Soil Erosion Assessment Research Articles

Assessment of Water-Induced Soil Erosion as a Threat to Natura 2000 Protected Areas in Crete Island, Greece

Water erosion is a major threat to biodiversity, according to the European Commission’s Soil Thematic Strategy, as it negatively affects soil structure, soil fertility and water availability for plants. The island of Crete (Southern Greece) has been characterized as a biodiversity hotspot including several Natura 2000 (N2K)-protected areas. The aim of this study was to model the soil loss rate in Crete regarding species richness, habitat types and their conservation status, as well as the MAES (Mapping and Assessment of Ecosystem and their Services) ecosystem types. To this end, the RUSLE soil erosion prediction model was implemented, using freely available geospatial data and cloud-computing processes. The estimated average soil loss in the study area was 6.15 t ha−1 y−1, while there was no significant difference between the terrestrial N2K (6.06 t ha−1 y−1) and non-N2K (6.19 t ha−1 y−1) areas. Notably, the natural habitats of principal importance for the conservation of biodiversity (referred to as “priority” areas), according to Annex I to Directive 92/43/EEC, are threatened by soil erosion with an estimated mean annual soil loss equal to 8.58 t ha−1 y−1. It is also notable that grasslands, heathland and shrubs and sparsely vegetated areas experienced the highest erosion rates among the identified MAES ecosystem types. The results showed that soil erosion is a serious threat to biodiversity in N2K-protected areas. Therefore, there is a need for systematic spatiotemporal monitoring and the implementation of erosion mitigation measures.

Sediment yield and soil erosion assessment by using empirical models for Shazand watershed, a semi-arid area in center of Iran

Sediment yield and soil erosion assessment by using empirical models for Shazand watershed, a semi-arid area in center of Iran

Quantification of Soil Erosion by Integrating Geospatial and Revised Universal Soil Loss Equation in District Dir Lower, Pakistan

This study is aimed to estimate soil erosion risk by integrating Revised Universal Soil Loss Equation (RUSLE) and geospatial tool in District Lower Dir, Eastern Hindu Kush. Soil erosion is among the biggest threats to agricultural production. Mountainous areas of Pakistan are exposed to erosion hazards due to immature geology, fragile slope, and deforestation. RUSLE factors were derived from data acquired from various sources. The Rainfall erosivity (R) factor was derived from monthly data obtained from Pakistan Meteorological Department, Peshawar. The soil erodibility (K) factor was prepared from the map of soil, Survey of Pakistan. The topography (LS) factor was calculated from 12.5 m DEM downloaded from the Alaska Satellite Facility. The cover management (C) factor was calculated from the Red and Near-Infrared band of Landsat 8 satellite image downloaded from USGS earth explorer. The Digital Elevation Model (DEM) and Landsat image were integrated to prepare the Support practice (P) factor. These factors were combined to assess soil erosion in the study area. The estimated soil erosion ranges between 0-25407 tons/hectare/year, with a mean soil loss of 230 tons/hectare/year. The erosion zonation map was then prepared and was classified as very low, low, moderate, high, and very high erosion. 22 % of the district was affected by low to moderate erosion while 67 % area is affected by very high erosion. The areas having more rainfall and steep slopes are more exposed to erosion hazards. Therefore, Erosion control activities are essential in those areas where erosion is high to assure a viable ecosystem.

Assessment of soil erosion in cultivated fields using a survey methodology for rills in the northern part of Taraba State, Nigeria

This study assessed soil erosion from 10 cultivated fields in the two comparable topographies using a survey methodology that focused on rills. The results revealed that rills were developed in all the 10 surveyed farms of the two contrasting land-use sites. The farm sizes on the hill slope site were lower but had the highest total numbers of rills compared to flatland farms. The field measurement of the rills parameters revealed greater length and depth of erosion in the hill slopes farm areas than on the flatland farm site. The magnitude and rates of rill erosion were also much higher and within the threshold range for the country-cultivated fields considered severe on the hill slope site than on the flatland site. This unveiled that soil erosion inform of rill erosion is a threat to agricultural production in the hill slope site. It was recommended that the expectations and perceptions of farmers be integrated into future studies to provide empirical evidence of farmers’ preference for cultivating hillslope sites while there are flatlands.

