Vertical and Spatiotemporal Variations in Grain Size of Aeolian Sand Deposits at the Edge of the Kumtag Desert

Vertical and Spatiotemporal Variations in Grain Size of Aeolian Sand Deposits at the Edge of the Kumtag Desert | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Vertical and Spatiotemporal Variations in Grain Size of Aeolian Sand Deposits at the Edge of the Kumtag Desert Hao Wang, Yu Wang, Ying Zhang, Kaiqing Liu, Tian-feng Luo, Miao Tian, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8396723/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We characterize height-resolved grain-size stratification in captured (trapped) aeolian sand across four geomorphic units at the edge of the Kumtag Desert, and assess the roles of wind forcing, vegetation roughness, and topographic effects. From 2023 to 2025, seven five-layer QN-JSY collectors were deployed at a hill-crest semi-fixed sand area (MT1), the vegetated left bank of the Danghe Reservoir (LB1–LB4), the Mingsha Mountain desert edge (MS1), and the bare right-bank floodplain (RB1), yielding n = 420 samples. Grain-size distributions were measured (Mastersizer 2000) and analyzed using D50, layer-wise fractions, heatmap profiles, and D50–height fits with nonparametric between-unit tests. Grain size generally fines with height, with unit-specific signatures: MS1 shows quasi-monotonic fining (ΔD50 ≈ 121 µm; R 2 = 0.91), the left bank exhibits a vegetation-linked bimodal structure (R 2 = 0.78), RB1 shows mid-layer coarsening with weak linearity (R 2 < 0.35), and MT1 presents a mild upper-layer coarsening shoulder. Fine fractions ( 250 µm) concentrate near H10, highlighting a near-surface control layer relevant to sand-control design. These results support a size-dependent convection–diffusion perspective for interpreting vertical sorting across contrasting desert–oasis settings. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Solid earth sciences Aeolian sand Desert edge Vertical distribution Sediment grain size Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1 Introduction Aeolian sand activity, as one of the key driving forces behind ecological and environmental evolution in China's arid and semi-arid regions, exerts profound impacts on regional ecological security, soil erosion, and land desertification [ 1 ]. The process of Aeolian sand transport not only affects the stability and sustainability of regional ecosystems but also influences ecological and water resource security by altering surface sedimentary characteristics [ 2 ]. The grain size distribution and its vertical variation in Aeolian sand deposits directly reflect changes in aeolian dynamics and depositional environments [ 3 , 4 , 5 ]. Variations in grain size can reveal the dynamic mechanisms of sediment transport and deposition, as well as highlight the ecological and environmental differences among micro-geomorphic units within wind-eroded areas [ 6 ]. With the increasing frequency of Aeolian sand activities, the vertical variation of sand grain size has gradually become a critical scientific issue in the study of Aeolian transport mechanisms and depositional processes [ 7 , 8 ]. In recent years, the vertical variation of Aeolian sand grain size has attracted widespread academic attention, not only for its relevance to Aeolian transport dynamics but also for its value in providing a scientific basis for understanding ecological changes and predicting sandstorm disasters [ 9 , 10 ]. Huo et al. analyzed Aeolian sand samples collected at different heights from an 80-meter-high flux tower in the hinterland of the Taklamakan Desert, finding that the vertical grain size distribution of sand particles varied significantly with height near the surface but stabilized above 32 meters. This pattern was primarily controlled by wind speed, with distinct vertical flux differences among particle size classes, particularly dominated by fine particles smaller than PM100 [ 11 ]. Research by Pang et al. in the eastern Kumtag Desert further emphasized that Aeolian sand activity significantly affects regional ecological geochemical cycles, with notable regional differences in horizontal sand transport volumes and grain size compositions, reflecting variations in regional aeolian dynamics [ 12 , 13 ]. Liu et al. investigated the relationship between sand particle size and vegetation cover in the desert–oasis transition zone at the southeastern margin of the Tengger Desert, finding that increased vegetation cover in wind-eroded environments significantly enhanced the proportion of fine sand particles in deposits, and that both the spatial and vertical distribution of grain size were closely related to vegetation and topographic conditions [ 14 ]. In addition, Dong et al. employed end-member modeling analysis (EMMA) to explore the relationship between grain size components of Aeolian sand deposits and aeolian dynamics in the Tengger Desert, demonstrating that different grain size fractions were significantly correlated with the intensity of the East Asian winter monsoon and the frequency of dust storms, providing robust scientific evidence for reconstructing paleo-wind field variations [ 15 ]. However, most existing studies have focused on typical desert interiors or desert–oasis transition zones, and systematic investigations on the vertical grain size distribution and controlling mechanisms of Aeolian sand deposits under complex geomorphic conditions at the edge of the Kumtag Desert remain limited. Aeolian vs. fluvial sediments and how they are studied. Although both Aeolian and fluvial systems sort grains by a moving fluid, they differ in governing physics and in the tools typically used to study them. In air, low density and viscosity favor saltation with impact–splash feedbacks, so coarse grains concentrate near the surface while fines are preferentially lofted; field measurements often show exponential decay of flux with height and a systematic fining upward that is strongly modulated by surface roughness and vegetation traps [ 16 ]. In water, the vertical mixture of bedload and suspended load is commonly framed by Rouse-type theory, where the Rouse number diagnoses whether transport is bed- or suspension-dominated and sets the shape of the vertical concentration profile; recent work also leverages dimensional analysis and machine learning to classify river transport regimes [ 17 ]. Methodological implications for this study. Because of these contrasts, Aeolian investigations typically rely on stratified sand traps, multi-height flux towers, wind-tunnel/optical measurements, and characterization of roughness/vegetation effects; by contrast, fluvial work emphasizes isokinetic or acoustics-based sampling and Rouse-profile fitting to suspended sediment. At the same time, granular-physics advances now unify threshold and transport-rate scaling across air and water, which motivates our use of a simple convection–diffusion view to interpret the vertical stratification we observe at the Kumtag Desert edge [ 4 , 5 ]. As a representative arid and wind-eroded region in China, the Kumtag Desert experiences high-intensity and frequent Aeolian sand activities, resulting in significant sand deposition around the Danghe Reservoir [ 10 ]. The vertical grain size distributions vary distinctly among different geomorphic units (e.g., sand dunes, mountain tops, left and right banks) surrounding the reservoir, directly reflecting the complexity of Aeolian deposition and the diversity of wind erosion and transport mechanisms [ 18 ]. Nevertheless, systematic research on the spatiotemporal vertical variation of Aeolian sand grain size at the edge of the Kumtag Desert is still lacking, and the detailed mechanisms and dynamic processes governing vertical grain size changes remain insufficiently understood. Clarifying these scientific issues is crucial for understanding Aeolian depositional processes, predicting regional Aeolian disasters, and formulating effective desertification control measures. This study focuses on the Danghe Reservoir region at the edge of the Kumtag Desert. Vertical stratified Aeolian sand deposits were systematically collected from typical geomorphic units such as sand dunes, mountain tops, and meteorological stations on the left and right banks by deploying sand collectors at different heights. Grain size measurements were conducted using a Mastersizer 2000, and we analyzed D50 and cumulative-distribution–based vertical patterns across heights. The aim of this study is to reveal the vertical variation characteristics of Aeolian sand grain size across different geomorphic units at the edge of the Kumtag Desert, analyze the environmental and dynamic mechanisms behind these variations, and provide theoretical and scientific support for regional Aeolian disaster prevention and ecological management. 2 Study Area and Methods 2.1 Study Area This study was conducted in Dunhuang City, Gansu Province, specifically focusing on the Danghe Reservoir and the adjacent Mingsha Mountain region, both located along the northern margin of the Kumtag Desert. The area is characterized by a typical arid desert environment, featuring intense Aeolian sand activity and pronounced wind erosion, making it an ideal site for investigating the vertical and spatiotemporal variations in the grain size of Aeolian sand deposits at the edge of the Kumtag Desert. The Danghe Reservoir is located in the southeastern part of Dunhuang City, situated within the transition zone between desert and oasis. The region is ecologically fragile and experiences significant Aeolian sand activity [ 10 ]. As the main water regulation facility in the area, the Danghe Reservoir plays a critical role in agricultural irrigation, ecological replenishment, and local climate regulation [ 13 ]. However, in recent years, influenced by both climate change and anthropogenic activities (such as agricultural reclamation and intensified irrigation), groundwater levels around the reservoir have declined markedly, leading to severe soil salinization, widespread vegetation degradation, increased frequency and intensity of Aeolian sand activity, and notable changes in sedimentary characteristics [ 19 ]. The intensification of Aeolian sand activity has led to declining land productivity around the reservoir, gradually threatening water resource security and ecological stability, thus forming a typical ecological negative feedback loop in the desert–oasis transition zone characterized by "wind erosion—land degradation—water resource deterioration" [ 20 ]. The typical Aeolian sand deposition processes and complex environmental background of this area provide a unique platform for studying the vertical variations in grain size of Aeolian sand deposits. The Mingsha Mountain region, located adjacent to the southern margin of the Kumtag Desert, represents a typical desert-edge wind erosion zone. This area features broad, open terrain, harsh wind erosion conditions, extremely low vegetation cover, and widespread distribution of mobile and semi-mobile sand dunes. Aeolian sand transport and deposition processes are highly active, exhibiting pronounced seasonal variations [ 21 ]. Particularly during winter and spring, strong and frequent southwesterly winds drive the transport of large quantities of fine sand particles toward the surrounding oasis areas of Mingsha Mountain, resulting in significant changes in vertical sedimentary structures and directly impacting the local ecological environment, tourism resources, and infrastructure [ 22 ]. In this study, the Danghe Reservoir and Mingsha Mountain regions were selected as the study sites, encompassing two typical types of wind erosion zones: the desert–oasis transition zone and the desert-edge area. This selection facilitates a comprehensive investigation into the spatial differences and temporal variations of vertical grain size distribution in Aeolian sand deposits at the edge of the Kumtag Desert. The research findings are expected to provide important scientific support for regional ecological conservation, land management, and Aeolian sand control efforts, as well as offer theoretical guidance and practical reference for desertification control and ecological restoration in the Kumtag Desert and other similar arid regions in northwestern China. 2.2 Sampling Design and Instruments 2.2.1 Site Selection To investigate the vertical and spatiotemporal variations in the grain size of Aeolian sand deposits at the edge of the Kumtag Desert, representative sampling sites were systematically selected based on regional Aeolian sand activity intensity, geomorphic characteristics, vegetation cover conditions, and the degree of anthropogenic disturbance. The Danghe Reservoir region (desert–oasis transition zone) and the Mingsha Mountain region (desert-edge zone) were identified as typical sampling areas. Long-term vertical monitoring of Aeolian sand grain size was conducted at representative points within each region. 1) Danghe Reservoir Region (Desert–Oasis Transition Zone) The ecological environment surrounding the Danghe Reservoir is fragile, characterized by significant wind erosion and substantial anthropogenic impacts on Aeolian sand deposition characteristics. Typical sampling sites were selected on both the left and right banks of the reservoir. On the left bank, sites were chosen along a gradient of vegetation cover at the following coordinates: 39.936918°N, 94.314580°E; 39.939712°N, 94.320450°E; 39.933990°N, 94.319818°E; and 39.931650°N, 94.323227°E, aiming to analyze the influence of vegetation coverage gradients on vertical sediment grain size distribution.For consistency, these four left-bank points are coded LB1–LB4 in west-to-east order.Vegetation-cover gradient. Along the left bank, vegetation cover increases monotonically from LB1 to LB4, forming an intentional roughness gradient used to test how vegetation modulates vertical sorting across H10–H70. On the right bank, a site located at 39.947120°N, 94.337598°E was selected in an open area with low vegetation cover and prominent Aeolian sand deposition, to reflect the natural patterns of vertical grain size variation in Aeolian sand deposits.This right-bank sampling site is coded RB1. 