Regional soil erosion risk mapping using RUSLE,GIS, and remote sensing: a case study in Miyun Watershed,North China

This paper applied the Revised Universal Soil Loss Equation (RUSLE), remote-sensing technique, and geographic information system (GIS) to map the soil erosion risk in Miyun Watershed, North China. The soil erosion parameters were evaluated in different ways: the R factor map was developed from the rainfall data, the K factor map was obtained from the soil map, the C factor map was generated based on a back propagation (BP) neural network method of Landsat ETM+ data with a correlation coefficient (r) of 0.929 to the field collected data, and a digital elevation model (DEM) with a spatial resolution of 30 m was derived from topographical map at the scale of 1:50,000 to develop the LS factor map. P factor map was assumed as 1 for the watershed because only a very small area has conservation practices. By integrating the six factor maps in GIS through pixel-based computing, the spatial distribution of soil loss in the upper watershed of Miyun reservoir was obtained by the RUSLE model. The results showed that the annual average soil loss for the upper watershed of Miyun reservoir was 9.86 t ha⁻¹ ya⁻¹ in 2005, and the area of 47.5 km² (0.3%) experiences extremely severe erosion risk, which needs suitable conservation measures to be adopted on a priority basis. The spatial distribution of erosion risk classes was 66.88% very low, 21.90% low, 6.19% moderate, 2.90% severe, and 1.84% very severe. Among all counties and cities in the study area, Huairou County is in the extremely severe level of soil erosion risk, about 39.6% of land suffer from soil erosion, while Guyuan County in the very low level of soil erosion risk suffered from 17.79% of soil erosion in 2005. Therefore, the areas which are in the extremely severe level of soil erosion risk need immediate attention from soil conservation point of view.     

The combined RUSLE/SDR approach integrated with GIS and geostatistics to estimate annual sediment flux rates in the semi-arid catchment,Turkey

Quantitative evaluation of the spatial distribution of the erosion risk in any watershed or ecosystem is one of the most important tools for environmentalists, conservationists and engineers to plan natural resource management for the sustainable environment in a long term. This study was performed in the semi-arid catchment of the Saraykoy II Irrigation Dam, Cankiri, located in the transition zone between the Central Anatolia Steppe and the Black Sea Forests of Turkey. The total area of the catchment is 262.31 ha. The principal objectives were to quantify both potential and actual soil erosion risks by the Revised Universal Soil Loss Equation (RUSLE) and to estimate the amount of sediments to be delivered from the hillslope of the catchment to the reservoir of the dam using the sediment delivery ratio (SDR) in combination with the RUSLE model. All factor and sub-factor calculations required for solving the RUSLE model and SDR in the catchment were made spatially using DEM, GIS and Geostatistics. As the main catchment was divided into twenty-five sub-catchments, the predicted actual soil loss (by the model) was 146,657.52 m³ year^?1 and the weighted average of SDR estimated by areal distribution (%) of the sub-watersheds was 0.344 for whole catchment, resulted in 50,450.19 m³ year^?1 sediment arriving to the reservoir. Since the Dam has a total storage capacity of 509 × 10³ m³, the life expectancy of the Dam is estimated as 10.09 year. This estimation indicated that the dam has a relatively short economic life and there is a need for water-catchment management and soil conservation measures to reduce erosion.     

Spatial prediction of soil erosion risk by remote sensing,GIS and RUSLE approach: a case study of Siruvani river watershed in Attapady valley,Kerala, India

Siruvani watershed with a surface area of 205.54 km² (20,554 hectare), forming a part of the Western Ghats in Attapady valley, Kerala, was chosen for testing RUSLE methodology in conjunction with remote sensing and GIS for soil loss prediction and identifying areas with high erosion potential. The RUSLE factors (R, K, LS, C and P) were computed from local rainfall, topographic, soil classification and remote sensing data. This study proved that the integration of soil erosion models with GIS and remote sensing is a simple and effective tool for mapping and quantifying areas and rates of soil erosion for the development of better soil conservation plans. The resultant map of annual soil erosion shows a maximum soil loss of 14.917 t h⁻¹ year⁻¹ and the computations suggest that about only 5.76% (1,184 hectares) of the area comes under the severe soil erosion zone followed by the high-erosion zone (11.50% of the total area). The dominant high soil erosion areas are located in the central and southern portion of the watershed and it is attributed to the shifting cultivation, and forest degradation along with the combined effect of K, LS and C factor. The RUSLE model in combination with GIS and remote sensing techniques also enables the assessment of pixel based soil erosion rate.     

