Calculate Soil Erosion Research Articles

Impacts of Land Use Conversion on Soil Erosion in the Urban Agglomeration on the Northern Slopes of the Tianshan Mountains

The serious problem of soil erosion not only has a profound impact on people’s lives but also results in a series of ecological and environmental challenges. To determine the impact of changes in land use type on soil erosion in the urban agglomeration on the northern slopes of the Tianshan Mountains, this study commences by employing the InVEST-SDR (integrated valuation of ecosystem services and tradeoffs–sediment delivery ratio) model to calculate soil erosion levels spanning from 2000 to 2020. Subsequently, it forecasts land use and land cover (LULC) conditions for the year 2030 under three scenarios: Q1 (natural development), Q2 (ecological protection), and Q3 (economic priority). This projection is accomplished through the integration of a coupled Markov chain and multi-objective planning model (MOP) alongside patch-generating land use simulation (PLUS) models. Ultimately, based on these outcomes, the study predicts soil erosion levels for the year 2030. There has been a consistent decline in soil erosion from 2000 to 2020 with high-intensity erosion concentrated in the Tianshan Mountain region. Grasslands, glaciers, and permafrost are identified as the most erosion-prone land types in the study area, with forests exhibiting the highest capacity for soil retention. Converting from grassland and barren land to forest within the same area results in a substantial reduction in soil erosion, specifically by 27.3% and 46.3%, respectively. Furthermore, the transformation from barren land to grassland also leads to a noteworthy 19% decrease in soil erosion. Over the past two decades, the study area has witnessed a significant decline in the area of grasslands, with a notable shift towards barren and impervious surfaces due to economic development and mining activities. The three predicted scenarios depict significant expansion towards barren land, grassland, and impervious area, respectively. Soil erosion decreases under different shared socio-economic pathway (SSP) scenarios relative to 2020. There is an increase in soil erosion in the Q1 scenario and in the Q3 scenario, whereas the amount of soil erosion in the Q2 scenario exhibits a continued decrease when only the effect of land change on soil erosion is considered. Persistently rapid economic development can exacerbate soil erosion problems, underscoring the need to find a balance between economic growth and ecological conservation. As economic expansion slows down, greater emphasis should be placed on environmental protection to maintain ecological stability.

Interactive Effects of Ecological Land Agglomeration and Habitat Quality on Soil Erosion in the Jinsha River Basin, China

Soil erosion is a significant global environmental issue and a crucial aspect of global change. Exploring the interactive effect of ecological land agglomeration and habitat quality on soil erosion can effectively guide the positive intervention of ecological restoration activities. The study calculated the comprehensive ecological land agglomeration with Fragstats 4.2 and the habitat quality with InVEST 3.7.0 for the years 2000, 2010, and 2020 within the Jinsha River Basin in Yunnan, China. In addition, the RUSLE model was utilized to calculate soil erosion in the study area. The Geographic and Temporally Weighted Regression (GTWR) model was employed to obtain the regression coefficients and their spatial and temporal variations. The findings of this study revealed the following: (1) During the study period, there was an overall 29.06% reduction in the soil erosion modulus with an annual rate of 1.70% reduction on average, accompanied by an increase in both the comprehensive ecological land agglomeration and habitat quality. Soil erosion was more severe in the eastern regions than in the western ones and the other two indicators were higher in the northeast and southwest. (2) The GTWR results demonstrate that comprehensive ecological land agglomeration and habitat quality were negatively correlated with soil erosion, with results of −0.1383 and 0.0021, respectively. However, in northwest regions, there was a significant positive correlation between habitat quality and soil erosion. (3) The interaction term between comprehensive ecological land agglomeration and habitat quality was significantly negatively correlated with soil erosion with a result of −0.0299, and the interaction coefficients have regional variations. This study offers valuable guidance for land-use development and soil and water conservation in the Jinsha River Basin.