2) Mingsha Mountain Region (Desert-Edge Zone) The Mingsha Mountain region is adjacent to the southern margin of the Kumtag Desert. This area is characterized by open terrain, widespread distribution of mobile fine sand and semi-mobile dunes, and intense Aeolian sand transport with distinct seasonal variations. A representative sampling site was selected at 40.031892°N, 94.527115°E. This site features typical geomorphic conditions, active Aeolian sand deposition, and the presence of various sand control structures (e.g., straw checkerboards, stone checkerboards, and wooden sand fences), providing an excellent research setting for analyzing the effects of protective measures on the vertical grain size distribution of Aeolian sand deposits in desert-edge environments.This desert-edge sampling site is coded MS1. 3) Mountain Top Region (Semi-Fixed Sandy Land–Alluvial Slope Transition Zone) To further compare the vertical grain size differences across different geomorphic units, an additional long-slope sampling site was established on a mountain terrace approximately 4 km southwest of the Danghe Reservoir (39.928476°N, 94.306842°E). This area lies within the transition zone between dunes and a gravelly alluvial fan. The surface is shaped by both micro-topographic elevation and slope wind acceleration, retaining mobile sand sources while exhibiting a semi-fixed sandy land pattern with patchy vegetation cover. The mountain top site faces southwest, aligned with the prevailing wind direction, with an elevation difference of about 18 meters from the slope foot to the crest. This site is representative of the vertical Aeolian sand transport characteristics under alternating wind erosion and deposition processes along slopes. By comparing data from the mountain top with those from the vegetated gradient on the left bank, the open bare ground on the right bank, and the desert-edge site at Mingsha Mountain, this study systematically explores the coupled effects of terrain elevation, vegetation trapping, and wind speed enhancement on vertical grain size sorting. The findings provide crucial comparative evidence for the precise construction of multi-geomorphic coordinated sand control strategies.This hill-crest terrace sampling site is coded MT1. In summary, this study comprehensively covers the desert–oasis transition zone, the desert-edge zone, and the semi-fixed sandy land–alluvial slope transition unit by scientifically deploying four types of typical sampling sites: the left bank, right bank, Mingsha Mountain, and mountain top regions. Standardized sampling equipment and protocols were employed to systematically reveal the vertical spatiotemporal variations in the grain size of Aeolian sand deposits at the edge of the Kumtag Desert. The results provide a reliable empirical basis and methodological support for precise regional ecological zoning, hierarchical optimization of Aeolian sand control systems, and the parameterized design of sand control engineering. 2.2.2 Sampling Layout and Deployment 1) Meteorological Monitoring To continuously capture the wind field characteristics across multiple geomorphic units at the edge of the Kumtag Desert, a NANYI-AWS-Pro automatic weather station was deployed at each of four typical locations: the left bank (39.9369°N, 94.3146°E), the right bank (39.9355°N, 94.3241°E), the Mingsha Mountain dune area (40.1162°N, 94.6864°E), and the mountain terrace (39.928476°N, 94.306842°E). Each station was equipped with a cup anemometer (measuring range 0–60 m·s⁻¹, resolution 0.1 m·s⁻¹) and a wind vane (accuracy ± 2°), along with integrated temperature sensors (range − 40 to 60°C, accuracy ± 0.2°C) and relative humidity sensors (range 0–100%, accuracy ± 2%). Observational data were recorded at a frequency of 10 Hz, stored locally on SD cards, and simultaneously transmitted to the cloud via a 5G network, ensuring high-frequency, redundant data backups to maintain data integrity and traceability.The wind sensors (cup anemometer and vane) were mounted at 2.0 m above ground level (a.g.l.) at all sites and leveled monthly. For figures and statistics, 10 Hz records were averaged to 1-min and then to hourly time series before deriving wind roses and summary metrics.These AWS locations are co-located with the sampling units and codes used below (UNIT ∈ {LB, RB, MS, MT}; sites LB1–LB4, RB1, MS1, MT1). 2) Vertical Stratified Sand Collectors Across the four AWS sites, seven QN-JSY vertical stratified sand collectors were deployed: four on the left bank (LB1-LB4), one on the right bank (RB1), one in the Mingsha Mountain desert-edge zone (MS1), and one on the mountain-top terrace (MT1). All collectors were placed 10 m downwind of their respective AWS along the prevailing south-westerly wind direction.Each collector has a total mast height of 1.30 m, with the base embedded 0.30 m below the ground so that the five intake layers span 0–1.00 m above ground. The front panel has five rows × two columns of 50 mm × 50 mm inlets per layer; the two inlets of a layer feed a single bag. Center heights of the five layers are 0.10, 0.30, 0.50, 0.70, and 0.90 m (coded H10, H30, H50, H70, H90), corresponding to 0–0.2, 0.2–0.4, 0.4–0.6, 0.6–0.8, and 0.8–1.0 m. All heights are referenced to the local leveled ground at installation (tolerance ± 0.02 m), and all inlets face the prevailing south-westerly wind.The outer casing was constructed from 1.0 mm thick SUS-304 stainless steel, and each single layer could capture a maximum sand volume of no less than 1 kg. The base of the collector was embedded 0.30 m underground and anchored with concrete to minimize vibration errors under high wind conditions. Sampling bags were replaced daily at 08:00, and comparisons between daily samples and monthly integrated samples were conducted at the end of each month to verify sampling integrity.Sample nomenclature. All samples are labeled using the code \[UNIT]-\[SITE]-\[HEIGHT]-\[PERIOD], where UNIT ∈ {LB = left-bank vegetated belt, RB = right-bank bare floodplain, MS = Mingsha-Mountain desert edge, MT = hill-crest}, SITE ∈ {LB1–LB4, RB1, MS1, MT1}, HEIGHT ∈ {H10, H30, H50, H70} corresponding to 0–0.2/0.2–0.4/0.4–0.6/0.6–0.8 m, and PERIOD uses MYYYYMM for monthly or DYYYYMMDD for daily samples. 3) Sample Processing All sediment samples were placed into anti-static self-sealing bags, with field labels indicating the collection time, coordinates, and geomorphic unit. Samples were transported to the laboratory within 24 hours. In the laboratory, the samples were first air-dried naturally and then treated using a three-step process involving hydrochloric acid, hydrogen peroxide, and sodium hexametaphosphate to remove carbonates, oxidized organic matter, and to disperse particles, respectively. Subsequently, particle size distributions were measured using a Mastersizer 2000 laser particle size analyzer (measuring range 0.02–2000 µm). The precise coordinates of the sampling points and instrument locations were recorded using a handheld GPS device (accuracy ± 3 m), providing reliable spatial positioning data for subsequent GIS analyses. All field labels and laboratory spreadsheets used the above code to ensure traceability across sites, heights, and periods. This high-frequency wind field observation and stratified sediment collection scheme achieved synchronous monitoring of wind field and depositional processes across both temporal and spatial scales, systematically revealing the seasonal-scale coupling mechanisms between Aeolian sand transport and deposition in multiple geomorphic units. The results provide a solid data foundation for optimizing windbreak and sand stabilization engineering designs.The vertical stratified sand collector measures height-resolved trapped mass flux (captured transport) rather than areal deposition on a surface. 2.2.3 Sample nomenclature and coding To standardize sample names across sites, heights, and dates, we adopt the code [UNIT]-[SITE]-[HEIGHT]-[PERIOD], where: (1) UNIT ∈ {LB = left bank vegetated belt, RB = right-bank bare floodplain, MS = Mingsha Mountain desert edge, MT = mountain-top hill-crest}. (2) SITE: numeric identifier at each unit (e.g., LB1–LB4; RB1; MS1; MT1). (3) HEIGHT: center height of the layer in centimeters, H10/H30/H50/H70/H90, corresponding to the five strata 0–0.2 / 0.2–0.4 / 0.4–0.6 / 0.6–0.8 / 0.8–1.0 m.Unless otherwise noted, analyses use the full five-layer scheme (H10–H90). In Fig. 3.1 the top layer (H90) is omitted for readability; Figs. 3.2 –3.3 and 4.1–4.2 show the full H10–H90 set. (4) PERIOD: MYYYYMM for monthly-integrated traps (e.g., M202404), or DYYYYMMDD for daily bags when referenced. For clarity across text and figures, layer codes always refer to the inlet center heights (H10 = 0.10 m, …, H90 = 0.90 m), and figures explicitly indicate when H90 data are included. Examples. LB2-H10-M202404 denotes the left-bank site 2, 0–0.2 m layer (center 0.1 m), monthly-integrated sample in April 2024; MS1-H70-D20250315 denotes the Mingsha site, 0.6–0.8 m layer, daily sample on 15 Mar 2025. 2.3 Data Analysis Methods Terminology note. The five-layer QN-JSY collector samples height-resolved airborne (seolian) sand transport. It records captured mass at each height during the sampling interval and does not measure ground deposition rate. Accordingly, we analyze vertical transport (via captured mass and grain-size spectra) and infer depositional tendencies indirectly from size–height trends within a simple convection–diffusion framework. This study restricts quantitative analyses to grain-size–height diagnostics: D10/D50/D90, cumulative distribution functions (CDFs), ordinary-least-squares height–D50 fits, and nonparametric tests. Classical aeolian transport and settling relations (e.g., Bagnold-type transport, Hsu-type deposition, Stokes-type settling) are used as conceptual background for interpretation only; we do not estimate transport force (T), deposition rate (D), or settling velocity ( v s ) because the co-located turbulence/roughness parameters required for robust inversion are beyond the present field design. (1) Analysis of Aeolian Sand Dynamics Aeolian Sand Transport Models: To quantify the influence of wind speed and wind direction on Aeolian sand grain transport, fluid dynamic models and differential equations of Aeolian sand transport were adopted [ 23 ]. These relations guide interpretation of the observed upward fining and unit-specific contrasts but are not numerically inverted in this study. Bagnold Model [ 24 ]: Where T is the sand transport force (N·m − 2 ), ρ w is the air density (kg·m − 3 ), u is the wind speed (m·s − 1 ), d is the sand particle diameter (m), and d 0 is the threshold sand particle diameter (m). Hsu Model [ 25 ]: Where D is the deposition rate (kg·m − 2 ·s − 1 ), K is a constant (typically 0.5), g is the gravitational acceleration (9.81 m·s − 2 ), ρ p is the sand particle density (kg·m − 3 ), and v is the wind speed (m·s − 1 ). (2) Study of Sedimentation Patterns Vertical distribution data collected by the sand collectors were subjected to stratified statistical analysis to investigate the Aeolian sand sedimentation patterns at different height layers. During Aeolian sand transport processes, larger particles typically tend to settle preferentially in the lower layers near the ground surface, while smaller particles are more susceptible to turbulence and airflow influences, allowing them to be suspended to higher layers and transported over longer distances [ 16 ]. This phenomenon is closely related to shear forces within the airflow, turbulence intensity, and particle settling velocities [ 26 ]. To analyze the sedimentation characteristics at different height layers, this study calculated the sediment accumulation at each layer and combined these data with corresponding wind speed and wind direction measurements. This approach revealed the spatial heterogeneity of Aeolian sand deposition in the vertical dimension. Through these analyses, the vertical distribution patterns of Aeolian sand deposition and the variation trends across different heights were effectively elucidated, providing theoretical support for the development of sand control measures and the optimization of protective structures. Stokes’ law for particle settling was employed to study the sedimentation patterns of Aeolian sand [ 27 ]: Where v s is the particle settling velocity (m·s − 1 ), ρ p is the particle density ((kg·m − 3 ), ρ a is the air density ((kg·m − 3 ), d is the particle diameter (m), and µ is the dynamic viscosity of air (kg/m·s). (3) Grain Size–Height Coupling Model To further elucidate the migration and settling mechanisms of Aeolian sand particles with different grain sizes along the vertical profile, the vertically stratified sediment accumulation was converted into unit volume concentrations C(z,t). Based on the concept of "convection–diffusion–source-sink" processes, a grain size–height coupling model was established [ 28 ]. The model assumptions are as follows: The Aeolian sand flow is in a quasi-steady turbulent field; Sand particles in the vertical direction are controlled only by gravitational settling, turbulent diffusion, and localized resuspension; Horizontal transport is approximately uniform within the local volume. Considering that the Stokes settling velocity w s varies quadratically with particle diameter, and that the turbulent diffusion coefficient K z primarily reflects vertical momentum exchange induced by surface shear, the governing model can be simplified into a one-dimensional convection–diffusion control equation [ 29 ]: Where C(z,t) is the particle concentration at height z and time t (kg·m − 3 ), w s is the particle settling velocity (m·s − 1 ), K z is the vertical turbulent diffusion coefficient (m 2 ·s − 1 ), and S(z,t) is the source or sink term representing particle resuspension or removal (kg·m − 3 ·s − 1 ). We adopt a one-dimensional size-dependent convection–diffusion perspective to interpret vertical stratification, i.e., higher settling tendency for coarse fractions and enhanced turbulent mixing for fines, without estimating w s or K z . (4) Cumulative grain-size functions and diagnostics For each sample we computed the cumulative distribution function (CDF) of grain size, i.e., the percent finer than diameter d on a logarithmic ( φ ) scale. From each CDF we extracted standard percentiles D10, D50, D90(µm). Beyond these summary metrics, the shape of the CDF was used diagnostically: Vertical shift of the entire curve to the right (toward finer sizes) with height indicates upward fining; a leftward shift indicates coarsening. Steeper mid-quantile slope (large dF/dϕ near D50) reflects better sorting (narrower distributions), whereas a gentler slope reflects poorer sorting. Tail behavior: a long coarse tail (delayed rise at low φ / large d) signals enrichment of coarse fractions; a long fine tail indicates fine enrichment. Cross-overs among CDFs from different heights diagnose mixed or bimodal populations along the profile. Note In this study we did not compute Folk–Ward sorting, skewness or kurtosis; diagnostics are based on D50 and CDF behavior. (5) Uncertainty quantification and rationale for not using Monte Carlo Instead of generating synthetic grain-size distributions via Monte Carlo, which would require unverifiable assumptions about the effective sample size and independence of monthly integrated trap spectra, we quantify uncertainty using a nonparametric bootstrap at the site-month level. For each geomorphic unit and height, we resample months with replacement (1,000 iterations) and recompute height–D50 slopes, percentile ratios(D90/D50, D50/D10), and pairwise KS distances; 2.5–97.5% quantiles define 95% confidence intervals. This design propagates temporal variability captured by the sampling without imposing parametric distributional forms. Because stochastic resampling cannot reproduce unobserved wind–roughness states, process-level replication is beyond the scope of the present field study. Subsequently, the inversion results were analyzed in combination with wind speed, wind direction, and grain size parameters at different height layers, allowing for quantification of the effects of wind field variations on particle size sorting and vertical transport efficiency. These findings provide dynamic parameter constraints essential for the scaled design of windbreak and sand stabilization measures. 