Soil Erosion Risk Assessment Research Articles

Soil Erosion Estimation of Kunthippuzha Watershed Using GIS and RUSLE Model

Soil erosion is an environmental crisis in the world today that threatens the natural environment and also agriculture. Erosion removes the top fertile soil, degrades soil fertility, water quality and soil productivity. The soil erosion risk assessment is helpful for land evaluation in the regions where soil erosion is a major threat for sustainable agriculture. Modelling can provide a quantitative and consistent approach to estimate soil erosion and sediment yield under a wide range of conditions. The soil erosion and erosion prone areas in the Kunthippuzha sub-watershed of Bharathapuzha river basin, Kerala, India was estimated using RUSLE. The study area is having a drainage area of 950 km2 up to the gauging station. The estimation was done for 1990 to 2021. The estimated rainfall erosivity, soil erodibility, Slope length and slope steepness factor and crop management factors range from 929.36 and 980.38 MJ mm ha-1 h-1 yr-1, 0.01387 to 0.0385 t ha h ha−1 MJ−1 mm−1, 0 to 9.77 and 0.057796 to 1.0999. The results indicate that the estimated total annual potential soil loss of about 842175 t/y is comparable with the measured sediment of 845500 t/yr during the years 1990-2021. The soil erosion rate categorized into six classes based on the erosion severity, the major portion (60%) of the study area comes under very slight erosion zone and only a small portion (14%) comes under severe and very severe erosion zone. Result suggests the area of the north-eastern part suffers from a high soil erosion risk due to steep slope. The results can certainly aid in implementation of soil management and conservation practices to reduce the soil erosion in the Kunthippuzha sub-watershed.

Assessment of soil erosion risk using RUSLE model, SATEEC system, remote sensing, and GIS techniques: a case study of Navroud watershed

Assessment of soil erosion risk using RUSLE model, SATEEC system, remote sensing, and GIS techniques: a case study of Navroud watershed

Risk assessment of flash flood and soil erosion impacts on electrical infrastructures in overcrowded mountainous urban areas under climate change

Risk management of flash floods in arid mountainous cities is challenged by the lack of proper data and the unreadiness of infrastructures to handle large floods. Climate projections predict increasing frequency of extreme droughts and floods over these arid zones aggravating the impacts of flash floods by increasing the hardness of the topsoil, making it less efficient at absorbing rain water. This study assesses the hazards of steadily increasing flash floods on voltage towers around Makkah city using hydrological modeling to simulate flow velocity and volume and erosion intensity of floods. Hydrological modeling estimated the maximum discharge rates at Wadi Numan and Wadi Al-Sharaya outlets as 3142 and 2543 m3/s, respectively. Extreme soil erosion rates are encountered in the lower reaches of these basins (11% of the total area) and severe erosion rates (3.6%) were reported in the planned voltage tower path. Catchment's lower reaches are proved highly vulnerable to soil erosion due to the lack of vegetation cover and high flow accumulations. The study alarms revisiting the risk management policies for infrastructure projects in arid mountainous cities considering the climate change impacts on increasing the frequency of unprecedented droughts and floods and their aggravated destructive impacts.

Soil erosion risk assessment using geospatial technologies and RUSLE model in Bore Guda Watershed, Southwestern Ethiopia

ABSTRACT Soil loss is one of the critical environmental problems caused by poor land use system, especially in developing countries. The loss of top soil through erosion in developing countries leads to loss of fertile soil which in turn increases the problem of food security. This study used the integration of the revised universal soil loss equation (RUSLE) model and geospatial techniques to assess the annual soil loss at Bore Guda watershed of Gibe River basin in southwestern parts of Ethiopia. To calculate the annual soil loss five factors, namely, rainfall erosivity, soil erodibility, topographic, cover management, and conservation practices were considered. The final soil loss severity classes were categorized into low, moderate, high, very high, and severe. The findings of the study revealed that the annual soil loss in the study area ranges from 0 to 775.784 ton/ha/year with average annual soil loss of 54.68 ton/ha/year. The results clearly showed that about 199.05 km2 (9.2%) and 872.63 km2 (41.29%) of the study area were identified as very high and severe soil loss, respectively. The results clearly showed that substantial areas categorized under severe erosion severity classes. Agricultural intensification caused by rapid population growth and high demand for more food is aggravating the problem of soil erosion. The increasing conversion due to agricultural intensification played a significant role in soil loss. The results of the study support decision makers, land administration, environmentalists, and others about the urgency of the situation and how to manage landscapes to minimize the severity of soil loss.

