Quantify Soil Erosion Research Articles

Geographic information system (GIS) based soil loss estimation using RUSLE model for soil and water conservation planning in anka_shashara watershed, southern Ethiopia

Water induced soil erosion has been continued to threaten the land resources in sub humid northwestern highlands of Ethiopia. Human-induced land cover (LC) changes due to improper land management practices are contributing factors in deteriorating soil quality. Soil and water conservation measures have been implemented without site-specific scientifically quantified soil erosion data and priority bases in this regard; this study was conducted with a view to quantifying soil erosion in Anka-Shashara watershed. To do this, we have opted to use the RUSLE model based on geographic information systems. By collecting data on rainfall, soils, vegetation, slopes and conservation practices separately as a layer and determining the pixel values for each of these factors, a quantified assessment of erosion in the basin is obtained. The result reveals that the mean annual soil loss (15.22t/ha/yr) of the most parts of study area falls in tolerable levels and almost 66% (1594 ha), moderate level about 23% and 10% at high level of watershed. The soil loss of watershed is classified in to four main classes. None to Slight rates of soil erosion (0-10t/ha) is cover the almost 66% of watershed moderate about 23% and high about 10% of erosion rate as seen from the erosion rate distribution map. The upstream of catchment needs management and protection of those existing resources and the fundamental attention for SWC as recommended bases. Therefore, to reduce sedimentation problem and ensue sustainability of the watershed need to special attention for implementing recommended SWC intervention at the earlier.

Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements

Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.

SOIL ERODIBILITY AND SOIL SUSCEPTIBILITY TO EROSION BY WATER IN SHONKOLA MOUNTAINOUS AREA OF SORO DISTRICT, ETHIOPIA

Although the problem of soil erosion is common in all countries, it is one of the more persisting hazards in developing countries like Ethiopia which is a country located in tropic where there is higher precipitation and is vulnerable to almost all forms of environmental degradation which is also resulted from poor resource conservation practices. The main objective of this study is to determine soil erodibility and susceptibility to soil erosion in Shonkola mountainous area, Soro district. To determine soil erodibility and susceptibility to soil erosion, the percentage of primary soil separates (silt, very fine sand, and clay); percentage of organic matter; soil structure classes; and soil permeability were estimated. Soil samples were collected from 56 locations at an average depth of 15 cm to employ soil texture nomograph. Thus, soil erodibility of Shonkola area is determined and mapped to envisage the area that is highly susceptible for soil erosion. The soil erodibility value ranges from 0.01426 to 0.04001. It was found that the soil erodibility increases as organic content decreases, soil texture becomes finer and less permeable, and structure becomes poor. Prediction of soil erodibility and susceptibility to soil erosion is of great importance for soil erosion quantification, using GIS. The farm-unit level identification of soil types and existing specific problems are crucial in planning and implementation of any soil management strategies.

A comprehensive modeling framework to evaluate soil erosion by water and tillage

Soil erosion is significantly increased and accelerated by unsustainable agricultural activities, resulting in one of the major threats to soil health and water quality worldwide. Quantifying soil erosion under different conservation practices is important for watershed management and a framework that can capture the spatio-temporal dynamics of soil erosion by water is required. In this paper, a modeling framework that coupled physically based models, Water Erosion Prediction Project (WEPP) and MIKE SHE/MIKE 11, was presented. Daily soil loss at a grid-scale resolution was determined using WEPP and the transport processes were simulated using a generic advection dispersion equation in MIKE SHE/MIKE 11 models. The framework facilitated the physical simulation of sediment production at the field scale and transport processes across the watershed. The coupled model was tested using an intensively managed agricultural watershed in Illinois. The impacts of no-till practice on both sediment production and sediment yield were evaluated using scenario-based simulations with different fractions of no-till and conventional tillage combinations. The results showed that if no-till were implemented for all fields throughout the watershed, 76% and 72% reductions in total soil loss and sediment yield, respectively, can be achieved. In addition, if no-till practice were implemented in the most vulnerable areas to sediment production across the watershed, a 40% no-till implementation can achieve almost the same reduction as 100% no-till implementation. Based on the simulation results, the impacts of no-till practice are more prominent if implemented where it is most needed.

