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A Wind Erosion Equation1
Soil Science Society of America Journal
Abstract
ABSTRACT The amount of erosion, E, expressed in tons per acre per annum, that will occur from a given agricultural field can be expressed in terms of equivalent variables as: E = , is field length along the prevailing wind erosion direction, and V is equivalent quantity of vegetative cover. The 5 equivalent variables are obtained,by grouping,some,and,converting,others of the,11 primary variables now,known,to govern,wind,erodibility. Rela- tions among,variables are extremely complex. Charts and tables have been developed,to permit graphical solutions of the equa- tion. The equation,is designed,to serve the twofold,purpose of providing a tool to (i) determine,the potential erosion from a particular field, and (ii) determine what field conditions of soil cloddiness, roughness, vegetative cover, sheltering by bar- riers, or width and orientation of field are necessary to reduce potential erosion,to a tolerable amount. Examples,of these applications of the equation,are presented. Weaknesses,in the equation,and,areas needing,further research are discussed. T HE WIND EROSION EQUATION was,developed,by the
... Therefore, vegetation coverage has been considered a key parameter in popular wind erosion models, such as Wind Erosion Equation (WEQ) (Woodruff & Siddoway, 1965), Revised WEQ (RWEQ) (Fryrear, Saleh, & Bilbro, 1998), Wind Erosion Prediction System (WEPS) (Böhner et al., 2003;Hagen, 1991;Shao, Raupach, & Leys, 1996;Woodruff & Siddoway, 1965). ...
... Therefore, vegetation coverage has been considered a key parameter in popular wind erosion models, such as Wind Erosion Equation (WEQ) (Woodruff & Siddoway, 1965), Revised WEQ (RWEQ) (Fryrear, Saleh, & Bilbro, 1998), Wind Erosion Prediction System (WEPS) (Böhner et al., 2003;Hagen, 1991;Shao, Raupach, & Leys, 1996;Woodruff & Siddoway, 1965). ...
Soil wind erosion is one of the key earth surface processes in arid and semi‐arid regions. Soil wind erosion not only leads to land desertification but also serves as an important source of fine particulate matter in the atmospheric environment. Accurate assessment of soil wind erosion and its temporal and spatial distributions is critical for planning and implementing soil conservation measures. As an important factor in wind erosion control, vegetation coverage has been included in almost all the major wind erosion models. The traditional models, however, usually overestimate wind erosion rate because they rely solely on photosynthetic vegetation coverage (PVC) but overlook nonphotosynthetic vegetation coverage (NPVC), such as fallen leaves and branches covering and protecting the soil. In the current study, field surveys, phenological data and fractional vegetation coverage (FVC) derived from the normalized difference vegetation index (NDVI) were employed to examine the temporal and spatial evolution of both PVC and NPVC in the wind erosion region on the Qinghai–Xizang Plateau (QXP). The results reveal significant variations in phonology across QXP. During 2000–2020, the growing season started on Julian Days 124–150, i.e., corresponding to the last month of spring, and ended on Julian Days 242–296, i.e., covering almost the first half of autumn, in the wind erosion‐prone areas of QXP. The vegetation greening initially began in the northern basins and southern river valleys with lower elevations and higher air temperatures, followed by the plateau areas with higher elevations and lower temperatures. Whereas, an opposite trend was manifested in the evolution of senescence. Approximately 40.7% of the area in arid, semi‐arid and extreme arid regions had never been observed greening. Owing to the combined influence of topography and climate, the vegetation coverage exhibited a decreasing trend from the southeast to the northwest of QXP. The mean annual FVC during the growing and nongrowing seasons were 36.2% and 24.4%, respectively. During the growing season, moreover, the FVC was approximately 1.04–1.37 times greater than the PVC. Regarding the interannual trend, the vegetation coverage increased from 2000 to 2020 in general. The mean annual FVC over the entire study region increased by 0.15% and 0.14% during the growing and nongrowing seasons, respectively, over the past 20 years. The temporal trend, however, varied among different areas. During the growing season, FVC remained basically unchanged in 37.2%, experienced mild improvements in 42.0% and underwent mild degradations in 20.8% of the study region. These findings hold important implications for understanding soil wind erosion processes and improving wind erosion models on QXP.
... The former is an indicator of the susceptibility of the soil mass to detachment into individual soil particles, and the latter is an indicator of the erosive energy of the wind to cause transport of the soil particles. Wind erodibility of soil (WE), as a major indicator of wind erosion, was used for the prediction of wind erosion by the soil loss equation (Woodruff and Siddoway 1965). In general, wind erodibility depends on many factors such as topographic position, slope steepness and soil management that cause detachment of the soil mass, e.g. ...
... One kilogramme of each sample was sieved through 0.84 mm sieve, and the percentage of particles greater than 0.84, referred to hereafter as non-erodible soil particles (NEP), was obtained. A standard table developed by Woodruff and Siddoway (1965) was used to estimate WE. The soil samples were، then, crushed, passed through 2-mm sieve and saved for physical and chemical analysis. ...
Wind erodibility of a soil (WE) is a major indicator of itssusceptibility to wind erosion under a given climatic condition. This studyis part of a national project for assessing and mapping wind erodibility ofsoils in Sudan. It was undertaken to generate WE data for the Red SeaState. Three replicate surface soil samples were collected, randomly, fromtwenty-eight geo-referenced farms spread in the State. Non-erodible soilparticles (NEP>0.84mm) and selected soil properties were measuredusing standard procedures. The mean NEP values ranged from 17.0% to57.2% with an overall mean coefficient of variation of replicatedeterminations equal to 4.7%. The equivalent WE ranged from 49.6 to244.0 ton/ha. The results showed a highly significant (P<0.001) increaseof NEP with increase of clay (C) and organic matter (OM), and decreasewith increase of sand and sand plus silt (S+Si) expressed, successively, asratios of clay, clay plus OM and clay plus CaCO3 . The reverse trendswere obtained for the relations of WE and the various soil properties andtheir ratios. Clay, (Si+S)/C, (Si+S)/(C+OM) and (Si+S)/(C+CaCO3 )accounted for 80%, 81%, 81% and 82% of the variation of NEP, and77%, 78%, 78%, and 79% of the variation of WE. Multiple regressionsrelationships of NEP or WE with clay, sand, CaCO 3 and OM gavecoefficients of determinations equal to 81% and 80%, respectively. Thus,it was recommended that clay, (Si+S)/C, (C+OM), (C+CaCO 3 ) ormultiple regression equations may be used for predicting NEP. However,(Si+S)/(C+CaCO3 ) is recommended for predicting NEP, and in all casesthe equivalent WE can be obtained from the standard table. A table forwind erodibility groups was developed for the State.
... Accurate prediction of aeolian soil displacement is critical for mitigating land degradation and protecting socioeconomic assets, necessitating the development of process-based aeolian transport models that quantify erosion magnitude and spatiotemporal patterns [1]. Various validated wind erosion models are currently available [2], including the Wind Erosion Equation (WEQ) model [3], the Texas Tech Erosion Analysis Model (TEAM) [4], the Wind Erosion Stochastic Simulator (WESS) [5], the ...
Ecosystem service research is essential to identify the contribution of the ecosystem to human welfare. As an important ecological barrier zone, the Kubuqi Desert supports the use of a crucial wind erosion prevention service (WEPS) to improve the ecological environment quality. Based on the Revised Wind Erosion Equation (RWEQ) model, the spatial and temporal changes of WEPS in the Kubuqi Desert region were simulated from 2000 to 2022, and the impacts and interactions of natural and socio-economic factors including numerical and typological variables on the spatial pattern of wind and sand control services in the region were analyzed by using geographical detector. From 2000 to 2022, the total WEPS provided in the Kubuqi Desert ranged from 0.35 × 10⁷ t to 1.26 × 10⁷ t. The average WEPS per unit area was between 0.19 kg m⁻² to 0.68 kg m⁻².WEPS has a higher spatial distribution in the east and a lower spatial distribution in the west. Soil type was the most important driver of the actual wind erosion (SL), with vegetation cover, elevation, mean annual temperature, mean annual wind speed, and mean annual precipitation as the main drivers, and population size and GDP as secondary drivers. The interaction analysis showed that the interaction of weather factor, vegetation factor and soil factor is the dominant factor influencing the amount of soil wind erosion in the Kubuqi Desert.
