Rainfall Erosivity Factor Research Articles

Soil Loss Potential Assessment for Natural and Post-Fire Conditions in Evia Island, Greece

A devastating forest fire in August 2021 burned about 517 km2 of the northern part of Evia Island, affecting vegetation, soil properties, sediment delivery and the hydrological response of the catchments. This study focuses on the estimation of the annual soil loss in the study area under natural (pre-fire) and post-fire conditions. The assessment of the soil loss potential was conducted with the application of the Universal Soil Loss Equation (USLE), which is an empirical equation and an efficient way to predict soil loss. The USLE factors include rainfall erosivity (R), soil erodibility (K), the slope and slope length factor (LS), the cover management factor (C) and the erosion control practice factor (P). The USLE quantified the annual soil erosion (in t/ha/year) for both pre- and post-wildfire conditions, and the study area has been classified into various soil loss categories and soil erosion intensity types. The results showed that the annual soil loss before the forest fires ranged from 0 to 1747 t/ha, with a mean value of 253 t/ha, while after the fire the soil loss significantly increased (the highest annual soil loss was estimated at 3255 t/ha and the mean value was 543 t/ha). These values demonstrate a significant post-fire change in mean annual soil loss that corresponds to an increase of 114% compared to the pre-fire natural condition. The area that is undergoing high erosion rates after the extreme wildfire event increased by approximately 7%, while the area of moderate rates increased by 2%. The calculated maximum potential of soil erosion, before and after the 2021 extreme wildfire event, has been visualized on spatial distribution maps of the average annual soil loss for the study area. The present study underlines the significant post-fire increase in soil loss as part of the identification of the more vulnerable to erosion areas that demand higher priority regarding the protective/control measures.

Spatial pattern of soil erosion using RUSLE model and GIS software at the Saf Saf watershed, Algeria

Soil erosion is one of the problems threatening the Algerian environment. In agriculture, soil erosion leads to the thinning of the topsoil under the effect of the natural erosive forces of water, or under the effect of agricultural activities. The present study aims to estimate average soil loss rate and to identify vulnerable zones. Through the integration of RUSLE model at the Saf Saf watershed, various parameters are utilized such as the rainfall erosivity factor (R), soil erodibility factor (K), slope length - slope factor (LS), crop management factor (C) and practice management factor (P). All these parameters are prepared and processed through a geographic information system (GIS) and remote sensing using various database sources. The results reveal that the river basin has an average annual soil loss of 3.9 t ha−1 yr−1, and annual soil loss of 4.53 million tonnes for the period 1975-2017. Meanwhile, eighty five percent of the study area is experiencing acceptable rate of soil erosion loss, which is ranging between 0 to 5 t ha−1 yr−1. The present study of risk assessment can contribute to understand the spatial pattern of soil erosion in order to use appropriate conservation practices for sustainable soil management.

Soil erosion risk assessment and treatment priority classification: A case study on guder watersheds, Abay river basin, Oromia, Ethiopia

