Measured Soil Loss Research Articles

Traditional gold mining in the highlands of Ethiopia: Its effect on soil loss and possible reclamation measures

Traditional gold mining (TGM) is among the off-farm income sources of smallholder communities in the highlands of Ethiopia. However, information on their impact on land degradation, taking soil loss as a key indicator, was scant. Hence, this study aimed at i) analyzing local communities’ attitudes toward TGM practice, ii) TGM impact on soil loss, and iii) possible restoration measures for the degraded gold-mined landscapes. For the evaluation of community perceptions, 96 beneficiaries were selected from three gold mining sites following stratified random sampling techniques. Soil loss measurement was done through measurement of i) the mass of soil excavated and transported to the river (TR) from 96 individuals and ii) soil volume excavated in 45 excavated soil pits (EP). Descriptive statistics and one-way ANOVA were used to analyze soil loss data, while community perception was analyzed qualitatively. The survey results indicated that the average soil loss from TR and EP were 6,075.97±8.9 t ha-1 yr-1 and 32,549.99±753.68 t ha-1, respectively. The magnitude of soil loss also showed a significant (p<0.001) difference among the three sites. Due to a lack of awareness and hiding the reality, most of the gold miners believe that TGM practice has no negative impact on the soil resource, and no post-mined reclamation strategy is implemented. It can be concluded that the existing TGM practices are not done in an environmentally friendly approach and result in adverse soil loss. Hence, stabilizing the topography of the mined sites via backfilling of mined pits and topsoil conservation and amendment measures can be suggested.

Geospatial modeling and mapping of soil erosion in India

Soil erosion generally removes the topmost fertile layer of soil, affecting agricultural productivity on a larger scale. As a significant portion of the Indian economy depends on agricultural productivity, granular assessment of the impact of soil erosion becomes critical. However, a national-scale assessment of soil erosion and an impact classification system currently doesn’t exist over India. Given the resource-intensive and time-consuming nature of field experiments required for the measurement of soil loss across a vast country, the Revised Universal Soil Loss Equation (RUSLE) was extensively utilized for soil erosion calculations due to its simplicity, streamlined data requirements, and accuracy. This study estimates the yearly potential soil loss throughout India at a spatial resolution of 250 m and quantifies its variability considering districts, soil texture, soil type, land use and land cover, and basins. The relative importance of individual and combined impact of multiple parameters on quantified soil loss has been assessed using a random forest model. Rainfall erosivity (R-factor) emerges as the most crucial feature in estimating soil erosion in Indian conditions while rainfall intensity, combined with the topographic factor, demonstrated the highest influence on soil erosion in Indian conditions when the combined impact was assessed. Further, we mapped the Sediment Delivery Ratio (SDR) and Specific Sediment Yield (SSY) to assess the actual soil loss reaching downstream of basins across the national boundary. The national mean values for Sediment Delivery Ratio (SDR) and Sediment Yield (SSY) stand at 0.11 and 2.61 tons per hectare per year, respectively. The yearly potential soil loss for India is calculated at 21 tons per hectare per year, and nine out of the twenty districts with the highest susceptibility to soil erosion are in the state of Assam. Finally, a novel impact-based erosion-severity classification system has been introduced which finds that 29.46% of the landmass is prone to minor erosion while 3.17% experiences catastrophic erosion. This is the first comprehensive national-scale assessment of both soil erosion and sediment yield mapping over India, and the consequent classification system will enable the planning and implementation of soil conservation strategies locally as well as nationally.

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.

Erosivity Factor of the Revised Universal Soil Loss Equation (RUSLE) - A Systematized Review

The Revised Universal Soil Loss Equation (RUSLE) is a globally accepted erosion model which has gained good acceptability. Among the five influences of the RUSLE method of soil erosion estimation, the erosivity factor (R) represents rainfall event’s ability to produce erosion. It is mainly affected by rainfall intensity and kinetic energy of the rain. The erosion index represented by EI30 is the most common R-factor estimation method. Due to the non-availability of rainfall intensity data in many watersheds, researchers have developed methods for erosivity estimation using rainfall depth. The Modified Fournier Index method has gained popularity. Recently, different models using machine learning techniques and ANN are also being set up to establish the R-factor for soil loss estimation. These models can estimate the R-factor quickly and more accurately. They can even predict the R-factor for the future to predict soil loss and plan conservation measures accordingly. An attempt has been made here to review different methodologies proposed by scientists across the globe for arriving at the R-factor for soil loss estimation using RUSLE model.