... In the field of wind erosion research, the Wind Erosion Equation (WEQ) and its revised version, the Revised Wind Erosion Equation (RWEQ), have been widely applied in the United States and other regions since the 1980s. These models are favored for their flexibility and ease of computation; however, their accuracy heavily depends on the quality of input data and the precision of observational data 29,30 . ...
This study addresses the critical ecological challenges of soil wind and water erosion in the agro-pastoral ecotone of northern China, both of which significantly contribute to soil degradation. Understanding the relative contributions of these erosion types is essential for developing effective control measures. Using the¹³⁶Cs tracer method, we quantified the ratio of soil wind erosion to water erosion under varying topographic and geomorphic conditions. The results revealed that cropland has experienced the most severe erosion in recent decades. Specifically, on gentle slopes (6°–8°), the rate of water erosion exceeded wind erosion by approximately eightfold. On steeper slopes (10°–15°), this trend was even more pronounced, with water erosion surpassing wind erosion by a factor of approximately 27. These findings were corroborated by measured data from a previous study area. Overall, water erosion is the dominant process in the agro-pastoral ecotone of northern China, with wind erosion playing a secondary role. Future erosion prevention strategies should prioritize hydraulic erosion control measures, particularly on sloping cropland. Furthermore, advancing research on the compound mechanisms of wind and water erosion is imperative for developing integrated mitigation strategies, ultimately supporting the sustainable development of the region’s ecological environment.
... By discerning various erosion risk intensities across desert grassland zones through this dual-lens, the findings hold substantial implications for directing wind erosion mitigation strategies within these regions. Historically, wind erosion assessments predominantly relied on empirical models like WEQ 116,117 , and WEPS 85 . Despite their regional specificity [118][119][120][121] , these models often lack foundational grounding in theoretical and physical processes 66,122 and necessitate localized adaptations and confirmatory testing 67 . ...
Wind erosion is a major ecological challenge in the Inner Mongolia desert grassland, directly impacting regional ecological stability and sustainable development. To gain a deeper understanding of the wind erosion process and its key driving factors, this study carefully assembled 21 indicators and quantified each factor’s relative contribution to wind erosion using ¹³⁷Cs tracing technology and geographic detectors. Data collection methods included the acquisition of soil samples, meteorological data, and vegetation cover information through remote sensing technology, along with field measurements taken at several sampling points within the study area. Data analysis was conducted using geographic detectors, which spatially identified the key factors influencing wind erosion and quantitatively assessed their contributions. Additionally, the spatial distribution of risk zones was accurately identified, analyzing wind erosion intensity and distribution patterns across different regions. The results showed that soil organic matter (SOM), vegetation height (VH), average annual precipitation (PPT), average annual temperature (AAT), and potential evaporation (PE) were the primary contributors to wind erosion, with their explanatory powers for the wind erosion modulus being 0.60, 0.51, 0.51, 0.48, and 0.44, respectively. This study provides important theoretical support for regional wind erosion control and proposes targeted management strategies to improve management efficiency and promote ecological protection and sustainable development.
... With the development and integrated application of technologies, such as geographic information systems (GIS), remote sensing, and model simulation, research on wind erosion models based on physical processes and statistical experience, has also advanced. Major wind erosion models proposed include the Wind Erosion Equation (WEQ) (Woodruff and Siddoway 1965), the Texas Erosion Analysis Model (TEAM) based on wind speed profile development (Gregory et al. 2004), the Bocharov model incorporating human activity factors (Bocharov 1985), the Revised Wind Erosion Equation (RWEQ) (Fryrear et al. 2000), the process-based Wind Erosion Prediction System (WEPS) (Hagen 2004), the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Stein et al. 2015), and the Wind Erosion Assessment Model (WEAM) (Lu and Shao 2001). Among these models, the RWEQ model is not only computationally simple and requires a small amount of data but also comprehensively considers various factors such as wind speed, precipitation, evaporation, soil moisture and texture, topographic features, vegetation, and snow cover (Kaplan, Basaran, and Erpul 2024). ...
The Loess Plateau is one of the most severely eroded regions in China. Assessing the spatiotemporal changes in wind erosion control services is of significant importance for the ecological protection of the Loess Plateau. The dynamic changes in these services are primarily influenced by human activities and natural climate changes. However, existing studies lack a quantitative evaluation of the contributions of climate change and human activities to the spatiotemporal changes in wind erosion control services on the Loess Plateau. This study utilized a modified soil wind erosion equation and partial derivative method to investigate the response characteristics of wind erosion control service changes on the Loess Plateau to climatic factors and human activities and quantified the relative contributions of these two driving factors. Additionally, the key threshold points of precipitation and grazing affecting soil wind erosion were found by constraint-line method. The results indicated that the amount of the sand fixation per unit area on the Loess Plateau increased slowly at a rate of 0.05 t·hm⁻²·a⁻¹ from 2000 to 2013 and then increased at a rate of 0.97 t·hm⁻²·a⁻¹ from 2013 to 2020. Spatially, 47.55% of the area showed an increasing trend, while 41.32% of the area showed a decreasing trend. Overall, the contribution of climate change to the variation in sand fixation services on the Loess Plateau is greater than that of human activities, but their contributions exhibit spatial heterogeneity. In the A2, B1, and B2 subregions, the decrease in sand fixation services and the increase in the A2 subregion are primarily influenced by human activities. Between 2000 and 2020, the average contributions of wind speed and precipitation to SR (sand fixation per unit area) on the Loess Plateau were 0.243 t·hm-2·a-1 and 0.09 t·hm-2·a-1, respectively, making them the most significant climatic drivers of SR changes. In coal mining areas on the Loess Plateau, the amount of the sand fixation per unit area mainly showed a decreasing trend (52.30%), while the conversion of cropland and unused land to grassland significantly promoted an increase in the amount of sand fixation, accounting for 50.30% of the total increase. After grazing intensity reaches a threshold (around 13.51%–35.84%), SR declines rapidly with further increases in grazing intensity. Various major ecological projects have also significantly promoted the increase in wind erosion control. The study results can provide empirical support and a basis for formulating ecological restoration plans for different ecological zones on the Loess Plateau, as well as for soil wind erosion control in coal mining and grazing areas.
... Land 2024, 13, 1991 2 of 18 In fact, as early as the 1960s, the first-generation wind erosion equation (WEQ) developed by the United States Department of Agriculture had already been used to study dust emissions from bare land [14,15]. With a deeper understanding of wind erosion mechanisms, increasingly advanced models have been developed, such as the Revised Wind Erosion Equation (RWEQ) [16], the Wind Erosion Prediction System (WEPS) [17], and the Wind Erosion Model Simulation System (IWEMS) [18,19]. ...
Soil fugitive dust (SFD) is a significant contributor to environmental particulate matter (PM), which not only pollutes and affects air quality but also poses risks to human health. The emission inventory can provide a basis for the effective prevention and control of SFD pollution. However, current emission inventories with low resolution and frequency make it difficult to assess dust emissions accurately. Obtaining monthly high-resolution bare soil information is one of the solutions for compiling SFD emission inventories. Taking Daxing District, Beijing, as a case study, this study first extracted bare soil for each month of 2020, 2021, and 2022, respectively, using high-spatial-resolution remote sensing satellite data, and then constructed a 10 m-size emission grid and monthly SFD emission inventories based on the wind erosion equation by inputting vegetation cover factor, meteorological data, and soil erosion index. The total emissions of TSP, PM10, and PM2.5 in Daxing District from 2020 to 2022 were 3996.54 tons, 359.26 tons, and 25.25 tons, respectively. Temporally, the SFD emissions showed a decreasing trend over the years and were mainly concentrated in the winter and spring seasons. Spatially, the SFD emissions were predominantly concentrated in the southern and northern areas. And the emissions of PM10 exhibit a significantly stronger correlation with wind speed and the extent of bare soil area.