Quantification of soil erosion and sediment yield for ungauged catchment using the RUSLE model: Case study for Lake Basaka catchment in Ethiopia

AbstractSoil erosion is a monumental land degradation problem in many parts of the world, threatening the well‐being of humans and ecosystems. The Lake Basaka catchment is heavily affected by serious land degradation problems related to land use–land cover changes (LULC), population dynamics and settlement, introduction of large‐scale irrigation schemes, etc. Accordingly, the objective of the present study was to quantify the magnitude of soil lost from the Lake Basaka catchment and the sediment subsequently delivered to the lake on the basis of the limited available dataset in a distributed manner using the RUSLE model in a GIS environment. The results indicated about 36 million m3 of soil were lost from the catchment between 1973 and 2007. Soil erosion and sedimentation in the lake catchment continue to increase from time to time, being attributed to the significant LULC occurring in the catchment. About 23 cm of the economically productive top soil in the catchment was lost during the study period (1960–2015), which translates into a significant degradation of the catchment (e.g. further reduction of infiltration capacity, thereby accelerating run‐off and erosion rates). Similarly, about a 1.3 m deep sediment layer was deposited in the lake during the same period. The computed sediment yield indicates about 14% of the lake incremental volume and depth in recent periods (post‐2000s) is attributable to sedimentation. Certain coincidental changes are also occurring in the catchment, including organic matter declines, soil nutrient depletion and soil depth decrement, which may lead to decreased production and productivity and associated ecosystem imbalances. The present study identified the potential areas that contribute significantly to erosion and sedimentation, thereby providing guidance regarding where land use/cover practices must be implemented to limit/reduce, if not avoid, the impacts of erosion and subsequent sedimentation in Lake Basaka.

Assessment of the risk of soil erosion using RUSLE method and SWAT model at the M’dez Watershed, Middle Atlas, Morocco

The preservation of soil resources is a primary global concern and a permanent challenge for all Mediterranean countries. In Morocco, the capacity of dam reservoirs continues to decline from one year to the next due to the rate of siltation, mainly due to the phenomenon of water erosion. Indeed, the origins of this erosion are generally related to land use planning, deforestation, agricultural practices and low vegetation cover. However, it is imperative to quantify soil erosion and its spatial distribution to achieve sustainable land use and governance of this resource. The SWAT hydro-agricultural model and the integrated RUSLE model were used to assess soil losses and characterize the degraded areas of the M’dez watershed, located in the upper Sebou, north of the Middle Atlas, and extend on an area of 3350 km2. The results obtained during this work show that the average soil losses estimated by the two models are consistent. For the SWAT model, the specific degradation of the watershed is estimated at 3.95 t/ha/year, whereas for the RUSLE model, the average loss of the basin is estimated at 2.94 t/ha/year). However, the use of these two models (SWAT and RUSLE), for the assessment and characterization of degraded areas at the level of Moroccan watersheds, has become a much sought-after approach for good soil conservation management.

Watershed prioritization for soil conservation measures using USPED model in Nagwan Watershed

Soil is a backbone for human livelihoods and plant growth. In present scenarios, soil erosion becoming a worldwide environmental calamity for impends the natural environment, agriculture, and aquatic life. Consequently, it’s very important to evaluate the erosion quantity and prepare the map of the erosionprone area for sustainable planning and management of watershed based on soil conservation practices. In the present study, soil loss for Nagwan watershed was quantified using Unit Stream Power-based Erosion Deposition (USPED) model which is based on Revised Universal Soil Loss Equation (RUSLE). The GIS-based dataset consists of various thematic layers of RUSLE factors such as rainfall erosivity (R), soil erodibility (K), topographic factor (LS), crop management factor (C), and support practice factor (P) have been formulated by using GIS (Geographic Information System) software i.e. ILWIS. The thematic layers of considered factors are integrated into a GIS environment which helps to evaluate the soil loss. According to USPED model, approximately 8.26%, 28.42%, 23.51% and 15.6% of the basin area has very low, low, moderate, high risk and very high erosion risks, respectively. It was found that out of 42 subwatersheds, 11 sub-watersheds comes under very high, 7 under high, 7 under moderate and rest under low and very low priority. The study emphasized the prioritization of smaller scale hydrological units’ i.e. micro-watersheds is an ideally recommended unit to commence the soil and water conservation practices. Outcomes of the analysis suggested that the USPED model is an effective tool to quantify soil erosion and deposition for the watershed.