Unveiling the Wrath of Erosion: Assessing the Impacts of Soil Erosion on the Coastal Region of Bangladesh

Study on soil erosion in the coastal region of Bangladesh is crucial due to its severe environmental and socio-economic impacts. Understanding erosion patterns, identifying vulnerable areas, and developing effective conservation strategies are essential to safeguard agricultural productivity, preserve coastal ecosystems, and protect the livelihoods of coastal communities. This study focuses on soil erosion in the low-lying, flood-prone coastal regions of Bangladesh. The Revised Universal Soil Loss Equation (RUSLE) technique was used to calculate soil erosion rates. The southern section of the study area, characterized by high rainfall erosivity (R) and soil erodibility (K), exhibited the highest erosion rates at 1446 t/ha/year. The region experiences significantly higher rainfall erosivity than other parts of the world. Cox’s Bazar, Chittagong, Feni, and Patuakhali receive over 3000 mm/yr of precipitation, while Teknaf and Hatiya surpass 4000 mm/yr. The vulnerability of the soft, sand-rich topsoil to water-driven gully and rill erosion contributes to the high erodibility factor. Conversely, the northwest and low-lying marshy areas exhibit low erosion rates due to the flat terrain and deposition of eroded soil mass from the hinterland. The hilly areas of Chittagong, Teknaf, and Cox’s Bazar experience above-average erosion rates. Effective soil conservation and management strategies are essential for reducing soil erosion in Bangladesh’s coastal regions, considering local environmental conditions such as precipitation and soil type. The findings of this research provide valuable insights for future studies and policies on soil erosion management in the coastal districts of Bangladesh. By guiding efficient conservation efforts, this information can help mitigate the adverse impacts of soil erosion on the environment and economy. IUBAT Review, 6(2): 1-24

Spatial soil loss prediction impacted by long-term land use/land cover change: a case study of Swat District.

Soil erosion is a destructive consequence of land degradation caused by deforestation, improper farming practices, overgrazing, and urbanization. This irreversible effect negatively impacts the limited renewable soil resource, causing soil truncation, reduced fertility, and unstable slopes. To address the anticipation of erosion modulus resulting from long-term land use and land cover (LULC) changes, a study was conducted in the Swat District of Khyber Pakhtunkhwa (Kpk), Pakistan. The study aimed to predict and evaluate soil erosion concerning these changes using remote sensing (RS), geographic information systems (GIS), and the Revised Universal Soil Loss Equation (RUSLE) model. We also evaluated the impact of the Billion Tree Tsunami Project (BTTP) on soil erosion in the region. Model inputs, such as rainfall erosivity factor, topography factor, land cover and management factor, and erodibility factor, were used to calculate soil erosion. The results revealed that significant soil loss occurred under 2001, 2011, and 2021 LULC conditions, accounting for 67.26%, 61.78%, and 65.32%, falling within the category of low erosion potential. The vulnerable topographical features of the area indicated higher erosion modulus. The maximum soil loss rates observed in 2001, 2011, and 2021 were 80 t/ha-1/year-1, 120 t/ha-1/year-1, and 96 t/ha-1/year-1, respectively. However, the observed reduction in soil loss in 2021 as compared to 2001 and 2011 suggests a positive influence of the BTTP on soil conservation efforts. This study underscores the potential of afforestation initiatives like the BTTP in mitigating soil erosion and highlights the significance of environmental conservation programs in regions with vulnerable topography.

