with vegetation, and micro-water harvesting with abandoned well recharge. These actions should be carried out over time to make sure to work.

Predicting soil erosion risk using the revised universal soil loss equation (RUSLE) model and geo‐spatial methods

AbstractAnthropogenic activities like overgrazing, deforestation and mismanaged land use accelerate soil erosion (SE), causing nutritional and organic matter loss. In this study, we predicted the annual rate of soil loss in the Salt Range, extending south from the Pothohar plateau, Pakistan, using the Revised Universal Soil Loss Equation (RUSLE). The RUSLE model parameters and erosion probability zones were estimated using remote sensing and Geo‐Spatial methods. The annual average soil loss rates were calculated by considering five geo‐environmental factors, that is, slope length and steepness (LS), rainfall erosivity (R), cover management (C), soil erodibility (K), and conservation practice (P) range from 0–559 527, 1404–4431, 0–1, −0.14 to 1.64, and 0.2–122 respectively. This research determined that the yearly average rate of SE in the Salt Range varies from over 50 to above 350 . The distribution of land area across different SE probability zones reveals that a small portion (2.11%) is classified as High, a moderate portion (7.13%) falls under the category of Moderate, while the majority (90.7%) is classified as Low in terms of proneness towards erosion. The land devoid of vegetation and characterized by steep slopes is especially prone to SE. The Salt Range is highly vulnerable to SE risk due to climatic variations and improper land use practices. The result provides a spatial distribution of SE across the salt range, utilized for management planning processes and conservation at the policy level among decision‐makers and land‐use planners.

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.