Spatial and temporal variability of rainfall erosivity (R-factor) for Vashisthi River basin erosion: Implications for the Revised Universal Soil Loss Equation

Spatial and temporal variability of rainfall erosivity (R-factor) for Vashisthi River basin erosion: Implications for the Revised Universal Soil Loss Equation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial and temporal variability of rainfall erosivity (R-factor) for Vashisthi River basin erosion: Implications for the Revised Universal Soil Loss Equation Vivek R. KasarKokil, Dr. Narhari Chaudhari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5853949/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract A major issue brought on by land degradation, increased agricultural productivity, and other human activities is soil erosion. Planning and conservation efforts within a basin or watershed benefit from an assessment of soil erosion. Soil erosion is primarily influenced by rainfall patterns and land cover, and modeling soil erosion is crucial for assessing the degree of land degradation. Under a variety of circumstances, modeling can offer a quantitative and reliable method for estimating soil erosion and sediment output. In the current work, soil loss in the Konkan region of India of the Vashisthi Basin has been estimated using the Revised Universal Soil Loss Equation (RUSLE), a soil loss model integrated with GIS rainfall data for previous 16 years (2006–2021) referred to analyses study.The RUSLE model, in conjunction with elements such as the Rainfall Erosivity Factor (R), for case study of Vashisthi River Basin (VRB) of Konkan, Maharashtra (India) given results with average value for Chiplun (30978.105 MJ.mm/ha/hr/y), Dapoli (30701.941 MJ.mm/ha/hr/y), Khed (28807.257 MJ.mm/ha/hr/y),Guhagar (24210.344 MJ.mm/ha/hr/y) Vashisthi River Soil Erosion RUSLE GIS Figures Figure 1 Figure 2 1. Introduction Forgotten facts about soil erosion and what are its environmental and economic consequences, some of the major environmental problems like soil erosion damage the effectiveness in a progressive shift of agriculture toward greater chemical inputs follows closely and brings forth certain negative consequences like loss of topsoil (Pimentel et al., 1995 ). The satisfaction of this need is restricted to some extent as the land also loses its capacity to support vegetative growth and expose below the average water bodies (Lal, 2001 ). It leads also to the environmental degradation. An estimated surface water loss is estimated to be in millions of hectares as a result of soil erosion and this has contributed to huge economic losses because of crop failures of increased cost of inland adaptation and water treatment (Eswaran, Lal, & Reich, 2001 ). It is mainly due to presence of manmade activities such as agriculture and natural ones such as topography and precipitation among others that river basins are the most affected by soil erosion (Wischmeier& Smith, 1978). In order to avoid land resources degradation, ensure appropriate water use, and understand sediment transport and deposition processes it is crucial to study soil erosion within river basins. Most management strategies effectively enhance the mitigation of soil loss which in turn leads to a portion of preserved ecosystems services, water cleansing and healthy soils maintenance (Boardman & Poesen, 2006 ). Soil erosion is a significant issue resulting from agricultural intensification, land degradation, and other human activities (Ganasri & Ramesh, 2015 ). RUSLE is popular within the soil erosion studies due to its applicability and versatility. RUSLE is a comprehensive advance to the original U.S. S.L.E., with improvements that allow for more accurate forecasts of soil erosion within a variety of parameters (Renard et al, 1997 ). There are many other applications of RUSLE in estimating soil erosion losses spatially. For example, in the work of Pandey et al., ( 2007 ) RUSLE was applied in the watershed in India and it was found that the method is quite useful in locating critical erosion areas in the watershed. Jain et al., ( 2001 ) applied the RUSLE model of spatial soil erosion in the Himalayas and established that steep hillslopes with high rainfall are prone to considerable erosion. Differences in soil loss estimates between the RUSLE and USLE vary from more to less erosion for individual locations depending on specific factor value changes (Renard et al. 1991 ) Soil erosion, driven by physical, chemical, and biological processes, is a primary contributor to soil deterioration, compromising soil quality and disrupting the delicate soil matrix balance. This degradation has severe environmental implications (Bonthagorla et al., 2015). In the 21st century, soil erosion has emerged as a pressing global environmental concern, significantly impacting soil fertility, land degradation, and agricultural productivity, thereby threatening the planet's ecological balance. Of the 329 million hectares of land in India, Maji et al. (2010) estimate that 120.4 million have been degraded (68% as a result of water erosion). Due to this, 5.3 giga-tonnes of soil are lost annually (Babu et al. 1983). Over 80% of Ratnagiri district's area is subject to severe to extremely severe erosion classes (20–40 t ha-1 year-1) (Salunkhe et al. 2018 ). The dynamic combination of minerals and organic matter that makes up soil allows plants to flourish or is able to sustain plant growth. The most important resource that supplies the foundation for life on Earth is soil. Soil erosion poses a significant risk. It negatively impacts the primary sector directly and indirectly impacts the entire economy. Both on and off the site, soil erosion has serious repercussions. (Saroha 2017). Soil, a vital resource, is under threat from erosion due to climate, land use, slope steepness, and ecological disasters (Alewell et al., 2019; Parveen et al., 2012). Erodibility, erosivity, and land management practices determine soil health. Topography and vegetation cover significantly influence soil erosion risks (Prasannakumar et al., 2011). Topsoil is carried away by wind and water in a process known as soil erosion. Erosion of the soil is a continuous process that might occur rapidly or gradually. It causes soil collapse, environmental degradation, and a persistent loss of topsoil, among other problems. Soil erosion is influenced by two key factors: soil erodibility factor (K USLE ) and topography factor (LS USLE ), as highlighted by Wu & Chen ( 2012 ). Soil particles are taken away by rivers, valleys, streams, oceans, and distant locations throughout this process. According to Barman et al. (2020), the Revised Universal Soil Loss Equation (RUSLE) is a suitable and useful model for assessing soil erosion at the local or regional level. A steep land slope, drought, wind, water, and tillage are some of the variables that can increase soil erosion; water erosion is the most common and major kind of soil erosion, though (Kanungo and Sharma, 2014). Soil erosion is accelerated by human activities like mining, construction, farming, grazing, and leisure. Farmers and agricultural communities must protect soil, as stressed by Obreschkow (2011). India's average soil erosion rate is 16.35 t/ha/yr, exceeding acceptable limits (Babu et al., 1983). This results in 61% soil displacement, 10% reservoir accumulation, 29% permanent ocean loss the gradual process of soil formation is caused by interactions between parent material, climate, biotic, slope, and time factors. Fertile productive base is depleted and deteriorated as a result of soil erosion. Sustainable development necessitates the conservation and management of soil. Erosion rates depend on hydrology, structure, terrain, soil surface conditions, and their interactions (Saroha 2017). Sehgal and Abrol (1994 and 1997) estimate that water and wind erosion cover 162.4 (1994) and 167.0 (1997) million hectares in India, with 91% of this area eroded by water. When integrated with geographic information systems, the RUSLE model provides a quantitative technique for assessing soil erosion across a large area, aiding local and regional conservation and management programs. Soil erosion also depletes nutrients and organic materials. Nutrient loss and humus or organic matter degradation affect 3.7 million hectares, according to Sehgal and Abrol (1994). Since 61% of eroded soil is only relocated, just 39% is lost. Consequently, 1.8 million tonnes of nitrogen, 26.3 million tonnes of potassium, and 0.8 million tonnes of phosphorus are lost every year. Food and economic insecurity result from decreased agricultural productivity and production due to soil erosion. Soil erosion in India results from natural and human-induced factors. Regular, reliable data collection is crucial to understand its dynamics (Sarroh, 2017). Soil erosion poses significant environmental and economic risks. Annual erosion rates identify risk zones in various categories i.e. High-risk (severe erosion), Moderate-risk (significant erosion), Low-risk (minimal erosion). Prior research mostly examined the shifting sediment load and sediment trapping brought on by the building and maintenance of dams. That being said, knowledge of soil deposition and erosion is equally crucial. (Chuenchum et.al, 2020) 2. Comparatives Studies on RUSLE Applications in Soil Erosion The rates of erosion are determined by a combination of major factors such as soil surface conditions, topography, hydrology, structure, and interactions between all of these. The equation for universal soil loss (USLE) is as follows: A = R × K × S x L × C × P. ------------------- (01) The variables S, L, C, P, R, K, and E represent the slope steepness, slope length, cover and management component, rainfall erosivity factor, soil erodibility factor, and soil loss in tonnes per hectare yearly, respectively. Each of these factors is briefly addressed in relation to the problems associated with soil erosion in India in the section that follows. 2.1 Rainfall Erosivity Factor (R) : Represents rainfall's kinetic energy and erosive potential. Ram Babu et al. (1978) calculated R-factor values for India's zones, creating an iso-erodent map using data from 225 stations (45 observed, 180 computed). 2.2 Soil Erodibility Factor (K) :Gurmel et al. (1981) estimated soil erodibility factors for Indian sites. Erosion is higher in red, laterite, and black soils. Continuous heavy tillage breaks down soil structure and reduces humus, increasing erodibility. 2.3 Topography : Slope Slope length and steepness: Slopes that are convex, long, and steep erode more soil. Watershed drainage networks and sediment yields are regulated by topography and geology. To reduce runoff and raindrop-induced soil erosion, grass or other vegetative cover should be on the soil's surface. 