Assessing soil erosion risk at national scale in developing countries: The technical challenges, a proposed methodology, and a case history

Through an extensive bibliographic review, this contribution underlines the urgency and challenges to quantify soil erosion rates (ERs) in developing countries. It subsequently elaborates on the combined application of GIS-based RUSLE, generalized likelihood uncertainty estimation (GLUE) principles and sediment delivery ratio functions (SDR) to quantify ERs at country scale for these countries, as they commonly have limited measurements to that purpose. The methodology, termed RUSLE-GGS (RUSLE-GIS-GLUE-SDR) herein, comprises the following sequence: (1) construction of ER samples using RUSLE-GIS based on freely available local/global geoenvironmental observations and field relations, (2) construction of area-specific sediment yield samples utilizing SDR transfer functions, and (3) assessment of the most behavioral samples by means of bias analysis and cross validation. Its application to Peru allows obtaining 5-km resolution ER and potential erosion maps for the years 1990, 2000, and 2010. RUSLE-GGS is highly replicable and could potentially be used as an initial standard and systematic method to estimate ERs in developing countries through the active participation of local scientists. Thus, it potentially can contribute to improve the capacity building in such countries and set an initial frame to compare the evolution of soil erosion in their territories towards attaining Goal 15 of the UN 2030 Agenda for Sustainable Development.

Changes of Soil Erosion and Possible Impacts from Ecosystem Recovery in the Three-River Headwaters Region, Qinghai, China from 2000 to 2015

Soil erosion poses a great threat to the sustainability of the ecological environment and the harmonious development of human well-being. The revised universal soil loss equation (RUSLE) was used to quantify soil erosion in the Three-River Headwaters region (TRH), Qinghai, China from 2000 to 2015. The possible effects of an ecosystem restoration project on soil erosion were explored against the background of climatic changes in the study area. The model was validated with on-ground observations and showed a satisfactory performance, with a multiple correlation coefficient of 0.62 from the linear regression between the estimations and observations. The soil erosion modulus in 2010–2015 increased 6.2%, but decreased 1.2% compared with those in the periods of 2000–2005 and 2005–2010, respectively. Based on the method of overlay analysis, the interannual change of the estimated soil erosion was dominated by climate (about 64%), specifically by precipitation, rather than by vegetation coverage (about 34%). Despite some uncertainties in the model and data, this study quantified the relative contribution of ecological restoration under global climatic change; meanwhile the complexity, labor-intensiveness and long-range character of ecological restoration projects have to be recognized. On-ground observations over the long-term, further parameterization, and data inputs with higher quality are necessary and essential for decreasing the uncertainties in the estimations.

Assessing and mitigating the effects of agricultural soil erosion on roadside ditches

PURPOSE: Roadside ditches line more than 6.3 million km of roadways in the USA, dissecting the natural topography and altering the flow of runoff from the catchments that drain into them. In agricultural regions, more than 30% of a watershed may directly drain into the roadside ditch system. Quantifying soil erosion and sediment export from agricultural watersheds is a crucial component when considering long-term soil sustainability. MATERIALS AND METHODS: Our study evaluated the relation of catchment soil erosion and ditch sedimentation at six representative roadside ditches in Lime Creek watershed (eastern Iowa) and quantified the effectiveness of possible catchment conservation practices to reduce soil erosion and ditch sedimentation. RESULTS AND DISCUSSION: Study results provide clear evidence linking roadside ditches to the agricultural catchments that drain into them. Among the six ditch sites, catchment erosion was found to be inversely related to sediment storage within the ditch due to erosive power of water entering the ditches from their basins. Of four catchment scenarios to reduce soil erosion, no-till with cover and graded terrace did not require land to be taken out of production and provided the most significant reductions in catchment erosion rates. CONCLUSIONS: Results indicated that reducing nutrient and sediment loads to ditches by incorporating in-field conservation practices in ditch catchments may be more economical and environmentally sustainable than current management practices for both farmers and roadway managers because they trap detached soil sediments before they enter the ditch.

Assessment of areal water and tillage erosion using magnetic susceptibility: the approach and its application in Moroccan watershed.