Soil erosion is the most persistent environmental problem in the Upper Blue Nile River (UBNR) basin of Ethiopia. Guder River is one of thetributaries of UBNR basin which critically required soil conservation practices. The main objective of this particular research article was to appraise soil erosion hazard priority classification with an easy and uncomplicated erosion modelling tool, the universal soil loss equation (USLE) using GIS software and RS data. Remote Sensing data such as annual mean precipitation, land-use land-cover, and soil map, digital elevation model map were used to determine the USLE factor values. The average annual rainfall data was derived from the widely used climate dataset CRU TS (Climatic Research Unit gridded Time Series) and converted to rainfall erosivity factor. Soil Erodibility Factor Soil (K) was calculated from FAO soil data “Digital Soil Map of the World - ESRI shapefile format”. Topographic Factor (LS) was delineated from a 30m digital elevation model. Cover Factor (C) and Support Practice Factor (P) were estimated from a 20m Ethiopia Sentinel2 Land-use Land-cover year, 2016. The study classified the Guder watersheds into different kinds of severity classes for prioritization of soil and water management options and conservation strategy. The mean annual soil eroded for the whole sub-basin was estimated at 25.23 tha−1y−1. The study output outcomes demonstrated that about 0.1% (426ha) 6.9% (46764 ha), 8.9% (60055 ha), and 19.8 % (134320ha) have been under Catastrophic, very severe, severe, high erosion severity class respectively. About half of the Guder sub-basin has been underneath a very slight erosion. Nevertheless, the area covered by very severe erosion was 6.9%, and the annual percent of sum-total soil erosion accounted for was 46.86%. The second and third in magnitude soil lost annually from the sub-basin with regards to per cent of total soil loss were severe (26.53%), and high (21.53%) respectively. In only 7% of the area under investigation, soil erosion estimated was to go beyond 100 t/ha/yr. erosion rate. District wise erosion affected and hotspot areas were identified: Middle of Steep slopes Mountainous parts of Ginde Beret, Jeldu, Ifata, Ambo, parts Ababo and Horo Guduru located in the study area borderline, Toke Kutaye, along the boundary of Midakegn and Cheliya were found in severe to very severe erosion. Finally, the study proposed that the government, policymakers, and soil and water management agents plan and implement the conservation measures and give awareness to stakeholders for optimum use of limited precious resources.

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

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

Assessment of soil loss rate using GIS–RUSLE interface in Tashat Watershed, Northwestern Ethiopia

The erosion of soil is one of the most difficult and ongoing problems caused by deforestation, improper cultivation, uncontrolled grazing, and other anthropogenic activities. As a result, assessing the level and quantity of soil erosion is essential for agricultural productivity and natural resource management. Thus, the goal of this study was to quantify soil loss rates and identify hotspot locations in the Tashat watershed, Abay basin, Ethiopia. Thematic factor maps, comprising rainfall erosivity factor (R), soil erodibility factor (K), topography factor (LS), cover and management factor (C), and conservation practices factor (P), were integrated using remote sensing data and the GIS 10.3.1 environment to estimate soil loss using RUSLE. The findings indicated that the watershed annual soil loss varies from none in the lower part to 3970.6 t ha−1 year−1 in the middle, with a mean annual soil loss of 64.2 t ha−1 year−1. The total estimated annual soil loss was 61,885,742.9 tons from the total watershed area of 48,348.4 ha. The majority of these soil erosion-affected places are geographically located in the watershed middle steepest slope portion, where Cambic Arenosols with higher soil erodibility character than other soil types in the research area predominate. Thus, sustainable soil and water conservation techniques should be implemented in the steepest middle section of the study area by respecting and acknowledging watershed logic, people, and watershed potentials.

Rainfall Erosivity Factor Update in Uruguay

The Universal Soil Loss Equation (USLE) and its revised form (RUSLE) are the soil erosion models incorporated into the EROSION 6.0 software. This, in turn, is the soil conservation planning tool used to fulfill the soil conservation regulations in Uruguay. The objective of this technical note was to use the methodology proposed by Renard and Freimund (1994) to update the rainfall erosivity factor (R factor) values incorporated into the EROSION 6.0 software. Additionally, it had the following specific objectives: to evaluate the impact of the length of the rainfall data series on the estimation of the R factor and to compare the updated values to the ones that are currently used. The rainfall data base used to estimate the R factor comprises the 1980-2009 period in different Uruguay locations. The equations used in this work allowed to update the R factor, and to obtain R factor values that agree with published R values for the region. The regression line intercept and slope between the 30 yr R factor estimated by both equations did not differ from zero (P < 0.29), and one (P < 0.70), respectively. The length of rainfall data series affects the R factor value, given the high interannual rainfall variability. We recommend using R factors based on 30 years of pluviometric records.

