Estimate Soil Loss Research Articles

Soil erosion assessment and identification of erosion hotspot areas in the upper Tekeze Basin, Northern Ethiopia

Soil erosion is a major environmental problem in Ethiopia, reducing topsoil and agricultural land productivity. Soil loss estimation is a critical component of sustainable land management practices because it provides important information about soil erosion hotspot areas and prioritizes areas that require immediate management interventions. This study integrates the Revised Universal Soil Loss Equation (RUSLE) with Google Earth Engine (GEE) to estimate soil erosion rates and map soil erosion in the Upper Tekeze Basin, Northern Ethiopia. SoilGrids250 m, CHIRPS-V2, SRTM-V3, MERIT Hydrograph, NDVI from sentinel collections and land use land cover (LULC) data were accessed and processed in the GEE Platform. LULC was classified using Random forest (RF) classification algorithm in the GEE platform. Landsat surface reflectance images from Landsat 8 Operational land imager (OLI) sensors (2021) was used for LULC classification. Besides, different auxiliary data were utilized to improve the classification accuracy. Using the RUSLE-GEE framework, we analyzed the soil loss rate in different agroecologies and LULC types in the upper Tekeze basin in Waghimra zone. The results showed that the average soil loss rate in the Upper Tekeze basin is 25.5 t ha−1 yr−1. About 63 % of the basin is experiencing soil erosion above the maximum tolerable rate, which should be targeted for land management interventions. Specifically, 55 % of the study area, which is covered by unprotected shrubland is experiencing mean annual soil loss of 34.75 t ha−1 yr−1 indicating the need for immediate soil conservation intervention. The study also revealed evidence that this high mean soil loss rate of the basin can be reduced to a tolerable rate by implementing integrative watershed management and exclosures. Furthermore, this study demonstrated that GEE could be a good source of datasets and a computing platform for RUSLE, in particular for data scarce semi-arid and arid environments. The results from this study are reliable for decision-making for rapid soil erosion assessment and intervention prioritization.

Quantitative assessment of morphometry and GIS integrated RUSLE model-based soil loss estimation from Pahuj river basin, central India

Quantitative assessment of morphometry and GIS integrated RUSLE model-based soil loss estimation from Pahuj river basin, central India

Prevalence, causes, and consequences of agricultural land abandonment: A case study in the Region of Murcia, Spain

Agricultural land abandonment has received increasing attention from the scientific community and decision makers in recent years. In this study, we aimed to identify the current status of agricultural land abandonment in Europe, investigate its main causes and consequences, and propose management solutions based on future projections. We selected the Region of Murcia (RM) in southeastern Spain as the study area as it has been identified as a hotspot of agricultural land abandonment. A large number of areas with severe erosion (gullies and piping) attributed to abandonment have been identified in the Neogene and Quaternary basins. Morphometric analyses of various fields and estimates of soil loss were performed in 57 abandoned land areas in the RM. Additionally, physical and chemical soil analyses were performed in 15 selected areas. The results showed that abandoned crop terraces in loam soil promote piping processes owing to their flat surfaces and the formation of a hydraulic gradient. Furthermore, soils with a poor structure, fine texture, and low organic matter content are vulnerable to erosion processes. Additionally, increased exchange sodium percentage (ESP), electrical conductivity (EC), sodium absorption ratio (SAR), and soluble sodium percentage (SSP) contribute to these processes. The results of this study confirmed the detrimental effects of abandoning agricultural land. We found that multiple contributing factors led to severe erosion on abandoned land in the RM. Therefore, efficient land use planning and management are required, especially for land abandonment in Neogene and Quaternary basins in semi-arid regions.