Evaluating soil erosion by introducing crop residue cover and anthropogenic disturbance intensity into cropland C-factor calculation: Novel estimations from a cropland-dominant region of Northeast China

The black-land in Northeast China is one of three black-lands in the world and has become the largest grain producing area and commercial grain base of China. Therefore, black-land soil erosion caused by natural factors and human activities has attracted much attention. The Revised Universal Soil Loss Equation (RUSLE) developed by the Agriculture Research Service of the United States Department of Agriculture provides a comprehensive framework for assessing soil erosion and has been widely applied all over the world. Compared with factors such as rainfall erosivity, soil erodibility, topography, and conservation practice, the vegetation coverage and management factor (C-factor in RUSLE) of cropland encompasses the most easily optimized measures. However, in some studies, the quantification of C-factor only considers vegetation cover and ignores other farmland management measures due to the limitation of field management information at regional scale, which implies a huge room for improvement in quantification of C-factor. In this study, the quantification of C-factor considered not only vegetation cover but also crop residue cover, agricultural machinery total power, and fertilizer application rate. Among them, the former is related to weakening soil erosivity, and the latter two are related to enhancing soil erosivity. Monitored sediment transport modulus (STM) at the sub-watershed scale was used to compare changes in the estimation accuracy of the soil erosion modulus (SEM) before and after improving the C-factor. The results showed that the improved cropland C-factor in RUSLE produced a better linear fit accuracy between SEM and STM, with an average increase of 0.115 for R2. Moreover, the order of SEM from high to low in different years was: 12.01 t·ha−1·y−1 (2019), 11.43 t·ha−1·yr−1 (2005), 11.17 t·ha−1·yr−1 (2010), 11.01 t·ha−1·yr−1 (2015), and 10.30 t·ha−1·yr−1 (2000), which was positively correlated with interannual variation of precipitation in Northeast China. Spatially, the order of multi-year average SEM in agricultural zones from high to low was as follows: 22.53 t·ha−1·yr−1 (Liaoning Plain and Hilly Zone; LPH), 20.44 t·ha−1·yr−1 (Baekdu Mountain Zone; BM), 15.11 t·ha−1·yr−1 (Western Liao River Zone; WLR), 11.36 t·ha−1·yr−1 (Lesser Khingan Mountain Zone; LKM), 7.56 t·ha−1·yr−1 (Greater Khingan Mountain Zone; GKM), 3.41 t·ha−1·yr−1 (Sanjiang Plain Zone; SJP), 3.14 t·ha−1·yr−1 (Songnen Plain Zone; SNP), and 2.06 t·ha−1·yr−1 (Hulunbuir Grassland Zone; HG), whereas the soil loss amount from high to low in various agricultural zones was in the order of BM > LPH > WLR > GKM > LKM > SNP > SJP > HG. Our study verified the feasibility of improving the C-factor of croplands in areas where cropland is the dominant land-use type. Moreover, our method will contribute to the use of RUSLE with higher precision in other regional-scale soil erosion assessments worldwide.

