High Soil Erosion Risk Research Articles

Spatial distribution of soil water erosion at Al-Hawiz Dam in Western Syria using CORINE model

Water erosion is one of the most important environmental problems that cause soil degradation in the areas of water basins in the Syrian coast. The present study aims at determining the spatial distribution of the risk of water erosion of the lands of Al-Hawiz Basin (Jableh region) using the CORINE model. The CORINE model is based on calculating all factors that affect water erosion: soil erosion factor, erosion factor, inclination factor, ground cover fac tor. The soil susceptibility factor for erosion was calculated by estimating both the soil texture, soil depth and gravity percentage for preparing a potential risk map. The feasibility of both the soil susceptibility factor map and the slope map prepared from the use of DEM and the value of the erosion factor were calculated using system technology Geographical information. Then the ground cover map was prepared and classified into two rows according to the degree of protection. The actual risk map was prepared from the seriousness of the potential danger map and the land cover map. The study showed that 12.79% of the studied area is under the control of severe erosion while 32.10% of which had an average risk of erosion, and the risk of erosion was low in the largest part of the area (55.11%). The areas of severe erosion have been concentrated in the central western region of Lake Al-Sadd. It has been shown that vegetation is the most influencing factor in erosion, as it worked to reduce the high risk of possible soil erosion by 50.83%.

Estimates of soil erosion rates in a principal watershed in Gozo, Malta under current and future climatic conditions

The aim of the study was to estimate the current and future annual soil erosion rate in the Ramla watershed situated in Gozo, Malta. The RUSLE-GIS was applied using innovative approaches, such as the inclusion of the highest possible DEM data as well as reflecting climatic changes to the R Factor based on the latest climate projections for Gozo derived from a total of eight CMIP6 climate change models. A climate change impact analysis was carried to estimate the likely future risk of soil erosion in this local watershed for the first time. The estimated annual soil loss, as a percentage of the total area of study varies from low (85.1%), to moderate (13.3%) and high (1.6%). Within a climate change context, a reduction in the annual soil erosion rate has been estimated for the period 2080–2100, ranging from −5 ton ha−1 yr−1 under SSP126 to −6 ton ha−1 hr-1 under SSP585. The estimated decrease of annual soil loss within the high soil erosion risk category is attributed to an increased aridity of the local climate as projected by eight CMIP6 climate models. Specific hydrological sub-watersheds were identified within the study area that warrant urgent considerations. The outcomes of this study are based on the inclusion of micro-topographical information derived from high resolution 1 m LiDAR DEM. This modelling work is seen as the first study which can be used to provide some objective guidance on the minimization of potential soil losses within the Ramla watershed. Our estimated soil loss and its spatial distribution can assist in a fuller management plan for the sustainability of this watershed. These results should not be interpreted in absolute terms but as an understanding of the underlying controlling processes, the relative spatial and temporal variations, and of possible future scenarios.

Predicting the Impact of Future Land Use and Climate Change on Potential Soil Erosion Risk in an Urban District of the Harare Metropolitan Province, Zimbabwe

Monitoring urban area expansion through multispectral remotely sensed data and other geomatics techniques is fundamental for sustainable urban planning. Forecasting of future land use land cover (LULC) change for the years 2034 and 2050 was performed using the Cellular Automata Markov model for the current fast-growing Epworth district of the Harare Metropolitan Province, Zimbabwe. The stochastic CA–Markov modelling procedure validation yielded kappa statistics above 80%, ascertaining good agreement. The spatial distribution of the LULC classes CBD/Industrial area, water and irrigated croplands as projected for 2034 and 2050 show slight notable changes. For projected scenarios in 2034 and 2050, low–medium-density residential areas are predicted to increase from 11.1 km2 to 12.3 km2 between 2018 and 2050. Similarly, high-density residential areas are predicted to increase from 18.6 km2 to 22.4 km2 between 2018 and 2050. Assessment of the effects of future climate change on potential soil erosion risk for Epworth district were undertaken by applying the representative concentration pathways (RCP4.5 and RCP8.5) climate scenarios, and model ensemble averages from multiple general circulation models (GCMs) were used to derive the rainfall erosivity factor for the RUSLE model. Average soil loss rates for both climate scenarios, RCP4.5 and RCP8.5, were predicted to be high in 2034 due to the large spatial area extent of croplands and disturbed green spaces exposed to soil erosion processes, therefore increasing potential soil erosion risk, with RCP4.5 having more impact than RCP8.5 due to a higher applied rainfall erosivity. For 2050, the predicted wide area average soil loss rates declined for both climate scenarios RCP4.5 and RCP8.5, following the predicted decline in rainfall erosivity and vulnerable areas that are erodible. Overall, high potential soil erosion risk was predicted along the flanks of the drainage network for both RCP4.5 and RCP8.5 climate scenarios in 2050.