Distribution Characteristics of Rainfall Erosivity in Jiangsu Coastal Areas

The issue of regional soil and water loss caused by human activity is particularly severe in coastal regions. Since coastal reclamation areas are a valuable land reserve resource, it is of practical significance to understand the distribution characteristics of rainfall erosion and its impact on soil erosion for the prediction, evaluation, and management of regional soil and water resources. Rainfall erosivity should be updated and estimated from simplified indices. This paper analyzed the observed rainfall data of field runoff plots in Dongtai City, Jiangsu Province, between 2011 and 2017. According to the standard of erosive rainfall in coastal areas, reporting 10.8 mm of rainfall or 7.6 mm·h−1 of I30 (maximum 30 min rainfall intensity), the annual average erosive rainfall frequency in Dongtai City was 37.7 and the annual erosive rainfall was 1082.0 mm on average, which accounted for 51.6% and 90.6% of the total rainfall frequency and the total rainfall, respectively. Moreover, the annual average rainfall erosivity in the region from 2011 to 2017 was 7717.4 MJ·mm·hm−2·h−1. The annual distribution of rainfall erosivity was irregular, with an average monthly erosivity value of 4501.8 MJ·mm·hm−2·h−1. Since the accumulated rainfall erosivity of Dongtai City in the flood season (May to September) accounted for 88.1% of the total rainfall erosivity, it is essential to focus on preventing soil and water loss in the flood season. This paper established a rainfall-based model and a composite model and intensity appropriate for a single event and monthly rainfall erosivity in the region. Both models can be used to calculate the annual rainfall erosivity, but only the composite model based on rainfall amount and intensity is recommended for calculating single and monthly rainfall erosivity levels in Jiangsu coastal areas. The empirical formulas in Jiangsu coastal areas can be updated using more recent rainfall data and assess soil erosion risk accurately.

Analysis of the Asymmetric Characteristic of Extreme Rainfall Erosivity in 8 Provinces of Southern China during 1961–2020

The roles of rainfall with various intensities in hydraulic erosion processes are obviously different. In-depth knowledge about the spatiotemporal variation in extreme rainfall erosivity is critical for soil erosion risk assessment and formulation of response measures. In the period of 1961–2020, more than 390,000 erosive rainfall data from 212 meteorological stations were collected to explore the erosion characteristics of different intensities of rainfall erosivity across southern China. The asymmetric characteristic of extreme rainfall erosion was analyzed and expressed by an asymmetric change index (ACI) defined in this study. Correlation analysis was applied to study the impact of global extreme climate events on the ACI. The results showed that different patterns of precipitation had evident asymmetric characteristics in rainfall erosivity, and fewer high-intensity precipitation events contributed to much higher total rainfall erosivity, particularly for rainstorms (≥100 mm). The ACI of the rainfall erosivity exhibited evident spatial heterogeneity; insignificant increasing trends were found in the ACIs for heavy rainfall, torrential, and rainstorm erosivities over the past 60 years. The change rate of ACI varied greatly on both monthly and interdecadal scales, and the ACI of rainstorms showed the maximum linear increasing trend on the long-time scale. There were significantly high correlations between the ACI and erosive rainfall days and the ACI and erosive rainfall (p < 0.01), and the correlation coefficients were relatively higher from May to October, where the precipitation was concentrated. Additionally, the El Niño-Southern Oscillation and tropical cyclones clearly influenced the spatiotemporal distribution of the ACI and rainfall erosivity but were limited to specific periods and regions. These results could provide a reference for extreme soil erosion event monitoring and control in southern China.