Environmental Threat of Soil Erosion in the Gwang Khola Watershed, Chure Region of Nepal

Chure, also known as the foothills of the Himalayas that extends from east to west of Nepal, is an essential region due to the hotspot of biological diversity and various natural resources, including recharge groundwater for the Tarai region of the country. However, the Chure region has a high rate of soil erosion due to human activities and natural processes, a severe issue in the Chure region. This study looked at soil erosion in the Gwang Khola watershed in the Chure region. We used the Revised Universal Soil Loss Equation (RUSLE) model to measure soil loss by soil erosion. The results showed that about 547,992.9 tons of soil were lost annually in the Gwang Khola watershed. The results indicated that 5,259.87 hectares of the land area were in a very low-risk zone for soil erosion. Similarly, 317.79 hectares of land had a moderate risk of soil degradation, while 13.59 hectares of forest area posed a high threat. The extreme-risk erosion area was situated above 1,250 m. In contrast, the moderate and the low-risk regions of soil erosion had a lower elevation range of 950 m to 1,250 m and 650 m to 950 m, respectively. The findings of this study may aid planners and policymakers in preventing soil erosion and protecting the ecosystem in this watershed and those with similar circumstances.

Rainfall erosion of natural fibers cement treated mud

This study evaluated the possible application of inexpensive mud stabilization treatments for the purpose of reducing soil erosion. The soil erosion was investigated by measuring soil loss over the surface of compacted specimens made by natural fibers cement treated mud exposed to a rainfall test sequence. The compacted specimens were made by adding 5 kg/m3 to 50 kg/m3 cement and 0 kg/m3 to 20 kg/m3 natural fibers to 40% to 60% water content mud. The results showed that the increasing of either cement content or natural fibers content reduced the soil erosion from 50% to 80%. Furthermore, a numerical function was developed to predict the value of soil loss of the treated mud based on the ratio of cement content, natural fibers content and initial water content.

Evaluation of Spatial-Temporal Variation of Soil Loss and Best Conservation Measures in an East Africa Catchment

Soil conservation (SC) is essential to maintain the reservoir service life and increase the yield since soil erosion is a major global concern that adversely affects not only the storage capacity but also the land fertility. This study evaluates the spatio-temporal variation of soil erosion using the popular SWAT model and identifies the best SC practice for Tekeze watershed located in the Northern part of Ethiopia. To accomplish this, four soil conservation management scenarios involving baseline, terracing, contouring, and grassed waterway scenarios are selected for soil loss evaluation. The SWAT model was calibrated and validated with R2 values of 0.7 and 0.9 and NSE values of 0.8 and 0.7, respectively, indicating satisfactory model performance. Five sub-basins of the catchment were found to be more susceptible to erosion with an average annual soil loss of 25.15 tons/ha/yr. Employment of the proposed SC measures in the sub-watershed erosion was reduced by 35.18%, 27.11%, and 18.76%, respectively, which is significant when compared with the baseline scenario. Since the investment cost of execution of an SC measure in a large watershed is very high, priority areas are also identified for cost savings as well as improved work efficiency.

A Review of Road Design for Wind Farms in China

Complex terrain conditions of wind farms, the large weight and the long size of wind turbine equipment, the high economic requirements of transportation on roads, and a shortage of relevant standards and codes lead to difficulty in road design and route selection of wind farms. In addition, frequent excavations and landfills in wind farm construction have seriously destroyed vegetation cover and soil structure on the primary surface, resulting in a large amount of water and soil loss and ecological damage to the wind farm. In view of the above problems, this paper reviews the relevant research status of the wind farm road route selection method, circular curve, longitudinal section design index, and water and soil loss problems, and summarizes them. Several calculation models for circular curve widening, the recommendation of a minimum radius under different restrictions, calculation models for longitudinal slope and vertical curve, and recommended values are listed. The causes of water and soil losses in wind farms are discussed and the characteristics of the diversity of erosion and the uneven distribution of time and space are put forward. Prediction models of wind farm water and soil loss and comprehensive prevention measures are given. The existing problems in the road design field of wind farms are put forward and the future development direction in this field is forecasted.