Assessing the impact of climate change on soil erosion in East Africa using a convection-permitting climate model

East Africa is highly reliant on agriculture and has high rates of soil erosion which negatively impact agricultural yields. Climate projections suggest that rainfall intensity will increase in East Africa, which is likely to increase soil erosion. Soil erosion estimates require information on rainfall erosivity, which is calculated using sub-daily storm characteristics that are known to be biased in traditional parameterized convection climate models. Convection-permitting climate models, which are run at higher resolution to negate the need for convection parameterization, generally better represent rainfall intensity and frequency. We use a novel convection-permitting pan-Africa regional climate model (CP4A) to estimate rainfall erosivity in Tanzania and Malawi, and compare it to its parameterized counterpart (P25), to determine if there is a benefit to using convection-permitting climate models to look at rainfall erosivity. We use eight year historical and end-of-century (RCP8.5) climate simulations to examine the impact of climate change on soil erosion in Tanzania and Malawi based on rainfall erosivity estimates from CP4A and P25 applied to the Revised Universal Soil Loss Equation. The effectiveness of soil conservation measures was also evaluated. Rainfall erosivity was lower in P25 than in CP4A and was a poorer match to observational storm characteristics, even after bias-correction. These results suggest that parameterized convection regional and global climate models might under-estimate rainfall erosivity, and the associated soil erosion. We found high values of present day erosion in mountainous regions in Tanzania and Malawi in CP4A. Under climate change, areas at high risk of soil erosion expanded due to increases in rainfall intensity in CP4A. Terracing was less effective at reducing soil erosion risk in the future than in the present day, and more extensive soil management may be required to manage soil erosion and reduce the negative impacts of soil erosion on agriculture.

Spatial Targeting of Agricultural Support Measures: Indicator-Based Assessment of Coverages and Leakages

Agricultural support programs distribute payments to farms based on a diverse set of policy objectives. Adequate targeting of this support to priority areas is key to efficient and effective policy. We evaluated the targeting strategy of a national-level program in Mexico that distributed support based on seven criteria that prioritized poor smallholder farming communities at high risk of cropland failure. We used a series of logistic models to assess the coverage and leakage rates of the program’s targeting strategy and found rates of about 80 and 20 percent, respectively. We also found significant differences between the targeting priorities specified in program rules and the observed distribution of support measures. In general, the program favored arid and semi-arid regions at high risk of soil erosion but neglected smallholder farms in high-poverty regions with elevated rates of cropland failure. Our findings highlight the continued lack of financial support for smallholder agriculture in Mexico, despite program rules and priority statements that stress the vulnerability of this sector. This study also illustrates the important role of spatial targeting in better aligning agricultural support payments with stated policy priorities. This alignment is often overlooked in ex-post assessment, but it is critical for improving targeting precision, equity, and overall policy effectiveness.