Statistical evaluation of proxies for estimating the rainfall erosivity factor

Considering the high-temporal-resolution rainfall data requirements for calculating the Rainfall Erosivity factor (that is, the R-factor), studies have developed a large number of proxies for the R-factor (PR). This study aims to evaluate 15 widely used proxies, which were developed in various countries using daily, monthly, or yearly rainfall data, in terms of correlation and statistical equality with the R-factor by using the 6-min pluviographic data from 28 stations in Australia. Meng’s test was applied to rank the correlations. Although the Meng’s test indicated that the correlation between Rainfall Erosivity (R) and Rainfall Erosivity calculated by the proxy model (PR) generally increased with a finer time resolution of the rainfall data (in the order of year, month, and day), the 15 PRs under examination were all highly correlated with R (r > 0.62, p < 0.004), implying that all of them can be reasonably used as an R predictor. A direct estimation of the R-factor using PRs produced a mean relative error (MRE), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NSE) with a mean of 50.0%, 1392 MJ mm ha−1 h−1 a−1, and 0.17, respectively. The linear calibrations improved the accuracy of the estimation and produced an MRE, RMSE, and NSE with a mean of 36.0%, 887 MJ mm ha−1 h−1 a−1, and 0.70, respectively. Finally, suitable proxies for instances where only daily, monthly, or yearly rainfall data are available were recommended.

Remote Sensing Quantitative Research on Soil Erosion in the Upper Reaches of the Minjiang River

The upper reaches of the Minjiang River are an important ecological barrier to the upper reaches of the Yangtze River and Chengdu Plain. They are also the water resources of Chengdu Plain. To protect the headwater ecosystems, it is necessary to carry out quantitative research on soil erosion in this area. This study mainly applies the USLE model to quantitatively evaluate the spatial distribution of ecosystem services on soil erosion and soil conservation. The evaluation is based on the following data: remote sensing image, meteorological radiation data, DEM, soil types, physical and chemical properties of the soil, vegetation types, and land use status. With advanced Earth observation technology, satellite remote sensing images about terrestrial vegetation and other evaluation parameters can be obtained in real time. The seasonal changes in vegetation coverage and the physical and chemical properties of soil have been fully considered. The results show that 1) the value of rainfall erosivity factor (R) is between 85.02 and 588.69 MJ•mm/(hm2•h•a), and its spatial distribution is consistent with that of annual average precipitation. 2) Soil erodibility factor (K) is between 0.12 and 0.30 (t hm2 h) •(hm2 MJ mm), showing zonal distribution, and is related to soil types. 3) Slope length and gradient factor (LS) range from 0.03 to 46.16. It is positively correlated with topographic relief. 4) Soil conservation measure factor (P) ranges from 0 to 1 and is determined by the land use and coverage. 5) Rainfall has a dominant impact on soil erosion, and the study area with violent and intense erosion reaches 17,302.17 km2, accounting for 69.81% of the total area. The soil conservation amount (T) in the study area is 283.45 million tons, and the ecosystem services are worth 434.48 million yuan. 6) RS and GIS techniques enable quick estimation. However, this assessment contains considerable uncertainty. It is still hard to reveal the physical process of soil erosion with empirical models.

Characteristics and predictive models of hillslope erosion in burned areas in Xichang, China, on March 30, 2020