Estimation of Soil Loss in the Nanoi River Basin using Geospatial Techniques

Objectives: The main purpose of this research is to identify severe soil loss areas in the Nanoi river basin of Assam, India to suggest appropriate soil management planning in the river basin. Methods: The Revised Universal Soil Loss Equation (RUSLE) integrates geospatial technologies to assess overall soil loss in the Nanoi river basin which provides a faster and more accurate estimation. It is possible to understand soil erosion patterns more thoroughly using RUSLE which makes sustainable soil management easier. Findings: According to the RUSLE equation, the Nanoi river basin experiences an estimated total soil loss of 18,562.5 tons per year, with an average annual soil loss of 0.32 t/h/y. This suggests that approximately 0.64 km2 of the area falls into the Moderate, high and Extreme soil erosion sensitivity zones. Furthermore, a final map is generated to display different areas with varying levels of soil loss rates. Novelty: The geospatial approaches used produce precise findings at a reasonable price and demonstrate the severity of soil loss in the river basin. If soil loss continues at the current rate, there is a high likelihood that certain areas on both sides of the river, particularly in the downstream region of the basin, will experience fluvial hazards, such as drainage congestion and floods. Hence, the results generated will be helpful in the management practices of the river basin. Keywords: Soil loss, Soil erosion, Geospatial, Revised universal soil loss equation, River basin

Utilizing GIS and remote sensing for soil loss estimation in Kopai River basin: an application of the RUSLE model

Utilizing GIS and remote sensing for soil loss estimation in Kopai River basin: an application of the RUSLE model

Soil Loss Estimation and Sub-Watersheds Prioritization for Management Using GIS-Based RUSLE Technique in Halaba-Bilate Watershed, Ethiopia

Soil Loss Estimation and Sub-Watersheds Prioritization for Management Using GIS-Based RUSLE Technique in Halaba-Bilate Watershed, Ethiopia

Estimation of soil erodibility in Peninsular Malaysia: A case study using multiple linear regression and artificial neural networks

Soil erodibility (K) is an essential component in estimating soil loss indicating the soil's susceptibility to detach and transport. Data Computing and processing methods, such as artificial neural networks (ANNs) and multiple linear regression (MLR), have proven to be helpful in the development of predictive models for natural hazards. The present case study aims to assess the efficiency of MLR and ANN models to forecast soil erodibility in Peninsular Malaysia. A total of 103 samples were collected from various sites and K values were calculated using the Tew equation developed for Malaysian soil. From several extracted parameters, the outcomes of correlation and principal component analysis (PCA) revealed the influencing factors to be used in the development of ANN and MLR models. Based on the correlation and PCA results, two sets of influencing factors were employed to develop predictive models. Two MLR (MLR-1 and MLR-2) models and four neural networks (NN-1, NN-2, NN-3, and NN-4) optimized using Levenberg-Marquardt (LM) and scaled conjugate gradient (SCG) were developed and evaluated. The model performance validation was conducted using the coefficient of determination (R2), mean squared error (MSE), root mean squared error (RMSE), and Nash-Sutcliffe efficiency coefficient (NSE). The analysis showed that ANN models outperformed MLR models. The R2 values of 0.446 (MLR-1), 0.430 (MLR-2), 0.894 (NN-1), 0.855 (NN-2), 0.940 (NN-3), and 0.826 (NN-4); MSE values of 0.0000306 (MLR-1), 0.0000315 (MLR-2), 0.0000158 (NN-1), 0.0000261 (NN-2), 0.0000318 (NN-3), and 0.0000216 (NN-4) suggested the higher accuracy and lower modelling error of ANN models as compared with MLR. This study could provide an empirical basis and methodological support for K factor estimation in the region.

Estimation of erosion rate in a watershed of a deforestation area in the Brazilian Amazon, eastern of Mato Grosso