Measuring Annual Sedimentation through High Accuracy UAV-Photogrammetry Data and Comparison with RUSLE and PESERA Erosion Models

Model-based soil erosion studies have increased in number, given the availability of geodata and the recent technological advances. However, their accuracy remains rather questionable since the scarcity of field records hinders the validation of simulated values. In this context, this study aims to present a method for measuring sediment deposition at a typical Mediterranean catchment (870 ha) in Greece through high spatial resolution field measurements acquired by an Unmanned Aerial Vehicle (UAV) survey. Three-dimensional modeling is considered to be an emerging technique for surface change detection. The UAV-derived point cloud comparison, applying the Structure-from-Motion (SfM) technique at the Platana sediment retention dam test site, quantified annual topsoil change in cm-scale accuracy (0.02–0.03 m), delivering mean sediment yield of 1620 m3 ± 180 m3 or 6.05 t ha−1yr−1 and 3500 m3 ± 194 m3 or 13 t ha−1yr−1 for the 2020–2021 and 2021–2022 estimation. Moreover, the widely applied PESERA and RUSLE models estimated the 2020–2021 mean sediment yield at 1.12 t ha−1yr−1 and 3.51 t ha−1yr−1, respectively, while an increase was evident during the 2021–2022 simulation (2.49 t ha−1yr−1 and 3.56 t ha−1yr−1, respectively). Both applications appear to underestimate the net soil loss rate, with RUSLE being closer to the measured results. The difference is mostly attributed to the model’s limitation to simulate gully erosion or to a C-factor misinterpretation. To the authors’ better knowledge, this study is among the few UAV applications employed to acquire high-accuracy soil loss measurements. The results proved extremely useful in our attempt to measure sediment yield at the cm scale through UAV-SfM and decipher the regional soil erosion and sediment transport pattern, also offering a direct assessment of the retention dams’ life expectancy.

GIS based soil loss assessment using RUSLE model: A case of Horo district, western Ethiopia

Land degradation in the form of soil erosion is a worldwide challenge and make environmental problem that affects crop yields, makes livelihoods difficult, and creates crises. The main objective of this study was to measure soil loss using the Revised Universal Soil Loss Equation (RUSLE) Model in Horo district, Western Ethiopia. RUSLE with a Geographical Information System (GIS) was used to quantify soil loss using rainfall, soil, a digital elevation model (DEM), and satellite image datasets as factor value inputs. Those factors are erosivity (R), erodibility (K), topography (LS), cover management (C), and conservation support practice (P) layer values that can be interactively used using weighted overlay in ArcGIS 10.8. The result shows that the maximum and minimum potential annual soil loss of the study area ranged from nil (0.01 t/ha/yr) on plain surfaces to 216.01 t/ha/yr. The average annual soil loss rate in the study area was 13.27 t ha/yr. The highest mean annual soil loss of 216.01 t/ha/yr were observed from farmland and it was the largest portion of the study area, which covered about 64243.02 ha and represented about 73.75% of the total. As a result, forest land (16383.23 ha) was the second-largest, accounting for 18.81% of the total area. Consequently, the study revealed that the farmland was more vulnerable to erosion than other land uses and land cover types. Hence, information on average annual soil loss is important for selecting appropriate conservation measures to reduce on-site soil loss and its off-site effects. Therefore, farmers and other expected bodies should have focused on soil conservation and management practices at the highest soil loss severity classes, which must get priority for conservation by stakeholders, agents, and the government.