Soil erosion constitutes a serious threat for sustainable agriculture in many countries. Magnetic susceptibility of soil is a fast, cheap, and non-destructive technique that could be used to quantify soil erosion or soil redistribution on a long-term scale. This study attempts to analyze the variation of magnetic susceptibility in soil profiles having the same lithology and climatic conditions, but different land uses and slope gradients in a subcatchment in northern Morocco. Soil cores were collected on forested, cultivated, and pasture lands. Each core was associated to a field unit (also called a homogeneous unit) characterized by a set of four cited erosion factors. The samples were measured for mass-specific low-frequency magnetic susceptibility (χlf) and frequency-dependent magnetic susceptibility (χfd). The linear correlation of χlf and χfd indicates the homogeneity of magnetic population in soil. It supports the use of empirical models based on comparisons of χlf to predict the value of magnetic parameter after tillage homogenization and removal of soil material from the surface, and to estimate soil erosion or redeposition. The study built a methodology improving these empirical models and enabling a quantitative approach of the phenomenon. Two models, namely "tillage homogenization" (as improved in this study) and the proposed "simple correlation" result in globally similar estimates of erosion, while another model, the "simple proportional" model, underestimates it. The results give an estimate of long-term erosion (deposition) in sampled units and allow drawing of an areal soil redistribution map in the watershed.

Quantitative Assessment of Soil Erosion Based on CSLE and the 2010 National Soil Erosion Survey at Regional Scale in Yunnan Province of China

Regional soil loss assessment is the critical method of incorporating soil erosion into decision-making associated with land resources management and soil conservation planning. However, data availability has limited its application for mountainous areas. To obtain a clear understanding of soil erosion in Yunnan, a pixel-based estimation was employed to quantify soil erosion rate and the benefits of soil conservation measures based on Chinese Soil Loss Equation (CSLE) and data collected in the national soil erosion survey. Results showed that 38.77% of the land was being eroded at an erosion rate higher than the soil loss tolerance, the average soil erosion rate was found to be 12.46 t∙ha−1∙yr−1, resulting in a total soil loss of 0.47 Gt annually. Higher erosion rates mostly occurred in the downstream areas of the major rivers as compared to upstream areas, especially for the southwest agricultural regions. Rain-fed cropland suffered the most severe soil erosion, with a mean erosion rate of 47.69 t∙ha−1∙yr−1 and an erosion ratio of 64.24%. Lands with a permanent cover (forest, shrub, and grassland) were mostly characterized by erosion rates an order of magnitude lower than those from rain-fed cropland, except for erosion from sparse woods, which was noticeable and should not be underestimated. Soil loss from arable land, woodland and grassland accounted for 52.24%, 35.65% and 11.71% of the total soil loss, respectively. We also found significant regional differences in erosion rates and a close relationship between erosion and soil conservation measures adopted. The CSLE estimates did not compare well with qualitative estimates from the National Soil Erosion Database of China (NSED-C) and only 47.77% of the territory fell within the same erosion intensity for the two approaches. However, the CSLE estimates were consistent with the results from a national survey and local assessments under experimental plots. By advocating of soil conservation measures and converting slope cropland into grass/forest and terraced field, policy interventions during 2006–2010 have reduced soil erosion on rain-fed cropland by 20% in soil erosion rate and 32% in total soil loss compared to the local assessments. The quantitative CSLE method provides a reliable estimation, due to the consideration of erosion control measures and is potentially transferable to other mountainous areas as a robust approach for rapid assessment of sheet and rill erosion.

Estimating soil degradation in montane grasslands of North-eastern Italian Alps (Italy)

Grasslands cover a large portion of the terrestrial ecosystems, and are vital for biodiversity conservation, environmental protection and livestock husbandry. However, grasslands are degraded due to unreasonable management worldwide, i.e., soil erosion indirectly due to the damage of overgrazing on vegetation coverage and soil texture. An in-depth investigation is necessary to quantify soil erosion in alpine pastures, in order to manage grasslands more sustainably. In this work, we collected freely available satellite images and carried out intensive field surveys for the whole Autonomous Province of Trento (Northeastern Italian Alps) in 2016. The area (and volume) of soil erosions were then estimated and shown in maps. The average of the depths of soil erosion measured in field was used as a reference for estimating soil erosion of the entire study area. High-resolution DEMs difference in soil surface conditions was also computed in two representative areas between pre- and post-degradation to estimate the volume and the average depth of eroded soils. The degradation of soil in the study areas has been estimated in 144063 m2 and an estimated volume of 33610 ± 1800 m3. Results indicate that our procedure can serve as a low-cost approach for a rapid estimation of soil erosion in mountain areas. Mapping soil erosion can improve the sustainability of grazing management system and reduce the risk of pastureland degradation at large spatial scales.