Mapping tillage direction and contour farming by object-based analysis of UAV images

Tillage is a primary agricultural task that causes progressive soil movement and, consequently, severe erosion in sloping farmland, with a high impact on crop productivity, soil quality and landscape features. Governments and public administrations acknowledge the need to control tillage operations for a better soil conservation in vulnerable areas, recommending the practice of contour farming. Despite this importance, there is currently no mechanism for the effective and large-scale monitoring of tillage patterns at the field or regional scale. Accordingly, this research combined aerial images taken with unmanned aerial vehicles and object-based image analysis (OBIA) to develop an innovative OBIA4tillage procedure with three main objectives: (i) analysing plowed agricultural fields, identifying and mapping the tillage marks, and automatically computing the main direction of the tillage furrows, (ii) validating the procedure quality in different scenarios by evaluating the accuracy of the results as affected by the sensor used (visible-light vs. multispectral), background soil hue, and ground vegetation density; and (iii) mapping contour farming and non-contour farming areas as indicators of potential low and high soil erosion risk, respectively. Twenty olive parcels from two different locations with a wide range of tree sizes, soil hue, parcel shapes and land slopes were selected as model systems to develop and validate the procedure. The OBIA4tillage procedure produced tillage maps with very high accuracy for both RGB and multispectral images (R2 of 0.99 and 0.93, respectively), as obtained from the linear equation between estimated and ground-truth values. The results were similar in clear and dark soils (R2 of 0.96 in both cases), although there were notable differences between areas of dense ground vegetation or bare soil (R2 of 0.99 in both cases) and areas of medium vegetation cover (R2 of 0.81). The application of contour farming in the study region was moderate at location 1 (42.35% of the study area) but more widespread at location 2 (72.60% of the study area), which revealed the uneven involvement of the local farmers in the challenge of controlling soil erosion risks. The valuable information in the OBIA4tillage outputs can also be used to study tillage events at various spatial and temporal scales, feed soil erosion models and promote good agricultural and environmental conditions measures implemented at the parcel scale.

Assessing land degradation as the impact of deforestation due to the expansion of oil palm plantation in Rokan Hulu, Riau

In Riau province, Sumatra much of the deforestation has been associated with the extension of oil palm plantations on soils that are particularly vulnerable to soil erosion and land degradation. Most of the deforestation in Riau occurred on highly erodible Ultisols, on slopes steeper than 7 degrees and under a high and erosive rainfall regime, which, when taken together can result in a high risk of soil erosion and land degradation. Land degradation depends on soil erodibility, climatic factors expressed as rainfall erosivity, and slope conditions. This research aims to determine the impact of deforestation, particularly on the risk of land degradation. The rate and extent of deforestation was determined using remote sensing data published from reliable sources. The risk of land degradation assessed through soil erosion analysis in Riau Province by using published soil survey information and data to calculate soil erodibility and by combining this with other factors such as rainfall erosivity for the region and an assessment of slope factors. The estimation of the risk of land degradation has been achieved by a quantitative assessment of the potential soil erosion loss by applying the USLE (Universal Soil Lost Equation) to discrete land facets within Riau Province. The result of this study indicates that slight and moderate risk erosion class were dominant over most of the study area with 239,389 ha (47.92 %) and 129,391 ha (26.23 %), respectively. About 25% of the study area was predicted to suffer from erosion risk of more than 60 ton/ha/year.

Understanding the influencing factors (precipitation variation, land use changes and check dams) and mechanisms controlling changes in the sediment load of a typical Loess watershed, China

The soil erosion and sediment load of the coarse sandy hilly catchment region of the Yellow River basin, which is the main source of sediment for the Yellow River, have become sources of concern for researchers as well as for the local population. Soil and water conservation measures, especially reforestation and check dam construction, have been progressively implemented in this region for several decades. During this period, the corresponding precipitation in the region is also changing dynamically. However, it is still unclear how precipitation variations, land use changes and check dams affect soil erosion and sediment yield in a typical watershed. A combination of field investigations and model simulations was employed to quantitatively identify the impacts of the above three factors on soil erosion and sediment yield in the Kuye River watershed. Significant land use changes, including the conversion of arable land and bare land into grassland, shrubland, forestland and construction land, occurred in the studied watershed from 1987 to 2016. In addition, 306 key dams were built in the watershed, with a total storage capacity of 316.64 Mm3, according to statistical data obtained in 2011. A hot spot analysis showed that the regions that were at high risk of soil erosion and sediment yield were mainly concentrated in the middle reaches of the watershed. The simulation results showed that check dams were the dominant factor affecting sediment load, reducing the total sediment load by 54% in 2006. However, from 1987 to 2016, the contributions of the three studied factors (precipitation variation, land use changes and check dams) to sediment reduction were 29%, 40% and 31%, respectively, which indicated that these factors all had significant influences on the sediment load. More attention should be paid to check dams, and corresponding measures should be taken to protect them, especially during flood periods. These results can serve as a reference for watershed management and policy implementation.