... Early work at the High Plains Wind Erosion Laboratory was conducted by SCS engineers and scientist and included setting up wind tunnels and experiments to understand wind erosion mechanics, major influencing factors, and wind erosion control methods (Chepil, 1958;Chepil and Woodruff, 1963). In 1965, the WEQ was published (Woodruff and Siddoway, 1965), and the procedure was used by SCS for many years to estimate wind erosion losses and design wind erosion control practices. More recently, the ARS Wind Erosion Research Unit, originally located in Manhattan, Kansas, and then moved to Fort Collins, Colorado, has developed a process-based Wind Erosion Prediction System (WEPS), that the USDA Natural Resources Conservation Service (NRCS, formerly SCS) has implemented in their field offices for wind erosion control planning purposes (Hagen, 1991;Wagner, 2013). ...
HIGHLIGHTS
Soil erosion research has evolved over a century to address critical societal needs.
This collection features 12 articles from the 2023 ASABE Soil Erosion Research Symposium.
Links to Symposium proceedings and presentations are provided.
Soil erosion modeling, methods, and measurement advances are highlighted from the collection.
Abstract. This article introduces a collection of 12 articles featured from a decennial international American Society of Agricultural and Biological Engineers (ASABE) soil erosion research symposium in Aguadilla, Puerto Rico, in January 2023. Over 130 engineers and scientists met to discuss current soil erosion issues, research techniques, and erosion prediction technologies. In addition to the meeting presentations and proceedings (116 abstracts or papers available online), twelve articles were published in a special collection in the ASABE journals. Information on the ASABE Soil Erosion Research Under a Changing Climate international symposium and details on the special collection of articles are provided in this paper. Articles are aggregated into soil erosion-related topical areas of modeling, methods, and measurement, and article highlights are presented. A common theme was the need for standardized measurements and methods to compare processes and impacts across geographic and climate regions and to assess model performance and prediction for extension of methods to other watersheds. Keywords: Erosion control, Hydrology, Sediment, Sedimentation, Soil conservation, Soil erosion modeling, Soil pollution, Water quality.
... To estimate the extent of wind erosion on a large scale, wind erosion prediction models based on remote sensing and geographic information system have been developed [15], such as the wind erosion equation (WEQ) [16]; erosion-productivity impact calculator [17]; ...
The Northern Slope of the Tianshan Mountains (NSTM) is characterized by complex and diverse terrain, which represents a fragile ecological environment. Soil wind erosion is a key factor affecting the natural ecosystem and the social development of the region, but it has not been well understood until now. In this study, the revised wind erosion equation (RWEQ) was employed to display the spatial and temporal characteristics of soil wind erosion in the NSTM from 2000 to 2018. In addition, the main driving factors of wind erosion were analyzed. The results showed that approximately 94.25% of the NSTM experienced soil wind erosion, with a multi-year average actual soil wind erosion modulus of 6556.40 t·km⁻²·a⁻¹. From 2000 to 2018, the actual soil wind erosion modulus in the NSTM showed a trend of fluctuational increase, with an increase rate of 44.65 t·km⁻²·a⁻², but the area affected by soil wind erosion exhibited a downward trend. The wind erosion rate decreased in 76.38% of the total area, except for some areas such as Hami, with an increasing trend of soil wind erosion. The wind factor in RWEQ showed a significant linear relationship with the soil wind erosion modulus (r = 0.62, p < 0.01). Land use changes also have a critical impact on the soil wind erosion. The results of geographical detectors show that the combined effect of weather factor and vegetation factor can explain more than 60% of the changes in soil wind erosion.
... The commonly used wind erosion models are shown in Table 1. The average soil loss in an area [27] Revised Wind Erosion Equation (RWEQ) Empirical/ Process-based The average soil loss in an area (including short-term average soil loss) [28] [29] Wind Erosion Prediction System (WEPS) Process-based Soil loss in terms of its direction and magnitude over the land surface [30] [31] Single-event Wind Erosion Evaluation Program (SWEEP) Process-based Soil loss in terms of its direction and magnitude over the land surface [32] Erosion Productivity Impact Calculator (EPIC) Empirical Soil loss over a specific period [33] Agricultural Policy Environmental eXtender (APEX) Empirical Soil loss over a specific time frame [28] Texas Erosion Analysis Model (TEAM) Process-based Total soil loss, soil movement rate over the field [34] Wind Erosion on European Light Soils (WEELS) Process-based Soil loss per unit time [35] Wind Erosion Assessment Model (WEAM) Process-based Soil loss over a specific period in tons per acre [5] Dynamic Model of Soil Wind Erosion (DMSWE) Process-based Amount of soil transport at downwind border [36] wind erosion around the world [9] [36]- [38]. Additionally, a comprehensive review of these wind erosion models has been discussed in reference [28]. ...
Wind erosion represents a formidable environmental challenge and has seri-ous negative impacts on soil health and agricultural productivity, particularlyin arid and semi-arid areas. The complex dynamics of wind erosion make itslarge-scale monitoring and quantification a daunting task. To facilitate themonitoring and quantification of wind erosion, various scientific approachesand methods have been employed. These include sophisticated wind erosionequations and models, wind tunnel experiments, and the application of radi-onuclides. Additionally, researchers have assessed soil physicochemical prop-erties, used anemometers for wind speed measurement, and deployed dustcollectors for particle capture. Remote sensing technologies, wind erosion mon-itoring stations, and evaluations of wind barriers have also been utilized. Re-cently, the adoption of machine learning methods has gained popularity. De-spite their value, each of these techniques has limitations in capturing the fullspectrum of the wind erosion process. This paper examines these limitationsand assesses the effectiveness of each method in the context of wind erosionstudies. It also outlines directions for future research and suggests pathwaysthat could enhance the understanding and management of wind erosion.
... In the dynamic classification system of soil wind erosion influencing factors, ridges belong to the class of surface roughness disturbance factors (Zou et al., 2014). They appear in various wind erosion prediction models, such as the Wind Erosion Equation (WEQ), the Revised Wind Erosion Equation (RWEQ) and the Wind Erosion Prediction System (WEPS) (Fryrear et al., 2001;Hagen, 1991;Woodruff & Siddoway, 1965). ...
In arid regions, the use of ridge cover as a traditional agricultural strategy has been effective in mitigating soil wind erosion. However, few studies have focused on the sand transport characteristics of different micro‐ridge spacings and heights. This study aimed to identify the mechanism by which ridges change the near‐surface sand transport. In a controlled wind tunnel environment, the aeolian sand flux structure and sand transport flux (qz) at heights from 0 to 0.7 m were measured. The results showed that, compared with no ridges, ridge covering could significantly modify the structure of aeolian sand flux near the surface, yielding a substantial reduction in the proportion of sand transport to the total sand transport in the 0‐ to 0.1‐m height layer. In the absence of ridges, blown sand was mainly concentrated in the 0‐ to 0.1‐m height layer, and qz decreased exponentially as the height increased. With various micro‐ridge spacings and heights, when H was less than 0.1 m and L was more than 15H, the blown sand also remained concentrated in the 0‐ to 0.1‐m height layer, and qz decreased exponentially as the height increased. When H increased to 0.15 m from 0.1 m and L decreased from 15H to 5H, the height of the blown sand was concentrated in the 0.3‐ to 0.4‐m layer. The blown sand layer of most ridge structures was concentrated at a height of 0.1–0.4 m, and the structure of the aeolian sand flux resembled an ‘elephant nose effect’. The total sand transport rate of all ridge structures was significantly lower than that with no ridges, indicating that a reasonable ridge structure can effectively prevent wind erosion.