Laser-scanner used in a wind tunnel to quantify soil erosion

Laser-scanner used in a wind tunnel to quantify soil erosion

Quantitative assessment of rill and interrill soil erosion in Romania

AbstractSoil erosion is an important geomorphological process with potential negative consequences especially on land agricultural potential. Unsuitable agricultural practices may increase soil erosion, leading to rapid loss of soil fertility and decrease of crop production. It is therefore important to correctly quantify soil erosion rates in order to adapt agricultural practices and implement proper conservation measures. This study attempts to assess the rill and interrill erosion in Romania, using the Romanian soil erosion model and GIS techniques. The database includes the digital terrain model, the soil map of Romania, the land use map of Romania and the rainfall erosivity regions. The results show that the high and very high erosion risk classes include 4.1% of the Romanian territory (9,627 km2). Most of this land is present in the hilly and plateau areas (Subcarpathians, Moldavian Plateau, Getic Plateau, Western Hills, Dobrogea Plateau). The model was validated by comparison of its predictions with long‐term erosion measurements from different locations in the country. Comparison with previous non‐GIS assessments of soil erosion at national level shows that the total estimated rill and interrill erosion in our study was very close to previous estimates. Comparison with the RUSLE 2015 model computed for Europe as a whole reveals that the two models assign almost 54% of their shared area to the same erosion class, while for 39% of the territory there is one class difference between the models. The results can be used for evaluations of erosion risk at national and regional scales.

Assessment of Soil Erosion in Uma Oya Catchment, Sri Lanka

Soil erosion is one of the major threats which affects farm productivity and water quality in river catchments. Quantification of human-induced soil loss is an imperative measure in catchment management. Among the catchments in Sri Lanka, the highest sediment yield has been reported from the Upper Uma Oya catchment (UOC) which is a sub-catchment of the Upper Mahaweli catchment. The Mahaweli watershed is the most important watershed in Sri Lanka since the size and the economic importance of it. Hence, this study was conducted considering the ten selected sub-watersheds of Uma Oya Watershed (UOW) in Sri Lanka to quantify soil erosion. Soil erosion of UOW was calculated using Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) Sediment Delivery Ratio (SDR) model. It was found that the current soil erosion rates in the watershed is varied between 14.0–648.6 t/ha/yr. Results of the study disclose that the soil erosion rates of UOW are 3 to 130 times faster than the soil loss tolerance in Sri Lanka. Findings of the study suggest that the implementation of sustainable watershed management options are crucial in order to reduce soil erosion and to enhance the quality of the watershed.

Predicting water erosion in arid lands using the GIS-based RUSLE model: A case study of Bedour catchment, central Tunisia

Abstract This study was conducted with a view to quantifying soil erosion in arid lands of Tunisia. To do this, we have opted to use the RUSLE model based on geographic information systems. By collecting data on rainfall, soils, vegetation, slopes and conservation practices separately as a layer and determining the pixel values for each of these factors, a quantified assessment of erosion in the basin is obtained. The data superposition and computing, following the model equations and protocol, allowed us to know the spatialized water erosion values at the pixel level. For the whole catchment, the study showed values oscillating between 0 and 163 Mgꞏha−1ꞏyear−1 with an average annual rate of 3 Mgꞏha−1ꞏyear−1. With such a low R (rainfall erosivity) factor (between 21.43 and 21.88 MJꞏmmꞏha–1ꞏh–1ꞏyear–1) itself related to low monthly and annual rainfall amounts, the region experiences locally very high annual erosion rates. Soil protection through conservation practices has saved the basin from even higher erosion. While plains cultivated and equipped with contour benches often suffer from low rates of erosion (less than 2 Mg·ha–1·year–1), unused slopes are neglected without protection, resulting in significantly high rates of erosion.