2.4 Support Conservation Practice Factor (P) : The extent, scope, and types of conventional and modern soil erosion prevention strategies affect soil erosion. Terrace farming, contour bunding, check dams, contour tillage, strip cropping and shelter belts reduce soil losses. Compared to the R and K correlations, the experiments to obtain data for the L, S, and C factors take less time. Therefore, such tests can be postponed. R. Lal (1994). A difficult problem in India's irrigated and rain-fed regions is soil erosion. The potential for agricultural output is ultimately impacted by a number of factors, including vegetation, rainfall, land slope, soil characteristics, and more. The soil erosion models given in Table 1 below are based on the Indian setting. (Kar et al., 2022), Table 1 Soil erosion models applied in Indian Context: Region Model Purpose Climate Data source Remarks Pambar River Basin (Idukki district, Kerala), area 288.53 km 2 RUSLE and TLSD To forecast the average annual rate of soil erosion and deposition and to pinpoint regions that are particularly vulnerable to either Rainfall in the tropical mountainous river basin ranges from 1533 mm (U/S) to 852 mm (D/S). Meteorological stations provide rainfall data, field sampling provides soil qualities, the Survey of India Toposheet, 1: 50,000 scale provides elevation data, and IrRS-P6 LISS-III provides vegetation characteristics. ) In comparison to silt loam textured soil, sandy loam and loamy sand soil have comparatively lower "K" values. In comparison to humid locations, semiarid sub-basins with reduced vegetation cover exhibit higher levels of soil erosion. Thus, plans for climate-specific management ought to be developed. Pathri Rao sub-watershed (Haridwardistrict, Uttarakhand), area 44 km 2 RUSLE-3D For the purpose of planning soil conservation, to forecast soil loss and the spatial distribution of soil erosion hazards semi-arid, subtropical climate with 1044 mm of rainfall Rainfall data and a field survey of farmers are combined with ResourceSat-1 LISS-IV (5.8 m resolution), IKONOS (1 m resolution), and toposheet (1: 25,000). The main component influencing soil erosion control is topographic factor (LS). Gumti River Basin (Tripura), area 2492 km2 USLE To determine how much soil has been lost. Humid subtropical climate with 335.27 mm of rainfall Data on rainfall (IMD), soil (NBSSLUP), ASTER DEM (resolution of 30 m), and LISS III Rainfall has less of an impact on soil erosion than LULC. The predicted soil loss should be verified using the field-measured soil loss data. Source: Kar et.al. (2022), Current Science, vol. 122, no.5 3. Methodology 3.1 Study Area : The site selected for this study was Vashishthi River in Konkan, Maharashtra, India. The river covers an area of ​​approximately 2238 square kilometers and is characterized by hills, plateaus and river valleys with temperatures varying from 12°C-34°C in summer. Temperatures vary between 23.1°C − 32.9°C after the rainy season. Hot during the day and cool at night with very low humidity, the average annual rainfall in the catchment is about 3391mm. The basin supports a variety of land uses such as agriculture, forest cover, and urban settlements, each contributing to land degradation to varying degrees 3.2 Objectives : The present work seeks to determine the soil erosion risk in upper reaches of Vashisthi river basin (Konkan, Maharashtra) using RUSLE model along with GIS based techniques. Estimate Rainfall Errosivity studies of soil loss over the timescale of relatively more than once a year in case of high intensity and high rainstorm events. (R, LS & C Factors of RUSLE Model) Determine and present the geographical areas that are susceptible to high erosion. Determine the current extent of soil erosion and the role of land use management regimes. 3.3 Data collection methods Data was acquired from multiple sources to address key study parameters. Land usage and vegetation cover were determined using LANDSAT/Sentinels satellite images. To calculate soil moisture content (K), soil texture and organic matter were sampled from several river basin areas. The TRMM &IITM website gives a complete database of rainfall intensity and dispersion for the previous 16 years (2006–2021). Geographic information was also collected using SRTM 90 m digital elevation data, which provided high-resolution DEMs for slope analysis. GIS techniques were essential for data processing and analysis. Land usage, soil characteristics, topography, and rainfall distribution were digitised and analysed using ArcGIS. Thematic maps for each RUSLE factor were created using raster and vector data processing. We estimated basin rainfall erosivity using spatial interpolation methods like kriging. GIS allowed data layers to be integrated to accurately model soil erosion. 3.4 Steps for integrating RUSLE and GIS for soil erosion estimation Integrating RUSLE with GIS required several systematic steps : 1. Data pre-processing : The raw data were processed to create uniform spatial resolution for all input layers and coordinate system. 2. Factor computation : GIS tools were used to determine each of the RUSLE factors (R, K, LS, C, and P). For instance, the slope-flux accumulation approach was used to generate the LS factor from the DEMs. 3. Rasterization : All objects were converted to raster format to facilitate spatial analysis. Each raster level represented a specific RUSLE factor. 4. Model application : The RUSLE equation A = R×K×LS×C×PA = R×K×LS×C×P was applied to the GIS environment by overlaying raster levels and summing them. 5. Soil erosion map : The resulting soil erosion calculations were plotted to simulate soil erosion in the river basin. Based on the shape of the erosion, the areas with the highest risk as erosion were identified. 4. River Basin DEM Generation : With help of GIS software digital elevation model is prepared for vashisthi river basin. 4.1 Explanation Location ID : Unique identifier for each sample location within the Vashisthi River Basin. Rainfall Erosivity (R) : Measures the potential of rainfall to cause erosion, expressed in MJ mm ha⁻¹ h⁻¹ yr⁻¹. This is calculated based on historical rainfall data and indicates the energy and intensity of rainfall at each location. 4.2 Method for Estimation of Rainfall Erosivity Factor (R) : The Rainfall Erosivity Factor (R) was calculated using 16 years (2006–2021) of rainfall data from the Indian Meteorological Department (IMD). This study employed the Wischmeier and Smith (1978) equation: R = \(\:{\sum\:}_{i=1}^{12}1.735\times\:10\) (1.5 log 10 \(\:\left(\frac{{p}_{i}^{2}}{p}\right)\) – 0.08188) Where, R = rainfall erosivity factor (MJ mm ha − 1 h − 1 y − 1 ). Pi = monthly rainfall (mm). P = annual rainfall (mm). The term \(\:\left(\frac{{p}_{i}^{2}}{p}\right)\) , Fournier Index is the term commonly used to refer to the calculation above. When the rainfall intensity data is unavailable, the aforementioned method is utilized to estimate the annual rainfall erosivity factor (R). The map in Fig. 2 shows that for the previous 16 years (2006–2021) the minimum potential rainfall in Guhagar taluka lies between 24000 to 24250 MJ.mm/ha/hr/y and the most affected Erosivity is in Dapoli and Chiplun taluks in the range of 30500 to 31000 MJ.mm/y. and/hr/y. y. Numerous factors, such as soil type, slope, and vegetation cover, affect how much soil is eroded by runoff. This provides information about the Vashishthi catchment's possible rainfall loss. 4.3 Slope Length and Steepness (LS) : A dimensionless factor representing the effect of topography on erosion. It combines a long slope (L), and a steep slope (S) to show how the terrain affects flow and suspended capacity. 4.4 Data sources and equation used to find LS Factor Table No.2 LS factor equations LS Factor Data source \(\:{\left(\frac{flow\:accumulation*cellsize}{22.13}\right)}^{0.4}{\left(\frac{\text{sin}\beta\:}{0.0896}\right)}^{1.3}\) RUSLE Equation \(\:{\left(\frac{⋋}{22.13}\right)}^{m}\) (65.41Sin 2 θ + 4.56Sinθ + 0.065) USLE Equation These are the equations are referred from the Literature survey. Where, ⋋ = Slope length m = Equivalent slope θ = Angle of slope We are using the USLE Equation having equations a) LS factor \(\:{\left(\frac{⋋}{22.13}\right)}^{m}\) (65.41Sin 2 θ + 4.56Sinθ + 0.065) b) C factor -0.2988*NDVI + 0.5621 Where, NDVI = Normalized difference vegetation index 5. Data Analysis 5.1 Generating Input Data for RUSLE Data processing for the RUSLE model required several basic steps to prepare the data input. Rainfall data were processed to calculate precipitation rate (R). Long-term rainfall data from nearby weather stations was examined to arrive at this conclusion. The texture, structure, and organic matter of soil samples taken from different locations were examined in order to quantify the soil moisture content (K) (Renard et al., 1997 ). Topographic data including mountain heights and slopes High digital elevation using GIS tools -derived from models (DEMs). This information is necessary to estimate slope length and slope steepness (LS) (Moore & Burch, 1986). 5.2Annual rainfall distribution: Graph. 5.1 shows the annual rainfall of four months for past 41 years in Konkan Region (1976–2016). In this graph we can observe that the regression line is in progressing manner and this regression line shows that the rainfall is increasing annually. So, we can predict that as the rainfall is increasing annually there is possibility of having erosion. 5.6.2 Return period VS Annual Rainfall: Above graph shows the return period of 41 year (1976–2016) of Konkan Region. Annual Rainfall (mm) is generally plotted on the y-axis using logarithmic scale and the Return periods are plotted on the x-axis, The x-axis scale is a modified probability scale, so that the resulting rainfall frequency appears as a curve shape and Regression line shows the equation Y = 487.18In(X) + 2267.8 5.2Annual Rainfall Erosivity Factor (R) for Chiplun, Khed, Dapoli and Guhagar Taluka from Year 2006 to 2021 Major Key Points from Table are as follows : i) The annual Rainfall Erosivity Factor (R) for Chipluntaluka in which the maximum rainfall erosivity is 44920.47 MJ.mm/ha/hr/y in 2013 and minimum rainfall erosivity is18648.99 MJ.mm/ha/hr/y in 2008. ii)The annual Rainfall Erosivity Factor (R) for Dapoli taluka in which the maximum rainfall erosivity is 64071.71 MJ.mm/ha/hr/y in 2013 and minimum rainfall erosivity is15891.23868 MJ.mm/ha/hr/y in 2008. iii)The annual Rainfall Erosivity Factor (R) for Khedtaluka in which the maximum rainfall erosivity is 45952.92 MJ.mm/ha/hr/y in 2013 and minimum rainfall erosivity is15081.8406 MJ.mm/ha/hr/y 2017. iv)The annual Rainfall Erosivity Factor (R) for Guhagar taluka in which the maximum rainfall erosivity is 41884.858MJ.mm/ha/hr/y in 2017 and minimum rainfall erosivity is7607.97MJ.mm/ha/hr/y in 2006. Therefore, from this observation it is observed that in 2013 maximum rainfall is occurred in Vashishti watershed region. The study found that the amount of soil erosion caused by rainfall varied across the river basin. In conclusion, we found out the R factor of our four stations points which are as follows: R in MJ.mm/ha/hr/y Chiplun Dapoli Khed Guhagar Mean 30978.10 30701.94 28807.26 24210.35 Standard Deviation 7660.30 12899.51 9086.79 10815.4 Minimum 18648.99 15891.24 15081.84 7607.97 Maximum 44920.47 64071.71 45952.92 41884.86 6. Results 6.1 Presentation of Soil Erosion Maps and Statistical Data The results of the soil erosion study are presented through detailed maps and statistical data, illustrating the spatial distribution and severity of soil erosion across the Vashisthi river basin (Konkan, Maharashtra). The maps were generated using GIS tools, showcasing areas with varying levels of soil loss. These visual representations help in understanding the extent and distribution of soil erosion, providing a clear picture of the areas most affected (Millward& Mersey, 1999). The statistical data, including mean, median, and standard deviation of soil loss rates, are summarized to quantify the overall erosion within the basin. The objectives defined during project work are giving results in term of various aspects. Major 6.2 Key points are as follows: Rainfall erosivity factor (R) using GIS & RS for case study of Vashisthi River Basin(VRB) given results with average value for 1) Chiplun (30978.105 MJ.mm/ha/hr/y) 2) Dapoli (30701.941 MJ.mm/ha/hr/y) 3) Khed (28807.257 MJ.mm/ha/hr/y) 4) Guhagar (24210.344 MJ.mm/ha/hr/y) In GIS-based soil erosion investigations, slope length (L factor) and steepness (S factor) are important elements to consider. A modern digital elevation model can calculate both. In soil erosion research aided by GIS, the slope length factor (L factor) and slope steepness factor (S factor) are significant elements established by contemporary digital elevation models (DEMs). 