... Developments in the implementation of geophysical models in geoinformation systems (GIS) have made the process of erosion by wind modelling more flexible (Rousseva et al., 2016) A quantitative assessment of the risks of erosion by wind is important for the proper implementation of these measures. The first established erosion by wind prediction model was the erosion by wind equation (WEQ) (Woodruff & Siddoway, 1965), which calculates the potential soil loss in the study area through factors affecting the development of erosion by wind (climatic, soil erodibility, topography, vegetation). This research aimed to calculate the soil loss due to erosion by wind on the agricultural soils of Dedoplistskaro Municipality. ...
Wind erosion plays a significant role in the degradation of agricultural land. When formulating strategies for mitigating wind erosion, it is crucial to possess precise quantitative data pertaining to the possible soil loss. Various types of equations and models are developed for this objective. This article used the WEQ to quantify the mean annual soil loss resulting from wind erosion on agricultural soils within the Dedoplistskaro Municipality in Eastern Georgia. The area of eastern Georgia experiences a higher degree of wind erosion because of its specific meteorological characteristics. The agricultural soils in the study area have been identified based on the land use classification provided by the ESA in 2021. The climate data for the research area has been obtained from the GWA and MODIS open-access satellite images. The WSD was used as the primary data source for the computation of the soil erodibility index. To evaluate the impact of vegetation cover, the LAI was chosen, which was derived from the yearly average NDVI data acquired using Sentinel 2. The width of open plots was determined by applying satellite-based Land Use and Land Cover (LULC) open access data as well as data acquired from the Ministry of Agriculture and Environmental Protection of Georgia. This data specifically pertains to windbreaks and plots that were occupied by perennial crops. The mathematical computations were executed via the web platforms GEE and ArcMap 10.8. Subsequently, a raster file depicting the probable soil loss resulting from wind erosion on the agrarian soils within the Dedoplistskaro municipality was obtained.
... En el Oeste, no existieron diferencias entre manejos (P>0,05), siendo el valor promedio 55% (Figura 2a). Este valor se encuentra por encima del 40%, valor equivalente al umbral considerado para una erosión tolerable (Woodruff & Siddoway, 1965). Esto significa que independientemente del manejo, la estructura del suelo está condicionada por su textura, ya que el alto contenido de arena y el bajo contenido de MO limitan el desarrollo de agregados resistentes a la erosión eólica (Tatarko, 2001). ...
In the South of Córdoba province, the simplification of agricultural production systems has increased the risk of wind erosion, due to changes in the dynamics of vegetation cover and land use. Therefore, our objectives were to evaluate the soil erodible fraction by wind in different managements and environments in the south of Córdoba and relate it to soil properties. For them, in three macro-environments (West, Central and East) and four managements: Forest (P), Mixed (M), Agriculture without peanuts (A0) and Agriculture with peanuts (A1), undisturbed samples were taken for the determination of the wind erodible fraction (FE) using a rotary sieve. In addition, samples were taken for the determination of organic matter (OM) and texture. There was interaction between management and the macro-environment for FE. In the West, the FE was similar between handlings and with values above the threshold, in the East it was also similar between handlings, but with values below the threshold. In the center, the FE of A1 was higher, presenting values above 40%. The FE was related in a linear and negative way with the OM and in a linear and positive way with the sand content. In soils with more than 70% sand, the use of service crops is necessary to control erosion, while this threshold is reduced to 60% for those sequences with peanuts.
... The Revised Wind Erosion Equation (RWEQ) is a prevalent empirical model for calculating wind erosion. The development of this model marks the first step in establishing a theoretical system for soil wind erosion (Woodruff & Siddoway, 1965). The RWEQ leverages several environmental factors, including climate, soil attributes, surface roughness, vegetation cover, and other parameters, to estimate soil wind erosion primarily by employing Eqs. ...
The arid regions of northwest China suffer from water shortages, low land quality, and a fragile ecological environment, while social and economic development has increased the ecological and environmental load. The spatiotemporal pattern and evolutionary trend of ecological environmental quality were investigated by constructing a remote sensing-based ecological environmental index (EQI) evaluation model incorporating four indicators: drought index (DI), soil erosion index (SEI), greenness index (GI), and carbon exchange index (CEI). The study found that between 2001 and 2020, the DI, the SEI, and the CEI in the northwest arid region exhibited a downward trend with reduction rates of − 3e−05, −0.0006, and −0.0018, respectively. However, the GI demonstrated an upward trend, with a growth rate of 0.002. The average EQI in 2020 was 0.315, indicating a fair grade, with only 11.56% falling above the medium level. A general increasing trend was observed throughout the study period in EQI, with an incremental rate of 0.0002. Areas with future improvements in EQI accounted for 57.547% and were principally located in the eastern part of Inner Mongolia, Qinghai, and the northern and southern portions of Xinjiang. Notably, land use was significantly correlated with EQI (p < 0.01), with a hierarchy of effects that ran: forest land (0.678) > cultivated land (0.422) > grassland (0.382) > wasteland (0.138). The highly robust findings presented here offer innovative methods for ecological and environmental monitoring in the arid region of the northwest, with potential implications at an international scale.
... The wind erosion equation (WEQ) was initially developed by Woodruff and Siddoway (1965) as the first step in establishing a comprehensive theoretical system for understanding and predicting wind erosion. Over the years, significant enhancements and refinements have been made to this equation, leading to the development of the revised wind erosion equation (RWEQ) by Fryrear et al. (2000). ...
The Chinese Loess Plateau is an ecologically fragile area in China and globally, which plays a crucial role in soil and water conservation and ecological restoration. To address the problem of soil erosion in this region, China has implemented the Grain for Green project since 2000. This study aims to evaluate the effectiveness of the Grain for Green project by analyzing the temporal and spatial changes in soil erosion on the Loess Plateau from 2000 to 2021. The results indicated that the average erosion intensities of water and wind erosion from 2000 to 2021 were 16.66 and 10.73 t·ha-1·yr-1, respectively. A downward trend was observed during the study period, with change rates of -0.45 and -0.32 t·ha-1·yr-1, respectively. The Gully region showed the highest intensity of water erosion, with a decrease rate of -0.80 t·ha-1·yr-1. On the other hand, the Sandy region experienced the highest intensity of wind erosion, with a decrease rate of -1.62 t·ha-1·yr-1. When analyzing erosion intensity in different land use types, it was found that bare land has the highest average erosion intensity (water erosion: 68.74 t·ha-1·yr-1, wind erosion: 183.29 t·ha-1·yr-1), while forest land has the lowest (almost no water erosion, wind erosion less than 0.5 t·ha-1·yr-1). Regarding different landforms, water erosion generally increases with slope, while wind erosion mainly occurs in flat areas with less than 5°. Enhancing vegetation coverage in each subregion is recommended to control soil erosion. Specifically, suggested increments were 4.51% for the Gully region, 3.65% for the Hilly-gully region, 2.89% for the Valley Plain region, 2.74% for the Earth-rock Mountain region, 1.49% for the Irrigation region, and 0.87% for the Sandy region.
... With the development and comprehensive application of geographic information systems (GISs) and other technologies, statistical and empirical soil erosion modeling has been developed. For soil erosion by wind, the wind erosion equation (WEQ) [19], Texas erosion analysis models (TEAMs) [20], the Bocharov model [21], the revised wind erosion equation (RWEQ) [22], and the wind erosion prediction system (WEPS) [23] have been put forward successively. For soil erosion by water, the current models include the universal soil loss equation (USLE) [24], the revised universal soil loss equation (RUSLE) [25], Chinese soil loss equation (CSLE) [26], water erosion prediction project (WEPP) [27], LImburg soil erosion model (LISEM) [28], European soil erosion model (EUROSEM) [29], and so on. ...