Soil erosion hazard assessment using the RUSLE model for the Taung Watershed of Ramotswa Agricultural District in Botswana

The Revised Universal Soil Loss Equation (RUSLE) model integrated with Remote Sensing (RS) and Geographical Information Systems (GIS) was positively validated to spatially assess soil erosion risk in the Taung Watershed of Ramotswa Agricultural District during the period 2000 – 2020. The estimated annual soil erosion averaged 12.04 and 12.74 t/ha/year in 2000 and 2020, respectively. The Land Use/Land Cover (LULC) and soil type were found to be key determinants of high soil erosion hazard when using RUSLE. The soil erosion assessment tools enabled prioritization of high erosion prone areas in the study area for soil conservation planning and watershed management.

Soil erosion estimation and risk assessment at watershed level: a case study of Neshe Dam Watershed in Blue Nile River basin, Ethiopia

ABSTRACT Accelerated erosion caused by human activities is the major triggering factor for the loss of soil and water resources in Ethiopia. Soil erosion is commonly regarded as a major environmental problem that affects the sustainability of agricultural production and downstream reservoir. To facilitate urgent policy intervention in sustainable land management, evaluating the amount of annual soil loss is inevitable. In this study, the Revised Universal Soil Loss Equation (RUSLE) and Geographic Information System (GIS) are integrated to analyse the amount of soil loss in the Neshe Dam Watershed of Ethiopia. The total amount of soil loss from the watershed (33,376 hectares) is about 1,252,935 tons per year. Furthermore, the potential average annual soil loss for the whole watershed is anticipated at 37.54 t ha−1 yr−1. In terms of sub-watersheds, very high soil loss (56.62 t ha−1 yr−1) was observed in sub-watershed B accounts (for 16.3%) and the least (16.06 t ha−1 yr−1) observed in sub-watershed E accounts (for 23.6%). The study concluded that if proper measures are not taken now, the accelerated erosion in the watershed will jeopardize the sustainability of agricultural production within the watershed and energy generated from the dam in the long term.

Assessment of soil erosion by SLEMSA model using remote sensing and GIS: A case study of Taung Watershed of Ramotswa Agricultural District in Botswana

The Soil Loss Estimation Model for Southern Africa (SLEMSA) model integrated with Remote Sensing (RS) and Geographical Information Systems (GIS) was positively validated to spatially assess soil erosion risk in the Taung Watershed of Ramotswa Agricultural District during the period 2000 – 2020. The estimated annual soil erosion averaged 11.77 and 12.57 t/ha/year in 2000 and 2020, respectively. The Land Use/Land Cover (LULC) and topography were found to be key determinants of high soil erosion risk when using SLEMSA. The soil erosion assessment tools enabled prioritization of high erosion prone areas in the study area for soil conservation planning and watershed management.

تقييم مخاطر انجراف التربة في حوض الأزرق باستخدام نموذج تقييم التربة والمياه ونظم المعلومات الجغرافية والاستشعار عن بعد