Wildfires often greatly aggravate hillslope erosion, which is a complex, highly dynamic and constantly changing water-soil interaction process. In this study, the remote sensing interpretation, field investigation, in-situ soil erosion pins experiments, and laboratory test were used to examine the controlling factors of post-fire hillslope erosion after the Xichang Fires occurred on March 30, 2020, in southwest Sichuan Province, China. We developed an empirical model to predict predicting post-fire hillslope erosion, and evaluated the model performance at watershed scale combined with the Sediment Delivery Ratio model (SDR). The controlling factors of hillslope erosion (fire severity, soil properties, rainfall, and topography) were collected. The correlations between rainfall events-based hillslope erosion depth (HED) and the controlling factors were examined. The results show that the fire severity has the strongest correlation, followed by soil properties, rainfall, and topography. The new predictive models were developed using multiple linear and non-linear (power law relationship) regression based on the normalized differenced Normalized Burn Ratio (ndNBR), soil erodibility (K), accumulated rainfall erosivity (∑R) and topographic factor (LS). Then, the methods were evaluated by comparing the predicted and observed values on the testing subset based on the statistical coefficients including R-squared, root mean squared error (RMSE) and discrepancy ratio (DR). The results suggest that the developed empirical model based on multiple non-linear (power law relationship) has a better performance (R-squared: 0.600, RMSE: 1.948), and the discrepancy ratio of 87.2% data range from 0.5 to 2. The model was used to estimate the spatial distribution of hillslope erosion depth across the entire study area, and the sediment yield at watershed scale (WSY) combined with the SDR model. The comparison between the observed sediment mobilized by debris flow and the predicted WSY shows that the model combined with the SDR showed an acceptable performance at watershed scale.

Modelovanie vodnej erózie na poľnohospodárskej pôde okresu Banská Bystrica

Relevant information and data on the intensity of water erosion and its area distribution are essential for selecting the most appropriate agricultural land management in an erosion-sensitive landscape. The land users can target appropriate soil protection measures (based on a correct evaluation of the obtained information) to those parts of the territory most significantly affected by erosion-accumulation processes. The USLE erosion prediction model is a suitable tool for evaluating the area from the long-term prediction of water erosion intensity and its area distribution and proposing appropriate anti-erosion measures. Relief, soil erodibility, rainfall erosivity, soil protection effect of vegetation cover, and agro-techniques are the main erosion factors that significantly influence the start and process of erosion-accumulation processes in a given locality. These factors are considered in the model's basic structure. The model has been updated and detailed several times with the aim of a more precise assessment of the territory. An updated digital layer of rainfall erosivity factor (R-factor) generated based on its updated values during its last update was incorporated into its structure. USLE erosion model refinement was achieved based on updating the digital layer of the rainfall erosivity factor (R-factor), which was generated based on its updated values. The results are more informative and precise outputs concerning the intensity of water erosion and its area distribution in specific soil-climatic and geomorphological conditions.

Mapping rainfall erosivity over India using multiple precipitation datasets

Rainfall erosivity is a measure of the erosive force of rainfall which represents the potential of rain to cause soil erosion. A large proportion of the total eroded soil in India is due to erosion by water, and rainfall erosivity is one of the major components. The current assessments of rainfall erosivity in India are however largely based on rain-gauge recordings and surveys which hinders its estimation and understanding over large areas. Growing availability of remotely-sensed gridded precipitation datasets presents an unprecedented opportunity to study long-term rainfall erosivity over varied terrains and address some of the limitations of point data-based estimations. In this study, multiple national and global gridded precipitation datasets were utilized to develop a high-resolution rainfall erosivity factor (R-factor) map to highlight areas prone to rainfall-induced erosion. Further, a large selection of empirical equations from literature were employed for estimating rainfall erosivity to provide a comparative analysis of these commonly adopted methods. The calculated rainfall erosivity is also compared with alternative methods to estimate R-factor such as Fournier Index (FI) and Modified Fournier Index (MFI). It was observed that MFI is highly correlated with rainfall erosivity, and an equation was finally proposed to estimate R-factor using MFI. This is the first such national-scale assessment of rainfall erosivity over India using gridded precipitation datasets, which will aid in understanding and mitigating rainfall-induced erosion.