Application of Geo-informatics for Soil Erosion Mapping

Soil erosion is a most severe environmental problem in hilly area. The study is carried out on Upper Bagmati River basin, North of Kathmandu valley having an area of 61 Sq.km. (approx). Universal Soil Loss Equation (USLE) model, with Geographic Information System (GIS) has been used to quantify the soil loss. Erosion modelling requires huge amount of information and data, usually coming from different sources and available in different formats and scales and for management of these data, GIS was used, which helped considerably in organizing the spatial data representing the effects of each factor affecting soil erosion. Five essential parameters of USLE Rainfall erosivity factor (R), Soil erodibility Factor (K), Slope length and steepness (LS) factor, Cropping management factor (C) and Support practice factor (P) have been used to estimate soil loss amount in the study area. All of these layers have been prepared in Arc GIS using various data sources and data preparation methods. DEM was prepared from the contour data with the interval of 20m which was used to generate LS factor. The monthly rainfall data (2010) of 17 rain gauge stations within the catchment area have been used to predict the R factor. K, C and P factors in basin area are adopted from the literature. The spatial distribution map of soil loss of the basin has been generated and classified into six categories depending on the calculated soil erosion amount. The annual predicted soil loss ranges between 0 and 292.878 t/ha/y. Low soil loss (mean 9.7 t/ha/y) have been recorded under forested areas. The high rate (mean 40.4 t/ha/y) of soil erosion was found in the cultivation area.

Rainfall erosivity estimation models for the Tibetan Plateau

Many models have been developed internationally to estimate rainfall erosivity. However, the effectiveness of these pre-existing models has not been verified for the Tibetan Plateau with large spatiotemporal heterogeneity of rainfall due to lack of high-temporal resolution rainfall data. The purpose of this study was to develop models to accurately estimate daily and semi-monthly rainfall erosivity values in the Tibetan Plateau. We used one-minute resolution rainfall data from 1,182 weather stations to develop rainfall erosivity models over the Tibetan Plateau for the first time. The particle swarm optimization (PSO) method was performed to determine the newly developed model parameters. The newly developed models were compared with three internationally common existing models (exponentially seasonal1Exponentially seasonal model denoted Richardson model (1983) that was revised as R =αP1.7265 by Xie et al. (2016).1, cosine2Cosine model denoted Yu and Rosewell model (1996) that was revised as R=0.26861+0.5412cosπ6M-7π6P1.7265 by Xie et al. (2016).2, and exponentially semi-month3Exponentially semi-month model denoted Zhang model (2002) that was expressed as R=α∑j=1mPjβ,α= 21.586β- 7.1891 and β=0.8363+18.177Pd12+24.455Py12.3) to evaluate their effectiveness. Our results showed that (1) the three existing models all overestimated the values of mean annual rainfall erosivity in the Tibetan Plateau because limited rainfall data in the area were used to develop these models; (2) the new models greatly reduced computational error values of mean annual rainfall erosivity (the symmetric mean absolute percentage error (SMAPE) of ≤ 23%), relative to the three existing models (SMAPE of ≥ 40%); (3) the new semi-monthly models (SMAPE of 17.7 – 29.2%) generally outperformed the new daily models (SMAPE of 17.9 – 30.7%) because of their better ability to estimate the values of extreme rainfall erosivity. It is advisable to use the semi-monthly model rather than daily model to compute rainfall erosivity when using universal soil loss equation (USLE) and its derived models to calculate soil erosion. That will benefit to reduce the time-scale conversion errors between event-based and daily data when lacking high-temporal resolution data and using daily rainfall data to analyze the influence of rainfall on soil erosion.

The sediment budget and migration of 137Cs in Chernobyl affected area: 30 years of investigations in the Plava River basin, Tula region

The Plava River basin (Tula region) is considered the most polluted with Chernobyl fallout region in Russian Federation. Detailed studies of sediment redistribution and migration of 137Cs produced by Chernobyl were conducted in the basin. The article provides an overview of results from various studies conducted during the last 30 years estimating the rate of erosion and sediment redistribution based on different methodology including 137Cs. Sediment budgets for different parts of the fluvial network were developed based on estimated rates of erosion and accumulation of sediments within the Plava River basin for the post-Chernobyl period. It has been established that eroded from agricultural fields sediments accumulate on the slopes of interfluves (38.4%) and in the bottoms of dry valleys (2738%). Part of the sediment delivered by slope runoff and temporary watercourses from the slopes of the interfluves to the bottoms of river valleys was redeposited on river floodplains (1011%), and the remaining part (13.124.7%) passed into the river sediment load. The value of basin contribution of sediments to the Plava River sediment runoff is somewhat overestimated due to the specifics of the erosion models used to calculate soil erosion from arable land. It has been established that for the period of more than 25 years since the Chernobyl accident, 5% of 137Cs deposits were lost due to soil erosion (less than 0.2% annually). According to evaluated sediment budget, only a quarter of mobilized material leaves basin as a sediment yield. Thus, only about 1% of the initial 137Cs fallout was removed from the Plava basin. The perspective directions for research on sediment redistribution in the plain river basins located in the temperate zone using 137Cs as a technogenic tracer are outlined.