Risk assessment of soil erosion in Central Asia under global warming

Soil erosion is a threat to the ecological environment and human health. Climate change is closely related to soil erosion, and the ecological environment in Central Asia is sensitive to climate change. However, there is a lack of comprehensive risk assessments of the soil erosion by wind and water in Central Asia under climate change. This study used climate model data to simulate the spatiotemporal variation of wind and water erosion in Central Asia during the current period (1996–2015) and in the future with global warming of 1.5 °C and 2.0 °C (GW1.5 °C and GW2.0 °C, respectively) compared with the pre-industrial levels. The variations of exposure of population to soil erosion in the future were assessed. The results showed that the overall wind erosion in Central Asia is decreasing, but the area in which increasing wind erosion occurs will increase (2.6% under GW1.5 °C, and 8.6% under GW2.0 °C) compared with the current period. Water erosion in Central Asia is increasing, and the area in which increasing water erosion occurs will increase by 10.2% under GW1.5 °C, and 20.3% under GW2.0 °C. The central arid desert shrub area of Central Asia is the only area where both wind and water erosion increase significantly. Wind speed and precipitation are the main factors that contribute to wind and water erosion. Higher vegetation cover helps to reduce wind and water erosion. Under global warming, more population in Central Asia will be affected by soil erosion, primarily in the upper reaches of the Amu River and the lower reaches of the Syr River. This study should provide a scientific basis for formulating the ecological development plans in Central Asia to meet the United Nations Sustainable Development Goals (SDGs).

Future projection and assessment of soil erosion and deposition by water in Manipur River basin using USPED model

Manipur River basin is seriously affected by soil erosion. Assessment of soil erosion in such watersheds is useful in identifying causes and associated risks. This study aims to assess spatio-temporal change in soil erosion/deposition in the past, and project it for the future using the Unit Stream Power Erosion/Deposition model based on rainfall erosivity, soil erodobility, soil cover, slope length and steepness factor as the input parameter. From the model results, the area under extreme to slight erosion intensity was found to be 3.82%, 5.54%, 5.44%, 5.28% and 6.74% for the year 2007, 2011, 2014, 2017 and 2030, whereas for the deposition it was found to be 3.72%, 4.9%, 5.21%, 5.3% and 6.3% for the year 2007, 2011, 2014, 2017 and 2030 respectively. This study also indicates that the study area is suffering from moderate soil erosion but it is most likely to transit into severe in the future.

Impact of Conservation Agriculture on Soil Erosion in the Annual Cropland of the Apulia Region (Southern Italy) Based on the RUSLE-GIS-GEE Framework

The processes of soil erosion and land degradation are more rapid in the case of inappropriate agricultural management, which leads to increased soil loss rates. Moreover, climatic conditions are one of the most important determining factors affecting agriculture, especially in the Mediterranean areas featuring irregular rainfall and high summer temperatures. Conservation agriculture (CA) can make a significant contribution to reducing soil erosion risk on the annual cropland (ACL) of the Mediterranean region in comparison with conventional management (CM). The objective of this study is to provide soil loss rate maps and calculate the values for each altitude and slope class and their combination for the Apulia region in four annual production cycles for the scenarios CM and CA. The present study estimates the significance of the adoption of CA on soil erosion assessment at regional scale based on the Revised Universal Soil Loss Equation (RUSLE) model. The parameters of the RUSLE model were estimated by using remote sensing (RS) data. The erosion probability zones were determined through a Geographic Information System (GIS) and Google Earth Engine (GEE) approach. Digital terrain model (DTM) at 8 m, ACL maps of the Apulia region, and rainfall and soil data were used as an input to identify the most erosion-prone areas. Our results show a 7.5% average decrease of soil loss rate during the first period of adoption of the four-year crop cycle—from 2.3 t ha−1 y−1 with CM to 2.1 t ha−1 y−1 with the CA system. CA reduced soil loss rate compared to CM in all classes, from 10.1% in hill class to 14.1% for hill + low slope class. These results can therefore assist in the implementation of effective soil management systems and conservation practices to reduce soil erosion risk in the Apulia region and in the Mediterranean basin more generally.