Evaluating the applicability of the water erosion prediction project (WEPP) model to runoff and soil loss of sandstone reliefs in the Loess Plateau, China

Soil erosion is one of the most serious environmental issues, especially in vulnerable areas such as the Pisha sandstone regions located in the Loess Plateau (China). In these types of reliefs, long-term studies monitoring runoff and soil loss are scarce, and even more considering the efficiency of different soil management techniques applied to reduce land degradation. In this study, seven years (2014–2020) of in-situ measurements of surface runoff and soil loss for different land uses (forestland, shrubland, grassland, farmland, and bare land) in a Pisha Sandstone environment at the Loess Plateau were conducted. We applied the Water Erosion Prediction Project (WEPP) model combining the large database with the precipitation regimes. Our results showed that runoff volume coming from observed and simulated data exhibited significant differences among them depending on the different vegetation types. Runoff and soil loss were different among diverse land use types as follows: farmland > grassland > shrubland > forestland. After conducting a calibration, we found satisfactorily simulated surface runoff and sediment yield based on precipitation regimes and land uses at sandstone reliefs. Simulation performance of surface runoff was better than sediment yield. The range of standard error of the model simulation for event and annual values of runoff were 4.71 mm and 12.19 mm, respectively. The standard error for event and annual values of soil loss were 4.19 t/hm2 and 21.86 t/hm2. In the calibration group, R2 of runoff and soil loss were 0.92 and 0.86 respectively, while Nash-Sutcliffe coefficient (E) reached 0.90 and 0.85, respectively. In the validation group, the R2 for both runoff and soil loss were 0.82 and 0.56, respectively. Nash-Sutcliffe coefficient (E) were 0.77 and 0.54 for the runoff and sediment yield. We concluded that using a detailed monitoring dataset, the WEPP model could accurately simulate and predict water erosion in the hillslopes of Pisha sandstone area.

Integration of GIS and Remote Sensing with RUSLE Model for Estimation of Soil Erosion

Globally, soil erosion is a significant problem contributing to nutrient loss, water quality degradation, and sand accumulation in water bodies. Currently, various climate factors are affecting the natural resources entire worldwide. Agricultural intensification, soil degradation, and some other human impacts all contribute to soil erosion, which is a significant issue. Management and conservation efforts in a watershed can benefit from a soil erosion study. Modeling can establish a scientific and accurate method to calculate sediment output and soil erosion below a variety of circumstances. The measured soil loss tolerance was compared to the risk of soil erosion (T value).In this study, GIS and remote sensing techniques have been integrated with the Revised Universal Soil Loss Equation (RUSLE) model to estimate soil loss in the Mayurakshi river basin of eastern India. To determine soil erosion-prone areas, rainfall, land use, and land cover maps, as well as a digital elevation model (DEM), were used as input. The annual soil loss in the basin area is estimated to be 4,629,714.8 tons. Accordingly, the study basin was categorized into five soil loss severity classes: very low (40.92%), low (49%), moderate (6.5%), high (2.4%) and very high (1.18%) risk classes. Soil erosion rates ranged from very slight to slight throughout the majority of the region. The section of the basin’s lower plain has been discovered to be least affected by soil loss. The results of study area can be helpful to conservation of soil management practices and watershed development program in the basin area.

The nexus between land use, land cover dynamics, and soil erosion: a case study of the Temecha watershed, upper Blue Nile basin, Ethiopia.