The study aims at evaluating soil erosion in Azraq Basin using soil and water assessment model, GIS and remote sensing. The soil erosion has important negative effects on the ground resources In the basin. As other basins in Jordan, Azraq Basin suffers extremely from soil erosion that resulted from the decrease of soil productivity before being eroded. Soil erosion is related to many environmental problems such as sedimentation in the river channels and dams, and deterioration water quality, so strategies for managing water basins and protecting soil from Erosion are needed. The results of the study showed that Azraq Basin has annually an average of 99.8 Mm of rainfall; the majority lost by evaporation process, and 73.8% of the total precipitation is wasted because of the actual evaporation process, whereas surface runoff has 8.9% of the total precipitation of the Basin. The study reveals that about 24.5% of Azraq Basin area suffers from severe soil erosion mainly In Wadi Al-Ghadf and Modaisisat. The Study also reveals that Wadi Modaissat is the largest basin in Azraq Basins, it Produces 58.4% of the total eroded materials. تهدف هذه الدراسة إلى تقييم مخاطر انجراف التربة لحوض الأزرق باستخدام نموذج تقييم التربة والمياه والتقنيات ونظم المعلومات الجغرافية والاستشعار عن بعد؛ وذلك لما له من آثار سلبية على الموارد الأرضيَّة في الحوض، حيث يعاني حوض الأزرق كغيره من الأحواض المائية في الأردن من مشكلة انجراف التربة، والناتجة عن انخفاض إنتاجية التربة في المنطقة قبل تعرضها للانجراف. كما يرتبط انجراف التربة بالعديد من المشكلات البيئية الأخرى، مثل: تموضع الرواسب في مجاري الأودية ، والسدود المائية وتدهور نوعيه المياه، مما يتطلب وضع استراتيجيات لإدارة الأحواض المائية وحماية تربتها من الانجراف. وتوصَّلت الدراسة إلى أن حوض الأزرق يتلقى معدل مطري يبلغ 99.8 ملم / سنويًا، يفقد الجزء الأعظم منها بواسطة عملية التبخر، حيث إن 73.8% من مجمل الهطول المطري يذهب نتيجة عملية التبخر الفعلي، في حين إن الجريان السطحي يستأثر بـ 8.9% من إجمالي الهطول المطري على حوض الأزرق، كما أوضحت الدراسة أن 24.5% من مساحة حوض الأزرق تصنَّف بأنها ذات معدل انجراف شديد جدًا للتربة، وتتمثل هذه المساحة في: حوضي واديي الغدف والمديدسسات. وأظهرت الدراسة أن حوض وادي المديدسسات هو أكثر الأحواض الفرعية لحوض الأزرق في معدل العائد الرسوبي المتوقع والذي يشكل ما نسبته 58.4% من أصل المواد المنجرفة فيه.

Spatial Assessment of Soil Erosion Risk Using RUSLE Embedded in GIS Environment: A Case Study of Jhelum River Watershed

The watershed area of the Mangla Reservoir spans across the Himalayan region of India and Pakistan, primarily consisting of the Jhelum River basin. The area is rugged with highly elevated, hilly terrain and relatively thin vegetation cover, which significantly increases the river’s sediment output, especially during the monsoon season, leading to a decline in the reservoir’s storage capacity. This work assesses the soil erosion risk in the Jhelum River watershed (Azad Jammu and Kashmir (AJ&K), Pakistan) using the Revised Universal Soil Loss Equation of (RUSLE). The RUSLE components, including the conservation support or erosion control practice factor (P), soil erodibility factor (K), slope length and slope steepness factor (LS), rainfall erosivity factor (R), and crop cover factor (C), were integrated to compute soil erosion. Soil erosion risk and intensity maps were generated by computing the RUSLE parameters, which were then integrated with physical factors such as terrain units, elevation, slope, and land uses/cover to examine how these factors affect the spatial patterns of soil erosion loss. The 2021 rainfall data were utilized to compute the rainfall erosivity factor (R), and the soil erodibility (K) map was created using the world surface soil map prepared by the Food and Agriculture Organization (FAO). The slope length and slope steepness factor (LS) were generated in the highly rough terrain using Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM). The analysis revealed that the primary land use in the watershed was cultivated land, accounting for 27% of the area, and slopes of 30% or higher were present across two-thirds of the watershed. By multiplying the five variables, the study determined that the annual average soil loss was 23.47 t ha−1 yr−1. In areas with dense mixed forest cover, soil erosion rates ranged from 0.23 t ha−1 yr−1 to 25 t ha−1 yr−1. The findings indicated that 55.18% of the research area has a low erosion risk, 18.62% has a medium erosion risk, 13.66% has a high risk, and 11.6% has a very high erosion risk. The study’s findings will provide guidelines to policy/decision makers for better management of the Mangla watershed.

Geoinformatic approach to potential soil erosion risk assessment in Tulasi watershed

The disintegration of soil grains caused by raindrops initiates the erosion process and is the primary source of erosion on small and steep slopes. Soil loss occurs all across the world, but poor countries are more vulnerable to it due to failure to replenish lost soil and minerals. Asian countries have been highlighted as one of the locations on the globe where soil erosion has proven to be a difficult challenge in terms of development and land resource conservation. Because India is the world's second most populous country and agriculture is the backbone of the Indian economy, sustainable land management approaches are urgently needed to protect production potential and provide an adequate supply of healthy food for millions. The Tulasi watershed in Maharashtra, India, is distinguished by heavy rain during the monsoon season, undulating and rocky topography and bare hilltops, all of which increase runoff capacity and make top soil more prone to detachment and loosening. In light of this, the current study employs geo-information technologies and the RUSLE model to derive erosion factors such as rainfall erosivity, soil erodibility, slope length and steepness, land cover and management and to estimate the amount and spatial distribution of soil loss in the study area.