Modeling soil erosion using RUSLE and GIS in a watershed occupied by rural settlement in the Brazilian Cerrado

The installation of a rural settlement complex in the watershed stream Indaiá has promoted changes in land-use and vegetation cover dynamics; however, the effects of intensive agriculture and cattle farming in rural settlements on soil loss rates are not well known. Predictive models implemented in geographic information systems have proven to be effective tools for estimating erosive processes. The erosion predictive model Revised Universal Soil Loss Equation (RUSLE) is a useful tool for analyzing, establishing and managing soil erosion. RUSLE has been widely used to estimate annual averages of soil loss, by both interrill and rill erosion, worldwide. Therefore, the aim of this work was to estimate the soil loss in the watershed stream Indaiá, using the RUSLE model and geoprocessing techniques. To estimate soil loss, the following factors were spatialized: erosivity (R), erodibility (K), topography (LS), land-use and management (C) and conservation practices (P); the annual soil loss values were calculated using the RUSLE model equation. The estimated value of soil loss in the hydrographic basin ranged from 0 to 4082.16 Mg ha^?1 year^?1 and had an average value of 47.81 Mg ha^?1 year^?1. These results have demonstrated that 68.16 % of the study area showed little or no soil loss based on the Food and Agriculture Organization’s (FAO 1980) classification. When comparing the average value of soil loss obtained using the RUSLE model with the Natural Potential for Erosion, a 16-fold reduction in soil was found, which highlighted the fact that vegetation cover (C factor) has a greater influence than other factors (R, K and LS) on soil loss prediction attenuation. These results lead to the conclusion that soil loss occurs by different methods in each settlement in the basin and that erosive processes modeled by geoprocessing have the potential to contribute to an orderly land management process.     

Geospatial assessment of soil erosion vulnerability at watershed level in some sections of the Upper Subarnarekha river basin, Jharkhand, India

Undulating landscapes of Chhotanagpur plateau of the Indian state of Jharkhand suffer from soil erosion vulnerability of varying degrees. An investigation was undertaken in some sections of the Upper Subarnarekha River Basin falling within this state. An empirical equation known as Universal Soil Loss Equation (USLE) was utilized for estimating the soil loss. Analysis of remote sensing satellite data, digital elevation model (DEM) and geographical information system (GIS)–based geospatial approach together with USLE led to the soil erosion assessment. Erosion vulnerability assessment was performed by analyzing raster grids of topography acquired from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM data. LANDSAT TM and ETM+ satellite data of March 2001 and March 2011 were used for inferring the land use–land cover characteristics of the watershed for these years, respectively. USLE equation was computed within the GIS framework to derive annual soil erosion rates and also the areas with varying degrees of erosion vulnerability. Erosion vulnerability units thus identified covered five severity classes of erosion ranging from very low (0–5 ton ha^?1 yr^?1) to very severe (> 40 ton ha^?1 yr^?1). Results indicated an overall increase of erosion in the year 2011 as compared to the erosion computed for the year 2001. Maximum soil erosion rate during the year 2001 was found up to 40 ton ha^?1 yr^?1, whereas this went up to 49.80 ton ha^?1 yr^?1 for the year 2011. Factors for the increase in overall erosion could be variation in rainfall, decrease in vegetation or protective land covers and most important but not limited to the increase in built-up or impervious areas as well.     

Multi-temporal annual soil loss risk mapping employing Revised Universal Soil Loss Equation (RUSLE) model in Nun Nadi Watershed, Uttrakhand (India)

The present comparative study is multi-temporal in nature. The Revised Universal Soil Loss Equation (RUSLE), remote sensing, and GIS were used to model the soil loss estimation for soil conservation and vegetation rehabilitation in Nun Nadi watershed for the years 2000 and 2009. The estimated mean soil loss for the year 2000 and 2009 is 3,283.11 and 1,419.39 Mg?ha^?1 year^?1, respectively. The study finds that about 80 % area has low or least risk of erosion and about 7 % is exposed to high or very high risk which indicates the improvement in terms of soil loss if we compare the data of both the time periods. The findings show that the rainfall, LULC change, and elevation are the main responsible factors for the soil loss in Nun Nadi watershed. Conservation measures have been adopted; however, the problem still remains serious and demands urgent attention.     