At the current times, soil erosion is the major problem that affects land and water resources, especially in Ethiopia's highlands. Due to the dynamics of land use land cover change, land degradation, and soil erosion increase significantly and result in a loss of fertile soil every year and lead reduction in agricultural production. This study was therefore designed to explore the land use land cover (LULC) dynamics from 1986 to 2020, to estimate mean annual soil erosion rates and identify erosion hotspot areas from 1986 to 2020, and finally, to evaluate the impacts of land use land cover change on soil loss of 1986 to 2020. For this, Landsat imageries of 4 years from 1986 to 2020 were used. Maximum likelihood supervised classification methods were used to classify LULCs. The dynamics of LULC change were used as an input for measuring soil loss by employing the combination of geospatial technologies with the revised universal soil loss equation (RUSLE). The LULC maps of 1986, 1997, 2009, and 2020 were used for identifying crop management (C) factor and conservation practice (P) factor. Rainfall erosivity factor (R), soil erodibility factor (K), and slope length and steepness factor (LS) were also used as sources of data. Based on the five factors, soil erosion intensity maps were prepared for each year. Results showed that the annual soil loss in the watershed ranged from 0 to 3938.66 t/ha/year in 1986, 0 to 4550.94 t/ha/year in 1997, 0 to 5011.21 t/ha/year in 2009, and 0 to 6953.23 t/ha/year in 2020. The annual soil loss for the entire watershed was estimated at 36.889, 42.477, 47.805, and 48.048 t/ha/year in 1986, 1997, 2009, and 2020, respectively. The mean soil loss of 1986, 1997, 2009, and 2020 was higher in cultivated land followed by shrub land, grazing land, and forest land. Mean soil loss increased from 1986 to 1997, from 1997 to 2009, and from 2009 to 2020. This is because of the expansion of agricultural land at the expense of grazing land and shrub land. Therefore, urgent soil and water conservation practices should be made in hotspot areas.

Adapting the WEPP Hillslope Model and the TLS Technology to Predict Unpaved Road Soil Erosion.

Unpaved road erosion have been recognized as important sediment sources in a watershed. To evaluate where and when road erosion occurs, the soil loss along road segments should be precisely predicted with process-based erosion models. Methods: The hillslope version of the Water Erosion Prediction Project (WEPP) was used to estimate soil loss from 20 typical road segments in the red soil region of South China. Terrestrial laser scanning (TLS)-measured soil losses were used to validate the model simulations. The results showed that the WEPP model could reasonably predict the total soil loss in relatively short (less than 100 m) and gentle (slope gradient lower than 10%) road segments. In contrast, soil loss would be underestimated for long or steep road segments. Detailed outputs along roads revealed that most of the peak soil loss rates were underestimated. It might due to the linear critical shear stress theory in the WEPP model. Additionally, the lack of upstream flow was found to be connected to the relatively low model efficiency. Nevertheless, the WEPP simulation could accurately fit erosion trend and predict the peak soil loss positions along road segments. Conclusions: The WEPP model could be adopted to evaluate the erosion risk of unpaved roads in the red soil region of South China.

Measurement of Water Soil Erosion at Sparacia Experimental Area (Southern Italy): A Summary of More than Twenty Years of Scientific Activity

The main purpose of this article is to give a general idea of the scientific activity that was carried out starting from the 2000s on the basis of the data collected in the plots installed at the Sparacia experimental station for soil erosion measurement in Sicily, South Italy. The paper includes a presentation of the experimental site, a description of the methods and procedures for measuring soil erosion processes both available in the literature and applied at the Sparacia station (sediment sampling and water level reading in the storage tanks for total erosion measurements; profilometer, and Structure from Motion technique for rill erosion measurements), and the main results obtained in the monitoring period in the experimental site. The latter concern the effects of plot size and steepness on soil loss, the measurement variability, the frequency analysis of soil loss, the rill erosion characterization, and the comparison between rill and interrill erosion rates. Each of these topics is addressed with multi-temporal analyses performed with increasing size of the available database, which allowed to draw robust conclusions. Soil loss did not vary appreciably with plot length in contrast with the assumption made in the USLE/RUSLE. The variability of the measurements of soil loss, runoff volume, and sediment concentration at the event scale in replicated plots decreased as the mean measured value increased. The normalized event soil loss was distributed according to a two-component distribution. A power relationship between rill volumes and lengths was established. The measurements also confirmed the morphological similarity between the channels of the rills and ephemeral gullies described by a power dimensionless relationship. Rill erodibility of the sampled clay soil varied over time, maintaining relatively low values. Finally, rill erosion was dominant relative to interrill erosion, and a more efficient sediment transport system through the rill network occurred as plot steepness increased.