Impacts of Ground Fissures on Soil Properties in an Underground Mining Area on the Loess Plateau, China

Mining-induced ground fissures are the main type of geological disasters found on the Loess Plateau, China, and cause great impacts on the soil properties around ground fissures. However, little research has been conducted on the quantitative relationship between ground fissures and changes in soil properties. To address this, 40 ground fissures in the Yungang mining area, Datong City, Shanxi Province, China, were investigated, and changes in soil properties (soil organic matter, soil moisture, field capacity, bulk density, soil porosity, and grain compositions) were revealed by the difference in soil properties between the edge and contrast points around ground fissures. Redundancy analyses were used to illustrate the relationships between the value (Si_DV) and percentage (Si_DP) of the difference in soil properties between the edge and contrast points, as well as the ground fissures. The characteristics of ground fissures that had a significant correlation according to Pearson correlation analysis with Si_DP were selected and analyzed via multivariate linear fitting model, random forest model, and Back Propagation (BP) neural network model, respectively. Results show that soil organic matter, soil moisture content, bulk density, field capacity, and the content of clay at the edge points were significantly less than those at the contrast points; conversely, soil porosity at the edge points was significantly greater. The average percentage of the difference between the edge points and contrast points of ground fissures in these six properties was 15.27%, while soil moisture content showed the greatest change (20.65%). The Si_DP was significantly correlated with the width, slope, and vegetation coverage of ground fissures; however, the vegetation coverage was the determining factor. BP neural network model had the greatest performance in revealing the relationships between ground fissures and changes in soil properties. The model for soil organic matter had the highest accuracy (R2 = 0.89), and all others were above 0.5. This research provides insights into the quantitative relationship between ground fissures and their impacts on soil physical properties, which can be used in conjunction with remote sensing images to rapidly assess soil erosion risks caused by mining on a large scale, given that soil physical properties are closely related to topsoil stability.

Assessment of soil erosion under long-term projections of climate change in Poland

Soil is a major natural resource fundamental for food production, and a habitat for living organisms. It is therefore extremely important to prevent its erosion. It is assumed that 50% of erosion at the global scale is caused by water. A relatively low number of studies model soil loss in Northern and Eastern Europe. Although that region currently contributes to total soil loss the least, the projected long-term climate change in Europe is likely to affect soil loss due to changes in rainfall characteristics. This study quantifies historical soil loss rates based on high-resolution datasets, and assesses the impact of the projected climate change on long-term average annual soil erosion rates in Poland, located in the temperate climate zone, using the Revised Universal Soil Loss Equation (RUSLE) model. The study investigates the projected changes for two future time horizons, namely: 2021-2050 (near future – NF) and 2071-2100 (far future – FF), under two different Representative Concentration Pathways (RCP) 4.5 and 8.5. To account for the uncertainty of individual climate models, an ensemble of nine EURO-CORDEX model scenarios was used. Based on the multi-model ensemble median, the average soil erosion rate in Poland for the historical period is 0.48 t ha−1 yr−1. The total annual soil loss in Poland is estimated at 14 Mt. The highest soil loss rate is observed in the south of Poland with mean annual erosion rate of 1.7 t ha−1 yr−1. Although it occupies only 16% of the territory of Poland, its share in total soil loss in the country is 62% (8.6 Mt year−1). The average projected potential soil erosion by water in the future increases gradually, reaching +7% (RCP4.5 NF), +8% (RCP8.5 NF), +13% (RCP4.5 FF), and +18% (RCP8.5 FF). The results of this study might be valuable for the development of the national soil conservation policy. The study fills the spatial gap in modelling of changes in soil erosion caused by climate in the Northern and Eastern part of Europe. Due to the lack of other national studies in the region, the directions of such changes could be transferrable to countries in the same climate zones, with a similar vegetation cover structure.