Spatial Modeling of Vegetation Cover for Soil Erosion Control Based on Arc GIS and the RUSLE Models

Deforestation in the Serayu watershed, Central Java province, Indonesia for agriculture and other uses leaves only 0.73% of vegetation. It has triggered a number of problems such as soil loss (erosion), landslides, floods and sedimentation downstream. Environmental damage control needs to be applied through appropriate conservation programs. This study aims to understand the distribution of soil erosion and the effectiveness of soil erosion control by using vegetation cover. Soil erosion modeling and its correlation to vegetation cover was performed by using an Arc GIS based model of the Revised Universal Soil Loss Equation (RUSLE) through five scenarios of vegetation landscape cover such as 10%, 15%, 20%, 25%, and 30% of the total area of the study site. Five parameters namely rain erosivity (R), soil erodibility (K), slope-length (LS) and crop management (C) and conservation practices (P) factor were used to calculate soil erosion. The results indicated 82.25 tons/ha/year soil erosion reduction due to enhancement of vegetation cover from the actual condition 0.73% to the 30% vegetation cover condition. The increase of 5% vegetation landscape cover (forest) detracted the soil erosion rate by 10,20 tons /ha/year. Very high and high soil erosion hazard levels were found in the northern, east, and west watershed areasKeywords: Soil erosion, RUSLE model, GIS, Serayu watershed, Vegetation cover

Effect of Land Use-Land Cover Change on Soil Erosion and Sediment Yield in Muger Sub-Basin, Upper Blue Nile Basin, Ethiopia

Land use and land cover (LULC) change have intense implications on soil resources, that requires research attention and global collaboration to take urgent actions. The present study attempted to analyze the impact of LULC change on soil erosion and sedimentation in Muger Sub-basin, southwestern parts of Ethiopia. Landsat TM 1986, Landsat ETM+ 2003, and Landsat OLI/TIRS 2020 are used for LULC analysis. We used rainfall erosivity, soil erodibility, slope length and steepness, cover management, and conservation practices to calculate soil erosion and sedimentation between 1986 and 2020. In this study, the integration of revised universal soil loss equation (RUSLE) model and geographic information system (GIS) are used to analyze the impact of LULC change on soil erosion and sedimentation. The average soil loss rate is about 53.2 ton/ha/year in 1986 and increased to 63.6, and 64 ton/ha/year in 2003, 2020, respectively. Result revealed that about 2707.7 ha (33%), 3124.5 ha (38.1%) and 3197.4 ha (38.9%) were exposed to severe erosion in 1986, 2003 and 2020, respectively. The mean sediment yields of the study area is estimated to 7.8 ton/ha/year in 1986, and later increased to 10, and 10.2 ton/ha/year in 2003, and 2020, respectively. Results revealed that the steep slope areas with crop land experience considerable soil loss, with mean soil loss increasing over all study period. Thus, urgent policy on the wise use of natural resources is essential not optional to reduce the current soil loss and sedimentation in Muger Sub-basin.