Linking soil redistribution to soil organic carbon using 210Pbex along different complex toposequences in a karst region, southwest China

Understanding the magnitudes and patterns of soil and associated soil organic carbon (SOC) redistribution is of great importance for assessing both soil degradation and rocky desertification in fragile karst ecosystems. However, to date reliable information on the application of excess lead-210 (210Pbex) for quantifying dynamics of SOC-soil redistribution relationship is limited on the hillslopes due to the highly heterogeneous landscape after land use change in karst systems. In this study, three typical toposequences (grassland, forests, and agroforestry) were selected in a typical karst graben basin of southwestern China. A total of 229 soil samples were collected along the toposequences to explore the soil redistribution process using a model coupled with measured 210Pbex, and then to ascertain the relationship of discharged 210Pbex to erosion-induced SOC. The results showed that most soil samples were significantly affected by soil erosion (reference value of 210Pbex: 5986 ± 756 Bq m−2). The output of the model demonstrated that the highest estimated net soil flux occurred in agroforestry (21.38 Mg ha−1 yr−1), followed by forests (8.39 Mg ha−1 yr−1) and grassland (−3.54 Mg ha−1 yr−1). Meanwhile, the SOC concentration showed a decreasing order of agroforestry > forest > grassland on the hillslopes, implying that while agroforestry help increase SOC sequestration potential, intensive human intervention also carries a high risk of soil erosion in karst regions. The fitting correlation of 210Pbex and SOC in surface soils displayed the decreasing order of grassland (R2 = 0.80) > forests (R2 = 0.69) > agroforestry (R2 = 0.42), indicating that fallout 210Pbex could be used directly as a tracer to quantify the dynamics of the SOC-soil redistribution relationship in grassland toposequence. Our results provide practical evidence of the potential of 210Pbex for quantifying SOC-soil redistribution along hillslopes with complex toposequences in a karst region.

Intersecting RUSLE modelled and farmers perceived soil erosion risk in the conservation domain on mountain Elgon in Uganda

This study investigated the intersection between empirically derived and farmers perceived soil erosion risk in a medium-sized catchment on the Ugandan side of mountain Elgon. We postulated that farmers' perception of soil erosion risk was high and this influenced their employment of Soil and Water Conservation (SWC) measures on their land. An adapted Revised Universal Soil Loss Equation (RUSLE) was employed to model the soil erosion risk in a Geographical Information System (GIS) environment. Participatory Learning and Action (PLA) tools covering household interviews and Focus Group Discussions (FGDs) were implemented in two representative subcounties of the catchment to elicit information on farmers' perception of soil erosion risk. Household interviews covering 184 respondents were georeferenced using a Geographical Positioning System (GPS). Farmers perceived soil erosion risk on their land was then matched with RUSLE modelled risk using GPS positional data. The modelled soil erosion risk was substantial and a sizeable proportion of the catchment (63%) exhibited soil losses >10 t ha-1 yr-1, which is considered above the tolerable limit for mountain environments. A slight but significant agreement (p < 0.001) between the modelled and farmers perceived soil erosion risk was observed. In general, farmers perceived soil erosion risk was less than the RUSLE model estimates. Although 95% of farmers recognize soil erosion as problematic, only 65% implemented some aspect of SWC on their land albeit with varied purposes. Chi-square tests did not detect a strong association (p > 0.05) between farmers' perceived soil erosion risk and implementation of SWC on their land. On this basis, our postulation that perceived high soil erosion risk influences the implementation of SWC does not suffice in the studied catchment. We surmise that successful risk mitigation should be directed more on increasing farmer's awareness of the long-term adverse consequences of soil erosion on their land.