Evaluation of sediment yield and soil loss by the MPSIAC model using GIS at Golestan watershed, northeast of Iran

Watershed degradation due to soil erosion and sedimentation is considered to be one of the major environmental problems in Iran. In order to address the critical conditions of watershed degradation in arid and semiarid regions, a study based on the Modified Pacific Southwest Inter-Agency Committee (MPSIAC) model was carried out at Golestan watershed, northeast of Iran. The model information layers comprising nine effective factors in erosion and sedimentation at the watershed site were obtained by digitalization and spatial interpolation of the basic information data in a GIS program. These factors are geology, soil, climate, runoff, topography, land cover, land use, channel, and upland erosion. The source data for the model were obtained from available records on rainfall and river discharge and sediment, topography, land use, geology, and soil maps as well as field surveys and laboratory analysis. The results of the MPSIAC model indicated that 60.75 % (194.4 km²) and 54.97 % (175.9 km²) of the total watershed area were classified in the heavy sedimentation and erosion classes, and the total basin sediment yield and erosion were calculated as 4,171.1 and 17,813.4 m³ km^?2 year^?1, respectively. In the sensitivity analysis, it was found that the most sensitive parameters of the model in order of importance were topography (slope), land cover and use, runoff, and channel erosion (R ²?=?0.92–0.94), while geology, climate (rainfall), soil, and upland erosion factors were found to have moderate effect to the model output (R ²?=?0.74–0.59).     

Geospatial assessment of soil erosion intensity and sediment yield: a case study of Potohar Region,Pakistan

Estimation of spatial extent of soil erosion, one of the most serious forms of land degradation, is critical because soil erosion has serious implications on soil fertility, water ecosystem, crop productivity and landscape beauty. The primary objective of the current study was to assess and map the soil erosion intensity and sedimentation yield of Potohar region of Pakistan. Potohar is the rainfed region with truncated and complex topography lying at the top of the Indus Basin, the world’s largest irrigation networks of canals and barrages. Spatially explicit Revised Universal Soil Loss Equation (RUSLE) Model integrated with Remote Sensing-GIS techniques was used for detecting/mapping of erosion prone areas and quantification of soil losses. The results show that the Potohar region is highly susceptible to soil erosion with an average annual soil loss of 19 tons ha^?1 year^?1 of which the maximum erosion (70–208 tons ha^?1 year^?1) was near the river channels and hilly areas. The sediment yield due to the erosion is as high as 148 tons ha^?1 year^?1 with an average of 4.3 tons ha^?1 year^?1. It was found that 2.06% of the total area falls under severe soil erosion, 13.34% under high erosion, 15.35% under moderate soil erosion while 69.25% of the area lies in the low (tolerable) soil erosion. Chakwal and Jhelum districts of the region are seriously affected by erosion owing to their topography and soil properties. The information generated in this study is a step forward towards proper planning and implementation of strategies to control the erosion and for protection of natural resources. It is, hence, necessary that suitable water harvesting structures be made to control water to prevent soil erosion and provision of water in the lean season in this region. Tree plantation and other erosion control practices such as strip cropping can also minimize soil erosion in this region.     

Integration of geospatial technologies with RUSLE for analysis of land use/cover change impact on soil erosion: case study in Rib watershed,north-western highland Ethiopia