The central region of Brazil, geographically located in Mato Grosso, presents changes in land cover with replacement of the forest for soybean and cotton cultivation, intensifying soil losses in a hydrographic basin located in the tropical climate. The objective of the research is to estimate the loss of soil in the Curisevo River Basin, from the Universal Soil Loss Equation. The methodologies used were the elaboration of a cartographic base in the QGIS geographic information system, with the spatial information of slopes, soil types and land cover, of the Curisevo River Basin. The rainfall erosivity was estimated with rainfall data from the National Institute of Meteorology and the erodibility of the soils was estimated according to the indications of the State of Mato Grosso. The land cover of the Curisevo River Basin was analyzed based on remote sensing images with the vectorization of the areas of the agriculture, pasture and forest classes. The spatial data of slopes of the Curisevo River Basin were obtained from the Shuttle Radar Topography Mission base, with spatial analysis treatment in QGIS. In the basin with an area of 1.18 million hectares, an erosion rate of 134,526 t/ha/year was estimated, corresponding to 16 million tons of sediments. This study contributes to the geographical studies, and the indices used in the equation to estimate soil losses can be analyzed by climatic, pedological, geomorphological and landscape analysis studies in land cover changes in a watershed.

Prioritization of Sub-Watershed Based on Soil Loss Estimation Using RUSLE Model

Soil erosion is one of the most crucial land degradation problems and is considered the most critical environmental hazard worldwide. The present study uses remote sensing data integrated with the geographical information system (GIS) technique and the revised universal soil loss equation (RUSLE) model for assessing the annual average soil loss of the Digaru watershed of India for 1999 and 2020. The estimated mean gross yearly soil loss from the entire watershed was 102716 t yr-1 in 1999 and 178931.6 t yr-1 in 2020. The overall average soil loss rate increased significantly between 1999 and 2020, rising from 4.73 t—ha-1yr-1 to 8.43 t—ha-1yr-1. The sub-watersheds are prioritized as high (≥ 40 t ha−1yr−1), moderate (20–40 t ha−1yr−1), and low (<20 t ha−1yr−1) based on the spatial distribution of soil erosion. Seven sub-watersheds have been grouped under low priority, followed by seven under moderate priority and one under high priority. This study demands instant attention for soil and water conservation efforts in highly eroded watershed areas.

Assessment of the extent of soil degradation over different land uses in the Kebbi area, northwestern Nigeria

This paper assessed soil degradation over land use in the Kebbi area, Northwestern Nigeria. The specific objectives entailed identifying major forms of soil loss in the study area, estimating soil loss, and examining the responses of soil physico-chemical properties over dominant land use types. Slope angles were determined using a GPS, a handheld Abney level, ranging poles, and a 30 m measuring tape while gully depths, widths, and lengths were taken at 5 m-30 m intervals. Key soil physico-chemical parameters were determined using standard procedures. Descriptive statistics such as mean, standard deviation, and coefficient of variation were used to summarize the data generated from the study. The analysis of variance and the student’s t-test were used to test for significant association between and within pairs of land uses. The results show that slopes range from 4⁰ - 16⁰, while the magnitude of soil loss ranged between 3580.32 m3 in K1 (Kalgo 1), 3550.89 m3 in G2 (Goru 2) and 161.01 m3 in A1 (Angwar Daji 1). Results show significance at p≤ 0.05 in bulk density values over the different land uses, with the highest bulk density value of 1.78 g/cm3 in badland and the lowest value of 1.35 g/cm3 in plantation land use. Total nitrogen ranges between 0.01 – 0.38%, with the lowest values in badland and scrubland, while soil base saturation is highest over plantation (76.3%) and lowest in badland (50.4%). The study concludes that the dominant geomorphic responses identified in the study area are gully initiation and development and varying amounts of soil degradation over the different land uses. The findings should facilitate policy initiation to rehabilitate degraded lands and adopt sustainable soil management practices like tree planting while linking geomorphological information to infrastructural planning and development.