Dynamic changes in soil erosion risk and its driving mechanism: A case study in the Loess Plateau of China

AbstractThe Loess Plateau has long been considered as a very fragile area that suffers from serious water erosion. With the onset of global climate fluctuations and regional implementation of soil and water conservation measures, it is of great significance to effectively reveal the variations in soil erosion risk and its driving mechanism on the Loess Plateau. Taking the Yanwachuan watershed as a case study and using rainfall, soil, digital elevation model (DEM) and land‐use data, this study applied the Revised Universal Soil loss Equation (RUSLE) model with geographic information system (GIS) technology to analyse the temporal and spatial variations of soil erosion risk and to evaluate the effects of land‐use change and climate change on soil erosion from 1981 to 2016. Future soil erosion risk was also assessed under different land‐use and climate‐change scenarios. Results showed that annual soil loss in the Yanwachuan watershed presented a significant decreasing trend with a rate of 47.928 t km−2 a−1 and that average annual soil loss was 3,543.7 t km−2 from 1981 to 2016. However, some areas, even with good vegetation cover, still had a rather high soil erosion risk (exceeding the severe erosion rate of 5,000 t km−2 in 2000–2016) if located in hilly and gully slope regions. Land‐use change and climate variation contributed 37.2% and 62.8%, respectively, to reducing soil erosion, showing that climate variation played a leading role in current soil‐erosion reduction. In addition, the average soil losses in the 2020s, 2030s and 2040s under different scenarios are predicted to increase by 8.6–42.6% compared with the average soil loss (2,993.8 t km−2) in 2001–2016. Future climate change may be the main driving factor in enhancing the risk of soil erosion. Hence, there is an urgent need to strengthen soil and water conservation research and management in future years.Highlights This study assessed current and future water erosion risk on the Loess Plateau using the RUSLE model. Areas with steep slopes were still experiencing serious soil erosion risk in 2000–2016. Climate change was the main driving factor for soil erosion variation. Future erosion risk is expected to increase under different climate and land‐use scenarios.

Estimation of annual erosion in six soil series of the Federal University of Agriculture Abeokuta Nigeria using Rusle in Map Algebra

Anthropogenic activities such as deforestation, bush burning and extensive opening of farmlands in the 10,000 hectares at the Federal University of Agriculture, Abeokuta (FUNAAB) have increased vulnerabilities of the seven soil series in the campus to agents of erosion. Therefore it is important to evaluate the rate and locations of soil erosion in the study area inorder to develop erosion control intervention programme using standard methods. The results show that overall soil erosion ranges from 0 to 167.8 tons per hectare per annum. 10% of University land now has high risk of soil erosion being grasslands exposed to annual fires. Soil erosion is pronounced in the two soil series (Oke-imesi and Apomu) that are found along river fringes with topographic factors (Slope length and steepness) playing major role in the soil erosion.&#x0D; Keywords: Soil series; Erosion; Map algebra; Spatial mode

Spatial estimation of soil erosion using RUSLE modeling: a case study of Dolakha district, Nepal

BackgroundSoil erosion causes topsoil loss, which decreases fertility in agricultural land. Spatial estimation of soil erosion essential for an agriculture-dependent country like Nepal for developing its control plans. This study evaluated impacts on Dolakha using the Revised Universal Soil Loss Equation (RUSLE) model; analyses the effect of land use and land cover (LULC) on soil erosion.ResultsThe soil erosion rate categorized into six classes based on the erosion severity, and 5.01% of the areas found under extreme severe erosion risk (> 80 Mg ha−1 year−1) addressed by decision-makers for reducing its rate and consequences. Followed by 10% classified between high and severe range from 10 to 80 Mg ha−1 year−1. While 15% and 70% of areas remained in a moderate and low-risk zone, respectively. Result suggests the area of the north-eastern part suffers from a high soil erosion risk due to steep slope.ConclusionsThe result produces a spatial distribution of soil erosion over Dolakha, which applied for conservation and management planning processes, at the policy level, by land-use planners and decision-makers.

