Simulated Soil Loss Research Articles

Assessing soil erosion and its drivers in agricultural landscapes: a case study in southern Bahia, Brazil

ABSTRACT Erosion is a worldwide threat to biodiversity conservation and agricultural yield, and it is linked to deforestation. In this study, we aim to assess soil loss in landscapes of Cachoeira River watershed, in southern Bahia, northeastern Brazil. We estimate the role of forests in diminishing soil erosion using the Revised Universal Soil Loss Equation (RUSLE). We compare real and simulated scenarios in which the forest was replaced by agricultural use, also comparing estimates of erosivity factor (R factor) derived from remote sensing and climatological station data. Real and simulated annual soil losses varied from 0 to 15.95 t/year and from 0 to 33.53 t/year along the watershed, respectively. However, only 0.04 and 1.67% of this area is highly and severely exposed to erosion, using data from climatological stations and remote sensing, respectively. Soil loss in the simulated deforested scenario was approximately two times higher than the real annual soil loss, indicating the importance of forest cover to mitigate soil erosion. Moreover, soil loss was 10.5 times greater when using precipitation data from remote sensing compared to climatological stations. Conclusively, the practice of agroforestry can be used as an alternative to avoid erosion.

Response of soil erosion to vegetation and terrace changes in a small watershed on the Loess Plateau over the past 85 years

Land use on the Chinese Loess Plateau has undergone dramatic changes over the past few decades. The implementation of a series of soil and water conservation measures has significantly altered the soil erosion, transportation, and deposition processes on the Loess Plateau. To effectively address and mitigate soil erosion, it is crucial to accurately quantify the soil loss rate and analyze the contributions of soil and water conservation measures over the past several decades. In this study, the Zhifanggou watershed, located in the hilly area of the Chinese Loess Plateau, is utilized as an illustrative example. Using historical data, remote sensing imagery, and on-site data of soil, vegetation, and soil conservation measures, we assessed the soil loss rates from 1938 to 2022 based on long-term land use changes, utilizing the Chinese Soil Loss Equation (CSLE) model. Furthermore, we employed a quantitative evaluation to assess the impacts of vegetation change and terrace construction on soil erosion. The findings of our study reveal significant transformations in land use. Farmland experienced an initial increase followed by a subsequent decline, while the opposite pattern was observed for forest land. The simulated soil loss rate for the entire watershed exhibited an upward trend, rising from 34.86 t·ha−1·yr−1 in 1938 to 104.11 t·ha−1·yr−1 in 1958, before declining to 56.98 t·ha−1·yr−1 in 1999 and reaching 5.87 t·ha−1·yr−1 in 2022. Attribution analysis showed that vegetation change exerted a dominant influence on recent soil erosion dynamics, accounting for 86.10 % of the total contribution in 2022, while terraces contributed 13.90 %. These findings clarify long-term soil erosion mechanisms and provide guidance for watershed soil and water conservation management.

A modified RUSLE model to simulate soil erosion under different ecological restoration types in the loess hilly area

Soil erosion is mainly affected by the rainfall characteristics and land cover conditions, and soil erosion modelling is important for evaluating land degradation status. The revised Universal Soil Loss Equation (RUSLE) have been widely used to simulate soil loss rate. Previous studies usually considered the general rainfall characteristics and direct effect of runoff with the event rainfall erosivity factor (Re) to produce event soil loss (Ae), whereas the fluctuation of rainfall intensity within the natural rainfall profile has rarely been considered. In this study, the relative amplitude of rainfall intensity (Ram) was proposed to generalize the features of rainfall intensity fluctuation under natural rainfall, and it was incorporated in a new Re (Re=RamEI30) to develop the RUSLE model considering the fluctuation of rainfall intensity (RUSLE-F). The simulation performance of RUSLE-F model was compared with RUSLE-M1 model (Re=EI30) and RUSLE-M2 model (Re=QREI30) using observations in field plots of grassland, orchard and shrubland during 2011–2016 in a loess hilly catchment of China. The results indicated that the relationship between Ae and RamEI30 was well described by a power function with higher R2 values (0.82–0.96) compared to QREI30 (0.80–0.88) and EI30 (0.24–0.28). The RUSLE-F model much improved the accuracy in simulating Ae with higher NSE (0.55–0.79 vs −0.11∼0.54) and lower RMSE (0.82–1.67 vs 1.04–2.49) than RUSLE-M1 model. Furthermore, the RUSLE-F model had better simulation performance than RUSLE-M2 model under grassland and orchard, and more importantly the rainfall data in the RUSLE-F model can be easily obtained compared to the measurements or estimations of runoff data required by the RUSLE-M2 model. This study highlighted the paramount importance of rainfall intensity fluctuation in event soil loss prediction, and the RUSLE-F model contributed to the further development of USLE/RUSLE family of models.