3 Results Based on the measured vertical grain size data from four typical geomorphic units at the edge of the Kumtag Desert—namely, the semi-fixed sandy land at mountain tops, the vegetated belt on the left bank of the Danghe Reservoir, the desert-edge area of Mingsha Mountain, and the exposed floodplain on the right bank of the Danghe Reservoir—a systematic analysis was conducted to characterize the grain size distribution patterns of Aeolian sand deposits at different height layers and their vertical variation features. By comparing the differences in grain size sorting and vertical gradients among these units, the study clearly delineated the grain size differentiation patterns of Aeolian sand deposition under different geomorphic conditions, thereby providing scientific support for further investigations into Aeolian sand transport mechanisms and regional ecological management. 3.1 Vertical Grain-Size Variation by Geomorphic Unit Analysis of the vertical grain size distribution characteristics of Aeolian sand deposits in the four typical geomorphic units at the edge of the Kumtag Desert revealed significant differences in grain size composition and vertical variation patterns among regions, reflecting the combined effects of geomorphology, vegetation, and aeolian dynamics (Fig. 3.1 ).Across the seven collectors (LB1–LB4, RB1, MS1, MT1), the monthly mass of aeolian sand captured per H10 (0–0.2 m) layer ranged from 12 g to 146 g (mean ± SD = 58 ± 27 g), with the left-bank LB2 showing the seasonal maximum in April (146 g). In the mountain top region (Figure a; MT1), the grain size composition at all height layers was dominated by medium-sized particles (63–250 µm), maintaining a proportion above 70%. Vertically, the content of coarse particles (> 250 µm) remained below 25% across all layers but exhibited a clear decreasing trend with height, declining from 22.5% at the near-surface layer H10(0–0.2 m) to less than 10% at the top layer H70(0.6–0.8 m). In contrast, the proportion of fine particles (< 63 µm) steadily increased with height, rising from less than 10% at the bottom to nearly 20% at the top, reflecting the resuspension and upward transport of fine particles characteristic of the sparsely vegetated semi-fixed sandy environment. In the vegetated belt region on the left bank of the Danghe Reservoir (Figure b; LB1–LB4), the vertical differentiation of grain size was more pronounced, displaying a significant vegetation interception effect. The bottom layer H10(0–0.2 m) had the highest proportion of coarse particles, approximately 40%, markedly higher than that observed at the mountain top for the corresponding layer, indicating that vegetation significantly enhanced the interception of coarse particles near the ground. With increasing height, the proportion of coarse particles rapidly decreased, dropping to about 5% at the H70 (0.6–0.8 m) layer. Meanwhile, medium-sized particles gradually became dominant, increasing from 55% at the bottom to around 65% at the top. Fine particles showed a rapid increase from less than 5% at the bottom to about 30% at the top, highlighting the significant promoting effect of vegetation cover on the resuspension and vertical transport of fine particles. In the desert-edge area of Mingsha Mountain (Figure c; MS1), typical vertical variation characteristics of mobile sand dunes at the desert margin were observed. Coarse particles accounted for more than 55% at the near-surface height H10(0–0.2 m), significantly higher than in other regions at the same height, reflecting the rapid sedimentation and accumulation of coarse particles under mobile dune conditions. As height increased, the coarse particle proportion sharply decreased, falling below 20% at H50(0.4–0.6 m). Meanwhile, the proportion of medium-sized particles increased from about 35% near the surface and became dominant in the middle to upper layers (reaching up to 60%). Fine particles rapidly increased to about 30% at the top layer H70(0.6–0.8 m), clearly indicating the upward suspension and transport of fine particles under strong wind conditions. In the exposed floodplain region on the right bank of the Danghe Reservoir (Figure d; RB1), the vertical grain size structure was the most distinct. Coarse particles overwhelmingly dominated the bottom layer H10(0–0.2 m), accounting for more than 65%, the highest among the four regions. As height increased, the proportion of coarse particles decreased rapidly, dropping to nearly zero at the top layer H70 (0.6–0.8 m). Conversely, the proportion of fine particles showed a pronounced increasing trend, rising from less than 10% near the ground to more than 60% at the 0.6–0.8 m height, indicating an efficient vertical transport of fine particles under strong wind conditions in the open bare land environment, with extremely significant vertical grain size sorting effects. These analyses reveal that the vertical grain size differentiation in the mountain top region is jointly regulated by topographic uplift and slope wind effects; in the left bank vegetated belt, vegetation roughness effects are the primary influence; in the Mingsha Mountain desert-edge region, the results reflect the dynamic coupling of strong winds and coarse particle sedimentation in mobile dunes; and in the open bare land of the right bank, a clear vertical sorting pattern of coarse and fine particles under strong winds is observed. These findings provide a deeper understanding of the typical vertical variation patterns of Aeolian sand grain size across different geomorphic units at the edge of the Kumtag Desert and offer scientific support for the optimization of regional ecological management measures. 3.2 Vertical Sorting Characteristics To further elucidate the vertical grain size sorting characteristics of Aeolian sand deposits in different geomorphic units at the edge of the Kumtag Desert, boxplot analyses of grain size data at various height layers were conducted for the mountain top, left bank vegetated belt, Mingsha Mountain desert-edge area, and right bank exposed floodplain regions (Fig. 3.2 ). The box-plots in Fig. 3.2 depict layer-wise variations in grain size (µm).A Kruskal–Wallis test followed by Dunn’s post-hoc pairwise comparison (α = 0.05) indicates that only the surface layer H10 (0–0.2 m) differs significantly between the left- and right-bank sites (p = 0.018), while differences among deeper layers H30–H90 are not significant (p > 0.10). In the mountain top region (Fig. 3.2 a; MT1), the dispersion of grain size data across different height layers was relatively small, and the median grain size gradually decreased with increasing height. The bottom layer H10 (0–0.2 m) exhibited the highest median grain size and the widest grain size distribution range, indicating a dominance of coarse particles. As height increased, the concentration of grain size data became more pronounced, the box size gradually narrowed, and the gap between the upper and lower quartiles decreased, demonstrating a clear vertical grain size sorting effect and reflecting the slow upward fining trend of Aeolian sand under sparse vegetation conditions in the semi-fixed sandy land. By the top layer H90 (0.8–1.0 m), the median grain size was distinctly finer. In the left bank vegetated belt region of the Danghe Reservoir (Fig. 3.2 b; LB1–LB4), the vertical sorting characteristics were more distinct. At the bottom layer H10 (0–0.2 m), the median grain size was relatively large, and the interquartile range was wide, indicating a significant presence of coarse particles and a broad range of grain size fluctuations. With increasing height, the median grain size decreased markedly, the box height gradually shrank, and a clear shift toward finer particles was observed. By the top layer H90 (0.8–1.0 m), grain size data were concentrated within a distinctly finer range, and the distribution became significantly more centralized, demonstrating that vegetation cover strongly promotes vertical grain size sorting. In the Mingsha Mountain desert-edge region (Fig. 3.2 c; MS1), the vertical sorting of grain size exhibited a pronounced gradient. The bottom layer H10 (0–0.2 m) had the largest median grain size and the widest distribution among all regions, reflecting the rapid near-surface deposition of coarse particles under the influence of strong winds at the desert margin. As height increased, the median grain size decreased rapidly, the distribution range narrowed significantly, and concentration improved. At middle to upper height layers H50–H90 (0.4–1.0 m), grain size data showed strong concentration trends with markedly smaller box sizes, indicating a clear upward fining trend, further confirming that vertical grain size sorting was strongest in the Mingsha Mountain region under strong wind conditions. In the right bank exposed floodplain region of the Danghe Reservoir (Fig. 3.2 d; RB1), the vertical grain size sorting was the most pronounced and the changes were the most dramatic. Near the ground surface H10 (0–0.2 m), both the median grain size and the distribution range reached their maximum values, with the box height being wide and extending significantly toward the coarse particle end, indicating a strong dominance of coarse particles. As height increased, the median grain size decreased rapidly, and the interquartile range also narrowed significantly. By the top layer H90 (0.8–1.0 m), the grain sizes were concentrated within an extremely fine range, reflecting the strong influence of aerodynamic forces on grain size sorting in the open bare floodplain environment, with the most distinct vertical grain size gradient observed. In summary, the mountain top region exhibited a slow vertical fining process under conditions of sparse vegetation and micro-topography; the left bank vegetated belt significantly enhanced vertical grain size sorting and increased data concentration; the Mingsha Mountain desert-edge region displayed a typical pattern of rapid coarse particle deposition and fine particle suspension under strong wind conditions; and the right bank exposed floodplain region, characterized by open terrain and lack of vegetation, showed the most pronounced vertical grain size sorting effect. These sorting characteristics further confirm that aeolian dynamics, vegetation cover, and topographic conditions exert significant influences on the vertical distribution of Aeolian sand grain size across different geomorphic units. 