Modeling the impact of land use land cover change on the estimation of soil loss and sediment export using InVEST model at the Rib watershed of Upper Blue Nile Basin, Ethiopia

Information on soil loss and sediment export is essential to identify vulnerable area of soil erosion and to inform conservation interventions in a given watershed. The goal of this study was to analyze the changes in soil loss and sediment exports in the Rib watershed of Ethiopia's Upper Blue Nile Basin. The study used spatial data by using a variety of data sources, including topographic maps, soil maps, meteorological data, and satellite images. Cultivated land, forests, grazing areas, shrubs, water bodies, and settlements were all identified in the study watershed. Soil loss and sediment export were calculated using the Integrated Valuation of Ecosystem Services and Tradeoff (In-VEST) model. The model was calibrated using the sediment yield data gathered in the sample watersheds. The results reveal that while the equivalent sediment export grew from 6.54 t/ha/year to 11.05 t/ha/year in 2000 and 2020, respectively, the average soil loss increased from 22.37 t/ha/year in 2000 to 33.38 t/ha/year in 2020. The largest rate of soil erosion was seen on cultivated land, which increased from 40.86 t/ha/year in 2000 to 53.9 t/ha/year in 2020. This relates to the expansion of the agricultural land. The soil loss and sediment export rates in sub-watersheds three (SW-3) and five (SW-5) were the highest, at 61.80 and 63.48 t/ha/year and 18.75 and 19.35 t/ha/year, respectively. The least amount of soil loss occurs in sub-watershed twelve (SW-12) (2.56 t/ha/year). This is because SW-12 is situated in the watershed's lower reaches fogera plain parts of the watershed experiencing less erosion. The result concluded that prioritizing those sub-watersheds is important for informed decision-making processes.

GIS based annual soil loss estimation with revised universal soil loss equation (RUSLE) in the upper Meki sub-catchment, rift valley sub-basin, Ethiopia

GIS based annual soil loss estimation with revised universal soil loss equation (RUSLE) in the upper Meki sub-catchment, rift valley sub-basin, Ethiopia

Estimation of Water Yield and Soil Erosion in Samanalawewa Watershed in Sri Lanka using GIS-based InVEST Model

Ecosystem services (ES) are a group of tasks performed by the earth's ecosystems that are required to support life and offer benefits to humanity. Thus, this study explores two of the water-related ecosystem services, water yield, and soil retention that were distributed in the Samanalawewa Watershed (SW) and its eight sub-watersheds in Sri Lanka, over 20 years (2000-2020) with five- year time intervals. Soil and water conservation and water yield play an important role in ecosystem management. Hydrological balance is a crucial component of the hydrological ecosystem services. Assessing water yield and soil erosion are pivotal for watershed management. Thus, this study estimated and mapped the water yield and soil erosion, using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST 3.9.2) Annual Water Yield (AWY) and Sediment Delivery Ratio (SDR) model respectively. According to the obtained results, in 2000, 2005, 2010, 2015 and 2020, the estimated mean annual water yield was 2.62x105, 2.6x105, 3.5x105, 3.58x105 and 2.75x105 m3 ha-1year-1, respectively. Furthermore, the estimated mean annual soil erosion rates for the above- mentioned years were 53.2, 52.9, 69.7, 87.7 and 70.2 t ha-1 year-1. The estimated soil loss values of the SW are 10 to 18 times greater than the soil erosion tolerance (5 t ha-1 year-1) in Sri Lanka. The results show that the years 2015 and 2005 have the highest and the lowest water yield and total soil loss values, respectively. The upper part of the watershed has relatively moderate water yield and soil loss values than the lower part. These findings would be beneficial in developing watershed management strategies and in the implementation of suitable soil and water conservation techniques within the watershed. 
 Keywords: Ecosystem services, InVEST, Samanalawewa watershed, Soil erosion, Water yield

A new approach to pedestal differentiation for soil loss estimation—a case study from a burnt area in north-central Portugal