Comparing two weather generator‐based downscaling tools for simulating storm intensification and its impacts on soil erosion under climate change

AbstractStorm intensification is projected to increase under climate change; however, it is quite challenging to statistically downscale storm intensification for climatic impact assessment. The objectives of this study are to compare two weather generator‐based tools in simulating daily precipitation extremes at a point scale, both with and without storm intensification options, and to further evaluate the responses of simulated surface runoff and soil loss to generated precipitation extremes under various cropping and tillage systems at a station in central Oklahoma, U.S.A. Results show that all data sources including raw GCM, raw RCM, and statistically downscaled GCM datasets are capable of providing information on future storm intensification, depending on particular ensemble members or models in each source. Thus, it is recommended that all data sources including as many members as possible should be used in model screening. Compared with the SYNthetic weather generaTOR (SYNTOR), the Generator for Point Climate Change (GPCC) tool tends to generate a stronger storm intensification, partially because GPCC takes into consideration the increased variance of monthly precipitation as projected by GCMs in adjusting daily precipitation variance for future climate generation. However, there are no discernable differences between storm intensification options with each tool because weaker signals of storm intensification are projected for the study site coupled with a projected decline in future annual precipitation. Simulated soil loss and surface runoff rates with GPCC were significantly greater than those with SYNTOR due to GPCC generating more frequent and heavier storms. Given the sizable differences in extreme precipitation generation, both tools could be used to generate a range of the potential impacts of storm intensification under climate change on soil erosion and surface hydrology.

Performance of the single‐event wind erosion evaluation program (SWEEP) model in assessing the impact of crop rotation, green manure, fertilizer, and tillage on wind erosion

AbstractAgricultural land management strategies have been applied to minimize wind erosion in the inland Pacific Northwest (iPNW) due to their profound influence on various soil properties and crop residue characteristics. However, simulating soil loss in response to these treatment strategies remains a challenge. The objective of this study was to test the performance of the single‐event wind erosion evaluation program (SWEEP) in simulating soil loss from agricultural land under contrasting tillage, crop rotation, fertilizer, and green manure treatments in the iPNW. These management strategies appeared to impact residue characteristics but had little or no impact on soil properties except tillage treatments in the short term (<2 years). Tillage appeared to have a greater impact on soil properties and residue characteristics than other treatments. As a result, the performance of SWEEP varied among disk‐tillage (DT), undercutter‐tillage (UT), and NT (no‐tillage/zero‐tillage) treatments. No or little difference was found in the performance of SWEEP between winter wheat‐summer fallow (WW‐SF) and WW‐oilseed‐SF (WW‐O‐SF) crop rotations and synthetic and biosolids fertilizer treatments. In addition, the performance of SWEEP was acceptable for both crop rotation and fertilizer treatments. In contrast, poor agreement between simulated and measured erosion was found for manure treatments in which SWEEP over‐estimated erosion by 313 and 210% for no manure and manure treatments, respectively. The over‐estimation of SWEEP may be due to the over‐estimation of abrasion flux from the fully crusted soil surface. Our results suggest that SWEEP can be used to identify control measures for windblown soil loss in the iPNW.