GIS-Based Soil Erosion Risk Assessment in the Watersheds of Bukidnon, Philippines Using the RUSLE Model

The sustainability of watersheds for supplying water and for carbon sequestration and other environmental services depends to a large extent on their susceptibility to soil erosion, particularly under changing climate. This study aimed to assess the risk of soil erosion in the watersheds in Bukidnon, Philippines, determine the spatial distribution of soil loss based on recent land cover maps, and predict soil loss under various rainfall scenarios based on recently reported climate change projections. The soil erosion risk assessment and soil loss prediction made use of GIS and the RUSLE model, while the rainfall scenarios were formulated based on PAGASA’s prediction of drier years for Bukidnon in the early-future to late-future. Results showed that a general increase in soil loss was observed in 2015, over the period from 2010 to 2020, although some watershed clusters also showed a declining trend of soil erosion, particularly the Agusan-Cugman and Maridugao watershed clusters. Nearly 60% of Bukidnon has high to very severe soil loss rates. Under extreme rainfall change scenario with 12.61% less annual rainfall, the soil loss changes were only +1.37% and −2.87% in the category of none-to-slight and very severe, respectively. Results showed that a decrease in rainfall would have little effect on resolving the excessive soil erosion problem in Bukidnon. Results of this study suggest that having more vegetative land cover and employing soil conservation measures may prove to be effective in minimizing the risk of soil erosion in the watersheds. This study provides valuable information to enhance the sustainability of the watersheds. The erosion-prone areas identified will help decision-makers identify priority areas for soil conservation and environmental protection.

Development of MLR and variedly optimized ANN models for forecasting the detachability and liquefaction potential index of erodible soils

• DLPI which quantifies soil erodibility potentials, was computed for southern Anambra. • MLR and four ANNs successfully predicted the DLPI of the study area. • Low modeling errors were obtained in the developed MLR and ANN models. • Soft computing algorithms can enhance soil erosion risk assessment and management. Artificial intelligence modeling techniques, such as artificial neural network (ANN) and multiple linear regression (MLR), have been found useful in the prediction of environmental hazard indicators, even in areas with data scarcity. The detachability and liquefaction potential index ( DLPI ) is a numerical model that quantifies soil erodibility potential. This paper aims to design ANN and MLR models that can predict the DLPI of erodible soils in southeastern Nigeria. Feedforward back-propagated ANN models (ANN1, ANN2, ANN3 and ANN4) optimized using different algorithms and output layer activation functions were proposed. To implement the four models, the geotechnical data of the eroding soils, which were used for the DLPI calculation, were utilized. Sixteen geotechnical variables analyzed on the soils were used as input variables. While ANN1 and ANN3 were optimized using scaled conjugate gradient algorithm, ANN2 and ANN4 were optimized using gradient descent algorithm. However, the output layers of the ANN1 and ANN2 were activated using identity function while those for the ANN3 and ANN4 were activated using hyperbolic tangent function. The results of the four models showed high performance with determination coefficient (R 2 ) values of 0.971 (ANN1), 0.998 (ANN2), 0.987 (ANN3), and 0.992 (ANN4). Further, low modeling errors were detected from the residual plots, sum of square errors (0.000-0.024), and relative errors (0.003-0.080); thus, confirming the reliability of the models. The MLR gave a perfect model for the DLPI prediction, with its multiple correlation coefficient = 1.000, R 2 = 1.000, R 2 adjusted = 1.000, and standard error of estimate = 0.000. Therefore, it suffices to say that the ANN and MLR algorithms are suitable for DLPI prediction.