Assessing the impact of land use and land cover change (LUCC) on soil erosion by wind and water is crucial for improving regional ecosystem services and sustainable development. In this study, the Revised Wind Erosion Equation (RWEQ) and Revised Universal Soil Loss Equation (RUSLE) were used to reveal changes in the extent of soil erosion by wind and water in the Qaidam Basin from 2000 to 2018 and the impact of LUCC on them. From 2000 to 2018, with global climate change, the areas and intensities of soil erosion by wind decreased, whereas those of soil erosion by water increased. With increased human activities, approximately 12.96% of the total area underwent conversion of the type of use: the areas of cropland, woodland, grassland, and construction land increased, whereas the areas of shrubbery, desert, and other unused land decreased. Land use/cover changes are positive to the soil erosion of water but negative to the soil erosion of wind. Among them, the changes in vegetation coverage of other unused land and grassland contributed to 83.19% of the total reduction in soil erosion by water. Converting other unused land to grassland reduced the total reductions in soil erosion by wind by 94.69%. These results indicate that the increase in vegetative cover and area of grasslands in the Qaidam Basin had a positive impact on the reduction in soil erosion. It is recommended that the arrangement of grasses, shrubs, and trees be optimized to prevent compound erosion by wind and water for protecting regional ecological environments.
... Many wind erosion models have been developed, including the Wind Erosion Equation (WEQ) model (Woodruff and Siddoway, 1965), the Pasak model (Pasak, 1973), the Bocharov model (Bocharov, 1984), the Wind Erosion Prediction System (WEPS; Hagen, 1991), the Wind Erosion Assessment Model (WEAM; Shao et al., 1996), and the Revised Wind Erosion Equation (RWEQ) model (Fryrear et al., 1998). These models represent equations that describe the various factors that affect wind erosion processes, and were mainly used to calculate soil loss from cultivated farmland, although they could also be used for other land with a significantly loose surface, such as sandy land with a low vegetation cover. ...
The commonly used wind erosion models, including the Revised Wind Erosion Equation (RWEQ), are mainly based on data from loose soils, for which the erodibility factor (EF) reflects the abundance of erodible aggregates, and cannot be applied to simulate erosion of compacted soils such as some grassland soils. Taking the Inner Mongolia Autonomous Region in northern China as the study area, we used the 137 Cs tracing technique to estimate the soil loss of grassland by wind erosion from 1963 to 2021, and used the results as criteria to verify the wind erosion rate (WER) predicted by RWEQ for the same period. We found that RWEQ accurately calculated the WER of cultivated farmland, but overestimated grassland WER. We believe that the main reason is that the equation did not consider the impact of soil compaction (SC) on EF, since the calculation methods for the weather factor, vegetation factor, surface roughness factor, and soil crust factor in RWEQ are applicable for grasslands. Therefore, we used the 137 Cs tracing results to extrapolate the grassland EF while keeping the climate, soil crust, vegetation cover, and roughness factors constant, and combined SC and the proportion of aggregates smaller than 0.84 mm in diameter to develop an empirical equation for grassland EF. Compared with the original model, the extended model (RWEQ ext) that included the factor of SC more accurately estimated the WER of compacted grassland soil and could be applied to the whole study area. The RWEQ ext results showed that tolerable and slight levels of wind erosion dominated the grassland and gobi surfaces in the study area, whereas moderate wind erosion occurred in grasslands with sparse vegetation cover, and severe, very severe, and destructive wind erosion mainly occurred on loose surfaces such as sandy land with low vegetation cover.
... It is the key, difficult problem in soil wind erosion research in recent years (Shao et al 1996, Alfaro et al 1998. Accounting for climate factors, soil erodibility, ridge length, crop residue and soil-surface roughness, a few models have been developed since the 1960s, including WEQ (Woodruff and Siddoway 1965), TEAM (Gregory et al 1988), WEAM (Shao et al 1996), RWEQ (Fryrear 1998) and WEPS (Hagen 1991). However, most models are based on empirical estimation and do not consider the wind erosion mechanism. ...
Anthropogenic dust (AD), as a crucial component of particulate matter, is defined as dust emitted through modifying or disturbing soil particles directly or indirectly associated with human activities in urban areas, croplands, pasturelands and dry lakes. The sources, characteristics, and impacts of AD remain poorly studied, in contrast to the large body of research on natural dust (ND). This review summarizes scientific findings published since the 1990s regarding the emissions, physical-chemical characteristics, and spatio-temporal distributions of AD from the micro to the global scale. AD accounts for 5%–60% of the global dust loading, with notable spread in existing estimates. Compared with ND, AD has more complex and variable compositions and physical-chemical properties. Influenced by human disturbances, AD exhibits small particle sizes, easily accessible critical friction velocity, and large emissions. Further research should improve the observations and simulations to investigate the complex interactions among AD, climate change, and human health.
... More specifically, for the 38 sampling sites, 13, 5, 8 and 1 site were in the moderate, strong, very strong and extremely strong erosion of wind erosion, respectively, indicating that wind erosion was very serious in the Siziwang Banner, which has resulted in the coarse sand soil in this region. Previous studies have also indicated that wind erosion could pose great adverse impacts on soil mechanical composition [38][39][40][41][42][43], thus the sandy soil in the study region can effectively reflect the impacts of soil erosion on soil quality. ...
As a natural ecological fragile region, the vast desert steppe in the Inner Mongolia has a developed animal husbandry, and thus posed great impacts on soil quality. In order to accurately evaluate the current situation of soil quality in the desert steppe, it is therefore imperative to adopt a suitable method to effectively assess the soil quality in the region. In this study, the minimum data set (MDS) was established with the help of principal component analysis, Norm value calculation, and correlation analysis, and four indicators, including organic matter, sand grains, soil erosion degree, and pH, were established to evaluate the soil quality of the desert steppe in the Siziwang Banner, a county in the Inner Mongolia. The results from the minimum data set (MDS) method were validated based on the total data set (TDS) method, and the validation indicated that the MDS method can be representative of the soil quality of the study area. The results indicated: 1) the soil quality index (SQI) of 0–30 cm in more than 90% of the study area falls in the range of 0.4 and 0.6 (medium level), while the better level (SQI ≥0.6) only accounted less than 10% of the study area; 2) For the MDS indexes, soil organic matter content at all depths decreased in the southern mountains, central hills, and northern plateau, which is consistent with the changing trends of SQI; 3) The sand grain was the dominant particle in the study region, which was in accordance with the intense wind erosion; 4) The negative correlation was found between the soil pH value and SQI (the high value in pH corresponded to the low value in SQI), which reflected that soil pH has a more stressful effect on the local vegetation. Overall, the MDS indexes in this study can objectively and practically reflect the soil quality in the study area, which can provide a cost effective method for SQI assessment in the desert steppe, which is important for the further grassland ecological construction and grassland management to improve the soil quality in the desert steppes.
... Efforts have been devoted to simulating and predicting wind-driven effects, including soil erosion, to control land degradation and implement appropriate agricultural management practices [4]. Various methods, ranging from empirical equations for average soil erosion [5,6] to advanced models predicting crop yields and conservation of natural resources [7][8][9], have been developed. ...
With the increasing construction activities in dry or degraded lands affected by wind-driven particle action, the deterioration of metal structures in such environments becomes a pressing concern. In the design and maintenance of outdoor metal structures, the emphasis has mainly been on preventing corrosion, while giving less consideration to abrasion. However, the importance of abrasion, which is closely linked to the terrain, should not be underestimated. It holds significance in two key aspects: supporting the attainment of sustainable development goals and assisting in soil planning. This study aims to address this issue by developing a predictive model that assesses potential material loss in these terrains, utilizing a combination of the literature case studies and experimental data. The methodology involves a comprehensive literature analysis, data collection from direct impact tests, and the implementation of a machine learning algorithm using multivariate adaptive regression splines (MARS) as the predictive model. The experimental data are then validated and cross-verified, resulting in an accuracy rate of 98% with a relative error below 15%. This achievement serves two primary objectives: providing valuable insights for anticipating material loss in new structure designs based on prospective soil conditions and enabling effective maintenance of existing structures, ultimately promoting resilience and sustainability.