Modelling the event‐based hydrological response of olive groves on steep slopes and clayey soils under mulching and tillage management using the SCS‐CN, Horton and USLE‐family models

AbstractThe SCS‐CN, Horton and USLE‐family models are used worldwide, but few examples are available for olive groves on steep and clayey soils under mulching of pruning residues. We evaluate the accuracy in predicting runoff and soil loss of a steep (20%) and clayey olive grove subject to three soil conservation practices: mechanical tillage (MT), mulching with pruning residues (NTR) and standard protection (SP), measured at plot scale in Southern Italy during 30 months under natural rainfall. The models were calibrated by adjusting the Curve Numbers (for the SCS‐CN model) and the C‐factors (MUSLE and USLE‐M); the Horton model was not calibrated. The model performance was assessed by qualitative and quantitative procedures. In all practices, the SCS‐CN was more accurate for runoff predictions (mean difference of 7% compared with corresponding observations) compared with the Horton (mean difference of 30%). For soil erosion, the MUSLE showed better performance in soils subject to MT or total protection (differences lower than 10%), while the USLE‐M was more reliable to simulate soil loss in mulched plots (differences of 8%). A set of Curve Numbers (95 for MT, 70 for SP and 85 for NTR) and C‐factors (0.4 for MT, 0.2 for SP and 0.1 for NTR) are proposed for applications in steep slope and clayey soil olive orchards using the SCS‐CN and USLE proposed models, respectively. Validation exercises in other environmental experimental conditions would enhance the export these models for runoff and erosion control in agricultural soils treated with mulching.

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.

Dynamic analysis of the determinants of trade-off and synergy between karst soil loss and water yield with integration of geomorphological differentiation

Soil and water loss is the core issue of rocky desertification. Affected by the complex and rapid environmental changes in karst areas, water and soil variables and their relationships changes significantly. Dynamically revealing the driving factors of the relationship between water and soil variables behind such changes is an urgent need to control rocky desertification. This study simulated soil loss and water yield in the Wujiang Basin in China from 2000 to 2018 by a modified RUSLE model and InVEST model, respectively. The relationships between soil loss and water yield were calculated by geographically weighted regression, and geographical detector considering stratification heterogeneity was used to quantitatively identify the determinants and their interactions of trade-off and synergy. The results showed that the area of the trade-off between water yield and soil loss in the basin and in different geomorphological types clearly increased over time, and accordingly, that of the synergy gradually decreased with time. At the basin scale, precipitation is the determinant for the trade-off and synergy, with an explanatory power between 5% and 45%, and the interaction between precipitation and elevation has the greatest explanatory power for the trade-off and synergy. In each geomorphological type, only three environmental factors significantly affected the trade-off and synergy between soil loss and water yield, namely precipitation, elevation, and lithology. The interaction of elevation and lithology dominantly affecting the trade-off in the low/middle elevation terrace indicates that the topographical factors of the region require further attention. The interaction between precipitation and elevation and the interaction between precipitation and lithology are worth noting in the other four geomorphological types.

Modeling surface runoff and soil loss response to climate change under GCM ensembles and multiple cropping and tillage systems in Oklahoma