7.1 Analysis of Spatial Patterns of Soil Erosion within the River Basin Spatial analysis revealed distinct patterns of soil erosion within the Vashisthi river basin (Konkan, Maharashtra) Areas with steep slopes, high rainfall intensity, and sparse vegetation cover exhibited significantly higher soil erosion rates. The integration of RUSLE factors into the GIS framework allowed for a detailed examination of how topography, rainfall, soil properties, and land use interact to influence erosion. The spatial patterns identified through this analysis are consistent with previous studies that highlight the importance of topographic and climatic factors in soil erosion (Moore & Burch, 1986; Renard et al., 1997 ). 7.2 Identification of High-Risk Areas for Soil Erosion The computed soil loss rates were used to identify high-risk sites for soil erosion. The majority of these locations are found in areas with high rainfall erosivity, steep slopes, and inadequate land cover management. The maps show that soil erosion is most likely to occur in the upper portions of the river basin, where deforestation and agricultural growth are common. These findings align with other studies that have documented similar erosion risks in hilly and mountainous regions (Pandey, Chowdary, & Mal, 2007 ; Zhang, Liu, & Xu, 2008). 7.3 Comparison of Soil Erosion Rates under Different Land Use and Management Practices In order to evaluate their effects on soil conservation, the study also examined soil erosion rates under various land use and management strategies. Comparing areas with extensive agriculture and deforestation to those with dense forest cover and efficient soil conservation techniques like contour farming and terracing, the former demonstrated noticeably reduced rates of erosion. For instance, locations with well-maintained vegetation cover had lower Cover Management Factors (C), resulting in reduced soil loss (Gyssels et al., 2005). The Support Practice Factor (P) also played a crucial role, with areas implementing contour farming or terracing exhibiting lower soil erosion rates (Lal, 1994). The efficiency of sustainable land management techniques in reducing soil erosion is demonstrated by these comparisons. The findings emphasize the necessity of focused soil conservation measures in high-risk regions in order to lessen erosion and encourage sustainable land use. 8.1 Discussion Interpretation of the Results in the Context of the Study Area The results of this study indicate significant variability in soil erosion rates across the Vashisthi river basin (Konkan, Maharashtra), primarily influenced by topography, land use, and rainfall intensity. The largest rates of soil loss are found in areas with steep slopes and significant rainfall erosivity, especially in the upper basin reaches. On the other hand, erosion rates are lower in areas with a lot of plant cover and good soil conservation techniques. In line with the findings of earlier research, our findings emphasize the crucial roles that topography and land cover play in defining patterns of soil erosion (Pandey, Chowdary, & Mal, 2007 ; Zhang, Liu, & Xu, 2008). Implications of Soil Erosion for Agriculture, Water Resources, and Infrastructure Soil erosion threatens agriculture, water resources, and infrastructure in the Vashisthi river basin (Konkan, Maharashtra). The loss of fertile topsoil affects agricultural productivity, requiring more fertilizers and other inputs to sustain crop yields (Pimentel et al., 1995 ). Furthermore, sedimentation in rivers and reservoirs can degrade water quality and diminish storage capacity, reducing water supply for irrigation, drinking, and industrial usage (Lal, 2001 ). Erosion-related damage to infrastructure, such as roads, bridges, and buildings, can result in increased maintenance and repair expenditures. These effects underline the importance of comprehensive soil conservation strategies to protect critical resources and infrastructure. Evaluation of the Effectiveness of RUSLE and GIS Techniques for Soil Erosion Studies The Vashisthi river basin in Konkan, Maharashtra, demonstrated a high degree of soil erosion that could be effectively assessed by combining RUSLE and GIS approaches. When paired with GIS's spatial data processing capabilities, the RUSLE model's empirical foundation and adaptability produced accurate estimations of soil loss rates (Renard et al., 1997 ). The identification of high-risk locations and the evaluation of various land management techniques were made possible by the display and analysis of spatial patterns of soil erosion made possible by GIS technologies. Other research have confirmed that this combination of RUSLE and GIS methodologies is a reliable method for simulating soil erosion (Millward& Mersey, 1999; Wang, Gertner, & Anderson, 2002 ). The study's findings can help policymakers design management scenarios and provide options for controlling soil erosion threats in the most efficient way possible, including prioritizing different sections of the basin for treatment. (Ganasri 2016 ). Soil loss tolerance and critical soil loss values might serve as valuable frameworks for selecting and executing conservation strategies in various micro-watersheds. (Bewket & Teferi 2009 ). 9. Recommendations for Soil Conservation and Management Practices Based on the findings of this study, several recommendations for soil conservation and management practices are proposed: Reforestation and Afforestation : Planting trees and restoring forest cover in erosion-prone areas can significantly reduce soil loss by stabilizing the soil and reducing runoff (Gyssels et al., 2005). Terracing and Contour Farming : Implementing terracing and contour farming techniques on steep slopes can help to slow down water flow and minimize soil erosion (Lal, 1994). Cover Crops and Mulching : Using cover crops and mulching in agricultural fields can protect the soil from erosion by maintaining soil moisture and providing a protective layer against raindrop impact (Pimentel et al., 1995 ). Sustainable Land Use Planning : Developing and enforcing land use policies that promote sustainable agricultural practices and limit deforestation can help to reduce soil erosion and preserve soil health (Eswaran, Lal, & Reich, 2001 ). Erosion Control Structures : Constructing check dams, sediment traps, and other erosion control structures can help to manage runoff and sediment transport in high-risk areas (Boardman &Poesen, 2006 ). These recommendations aim to mitigate soil erosion and enhance land sustainability in the Vashisthi river basin (Konkan, Maharashtra). By implementing these measures, the basin can achieve a balance between agricultural productivity, environmental protection, and infrastructure resilience. 10. Conclusion Summary of Key Findings and Their Significance This study assesses soil erosion in the Vashisthi River Basin (Konkan, Maharashtra) using RUSLE and GIS approaches. Findings indicate significant geographic variability in soil erosion rates, with steep slopes, heavy rainfall, and sparse vegetation cover contributing to high soil loss. High-risk areas were identified, highlighting topography and land cover's critical roles in soil erosion dynamics. These results provide valuable insights into soil erosion patterns, emphasizing the need for targeted conservation efforts to protect vulnerable areas and maintain land productivity. Contributions of the Study to Soil Erosion Research and River Basin Management This study contributes to the field of soil erosion research by demonstrating the effectiveness of combining RUSLE with GIS for spatial analysis and mapping of soil erosion. The integration of these techniques allows for accurate estimation and visualization of soil loss, facilitating better-informed decision-making for soil conservation and river basin management. The findings of this study can be utilized to influence the development and implementation of sustainable land management strategies such as reforestation, contour farming, and erosion control structures in order to reduce soil erosion and strengthen the Vashisthi river basin (Konkan, Maharashtra). This study contributes to the achievement of sustainable development goals for land and water resources by giving actionable information. Limitations of the Study and Areas for Future Research This study's findings on soil erosion in the Vashisthi River Basin (Konkan, Maharashtra) should be considered in light of the following limitations: - The RUSLE model's accuracy is contingent upon the quality and resolution of input data. - Data inaccuracies or gaps may affect the reliability of soil erosion estimates. Second, large storms and other extreme weather conditions can produce episodic soil erosion that may not be included in average annual estimates. These factors were not taken into consideration in our study. Thirdly, the model ignores the potential future mitigation effects of soil conservation measures that have not yet been put into practice. Future research should focus on addressing these limitations by incorporating higher-resolution data and considering the impact of extreme weather events on soil erosion. Additionally, exploring the integration of other erosion models with GIS and validating them with field measurements can enhance the robustness of soil erosion assessments. Long-term monitoring and evaluation of soil conservation practices would also provide valuable feedback for improving land management strategies and ensuring sustainable development in river basins. In conclusion, this study underscores the importance of understanding and managing soil erosion to protect environmental and economic resources. Through the application of advanced modelling and spatial analysis techniques, it offers a pathway for effective soil conservation and sustainable land use planning in the Vashisthi river basin (Konkan, Maharashtra) Declarations Declarations Ethics approval and consent to participate: Not applicable Consent to publication: All authors agree to publish the manuscript. Competing interest: The authors declare no competing interest. Data Availability declaration All data generated during this study are included in this published article Funding: “The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.” Author Contribution VRK wrote main manuscript, contributed to the study conception and design.NDC commented on previous versions of the manuscript.All authors read and approved the final manuscript Acknowledgement This work for experimental Study and Analysis purpose supported by Department Civil Engineering, Gharda Institute of Technology, Khed, Ratnagiri, India-415708 References B.P. Ganasri and H. Ramesh (2015). Assessment of soil erosion by RUSLE model using remote sensing and GIS - A case study of Nethravathi Basin. Geoscience Frontiers 7(1), 953-961.https://doi.org/10.1016/j.gsf.2015.10.007. S. S. Salunkhe, S. B. Nandgude1, D. M. Mahale, Tapas Bhattacharya S.Wandre (2018). Estimation of Soil Erosion and Nutrient Loss by USLE Model for Ratnagiri District. Advanced Agricultural Research & Technology Journal 2(1),Corpus ID: 201799052 V. Prasanna Kumar, H. Vijith, S. Abinod, N. Geetha. (2011). Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using Revised Universal. https://doi.org/10.1016/j.gsf.2011.11.003 B.P. Ganasri (2016). Assessment of soil erosion by RUSLE model using remote sensing and GIS - A case study of Nethravathi Basin.Geoscience Frontiers 7, and 953e961.Basin, Jharkhand. J. Geogr. Inf. Syst. 2012, 4, 588–596. https://doi.org/10.1016/j.gsf.2015.10.007 Binoy Kumar Barman (2020). Soil erosion assessment using revised universal soil loss equation model and geo-spatial technology: A case study of upper Tuirial river basin, Mizoram. AIMS Geosciences 6(4), 525–544. https://doi.org/ 10.3934/geosci.2020030 PavisornChuenchum (2020). Estimation of Soil Erosion and Sediment Yield in the Lancang–Mekong River Using the Modified Revised Universal Soil Loss Equation and GIS Techniques.Water135 (12), https://doi.org/org/10.3390/w12010135 V Prasannakumar, H Vijith , S Abinod , N Geetha. (2012). Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using Revised Universal Soil Loss Equation (RUSLE) and geo-information technology. Geoscience Frontiers 3(2), 209–15. https://doi.org/10.1016/j.gsf.2011.11.003 K G Renard, G R Foster, G A Weesies, J P Porter (1991). RUSLE: Revised universal soil loss equation. Journal of soil and Water Conservation, 46(1), 30–3. K G Renard, G R Foster, G A Weesies, D K McCool, D C Yoder (1997). Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE) In: Agriculture AHUDo, editor. Washington, DC. J Boardman, J Poesen (2006). Soil Erosion in Europe: Major Processes, Causes and Consequences. In J. Boardman & J. Poesen (Eds.), Soil Erosion in Europe (pp. 477-487). Wiley.DOI:10.1002/0470859202 H Eswaran, R Lal, P F Reich (2001). Land Degradation: An Overview. In E. M. Bridges, I. D. Hannam, L. R. Oldeman, F. W. T. Pening de Vries, S. J. Scherr, & S. Sompatpanit (Eds.), Responses to Land Degradation (pp. 20-35). Science Publishers. G Gyssels,,JPoesen, E Bochet , Y Li (2005). Impact of plant roots on the resistance of soils to erosion by water: A review. Progress in Physical Geography, 29(2), 189-217.https://doi.org/10.1191/0309133305pp443ra M K Jain, U C Kothyari, K G Ranga Raju (2001). Rainfall-runoff and soil erosion modeling using the distributed approach. Water Resources Research, 37(7), 1819-1832. R Lal. (1994). Soil Erosion Research Methods. Soil and Water Conservation Society.DOI:10.1201/9780203739358 R Lal, (2001). Soil Degradation by Erosion. Land Degradation & Development, 12(6), 519-539.https://doi.org/10.1002/ldr.472 AAMillward, &J E Mersey. (1999). Adapting the RUSLE to model soil erosion potential in a mountainous tropical watershed. CATENA, 38(2), 109-129.https://doi.org/10.1016/S0341-8162(99)00067-3 I D Moore &G J Burch (1986). Physical basis of the length-slope factor in the Universal Soil Loss Equation. Soil. Science Society of America Journal, 50(5), 1294-1298. A Pandey, V M Chowdary& B C Mal (2007). Identification of critical erosion-prone areas in the small agricultural watershed using USLE, GIS, and remote sensing. Water Resources Management, 21(4), 729-746.DOI: 10.1007/s11269-006-9061-z D Pimentel,C Harvey, P Resosudarmo, K Sinclair, D Kurz, M McNair, Blair, R. (1995). Environmental and Economic Costs of Soil Erosion and Conservation Benefits. Science, 267(5201), 1117-1123.DOI: 10.1126/science.267.5201.1117 W H Wischmeier, D DSmith (1978). Predicting Rainfall Erosion Losses – A Guide to Conservation Planning. USDA Agriculture Handbook No. 537. W Bewket, E Teferi (2009). Assessment of soil erosion hazard and prioritization for treatment at the watershed level: Case study in the Chemoga watershed, Blue Nile basin, Ethiopia. Land Degradation & Development, 20(6), 609-622.https://doi.org/10.1002/ldr.944 G Wang, G Gertner, A Anderson (2002). Spatial prediction and uncertainty analysis of topographic factors for the Revised Universal Soil Loss Equation (RUSLE). Journal of Soil and Water Conservation, 57(1), 1-8. https://doi.org/10.1016/S0924-2716(01)00035-1 Y Wu, &J Chen (2012). Modeling of soil erosion and sediment transport in the East River Basin in southern China. Science of the Total Environment, 441, 159-168.https://doi.org/10.1016/j.scitotenv.2012.09.057 V Prasannakumar, H Vijith, S Abinod, & N Geetha (2012). Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using RUSLE and geo-information technology. Geoscience Frontiers, 3(2), 209-215.https://doi.org/10.1016/j.gsf.2011.11.003 Kumar Kar, Saswat & Kumar, Suresh & Mariappan, Sankar & Patra, Sridhar & Singh, Rajkumar & Shrimali, s & Ojasvi, P. (2022). Process-based modelling of soil erosion: scope and limitation in the Indian context. Current Science. 122. 533-541. 10.18520/cs/v122/i5/533-541. Graphs Graphs 1 to 3 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Graphs.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5853949","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":421746088,"identity":"feb67adc-2c5b-4bd8-9b3a-334b663922ad","order_by":0,"name":"Vivek R. KasarKokil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYDACZiAEAX725gNASkKGeC2SPccSQFp4iLMHBAxu+BiAaMJaDI7zGBv8+GOXLzmD5/OrGzUWPAzsh49uwKvlMI9xYm9bsmW/dO8265xjQIfxpKXdwKdFspkt+QBvA7OB5Jyz24xz2IBaJHjMCGo5+OdPvYHBjZxnxjn/iNDCz8x8OJmH7TBIC/Pj3DYitRjLth03AAayGXNunwQPGyG/sPEfbJZ886faABiVjz/nfKuT42c/fAyvFhTtEmCSWOUgwPyBFNWjYBSMglEwcgAAaN9CdzcdlMwAAAAASUVORK5CYII=","orcid":"","institution":"Karmveer KakasahebWagh Institute of Engineering Education \u0026 Research, Nashik, Maharashtra, India-422003","correspondingAuthor":true,"prefix":"","firstName":"Vivek","middleName":"R.","lastName":"KasarKokil","suffix":""},{"id":421746089,"identity":"b4451bff-38fb-49df-82b1-6522162c8e92","order_by":1,"name":"Dr. Narhari Chaudhari","email":"","orcid":"","institution":"Gokhale Education Society’s R.H.Sapat College of Engineering Education \u0026 Research, Nashik, Maharashtra, India-422005","correspondingAuthor":false,"prefix":"Dr.","firstName":"Narhari","middleName":"","lastName":"Chaudhari","suffix":""}],"badges":[],"createdAt":"2025-01-18 08:38:05","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5853949/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5853949/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77703805,"identity":"3728edf2-cc6d-4ce9-974f-7dd141c9c58b","added_by":"auto","created_at":"2025-03-04 11:39:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":77602,"visible":true,"origin":"","legend":"\u003cp\u003eVashishti river basin DEM (Source: SRTM)\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5853949/v1/ee897506342066c603414341.jpg"},{"id":77702492,"identity":"58f0dfcc-047e-4ea9-976e-41652c1001e2","added_by":"auto","created_at":"2025-03-04 11:31:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":48355,"visible":true,"origin":"","legend":"\u003cp\u003eRainfall Erosivity Map (Source: GIS Developed Map)\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5853949/v1/08800963b01726cb329b87ad.jpg"},{"id":86738299,"identity":"c5306dfe-4bc3-41f8-bafb-e4fb67c73ddc","added_by":"auto","created_at":"2025-07-15 06:18:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1482025,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5853949/v1/9d11e33e-2f05-427d-9611-86d0bd17cc2b.pdf"},{"id":77702491,"identity":"ec5d01a9-c16d-460e-b33d-d58fc1d102e9","added_by":"auto","created_at":"2025-03-04 11:31:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":80949,"visible":true,"origin":"","legend":"","description":"","filename":"Graphs.docx","url":"https://assets-eu.researchsquare.com/files/rs-5853949/v1/6a28a2c947abc974c4cf90e8.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial and temporal variability of rainfall erosivity (R-factor) for Vashisthi River basin erosion: Implications for the Revised Universal Soil Loss Equation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eForgotten facts about soil erosion and what are its environmental and economic consequences, some of the major environmental problems like soil erosion damage the effectiveness in a progressive shift of agriculture toward greater chemical inputs follows closely and brings forth certain negative consequences like loss of topsoil (Pimentel et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). The satisfaction of this need is restricted to some extent as the land also loses its capacity to support vegetative growth and expose below the average water bodies (Lal, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). It leads also to the environmental degradation. An estimated surface water loss is estimated to be in millions of hectares as a result of soil erosion and this has contributed to huge economic losses because of crop failures of increased cost of inland adaptation and water treatment (Eswaran, Lal, \u0026amp; Reich, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt is mainly due to presence of manmade activities such as agriculture and natural ones such as topography and precipitation among others that river basins are the most affected by soil erosion (Wischmeier\u0026amp; Smith, 1978). In order to avoid land resources degradation, ensure appropriate water use, and understand sediment transport and deposition processes it is crucial to study soil erosion within river basins. Most management strategies effectively enhance the mitigation of soil loss which in turn leads to a portion of preserved ecosystems services, water cleansing and healthy soils maintenance (Boardman \u0026amp; Poesen, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Soil