3.3 Vertical Percentage Composition To further clarify the vertical distribution structures of Aeolian sand grain size components at different height layers within each geomorphic unit, this study classified particle sizes based on coarseness and fineness, and conducted a detailed analysis using percentage-stacked bar charts for the mountain top, left bank vegetated belt, Mingsha Mountain desert-edge area, and right bank exposed floodplain regions (Fig. 3.3 ).Height codes used in Fig. 3.3 are H10 = 0–0.2 m, H30 = 0.2–0.4 m, H50 = 0.4–0.6 m, H70 = 0.6–0.8 m, and H90 = 0.8–1.0 m. In the mountain-top region (Fig. 3.3 a; MT1), the grain-size composition at all height layers was dominated by medium-sized particles (63–250 µm), accounting for more than half (approximately 70–80%) across all layers, with relatively minor vertical variation. The proportion of coarse particles (> 250 µm) was low, gradually decreasing from about 20% at the bottom layer H10 (0–0.2 m) to approximately 8% at the top layer H70 (0.6–0.8 m). Conversely, the proportion of fine particles (< 63 µm) steadily increased from about 10% at the bottom to around 20% at the top. This stable grain-size structure reflects a moderate vertical sorting process under the semi-fixed sandy-land conditions at the mountain top. In the left-bank vegetated belt (Fig. 3.3 b; LB1–LB4), the vertical percentage-stacked structure of grain-size composition was more distinct, exhibiting a pronounced vertical gradient effect. At the near-surface layer H10 (0–0.2 m), the proportion of coarse particles was the highest, close to 40%, indicating effective interception of coarse particles by vegetation. With increasing height, the proportion of coarse particles sharply decreased, dropping to only about 5% at the top layer H70 (0.6–0.8 m). Meanwhile, medium-sized particles progressively became dominant, increasing from approximately 50% at the bottom to over 65% at mid to upper layers. Fine particles also increased significantly with height, reaching around 30% at the top, reflecting the strong promoting effect of vegetation cover on the resuspension and upward transport of fine particles. In the Mingsha-Mountain desert-edge region (Fig. 3.3 c; MS1), the stacking structure reflected the typical vertical grain-size sorting characteristics under strong wind conditions at the desert margin. At the near-surface layer H10 (0–0.2 m), coarse particles accounted for more than half (approximately 55%), the highest among the four regions. As height increased, the proportion of coarse particles decreased rapidly, while medium-sized particles gradually became dominant (reaching up to about 60%), and fine particles rapidly increased at the top layer H70 (0.6–0.8 m) to nearly 30%. This pattern clearly illustrates the process of rapid coarse-particle deposition and upward migration of fine particles under strong wind conditions in the Mingsha-Mountain region. In the right-bank exposed floodplain of the Danghe Reservoir (Fig. 3.3 d; RB1), the vertical stacking characteristics of grain-size composition were particularly pronounced. At the bottom layer H10 (0–0.2 m), the proportion of coarse particles was extremely high, reaching about 65%, the highest among the four regions. With increasing height, the proportion of coarse particles decreased rapidly and almost disappeared at the top layer H70 (0.6–0.8 m), while fine particles gradually became dominant, exceeding 60% at H70. This indicates that under strong wind conditions, the exposed floodplain environment exhibited an extremely significant vertical sorting effect between coarse and fine particles. In summary, differences in the percentage-stacked grain size distributions across geomorphic units revealed that the mountain top semi-fixed sandy land exhibited a moderate and stable vertical grain size structure; the left bank vegetated belt showed enhanced vertical sorting due to the vegetation interception effect; the Mingsha Mountain region reflected a typical pattern of rapid coarse particle deposition and fine particle suspension at the desert edge; and the right bank exposed floodplain displayed the strongest vertical grain size differentiation effect. These differences profoundly demonstrate the critical influence of geomorphic conditions, vegetation cover, and aeolian dynamics on the vertical grain size distribution structures of Aeolian sand deposits. 4. Discussion The vertical distribution characteristics of Aeolian sand grain size are not only controlled by regional aeolian dynamics but are also closely related to vegetation coverage, surface roughness, and topographic features of different geomorphic units [ 30 ]. The results presented in Chap. 3 demonstrated that the vertical grain size distributions in the semi-fixed sandy land at the mountain top, the vegetated belt on the left bank of the Danghe Reservoir, the desert-edge area of Mingsha Mountain, and the exposed floodplain on the right bank of the Danghe Reservoir exhibited significant differences. For methodological consistency, results are limited to grain-size–height diagnostics; no numerical estimates of T, D, or v s are reported. This chapter will further focus on the key features of vertical grain size variations across the different geomorphic units. Through comparative analysis of mean grain size, linear fitting, and visualization using grain size–height heatmaps, the intrinsic relationships between Aeolian sand transport processes and grain size sorting mechanisms under varying geomorphic conditions will be deeply explored. The aim is to provide theoretical support for the optimization of regional Aeolian sand control strategies and ecological protection measures. 4.1 Mean Grain-Size Trends by Height During the processes of Aeolian sand transport and deposition, the vertical differentiation of particle grain size not only reflects the interactions between aeolian dynamics and surface characteristics but also reveals the differences in depositional dynamics among various geomorphic units [ 31 ]. To further understand the seolian sand sedimentation patterns in the Danghe Reservoir and its surrounding areas, Fig. 4.1 illustrates the variation trends of median grain size (D50) at different height layers within 0–1.0 m across four typical geomorphic units: the semi-fixed sandy land at the mountain top (MT1), the vegetated belt on the left bank (LB1–LB4), the desert-edge area of Mingsha Mountain (MS1), and the exposed floodplain on the right bank (RB1). Height codes are H10 = 0–0.2 m, H30 = 0.2–0.4 m, H50 = 0.4–0.6 m, H70 = 0.6–0.8 m, and H90 = 0.8–1.0 m. Figure 4 − 1 presents the variation trends of the mean grain size (D50) of Aeolian sand deposits with height within the 0–1.0 m vertical profiles across four different geomorphic units: the mountain top, the left bank vegetated belt, the desert-edge area of Mingsha Mountain, and the exposed floodplain on the right bank of the Danghe Reservoir. Overall, the grain size variations in each geomorphic unit were influenced by aeolian dynamic sorting, displaying nonlinear fluctuations. However, notable differences in variation direction and amplitude were observed among the units, reflecting the combined effects of wind strength, surface roughness, and vegetation resistance. Mountain top (MT1). Grain size remained relatively stable at the bottom layer H10 (0–0.2 m) but increased sharply in the middle to upper layers (H30–H70), reaching a peak at H70. A similar mid- to upper-layer coarsening phenomenon has also been observed in the Taklamakan Desert, where Chen et al. noted that approximately 0.2 meters height represents the transition from saltation-dominated to suspension-dominated transport around an 80-meter aeolian flux tower [ 32 ]. Left bank vegetated belt (LB1–LB4). The mean grain size was largest at the bottom layer H10 and gradually decreased with increasing height, but a distinct secondary peak appeared at the middle height layer H50 before declining again. This “bimodal” variation pattern is consistent with findings by van der Wal (2000) in coastal dune vegetation restoration areas, where it was attributed to the combined effects of coarse particle retention and fine particle uplift induced by vegetation[ 33 ]. Mingsha-Mountain desert edge (MS1). D50 exhibited a nearly monotonous decreasing trend, with grain size progressively fining as height increased, except for a slight increase at H70. Similar vertical fining trends are widely observed in active dune areas. For instance, Wang et al. found that on gobi barelands, grain size continuously decreased above 0.3 meters, which aligns with the environmental conditions of low surface roughness and high shear stress typical of dune surfaces [ 34 ]. Right-bank bare floodplain (RB1). The amplitude of mean grain size variation was the smallest, showing a gentle pattern characterized by slightly coarser particles at mid-layers (H50) and finer particles at the top (H70/H90 if present). Such a weak convex trend has also been recorded in surface-crusted environments in moist settings. For example, Lancaster et al. observed that in moist intertidal zones, coarse particles concentrated around 0.3 meters height, with noticeable changes only under intensified wind conditions [ 35 ]. Overall, these different vertical trends indicate that the vertical grain size sorting of Aeolian sediments is strongly regulated by local aeolian dynamics, surface characteristics, and vegetation conditions [ 36 ]. Vegetation coverage can both intercept coarse particles at lower layers and alter turbulent structures to promote fine particle uplift, forming the “bimodal” grain size structure observed in this study and also by van der Wal et al. [ 37 ]. Conversely, on exposed and dry dune slopes, the gradual settling of heavy particles typically results in a monotonous fining trend, consistent with wind tunnel experiments and field observations [ 38 ]. The weak variation observed in the exposed floodplain could be attributed to localized surface moisture or crusting effects, which inhibit saltation transport and cause coarse particles to accumulate at mid-heights, similar to observations in moist beach environments [ 39 ]. In summary, the vertical grain size variation patterns revealed in this study are highly consistent with previous understandings of vertical grain size differentiation in Aeolian deposits, such as those described by Qian and Dong for sedimentation in floodplain areas of China, where Aeolian sand grain size distributions were found to be governed by the coupling of multiple factors [ 40 ]. Additionally, the identification of secondary grain size peaks at mid-layers further supports the importance of the mixed bed collision-splash mechanism as proposed in numerical simulations [ 4 , 5 ].. These findings enhance the understanding of Aeolian sediment budgets and grain size dynamics in the Danghe Reservoir region and lay a solid foundation for future research. 4.2 Height–Grain-Size Linear Fits Figure 4.2 presents ordinary-least-squares fits between median grain size (D50) and height (h) within 0–1.0 m for the four geomorphic units: mountain top (MT1), left-bank vegetated belt (LB1–LB4, aggregated), Mingsha-Mountain desert edge (MS1), and right-bank bare floodplain (RB1). Height codes: H10 = 0–0.2 m, H30 = 0.2–0.4 m, H50 = 0.4–0.6 m, H70 = 0.6–0.8 m, H90 = 0.8–1.0 m. For the left bank, site-level series from LB1–LB4 were combined (mean of layer medians) before fitting to ensure naming consistency. Mechanistic interpretation. Negative slopes signal dominance of settling over mixing for coarse grains under strong winds (MS1), whereas weak or slightly positive slopes indicate topographic speed-up or boundary-condition damping (MT1, RB1). Vegetation increases the magnitude of the negative slope by simultaneously trapping coarse grains at H10 and enhancing mixing for fines above the canopy (LB1–LB4). Within the convection–diffusion lens, slope magnitude scales with K z / w ₛ for fines, while R 2 reflects the temporal steadiness of the local wind/roughness state [ 29 ]. All four units exhibit statistically significant linear relationships (p < 0.05), but the slope sign and coefficients of determination (R 2 ) differ, reflecting the coupled controls of regional wind regime, surface roughness, and vegetation resistance [ 41 ]. Mountain top (MT1). A weak positive slope with the lowest R 2 indicates that slight topographic undulations and wind direction shear led to localized coarse particle retention in the middle to upper layers, thereby weakening the typical “finer with height” trend. This characteristic corresponds well with the redeposition peak height observed by Qian et al. (2019) on ridges in the Qaidam Basin [ 42 ]. Left-bank vegetated belt (LB1–LB4). A moderate negative slope and moderate R 2 reflect strong interception of coarse particles near H10 by vegetation and vegetation-induced perturbations to near-surface turbulence that promote upward transport of fines, yielding a rapid fining trend [ 43 ]. Mingsha-Mountain desert edge (MS1). Both the most negative slope and the highest R 2 among units reveal a typical strong-wind sorting mechanism: rapid near-surface deposition of coarse particles and efficient upward transport of fine particles. The slope of − 0.62 mm·m − 1 obtained by Chen et al. (2015) from an 80-meter flux tower in the Taklamakan Desert closely matches the slope of − 0.59 mm·m − 1 found in this study [ 32 ]. Right-bank bare floodplain (RB1). Although the slope is negative, the R 2 is relatively low, implying that the D50–height relation is weakened by surface-moisture crusting and/or low roughness under a relatively stable wind field. A similar weak convex behavior was recorded for intertidal seolian deposition [ 44 ]. Overall, the differences in linear fitting parameters among the geomorphic units further confirm that topographic undulations determine the locations of near-surface shear velocity transitions, vegetation cover enhances grain size sorting by altering turbulent pulsation structures, and strong wind zones exhibit clearer coarse–fine shear stratification. This regional comparison is highly consistent with both domestic and international field observations and provides reliable quantitative boundary conditions for subsequent heatmap clustering analyses and numerical simulations [ 45 ]. 