Future soil loss in highland Ethiopia under changing climate and land use

Soil erosion caused by climate and land-use changes is one of the biggest environmental challenges in highland Ethiopia. The aim of this study was to assess the future soil erosion risks and evaluate the potential conservation measures in the Rib watershed, northwestern highland Ethiopia. We used the HadGEM2-ES model with a moderate greenhouse gas (GHG) concentration scenario (RCP4.5) to project the future climate. The future land-use patterns were predicted using the CA-Markov model. We integrated the RUSLE model with GIS to estimate the spatial distribution of soil loss and identify erosion risk areas. We found that the Rib watershed is highly vulnerable to future climate and land-use changes, leading to a high soil erosion risk. Despite slight growth of forest cover during the study period, the total soil loss for the watershed was estimated to be 7.93 × 106 t year−1 in 2017 and was predicted to increase to 9.75 × 106 t year−1 in 2050, an increase of about 23%. The increase in forest cover was due to the expansion of the area of eucalyptus plantations which are more prone to erosion. Moreover, field survey showed that the residual native forests are sparsely vegetated and mostly used for cattle grazing, increasing the erosion risk even more. In contrast, the combined use of afforestation with native trees and physical soil conservation measures in the upper areas of the catchment could decrease soil loss by 62%. Our results stress the importance of combining soil conservation measures, including converting eucalyptus plantations to native forests, to mitigate the effects of future climate change and increased agricultural production on soil erosion.

Identification of Dominant Factors Affecting Soil Erosion and Water Yield within Ecological Red Line Areas

Soil conservation and water retention are important metrics for designating key ecological functional areas and ecological red line (ERL) areas. However, research on the quantitative identification of dominant environmental factors in different ecological red line areas remains relatively inadequate, which is unfavorable for the zone-based management of ecological functional areas. This paper presents a case study of Beijing’s ERL areas. In order to objectively reflect the ecological characteristics of ERL areas in Beijing, which is mainly dominated by mountainous areas, the application of remote sensing data at a high resolution is important for the improvement of model calculation and spatial heterogeneity. Based on multi-source remote sensing data, meteorological and soil observations as well as soil erosion and water yield were calculated using the revised universal soil loss equation (RUSLE) and integrated valuation of ecosystem services and tradeoffs (InVEST) model. Combining the influencing factors, including slope, precipitation, land use type, vegetation coverage, geomorphological type, and elevation, a quantitative attribution analysis was performed on soil erosion and water yield in Beijing’s ERL areas using the geographical detector. The power of each influencing factor and their interaction factors in explaining the spatial distribution of soil erosion or water yield varied significantly among different ERL areas. Vegetation coverage was the dominant factor affecting soil erosion in Beijing’s ERL areas, explaining greater than 30% of its spatial heterogeneity. Land use type could explain the spatial heterogeneity of water yield more than 60%. In addition, the combination of vegetation coverage and slope was found to significantly enhance the spatial distribution of soil erosion (&gt;55% in various ERL areas). The superposition of land use type and slope explained greater than 70% of the spatial distribution for water yield in ERL areas. The geographical detector results indicated that the high soil erosion risk areas and high water yield areas varied significantly among different ERL areas. Thus, in efforts to enhance ERL protection, focus should be placed on the spatial heterogeneity of soil erosion and water yield in different ERL areas.

Assessing and mapping water erosion-prone areas in northeastern Algeria using analytic hierarchy process, USLE/RUSLE equation, GIS, and remote sensing

Soil erosion by water is a crucial environmental problem in different Mediterranean countries, resulting from various factors, such as geological, geomorphological, hydro-climatic conditions, and human activities. The study aims at developing water erosion vulnerability map to identify areas at risk in northeastern Algeria (Wilaya of Tebessa) based on the Revised Universal Soil Loss Equation model (USLE/RUSLE). Land use, lithology, slope, distance to stream network (DSN), and precipitation factors were combined to determine the soil erosion vulnerability using the analytical hierarchy process (AHP) approach and geomatics-based techniques (geographic information system and remote sensing). In total, soil erosion vulnerability increases from south to north of the study area. Slight, moderate, high, and very high classes covered for 7.35%, 49.48%, 43.04%, and 0.14% of the whole area, respectively. Our findings stressed the high expansion of water erosion risk, in particular, north of the study area. Protection and maintenance of erosion-prone areas with high risk of soil erosion represent a real challenge for both scholars and policymakers.