Evaluation of the Risk Induced by Soil Erosion on Land Use. Case Study: Guruslău Depression

Changes in land use, increasing of agricultural areas to the detriment of wooded ones, and poor management of agricultural land, along with the impact of current changes in the climate (reflected in the increase of the climate aggression index) makes soil erosion one of the main risks associated with improper land use, with a direct impact on its productivity and an indirect impact on human beings. The aim of this study is to assess the risk induced by surface soil erosion on land use, using as our main method of investigation the development of two models of integrated spatial analysis of the territory: a derived model of the universal soil loss equation (USLE) and a qualitative model that integrates the result of soil erosion assessment with the database representing the land use. This was carried out in order to highlight the impact on the territory. The spatial analysis models were developed on a structure of vector spatial databases, through which the soil type, soil texture, climate aggression coefficient, and land use were mapped, and alphanumeric databases, representing the market cost of land, in EUROs, that highlight the quality of cultivated land (in terms of productive economic potential). The induced risk estimation is based on a qualitative rating of soil erosion vulnerability on a scale from 1 to 5 (1-low vulnerability; 5-high vulnerability) and of the reduction of the economic value of the land (according to the vulnerability rating). The implemented methodology highlights the quantitative risk, with a maximum value of about 46.000 EUROs, spatially identified on large surfaces on the outskirts of the Jibou municipality. It is mainly caused by the impact of soil erosion on large areas of orchards, which provide necessary products for human consumption. The present methodology can be implemented on similar areas and can be used as a model of good practices in risk assessment based on financial losses by local public authorities.

Soil quality indicators along a degradation gradient in central Iran: comparison of two regions with contrasting grazing systems

ABSTRACT Soil degradation poses serious environmental problems and requires quantitative assessment. This study aimed to investigate the soil properties in rangelands with different management conditions. Based on the assessment of soil erosion, vegetation cover, vegetation composition, and vegetation vigor, 56 sites were selected in rangelands with moderate, poor, and very poor conditions located in 15 grazing-free areas and one wildlife refuge region. Soil samples (0–10 cm layer) were collected from these sites through a systematic-random method to compare their physical and chemical properties. Results showed that soil organic carbon (OC) and total nitrogen content were significantly different between sites with different range conditions. The mean weight diameter of water-stable aggregates and structural stability index in moderate rangelands were 1.56 mm and 3.98%, respectively, and declined towards more degraded rangeland (i.e. 0.84 mm and 0.83%). Grazing-free rangelands exhibited a relatively larger OC deficit; hence specific management measures and control of grazing are required to enhance the quality of soil in these sites. This study highlighted the role of organic matter in preserving soil structural stability. Increasing protection level of rangelands and decreasing grazing pressure may preserve soil quality and combat desertification in these arid regions.

Soil Erosion Assessment of Alpine Grassland in the Source Park of the Yellow River on the Qinghai-Tibetan Plateau, China

The source park of the Yellow River (SPYR), as a vital ecological shelter on the Qinghai-Tibetan Plateau, is suffering different degrees of degradation and desertification, resulting in soil erosion in recent decades. Therefore, studying the mechanism, influencing factors and current situation of soil erosion in the alpine grassland ecosystems of the SPYR are significant for protecting the ecological and productive functions. Based on the 137Cs element tracing technique and machine learning algorithms, five strategic variable selection algorithms based on machine learning algorithms are used to identify the minimal optimal set and analyze the main factors that influence soil erosion in the SPYR. The optimal model for estimating soil erosion in the SPYR is obtained by comparisons model outputs between the RUSLE and machine learning algorithms combined with variable selection models. We identify the spatial distribution pattern of soil erosion in the study area by the optimal model. The results indicated that: (1) A comprehensive set of variables is more objective than the RUSLE model. In terms of verification accuracy, the simulated annealing -Cubist model (R = 0.67, RMSD = 1,368 t km–2⋅a–1) simulation results represents the best while the RUSLE model (R = 0.49, RMSD = 1,769 t⋅km–2⋅a–1) goes on the worst. (2) The soil erosion is more severe in the north than the southeast of the SPYR. The average erosion modulus is 6,460.95 t⋅km–2⋅a–1 and roughly 99% of the survey region has an intensive erosion modulus (5,000–8,000 t⋅km–2⋅a–1). (3) Total erosion loss is relatively 8.45⋅108 t⋅a–1 in the SPYR, which is commonly 12.64 times greater than the allowable soil erosion loss. The economic monetization of SOC loss caused by soil erosion in the entire research area was almost $47.90 billion in 2014. These results will help provide scientific evidences not only for farmers and herdsmen but also for environmental science managers and administrators. In addition, a new ecological policy recommendation was proposed to balance grassland protection and animal husbandry economic production based on the value of soil erosion reclassification.