Soil erosion rates on sloping cropland fragment underlain by contrasting lithologies

This study examines the pattern of soil erosion rates in a cropland fragment underlain by limestone and purple sandstone (referred to as the CLP) in the Three Gorges Reservoir Region (TGRR) using the 137Cs and 210Pbex techniques. A total of 96 bulk cores and 18 sectioned cores were collected from the CLP. In addition, 4 bulk and 4 sectioned cores were extracted from each of two reference sites. Based on the 137Cs and 210Pbex inventories obtained from these cores, the soil erosion rates were assessed. The results indicate that the 137Cs and 210Pbex inventories both increase downslope, while the calculated soil erosion rates both decrease. This implies that the intensity of soil erosion decreases downslope, which is a combined response to the effect of gravity, the short slope length, and the gentle gradient of the cropland. The soil erosion rates derived from 137Cs were generally higher than those determined using 210Pbex. This shows that soil erosion was more intense from 1954 onward than during the period between 1920 and 1954, reflecting the impact of disturbance by cultivation, which has increased rapidly over the past 60 years. The soil erosion rate on the limestone area was similar to that in the purple sandstone area, but higher than that reported from karst regions experiencing rocky desertification. This suggests that, providing that there is no evidence of karst rocky desertification, the soil erosion intensity in cropland underlain by limestone is similar to that underlain by purple sandstone. This result provides a new insight into the pattern of soil erosion intensity on the cultivated hillslopes of karst and non-karst regions at the scale of the cropland fragment. It will also help to improve our understanding of surface processes in karst and non-karst areas subject to intense cultivation.

Use of Monitoring Approaches to Verify the Predictive Accuracy of the Modeling of Particle-Bound Solid Inputs to Surface Waters

For particle-bound substances such as phosphorus, erosion is an important input pathway to surface waters. Therefore, knowledge of soil erosion by water and sediment inputs to water bodies at high spatial resolution is essential to derive mitigation measures at the regional scale. Models are used to calculate soil erosion and associated sediment inputs to estimate the resulting loads. However, validation of these models is often not sufficiently possible. In this study, sediment input was modeled on a 10 × 10 m grid for a subcatchment of the Kraichbach river in Baden-Wuerttemberg (Germany). In parallel, large-volume samplers (LVS) were operated at the catchment outlet, which allowed a plausibility check of the modeled sediment inputs. The LVS produced long-term composite samples (2 to 4 weeks) over a period of 4 years. The comparison shows a very good agreement between the modeled and measured sediment loads. In addition, the monitoring concept of the LVS offers the possibility to identify the sources of the sediment inputs to the water body. In the case of the Kraichbach river, it was found that around 67% of the annual sediment load in the water body is contributed by rainfall events and up to 33% represents dry-weather load. This study shows that the modeling approaches for calculating the sediment input provide good results for the test area Kraichbach and the transfer for a German wide modeling will produce plausible values.

RETRACTED ARTICLE: Environmental change of rainfall erosivity based on GIS system and architectural design of sponge city

With the continuous development of society and the continuous improvement of scientific and technological level, it has greatly promoted the development of many fields. Especially civil engineering, civil engineering plays an important role in the development of China. Civil engineering survey is an indispensable part of civil engineering work. The application of GIS system technology in engineering survey helps to improve the accuracy and efficiency of survey, ensure the orderly development of the project, and improve the construction quality. However, rainfall erosivity reflects the potential ability of rainfall to cause soil erosion. Many scientists use R value of rainfall erosion rate to calculate soil erosion coefficient and draw curve equation. Therefore, it is an important guideline to study the spatial and temporal distribution characteristics of R value, establish simple regression function, quantitative prediction of soil and water loss, and soil and water conservation plan. With the continuous changes of economy and society, the global environment is also changing, which inevitably affects human beings. With the accelerating process of new urbanization, urban planning and architectural design are also deepening. On the one hand, it is necessary to improve the urban living environment and seriously implement the construction of sponge city. On the other hand, the green roof design of urban rainwater drainage and storage must be coordinated with it. The sustainable development of economy and society promotes the strong rise of construction industry. Architectural design is an important part of construction engineering, which can represent the construction standard of construction engineering. Based on the research of GIS system and rainfall erosivity environment change, this paper applies it to the architectural design of sponge City, and then promotes the vigorous development of urban construction.