erosion is a significant issue resulting from agricultural intensification, land degradation, and other human activities (Ganasri \u0026amp; Ramesh, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRUSLE is popular within the soil erosion studies due to its applicability and versatility. RUSLE is a comprehensive advance to the original U.S. S.L.E., with improvements that allow for more accurate forecasts of soil erosion within a variety of parameters (Renard et al, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). There are many other applications of RUSLE in estimating soil erosion losses spatially. For example, in the work of Pandey et al., (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) RUSLE was applied in the watershed in India and it was found that the method is quite useful in locating critical erosion areas in the watershed. Jain et al., (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) applied the RUSLE model of spatial soil erosion in the Himalayas and established that steep hillslopes with high rainfall are prone to considerable erosion. Differences in soil loss estimates between the RUSLE and USLE vary from more to less erosion for individual locations depending on specific factor value changes (Renard et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1991\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eSoil erosion, driven by physical, chemical, and biological processes, is a primary contributor to soil deterioration, compromising soil quality and disrupting the delicate soil matrix balance. This degradation has severe environmental implications (Bonthagorla et al., 2015). In the 21st century, soil erosion has emerged as a pressing global environmental concern, significantly impacting soil fertility, land degradation, and agricultural productivity, thereby threatening the planet's ecological balance. Of the 329\u0026nbsp;million hectares of land in India, Maji et al. (2010) estimate that 120.4\u0026nbsp;million have been degraded (68% as a result of water erosion). Due to this, 5.3 giga-tonnes of soil are lost annually (Babu et al. 1983). Over 80% of Ratnagiri district's area is subject to severe to extremely severe erosion classes (20\u0026ndash;40 t ha-1\u0026nbsp;year-1) (Salunkhe et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe dynamic combination of minerals and organic matter that makes up soil allows plants to flourish or is able to sustain plant growth. The most important resource that supplies the foundation for life on Earth is soil. Soil erosion poses a significant risk. It negatively impacts the primary sector directly and indirectly impacts the entire economy. Both on and off the site, soil erosion has serious repercussions. (Saroha 2017). Soil, a vital resource, is under threat from erosion due to climate, land use, slope steepness, and ecological disasters (Alewell et al., 2019; Parveen et al., 2012). Erodibility, erosivity, and land management practices determine soil health. Topography and vegetation cover significantly influence soil erosion risks (Prasannakumar et al., 2011). Topsoil is carried away by wind and water in a process known as soil erosion. Erosion of the soil is a continuous process that might occur rapidly or gradually. It causes soil collapse, environmental degradation, and a persistent loss of topsoil, among other problems. Soil erosion is influenced by two key factors: soil erodibility factor (K\u003csub\u003eUSLE\u003c/sub\u003e) and topography factor (LS\u003csub\u003eUSLE\u003c/sub\u003e), as highlighted by Wu \u0026amp; Chen (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSoil particles are taken away by rivers, valleys, streams, oceans, and distant locations throughout this process. According to Barman et al. (2020), the Revised Universal Soil Loss Equation (RUSLE) is a suitable and useful model for assessing soil erosion at the local or regional level.\u003c/p\u003e \u003cp\u003eA steep land slope, drought, wind, water, and tillage are some of the variables that can increase soil erosion; water erosion is the most common and major kind of soil erosion, though (Kanungo and Sharma, 2014). Soil erosion is accelerated by human activities like mining, construction, farming, grazing, and leisure. Farmers and agricultural communities must protect soil, as stressed by Obreschkow (2011). India's average soil erosion rate is 16.35 t/ha/yr, exceeding acceptable limits (Babu et al., 1983). This results in 61% soil displacement, 10% reservoir accumulation, 29% permanent ocean loss the gradual process of soil formation is caused by interactions between parent material, climate, biotic, slope, and time factors. Fertile productive base is depleted and deteriorated as a result of soil erosion. Sustainable development necessitates the conservation and management of soil. Erosion rates depend on hydrology, structure, terrain, soil surface conditions, and their interactions (Saroha 2017). Sehgal and Abrol (1994 and 1997) estimate that water and wind erosion cover 162.4 (1994) and 167.0 (1997) million hectares in India, with 91% of this area eroded by water. When integrated with geographic information systems, the RUSLE model provides a quantitative technique for assessing soil erosion across a large area, aiding local and regional conservation and management programs. Soil erosion also depletes nutrients and organic materials. Nutrient loss and humus or organic matter degradation affect 3.7\u0026nbsp;million hectares, according to Sehgal and Abrol (1994). Since 61% of eroded soil is only relocated, just 39% is lost. Consequently, 1.8\u0026nbsp;million tonnes of nitrogen, 26.3\u0026nbsp;million tonnes of potassium, and 0.8\u0026nbsp;million tonnes of phosphorus are lost every year. Food and economic insecurity result from decreased agricultural productivity and production due to soil erosion. Soil erosion in India results from natural and human-induced factors. Regular, reliable data collection is crucial to understand its dynamics (Sarroh, 2017). Soil erosion poses significant environmental and economic risks. Annual erosion rates identify risk zones in various categories i.e. High-risk (severe erosion), Moderate-risk (significant erosion), Low-risk (minimal erosion). Prior research mostly examined the shifting sediment load and sediment trapping brought on by the building and maintenance of dams. That being said, knowledge of soil deposition and erosion is equally crucial. (Chuenchum et.al, 2020)\u003c/p\u003e"},{"header":"2. Comparatives Studies on RUSLE Applications in Soil Erosion","content":"\u003cp\u003eThe rates of erosion are determined by a combination of major factors such as soil surface conditions, topography, hydrology, structure, and interactions between all of these.\u003c/p\u003e \u003cp\u003eThe equation for universal soil loss (USLE) is as follows:\u003c/p\u003e \u003cp\u003e \u003cb\u003eA\u0026thinsp;=\u0026thinsp;R \u0026times; K \u0026times; S x L \u0026times; C \u0026times; P. ------------------- (01)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe variables S, L, C, P, R, K, and E represent the slope steepness, slope length, cover and management component, rainfall erosivity factor, soil erodibility factor, and soil loss in tonnes per hectare yearly, respectively. Each of these factors is briefly addressed in relation to the problems associated with soil erosion in India in the section that follows.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.1 Rainfall Erosivity Factor (R)\u003c/b\u003e: Represents rainfall's kinetic energy and erosive potential. Ram Babu et al. (1978) calculated R-factor values for India's zones, creating an iso-erodent map using data from 225 stations (45 observed, 180 computed).\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.2 Soil Erodibility Factor (K)\u003c/b\u003e:Gurmel et al. (1981) estimated soil erodibility factors for Indian sites. Erosion is higher in red, laterite, and black soils. Continuous heavy tillage breaks down soil structure and reduces humus, increasing erodibility.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.3 Topography\u003c/b\u003e: Slope Slope length and steepness: Slopes that are convex, long, and steep erode more soil. Watershed drainage networks and sediment yields are regulated by topography and geology.\u003c/p\u003e \u003cp\u003eTo reduce runoff and raindrop-induced soil erosion, grass or other vegetative cover should be on the soil's surface.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.4 Support Conservation Practice Factor (P)\u003c/b\u003e: The extent, scope, and types of conventional and modern soil erosion prevention strategies affect soil erosion. Terrace farming, contour bunding, check dams, contour tillage, strip cropping and shelter belts reduce soil losses. Compared to the R and K correlations, the experiments to obtain data for the L, S, and C factors take less time. Therefore, such tests can be postponed. R. Lal (1994). A difficult problem in India's irrigated and rain-fed regions is soil erosion. The potential for agricultural output is ultimately impacted by a number of factors, including vegetation, rainfall, land slope, soil characteristics, and more. The soil erosion models given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below are based on the Indian setting. (Kar et al., 2022),\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSoil erosion models applied in Indian Context:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePurpose\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eClimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRemarks\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePambar River Basin\u003c/p\u003e \u003cp\u003e(Idukki district,\u003c/p\u003e \u003cp\u003eKerala), area\u003c/p\u003e \u003cp\u003e288.53 km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRUSLE and\u003c/p\u003e \u003cp\u003eTLSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo forecast the average annual rate of soil erosion and deposition and to pinpoint regions that are particularly vulnerable to either\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRainfall in the tropical mountainous river basin ranges from 1533 mm (U/S) to 852 mm (D/S).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMeteorological stations provide rainfall data, field sampling provides soil qualities, the Survey of India Toposheet, 1: 50,000 scale provides elevation data, and IrRS-P6 LISS-III provides vegetation characteristics.\u003c/p\u003e \u003cp\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIn comparison to silt loam textured soil, sandy loam and loamy sand soil have comparatively lower \"K\" values. \u003c/p\u003e \u003cp\u003eIn comparison to humid locations, semiarid sub-basins with reduced vegetation cover exhibit higher levels of soil erosion. Thus, plans for climate-specific management ought to be developed.