Rainwater transformation to runoff and soil loss at the surface and belowground on soil-mantled karst slopes under rainfall simulation experiments

Karst slopes have thin soil layers covering bedrock and feature multiple near-surface soil-rock interfaces (SRIs), which can produce hydrological processes that differ from those of nonkarst slopes. This study investigated the differences in the proportions of rainwater transformed into surface and belowground runoff and measured soil loss on soil-mantled karst slopes with different hydrological situations: bedrock with fissures (HS-1) and without fissures (HS-2). The rainfall intensity was 50 mm/h. Three slope gradients (5°, 15° and 25°) were applied, and surface runoff (SR), subsurface flow (SF), underground fissure flow (UFF) and soil-rock interface flow (SRIF) were collected during rainfall. No SR was produced on 5° slopes in either treatment. The proportion of rainwater absorbed by soil (RH) was approximately 34%-46% of the rainwater, with no significant differences (p > 0.05) between HS-1 and HS-2. On the 5° and 15° slopes, 28%-61% of the rainwater was transformed into UFF under HS-1, and 29%-50% of the rainwater was transformed into SRIF under HS-2. On the 25° slope, the SRIF and UFF proportions were<18%. Bedrock fissures greatly affected the type of runoff but had little effect on the near-surface soil water. The SR and SF amounts were 29%-67% and 3%-38% higher in HS-2 than in HS-1, respectively. Due to bedrock fissures, on average, 6% of rainwater was lost as underground flow during rainfall, and the extra rainwater, 30% on average, flowed along the SRI underground, potentially forming UFF. This difference occurs because the infiltrating rainwater that reaches the SRI can freely drain through bedrock fissures in HS-1 but not in HS-2. The soil loss via SF, SRIF and UFF was very low (<0.05 g/L). Therefore, slope gradients and bedrock fissures both have major effects on rainwater transformation, and soil loss via rainfall impact is very slow on soil-mantled karst slopes.

Evaluation of climate change impact on soil erosion in the integrated farming system based hilly micro-watersheds using Revised Universal Soil Loss Equation

Increased rainfall intensity under changing climate scenarios threatens the sustainability of cropland due to soil erosion in the Himalayan mountain ecosystem. This study aimed to identify the resilient integrated farming system (IFS) based land use practices in hilly micro-watersheds (HMWs) under projected climate change scenarios. The IFS based land uses considered were livestock with fodder crops (HMW1), forestry (HMW2), agroforestry (HMW3), food and fodder crops (HMW4), agri-horti-silvi-pasture (HMW5), horticulture (HMW6), and abandoned shifting cultivation (HMW7, as traditional jhum), established on the hill slope (>32.0 to 53.0%) in Meghalaya, India. The Revised Universal Soil Loss Equation (RUSLE) was calibrated and validated satisfactorily (RMSEa: 0.3 to 1.2 t ha−1 yr−1; NSE: 0.6 to 0.9) using 24 years measured soil loss data for each HMWs. The model was most sensitive to soil erodibility (S = 0.9 to 1.1) followed by plant cover (S = 0.6 to 1.0) in simulating the soil loss in the hilly micro-watersheds. Simulated soil losses in different HMWs ranged from 11.5 to 32.7 t ha−1 yr−1 under the baseline climate (1976 to 2005). The soil loss is projected to increase in the range of 7.3 to 15.3% under different RCPs (RCP2.6 to RCP8.5). The HMW with shifting cultivated IFS were the most vulnerable to climate change, as soil loss increased by 1.7 to 8.4 t ha−1 yr−1 over baseline (32.7 t ha−1 yr−1). However, HMW with horticulture based IFS followed by agroforestry showed the most resilience to climate change and manifested a minimal increase (0.6 to 3.1 t ha−1 yr−1) over baseline soil loss (11.5 to 12.0 t ha1 yr−1). Adoption of horticultural and agroforestry based IFS on the hill slope of Eastern Himalayan ecosystem is an effective measure to control soil erosion.