Study of hanging valley in loess-paleosoil sediments with soil erosion assessment using nuclear and erosion potential methods

This paper is dealing with soil erosion assessment using two different approaches: nuclear model and erosion potential method, also known as Gavrilovic's method. Complex valley systems on Titel Loess Plateau were selected for investigation. Radiocaesium is favored in many studies as an optimal erosion tracer due to its relatively long half-life, negligible renewal in the environment and strongly binding ability onto soil particles. The use of gamma-spectrometry in environmental testing laboratories acts as a precise and fast measurement technique for determination of 137Cs activity concentrations, without the need for complicate preparation of samples. Annual erosion and deposition rates were estimated according to three conversion models for uncultivated land: the profile distribution model with two years of dominant fallout of 137Cs (1963 and 1986) and the diffusion and migration model using WALLING software. The applied nuclear models were validated by comparison with erosion potential model which is the most relevant empirical model for erosion processes in torrent valleys. The obtained results indicate a good agreement with overall low values of average annual soil erosion rates on all soil profiles in the investigated area. Correlation analysis has determined the different influence of slope, terrain curvature, and vegetation index on the erosion models.

Soil erosion assessment in Ethiopia: A review

Soil erosion is a critical problem affecting rural livelihoods in Ethiopia. Large numbers of studies have been undertaken to identify critical areas of soil loss and prioritize conservation options. With the advancement of geospatial analysis techniques, the use of spatially distributed soil erosion assessment options has become increasingly common. The lack of database and documentation related to soil erosion assessment undermines coordination leading to duplication of efforts and in some instances generating contradictory results. The purpose of this study is to (1) review existing knowledge related to the extent and spatial distribution of soil erosion, (2) document the associated methods employed to assess soil erosion, and (3) assess the spatio-temporal dynamics of soil erosion and the determinant factors in Ethiopia. The review shows that there are about 170 peer-reviewed papers published in scientific journals related to soil erosion in Ethiopia. In those scientific articles, 15 different approaches were used to assess soil loss and sediment yield at different scales. Considering the data set, soil loss rate in Ethiopia varies between 0 and 220 t ha^–1 y^–1, and sediment yield ranges between 2 and 70 t ha^–1 y^–1. Based on the database, the national average gross soil erosion rate is estimated to be 38 t ha^–1 y^–1, while the net sediment yield is about 26 t ha^–1 y^–1. Generally, the observed gross soil loss is slightly lower than the mean value from cultivated lands reported in previous estimates (42 t ha^–1 y^–1) by Hurni (1993). The estimate made using the Revised Universal Soil Loss Equation gives the highest soil loss (51 t ha^–1 y^–1), while that based on field-survey approaches gives the lowest (20 t ha^–1 y^–1). The highest average net soil loss rate (40 t ha^–1 y^–1) is obtained using plot-level measurements, while the lowest (18 t ha^–1 y^–1) is obtained using the AGricultural Non-Point Source pollution model. The highest average soil erosion rate is observed in the moist agro-ecological zone (57 ± 7.8 t ha^–1 y^–1), while the lowest is obtained in the submoist (23.6 ± 2.7 t ha^–1 y^–1) following the arid zone (28.8 ± 6.5 t ha^–1 y^–1). The wide range of soil erosion estimates imply spatio-temporal dynamics of soil erosion in the country, which is mainly a reflection of heterogeneity of the various sites, mainly associated with different values of cover and management factors. Such knowledge can enable making informed conservation decisions by focusing on critical hotspots.