In recent times, soil erosion interlocked with land use and land cover (LULC) changes has become one of the most important environmental issues in developing countries. Evaluation of this complex interaction between LULC change and soil erosion is indispensable in land use planning and conservation works. This paper analysed the impact of LULC change on soil erosion in the north-western highland Ethiopia over the period 1986–2016. Rib watershed, the area with dynamic LULC change and severe soil erosion problem, was selected as a case study site. Integrated approach that combined geospatial technologies with revised universal soil loss equation model was utilized to evaluate the spatio-temporal dynamics of soil loss over the study period. Pixel-based overlay of soil erosion intensity maps with LULC maps was carried out to understand the change in soil loss due to LULC change. Results showed that the annual soil loss in the study area varied from 0 to 236.5 t ha^?1 year^?1 (tons per hectare per year) in 1986 and 0–807 t ha^?1 year^?1 in 2016. The average annual soil loss for the entire watershed was estimated about 40 t ha^?1 year^?1 in 1986 comparing with 68 t ha^?1 year^?1 in 2016, a formidable increase. Soil erosion potential that was estimated to exceed the average soil loss tolerance level increased from 34.5% in 1986 to 66.8% in 2016. Expansion of agricultural land at the expense of grassland and shrubland was the most detrimental factor for severe soil erosion in the watershed. The most noticeable change in soil erosion intensity was observed from cropland with mean annual soil loss amount increased to 41.38 t ha^?1 year^?1 in 2016 from 26.60 in 1986. Moreover, the most successive erosion problems were detected in eastern, south-eastern and northern parts of the watershed. Therefore, the results of this study can help identify the soil erosion hot spots and conservation priority areas at local and regional levels.     

Investigation of RS and GIS techniques on MPSIAC model to estimate soil erosion

Soil erosion due to surface water is a standout among the serious threat land degradation problem and an hazard environmental destruction. The first stage for every kind of soil conservation planning is recognition of soil erosion status. In this research, the usability of two new techniques remote sensing and geographical information system was assessed to estimate the average annual specific sediments production and the intensity erosion map at two sub-basins of DEZ watershed, southwest of Lorestan Province, Iran, namely Absorkh and Keshvar sub-basins with 19,920 ha, using Modified Pacific Southwest Inter-Agency Committee (MPSIAC) soil erosion model. At the stage of imagery data processing of IRS-P6 satellite, the result showed that an overall accuracy and kappa coefficient were 90.3% and 0.901, respectively, which were considered acceptable or good for imagery data. According to our investigation, the study area can be categorized into three level of severity of erosion: moderate, high, and very high erosion zones. The amount of specific sediments and soil erosion predicted by MPSIAC model was 1374.656 and 2396.574 m³ km^?2 year^?1, respectively. The areas situated at the center and south parts of the watershed were subjected to significant erosion because of the geology formation and ground cover, while the area at the north parts was relatively less eroded due to intensive land cover. Based on effective of nine factors, the driving factors from high to low impact included: Topography > Land use > Upland erosion > Channel erosion > Climate > Ground cover > Soil > Runoff > Surface geology. The measured sediment yield of the watershed in the hydrometric station (Keshvar station) was approximately 2223.178 m³ km^?2 year^?1 and comparison of the amount of total sediment yield predicted by model with the measured sediment yield indicated that the MPSIAC model 38% underestimated the observed value of the watershed.     

Bright side? The impacts of Three Gorges Reservoir on local ecological service of soil conservation in southwestern China

Three Gorges Reservoir in China was running since June 2003, and its impacts on soil erosion (SE) and soil conservation (SC) have attracted major public attentions. We quantified the soil conservation service of ecosystems in the Three Gorges Reservoir Area (TGRA) based on a GIS platform using the universal soil loss equation. We revealed the changes of spatial and temporal patterns of soil conservation (SC) and soil erosion (SE) after project construction as well as impact factors on local SE and SC. Results showed that the total amounts and mean capacity of soil conservation services in the TGRA were 15.38-billion t a^?1 and 2134.73 t ha^?1 a^?1, respectively. Northeast reservoir area owned better services than the southwest, and the regions with a capacity of >5000 t ha^?1 a^?1 were primary located in mountain areas. An increasing trend in SC appeared in the TRGA and “with increasing SC” totaled 22690.5 km² (38.9%), while the areas “with decreasing SC” amounted to 3460.4 km² (5.9%) between 2000 and 2010. Moreover, the pattern of changing SC was continuous in this area. The spatial characteristics of soil conservation service in the TGRA were primarily affected by slope, climate and terrain features. In addition, the reforestation and/or forest protection would contribute to soil erosion control in the TGRA. The results revealed a great spatial heterogeneity of soil conservation service in this region, which may provide useful suggestions for land management, soil erosion control and ecosystem protection in the TGRA in China.     