To develop effective conservation practices to respond to future climatic challenges, the effects of various cropping and tillage systems on surface runoff and soil loss need to be evaluated under extensive geographical conditions. This study used a total of 100 climate scenarios generated from 25 downscaled General Circulation Model (GCM) projections under two Representative Concentration Pathways (RCP4.5 and 8.5) during 2021–2050 and 2051–2080. Those 100 future scenarios were combined with 29 cropping and tillage systems to simulate surface runoff, soil erosion, and crop production response to climate change using the Water Erosion Prediction Project (WEPP) model. The results showed that average annual precipitation in central Oklahoma was projected to significantly decrease by 4–6% (p < 0.05) during the two time periods for both RCP scenarios. Mean annual temperatures were projected to significantly increase (p < 0.01) by 1.74 ℃ for RCP4.5 and 1.99 ℃ for RCP8.5 during 2021–2050, and 2.65 ℃ for RCP4.5 and 3.90 ℃ for RCP8.5 during 2051–2080. Annual runoff and soil loss averaged over the two RCPs and all crop types was projected to decrease by approximately 1% and 3%, respectively. Except for cotton, crop yields were predicted to decrease by 10.3%–18.3% during 2021–2080. Simulated annual runoff depth and soil loss separately followed the order of reduced tillage (RT) > delayed tillage (DT) > no-till (NT) > conventional tillage (CT) and RT > CT > DT > NT under future climate scenarios, respectively. If economically feasible, no-till and the crop-alfalfa rotation were the most effective soil conservation method on farmlands to combat projected future erosion due to changing precipitation and temperatures.

Soil loss and PM10 emissions from agricultural fields in the Junggar Basin over the past six decades

The Junggar Basin in north Xinjiang Province is regarded as one of the important dust sources in China. There is, however, a lack of information on long-term dust emissions originating from the basin. In these largely agricultural communities, wind erosion is a major concern that results in a threat to sustainable agriculture and environmental quality. This study assesses dust emissions from the agricultural community of the Junggar Basin in north Xinjiang based on soil and land use types extracted from remote sensing data in response to weather and climate over the past six decades. The Wind Erosion Prediction System (WEPS) was used to simulate annual soil loss and PM10 (particulate matter ≤10 μm in aerodynamic diameter) emissions at 11 meteorological stations across the Junggar Basin. From 1958 to 2018, annual soil loss and PM10 emissions significantly decreased 3.65 and 0.2 kg m^–2 y^–1, respectively. This decrease was likely due to decreasing wind speed, but also associated with increased precipitation and temperature, and decreased solar radiation. Wind erosion occurred most frequently during April and May, accounting for 39% of the annual soil loss and 40% of the annual PM10 emission. In contrast, no erosion occurred in January, February, and December as a result of low temperature (&lt;–8.6°C), frozen soil conditions, and snow cover. Wind erosion risk appeared to decrease during the past six decades in response to observed climate change across the basin; however the southeast part of the basin experienced increasing wind erosion risk over the six decades. Projects to control wind erosion risk and combat dust emission should be given priority in the southeast part of the basin. Frequency of dust events was compared to simulated erosion; a strong linkage was found between simulated soil loss and frequency of dust storms based on regression analysis. This demonstrated that the modelling was acceptable in the conditions of this study and the results were prone to be reliable.

Estimating Soil Loss for Sustainable Crop Production in the Semi-deciduous Forest Zone of Ghana

Quantitative information on soil loss is relevant for devising soil conservation and crop management strategies to ensure sustainable fertility management and crop production. Estimations from runoff/erosion plots are expensive and laborious and thus requiring the exploration of other less expensive but reliable methods such as modeling. This study aimed to estimate current and future rates of soil loss for conservation planning toward sustainable crop production in the semi-deciduous forest zone of Ghana. The Universal Soil Loss Equation (USLE), which took into consideration the rainfall characteristics of the study area, inherent soil physicochemical and hydraulic properties, variations in slopes and terrain differences, land cover types, and soil management practices, was employed to estimate the magnitude and rate of soil loss in the study area. Output from three regional climate models (RCMs) from Coordinated Regional Climate Downscaling Experiment including CCCma-CanESM2, GFDL-ESM2M, and HadGEM2-ES were used to estimate the impact of climate change on soil erosion in the study area. The results showed that soil loss estimated for bare soils was high ranging from 12.7 to 163.8 t ha−1 year−1 largely due to variation in slopes coupled with soil physicochemical and hydraulic properties. The simulated annual soil losses under various land cover options showed variable degrees of soil loss for maize cultivation under conventional tillage (8.2–106.5 t ha−1 year−1), soya bean monocropping (4.4–57.3 t ha−1 year−1), and low soil loss for oil palm plantation with grass or leguminous cover (2.5–32.8 t ha−1 year−1). Evaluation of the RCMs showed excellent performance for CCCma-CanESM2 and GFDL-ESM2M. Predictions of climate change impact using outputs from CCCma-CanESM2 and GFDL-ESM2M indicated that 9–39% increase in soil loss is expected by 2070, and it will be more severe (16–42%) by 2100. The model predictions indicate that the adoption of site-specific land cover management strategies such as tree–cover crop intercropping and reduced tillage has a huge potential to reduce soil loss and sustain soil fertility. The model can be used as an advisory tool for mapping areas for appropriate cropping systems for a particular site.