... wind erosion equations (Fryrear et al., 1998;Woodruff & Siddoway, 1965) based upon reparameterizing the ratio of the transport rate on a vegetated surface to the equivalent transport over bare sand. The authors noted that the equation needed to be tested in field conditions, echoing ongoing challenges associated with wind tunnel studies (section 3.3). ...
Coastal dunes are found along the sandy coasts of oceans, seas, and large lakes all around the world. They are dynamic landforms that evolve along complex morphological and biological continua in response to a range on controls linked to climate, sea level, sediment movement, vegetation cover, and land use. By collating research across the full spectrum of processes shaping different types and sizes of dunes and smaller aeolian bedforms, special issues can aid researchers to identify new research directions and methods emerging from the discipline. This editorial summarizes the 25 contributions to the special issue Coastal dunes: links between aeolian processes and landform dynamics. We grouped the contributions into four broad themes: (1) long‐term dune evolution, (2) short‐term aeolian transport, (3) research methods, and (4) coastal dune management. Contributions to the special issue demonstrate that research interest in coastal dunes, and particularly the impacts of human interventions on dunes, continues to grow. It also shows how aeolian research on coastal dunes covers a range of temporal and spatial scales, from ripple dynamics and instantaneous airflow‐transport processes to dune field evolution with rising sea levels and large‐scale dune stage shifts. We highlight potential avenues for future research including vegetation roughness characteristics and their effect on wind flow and sediment transport, the challenges of upscaling short‐ and small‐scale results to larger and longer spatiotemporal scales, and the study of both natural and managed dune landscapes.
... Owen [22] developed a wind speed profile for the leapfrog layer in 1964 and obtained the mass flux of particles per unit width perpendicular to the fluid plane based on two complementary assumptions about the interaction between turbulent winds and uniform sand or soil particle motion. The wind erosion equation (WEQ) is the first model that estimated annual wind erosion in the field in 1965 and includes 5 groups of 11 variables: climate factors, soil erodibility, soil surface roughness, field length, and crop residue [23]. The main limitation of the WEQ model is its poor regional adaptability. ...
Wind erosion can cause high dust emissions from agricultural land and can lead to a significant loss of carbon and nutrients from the soil. The carbon balance of farmland soil is an integral part of the carbon cycle, especially under the current drive to develop carbon-neutral practices. However, the amount of global carbon lost due to the wind erosion of farmland is unknown. In this study, global farmland dust emissions were estimated from a dust emission inventory (0.1° × 0.1°, daily) built using the improved Community Multiscale Air Quality Modeling System–FENGSHA (CMAQ-FENGSHA), and global farmland organic carbon losses were estimated by combining this with global soil organic carbon concentration data. The average global annual dust emissions from agricultural land from 2017 to 2021 were 1.75 × 10⁹ g/s. Global dust emissions from agricultural land are concentrated in the UK, Ukraine, and Russia in Europe; in southern Canada and the central US in North America; in the area around Buenos Aires, the capital of Argentina, in South America; and in northeast China in Asia. The global average annual organic carbon loss from agricultural land was 2970 Gg for 2017–2021. The spatial distribution of emissions is roughly consistent with that of dust emissions, which are mainly concentrated in the world’s four major black soil regions. These estimates of dust and organic carbon losses from agricultural land are essential references that can inform the global responses to the carbon cycle, dust emissions, and black soil conservation.
... In the 1930s, Dust Bowl events in the Great Plains region of the United States triggered the development of empirical wind erosion models (Tatarko et al., 2013). Continuous efforts led to the wind erosion equation (WEQ), the first empirical model, based on wind tunnel experiments and field measurements, integrating different factors, for assessing annual soil loss (Woodruff & Siddoway, 1965). The revised WEQ (RWEQ) replaced WEQ as a tool to estimate erosion (Fryrear et al., 2001) and has been extensively tested and found to be in good agreement with on-field measurements (Buschiazzo & Zobeck, 2008). ...
Regional assessments of the wind erosion risk are rare and vary due to the methods used and the available data to be included. The adaptation of existing methods has the advantage that the results can be compared directly. We adopted an already successfully applied methodology (ILSWE—applied in East Africa), to investigate the spatiotemporal variability of the wind erosion risk between 2005 and 2019 in Southern Africa. The approach integrates climatic variables, a vegetation index, and soil properties to describe the potential impact of wind erosion at the landscape scale. The annual and seasonal variability is determined by the vegetation cover, whereas droughts and strong El Niño events had only regional effects. We estimated that 8.3% of the study area experiences a moderate to elevated wind erosion risk over the 15‐year period with annual and inter‐annual fluctuations showing a slight upward trend. In general, the desert and drylands in the west have the highest proportion of risk areas, the moist forests in the east are characterized by a very low risk of wind erosion, while the grasslands, shrublands, and croplands in the interior most likely react to changes of climatic conditions. The validation process is based on a comparison with the estimated frequency of dust storms derived from the aerosol optical depth and angstrom exponent and revealed an overall accuracy of 65%. The results of this study identify regions and yearly periods prone to wind erosion to prioritize for further analysis and conservation policies for mitigation and adaptation strategies.
Processes of soil fertility degradation described are soil erosion (water and wind erosion), nutrient removal in produce and plant residues, oxidation (including crop residue burning), reduction (including denitrification, waterlogging and anaerobiosis) and leaching, and loss of fauna, flora and microbial diversity. Acidification, salinisation and sodification, and soil aggregate breakdown in the semi-arid and sub-humid regions are the dominant processes of soil fertility degradation. Consequences and magnitude of soil fertility degradation including organic C and N losses and decline in productivity from these processes are considered and briefly potential management solutions suggested for restoration.
Utilizing its beauty, charm and comfort for the youngest and allowing them to grow in such an interior and acquire the
characteristics that this style brings, we can assume what kind of person it will be later in life. It is an honor to work for children.
In the face of climate change and human activities, Central Asia’s (CA) terminal lake basins (TLBs) are shrinking, leading to deteriorating natural environments and serious soil wind erosion (SWE), which threatens regional socio-economic development, human health, and safety. Limited research on SWE and population exposure risk (PER) in these areas prompted this study, which applied the RWEQ and a PER model to assess the spatiotemporal changes in SWE and PER in TLBs in CA, including the Ili River Basin (IRB), Tarim River Basin (TRB), Syr Darya River Basin (SRB), and Amu Darya River Basin (ARB), from 2000 to 2020. We analyzed the driving factors of SWE and used the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to simulate dust event trajectories. The findings from 2000 to 2020 show a spatial reduction trend in SWE and PER, with primary SWE areas in the Taklamakan Desert, Aral Sea Basin, and Lake Balkhash. Significant PER was observed along the Tarim River, near Lake Balkhash, and in the middle and lower reaches of the ARB and SRB. Over the past 21 years, temporal trends in SWE have occurred across basins, decreasing in the IRB, but increasing in the TRB, SRB, and ARB. Dust movement trajectories indicate that dust from the lower reaches of the SRB and ARB could affect Europe, while dust from the TRB could impact northern China and Japan. Correlations between SWE, NDVI, temperature, and precipitation revealed a negative correlation between precipitation and NDVI, suggesting an inhibitory impact of precipitation and vegetation cover on SWE. SWE also varied significantly under different LUCCs, with increases in cropland, forestland, and desert land, and decreases in grassland and wetland. These insights are vital for understanding SWE and PER in TLBs and offer theoretical support for emergency mitigation in arid regions.