Assessment of soil erosion risk using SWAT model

Soil erosion is one of the most serious land degradation problems and the primary environmental issue in Mediterranean regions. Estimation of soil erosion loss in these regions is often difficult due to the complex interplay of many factors such as climate, land uses, topography, and human activities. The purpose of this study is to apply the Soil and Water Assessment Tool (SWAT) model to predict surface runoff generation patterns and soil erosion hazard and to prioritize most degraded sub-catchment in order to adopt the appropriate management intervention. The study area is the Sarrath river catchment (1,491 km²), north of Tunisia. Based on the estimated soil loss rates, the catchment was divided into four priority categories for conservation intervention. Results showed that a larger part of the watershed (90 %) fell under low and moderate soil erosion risk and only 10 % of the watershed was vulnerable to soil erosion with an estimated sediment loss exceeding 10 t?ha^?1?year^?1. Results indicated that spatial differences in erosion rates within the Sarrath catchment are mainly caused by differences in land cover type and gradient slope. Application of the SWAT model demonstrated that the model provides a useful tool to predict surface runoff and soil erosion hazard and can successfully be used for prioritization of vulnerable areas over semi-arid catchments.     

Geospatial approach in mapping soil erodibility using CartoDEM – A case study in hilly watershed of Lower Himalayan Range

Soil erodibility is one of the most important factors used in spatial soil erosion risk assessment. Soil information derived from soil map is used to generate soil erodibility factor map. Soil maps are not available at appropriate scale. In general, soil maps at small scale are used in deriving soil erodibility map that largely generalized spatial variability and it largely ignores the spatial variability since soil map units are discrete polygons. The present study was attempted to generate soil erodibilty map using terrain indices derived from DTM and surface soil sample data. Soil variability in the hilly landscape is largely controlled by topography represented by DTM. The CartoDEM (30 m) was used to derive terrain indices such as terrain wetness index (TWI), stream power index (SPI), sediment transport index (STI) and slope parameters. A total of 95 surface soil samples were collected to compute soil erodibility factor (K) values. The K values ranged from 0.23 to 0.81 t ha^?1R^?1 in the watershed. Correlation analysis among K-factor and terrain parameters showed highest correlation of soil erodibilty with TWI (r ²= 0.561) followed by slope (r ²= 0.33). A multiple linear regression model was developed to derive soil erodibilty using terrain parameters. A set of 20 soil sample points were used to assess the accuracy of the model. The coefficient of determination (r ²) and RMSE were computed to be 0.76 and 0.07 t ha^?1R^?1 respectively. The proposed methodology is quite useful in generating soil erodibilty factor map using digital elevation model (DEM) for any hilly terrain areas. The equation/model need to be established for the particular hilly terrain under the study. The developed model was used to generate spatial soil erodibility factor (K) map of the watershed in the lower Himalayan range.     

Estimating of water erosion in semiarid regions using RUSLE equation under GIS environment

Water erosion is one of the main forms of land degradation in Algeria, with a serious repercussion on agricultural productivity. The purpose of this study is to estimate the soil loss of Wadi El-Ham watershed in the center of Algeria, this study aims also to evaluate the effectiveness and reliability of the use of the Revised Universal Soil Loss Equation (RUSLE) under a Geographic Information System in this field. The RUSLE model involves the main factors of erosion phenomena, namely, rain aggressiveness, soil erodibility, topographic factor, land cover index and the anti-erosive practices factor. Using this approach, the specific erosion in Wadi El-Ham watershed is estimated as 5.7 (t/ha/yr) in the entire watershed area. This result is compared to the measured suspended sediment at the Rocade-Sud gauging station situated outlet the watershed. These data consist of 1293 instantaneous measures of the water discharge and the suspended sediment concentration recorded during 21 years. Through this comparison, the used approach of RUSLE under GIS estimates the soil loss in Wadi El-Ham in Hodna region of Algeria with an error of 7.5%. Consequently, the results obtained in cartographic format make it possible to target the areas requiring priority action for a larger scale analysis to find appropriate solutions to combat erosion and to protect the natural environment.     