GeoWEPP과 SWAT 모델을 이용한 산지 유역 강우-유출량 특성 분석

Due to recent climate change, continuous soil loss is occurring in the mountainous watershed. The development of geographic information systems allows the spatial simulation of soil loss through hydrological models, but more researches applied to the mountain watershed areas in Korea are needed. In this study, prior to simulating the soil loss characteristics of the mountainous watershed, the field monitoring and the SWAT and GeoWEPP models were used to simulate and analyze the rainfall and runoff characteristics in the mountainous watershed area of Jirisan National Park. As a result of monitoring, runoff showed a characteristic of a rapid response as rainfall increased and decreased. In the simulation runoff results of calibrated SWAT models, R², RMSE and NSE was 0.95, 0.03, and 0.95, respectively. The runoff simulation results of the GeoWEPP model were evaluated as 0.89, 0.30, and 0.83 for R², RMSE, and NSE, respectively. These results, therefore, imply that the runoff simulated through SWAT and GeoWEPP models can be used to simulate soil loss. However, the results of the two models differ from the parameters and base flow of actual main channel, and further consideration is required to increase the model’s accuracy.

Critical standing crop residue amounts for wind erosion control in the inland Pacific Northwest, USA

Crop residue is an important factor influencing wind erosion of cultivated soils. Establishing soil surface protection afforded by standing crop residue is critical for land managers seeking to reduce or prevent soil loss by wind erosion and the impacts of blowing dust from agricultural lands. The objectives of this study were to evaluate the effect of standing residue on soil wind erosion in the inland Pacific Northwest (iPNW), USA, and test the performance of the plant factor algorithm of the Agricultural Policy/Environmental eXtender (APEX) and Revised Wind Erosion Equation (RWEQ) models in influencing soil loss. The effect of standing winter wheat (Triticum aestivum L.), spring canola (Brassica napus L.), and chickpea (Cicer arietinum) residue on wind erosion, remaining from major commodity crops in the region, was tested in a laboratory wind tunnel using four levels of residue density. The impact of standing residue in controlling wind erosion was compared and analyzed in terms of residue density and their respective frontal area index. Our results show that residue at a density characteristic of the production environment (110 standing residue elements m−1 for winter wheat, 20 standing elements m−1 for canola, and 16 standing elements m−1 for chickpea) provided significant protection to the soil surface from wind erosion. Soil loss at this level of residue density was reduced by 73.3, 53.4, and 60.9% for respectively winter wheat, spring canola, and chickpea (frontal area indexes are 0.172, 0.104, and 0.026 respectively) compared with a surface without residue. The soil surface was found to be at significant risk from wind erosion when residue densities of the three crop types were <50% of the typical production amounts. Although not consistently significant, soil loss decreased as wind direction shifted from parallel to perpendicular with the standing residue row. The APEX model adequately simulated winter wheat and spring canola residue protection but had low accuracy in representing chickpea residue effects relative to the wind tunnel experiments. In contrast, the RWEQ model appeared inadequate in simulating soil loss for the winter wheat and canola treatments but adequately represented chickpea residue effects. Differences in model accuracy for different crop types must be considered by producers and managers to determine whether model information used to select practices to control wind erosion are likely to result in under- or over-protection of soil resources.