5. Conclusions This study quantified the vertical grain-size structure of seolian sand across four geomorphic units—MT1 hill-crest, LB1–LB4 left-bank vegetated belt, MS1 Mingsha-Mountain desert edge, and RB1 right-bank bare floodplain—using five-layer trapping (H10–H90), grain-size statistics, and linear fits. (1) Unit-specific vertical trends. All profiles exhibit upward fining, but with distinct signatures: MS1 shows quasi-monotonic fining with ΔD50 ≈ 121 µm and R 2 = 0.91; the left bank (LB1–LB4) presents a bimodal pattern with secondary mid-layer coarsening (overall R 2 = 0.78); RB1 displays mid-layer coarsening and weak linearity (R 2 < 0.35); MT1 shows a mild upper-layer coarsening shoulder superimposed on the general fining trend. (2) Component stacking. Heat-map/stack analyses indicate fines ( 250 µm) concentrate within H10 (0–0.2 m), defining a near-surface control layer for sand-control design. (3) Vertical distribution shape (qualitative). With height the grain-size distributions narrow and the fine-end dominance increases, most pronounced at MS1; the left bank retains mid-layer mixing consistent with vegetation-induced bimodality, while RB1 is coarse-rich near H10 and rapidly fine-dominated aloft. (4) Between-unit differences. Nonparametric tests (Kruskal–Wallis with Dunn’s post-hoc) show a significant difference between left- and right-bank sites only at H10 (p = 0.018); deeper layers are not significantly different (p > 0.10). (5) Mechanistic implications. A size-dependent convection–diffusion framework, constrained by the observed D50–height relations, supports the view that wind regime, vegetation roughness, and topographic acceleration jointly control vertical stratification through differential settling ( w s ) and turbulent diffusion ( K z ). These parameters provide actionable bounds for optimizing windbreak height, checkerboard spacing, and sand-fence layouts in desert–oasis ecotones, with priority intervention in the 0–0.4 m layer. Limitations and outlook.The study uses one site per non-left-bank unit and lacks fully co-located, high-frequency wind/roughness measurements; seasonal extremes are under-represented. Future work should extend multi-site, multi-season observations and pair trapping with in situ turbulence/roughness sensors or wind-tunnel tests to achieve process-level replication and strengthen model calibration. Future work. We will extend observations to multi-season and extreme-event periods, add co-located wind/turbulence and surface-roughness measurements, and broaden spatial replication across additional left/right-bank, dune-edge, and hill-crest sites. Controlled experiments and numerical modeling will be used to isolate vegetation/topography effects and inform sand-control design, and the harmonized LB/RB/MS/MT (H10–H90) datasets will be released to support reproducibility. Declarations Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by the National Natural Science Foundation of China, Regional Fund: Study on the Mechanism of Heavy Metal Migration and Transformation in Alpine Inland Rivers Due to Cascading Dam Construction (Grant No. 52169015); China’s National Key Research and Development Program: Targeted Depolymerization and Enhanced Pretreatment Technology for Diversified Biomass Alcohol Feedstocks (Grant No. 2022YFB4201901-1); Gansu Provincial Major Science and Technology Project: Key Technology R&D for Large-Scale Disposal and Recycling of Plateau Summer Vegetable Waste (24ZDNA004). This work was supported by Incubation Program of Excellent Doctoral Dissertation-Lanzhou University of Technology. Author Contribution H.W.: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Writing – original draft. Y.W. (Yu): Conceptualization, Methodology, Investigation, Formal analysis, Writing – review & editing, Supervision. Y.Z.: Investigation, Data curation, Writing – review & editing. K.L.: Investigation, Data curation, Writing – review & editing. T.-f.L.: Investigation, Data curation, Writing – review & editing. M.T.: Investigation. W.R.: Investigation. X.Z.: Investigation. F.H.: Investigation. J.Z.: Investigation. Y.H.: Investigation. Y.W. (Yinuo): Investigation. E.Q.: Investigation. All authors reviewed and approved the final manuscript. Acknowledgement The authors gratefully acknowledge the colleagues and students from the College of Energy and Power Engineering, Lanzhou University of Technology, for their assistance with field sampling, hydrological monitoring, and laboratory analyses. We also thank the local hydrological and environmental agencies in the Danghe River Basin for providing long-term observational data and logistical support. The authors have reviewed and edited the output and take full responsibility for the content of this publication. 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The flux profile of a blowing sand cloud: A wind-tunnel investigation. Geomorphology [Internet] . 49 (3–4), 219–230. 10.1016/S0169-555X(02)00170-8 (2003). Rotnicka, J. Aeolian vertical mass-flux profiles above dry and moist sandy beach surfaces. Geomorphology [Internet] . 187 , 27–37. 10.1016/j.geomorph.2012.12.032 (2013). Qian, G., Dong, Z. B. & Luo, W. Equations for the near-surface mass-flux density profile of wind-blown sediments. Earth Surf. Processes Land. [Internet] . 36 (10), 1292–1299. 10.1002/esp.2151 (2011). Arens, S. M., van Boxel, J. H. & Abuodha, J. O. Z. Changes in grain size of sand in transport over a foredune. Earth Surf. Processes Land. [Internet] . 27 (11), 1163–1175. 10.1002/esp.418 (2002). Qian, G., Dong, Z., Liu, X. & Wang, T. Height and grain-size characteristics of aeolian redeposition peaks on ridge crests in the Qaidam Basin, NW China. Catena [Internet] . 182 , 104127. 10.1016/j.catena.2019.104127 (2019). Hesp, P. A., Dong, Y., Cheng, H. & Booth, J. Wind flow and sedimentation in artificial vegetation: Field and wind tunnel experiments. Geomorphology [Internet] . 337 , 165–182. 10.1016/j.geomorph.2019.03.020 (2019). Bauer, B. O. et al. Aeolian sediment transport on a beach: Surface moisture, wind fetch, and mean transport. Geomorphology [Internet] . 105 (1–2), 106–116. 10.1016/j.geomorph.2008.02.016 (2009). Dong, Z., Liu, X. & Qian, G. Using vertical grain-size profiles to constrain numerical simulation of aeolian transport and deposition. Aeolian Res. [Internet] . 32 , 144–153. 10.1016/j.aeolia.2018.02.004 (2018). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8396723","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":608357107,"identity":"223f8224-93c4-49a6-a4f8-c2e3533e272b","order_by":0,"name":"Hao Wang","email":"","orcid":"","institution":"Lanzhou University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Wang","suffix":""},{"id":608357114,"identity":"35288ecc-8344-4a18-a65c-ace4b317b690","order_by":1,"name":"Yu Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYDADNh4GxgcJFTWkaWE2eHDmGCnW8DCwST5sYSas0OD42cOvedsYovl4Dh+rSGxgY+Bv707Ar+VMXpo1UEtuG29b2o3EHTIMEmfObsCrxexAjpkxb9v/3DZ+HrMbiWfYGAwkcgloOf8GpAVoCz//t4LENmYitNzIMX4McVgPGwNRWuxvvDFjnHMOqIXnmLFEwpljPAT9ItmfY/zhTRlD7vye5Icff1TUyPG39+LXAgRsUjxIPB6c6pAA88cfxCgbBaNgFIyCkQsAILVGry8BNIcAAAAASUVORK5CYII=","orcid":"","institution":"Lanzhou University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Wang","suffix":""},{"id":608357116,"identity":"3e4e32f6-79f9-4859-9a69-a50bf941fd17","order_by":2,"name":"Ying Zhang","email":"","orcid":"","institution":"Gansu Provincial Water Environment Monitoring 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Technology","correspondingAuthor":false,"prefix":"","firstName":"Miao","middleName":"","lastName":"Tian","suffix":""},{"id":608357121,"identity":"62e84398-0382-44c6-bad6-5636c2af1f96","order_by":6,"name":"Weilong Ren","email":"","orcid":"","institution":"Lanzhou University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Weilong","middleName":"","lastName":"Ren","suffix":""},{"id":608357123,"identity":"81dc945d-ca00-49fe-b2e9-fd78aba4f5de","order_by":7,"name":"Xiaolong Zhang","email":"","orcid":"","institution":"Lanzhou University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiaolong","middleName":"","lastName":"Zhang","suffix":""},{"id":608357124,"identity":"623cd013-3eb8-4187-b47a-c93accc3b59d","order_by":8,"name":"Feiyan Hu","email":"","orcid":"","institution":"Lanzhou University of 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Technology","correspondingAuthor":false,"prefix":"","firstName":"Yinuo","middleName":"","lastName":"Wang","suffix":""},{"id":608357129,"identity":"ffaca749-91af-42d0-9908-557806a29364","order_by":12,"name":"Ejia Qinni","email":"","orcid":"","institution":"Lanzhou University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Ejia","middleName":"","lastName":"Qinni","suffix":""}],"badges":[],"createdAt":"2025-12-18 14:53:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8396723/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8396723/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105562839,"identity":"2f7e7b13-d341-45e1-ae90-c78305512d84","added_by":"auto","created_at":"2026-03-27 12:44:54","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11416083,"visible":true,"origin":"","legend":"\u003cp\u003eFig. 2.1 Location map of the Danghe Reservoir–Mingsha Mountain area.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8396723/v1/34388573b16968111f9b4764.jpeg"},{"id":105048908,"identity":"76b0d296-f107-47c7-b673-61abfaa19157","added_by":"auto","created_at":"2026-03-20 09:48:05","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":627627,"visible":true,"origin":"","legend":"\u003cp\u003eFig 2.2 Deployment of Sand Collectors in the Danghe Reservoir Region\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8396723/v1/a08bd6bf818a2dbe99e9b210.jpeg"},{"id":105048918,"identity":"041ce4f2-efe3-4f05-96b5-4c5bca250985","added_by":"auto","created_at":"2026-03-20 09:48:06","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":526248,"visible":true,"origin":"","legend":"\u003cp\u003eFig 2.3 Deployment of Sand Collectors in the Mingsha Mountain Region\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8396723/v1/8b9bb98db94ed6372afc323d.jpeg"},{"id":105048917,"identity":"21b9eae7-70e8-4df9-a1af-18e02cdc02aa","added_by":"auto","created_at":"2026-03-20 09:48:06","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":511978,"visible":true,"origin":"","legend":"\u003cp\u003eFig 2.4 Deployment of Sand Collectors in the Mountain Top Region\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8396723/v1/23efd3200737676064f1fa88.jpeg"},{"id":105562925,"identity":"27a75939-9e2f-4c5f-985e-d16dd480919b","added_by":"auto","created_at":"2026-03-27 12:45:16","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":534448,"visible":true,"origin":"","legend":"\u003cp\u003eFig 2.5 Deployment of Meteorological Stations\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8396723/v1/b8c4dcb605f399003cbbeac2.jpeg"},{"id":105562847,"identity":"e9d2d0e2-3bb2-4571-a651-37e9cb0cb8d1","added_by":"auto","created_at":"2026-03-27 12:44:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":216302,"visible":true,"origin":"","legend":"\u003cp\u003eFig 2.6 Schematic of the five-layer QN-JSY sand collector. The base is embedded 0.30 m; intake-center heights above ground are 0.10, 0.30, 0.50, 0.70, and 0.90 m (H10–H90), corresponding to 0–0.2, 0.2–0.4, 0.4–0.6, 0.6–0.8, and 0.8–1.0 m. Each layer has two 50 mm × 50 mm inlets feeding one bag.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8396723/v1/42cba0437c02e25e177cf7f6.png"},{"id":105048909,"identity":"9f7513f8-362e-4007-984a-8a3e0952b874","added_by":"auto","created_at":"2026-03-20 09:48:05","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":71136,"visible":true,"origin":"","legend":"\u003cp\u003eFig 3.1 Vertical grain-size distributions of seolian sand by height codes in the study area: (a) MT1 hill-crest section, (b) LB1–LB4 left-bank vegetated zone, (c) MS1 Mingsha-Mountain desert edge, and (d) RB1 right-bank bare floodplain. Height codes: H10 = 0–0.2 m, H30 = 0.2–0.4 m, H50 = 0.4–0.6 m, H70 = 0.6–0.8 m. Legends use the same H10–H70 order across panels.\u003c/p\u003e","description":"","filename":"41.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8396723/v1/b974c7cec6bf7e22a082291e.jpg"},{"id":105048913,"identity":"a7fc3cbd-c562-4c8b-8739-77ee485e455c","added_by":"auto","created_at":"2026-03-20 09:48:05","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":563925,"visible":true,"origin":"","legend":"\u003cp\u003eFig 3.2 Layer-wise box-plots of grain size (µm) for seolian sand at four geomorphic units: (a) MT1 hill-crest, (b) LB1–LB4 left-bank vegetated zone, (c) MS1 Mingsha edge, and (d) RB1 right-bank bare floodplain. Height codes: H10 = 0–0.2 m, H30 = 0.2–0.4 m, H50 = 0.4–0.6 m, H70 = 0.6–0.8 m, H90 = 0.8–1.0 m. Boxes show median and interquartile range; whiskers denote 1.5×IQR; points are outliers. Statistical comparisons are reported in the text (Kruskal–Wallis with Dunn’s post-hoc).\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8396723/v1/58eb95492d4d8e207f8d134f.jpeg"},{"id":105048911,"identity":"f56df45c-37d3-4d74-a25f-397e0c23b763","added_by":"auto","created_at":"2026-03-20 09:48:05","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":61846,"visible":true,"origin":"","legend":"\u003cp\u003eFig 3.3 Vertical percentage composition of seolian sand by height for four geomorphic units: (a) MT1 hill-crest section, (b) LB1–LB4 left-bank vegetated zone, (c) MS1 Mingsha-Mountain desert edge, and (d) RB1 right-bank bare floodplain. Height codes: H10 = 0–0.2 m, H30 = 0.2–0.4 m, H50 = 0.4–0.6 m, H70 = 0.6–0.8 m, H90 = 0.8–1.0 m. Size classes: Fine \u0026lt; 63 µm, Medium 63–250 µm, Coarse \u0026gt; 250 µm. Each stacked bar sums to 100%.\u003c/p\u003e","description":"","filename":"42.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8396723/v1/6e19c8983c6588fc5468782f.jpg"},{"id":105562940,"identity":"d2fb9ff8-f0fb-4d1e-b5ab-b18e8d81f04f","added_by":"auto","created_at":"2026-03-27 12:45:19","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":266896,"visible":true,"origin":"","legend":"\u003cp\u003eFig 4.1 Vertical trends of median grain size (D50, µm) with height for seolian sand at four geomorphic units: MT1 hill-crest, LB1–LB4 (mean ± SD) left-bank vegetated zone, MS1 Mingsha-Mountain desert edge, and RB1 right-bank bare floodplain. Height codes: H10 = 0–0.2 m, H30 = 0.2–0.4 m, H50 = 0.4–0.6 m, H70 = 0.6–0.8 m, H90 = 0.8–1.0 m. Lines connect layer mid-points.\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8396723/v1/19a57a5f3826f2a6803c4d7e.jpeg"},{"id":105048916,"identity":"134861ad-5f2b-4d67-a74d-30661e085819","added_by":"auto","created_at":"2026-03-20 09:48:05","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":452612,"visible":true,"origin":"","legend":"\u003cp\u003eFig 4.2 Least-squares fits of median seolian-sand grain size (D50, µm) versus height for four geomorphic units: MT1 hill-crest, LB1–LB4 left-bank vegetated zone (site-mean series), MS1 Mingsha-Mountain desert edge, and RB1 right-bank bare floodplain. Points denote layer medians; dashed curves are OLS fits. Height codes: H10 = 0–0.2 m, H30 = 0.2–0.4 m, H50 = 0.4–0.6 m, H70 = 0.6–0.8 m, H90 = 0.8–1.0 m.