Soil pedestals have long been used as qualitative indicators of soil splash erosion. In rangelands, plant-capped pedestals, generally grass tussocks, have also been used to quantitatively estimate soil loss since the first half of the twentieth century. In agricultural lands, forests, and badlands, stone-capped pedestals have been used as qualitative and semi-quantitative indicators of active, ‘extreme’ erosion. Little work has been reported on using capstone pedestal data for quantifying soil loss. We postulate that three distinct capstone pedestal types may be present in any given location and that a detailed analysis of a pedestal height histogram may be used to recognize their populations. This analysis can subsequently inform if soil loss can be reliably estimated and if so, which of the existing methods using pedestal height data will provide more accurate results. The three proposed capstone pedestal types are: (1) neo-pedestals formed underneath surface stones exposed by (partial) removal of the soil surface cover; (2) endo-pedestals formed underneath stones that were buried in the soil but have been exposed by erosion; and (3) phoenix-pedestals formed underneath stones from collapsed pedestals. In the pedestal height histogram of any given location, a skew to smaller heights may indicate the existence of endo- and/or phoenix-pedestals, which may be revealed as a bi-(or tri) modal distribution when using a smaller bin size. This concept was applied to a case study where soil loss had been monitored for control plots and mulched plots during a 5-year period following wildfire in a eucalypt plantation. We measured pedestal heights and used methods to quantitatively assess soil loss from soil pedestal data in the available literature. Soil pedestal data at the end of the 5-year period under or overestimated soil loss in the control treatment, with results ranging from 60 to 115% of measured soil loss, depending on the method. It is postulated that phoenix- and endo-pedestals may be a driving factor behind the observed discrepancies. We discuss how future research may provide more insight into dominant processes, and how frequency distributions may be used to select the best methods for estimating soil loss from pedestals.

Soil Loss Estimation by Water Erosion in Agricultural Areas Introducing Artificial Intelligence Geospatial Layers into the RUSLE Model

The existing digital soil maps are mainly characterized by coarse spatial resolution and are not up to date; thus, they are unable to support the physical process-based models for improved predictions. The overarching objective of this work is oriented toward a data-driven approach and datacube-based tools (Soil Data Cube), leveraging Sentinel-2 imagery data, open access databases, ground truth soil data and Artificial Intelligence (AI) architectures to provide enhanced geospatial layers into the Revised Universal Soil Loss Equation (RUSLE) model, improving both the reliability and the spatial resolution of the final map. The proposed methodology was implemented in the agricultural area of the Imathia Regional Unit (northern Greece), which consists of both mountainous areas and lowlands. Enhanced soil maps of Soil Organic Carbon (SOC) and soil texture were generated at 10 m resolution through a time-series analysis of satellite data and an XGBoost (eXtrene Gradinent Boosting) model. The model was trained by 84 ground truth soil samples (collected from agricultural fields) taking into account also additional environmental covariates (including the digital elevation model and climatic data) and following a Digital Soil Mapping (DSM) approach. The enhanced layers were introduced into the RUSLE’s soil erodibility factor (K-factor), producing a soil erosion layer with high spatial resolution. Notable prediction accuracy was achieved by the AI model with R2 0.61 for SOC and 0.73, 0.67 and 0.63 for clay, sand, and silt, respectively. The average annual soil loss of the unit was found to be 1.76 ton/ha/yr with 6% of the total agricultural area suffering from severe erosion (>11 ton/ha/yr), which was mainly found in the mountainous border regions, showing the strong influence of the mountains in the agricultural fields. The overall methodology could strongly support regional decision making and planning and environmental policies such as the European Common Agricultural Policy (CAP) and the Sustainable Development Goals (SDGs).

Does spatial resolution matter in the estimation of average annual soil loss by using RUSLE?-a study of the Urmodi River Watershed (Maharashtra), India.