Acoustic guided wave detection of grounding rod corrosion: equivalent circuit model and implementation

Acoustic pulse-echo systems, are effective for corrosion damage detection in buried individual ground rods without any need for excavation. In electric power distribution systems there are large numbers buried ground rods are used in substations to form a grounding networks that is critical for the protection of equipment and personnel working in the vicinity of the substation. Over time, these grounding rods will corrode and cease to provide adequate protection. In this work, we present an equivalent circuit model (ECM) for acoustic pulse-echo corrosion detection systems. The circuit model was used to develop a transducer configuration that effectively launches longitudinal acoustic waves that can accurately detect position and damage severity. We show that our model correctly predicts the temporal response from both undamaged rods and rods with machined simulated corrosion pits. The circuit model also accounts for loss, and the results show strong agreement with simulated soil-loss in laboratory experiments. Evidence of wave mode conversion is found in experimental data that is not captured by the model. Preliminary field measurements of copper-clad steel grounding rods are presented that show strong similarities to modeled results. This ECM is a promising basis for further development of pulse-echo systems for corrosion detection and may be useful for future tomographic analysis of field measurements.

Soil Erosion Modelling for Sustainable Environmental Management in Sebeya Catchment, Rwanda

Soil erosion models can be understood as a virtual laboratory that brings together data, observations and knowledge from different fields for sustainable environmental management. The present study was carried out on Sebeya catchment which is located in the Western Province of Rwanda. The main objective of this study was to develop a Universal Soil Loss Equation type of erosion model to be used in predicting soil loss and associated crop yields for sustainable agriculture management in Sebeya catchment at the level of parcels. USLE parameters were determined on each parcel in Sebeya catchment using map overlapping techniques as applied in Geographical Information System (GIS). Applying a combination of 0, 1, 2 and 3 soil erosion control measures on each of 259,673 parcels, the simulated annual soil loss for Sebeya catchment was 849.94; 143.27; 88.64 and 28.59 t/ha/yr respectively. Soil Loss and Crop Yield (SOLCY) model has been developed to predict soil loss and crop yields for each main cultivated crop in Sebeya catchment. A combination of 3 soil erosion control measures such as (bench terrace + mulching + drainage channels) has been found to be the most effective in reducing soil erosion on each parcel with slope range of (16 - 60)%. Farmers and agriculture technicians can use SOLCY model. Finally, researchers should develop similar models on other catchments based on SOLCY model design concept.

Selective Cropping as a Soil Conservation Practice: A Benefits Evaluation

HighlightsReplacing either a winter wheat or corn system with a spring wheat system improved soil retention by more than 19%.When grown for bioenergy production instead of forage hay, switchgrass improved soil retention by 40%.Replacement of field cropping systems with grassland systems improved soil retention by over 70%.Incorporating the environmental merits and demerits of cropping systems during on-farm and policy decision-making may go a long way toward ensuring food, feed, and energy security and environmental sustainability.Abstract. While policies that take land out of production provide a quick fix to erosion, the sustainability of such approaches has been questioned, particularly with the increasing demand for arable land. Policies that reconcile the needs for food, feed, fuel, and healthy ecosystems provide a more sustainable alternative approach to soil erosion management in agricultural lands. This study evaluates the impacts of such a policy that prioritizes the implementation of cropping systems with relatively high environmental benefits. The effectiveness of three field cropping systems and three grassland systems in reducing soil erosion is examined on a 6.6 ha field plot in central Texas over a 20-year period. Soil loss rates under each system are estimated using the Agricultural Policy/Environmental eXtender (APEX) model. A relational comparison of the simulated soil losses and water budget components is undertaken to determine the impact of alternative cropping and grassland systems on the landscape’s hydrologic and erosion processes. The results revealed that replacing a winter wheat system and a corn cropping system with a spring wheat system improved soil retention by 19% and 25%, respectively. Growing switchgrass for bioenergy production instead of forage hay improved soil retention considerably (by 40%). Irrespective of the grassland system adopted (restoration to native prairie or growing switchgrass for hay or bioenergy), the replacement of corn or wheat cropping systems with grassland systems increased soil retention by over 70%. This study shows that there is an opportunity to alleviate concerns about conservation programs that take cropland out of production and those that use food crops as fuel and feedstocks, while considerably reducing soil loss from agricultural lands. Incorporating the merits and demerits of cropping systems and why one system should be preferred over another during on-farm and policy decision-making may go a long way toward ensuring food, feed, and energy security and environmental sustainability. Keywords: Cropping systems, Soil retention, Sustainability, Switchgrass.