Evaluation of Rainfall Erosivity Factor Estimation Using Machine and Deep Learning Models

Rainfall erosivity factor (R-factor) is one of the Universal Soil Loss Equation (USLE) input parameters that account for impacts of rainfall intensity in estimating soil loss. Although many studies have calculated the R-factor using various empirical methods or the USLE method, these methods are time-consuming and require specialized knowledge for the user. The purpose of this study is to develop machine learning models to predict the R-factor faster and more accurately than the previous methods. For this, this study calculated R-factor using 1-min interval rainfall data for improved accuracy of the target value. First, the monthly R-factors were calculated using the USLE calculation method to identify the characteristics of monthly rainfall-runoff induced erosion. In turn, machine learning models were developed to predict the R-factor using the monthly R-factors calculated at 50 sites in Korea as target values. The machine learning algorithms used for this study were Decision Tree, K-Nearest Neighbors, Multilayer Perceptron, Random Forest, Gradient Boosting, eXtreme Gradient Boost, and Deep Neural Network. As a result of the validation with 20% randomly selected data, the Deep Neural Network (DNN), among seven models, showed the greatest prediction accuracy results. The DNN developed in this study was tested for six sites in Korea to demonstrate trained model performance with Nash–Sutcliffe Efficiency (NSE) and the coefficient of determination (R2) of 0.87. This means that our findings show that DNN can be efficiently used to estimate monthly R-factor at the desired site with much less effort and time with total monthly precipitation, maximum daily precipitation, and maximum hourly precipitation data. It will be used not only to calculate soil erosion risk but also to establish soil conservation plans and identify areas at risk of soil disasters by calculating rainfall erosivity factors.

Identification of areas vulnerable to soil erosion and risk assessment of phosphorus transport in a typical watershed in the Loess Plateau

Soil erosion is an increasingly serious eco-environmental problem and an important driver of phosphorus loss, which not only reduces soil productivity but also decreases water availability. The integration of the universal soil loss equation (USLE) and the geographic information system (GIS) technique is globally popular for erosion prediction and assessment. The Fen River basin is located in the east of the Loess Plateau and has eco-environmental problems of soil erosion and eutrophication because of excess phosphorus content. This study attempted to use the USLE model to evaluate soil erosion and the transport of the resulting particulate phosphorus in the Fen River basin under a GIS framework. The results showed that soil erosion in 15.8% of the study area exceeded 8000 t/(km2·a) and was mainly distributed in the upper Fen River basin. Soil erosion was greatest in the bareland area, with an average of approximately 1.22 × 104 t/(km2·a), followed by that in grassland. Soil erosion in the study area is most sensitive to the rainfall erodibility (R), followed by the soil erodibility (K), topographic factors including slope steepness (S) and slope length (L), the soil and water conservation factor (P), and the vegetation cover and management factor (C). Similar to soil erosion, the high-risk areas of particulate phosphorus transport were mainly concentrated in the upper reaches of the basin. The study also pointed out that the combined use of available data sources with the USLE model and GIS technique is a viable option to calculate soil erosion and assess the risk of particulate phosphorus transport, which could provide a scientific basis for reducing soil erosion and controlling phosphorus migration.

Soil erosion changes during the last 30 years and contributions of gully erosion to sediment yield in a small catchment, southern China

As a large weathered granite soil region in the world, southern China is experiencing severe sheet and rill erosion and gully erosion due to its special geological conditions, inappropriate land use, and high intensity precipitation. However, few studies have been conducted on the long-term dynamics of these two types of soil erosion and the sediment contribution of gully erosion in southern China. To assess this, estimates of soil erosion changes and the contribution ratio of gully erosion to sediment yield in a small catchment in southern China from 1989 to 2015 are quantified. A topographic map and three high-resolution satellite images were interpreted for land use changes to calculate soil erosion modulus of sheet and rill erosion by the Chinese soil loss equation (CSLE) and to estimate the amount of gully erosion by gully volume based on a generalized inverted triangular pyramid model multiplying bulk density. Results indicated that soil erosion modulus started as high as 10,442 t km−2 in 1989, followed by an initial decrease of 54% before increasing slightly. The total gully volume decreased by 37% overall, as did the cumulative amount of gully erosion. Between 2005 and 2010 positive total soil loss was calculated and the sediment contribution ratio of gully erosion was estimated to be 52.27%. It can be concluded that the combination of remote sensing images, the CSLE, and a new generalized gully model can effectively be used to quantify soil loss from sheet and rill erosion as well as gully erosion. This study provides a new insight to estimate gully erosion and the dynamic evolution of soil loss. Further studies should be performed in combination with field methods to validate this approach.