\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8396723/v1/ac2a7ac55f78cf8e2346a0ac.jpeg"},{"id":108048294,"identity":"331d7242-6c44-4c59-8856-8d872666268a","added_by":"auto","created_at":"2026-04-28 20:24:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":15691994,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8396723/v1/5248af9c-ba02-4c67-918b-0158b43958a0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Vertical and Spatiotemporal Variations in Grain Size of Aeolian Sand Deposits at the Edge of the Kumtag Desert","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAeolian sand activity, as one of the key driving forces behind ecological and environmental evolution in China's arid and semi-arid regions, exerts profound impacts on regional ecological security, soil erosion, and land desertification [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The process of Aeolian sand transport not only affects the stability and sustainability of regional ecosystems but also influences ecological and water resource security by altering surface sedimentary characteristics [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The grain size distribution and its vertical variation in Aeolian sand deposits directly reflect changes in aeolian dynamics and depositional environments [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Variations in grain size can reveal the dynamic mechanisms of sediment transport and deposition, as well as highlight the ecological and environmental differences among micro-geomorphic units within wind-eroded areas [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. With the increasing frequency of Aeolian sand activities, the vertical variation of sand grain size has gradually become a critical scientific issue in the study of Aeolian transport mechanisms and depositional processes [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, the vertical variation of Aeolian sand grain size has attracted widespread academic attention, not only for its relevance to Aeolian transport dynamics but also for its value in providing a scientific basis for understanding ecological changes and predicting sandstorm disasters [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Huo et al. analyzed Aeolian sand samples collected at different heights from an 80-meter-high flux tower in the hinterland of the Taklamakan Desert, finding that the vertical grain size distribution of sand particles varied significantly with height near the surface but stabilized above 32 meters. This pattern was primarily controlled by wind speed, with distinct vertical flux differences among particle size classes, particularly dominated by fine particles smaller than PM100 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Research by Pang et al. in the eastern Kumtag Desert further emphasized that Aeolian sand activity significantly affects regional ecological geochemical cycles, with notable regional differences in horizontal sand transport volumes and grain size compositions, reflecting variations in regional aeolian dynamics [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Liu et al. investigated the relationship between sand particle size and vegetation cover in the desert\u0026ndash;oasis transition zone at the southeastern margin of the Tengger Desert, finding that increased vegetation cover in wind-eroded environments significantly enhanced the proportion of fine sand particles in deposits, and that both the spatial and vertical distribution of grain size were closely related to vegetation and topographic conditions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In addition, Dong et al. employed end-member modeling analysis (EMMA) to explore the relationship between grain size components of Aeolian sand deposits and aeolian dynamics in the Tengger Desert, demonstrating that different grain size fractions were significantly correlated with the intensity of the East Asian winter monsoon and the frequency of dust storms, providing robust scientific evidence for reconstructing paleo-wind field variations [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, most existing studies have focused on typical desert interiors or desert\u0026ndash;oasis transition zones, and systematic investigations on the vertical grain size distribution and controlling mechanisms of Aeolian sand deposits under complex geomorphic conditions at the edge of the Kumtag Desert remain limited.\u003c/p\u003e \u003cp\u003eAeolian vs. fluvial sediments and how they are studied. Although both Aeolian and fluvial systems sort grains by a moving fluid, they differ in governing physics and in the tools typically used to study them. In air, low density and viscosity favor saltation with impact\u0026ndash;splash feedbacks, so coarse grains concentrate near the surface while fines are preferentially lofted; field measurements often show exponential decay of flux with height and a systematic fining upward that is strongly modulated by surface roughness and vegetation traps [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In water, the vertical mixture of bedload and suspended load is commonly framed by Rouse-type theory, where the Rouse number diagnoses whether transport is bed- or suspension-dominated and sets the shape of the vertical concentration profile; recent work also leverages dimensional analysis and machine learning to classify river transport regimes [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMethodological implications for this study. Because of these contrasts, Aeolian investigations typically rely on stratified sand traps, multi-height flux towers, wind-tunnel/optical measurements, and characterization of roughness/vegetation effects; by contrast, fluvial work emphasizes isokinetic or acoustics-based sampling and Rouse-profile fitting to suspended sediment. At the same time, granular-physics advances now unify threshold and transport-rate scaling across air and water, which motivates our use of a simple convection\u0026ndash;diffusion view to interpret the vertical stratification we observe at the Kumtag Desert edge [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs a representative arid and wind-eroded region in China, the Kumtag Desert experiences high-intensity and frequent Aeolian sand activities, resulting in significant sand deposition around the Danghe Reservoir [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The vertical grain size distributions vary distinctly among different geomorphic units (e.g., sand dunes, mountain tops, left and right banks) surrounding the reservoir, directly reflecting the complexity of Aeolian deposition and the diversity of wind erosion and transport mechanisms [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Nevertheless, systematic research on the spatiotemporal vertical variation of Aeolian sand grain size at the edge of the Kumtag Desert is still lacking, and the detailed mechanisms and dynamic processes governing vertical grain size changes remain insufficiently understood. Clarifying these scientific issues is crucial for understanding Aeolian depositional processes, predicting regional Aeolian disasters, and formulating effective desertification control measures.\u003c/p\u003e \u003cp\u003eThis study focuses on the Danghe Reservoir region at the edge of the Kumtag Desert. Vertical stratified Aeolian sand deposits were systematically collected from typical geomorphic units such as sand dunes, mountain tops, and meteorological stations on the left and right banks by deploying sand collectors at different heights. Grain size measurements were conducted using a Mastersizer 2000, and we analyzed D50 and cumulative-distribution\u0026ndash;based vertical patterns across heights. The aim of this study is to reveal the vertical variation characteristics of Aeolian sand grain size across different geomorphic units at the edge of the Kumtag Desert, analyze the environmental and dynamic mechanisms behind these variations, and provide theoretical and scientific support for regional Aeolian disaster prevention and ecological management.\u003c/p\u003e"},{"header":"2 Study Area and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eThis study was conducted in Dunhuang City, Gansu Province, specifically focusing on the Danghe Reservoir and the adjacent Mingsha Mountain region, both located along the northern margin of the Kumtag Desert. The area is characterized by a typical arid desert environment, featuring intense Aeolian sand activity and pronounced wind erosion, making it an ideal site for investigating the vertical and spatiotemporal variations in the grain size of Aeolian sand deposits at the edge of the Kumtag Desert.\u003c/p\u003e \u003cp\u003eThe Danghe Reservoir is located in the southeastern part of Dunhuang City, situated within the transition zone between desert and oasis. The region is ecologically fragile and experiences significant Aeolian sand activity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. As the main water regulation facility in the area, the Danghe Reservoir plays a critical role in agricultural irrigation, ecological replenishment, and local climate regulation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, in recent years, influenced by both climate change and anthropogenic activities (such as agricultural reclamation and intensified irrigation), groundwater levels around the reservoir have declined markedly, leading to severe soil salinization, widespread vegetation degradation, increased frequency and intensity of Aeolian sand activity, and notable changes in sedimentary characteristics [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The intensification of Aeolian sand activity has led to declining land productivity around the reservoir, gradually threatening water resource security and ecological stability, thus forming a typical ecological negative feedback loop in the desert\u0026ndash;oasis transition zone characterized by \"wind erosion\u0026mdash;land degradation\u0026mdash;water resource deterioration\" [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The typical Aeolian sand deposition processes and complex environmental background of this area provide a unique platform for studying the vertical variations in grain size of Aeolian sand deposits.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Mingsha Mountain region, located adjacent to the southern margin of the Kumtag Desert, represents a typical desert-edge wind erosion zone. This area features broad, open terrain, harsh wind erosion conditions, extremely low vegetation cover, and widespread distribution of mobile and semi-mobile sand dunes. Aeolian sand transport and deposition processes are highly active, exhibiting pronounced seasonal variations [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Particularly during winter and spring, strong and frequent southwesterly winds drive the transport of large quantities of fine sand particles toward the surrounding oasis areas of Mingsha Mountain, resulting in significant changes in vertical sedimentary structures and directly impacting the local ecological environment, tourism resources, and infrastructure [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, the Danghe Reservoir and Mingsha Mountain regions were selected as the study sites, encompassing two typical types of wind erosion zones: the desert\u0026ndash;oasis transition zone and the desert-edge area. This selection facilitates a comprehensive investigation into the spatial differences and temporal variations of vertical grain size distribution in Aeolian sand deposits at the edge of the Kumtag Desert. The research findings are expected to provide important scientific support for regional ecological conservation, land management, and Aeolian sand control efforts, as well as offer theoretical guidance and practical reference for desertification control and ecological restoration in the Kumtag Desert and other similar arid regions in northwestern China.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sampling Design and Instruments\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Site Selection\u003c/h2\u003e \u003cp\u003eTo investigate the vertical and spatiotemporal variations in the grain size of Aeolian sand deposits at the edge of the Kumtag Desert, representative sampling sites were systematically selected based on regional Aeolian sand activity intensity, geomorphic characteristics, vegetation cover conditions, and the degree of anthropogenic disturbance. The Danghe Reservoir region (desert\u0026ndash;oasis transition zone) and the Mingsha Mountain region (desert-edge zone) were identified as typical sampling areas. Long-term vertical monitoring of Aeolian sand grain size was conducted at representative points within each region.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003e1) Danghe Reservoir Region (Desert–Oasis Transition Zone)\u003c/h3\u003e\n\u003cp\u003eThe ecological environment surrounding the Danghe Reservoir is fragile, characterized by significant wind erosion and substantial anthropogenic impacts on Aeolian sand deposition characteristics. Typical sampling sites were selected on both the left and right banks of the reservoir. On the left bank, sites were chosen along a gradient of vegetation cover at the following coordinates: 39.936918\u0026deg;N, 94.314580\u0026deg;E; 39.939712\u0026deg;N, 94.320450\u0026deg;E; 39.933990\u0026deg;N, 94.319818\u0026deg;E; and 39.931650\u0026deg;N, 94.323227\u0026deg;E, aiming to analyze the influence of vegetation coverage gradients on vertical sediment grain size distribution.For consistency, these four left-bank points are coded LB1\u0026ndash;LB4 in west-to-east order.Vegetation-cover gradient. Along the left bank, vegetation cover increases monotonically from LB1 to LB4, forming an intentional roughness gradient used to test how vegetation modulates vertical sorting across H10\u0026ndash;H70. On the right bank, a site located at 39.947120\u0026deg;N, 94.337598\u0026deg;E was selected in an open area with low vegetation cover and prominent Aeolian sand deposition, to reflect the natural patterns of vertical grain size variation in Aeolian sand deposits.This right-bank sampling site is coded RB1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e2) Mingsha Mountain Region (Desert-Edge Zone)\u003c/h3\u003e\n\u003cp\u003eThe Mingsha Mountain region is adjacent to the southern margin of the Kumtag Desert. This area is characterized by open terrain, widespread distribution of mobile fine sand and semi-mobile dunes, and intense Aeolian sand transport with distinct seasonal variations. A representative sampling site was selected at 40.031892\u0026deg;N, 94.527115\u0026deg;E. This site features typical geomorphic conditions, active Aeolian sand deposition, and the presence of various sand control structures (e.g., straw checkerboards, stone checkerboards, and wooden sand fences), providing an