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathri Rao\u003c/p\u003e \u003cp\u003esub-watershed\u003c/p\u003e \u003cp\u003e(Haridwardistrict,\u003c/p\u003e \u003cp\u003eUttarakhand),\u003c/p\u003e \u003cp\u003earea 44 km\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRUSLE-3D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFor the purpose of planning soil conservation, to forecast soil loss and the spatial distribution of soil erosion hazards\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003esemi-arid, subtropical climate with 1044 mm of rainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRainfall data and a field survey of farmers are combined with ResourceSat-1 LISS-IV (5.8 m resolution), IKONOS (1 m resolution), and toposheet (1: 25,000).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe main component influencing soil erosion control is topographic factor (LS).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGumti River Basin\u003c/p\u003e \u003cp\u003e(Tripura), area\u003c/p\u003e \u003cp\u003e2492 km2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTo determine how much soil has been lost.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHumid subtropical climate with 335.27 mm of rainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eData on rainfall (IMD), soil (NBSSLUP), ASTER DEM (resolution of 30 m), and LISS III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRainfall has less of an impact on soil erosion than LULC. \u003c/p\u003e \u003cp\u003eThe predicted soil loss should be verified using the field-measured soil loss data.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSource: Kar et.al. (2022), Current Science, vol. 122, no.5\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003e \u003cb\u003e3.1 Study Area\u003c/b\u003e: The site selected for this study was Vashishthi River in Konkan, Maharashtra, India. The river covers an area of ​​approximately 2238 square kilometers and is characterized by hills, plateaus and river valleys with temperatures varying from 12\u0026deg;C-34\u0026deg;C in summer. Temperatures vary between 23.1\u0026deg;C \u0026minus;\u0026thinsp;32.9\u0026deg;C after the rainy season. Hot during the day and cool at night with very low humidity, the average annual rainfall in the catchment is about 3391mm. The basin supports a variety of land uses such as agriculture, forest cover, and urban settlements, each contributing to land degradation to varying degrees\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2 Objectives\u003c/b\u003e: The present work seeks to determine the soil erosion risk in upper reaches of Vashisthi river basin (Konkan, Maharashtra) using RUSLE model along with GIS based techniques.\u003c/p\u003e \u003cp\u003eEstimate Rainfall Errosivity studies of soil loss over the timescale of relatively more than once a year in case of high intensity and high rainstorm events. (R, LS \u0026amp; C Factors of RUSLE Model)\u003c/p\u003e \u003cp\u003eDetermine and present the geographical areas that are susceptible to high erosion.\u003c/p\u003e \u003cp\u003eDetermine the current extent of soil erosion and the role of land use management regimes.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Data collection methods\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eData was acquired from multiple sources to address key study parameters. Land usage and vegetation cover were determined using LANDSAT/Sentinels satellite images. To calculate soil moisture content (K), soil texture and organic matter were sampled from several river basin areas. The TRMM \u0026amp;IITM website gives a complete database of rainfall intensity and dispersion for the previous 16 years (2006\u0026ndash;2021). Geographic information was also collected using SRTM 90 m digital elevation data, which provided high-resolution DEMs for slope analysis. GIS techniques were essential for data processing and analysis. Land usage, soil characteristics, topography, and rainfall distribution were digitised and analysed using ArcGIS. Thematic maps for each RUSLE factor were created using raster and vector data processing. We estimated basin rainfall erosivity using spatial interpolation methods like kriging. GIS allowed data layers to be integrated to accurately model soil erosion.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Steps for integrating RUSLE and GIS for soil erosion estimation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eIntegrating RUSLE with GIS required several systematic steps\u003c/b\u003e:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e\u003cp\u003e1. \u003cb\u003eData pre-processing\u003c/b\u003e: The raw data were processed to create uniform spatial resolution for all input layers and coordinate system.\u003c/p\u003e \u003cp\u003e2. \u003cb\u003eFactor computation\u003c/b\u003e: GIS tools were used to determine each of the RUSLE factors (R, K, LS, C, and P). For instance, the slope-flux accumulation approach was used to generate the LS factor from the DEMs.\u003c/p\u003e \u003cp\u003e3. \u003cb\u003eRasterization\u003c/b\u003e: All objects were converted to raster format to facilitate spatial analysis. Each raster level represented a specific RUSLE factor.\u003c/p\u003e\u003cp\u003e4. \u003cb\u003eModel application\u003c/b\u003e: The RUSLE equation A\u0026thinsp;=\u0026thinsp;R\u0026times;K\u0026times;LS\u0026times;C\u0026times;PA\u0026thinsp;=\u0026thinsp;R\u0026times;K\u0026times;LS\u0026times;C\u0026times;P was applied to the GIS environment by overlaying raster levels and summing them.\u003c/p\u003e \u003cp\u003e5. \u003cb\u003eSoil erosion map\u003c/b\u003e: The resulting soil erosion calculations were plotted to simulate soil erosion in the river basin. Based on the shape of the erosion, the areas with the highest risk as erosion were identified.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4. River Basin DEM Generation\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e With help of GIS software digital elevation model is prepared for vashisthi river basin.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.1 Explanation\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLocation ID\u003c/b\u003e: Unique identifier for each sample location within the Vashisthi River Basin.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRainfall Erosivity (R)\u003c/b\u003e: Measures the potential of rainfall to cause erosion, expressed in MJ mm ha⁻\u0026sup1; h⁻\u0026sup1; yr⁻\u0026sup1;. This is calculated based on historical rainfall data and indicates the energy and intensity of rainfall at each location.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e4.2 Method for Estimation of Rainfall Erosivity Factor (R)\u003c/b\u003e: The Rainfall Erosivity Factor (R) was calculated using 16 years (2006\u0026ndash;2021) of rainfall data from the Indian Meteorological Department (IMD). This study employed the Wischmeier and Smith (1978) equation:\u003c/p\u003e \u003cp\u003eR = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sum\\:}_{i=1}^{12}1.735\\times\\:10\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e(1.5 log\u003c/sup\u003e\u003csub\u003e10\u003c/sub\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\frac{{p}_{i}^{2}}{p}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003csup\u003e\u0026ndash; 0.08188)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWhere,\u003c/p\u003e \u003cp\u003eR\u0026thinsp;=\u0026thinsp;rainfall erosivity factor (MJ mm ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e y\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003ePi\u0026thinsp;=\u0026thinsp;monthly rainfall (mm).\u003c/p\u003e \u003cp\u003eP\u0026thinsp;=\u0026thinsp;annual rainfall (mm).\u003c/p\u003e \u003cp\u003eThe term\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\frac{{p}_{i}^{2}}{p}\\right)\\)\u003c/span\u003e\u003c/span\u003e, Fournier Index is the term commonly used to refer to the calculation above. When the rainfall intensity data is unavailable, the aforementioned method is utilized to estimate the annual rainfall erosivity factor (R).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe map in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that for the previous 16 years (2006\u0026ndash;2021) the minimum potential rainfall in Guhagar taluka lies between 24000 to 24250 MJ.mm/ha/hr/y and the most affected Erosivity is in Dapoli and Chiplun taluks in the range of 30500 to 31000 MJ.mm/y. and/hr/y. y. Numerous factors, such as soil type, slope, and vegetation cover, affect how much soil is eroded by runoff. This provides information about the Vashishthi catchment's possible rainfall loss.\u003c/p\u003e \u003cp\u003e \u003cb\u003e4.3 Slope Length and Steepness (LS)\u003c/b\u003e: A dimensionless factor representing the effect of topography on erosion. It combines a long slope (L), and a steep slope (S) to show how the terrain affects flow and suspended capacity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Data sources and equation used to find LS Factor\u003c/h2\u003e \u003cp\u003e \u003cb\u003eTable No.2\u003c/b\u003e LS factor equations\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLS Factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\left(\\frac{flow\\:accumulation*cellsize}{22.13}\\right)}^{0.4}{\\left(\\frac{\\text{sin}\\beta\\:}{0.0896}\\right)}^{1.3}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRUSLE Equation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\left(\\frac{⋋}{22.13}\\right)}^{m}\\)\u003c/span\u003e\u003c/span\u003e(65.41Sin\u003csup\u003e2\u003c/sup\u003eθ\u0026thinsp;+\u0026thinsp;4.56Sinθ\u0026thinsp;+\u0026thinsp;0.065)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUSLE Equation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese are the equations are referred from the Literature survey.\u003c/p\u003e \u003cp\u003eWhere,\u003c/p\u003e \u003cp\u003e⋋ = Slope length m\u0026thinsp;=\u0026thinsp;Equivalent slope\u003c/p\u003e \u003cp\u003eθ\u0026thinsp;=\u0026thinsp;Angle of slope\u003c/p\u003e \u003cp\u003eWe are using the USLE Equation having equations\u003c/p\u003e\u003cp\u003e \u003cb\u003ea) LS factor\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\left(\\frac{⋋}{22.13}\\right)}^{m}\\)\u003c/span\u003e \u003c/span\u003e(65.41Sin\u003csup\u003e2\u003c/sup\u003eθ\u0026thinsp;+\u0026thinsp;4.56Sinθ\u0026thinsp;+\u0026thinsp;0.065)\u003c/p\u003e \u003cp\u003e \u003cb\u003eb) C factor\u003c/b\u003e \u003c/p\u003e\u003cp\u003e-0.2988*NDVI\u0026thinsp;+\u0026thinsp;0.5621\u003c/p\u003e \u003cp\u003eWhere,\u003c/p\u003e \u003cp\u003eNDVI\u0026thinsp;=\u0026thinsp;Normalized difference vegetation index\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Data Analysis","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Generating Input Data for RUSLE\u003c/h2\u003e \u003cp\u003eData processing for the RUSLE model required several basic steps to prepare the data input. Rainfall data were processed to calculate precipitation rate (R). Long-term rainfall data from nearby weather stations was examined to arrive at this conclusion. The texture, structure, and organic matter of soil samples taken from different locations were examined in order to quantify the soil moisture content (K) (Renard et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Topographic data including mountain heights and slopes High digital elevation using GIS tools -derived from models (DEMs). This information is necessary to estimate slope length and slope steepness (LS) (Moore \u0026amp; Burch, 1986).