The study investigates the influence of multispectral satellite data's spatial resolution on land degradation in the Urmodi River Watershed in which Kaas Plateau, a UNESCO World Heritage site, is located. Specifically, the research focuses on soil erosion and its risk zonation. The study employs Landsat 8 (30-m resolution) and Sentinel-2 (10-m resolution) data to assess soil erosion risk. The Revised Universal Soil Loss Equation (RUSLE) is used to quantify the average annual soil erosion output denoted by (A), by using its factors such as rainfall (R), soil erodibility (K), slope-length (LS), cover management (C), and support practices (P). R-factor was computed from MERRA-2 rainfalldata, K-factor was derived from field soil sample-based analysis, LS factor was from Cartosat Digital Elevation Model-based data. The C factor was derived from NDVI of Landsat 8 and Sentinel-2, and the P factor was prepared from LULC derived from Landsat 8, and Sentinel-2 was incorporated in the final integration. The soil erosion hazard map ranged from slight to extremely severe. Remote sensing (RS)-based parameters like Land Use Land Cover (LULC) are derived from the Landsat 8 and Sentine-2 satellite data and used to compute the difference in the final outcome of the integration. The study found similarities in average annual soil loss (A) in plain areas, but differences in final soil erosion risk zone (A) were influenced by LULC map variations due to different cell sizes, P factor, and slope gradient. Notable differences were observed in soil erosion risk categories, particularly in high to very severe zones, with a cumulative difference of 73.85 km2. In addition to this, a scatterplot between the final outputs was computed and found the moderate (R2 = 42.08%) correlation between Landsat 8 and Sentinel-2 imagery-based final average annual soil erosion (A) of RUSLE. The study area encompasses various landforms ranging from the plateau to pediplain, and in such situation, the water-led soil erosion categories vary depending on terrain condition along with its biophysical factors and, hence, need to analyze the need of such factors on the average annual soil erosion quantification. Different spatial resolution has an effect on the final output, and hence, there is a need to track this change at various spatial resolutions. This analysis highlights the significant impact of spatial resolution on land degradation assessment, providing precise identification of surface features and enhancing soil erosion risk zoning accuracy.

Assessment of soil erosion from rainwater runoff within a small non arable catchment

The article represents the dynamics of erosion-accumulation processes within the test catchment of the Lena basin over two time periods (1985-1990; 2015-2019) from rainfall-runoff, studied using the WaTEM/SEDEM model. The estimate of soil loss from rainfall-runoff was obtained, taking into account the deposition of part of the eroded soil within the catchment. This is one of the first works that estimates the magnitude of soil erosion within a poorly crop developed catchment from rainwater runoff, taking into account the deposition. It was determined that the measured sediment yield from the test catchment decreased over the two studied periods. The decrease in runoff from the studied territory is explained by a decrease in the intensity of agricultural activity in the catchment, as well as the forest area increase and grassland area reduction.

Assessment of soil erosion in the Boussellam watershed, Algeria: Integrated approach using the Erosion Potential Method (EPM) and GIS

Land degradation is a growing concern, exacerbated by recent climate change. Water erosion emerges as a crucial tool to address this issue. This study focuses on estimating soil loss from water erosion in the Boussellam valley watershed, a part of the ex-pansive Soummam basin in northeastern Algeria, characterized by a semi-humid to humid climate. Covering an expanse of 4,301 km2 with a perimeter of 420 Km, the basin's assessment incorporates often-overlooked factors such as temperature. Utilizing Geographic In-formation System (GIS) in conjunction with GAVRILOVIC's EPM (Erosion Potential Method) model, erosion projections for the year 2022 have been generated. The findings reveal that the entire Boussellam watershed experiences an average erosion rate of 8.50 tonnes per hectare annually. However, it is evident that the current protective measures implemented by decision-makers are suboptimal. To pinpoint the most vulnerable areas, GIS was employed to map and subsequently categorize them into five levels of erosion intensity: low, moderate, medium, high, and very high. These detailed maps will enable more precise and tailored interventions by decision-makers to effectively safeguard the regions most impacted by erosion.

An Estimation of Annual Soil Loss using Integrated Approach of RUSLE and Geospatial Techniques: A Case Study of Upper-Krishna River Basin, India

An Estimation of Annual Soil Loss using Integrated Approach of RUSLE and Geospatial Techniques: A Case Study of Upper-Krishna River Basin, India

Integration of RUSLE Model with Remote Sensing and GIS Tools for Soil Loss Estimation in the Kubanni Drainage Basin, Zaria, Nigeria

Integration of RUSLE Model with Remote Sensing and GIS Tools for Soil Loss Estimation in the Kubanni Drainage Basin, Zaria, Nigeria