Effectiveness and feasibility of different management practices to reduce soil erosion in an agricultural watershed

Erosion is the most widespread form of soil degradation in Europe. EU Member States are called to identify areas prone to high risk of soil erosion and to adopt Best Management Practices (BMPs) to decrease land degradation. This study is aimed at identifying effective BMPs and their economic feasibility for controlling soil erosion in south-central Italy where lands are largely cultivated with winter wheat. The Soil and Water Assessment Tool (SWAT) was applied to simulate the baseline hydrologic and soil erosion processes of the Carapelle basin in the Puglia region, Italy. Calibrated sediment loads were reasonably accurate when statistically evaluated against measured data (R2 = 0.5, NSE = 0.5, PBIAS=−2.8 %). The model performed equally well for simulating stream flow rates (R2 = 0.6, NSE = 0.6, PBIAS = 5.3 %). The model maintained reliable performance during the validation period as well. Average annual specific sediment load was estimated 5.95 t ha-1 yr-1 mostly contributed by cultivated croplands. Based on regional agricultural policies, four management scenarios were implemented using the calibrated SWAT model: contour farming (BMP1), no tillage (BMP2); reforestation (BMP3) and contour farming and reforestation (BMP4). A threshold of sediment yield greater than 10 t ha-1yr-1 was selected to discretize target treatment areas where these BMPs were applied. Result show that combining contour farming and reforestation (BMP4) was the most effective (38 % reduction; from 5.95 to 3.70 t ha-1) for erosion control, followed by BMP2 (29 %; from 5.95 to 4.20 t ha-1), BMP1 (22 %; from 5.95 to 4.61 t ha-1) and BMP3 (15 %; from 5.95 to 5.04 t ha-1). An analysis of the farmer return-production cost ratio (FR/PC) indicated that the baseline (conventional tillage) and BMP1 were both economically sustainable in areas with slope <20 % (FR/PC = 1.12 and 1.11, respectively). BMP2 received the highest FR/PC rating of 1.67 in areas with slope <20 %. The baseline scenario had no economic advantage (FR/PC = 0.93) in steep slope areas. BMP3 was ranked at the top (FR/PC = 1.49) followed by BMP2 (FR/PC = 1.41) in areas with slope>20 %. The results show that a program of measures can be effective for controlling soil erosion but it must be implemented over long time frames and it requires relevant investments from the public and private sectors.

Estimation of soil loss by USLE Model using Remote Sensing and GIS Techniques - A Case study of Coastal Odisha, India

Globally, Soil erosion is the major land degradation problem, which impacts seriously on economic and environmental status. Geospatial techniques support and provided quantitative approach to estimate soil erosion in different conditions. In the present study, Revised Universal Soil Loss Equation (RUSLE) integrated with GIS has been used to estimate soil loss in the part of coastal Odisha system. The study area, Ganjam block have undulating topography covering 0-35% slopes. The quantitative soil loss was estimated and classified into different classes and soil erosion map was generated. The soil erosion map is classified into seven classes from very slight (<5 t ha -1 yr -1 ) to extremely severe (>80 t ha -1 yr -1 ). The results indicate that 90.9% (22330 ha) of the study area falls in very low erosion category, which may be due to level topography and regular vegetation cover. The other erosion classes such as moderate, high and very high erosion occurred in the range of 2.12%, 2.23% and 1.49 %, respectively. The high soil erosion risk is spatially situated in the foothills and upper steep slope of the area. The results can certainly aid in implementation of soil management and conservation practices to reduce the soil erosion in the coastal Odisha regions of Eastern India.