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e5.2Annual rainfall distribution:\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGraph. 5.1 shows the annual rainfall of four months for past 41 years in Konkan Region (1976\u0026ndash;2016). In this graph we can observe that the regression line is in progressing manner and this regression line shows that the rainfall is increasing annually. So, we can predict that as the rainfall is increasing annually there is possibility of having erosion.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e5.6.2 Return period VS Annual Rainfall:\u003c/h2\u003e \u003cp\u003eAbove graph shows the return period of 41 year (1976\u0026ndash;2016) of Konkan Region. Annual Rainfall (mm) is generally plotted on the y-axis using logarithmic scale and the Return periods are plotted on the x-axis, The x-axis scale is a modified probability scale, so that the resulting rainfall frequency appears as a curve shape and Regression line shows the equation Y\u0026thinsp;=\u0026thinsp;487.18In(X)\u0026thinsp;+\u0026thinsp;2267.8\u003c/p\u003e \u003cp\u003e \u003cb\u003e5.2Annual Rainfall Erosivity Factor (R) for Chiplun, Khed, Dapoli and Guhagar Taluka from Year 2006 to 2021\u003c/b\u003e \u003c/p\u003e\u003cp\u003e \u003cb\u003eMajor Key Points from Table are as follows\u003c/b\u003e:\u003c/p\u003e \u003cp\u003ei) The annual Rainfall Erosivity Factor (R) for Chipluntaluka in which the maximum rainfall erosivity is 44920.47 MJ.mm/ha/hr/y in 2013 and minimum rainfall erosivity is18648.99 MJ.mm/ha/hr/y in 2008.\u003c/p\u003e \u003cp\u003eii)The annual Rainfall Erosivity Factor (R) for Dapoli taluka in which the maximum rainfall erosivity is 64071.71 MJ.mm/ha/hr/y in 2013 and minimum rainfall erosivity is15891.23868 MJ.mm/ha/hr/y in 2008.\u003c/p\u003e \u003cp\u003eiii)The annual Rainfall Erosivity Factor (R) for Khedtaluka in which the maximum rainfall erosivity is 45952.92 MJ.mm/ha/hr/y in 2013 and minimum rainfall erosivity is15081.8406 MJ.mm/ha/hr/y 2017.\u003c/p\u003e \u003cp\u003eiv)The annual Rainfall Erosivity Factor (R) for Guhagar taluka in which the maximum rainfall erosivity is 41884.858MJ.mm/ha/hr/y in 2017 and minimum rainfall erosivity is7607.97MJ.mm/ha/hr/y in 2006.\u003c/p\u003e \u003cp\u003eTherefore, from this observation it is observed that in 2013 maximum rainfall is occurred in Vashishti watershed region.\u003c/p\u003e \u003cp\u003eThe study found that the amount of soil erosion caused by rainfall varied across the river basin. In conclusion, we found out the R factor of our four stations points which are as follows:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR in MJ.mm/ha/hr/y\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChiplun\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDapoli\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKhed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGuhagar\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30978.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30701.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28807.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24210.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7660.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12899.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9086.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10815.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18648.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15891.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15081.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7607.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44920.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64071.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45952.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41884.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"6. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e6.1 Presentation of Soil Erosion Maps and Statistical Data\u003c/h2\u003e \u003cp\u003eThe results of the soil erosion study are presented through detailed maps and statistical data, illustrating the spatial distribution and severity of soil erosion across the Vashisthi river basin (Konkan, Maharashtra). The maps were generated using GIS tools, showcasing areas with varying levels of soil loss. These visual representations help in understanding the extent and distribution of soil erosion, providing a clear picture of the areas most affected (Millward\u0026amp; Mersey, 1999). The statistical data, including mean, median, and standard deviation of soil loss rates, are summarized to quantify the overall erosion within the basin.\u003c/p\u003e \u003cp\u003eThe objectives defined during project work are giving results in term of various aspects. Major 6.2 Key points are as follows:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRainfall erosivity factor (R)\u003c/b\u003e using GIS \u0026amp; RS for case study of Vashisthi River Basin(VRB) given results with average value for 1) Chiplun (30978.105 MJ.mm/ha/hr/y) 2) Dapoli (30701.941 MJ.mm/ha/hr/y) 3) Khed (28807.257 MJ.mm/ha/hr/y) 4) Guhagar (24210.344 MJ.mm/ha/hr/y)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIn GIS-based soil erosion investigations, slope length (L factor) and steepness (S factor) are important elements to consider. A modern digital elevation model can calculate both. In soil erosion research aided by GIS, the slope length factor (L factor) and slope steepness factor (S factor) are significant elements established by contemporary digital elevation models (DEMs).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e7.1 Analysis of Spatial Patterns of Soil Erosion within the River Basin\u003c/h2\u003e \u003cp\u003eSpatial analysis revealed distinct patterns of soil erosion within the Vashisthi river basin (Konkan, Maharashtra) Areas with steep slopes, high rainfall intensity, and sparse vegetation cover exhibited significantly higher soil erosion rates. The integration of RUSLE factors into the GIS framework allowed for a detailed examination of how topography, rainfall, soil properties, and land use interact to influence erosion. The spatial patterns identified through this analysis are consistent with previous studies that highlight the importance of topographic and climatic factors in soil erosion (Moore \u0026amp; Burch, 1986; Renard et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e7.2 Identification of High-Risk Areas for Soil Erosion\u003c/h2\u003e \u003cp\u003eThe computed soil loss rates were used to identify high-risk sites for soil erosion. The majority of these locations are found in areas with high rainfall erosivity, steep slopes, and inadequate land cover management. The maps show that soil erosion is most likely to occur in the upper portions of the river basin, where deforestation and agricultural growth are common. These findings align with other studies that have documented similar erosion risks in hilly and mountainous regions (Pandey, Chowdary, \u0026amp; Mal, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Zhang, Liu, \u0026amp; Xu, 2008).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e7.3 Comparison of Soil Erosion Rates under Different Land Use and Management Practices\u003c/h2\u003e \u003cp\u003eIn order to evaluate their effects on soil conservation, the study also examined soil erosion rates under various land use and management strategies. Comparing areas with extensive agriculture and deforestation to those with dense forest cover and efficient soil conservation techniques like contour farming and terracing, the former demonstrated noticeably reduced rates of erosion. For instance, locations with well-maintained vegetation cover had lower Cover Management Factors (C), resulting in reduced soil loss (Gyssels et al., 2005). The Support Practice Factor (P) also played a crucial role, with areas implementing contour farming or terracing exhibiting lower soil erosion rates (Lal, 1994).\u003c/p\u003e \u003cp\u003eThe efficiency of sustainable land management techniques in reducing soil erosion is demonstrated by these comparisons. The findings emphasize the necessity of focused soil conservation measures in high-risk regions in order to lessen erosion and encourage sustainable land use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e8.1 Discussion\u003c/h2\u003e \u003cp\u003e \u003cb\u003eInterpretation of the Results in the Context of the Study Area\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe results of this study indicate significant variability in soil erosion rates across the Vashisthi river basin (Konkan, Maharashtra), primarily influenced by topography, land use, and rainfall intensity. The largest rates of soil loss are found in areas with steep slopes and significant rainfall erosivity, especially in the upper basin reaches. On the other hand, erosion rates are lower in areas with a lot of plant cover and good soil conservation techniques. In line with the findings of earlier research, our findings emphasize the crucial roles that topography and land cover play in defining patterns of soil erosion (Pandey, Chowdary, \u0026amp; Mal, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Zhang, Liu, \u0026amp; Xu, 2008).\u003c/p\u003e \u003cp\u003e \u003cb\u003eImplications of Soil Erosion for Agriculture, Water Resources, and Infrastructure\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSoil erosion threatens agriculture, water resources, and infrastructure in the Vashisthi river basin (Konkan, Maharashtra). The loss of fertile topsoil affects agricultural productivity, requiring more fertilizers and other inputs to sustain crop yields (Pimentel et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Furthermore, sedimentation in rivers and reservoirs can degrade water quality and diminish storage capacity, reducing water supply for irrigation, drinking, and industrial usage (Lal, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Erosion-related damage to infrastructure, such as roads, bridges, and buildings, can result in increased maintenance and repair expenditures. These effects underline the importance of comprehensive soil conservation strategies to protect critical resources and infrastructure.