QUANTITATIFICATION OF SOIL EROSION USING CAUSATIVE FACTORS IN THE GIS ENVIRONMENT FOR SYLHET DISTRICT, BANGLADESH

The aim of this study was to enumerate the potential soil erosion in the Sylhet district of Bangladesh using the Revised Universal Soil Loss Equation (RUSLE) approach in the software-based windows. Using the rainfall erosivity factor (R), the soil erodibility factor (K), the cover management factor (C), the topographic factor (LS) and the support practices factor (P), the RUSLE formula quantified the possible soil loss rate. The soil erosion rate was classified into six classes based on the erosion severity. The result shows that the annual soil deterioration rate differs from 0 to 799.67 t/ha/year. However, almost about 97.71% of the total surface area is under low potential (54.1%) and moderate (43.61%) erosion risk. Rather about 2.29% area of Sylhet district was under average, high, very high and critic erosion risk. Result indicates that the north-eastern and central zone are highly vulnerable to soil erosion due to steep slope and P factor.

Projections of rainfall erosivity in climate change scenarios for the largest watershed within Brazilian territory

Global climate change can potentially threaten agricultural production due to endangered natural resources, such as rainfall patterns. Thus, extreme rainfall events can cause greater rainfall erosivity, consequently, greater soil erosion. Conversely, a reduction in rainfall amount can lead to water scarcity for the agriculture production process. This way, it is a foremost need to model climatic conditions under global climate change scenarios, particularly in places where rainfall data tends to increase. This work aimed to project rainfall erosivity in the major Brazilian watershed, the Tocantins-Araguaia river basin, throughout the 21st century under two Intergovernmental Panel for Climate Change Fifth Assessment Report (IPCC AR5) Scenarios, the Representative Concentration Pathways, RCP4.5 and RCP8.5 scenarios. This study uses the downscaling of four global climate models of the Coupled Model Intercomparison Project (CMIP5) by the Eta regional climate model, used by the Brazilian National Institute for Space Research. The average rainfall erosivity was calculated based on the Modified Fournier Index in three periods of 30-year length throughout the 21st century. Time series of R-factor were analyzed at rain gauge station points overlapping regional model grid cells over the basin for the 1961–2099 period. Projections indicated lower annual average rainfall erosivity values in comparison with historical data. Estimated mean rainfall erosivity values were 10,977.69 ± 526 MJ mm ha−1 h−1 yr−1 for the RCP4.5 scenario, and 10,379.71 ± 723 MJ mm ha−1 h−1 yr−1 for the most pessimistic climate change scenario, RCP8.5. The largest reductions of the mean R-factor reached 5,5% for the multi-model ensemble projections for near future, and 15.4% for the ensemble projections models for long-term, with the greatest decreasing trends under RCP8.5. Reductions greater than 2,000 MJ mm ha−1 h−1 are expected throughout the 21st century according to multi-model ensemble projections models under RCP8.5 scenario in most of the watershed. Decreasing rainfall erosivity factor in both RCP scenarios was due to a lower rainfall depth. However, the value of rainfall erosivity is still considered high and should be taken into account in soil conservation practices. Furthermore, the smaller rainfall amount indicates a possible reduction in water availability for crops of longer cycle, and increase in spatial variability of less intense rainfall.

Comment on “Changing climate and land use of 21st century influences soil erosion in India” by Pal et al., Gondwana Research, Volume 94, Pages 164–185

The source of the rainfall erosivity equation used by Pal et al. (2021) cannot be confirmed. It is a part of a growing trend of misusing empirical equations to estimate the rainfall erosivity factor R when the rainfall data are scarce. We provided a plausible source of the correct version of the rainfall erosivity equation in this comment.

Comment on “Multi-fractal characteristics of reconstructed landform and its relationship with soil erosion at a large opencast coal-mine in the loess area of China” by Shi et al. (2021)

We were intrigued by the soil erosion modeling conducted by Shi et al. (2021) on post-mining areas in China. However, we discovered that the authors employed an incorrect empirical equation to calculate the rainfall erosivity factor, which resulted in a severe underestimation of the true rate of soil erosion. We presented our findings and discussed a possible source for the true equation.

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.

Soil Erosion Risk Assessment for Prioritization of Soil Conservation Measures Using RUSLE Integrated with GIS in Chemoga Watershed, Upper Blue Nile Basin, Ethiopia.