Hillslope and point based soil erosion – an evaluation of a Landscape Evolution Model

AbstractExcessive soil erosion and deposition is recognised as a significant land degradation issue. Quantifying soil erosion and deposition is a non‐trivial task. One of these methods has been the mathematical modelling of soil erosion and deposition patterns and the processes that drive them. Here we examine the capability of a landscape evolution model to predict both soil erosion rate and pattern of erosion and deposition. This numerical model (SIBERIA) uses a Digital Elevation Model (DEM) to represent the landscape and calculates erosion and deposition at each grid point in the DEM. To assess field soil redistribution rates (SRR) and patterns the distribution of the environmental tracer 137Cs has been analysed. Net hill slope SRR predicted by SIBERIA (a soil loss rate of 1.7 to 4.3 t ha‐1 yr‐1) were found to be in good agreement with 137Cs based estimates (2.1 – 3.4 t ha‐1 yr‐1) providing confidence in the predictive ability of the model at the hillslope scale. However some differences in predicted erosion/deposition patterns were noted due to historical changes in landscape form (i.e. the addition of a contour bank) and possible causes discussed, as is the finding that soil erosion rates are an order of magnitude higher than likely soil production rates. The finding that SIBERIA can approximate independently quantified erosion and deposition patterns and rates is encouraging, providing confidence in the employment of DEM based models to quantify hillslope erosion rates and demonstrating the potential to upscale for the prediction of whole catchment erosion and deposition. The findings of this study suggest that LEMs can be a reliable alternative to complex and time consuming methods such as that using environmental tracers for the determination of erosion rates. The model and approach demonstrates a new approach to assessing soil erosion that can be employed elsewhere. © 2018 John Wiley & Sons, Ltd.

Quantifying water erosion hazard in central Tunisia A case study: the Leben watershed

This study was performed in order to quantify soil erosion in the arid lands of central Tunisia. To do this we used geographic information systems, remote sensing and the RUSLE prediction model. While the study area is characterized by reduced rainfall quantities and therefore a low R factor, other factors such as high soil erodibility, low vegetation cover and steep slopes resulted in locally high erosion rates. Indeed, the calculation of RUSLE factors revealed values between 0 and 731 tones×ha-1×year-1 with an average of 3.84 tones×ha-1×year-1. Whereas most catchment lands have low levels, the relief surrounding the catchment has relatively high rates due to the high value of slopes, the skeletal soils that compose them, but also due to the absence of protection, reforestation or crop management. This study also showed that despite their small extension compared to the watershed, main streams sectors, and especially their banks, record the highest erosion rates mainly due to the flow accumulation in the downstream sectors. Relative protection of the piedmont, which has saved them from higher erosion, can only be considered efficient and sustainable if the upstream hillsides are also protected. The comparison with previous studies carried out in Tunisia shows similarities in the results of some of them, considering the natural geographical variation of the R factor.

Estimating long-term erosion and sedimentation rate on farmland using magnetic susceptibility in northeast China

Soil loss is a global environmental issue. Increasing amounts of attention have been paid to assessing soil erosion and its distribution on cultivated slopes using magnetic susceptibility (MS), which is a rapid, reliable, economic and nondestructive method. However, the MS technique provides incomplete estimates of soil loss. The objective of this study is to employ MS as a tracer to estimate soil loss on cultivated slopes in northeast China. A typical degraded sloping farmland area with a 40-year history of cultivation and a reference forest site were selected. The eroded and deposition regions were distinguished and the tillage homogenization model was used to predict variations in MS with erosion depth. The soil erosion depth, deposition thickness, soil loss, and soil erosion rate were calculated by matching the MS profile measured in the farmland sites to the reference forest site. The results showed that nearly 64% of the area of the selected slope had been eroded, and sediment produced by erosion had been deposited on 36% of the slope as a whole. The largest MS values occurred on the lower portion of the slope (a deposition area), whereas the smallest values occurred on the middle portion of the slope (an eroded area). The average eroded soil depth was 44.5 cm or 1.1 cm·yr-1, and the average deposition thickness was 35.5 cm or 0.9 cm·yr-1. The total amount of soil loss from the sampling slope was 29,338.4 t, which corresponded to a rate of soil loss of 4,630.5 t·km-2·yr-1 over the 40-year cultivation period. This result was similar to those obtained by previous studies conducted in the same region using other methods and techniques. Thus, soil MS was demonstrated to be a reliable tool that can be used to quantify soil erosion on cultivated slopes in northeast China.