Assessing applicability of the WEPP hillslope model to steep landscapes in the northern Loess Plateau of China

The Water Erosion Prediction Project (WEPP) model has been extensively evaluated at lower slope gradients, but its applicability to steep slopes is not yet known. The objectives of this study were to evaluate the WEPP model’s ability to predict runoff and soil erosion on steep slopes in the hilly-gully region of the Loess Plateau of China, and to provide insights for adapting it for this region. Field runoff and soil loss data collected from six bare slope gradient plots, four bare slope length plots, and four cropped plots at the Ansai experimental station during 1985–1992 were used to calibrate, validate, and evaluate the WEPP hillslope model. Measured rainfall intensity (rainfall break point data) was directly used to minimize climate-induced prediction errors. Overall, the calibrated WEPP model predicted event, annual, and average annual runoff and soil loss for different slope gradients, slope lengths, and cropped conditions reasonably well with model efficiencies (ENS) being greater than 0.5 for most cases, but there is still room for improvement. Specifically, for the slope gradient plots, simulated runoff was somewhat insensitive to slope increases at large slope gradients while measured runoff tended to increase with slope steepness due to a decrease in surface storage capacity; however, simulated average annual soil loss, though satisfactory (ENS = 0.83), was slightly oversensitive to slope increases at large slope gradients. For the slope length plots, simulated average annual soil loss was overly responsive to slope length increases at steep slopes, indicating a potential deficiency in representing slope length influence in the model for large slope gradients. The use of the default 1-m rill spacing might have also affected the sensitivity of WEPP-predicted soil loss to slope gradients and lengths, because rill spacing varies with slope steepness and lengths and WEPP –predicted erosion is sensitive to rill spacing. For the cropped plots, WEPP tended to underpredict runoff and soil loss, indicating that the internal model adjustments of hydraulic conductivity and soil erodibility for the effects of factors like roots and residue need to be reexamined for use on steep slopes.

Predicting soil wind erosion potential under different corn residue management scenarios in the central Great Plains

Various models and simplified equations are available to predict wind erosion potential. However, their performance can be often site-specific, depending on soil characteristics and agronomic practices, warranting site-specific model validations. Thus, in this study, we 1) validated the wind erodible fraction (WEF) predictive equations by Fryrear et al. (1994) and López et al. (2007) and 2) estimated the total soil loss with the Single-event Wind Erosion Evaluation Program (SWEEP) using 3-yr measured data from six experiments located across a precipitation gradient in the central Great Plains. Each site had three corn (Zea mays L.) residue removal treatments: control (no removal), grazed, and baled. The measured and predicted WEF were significantly correlated. While the Fryrear et al. (1994) equation performed better than the López et al. (2007) equation, it underestimated WEF with 59% uncertainty across site-years. To reduce this underestimation and uncertainty, we developed a new statistical equation (WEF% = 84.3 + 2.64 × % silt-0.30 × % clay-7.43 × % organic matter-0.15 × % residue cover; r2 = 0.56). The predictive ability of the new equation was, however, no better than that of the existing predictive equations, suggesting the need for further refinement of WEF equations for the region. Simulated total soil loss by wind using the SWEEP model indicated that corn residue baling may increase soil loss if residue cover drops below 20% in the study region. Overall, the existing WEF equations could under- or over-estimate WEF based on site-specific residue management, warranting further model refinement and site-specific validation, whereas the SWEEP estimated soil loss corroborates the critical importance of maintaining sufficient residue cover (>20%) to reduce wind erosion.