Estimation of soil erosion rates in a subtropical mountain watershed using 137Cs radionuclide

The formulation of watershed management strategies to protect water resources threatened by soil erosion and sedimentation requires a thorough understanding of sediment sources and factors that drive soil movement in the watershed. This paper describes a study of medium-term water-driven soil erosion rates in a mountainous watershed of the Shihmen Reservoir in Taiwan. A total of 60 sampling sites were selected along a hillslope. At each sampling site, the inventory ¹³⁷Cs activity was determined and then calculated with the diffusion and migration model to derive soil erosion rates. The rates are one to two orders of magnitude lower than estimates using the Universal Soil Loss Equation, a soil erosion model often used in Taiwan. Results of multiple regression analysis indicate that the spatial variability of soil erosion rates is associated with the relative position of a sampling site to the nearest ridge and soil bulk densities (r ² = 0.33, p < 0.01). Finally, the patterns of soil redistribution rates on the hillslope follow the ¹³⁷Cs hillslope model as soil erosion increases in the downslope direction. No deposition site is found at footslope because soil deposition is swept away by regular flooding along the stream channel. This study is an important first step in using ¹³⁷Cs as a tracer of soil redistribution in mountainous watersheds of Taiwan.     

Identifying sediment sources from the inter-gully area and gully area in a small watershed in the Loess Hilly Region of China

Understanding the sediment source is very significant for erosion control in small watersheds. On the Loess Plateau of China, over 110 thousands of check dams were constructed in the past 60 years, which played an important role in controlling soil loss and also kept much information of erosion and sediment yield in the past. The objective of this investigation is to identify the sediment source by the ¹³⁷Cs tracing method in a small watershed in the Loess Hilly Region of China. Fifty-five sampling sites were selected from the watershed (44 from the inter-gully area, 7 from the gully sides and 4 from the reference sites), and a total of 114 soil samples were collected from three sediment profile cores at the Sidizui watershed check dam constructed in 1959. Based on the erosion rate from the inter-gully area by the ¹³⁷Cs models, and the total erosion amount deposited in the check dam since 1963, the relative contributions of sediment from the inter-gully area and gully area were estimated during 1963–2013. By comparing the difference of ¹³⁷Cs activities of surface soils from the check dam (deposited in the 2011–2013 flood events), the inter-gully and gully areas, the relative contributions of sediment derived from the two source areas to the flood sediment during recent years (2011–2013) were estimated by a simple mixing model. Results showed that the erosion rate from the inter-gully area was about 3054 t/km² a during 1963–2013, and the relative contributions of sediment from the inter-gully area and gully area were estimated to be 27 and 73%, respectively. The sediment from the inter-gully area was about 20% of the total sediment yield amount to the 2011–2013 floods, and from the gully area it was about 80%. The relative contribution of sediment from the inter-gully or gully area was not a fixed value in the watershed. Both of the inter-gully erosion and gully erosion should be simultaneously controlled, and more erosion control measures on the gully area should be taken in the subsequent watershed management in order to reduce the erosion amount in this region.     

Estimation of soil erosion using RUSLE in a GIS framework: a case study in the Buyukcekmece Lake watershed,northwest Turkey

This study was aimed at predicting soil erosion risk in the Buyukcekmece Lake watershed located in the western part of Istanbul, Turkey, by using Revised Universal Soil Loss Equation (RUSLE) model in a GIS framework. The factors used in RUSLE were computed by using different data obtained or produced from meteorological station, soil surveys, topographic maps, and satellite images. The RUSLE factors were represented by raster layers in a GIS environment and then multiplied together to estimate the soil erosion rate in the study area using spatial analyst tool of ArcGIS 9.3. In the study, soil loss rate below 1 t/ha/year was defined as low erosion, while those >10 t/ha/year were defined as severe erosion. The values between low and severe erosion were further classified as slight, moderate, and high erosion areas. The study provided a reliable prediction of soil erosion rates and delineation of erosion-prone areas within the watershed. As the study revealed, soil erosion risk is low in more than half of the study area (54%) with soil loss <1 t/ha/year. Around one-fifth of the study area (19%) has slight erosion risk with values between 1 and 3 t/ha/year. Only 11% of the study area was found to be under high erosion risk with soil loss between 5 and 10 t/ha/year. The severe erosion risk is seen only in 5% of the study area with soil loss more than 10 t/ha/year. As the study revealed, nearly half of the Buyukcekmece Lake watershed requires implementation of effective soil conservation measures to reduce soil erosion risk.     