Efficiency of Some Traps to Measure Flux Dust Suspension Particles by Wind Erosion

This study was to analyze the efficiency (E) of traps in holding suspended soil particles that cause dust, environmental and agricultural problems by wind erosion in NWCZ, Egypt. These vertical and passive sediment traps with different design in rotary and fixed reservoir box and different methods of measured soil loss. Rotary Distance (RD), Fixed Distance (FD), Rotary Point (RP) and Fixed Point (FP) traps were compared with traditional trap Big Spring Number Eight (BSNE). The variation between traps’ efficiency as a function of height was significantly (P≤0.05) and was as follow: RD, FD, RP, and FP more increase than BSNE. The performance of the trap types as an efficiency of suspension particles (assumed to possess E=1 for suspended particle sizes) was as follow RD> FD> RP> FP> BSNE; 99%, 98%, 88%, 85%, 65% respectively. The efficiency of traps in suspension particles size (<45, 45- 63 and >100 µm) were significant (P ≤ 0.05) and the best efficient were RD and FD. Therefore, RD and FD traps have direct methods for measuring soil loss by wind erosion and have the most acceptable, accurate, fastest and easiest in soil particle analyzed and therefore the most effective way to discriminate suspension particles.

Experience with the assessment of the USLE cover-management factor for arable land compared with long-term measured soil loss in the Swiss Plateau

The cover-management factor (C-factor) of the Universal Soil Loss Equation (USLE) is the most important factor for estimating the effects of farming practices or agricultural policy measures on soil loss rates. This study, conducted in Switzerland from 1987 to 2017, assessed the impact of mitigation measures on arable land by comparing modelled C-factor values and measured soil losses. C-factor values were calculated in detail for 203 fields for five different periods (1987–89, 1997–99, 1997–2006, 2003–09, 2010–14) using a C-factor tool adapted to Swiss conditions. The C-factor values were compared with the measured soil loss rates of the same fields from the three periods 1987–89, 1997/98–2006/07, and 2007/08–2016/17. Given various action programmes, the share of conservation tillage practices increased from 4% in 1997/98 to 85% in 2014/15. The mean annual soil loss was 0.71 t ha–1 yr–1 (1987–89) and decreased by over two-thirds from 0.74 t ha–1 yr–1 (1997/98–2006/07) to 0.20 t ha–1 yr–1 (2007/08–2016/17), while the mean C-factor values were 0.136 in 1987–89 and decreased by almost half from 0.094 (1997–99) to 0.050 (2010–14). This study demonstrates that, with an in-depth calculation of C-factor values over different periods, changes amounting to soil loss resulting from the implementation of mitigation measures can be satisfactorily reproduced for a region.

Soil Erosion Estimates in Arid Region: A Case Study of the Koutine Catchment, Southeastern Tunisia

Soil erosion remains one of the principal environmental problems in arid regions. This study aims to assess and quantify the variability of soil erosion in the Koutine catchment using the RUSLE (Revised Universal Soil Loss Equation) model. The Koutine catchment is located in an arid area in southeastern Tunisia and is characterized by an annual mean precipitation of less than 200 mm. The model was used to examine the influence of topography, extreme rainstorm intensity and soil texture on soil loss. The data used for model validation were obtained from field measurements by monitoring deposited sediment in settlement basins of 25 cisterns (a traditional water harvesting and storage technique) over 4 years, from 2015 to 2018. Results showed that slope is the most controlling factor of soil loss. The average annual soil loss in monitoring sites varies between 0.01 and 12.5 t/ha/y. The storm events inducing the largest soil losses occurred in the upstream part of the Koutine catchment with a maximum value of 7.3 t/ha per event. Soil erosion is highly affected by initial and preceding soil conditions. The RUSLE model reasonably reproduced (R2 = 0.81) the spatiotemporal variability of measured soil losses in the study catchment during the observation period. This study revealed the importance of using the cisterns in the data-scarce dry areas as a substitute for the classic soil erosion monitoring fields. Besides, combining modeling of outputs and field measurements could improve our physical understanding of soil erosion processes and their controlling factors in an arid catchment. The study results are beneficial for decision-makers to evaluate the existing soil conservation and water management plans, which can be further adjusted using appropriate soil erosion mitigation options based on scientific evidence.