Assessment of Spatial Soil Erosion Using RUSLE Model Integration with GIS and RS Tools: The Case of Gilgel Gibe-I Catchment, South West Ethiopia

Water-induced soil erosion is one of the serious environmental, agricultural, and socioeconomic problems in Ethiopian highlands. Accurate information on the rates of soil erosion helps environment protection and socio-economic development efforts of the nation. The objective of this research was to estimate annual soil loss, sediment yield, and map erosion risk areas of Gilgel Gibe-I (GG-I) catchment via integrating Revised Universal Soil Loss Equation (RULSE) model with Geographical Information System (GIS) and Remote Sensing (RS) technologies. The model inputs variables; rainfall erosivity (R), soil erodibility (K), topographic (LS), land cover (C) and land management (P) were derived from meteorological stations, Ethio-soil map, and satellite image of the catchment. The annual soil loss (t^-1ha^-1yr) was estimated using pixel-by-pixel ArcGIS map overlays to ensure the accuracy of RULSE output. The model output revealed on average 12.52 (t^-1ha^-1yr) soils was lost from GG-I catchment through sheet and rill erosion. The rates of soil loss were varying in the catchment, 59.8% of the catchment exposed to low rate (<5 t^-1ha^-1yr), 12.2% to moderate rate (5-12 t^-1ha^-1yr), 11.7% to high rate (12-30 t^-1ha^-1yr), and 6.6% to severe (>30 t^-1ha^-1yr). The annual sediment yield capacity of the catchment was 2.54 t^-1ha and delivery ration estimated 0.203% transported to outlet of the catchment-GGI hydropower dam. To combat the problems of GG-I hydropower dam siltation, land degradation, and low agricultural productivity an integrated natural resource management intervention is required throughout the catchment particularly in high and severe erosion risk areas.

Soil erosion and sediment yield assessment using RUSLE and GIS based approach in a forest watershed in Kandi region of Punjab

The Revised Universal Soil Loss Equation (RUSLE) integrated with the sediment delivery ratio (SDR) model employed in the GIS environment has been used to quantify the soil erosion and sediment yield for a small reservoir (Takarla dam) located in the Kandi region of Punjab, India. The catchment area of Takarla dam is 72.6 ha. The various layers including rainfall erosivity factor, soil erodibility factor, Slope length and steepness factor and crop management factor were prepared in ArcGIS to estimate the average annual soil loss. The SDR for the dam watershed was obtained by employing different empirical equations. The sedimentation rate and sediments collected in the dam was assessed for the last 20 years. The results of the present study revealed that the average annual soil loss varied from 0 to 12.8 t ha-1 year-1 for Takarla dam. The sediments deposited in the dam over 20 years estimated to be 142603.8 tonnes. The results of the present study may provide an insight for policy makers to design and execute the watershed management practices to reduce erosion hazard and sediment accumulation in the reservoirs.

Modelling and mapping of soil erosion in the north-eastern frontier Himalayan ranges of India using remote sensing and GIS

Soil erosion is a land degradable problem cause by natural resources. In context of North Eastern Himalayan ranges, it is a common soil degradation process due to fragile nature of soil and ecosystem. The aim of this study is to assess the soil erosion of Mahadevpur block, Namsai district of Arunachal Pradesh by RUSLE method using remote sensing and GIS environment. In this study, Sentinel 2 data of the study area was used to prepare land use/land cover (LU/LC) map by visual interpretation technique and ALOS digital elevation model (DEM) was used to develop slope, contour, flow direction and flow accumulation maps. Thematic raster layers of RUSLE factors (R, K, LS, C and P) were computed and used to estimate average annual soil loss by ArcGIS 10.3 software. The results showed that maximum area was (37.10%) under very slight soil loss class (< 5.0 t ha-1 yr-1). However, the extremely severe soil loss(> 80.0 t ha-1 yr-1) was observed in 7.0 % of the study area which was confined to terrain features such as lateral bar, meander scar, point bar, braided bar and channel bar. It was proved that, use of geospatial technologies in combination with RUSLE is a comprehensive approach, which helped in spatial assessment of soil erosion for better resource planning and taking up suitable conservation measures.