The Chinese Loess Plateau plays a crucial role in soil and water conservation and ecological restoration. China has implemented the Grain for Green project since the year 2000 to address the problem of soil erosion in the aforementioned region. This study aims to evaluate the effectiveness of the Grain for Green project by analyzing the temporal and spatial changes in soil erosion on the Loess Plateau from the year 2000 to 2021. The spatiotemporal characteristics of soil erosion on the Loess Plateau were evaluated from both water and wind erosion perspectives. The Revised Universal Soil Loss Equation (RUSLE) was used for water erosion assessment, while the Revised Wind Erosion Equation (RWEQ) was used for wind erosion assessment. The average annual water and wind erosion intensities were 14.56 and 3.95 t·ha− 1·yr− 1, respectively, during the study period. Vegetation coverage, erosive rainfall, and erosive wind intensity showed an increasing trend, while the conversion of land use types primarily involved transforming cropland, bare land, and shrubland into forest and grassland. The comprehensive dynamic changes in various factors resulted in a considerable decrease in water erosion, while wind erosion did not exhibit a remarkable trend over time. Overall, the Grain for Green project has achieved remarkable effectiveness. Increasing vegetation coverage in each subregion is recommended to control soil erosion further, with specific recommended increments as follows: Gully region (4.29%), Hilly–gully region (3.27%), Valley Plain region (2.18%), Earth–rock Mountain region (2.86%), Irrigation region (1.21%), and Sandy region (1.00%). Under optimized vegetation coverage conditions, the intensities of water and wind erosion decreased by 72.03% and 7.20%, respectively. However, 18.50% of the region still experienced water erosion intensity, and 6.72% experienced wind erosion intensity, which reached extremely slight or high levels. Therefore, these areas may require additional soil conservation measures to address soil erosion issues. Specific measures should be tailored to the actual conditions and be in accordance with the overall goals of Loess Plateau management and the development needs of the Yellow River Basin.
Cultivated histosols are subject to several degradation processes that jeopardize their sustainability. These include wind erosion, caused by winds that induce saltation, reptation and suspension of organic particles above a certain threshold. In order to limit soil loss, it is important to identify wind speed thresholds for particle saltation and suspension to guide corrective interventions. In order to describe these erosion processes on cultivated histosols, weather stations were installed at two sites during the summer of 2019 to measure wind speed, saltation and aerosol concentration in the air. A standard method (Stout and Zobeck, 1996) was used to extract the saltation threshold velocity, then adapted to determine a suspension threshold for fine material. Results suggest that this method can accurately determine a saltation threshold for cultivated histosols. It also better describes the suspension mechanism of aerosols, for which a high concentration was observed at low wind speeds, with a decrease for stronger winds and an increase above the saltation threshold.
The Tarim River Basin, China’s largest inland river basin, is renowned for its ecological fragility characterized by concurrent greening and desertification processes. Soil wind erosion emerges as a critical factor impacting the natural ecosystem of this region. This study employs a soil wind erosion model tailored to cultivated land, grassland, and desert terrains to analyze the multitemporal characteristics of and spatial variations in soil wind erosion across nine subbasins within the Tarim River Basin, utilizing observed data from 2010, 2015, and 2018. Additionally, this study investigates the influence of various factors, particularly wind speed, on the soil wind erosion dynamics. Following established standards of soil erosion classification, the intensity levels of soil erosion are assessed for each calculation grid within the study area alongside an analysis of the environmental factors influencing soil erosion. Findings indicate that approximately 38.79% of the total study area experiences soil wind erosion, with the Qarqan River Basin exhibiting the highest erosion modulus and the Aksu River Basin registering the lowest. Light and moderate erosion predominates in the Tarim River Basin, with an overall decreasing trend observed over the study period. Notably, the Qiemo River Basin, Dina River Basin, and Kaidu Kongque River Basin display relatively higher proportions of eroded area compared to their total subbasin area. Furthermore, this study underscores the substantial influence of the annual average wind speed on soil erosion within the study area, advocating for prioritizing soil and water conservation programs, particularly in the downstream regions of the Tarim River Basin, to mitigate future environmental degradation.
The Wind Erosion Equation, currently one of the primary methods for estimating fugitive soil dust emission inventory, is influenced by several factors. Taking the convergent areas of the Tibet Plateau, Loess Plateau, and Qinba Mountains in Western China, we have optimized the climate factor using the WRF model driven by ERA5 reanalysis data. Additionally, we have modified the vegetation cover factors via normalized difference vegetation index and considered the impacts of the land use and cover change. Subsequently, other factors were allocated utilizing geographic information system, and the grid-based fugitive soil dust emission inventory for the study area for 2019 was derived through calculation. Based on the climate factor and vegetation cover factor, we have come up with the monthly allocation coefficients. The study has revealed the following findings: (1) Climate factors are unevenly distributed throughout the focused region, with the Loess Plateau showing the highest value, followed by the Tibet Plateau and the Qinba Mountains. There are also significant variations in the distribution of these factors among municipalities and counties; (2) The order of vegetation cover factor, primarily influenced by regional background as well as agricultural and pastoral activities, in the Loess Plateau, Tibetan Plateau and Qinba Mountains, is consistent with that of the wind erosion index; (3) In 2019, fugitive dust emissions from total suspended particles, PM10, and PM2.5 reached 9835.9, 2950.8, and 491.8 kt/a, respectively. The Loess Plateau exhibited the highest emission intensity due to factors such as low vegetation coverage, precipitation, high wind speed and wind erosion index; (4) Climate factor and vegetation cover factor are the primary factors influencing the monthly allocation coefficients. In 2019, the highest monthly fugitive dust emissions were estimated in April, accounting for approximately 36.21% of the total. The second and third-highest were found in August and June, respectively. This phenomenon can be explained climatically, as the Loess Plateau, semi-arid and arid regions, did not experience a significant increase in rainfall corresponding to rising temperatures.
Worldwide, wind erosion takes center stage as a significant natural phenomenon, with wide ranging effects on air quality, human health, soil fertility, and the efficiency of agricultural processes. To develop an effective strategy for controlling wind erosion and soil degradation, it is necessary to identify the regions with the greatest soil erosion potential. In this regard, many wind erosion models are available that can be used to estimate the rate of soil erosion, allowing erosion control strategies to be assessed. A major factor in all wind erosion models is the inherent erodibility of soil. As it has been proven that the wind Erodible Fraction of soil (EF) is closely related to its erodibility, this parameter is of importance and used in many wind erosion models such as WEQ, RWEQ, EPIC, and APEX. To evaluate the influence of key soil characteristics, such as contents of sand, silt, clay, organic matter, and calcium carbonate on the EF, a dataset consisting of 293 records was compiled from published literature. The results indicate that soil texture has a more significant impact on the EF than the contents of organic matter and calcium carbonate. Moreover, it is noteworthy that within the existing body of the literature, a total of six proposed equations have been identified for the purpose of predicting the soil EF. Through an evaluation of the performance of the existing equations, it was determined that their effectiveness falls short of expectations. Therefore, an equation with acceptable accuracy is proposed in this study for predicting the EF of soil using a large dataset with a wide range of EF values.