excellent research setting for analyzing the effects of protective measures on the vertical grain size distribution of Aeolian sand deposits in desert-edge environments.This desert-edge sampling site is coded MS1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e3) Mountain Top Region (Semi-Fixed Sandy Land–Alluvial Slope Transition Zone)\u003c/h3\u003e\n\u003cp\u003eTo further compare the vertical grain size differences across different geomorphic units, an additional long-slope sampling site was established on a mountain terrace approximately 4 km southwest of the Danghe Reservoir (39.928476\u0026deg;N, 94.306842\u0026deg;E). This area lies within the transition zone between dunes and a gravelly alluvial fan. The surface is shaped by both micro-topographic elevation and slope wind acceleration, retaining mobile sand sources while exhibiting a semi-fixed sandy land pattern with patchy vegetation cover. The mountain top site faces southwest, aligned with the prevailing wind direction, with an elevation difference of about 18 meters from the slope foot to the crest. This site is representative of the vertical Aeolian sand transport characteristics under alternating wind erosion and deposition processes along slopes. By comparing data from the mountain top with those from the vegetated gradient on the left bank, the open bare ground on the right bank, and the desert-edge site at Mingsha Mountain, this study systematically explores the coupled effects of terrain elevation, vegetation trapping, and wind speed enhancement on vertical grain size sorting. The findings provide crucial comparative evidence for the precise construction of multi-geomorphic coordinated sand control strategies.This hill-crest terrace sampling site is coded MT1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn summary, this study comprehensively covers the desert\u0026ndash;oasis transition zone, the desert-edge zone, and the semi-fixed sandy land\u0026ndash;alluvial slope transition unit by scientifically deploying four types of typical sampling sites: the left bank, right bank, Mingsha Mountain, and mountain top regions. Standardized sampling equipment and protocols were employed to systematically reveal the vertical spatiotemporal variations in the grain size of Aeolian sand deposits at the edge of the Kumtag Desert. The results provide a reliable empirical basis and methodological support for precise regional ecological zoning, hierarchical optimization of Aeolian sand control systems, and the parameterized design of sand control engineering.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003cdiv class=\"Heading\"\u003e2.2.2 Sampling Layout and Deployment\u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003e1) Meteorological Monitoring\u003c/h3\u003e\n\u003cp\u003eTo continuously capture the wind field characteristics across multiple geomorphic units at the edge of the Kumtag Desert, a NANYI-AWS-Pro automatic weather station was deployed at each of four typical locations: the left bank (39.9369\u0026deg;N, 94.3146\u0026deg;E), the right bank (39.9355\u0026deg;N, 94.3241\u0026deg;E), the Mingsha Mountain dune area (40.1162\u0026deg;N, 94.6864\u0026deg;E), and the mountain terrace (39.928476\u0026deg;N, 94.306842\u0026deg;E). Each station was equipped with a cup anemometer (measuring range 0\u0026ndash;60 m\u0026middot;s⁻\u0026sup1;, resolution 0.1 m\u0026middot;s⁻\u0026sup1;) and a wind vane (accuracy\u0026thinsp;\u0026plusmn;\u0026thinsp;2\u0026deg;), along with integrated temperature sensors (range \u0026minus;\u0026thinsp;40 to 60\u0026deg;C, accuracy\u0026thinsp;\u0026plusmn;\u0026thinsp;0.2\u0026deg;C) and relative humidity sensors (range 0\u0026ndash;100%, accuracy\u0026thinsp;\u0026plusmn;\u0026thinsp;2%). Observational data were recorded at a frequency of 10 Hz, stored locally on SD cards, and simultaneously transmitted to the cloud via a 5G network, ensuring high-frequency, redundant data backups to maintain data integrity and traceability.The wind sensors (cup anemometer and vane) were mounted at 2.0 m above ground level (a.g.l.) at all sites and leveled monthly. For figures and statistics, 10 Hz records were averaged to 1-min and then to hourly time series before deriving wind roses and summary metrics.These AWS locations are co-located with the sampling units and codes used below (UNIT \u0026isin; {LB, RB, MS, MT}; sites LB1\u0026ndash;LB4, RB1, MS1, MT1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e2) Vertical Stratified Sand Collectors\u003c/h3\u003e\n\u003cp\u003eAcross the four AWS sites, seven QN-JSY vertical stratified sand collectors were deployed: four on the left bank (LB1-LB4), one on the right bank (RB1), one in the Mingsha Mountain desert-edge zone (MS1), and one on the mountain-top terrace (MT1). All collectors were placed 10 m downwind of their respective AWS along the prevailing south-westerly wind direction.Each collector has a total mast height of 1.30 m, with the base embedded 0.30 m below the ground so that the five intake layers span 0\u0026ndash;1.00 m above ground. The front panel has five rows \u0026times; two columns of 50 mm \u0026times; 50 mm inlets per layer; the two inlets of a layer feed a single bag. Center heights of the five layers are 0.10, 0.30, 0.50, 0.70, and 0.90 m (coded H10, H30, H50, H70, H90), corresponding to 0\u0026ndash;0.2, 0.2\u0026ndash;0.4, 0.4\u0026ndash;0.6, 0.6\u0026ndash;0.8, and 0.8\u0026ndash;1.0 m. All heights are referenced to the local leveled ground at installation (tolerance\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 m), and all inlets face the prevailing south-westerly wind.The outer casing was constructed from 1.0 mm thick SUS-304 stainless steel, and each single layer could capture a maximum sand volume of no less than 1 kg. The base of the collector was embedded 0.30 m underground and anchored with concrete to minimize vibration errors under high wind conditions. Sampling bags were replaced daily at 08:00, and comparisons between daily samples and monthly integrated samples were conducted at the end of each month to verify sampling integrity.Sample nomenclature. All samples are labeled using the code \\[UNIT]-\\[SITE]-\\[HEIGHT]-\\[PERIOD], where UNIT \u0026isin; {LB\u0026thinsp;=\u0026thinsp;left-bank vegetated belt, RB\u0026thinsp;=\u0026thinsp;right-bank bare floodplain, MS\u0026thinsp;=\u0026thinsp;Mingsha-Mountain desert edge, MT\u0026thinsp;=\u0026thinsp;hill-crest}, SITE \u0026isin; {LB1\u0026ndash;LB4, RB1, MS1, MT1}, HEIGHT \u0026isin; {H10, H30, H50, H70} corresponding to 0\u0026ndash;0.2/0.2\u0026ndash;0.4/0.4\u0026ndash;0.6/0.6\u0026ndash;0.8 m, and PERIOD uses MYYYYMM for monthly or DYYYYMMDD for daily samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003e3) Sample Processing\u003c/h3\u003e\n\u003cp\u003eAll sediment samples were placed into anti-static self-sealing bags, with field labels indicating the collection time, coordinates, and geomorphic unit. Samples were transported to the laboratory within 24 hours. In the laboratory, the samples were first air-dried naturally and then treated using a three-step process involving hydrochloric acid, hydrogen peroxide, and sodium hexametaphosphate to remove carbonates, oxidized organic matter, and to disperse particles, respectively. Subsequently, particle size distributions were measured using a Mastersizer 2000 laser particle size analyzer (measuring range 0.02\u0026ndash;2000 \u0026micro;m). The precise coordinates of the sampling points and instrument locations were recorded using a handheld GPS device (accuracy\u0026thinsp;\u0026plusmn;\u0026thinsp;3 m), providing reliable spatial positioning data for subsequent GIS analyses. All field labels and laboratory spreadsheets used the above code to ensure traceability across sites, heights, and periods.\u003c/p\u003e \u003cp\u003eThis high-frequency wind field observation and stratified sediment collection scheme achieved synchronous monitoring of wind field and depositional processes across both temporal and spatial scales, systematically revealing the seasonal-scale coupling mechanisms between Aeolian sand transport and deposition in multiple geomorphic units. The results provide a solid data foundation for optimizing windbreak and sand stabilization engineering designs.The vertical stratified sand collector measures height-resolved trapped mass flux (captured transport) rather than areal deposition on a surface.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003cdiv class=\"Heading\"\u003e2.2.3 Sample nomenclature and coding\u003c/div\u003e \u003cp\u003eTo standardize sample names across sites, heights, and dates, we adopt the code [UNIT]-[SITE]-[HEIGHT]-[PERIOD], where:\u003c/p\u003e \u003cp\u003e(1) UNIT \u0026isin; {LB\u0026thinsp;=\u0026thinsp;left bank vegetated belt, RB\u0026thinsp;=\u0026thinsp;right-bank bare floodplain, MS\u0026thinsp;=\u0026thinsp;Mingsha Mountain desert edge, MT\u0026thinsp;=\u0026thinsp;mountain-top hill-crest}.\u003c/p\u003e \u003cp\u003e(2) SITE: numeric identifier at each unit (e.g., LB1\u0026ndash;LB4; RB1; MS1; MT1).\u003c/p\u003e \u003cp\u003e(3) HEIGHT: center height of the layer in centimeters, H10/H30/H50/H70/H90, corresponding to the five strata 0\u0026ndash;0.2 / 0.2\u0026ndash;0.4 / 0.4\u0026ndash;0.6 / 0.6\u0026ndash;0.8 / 0.8\u0026ndash;1.0 m.Unless otherwise noted, analyses use the full five-layer scheme (H10\u0026ndash;H90). In Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e the top layer (H90) is omitted for readability; Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e\u0026ndash;3.3 and 4.1\u0026ndash;4.2 show the full H10\u0026ndash;H90 set.\u003c/p\u003e \u003cp\u003e(4) PERIOD: MYYYYMM for monthly-integrated traps (e.g., M202404), or DYYYYMMDD for daily bags when referenced.\u003c/p\u003e \u003cp\u003eFor clarity across text and figures, layer codes always refer to the inlet center heights (H10\u0026thinsp;=\u0026thinsp;0.10 m, \u0026hellip;, H90\u0026thinsp;=\u0026thinsp;0.90 m), and figures explicitly indicate when H90 data are included.\u003c/p\u003e \u003cp\u003eExamples. LB2-H10-M202404 denotes the left-bank site 2, 0\u0026ndash;0.2 m layer (center 0.1 m), monthly-integrated sample in April 2024; MS1-H70-D20250315 denotes the Mingsha site, 0.6\u0026ndash;0.8 m layer, daily sample on 15 Mar 2025.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Analysis Methods\u003c/h2\u003e \u003cp\u003eTerminology note. The five-layer QN-JSY collector samples height-resolved airborne (seolian) sand transport. It records captured mass at each height during the sampling interval and does not measure ground deposition rate. Accordingly, we analyze vertical transport (via captured mass and grain-size spectra) and infer depositional tendencies indirectly from size\u0026ndash;height trends within a simple convection\u0026ndash;diffusion framework.\u003c/p\u003e \u003cp\u003eThis study restricts quantitative analyses to grain-size\u0026ndash;height diagnostics: D10/D50/D90, cumulative distribution functions (CDFs), ordinary-least-squares height\u0026ndash;D50 fits, and nonparametric tests. Classical aeolian transport and settling relations (e.g., Bagnold-type transport, Hsu-type deposition, Stokes-type settling) are used as conceptual background for interpretation only; we do not estimate transport force (T), deposition rate (D), or settling velocity (\u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e) because the co-located turbulence/roughness parameters required for robust inversion are beyond the present field design.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(1) Analysis of Aeolian Sand Dynamics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAeolian Sand Transport Models:\u003c/p\u003e \u003cp\u003eTo quantify the influence of wind speed and wind direction on Aeolian sand grain transport, fluid dynamic models and differential equations of Aeolian sand transport were adopted [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. These relations guide interpretation of the observed upward fining and unit-specific contrasts but are not numerically inverted in this study.\u003c/p\u003e \u003cp\u003eBagnold Model [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"588\" height=\"97\"\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cem\u003eT\u003c/em\u003e is the sand transport force (N\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), \u003cem\u003eρ\u003c/em\u003e\u003csub\u003e\u003cem\u003ew\u003c/em\u003e\u003c/sub\u003e is the air density (kg\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), \u003cem\u003eu\u003c/em\u003e is the wind speed (m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003ed\u003c/em\u003e is the sand particle diameter (m), and \u003cem\u003ed\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e is the threshold sand particle diameter (m).\u003c/p\u003e \u003cp\u003eHsu Model [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"580\" height=\"82\"\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eD\u003c/em\u003e is the deposition rate (kg\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003eK\u003c/em\u003e is a constant (typically 0.5), \u003cem\u003eg\u003c/em\u003e is the gravitational acceleration (9.81 m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e), \u003cem\u003eρ\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e is the sand particle density (kg\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), and \u003cem\u003ev\u003c/em\u003e is the wind speed (m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003e(2) Study of Sedimentation Patterns\u003c/b\u003e \u003c/p\u003e \u003cp\u003eVertical distribution data collected by the sand collectors were subjected to stratified statistical analysis to investigate the Aeolian sand sedimentation patterns at different height layers. During Aeolian sand transport processes, larger particles typically tend to settle preferentially in the lower layers near the ground surface, while smaller particles are more susceptible to turbulence and airflow influences, allowing them to be suspended to higher layers and transported over longer distances [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This phenomenon is closely related to shear forces within the airflow, turbulence intensity, and particle settling velocities [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo analyze the sedimentation characteristics at different height layers, this study calculated the sediment accumulation at each layer and combined these data with corresponding wind speed and wind direction measurements. This approach revealed the spatial heterogeneity of Aeolian sand deposition in the vertical dimension.