ВОПРОСЫ ОЦЕНКИ СОСТОЯНИЯ ЭРОЗИОННОСТИ ПОЧВЫ В ЛЕСНЫХ МАССИВАХ МЕТОДАМИ ДИСТАНЦИОННОГО ЗОНДИРОВАНИЯ

Introduction. There are a number of factors influencing and accelerating erosion processes. At the same time, the erosion of the soil itself plays an important role. To assess the erosion state of soils, various methods and means of remote sensing are widely used. The purpose of the study. The purpose of the study is to somewhat simplify the well-known multiplicative model for studying soil erosion in forests by some combination of indicators of individual factors and the formation of new composite functionals – indicators of vegetation stress as part of an improved model. Methods. The functionals introduced in the article can be considered as indices of the state of plants in forest areas. The first component in these functionals is an indicator of stress, i.e. the greater this component, the deeper the state of stress of the plant. At the same time, the second component in these functionals is an indicator of the healthy state of vegetation. Therefore, in general, the weighted average of these indicators is an indicator of the state of vegetation. Results. An extremal property of this functional, called the plant state functional, is shown. It is determined that this functional can be defined as a linearly weighted or geometric mean. In the first case, in the graph of the dependence of this functional on the indicator of the reflected signal in the Green zone, to the right of the extremum (minimum) point, there is a zone of deterioration in the state of vegetation, and to the left is an improvement zone. In the second case, when using the geometric mean, this picture changes as follows: conditions are revealed when the extremum turns into a minimum or maximum. In the case of a maximum, to the right of the maximum point there is a zone of improvement in the state of vegetation, and to the left of deterioration. Practical depend_ ency. Based on the results obtained, new methods can be developed to assess the state of soil erosion using the results of spectral remote sensing. in forests. Discussion. The issue of assessing soil erosion in forest areas is considered. Based on the well-known model of soil erosion in forest areas, an improved erosion model is proposed that contains an extreme functional of the state of vegetation, depending on two indica% tors characterizing the nitrogen content in the plant and the stress level, respectively. The introduction of such an extreme factor into the multiplicative erosion model makes it possible to more objectively assess the degree of soil erosion.

RUSLE-based soil erosion assessment and erosion control evaluation in the Kabul Watershed

The development and conservation of water and soil in the Kabul River Basin are critical for ensuring its sustainable economic, social, and environmental progress. Since the watershed drains across the international borders into the Indus River Basin, it is key for both up- and downstream countries to thoroughly plan their resource development and management. Due to relief, soil and climatic conditions, as well as the recent deforestation the Kabul River Basin has been witnessing significant soil erosion by water, indicating the need for analyzing its specifics and deploying proper control measures. This study was carried out using the combination of the Revised Universal Soil Loss Equation (RUSLE) Model and GIS techniques to investigate the gross soil loss rates and their spatial distribution inside the target basin. Thus, the annual average soil loss rate was estimated at 15.1 tons/ha/year, pointing to severe local soil erosion. As to its spatial distribution, according to the study up to 99% of mean annual soil loss rates fell within the tolerable (0-5 tons/acre/year) category. Considering the soil loss rates, local topography, and community-based participatory approaches, the authors recommend conducting a further detailed diagnostic analysis to inform and support subsequent control measures such as deforestation prevention, construction of small check dams, terracing, trenching in hilly areas, revegetation (reforestation) of open- and grasslands, and rainwater harvesting.