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEvaluation of the Effectiveness of RUSLE and GIS Techniques for Soil Erosion Studies\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Vashisthi river basin in Konkan, Maharashtra, demonstrated a high degree of soil erosion that could be effectively assessed by combining RUSLE and GIS approaches. When paired with GIS's spatial data processing capabilities, the RUSLE model's empirical foundation and adaptability produced accurate estimations of soil loss rates (Renard et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The identification of high-risk locations and the evaluation of various land management techniques were made possible by the display and analysis of spatial patterns of soil erosion made possible by GIS technologies. Other research have confirmed that this combination of RUSLE and GIS methodologies is a reliable method for simulating soil erosion (Millward\u0026amp; Mersey, 1999; Wang, Gertner, \u0026amp; Anderson, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The study's findings can help policymakers design management scenarios and provide options for controlling soil erosion threats in the most efficient way possible, including prioritizing different sections of the basin for treatment. (Ganasri \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Soil loss tolerance and critical soil loss values might serve as valuable frameworks for selecting and executing conservation strategies in various micro-watersheds. (Bewket \u0026amp; Teferi \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"9. Recommendations for Soil Conservation and Management Practices","content":"\u003cp\u003eBased on the findings of this study, several recommendations for soil conservation and management practices are proposed:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eReforestation and Afforestation\u003c/b\u003e: Planting trees and restoring forest cover in erosion-prone areas can significantly reduce soil loss by stabilizing the soil and reducing runoff (Gyssels et al., 2005).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTerracing and Contour Farming\u003c/b\u003e: Implementing terracing and contour farming techniques on steep slopes can help to slow down water flow and minimize soil erosion (Lal, 1994).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCover Crops and Mulching\u003c/b\u003e: Using cover crops and mulching in agricultural fields can protect the soil from erosion by maintaining soil moisture and providing a protective layer against raindrop impact (Pimentel et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSustainable Land Use Planning\u003c/b\u003e: Developing and enforcing land use policies that promote sustainable agricultural practices and limit deforestation can help to reduce soil erosion and preserve soil health (Eswaran, Lal, \u0026amp; Reich, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eErosion Control Structures\u003c/b\u003e: Constructing check dams, sediment traps, and other erosion control structures can help to manage runoff and sediment transport in high-risk areas (Boardman \u0026amp;Poesen, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThese recommendations aim to mitigate soil erosion and enhance land sustainability in the Vashisthi river basin (Konkan, Maharashtra). By implementing these measures, the basin can achieve a balance between agricultural productivity, environmental protection, and infrastructure resilience.\u003c/p\u003e"},{"header":"10. Conclusion","content":"\u003cp\u003e \u003cb\u003eSummary of Key Findings and Their Significance\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study assesses soil erosion in the Vashisthi River Basin (Konkan, Maharashtra) using RUSLE and GIS approaches. Findings indicate significant geographic variability in soil erosion rates, with steep slopes, heavy rainfall, and sparse vegetation cover contributing to high soil loss. High-risk areas were identified, highlighting topography and land cover's critical roles in soil erosion dynamics. These results provide valuable insights into soil erosion patterns, emphasizing the need for targeted conservation efforts to protect vulnerable areas and maintain land productivity.\u003c/p\u003e \u003cp\u003e \u003cb\u003eContributions of the Study to Soil Erosion Research and River Basin Management\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study contributes to the field of soil erosion research by demonstrating the effectiveness of combining RUSLE with GIS for spatial analysis and mapping of soil erosion. The integration of these techniques allows for accurate estimation and visualization of soil loss, facilitating better-informed decision-making for soil conservation and river basin management. The findings of this study can be utilized to influence the development and implementation of sustainable land management strategies such as reforestation, contour farming, and erosion control structures in order to reduce soil erosion and strengthen the Vashisthi river basin (Konkan, Maharashtra). This study contributes to the achievement of sustainable development goals for land and water resources by giving actionable information.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations of the Study and Areas for Future Research\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis study's findings on soil erosion in the Vashisthi River Basin (Konkan, Maharashtra) should be considered in light of the following limitations:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e- The RUSLE model's accuracy is contingent upon the quality and resolution of input data.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e- Data inaccuracies or gaps may affect the reliability of soil erosion estimates.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eSecond, large storms and other extreme weather conditions can produce episodic soil erosion that may not be included in average annual estimates. These factors were not taken into consideration in our study. Thirdly, the model ignores the potential future mitigation effects of soil conservation measures that have not yet been put into practice.\u003c/p\u003e \u003cp\u003eFuture research should focus on addressing these limitations by incorporating higher-resolution data and considering the impact of extreme weather events on soil erosion. Additionally, exploring the integration of other erosion models with GIS and validating them with field measurements can enhance the robustness of soil erosion assessments. Long-term monitoring and evaluation of soil conservation practices would also provide valuable feedback for improving land management strategies and ensuring sustainable development in river basins.\u003c/p\u003e \u003cp\u003eIn conclusion, this study underscores the importance of understanding and managing soil erosion to protect environmental and economic resources. Through the application of advanced modelling and spatial analysis techniques, it offers a pathway for effective soil conservation and sustainable land use planning in the Vashisthi river basin (Konkan, Maharashtra)\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclarations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publication:\u003c/strong\u003e \u003cp\u003eAll authors agree to publish the manuscript.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interest:\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interest.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eData Availability declaration\u003c/strong\u003e \u003cp\u003eAll data generated during this study are included in this published article\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003e\u0026ldquo;The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u0026rdquo;\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eVRK wrote main manuscript, contributed to the study conception and design.NDC commented on previous versions of the manuscript.All authors read and approved the final manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis work for experimental Study and Analysis purpose supported by Department Civil Engineering, Gharda Institute of Technology, Khed, Ratnagiri, India-415708\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eB.P. 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Syst. 2012, 4, 588\u0026ndash;596. https://doi.org/10.1016/j.gsf.2015.10.007\u003c/li\u003e\n\u003cli\u003eBinoy Kumar Barman (2020). Soil erosion assessment using revised universal soil loss equation model and geo-spatial technology: A case study of upper Tuirial river basin, Mizoram. AIMS Geosciences 6(4), 525\u0026ndash;544. https://doi.org/ 10.3934/geosci.2020030\u003c/li\u003e\n\u003cli\u003ePavisornChuenchum (2020). Estimation of Soil Erosion and Sediment Yield in the Lancang\u0026ndash;Mekong River Using the Modified Revised Universal Soil Loss Equation and GIS Techniques.Water135 (12), https://doi.org/org/10.3390/w12010135\u003c/li\u003e\n\u003cli\u003eV Prasannakumar, H Vijith , S Abinod , N Geetha. (2012). Estimation of soil erosion risk within a small mountainous sub-watershed in Kerala, India, using Revised Universal Soil Loss Equation (RUSLE) and geo-information technology. 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Current Science. 122. 533-541. 10.18520/cs/v122/i5/533-541.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Graphs","content":"\u003cp\u003eGraphs 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Vashisthi River, Soil Erosion, RUSLE, GIS","lastPublishedDoi":"10.21203/rs.3.rs-5853949/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5853949/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA major issue brought on by land degradation, increased agricultural productivity, and other human activities is soil erosion. Planning and conservation efforts within a basin or watershed benefit from an assessment of soil erosion. Soil erosion is primarily influenced by rainfall patterns and land cover, and modeling soil erosion is crucial for assessing the degree of land degradation. Under a variety of circumstances, modeling can offer a quantitative and reliable method for estimating soil erosion and sediment output. In the current work, soil loss in the Konkan region of India of the Vashisthi Basin has been estimated using the Revised Universal Soil Loss Equation (RUSLE), a soil loss model integrated with GIS rainfall data for previous 16 years (2006\u0026ndash;2021) referred to analyses study.The RUSLE model, in conjunction with elements such as the Rainfall Erosivity Factor (R), for case study of Vashisthi River Basin (VRB) of Konkan, Maharashtra (India) given results with average value for Chiplun (30978.105 MJ.mm/ha/hr/y), Dapoli (30701.941 MJ.mm/ha/hr/y), Khed (28807.257 MJ.mm/ha/hr/y),Guhagar (24210.344 MJ.mm/ha/hr/y)\u003c/p\u003e","manuscriptTitle":"Spatial and temporal variability of rainfall erosivity (R-factor) for Vashisthi River basin erosion: Implications for the Revised Universal Soil Loss Equation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-04 11:31:18","doi":"10.21203/rs.3.rs-5853949/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"31d56559-9784-48a1-9b7d-35a9e059cb95","owner":[],"postedDate":"March 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-15T05:54:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-04 11:31:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5853949","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5853949","identity":"rs-5853949","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}