Cultivated organic soils are known to be highly susceptible to wind erosion. The aim of this research was to explore the relationships between organic soil properties and measured soil height variation (SHV) in a cultivated organic soil and to analyze field soil erodibility (wind erodibility index: WEI) from known models. In the fall, 81 georeferenced soil samples were collected at a depth of 0-10 cm and analyzed in the laboratory to determine different soil properties. At the same time, soil height variations were measured over a 35-day period. Simple and multiple linear regressions were used to explore relationships between soil properties and analyze SHV and WEI. The average SHV across the farm was - 0.19 mm per day, equivalent to a loss of 0.665 cm during the measurement period. Simple linear regression showed weak relationships with SHV. Nevertheless, the analysis showed that soil loss areas had a larger fraction of macro-aggregates. Conversely, deposition areas with low bulk density (BD) contained more fine material (organic matter [OM] and erodible material [EM]), suggesting wind erosion effects, i.e., deposition of fine matter. Many soil properties were found to correlate significantly with one another. Applying multiple linear regression with SHV and WEI as dependent variables and soil properties as independent variables produced the following results: a) 24% of the SHV was significantly explained by 6 soil properties (α=5%,p=0.001), BD, EM, mean weight diameter (MWD), elevation (Z), geometric mean diameter (GMD), and volumetric water (VW); and b) 75% of the variation in WEI was explained by 5 variables (α=5%,p=0.001), BD, VW, OM, MWD and GMD. This result shows that BD, VW, OM, MWD, EM, GMD, elevation Z are key soil properties in sustainable management of organic soil with > 80% OM and will be used in a wind erosion risk model adapted to organic soils.
Keywords: organic soil, wind erosion, bulk density, soil properties, soil loss, soil erodibility.
Similar to those for water erosion, wind erosion models are designed to: (1) evaluate how different soil management practices affect wind erosion and (2) assist with the design and establishment of management practices for reducing wind erosion rates and soil erodibility. The models allow researchers to evaluate the performance of different management actions under various climatic scenarios, soil conditions, and regions (Pi and Sharratt 2017). Wind erosion models can estimate the on-site (e.g., soil fertility and productivity decline) as well as the off-site (e.g., dust pollution) implications of wind erosion.
The continuous changes in the use of soil for agricultural and livestock activities, as well the geographical location on sites affected by continuous wind currents, have caused changes in soil characteristics in a period of 40 years. To identify such changes, this study was performed in the north of the state of Zacatecas. The results of soil from 300 samples obtained in the laboratory analysis in 1976 were compared against an equal number of samples obtained in 2016. “Raster” images were generated by georeferencing the sampling sites and interpolating with “kriging”. The coinciding determinations between databases showed the differences between them by overlapping both images. The wind erosion was estimated and the levels of erosion associated with the sites where the changes occurred. The image analysis shows a matching area where sand content and soil pH increased. Such surface represents 401 907 ha of 2 118 000 ha covered by the images, where calcium has decreased in 98% of the surface, as well as potassium (59%), organic matter (58%), magnesium (49%) and sodium (35%) in 40 years, identifying areas of accumulation and soil loss associated with values of wind erosion. The prevailing SW winds that cause wind erosion were identified with values from 8 to 32 Mg ha-1 in 100 209 ha of accumulation and 233 587 ha of losses in 40 years. Of the total identified, 199 753 ha are agricultural areas and 202 154 are for livestock use. The identification of areas should help define plans and public policies for technological intervention.
The contents of potentially toxic elements (V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd, and Pb) and emission characteristics of PM2.5 in soil fugitive dust (SFD) in six Yunnan cities (Baoshan, Kunming, Wenshan, Honghe, Yuxi, and Zhaotong) were investigated in this research. The results showed that the contents of Zn and Pb in PM2.5 of SFD were the highest around Honghe and Yuxi, respectively, while the contents of Mn were the highest in PM2.5 of SFD around the other four cities. The enrichment factor and correlation indicated that the potentially toxic elements’ pollution degrees of PM2.5 of SFD around Kunming, Yuxi, and Honghe were higher than those around the other three cities and that potentially toxic elements were generally affected by metal smelting activities, and in Zhaotong, were affected by coal burning activities, while in Wenshan and Baoshan were less affected by human activities. The total emission of PM2.5 of SFD in the six cities was 7705.49 t in 2018. The total emission factor of PM2.5 of SFD reached the highest level from January to May and the lowest level from July to October. The health risk assessment showed that the potentially toxic elements in PM2.5 of SFD for children in the six cities and for adults in Baoshan, Kunming, Honghe, and Zhaotong had non-carcinogenic risk (non-carcinogenic risk thresholds were greater than 1), and As contributes most to non-carcinogenic risk. The carcinogenic risk value of Cr in PM2.5 of SFD in Kunming and Zhaotong was between 1 × 10⁻⁶ and 1 × 10⁻⁴, which had a certain carcinogenic risk. More attention should be paid to alleviate health risks posed by particle-bound potentially toxic elements through SFD.
Wind erosion is a serious environmental hazard, which causes land degradation and air pollution and adversely affects human health. Dust emission generated by wind erosion is the most prominent aerosol source that directly or indirectly influences the global radiation balance. The chapter presents the factors influencing wind erosion and describes the mechanics of soil particle movement in wind erosion. The Wind Erosion Equation (WEQ), the first empirical wind erosion model for estimating the annual soil loss, and its revised version, the Revised WEQ (RWEQ), are discussed. A few popular process-based wind erosion models are introduced. The basic principles adopted for controlling wind erosion are presented. The chapter also describes the benefits of windbreaks and shelterbelts, two popular mechanical measures of wind erosion control. The design of the windbreaks and shelterbelts is discussed in terms of their height, length, continuity, density, orientation, and number of rows and plant species.
Publisher Summary The chapter illustrates the movement and abrasion of soil by wind. Movement is initiated when the pressure of the wind against the surface soil grains overcomes the force of gravity on the grains. The grains are moved along the surface of the ground in a series of jumps known as saltation. The higher the grains jump, the more energy they derive from the wind. The concentration of saltating grains increases with distance downwind till, if the eroding field is large enough, it becomes the maximum that a wind of a particular velocity can sustain. The impacts of the saltating grains initiate movement of larger and denser grains and of smaller dust particles. The saltating grains collide against massive materials and other grains and cause disintegration of all involved. The solution of the problem is dependent on the overall progress made in research, testing, and extension.
Relative field erodibility based on many previous wind tunnel and field measurements is merely an index of the quantity of soil loss that would occur under certain climatic conditions. This paper, based on additional measurements, shows the relationship between the relative field erodibility and the quantity of soil loss and presents a table for converting the relative field erodibility, as determined from seven major factors, to soil loss in tons per acre per annum as would occur under climatic conditions such as existed in the vicinity of Garden City, Kansas, during the years 1954 through 1956.
Use Fig. 5 to determine C'. C' = 50% for vicinity of Pratt
- E Determine
Determine E 3 = I'K'C. Use Fig. 5 to determine C'. C' = 50% for vicinity of Pratt, Kansas. E 3 = 125 X 1 X.50 = 62.5 tons/acre per annum.
Cut out mov-able E a — I'K'C' scale. Place it along E 2 = I'K' ordinate so that 62.5 on movable scale coincides with 125 on ordi-nate. Move to right, down along curved 125 line to inter-section of L' = 2,150 feet, then move horizontally left to movable E 3 scale and read E 4 = I
- L Db
L' — D f — Db = 2,750 — 600 = 2,150 feet. d) Use Fig. 9 to obtain E 4 — I', K', C', f(L'). Cut out mov-able E a — I'K'C' scale. Place it along E 2 = I'K' ordinate so that 62.5 on movable scale coincides with 125 on ordi-nate. Move to right, down along curved 125 line to inter-section of L' = 2,150 feet, then move horizontally left to movable E 3 scale and read E 4 = I', K', C', f(L') = 60 tons/acre per annum.
- J L Doughty
Doughty, J. L., and staff. 1943. Report of Investigations, Soil
Research Laboratory, Can. Dep. of Agr., Swift Current, Sask.,
37-39.
Wind-tunnel studies of fundamental problems related to windbreaks
- N P Woodruff
- A W Zingg
Woodruff, N. P., and A. W. Zingg. 1952. Wind-tunnel studies
of fundamental problems related to windbreaks, USDA. SCS-TP-112.
In the Great Plains prevailing wind erosion direction
In the Great Plains prevailing wind erosion direction. J. Soil
Water Conserv. 19 : 67-70.
13. ---,
, and ---. 1964. Wind
erodibility of knolly and level terrains. J. Soil Water Conserv.
19: 179-181.