How to Optimize Ecosystem Services Based on a Bayesian Model: A Case Study of Jinghe River Basin

Based on a Bayesian Network Model (BBN), we established an ecological service network system of the Jinghe River Basin in 2015. Our method consisted of using the distributed eco-hydrological model (Soil and Water Assessment Tool (SWAT) model) to simulate water yield, the Carnegie-Ames-Stanford Approach (CASA) model to estimate Net Primary Productivity (NPP), the Universal Soil Loss Equation (USLE) model to calculate soil erosion and the Crop Productivity (CP) model to simulate agricultural productivity to quantify the four ecosystem services. Based on the network established, the key variable subset and the visual optimal state subset, which we visualized, were analyzed and used to provide spatial optimization suggestions for the four kinds of ecosystem services studied. Our results indicate that water yield, concentrated in the middle and lower reaches of the mountain and river areas, is increasing in the Jinghe River Basin. NPP is continuously increasing and is distributed in the middle and lower reaches of the mountain areas on both sides of the river. Agricultural productivity also shows an upward trend, with areas of high productivity concentrated in the southern downstream mountain areas. On the contrary, the amount of soil erosion is declining, and the high erosion value is also declining, mainly in the upper reaches of the basin (in the Loess Hilly Area). Additionally, we found that a synergistic relationship exists between water yield, NPP and agricultural productivity, which can increase vegetation cover, leading to enhanced agricultural productivity. However, water yield can be reduced as required in order to balance the tradeoff between water yield and soil erosion. Clear regional differences exist in ecosystem services in the river basin. In the future, the two wings of the middle and lower reaches of the river basin will be the main areas of optimization, and it is likely that an optimal ecosystem services pattern can be reached.

Estimation of Sediment Yield and Maximum Outflow Using the IntErO Model in the Sarada River Basin of Nepal

Soil erosion is a severe environmental problem worldwide as it washes away the fertile topsoil and reduces agricultural production. Nepal, being a hilly country, has significant erosion disputes as well. It is important to cognise the soil erosion processes occurring in a river basin to manage the erosion severity and plan for better soil conservation programs. This paper seeks to calculate the sediment yield and maximum outflow from the Sarada river basin located in the western hills of Nepal using the computer-graphic Intensity of Erosion and Outflow (IntErO) model. Asymmetry coefficient of 0.63 was calculated, which suggests a possibility of large floods to come in the river basin in the future whereas the maximum outflow from the river basin was 1918 m³ s−1. An erosion coefficient value of 0.40 was obtained, which indicates surface erosion of medium strength prevails in the river basin. Similarly, the gross soil loss rate of 10.74 Mg ha−1 year−1 was obtained with the IntErO modeling which compares well with the soil loss from the erosion plot measurements. The IntErO model was used for the very first time to calculate soil erosion rates in the Nepalese hills and has a very good opportunity to be applied in similar river basins.