Soil erosion prediction and risk assessment using RUSLE model and GIS techniques in the Nangka watershed

Soil erosion in the Nangka watershed has always been a matter of concern. Over the last decade, soil erosion has led to continuous environmental problems. A thorough examination of the extent of the problem was required to identify an appropriate soil conservation strategy within the watershed. This study was conducted to observe erosion rates and map out the erosion hazard level. Erosion predictions were analysed by using the Revised Universal Soil Loss Equation (RUSLE) model with the help of ArcGIS software. RUSLE was selected because of its quantitative ability to estimate average annual soil erosion and its compatibility with the GIS interface. The potential hazard of soil erosion was classified and ranked into five class categories as set by the national authority. The results reveal that the Nangka watershed is prone to soil erosion with the annual average values ranging from 1.33 Mg·ha –1·y –1 to 2472.29 Mg·ha –1·y –1. High soil erosion rates of 9.8% are in severe (class IV) and very severe (class V) conditions, primarily in the upper course of the watershed. The low annual average of soil erosion (class I and class II), which accounted for 75.95% of the total erosion, mostly took place in the steepness below 35%. The remaining area of 14.25% within the watershed is in moderate condition (class III). It is expected that the results of this study will help the authority in the implementation of soil conservation measures.

Soil erosion risk and sediment yield assessment with Revised Universal Soil Loss Equation and GIS: The case of Nesha watershed, Southwestern Ethiopia

This research was administered to spatially predict the soil loss rate of the kaffa zone using a model estimate and GIS. Revised Universal Soil Loss Equation (RUSLE) adapted to Ethiopian conditions was accustomed estimate potential soil losses by utilizing information on rainfall erosivity (R) using interpolation of rainfall data, soil erodibility (K) using DSMW soil map, vegetation cover (C) using spot6 images, topography (LS) using Digital Elevation Model (DEM) and conservation practices (P) using DEM and satellite images. supported the analysis, the mean and total annual soil loss potential of the study area was 30 tons ha-1 year-1 and 36,264.5tons ha-1 year-1, respectively. The results also showed that about 2.89, 8.02, 15.31 and 73.78% of the study area were classified as slight, moderate, high, and very high with values ranging from 0 to 15,15 to50,50 to 200, and >200 tons ha-1 year-1, respectively. The study demonstrates that the RUSLE using GIS and RS provides a great advantage to spatially analyzing multi-layer of knowledge. The expected amount of soil loss and its spatial distribution could facilitate sustainable land use and management.

Quantitative soil erosion risk assessment due to rapid urbanization in the Cox’s Bazar district and Rohingya refugee camps in Bangladesh

Soil erosion due to anthropogenic interventions is an emerging threat to the south-eastern coastal districts of Bangladesh. In the recent decade, land degradation intensified this process in this region due to the settlement of the Forcibly Displaced Myanmar Nationals popularly known as ‘Rohingya’, who fled from Myanmar in 2017. To control soil erosion, it is necessary to identify the soil erosion locations and their intensity. The present study has attempted to quantify the regional soil loss (from 2015 to 2020) using the Revised Universal Soil Loss Equation model. In this method, soil erosion risk assessments are carried out by integration of different physiographic parameters in the spatial dimension. Results showed that the year 2015 and 2020 witnessed mean soil erosion at a rate of around 58.2 and 59 t ha− 1 yr− 1, respectively in the Cox’s Bazar district in which 20–21% of the study area was highly subjected to soil erosion. But in the Rohingya camps mean soil erosion for these periods is 59 and 78 t ha− 1 yr− 1, respectively with a cumulative increase of 32%. The sub-districts adjacent to the Rohingya refugee camps (Ramu, Ukhia, and Teknaf) have experienced intense erosion compared to the others. From 2015 to 2020, a large increase of about 4.54 (6%) t ha− 1 yr− 1 is observed in the Palongkhali union whereas a maximum decrease of about − 8.08 (23%) t ha− 1 yr− 1 is observed in the Pokkhali union. In Rohingya camps, the worst soil erosion-affected areas are the camps 8E, 10, 14, 15, 16, and 17 having more than 100 t ha− 1 yr− 1 mean soil loss. The geographic detector method is used to identify the influence of different factors on soil erosion. Land use change is found to be the major contributing factor to soil erosion increase from 2015 to 2020. The spatial variations are significantly controlled by slope and elevation factors. This work can help to understand the dynamics of soil erosion and the knowledge can be used for conservation planning in the Cox’s Bazar and Rohingya camp areas.