Investigating effects of land use change scenarios on soil erosion using CLUE-s and RUSLE models

Predicting soil erosion change is an important strategy in watershed management. The objective of this research was to evaluate land use change effects on soil erosion in the north of Iran using five land use scenarios. Three land use maps were created for a period of 25 years (1986–2010) to investigate land use transition and to simulate land use for the year 2030. Additionally, the RUSLE model was used to estimate erosion and the effect of land use change. The results showed that CLUE-s is suitable for modeling future land use transition using ROC curve. The median soil loss in the basis period was 104.52 t ha^?1 years^?1. Results indicate that the range of soil loss change is 2–32% in simulated period and soil loss value was higher than basis period in all scenarios. Thirty percent decrease in demand scenario has the lowest soil loss in simulated period, and the soil loss value under this scenario will be only 2% more than the basis period. Thus, the soil conversion effects resulted from the demand of each land use.     

Simulating climate change impact on soil erosion using RUSLE model <Emphasis Type="BoldItalic">−</Emphasis> A case study in a watershed of mid-Himalayan landscape

Climate change, particularly due to the changed precipitation trend, can have a severe impact on soil erosion. The effect is more pronounced on the higher slopes of the Himalayan region. The goal of this study was to estimate the impact of climate change on soil erosion in a watershed of the Himalayan region using RUSLE model. The GCM (general circulation model) derived emission scenarios (HadCM3 A2a and B2a SRES) were used for climate projection. The statistical downscaling model (SDSM) was used to downscale the precipitation for three future periods, 2011–2040, 2041–2070, and 2071–2099, at large scale. Rainfall erosivity (R) was calculated for future periods using the SDSM downscaled precipitation data. ASTER digital elevation model (DEM) and Indian Remote Sensing data – IRS LISS IV satellite data were used to generate the spatial input parameters required by RUSLE model. A digital soil-landscape map was prepared to generate spatially distributed soil erodibility (K) factor map of the watershed. Topographic factors, slope length (L) and steepness (S) were derived from DEM. Normalised difference vegetation index (NDVI) derived from the satellite data was used to represent spatial variation vegetation density and condition under various land use/land cover. This variation was used to represent spatial vegetation cover factor. Analysis revealed that the average annual soil loss may increase by 28.38, 25.64 and 20.33% in the 2020s, 2050s and 2080s, respectively under A2 scenario, while under B2 scenario, it may increase by 27.06, 25.31 and 23.38% in the 2020s, 2050s and 2080s, respectively, from the base period (1985–2013). The study provides a comprehensive understanding of the possible future scenario of soil erosion in the mid-Himalaya for scientists and policy makers.     

Mapping heatwave vulnerability in Korea

Analysis of event-based soil erosion magnitude with special return periods is essential to appropriately design strategies and adopt soil conservation practices. However, the spatiotemporal variations of soil erosion with different return periods, especially at national level, have not been adequately considered. Therefore, the present study aimed to zone rainfall erosivity index (R factor) as the most dynamic factor affecting variability of soil erosion rate, with different return periods in monthly, seasonal and annual time scales in Iran. Toward this attempt, the kinetic energy and maximum 30-min intensity (I ₃₀) over 12,000 available and accessible events of 70 stations were calculated during the common period of 1984–2004 and the corresponding R factor of the Universal Soil Loss Equation was then computed. Subsequently, the best-fitted frequency distributions were determined in all stations in three time scales using the EasyFit Software. The R factor was accordingly estimated for 2-, 5-, 10-, 25- and 50-year return periods. In addition, the inverse distance weighting technique was employed to determine and analyze the spatial variability patterns of R factor in different time scales using geographic information system. The results indicated that the frequency distributions fitted to study data were different in study time scales due to variability of spatiotemporal patterns of R factor. In addition, no specific spatial pattern of R factor could be recognized for different return periods and time scales. The average annual R factor was also found 1.41 MJ mm ha^?1 h^?1, whereas the respective R factor for different respective return periods of 2, 5, 10, 25 and 50 years was obtained 1.47, 2.62, 3.35, 4.48 and 5.54 MJ mm ha^?1 h^?1. These findings can be used for suitable decision making and effective environmental planning for land management Iran countrywide.