Simulating soil loss rate in Ekbatan Dam watershed using experimental and statistical approaches

Reservoir sedimentation resulting from water erosion is an important environmental issue in many countries where storage of water is crucial for economic and agricultural development. Therefore, this paper reports results from analysis of the soil hydrological response, i.e. soil water erosion, to simulated rainfall resulting in sediment accumulation at the reservoir of Ekbatan Dam (Hamedan province, Iran). Also, another objective of this study was to simulate the future trends in reservoir sedimentation (soil loss rate; SLR) from indoor rainfall simulator data by multiple linear regression (MLR) and Artificial Neural Networks (ANNs). For this research, three sampling points with different types of soils were chosen including clayey sand soil (SC-SM), silty soil (ML), and clayey soil (CL). The input parameters were slope gradient (sin θ), soil type (St), water content (w), dry density (ϒd), shear strength (τ), unconfined compressive strength (qu), permeability (k), and California bearing ratio (CBR). Using MLR and ANN methods, 7 models were developed with 2 constant predictors (i.e. sin θ and St) and 6 free predictors which were added in each step one by one. Among MLR models, model 5 with St, sin θ,ϒd, τ, w, and qu as input parameters was statistically significant. Among ANN models, model 4 with St, sin θ,ϒd, τ, and w as input parameters, 9 nodes, and 1 hidden layer was statistically significant. The root mean square error (RMSE), mean error (ME), and correlation coefficient (R) values were 1.433 kg/m2 h, 0.0195 kg/m2 h, and 0.698 for the MLR model and 0.38 kg/m2 h, 0.151 kg/m2 h, and 0.98 for the ANN model, respectively. These results show that the ANN model could better predict the SLR in comparison to the MLR model. The results also demonstrate that shear strength, among the strength parameters, had a greater impact on the SLR than compressive strengths (qu and CBR). Last but not the least, the reservoir sedimentation was estimated for all methods and compared with the observed data. The results indicate that the ANN model is more appropriate for forecasting/simulating the sediment yield for a small watershed.

Wind erosion and dust emissions in central Asia: Spatiotemporal simulations in a typical dust year

AbstractThe prediction of wind erosion and dust emissions is important for controlling erosion and identifying dust sources in arid and semiarid regions of the world. This study predicts quantitatively wind erosion and dust emissions in Xinjiang Province, central Asia. The wind erosion prediction system (WEPS) was used to simulate annual soil and PM10 (particulate matter ≤10 μm in aerodynamic diameter) loss at 64 meteorological stations across the province. Soil and PM10 loss were simulated from bare surfaces at all 64 stations and from cotton and wheat fields at 11 stations. Simulated annual bare soil and PM10 loss were lowest in the Junggar (soil and PM10 loss were, respectively, 121.7 and 7.6 kg m‐2) and Tarim basins (soil loss was 78.2 kg ha‐1 and PM10 loss was 6.5 kg m‐2) and highest in the Tu‐ha Basin (soil and PM10 loss were, respectively, 638.2 and 37.7 kg m‐2). Stations with the highest annual soil loss in the Tarim and Tu‐ha basins also had the highest number of days with wind speeds &gt;8 m s‐1. This indicated wind influenced erosion, but other factors such as soil type also affect wind erosion. The maximum monthly bare soil and PM10 loss occurred in May in the three basins, substantiating that dust storms occur most frequently during spring in the region. Simulated soil and PM10 loss were lower for cotton and wheat than bare soil, thus suggesting that maintaining vegetative cover during a portion of the year provided some protection to the soil surface from wind erosion. © 2018 John Wiley &amp; Sons, Ltd.