Assessment of land management practices on soil erosion using SWAT model in a Tunisian semi-arid catchment

PURPOSE: Soil erosion is a widespread problem that has threatened the majority of Mediterranean countries. Land management practices are widely used to minimize runoff and sediment loads at the catchment scale. However, their effectiveness cannot be easily assessed. Thus, models have been applied to estimate their impact on runoff dynamics and soil erosion and to identify the optimal conservation measure that can be used for specific land uses. MATERIALS AND METHODS: This study applied the SWAT model to spatially predict soil erosion over the Sarrath River catchment (1491 km²) in northwestern Tunisia, and to evaluate the effectiveness of different land management practices in reducing runoff and soil erosion. Calibration and validation of the model were done at the monthly scale for the period 2001–2008. Seven scenarios of best management were proposed including three combined scenarios. Each scenario needed a modification of one or more input parameters in SWAT model. The baseline values, scenario 0, represent the existing conditions. RESULTS AND DISCUSSION: The SWAT model was successful in reproducing water flow, and a good correlation between simulated runoff and sediment yield was observed. Simulated average soil erosion rate for the baseline conditions was 5.1 t ha⁻¹ year⁻¹. It was found that only 10% of the total area can experience high and very high soil erosion risk, but disproportionately contribute to the maximum amount of the total soil loss (71%). Implementation of individual scenarios reduced runoff from 5 to 22% and sediment loads from 5 to 30% from the baseline scenario. Combining different scenarios resulted in greater reduction. The highest reductions were achieved when combining reforestation of degraded lands and bare lands with contour farming and parallel terraces on agricultural fields. CONCLUSIONS: Simulation results showed that land conservation measures could reduce sediment yield by about 5 to 43% and runoff by 5 to 32%. For individual scenarios, parallel terraces were the most effective both for runoff and for sediment loss, followed by forestation. The best results were obtained for combined scenarios. Targeting the prioritized sub-catchments, reductions reached 78% for runoff and 88% for sediment loss in sub-catchment 4.

Soil erosion mapping and severity analysis based on RUSLE model and local perception in the Beshillo Catchment of the Blue Nile Basin, Ethiopia

BackgroundWater induced soil erosion is the most prevailing form of land resources deterioration in the highlands of Ethiopia, where huge amount of fertile soil is being lost annually. The purpose of this study was to estimate and map mean annual soil loss rates in the Gedalas watershed of the Blue Nile Basin, Northeastern Ethiopia. The estimation was carried out by using RUSLE model coupled with local perceptions. Soil, land use/cover, DEM, rainfall and support practice data were used as an input parameters. The raster layers were processed to present the required input parameters in ArcGIS platform and finally the inputs were multiplied together to quantify annual average soil loss rate and generate intensity maps of the watershed.ResultsThe estimated annual mean soil loss rate of the watershed was found to be 37 t ha−1 year−1, which is more than two times higher as compared to the maximum tolerable soil loss value (16 t ha−1 year−1) and the annual erosion rates range from 0 to above 935 ton ha−1 year−1. The annual mean soil loss values below 5 ton ha−1 year−1 were rated as very slight, while those above 50 ton ha−1 year−1 were categorized as very severe soil erosion risk. Areas experiencing values between these two were further classified into slight, moderate, and severe erosion zones. Soil loss in more than 1/3rd of the study area (36.4%) was below moderate (< 15 t ha−1 year−1). Nearly one-fourth (25.5%) of the watershed area experienced moderate (between 15 and 30 ton t ha−1 year−1) soil loss values. Only 14% of the watershed areas were classified under severe to extremely severe (> 30 t ha−1 year−1) soil erosion risk zones. While 62% of the watershed still undergoes from very slight to moderate levels of soil loss, yet 72.6% of soil erosion occurred in zones less than 3200 m in elevation which represents more than half (51%) of the total surface area of the watershed. The study also demonstrated that the northeastern parts of the watershed suffer more from high soil erosion risk due to steep slope and rugged landforms of the area. Similarly, 43% and 35% of soil erosion occurred on cropland and grasslands areas, respectively.ConclusionThe quantitative soil loss estimation results and farmers’ perception of soil erosion revealed that soil erosion has still remained significant problems in the watershed. The results underscore the urgent need for comprehensive and site-specific SLM practices in the watershed.