Long‐term impact of land use changes on soil erosion in an agricultural catchment (in the Western Polish Carpathians)

AbstractThe change in land use and land cover (LULC) from natural vegetation to agricultural in mountain areas usually dramatically accelerates soil erosion rates if the land is used for crop production. The aim of research was to calculate soil erosion magnitude basing on the Revised Universal Soil Loss Equation (RUSLE) in response to long‐term changes in LULC in an agricultural Homerka catchment (19.3 km2) of the Polish Carpathians. The changes in LULC were derived from cadastral maps for 1846 and orthophotomaps for 2009. Three variants with different factors, rainfall–run‐off erosivity (R), practice support (P), and LULC, were analysed to investigate which has had the greatest impact on soil erosion changes over the last 160 years. An increase in forest area (by 67.81%) and decrease in cultivated land (by 91.94%) were observed, primarily due to the collapse of the communist system. The estimated soil erosion using the RUSLE model in the analysed catchment decreased by 77% (from 18.13 t ha−1 yr−1 in 1846 to 4.11 t ha−1 yr−1 in 2009). The long‐term changes in soil erosion rates and their spatial distribution are mainly associated with transformation from cultivated land to forest or grassland. The P factor was responsible for reduction of soil erosion rates by 8%, a minor impact. Over the last 160 years, the average and maximum annual precipitation changes were also statistically insignificant. Results reveal a dominant role of human impact, particularly with respect to LULC on soil erosion changes in mid‐mountain areas.

The CSLE model based soil erosion prediction: Comparisons of sampling density and extrapolation method at the county level

The Universal Soil Loss Equation (USLE) is an empirical equation commonly applied to predict soil erosion throughout the world. In China, the Chinese Soil Loss Equation (CSLE) has been developed for estimating annual soil erosion by water on the basis of the USLE. It is proven that two factors (i.e., sampling density and extrapolation method) are important in the application of CSLE for soil erosion survey and evaluation. In this study, based on the SPOT 6 image (1.5 m resolution) and the 1:10,000 topographic map in Yishui county of the Yimeng Mountain Area (YMA), eastern China, two sampling densities (1% and 4%) were selected and three extrapolation methods (grid based calculation, direct extrapolation, and Kriging extrapolation based on sampling units) were adopted to compare their effect on the CSLE calculated soil erosion condition and status. Results showed that estimated soil erosion by direct extrapolation and Kriging extrapolation methods varies largely with sampling density. The discrepancy of the soil erosion area ratios calculated by the two methods are 6.5% and 7.7% under the 1% and 4% sampling density, and the relative discrepancies are 15.9% and 16.2%, respectively. But the grid calculation method is less affected by sampling density. The discrepancy of soil erosion area ratios is only 0.9% and 2.0%, respectively, under the 1% with 4% sampling density. Considering both field workload and prediction accuracy of soil erosion, the 1% sampling density with the grid calculation method is recommended when high resolution remotely sensed data are available. Otherwise, the 4% sampling density with either extrapolation method should be used. The research can provide useful reference information for ensuring the accuracy and reducing the survey workload on the application of CSLE in other areas.

Integrated universal soil loss equation (USLE) and Geographical Information System (GIS) for soil erosion estimation in A Sap basin: Central Vietnam

Central Vietnam is very susceptible to soil erosion due to its complicated terrain and heavy rainfall. The objective of this study was to quantify soil erosion in the A Sap river basin, A Luoi district, Thua Thien Hue Province, Vietnam, using the Universal Soil Loss Equation (USLE) and Geographical Information System (GIS). The results showed that 34% of land area lost accumulated to 10tha−1year−1 while 47% of the total area lost less than 1tha−1year−1. Natural forest land lost the most with an average of about 19tha−1year−1, followed by plantation forest with approximately 7tha−1year−1 and other agricultural lands at 3.70 and 1.45tha−1year−1 for yearly crops and paddy rice, respectively. Soil erosion was most sensitive to the topographic factor (LS), followed by the practice support factor (P), soil erodibility factor (K), cropping management (C), and the rainfall erosivity factor (R). Implications are that changes to the cultivated calendar and implementing intercropping are effective ways to prevent soil erosion in cultivated lands. Furthermore, introducing broad leaves trees for mountainous areas in A Sap basin was the most effective practice in reducing soil erosion. The study also pointed out that the combination of available data sources used with the USLE and GIS technology is a viable option to calculate soil erosion in Central Vietnam, which would allow targeted attention toward a solution is to reduce future soil erosion.