Assessing soil erosion risk in a peri-urban catchment of the Lake Victoria basin.

Soil erosion and sedimentation contribute to deteriorating water quality, adverse alterations in basin hydrology and overall ecosystem biogeochemistry. Thus, understanding soil erosion patterns in catchments is critical for conservation planning. This study was conducted in a peri-urban Inner Murchison Bay (IMB) catchment on the northern shores of Lake Victoria since most soil erosion studies in Sub-Saharan Africa have been focused on rural landscapes. The study sought to identify sediment sources by mapping erosion hotspots using the revised universal soil loss equation (RUSLE) model in appendage with field walks. RUSLE model was built in ArcGIS 10.5 software with factors including: rainfall erosivity, soil erodibility, slope length and steepness, land cover and support practices. The model was run, producing an erosion risk map and field assessments conducted to ground-truth findings and identify other hotspots. The percentage areas for RUSLE modelled erosion rates were: 66.8% for 0-2 tha-1year-1; 10.8% for 2-5 tha-1year-1; 10.1% for 5-10 tha-1year-1; 9% for 10-50 tha-1year-1 and 3.3% for 50-100 tha-1year-1. Average erosion risk was 7 tha-1year-1 and the total watershed erosion risk was 197,400 tyear-1, with croplands and steep areas (slope factor > 20) as the major hotspots (> 5 tha-1year-1). Field walks revealed exposed soils, marrum (gravel) roads and unlined drainage channels as other sediment sources. This study provided the first assessment of erosion risk in this peri-urban catchment, to serve as a basis for identifying mitigation priorities. It is recommended that tailored soil and water conservation measures be integrated into physical planning, focusing on identified non-conventional hotspots to ameliorate sediment pollution in Lake Victoria.

Using Google Earth Engine and GIS for basin scale soil erosion risk assessment: A case study of Chambal river basin, central India

Using Google Earth Engine and GIS for basin scale soil erosion risk assessment: A case study of Chambal river basin, central India

Assessment of soil erosion risk using an integrated approach of GIS and Analytic Hierarchy Process (AHP) in Erzurum, Turkiye

Assessing soil erosion hazards and mapping the spatial distribution of soil erosion have an essential role in sustainable forest management. In this study, the potential soil erosion risk was evaluated through the Analytical Hierarchy Process (AHP) and Geographic Information Systems (GIS) in the Oltu forest planning unit, Erzurum. Seven erosion-related criteria, including slope, bedrock type, relative relief, drainage density and frequency, rainfall, and land use/land cover (LULC) were used for the present assessment. According to the AHP analysis, the slope was the most influential factor (21%) followed by bedrock type (19%), land cover (17%), and relative relief (14%) in the soil erosion process. The soil erosion risk in the study area was strongly influenced by the LULC where 59.46% is bare land with high erosion risk and 12.07%, with the lowest risk, is in an area with any forest cover. The estimated soil erosion risk was classified into five different classes namely very low, low, moderate, high, and very high. The results showed that this study area is highly prone to soil erosion. The larger proportion of the area (39.16%) is exposed to high to very high erosion, mainly determined by forest cover and geomorphology. To analyze the accuracy of the soil erosion risk map, 40 points were selected randomly in this study area. In these points, predicted values were compared to the real values obtained by Google Earth-colored images. The area under the ROC curve (AUC) method was applied to validate the efficiency of the AHP which showed a satisfactory accuracy of 81.00%. Findings presented that including the more influencing factors with a slope instead of including only the slope contributes to a more accurate erosion risk map. This study highlighted that GIS-based multi-criteria decision-making is a valuable and practical tool for decision-makers and land managers in creating soil erosion susceptibility maps and determining high-priority areas that require conservation measures for sustainable land use management by reducing the economic and ecological impacts of soil loss. Also, this approach can be practically applied in other planning units.