Soil loss by runoff has become series problem in Ethiopian; particularly the northern Ethiopian highlands are among the most seriously affected regions in the country due to highly rugged topography, population pressure, over grazing and cultivation of steep lands and erosive nature of rainfall. Hence, soil erosion risk assessment is critically required for prioritization of soil conservation measures. The main objective of this study is to estimate soil erosion rate and identify hotspot areas of soil erosion to facilitate conservation management practices in Chemoga watershed, Upper Blue Nile Basin, Ethiopia. In this study, RUSLE method was used integrated with GIS technique to estimate soil erosion rate of a watershed and prioritize for soil and water conservation practice. In the study rainfall erosivity factor (R), Length slope factor (LS), soil erodability factor (K), vegetation cover factor (C) and erosion control factor (P) were used as an input to the RUSLE model. The rainfall erosivity factor has been determined from monthly precipitation data. The soil survey data was used to develop the soil erodability factor and the digital elevation model (DEM) of study area was also used to generate topographic factor (LS). The value of cover management factor and support practice factor were obtained from land use land cover map and validated with field observations. The results show that midstream and upstream parts of the watershed is more sensitive to soil erosion than the lower part. In the study, the erosion hazard maps were reclassified for the periodization of the watersheds. Based on the special distribution of the erosion hazards, 4.93% of the watershed area was classified sever soil erosion risk zone (30 up to 50 ton/ha/year) and 4.91% categorized as very sever soil erosion risk zone i.e above 50 ton/ha/year which are situated in the narrowest steep slope parts of the upper catchments of the watershed. These parts of the watersheds were identified as the primary erosion hotspot area which needs immediate attention in terms of management perspective.

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.

Assessment of Various Empirical Soil Loss Estimation Equations in Arid Regions

One of the most commonly used equations to estimate soil erosion is the revised universal soil loss equation (RUSLE). Based on the early approach developed by the Soil Conservation Service of USA, the rainfall erosivity factor (R-factor) in the RUSLE equation requires sub-daily rainfall data, which is usually not available. Other empirical equations estimate R-factor based on available rainfall data like annual and monthly rainfall data. In arid regions such as the Arabian Peninsula, several studies estimated the R-factor based on these empirical equations without calibration. We propose in this paper to assess the applicability of some of these empirical equations against R-factor values calculated using as a reference the RUSLE approach. For this data, data from 104 stations with sub-daily rainfall was collected. The reference R-factor was calculated for the 104 stations. The results of seven empirical equations were tested against the reference R-factor. Most of the tested equations significantly underestimated the R-factor. Furthermore, the obtained RMSE and MAE values were almost as high as the average R-factor, with MAPE exceeding 100%. Therefore, it is recommended not to apply these equations in arid regions. A recalibration of the form of equation that gave the best results, gave an RMSE of 280 (Mj·mm/(ha·hr)) and the MAPE dropped to 47.6%.

Study of soil erosion by using remote sensing and GIS techniques in Sone command area in Bihar, India

Soil erosion is caused by various reasons, but the land slope and poor land management are the main reasons. Identification of erosion-prone areas is the most important factor for agricultural planning and effective land management. This study includes an estimation of average annual soil loss in the Sone command area in Bihar and the preparation of spatially distributed soil erosion map for different land-use scenarios. The soil in the study area is generally clay loam. A well-known empirical equation i.e. RUSLE (Revised Universal Soil Loss Equation) has been used to estimate parameters and prepare spatially distributed maps. By using remote sensing and GIS techniques different soil loss parameters like rainfall erosivity (R), soil erodibility (K), slope length and steepness factor (LS), cover management factor (C) and conservation practice factor (P) are estimated and spatially distributed map of all these parameters are prepared. Average soil loss in the study area was found in the range 21 to 40 t.ha−1.yr−1 and the maximum value is found to be around 7000 t.ha−1.yr−1. It was also found that areas with high soil erosion rates are very low. Around 46% variation is observed between the maximum and minimum average soil loss rates.