\u003c/p\u003e \u003cp\u003eThrough these analyses, the vertical distribution patterns of Aeolian sand deposition and the variation trends across different heights were effectively elucidated, providing theoretical support for the development of sand control measures and the optimization of protective structures.\u003c/p\u003e \u003cp\u003eStokes\u0026rsquo; law for particle settling was employed to study the sedimentation patterns of Aeolian sand [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]:\u003c/p\u003e\u003cp\u003e\u003cimg 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4MCBiIMUXtfIlCiK2LBhA5xOJ3Q6HX755Rf2TREREVFceC1hioiIiChevZZpPiIiIqJ4lfBhyu12QxAE32aiGo0GNTU1GB8fV19KUbJarSE3bHU4HEhJSUFVVZW6REREtKQldJhyOBxYvXo1bt68iZaWFsiyjLGxMTidTmRlZfEByjHW1tYGnU4XdNv+vr4+eDwe7Nq1S10iIiJa0hI2TImiiJ07dwIAenp6fBt7ZWZmora2FgBgNBq5W3uMiKKIgYEBrFmzBpmZmX41SZJgt9uh1+uRl5fnVyMiIlrqEjZMDQ0Nwel0oqysLGAD0dHRUQDAkydP/M7Twimfd2lpacAjh8bHx/H48WNs2LABaWlpfjUiIqKlLmHDVCiiKOLatWsAwCmnGOIUHxERxauEDVN5eXnQ6/Ww2WxwOBzAzAhJZWUlnE4njEYj98KKEU7xERFRPEvYMJWWloauri7o9XqsXbsWGo0G69evx7JlyzA8PMw+qRjiFB8REcWzhA1TmAlUe/bsweXLl+H1ejE1NQW73R7QQ0XR4RQfERHFs4TeAb2qqgoulwt3794NGDGh2FAeJQQAAwMDfqNPDofDt6JSXSMiInpbJOzIlCRJcLlc6O3txYoVK1BXV4f+/n71ZRQlZYpvenoabrcbmNkotb6+HhcuXIDT6cTKlSuRlJSEzs5OX/8aERHR2yJhR6bGx8dx/fp1vHjxAvfu3YPH4/HVSkpKcOXKlYBmaZofSZJw4MABvHr1Cl1dXQCArKwsfP3116iuroYoiti7dy8GBweRn58Ps9nMKVYiInrrJGSYampqQk9PD5qbm33Te263Gw8ePMDPP//sa0zn1FN0HA4HDh48iPb2dn6OREQUtxJums9qtaK6uhqVlZV+fVKpqakoLy+H3W5HR0eH37QULUxjYyNycnIYpIiIKK4lXJhSdjefS7A9kShyVqsVLS0tXKVHRERxL+HCVFFREXQ6Hfbv34/Ozk5IkuSrud1uNDQ0QBAEXLp0iSv8FkiSJDQ2NkKn0yE9PV1dJiIiiisJF6Zyc3Px9OlTVFVV4dixY0hKSoJGo4FGo8GOHTuAmRVobIReOK/Xi4mJCXz77bf8HImIKO4lZAM6ERERUawk3MgUERERUSwxTBERERFFgWGKiIiIKAoMU0RERERRYJgiIiIiisL/AUt60NEUOo80AAAAAElFTkSuQmCC\" width=\"595\" height=\"73\"\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e is the particle settling velocity (m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003eρ\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e is the particle density ((kg\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), \u003cem\u003eρ\u003c/em\u003e\u003csub\u003e\u003cem\u003ea\u003c/em\u003e\u003c/sub\u003e is the air density ((kg\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), \u003cem\u003ed\u003c/em\u003e is the particle diameter (m), and \u003cem\u003e\u0026micro;\u003c/em\u003e is the dynamic viscosity of air (kg/m\u0026middot;s).\u003c/p\u003e \u003cp\u003e \u003cb\u003e(3) Grain Size\u0026ndash;Height Coupling Model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo further elucidate the migration and settling mechanisms of Aeolian sand particles with different grain sizes along the vertical profile, the vertically stratified sediment accumulation was converted into unit volume concentrations C(z,t). Based on the concept of \"convection\u0026ndash;diffusion\u0026ndash;source-sink\" processes, a grain size\u0026ndash;height coupling model was established [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe model assumptions are as follows:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe Aeolian sand flow is in a quasi-steady turbulent field;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSand particles in the vertical direction are controlled only by gravitational settling, turbulent diffusion, and localized resuspension;\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHorizontal transport is approximately uniform within the local volume.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eConsidering that the Stokes settling velocity \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e varies quadratically with particle diameter, and that the turbulent diffusion coefficient \u003cem\u003eK\u003c/em\u003e\u003csub\u003e\u003cem\u003ez\u003c/em\u003e\u003c/sub\u003e primarily reflects vertical momentum exchange induced by surface shear, the governing model can be simplified into a one-dimensional convection\u0026ndash;diffusion control equation [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"706\" height=\"70\"\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cem\u003eC(z,t)\u003c/em\u003e is the particle concentration at height \u003cem\u003ez\u003c/em\u003e and time \u003cem\u003et\u003c/em\u003e (kg\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e), \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e is the particle settling velocity (m\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003eK\u003c/em\u003e\u003csub\u003e\u003cem\u003ez\u003c/em\u003e\u003c/sub\u003e is the vertical turbulent diffusion coefficient (m\u003csup\u003e2\u003c/sup\u003e\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and S(z,t) is the source or sink term representing particle resuspension or removal (kg\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u0026middot;s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003eWe adopt a one-dimensional size-dependent convection\u0026ndash;diffusion perspective to interpret vertical stratification, i.e., higher settling tendency for coarse fractions and enhanced turbulent mixing for fines, without estimating \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e or \u003cem\u003eK\u003c/em\u003e\u003csub\u003e\u003cem\u003ez\u003c/em\u003e\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(4) Cumulative grain-size functions and diagnostics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor each sample we computed the cumulative distribution function (CDF) of grain size, i.e., the percent finer than diameter d on a logarithmic (\u003cem\u003eφ\u003c/em\u003e) scale. From each CDF we extracted standard percentiles D10, D50, D90(\u0026micro;m). Beyond these summary metrics, the shape of the CDF was used diagnostically:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eVertical shift of the entire curve to the right (toward finer sizes) with height indicates upward fining; a leftward shift indicates coarsening.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSteeper mid-quantile slope (large dF/dϕ near D50) reflects better sorting (narrower distributions), whereas a gentler slope reflects poorer sorting.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTail behavior: a long coarse tail (delayed rise at low φ / large d) signals enrichment of coarse fractions; a long fine tail indicates fine enrichment.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCross-overs among CDFs from different heights diagnose mixed or bimodal populations along the profile.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eNote\u003c/strong\u003e \u003cp\u003eIn this study we did not compute Folk\u0026ndash;Ward sorting, skewness or kurtosis; diagnostics are based on D50 and CDF behavior.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e(5) Uncertainty quantification and rationale for not using Monte Carlo\u003c/b\u003e \u003c/p\u003e \u003cp\u003eInstead of generating synthetic grain-size distributions via Monte Carlo, which would require unverifiable assumptions about the effective sample size and independence of monthly integrated trap spectra, we quantify uncertainty using a nonparametric bootstrap at the site-month level. For each geomorphic unit and height, we resample months with replacement (1,000 iterations) and recompute height\u0026ndash;D50 slopes, percentile ratios(D90/D50, D50/D10), and pairwise KS distances; 2.5\u0026ndash;97.5% quantiles define 95% confidence intervals. This design propagates temporal variability captured by the sampling without imposing parametric distributional forms. Because stochastic resampling cannot reproduce unobserved wind\u0026ndash;roughness states, process-level replication is beyond the scope of the present field study.\u003c/p\u003e \u003cp\u003eSubsequently, the inversion results were analyzed in combination with wind speed, wind direction, and grain size parameters at different height layers, allowing for quantification of the effects of wind field variations on particle size sorting and vertical transport efficiency.\u003c/p\u003e \u003cp\u003eThese findings provide dynamic parameter constraints essential for the scaled design of windbreak and sand stabilization measures.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eBased on the measured vertical grain size data from four typical geomorphic units at the edge of the Kumtag Desert\u0026mdash;namely, the semi-fixed sandy land at mountain tops, the vegetated belt on the left bank of the Danghe Reservoir, the desert-edge area of Mingsha Mountain, and the exposed floodplain on the right bank of the Danghe Reservoir\u0026mdash;a systematic analysis was conducted to characterize the grain size distribution patterns of Aeolian sand deposits at different height layers and their vertical variation features.\u003c/p\u003e \u003cp\u003eBy comparing the differences in grain size sorting and vertical gradients among these units, the study clearly delineated the grain size differentiation patterns of Aeolian sand deposition under different geomorphic conditions, thereby providing scientific support for further investigations into Aeolian sand transport mechanisms and regional ecological management.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Vertical Grain-Size Variation by Geomorphic Unit\u003c/h2\u003e \u003cp\u003eAnalysis of the vertical grain size distribution characteristics of Aeolian sand deposits in the four typical geomorphic units at the edge of the Kumtag Desert revealed significant differences in grain size composition and vertical variation patterns among regions, reflecting the combined effects of geomorphology, vegetation, and aeolian dynamics (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e).Across the seven collectors (LB1\u0026ndash;LB4, RB1, MS1, MT1), the monthly mass of aeolian sand captured per H10 (0\u0026ndash;0.2 m) layer ranged from 12 g to 146 g (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u0026thinsp;=\u0026thinsp;58\u0026thinsp;\u0026plusmn;\u0026thinsp;27 g), with the left-bank LB2 showing the seasonal maximum in April (146 g).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the mountain top region (Figure a; MT1), the grain size composition at all height layers was dominated by medium-sized particles (63\u0026ndash;250 \u0026micro;m), maintaining a proportion above 70%. Vertically, the content of coarse particles (\u0026gt;\u0026thinsp;250 \u0026micro;m) remained below 25% across all layers but exhibited a clear decreasing trend with height, declining from 22.5% at the near-surface layer H10(0\u0026ndash;0.2 m) to less than 10% at the top layer H70(0.6\u0026ndash;0.8 m). In contrast, the proportion of fine particles (\u0026lt;\u0026thinsp;63 \u0026micro;m) steadily increased with height, rising from less than 10% at the bottom to nearly 20% at the top, reflecting the resuspension and upward transport of fine particles characteristic of the sparsely vegetated semi-fixed sandy environment.\u003c/p\u003e \u003cp\u003eIn the vegetated belt region on the left bank of the Danghe Reservoir (Figure b; LB1\u0026ndash;LB4), the vertical differentiation of grain size was more pronounced, displaying a significant vegetation interception effect. The bottom layer H10(0\u0026ndash;0.2 m) had the highest proportion of coarse particles, approximately 40%, markedly higher than that observed at the mountain top for the corresponding layer, indicating that vegetation significantly enhanced the interception of coarse particles near the ground. With increasing height, the proportion of coarse particles rapidly decreased, dropping to about 5% at the H70 (0.6\u0026ndash;0.8 m) layer. Meanwhile, medium-sized particles gradually became dominant, increasing from 55% at the bottom to around 65% at the top. Fine particles showed a rapid increase from less than 5% at the bottom to about 30% at the top, highlighting the significant promoting effect of vegetation cover on the resuspension and vertical transport of fine particles.\u003c/p\u003e \u003cp\u003eIn the desert-edge area of Mingsha Mountain (Figure c; MS1), typical vertical variation characteristics of mobile sand dunes at the desert margin were observed. Coarse particles accounted for more than 55% at the near-surface height H10(0\u0026ndash;0.2 m), significantly higher than in other regions at the same height, reflecting the rapid sedimentation and accumulation of coarse particles under mobile dune conditions. As height increased, the coarse particle proportion sharply decreased, falling below 20% at H50(0.4\u0026ndash;0.6 m). Meanwhile, the proportion of medium-sized particles increased from about 35% near the surface and became dominant in the middle to upper layers (reaching up to 60%). Fine particles rapidly increased to about 30% at the top layer H70(0.6\u0026ndash;0.8 m), clearly indicating the upward suspension and transport of fine particles under strong wind conditions.\u003c/p\u003e \u003cp\u003eIn the exposed floodplain region on the right bank of the Danghe Reservoir (Figure d; RB1), the vertical grain size structure was the most distinct. Coarse particles overwhelmingly dominated the bottom layer H10(0\u0026ndash;0.2 m), accounting for more than 65%, the highest among the four regions. As height increased, the proportion of coarse particles decreased rapidly, dropping to nearly zero at the top layer H70 (0.6\u0026ndash;0.8 m). Conversely, the proportion of fine particles showed a pronounced increasing trend, rising from less than 10% near the ground to more than 60% at the 0.6\u0026ndash;0.8 m height, indicating an efficient vertical transport of fine particles under strong wind conditions in the open bare land environment, with extremely significant vertical grain size sorting effects.\u003c/p\u003e \u003cp\u003eThese analyses reveal that the vertical grain size differentiation in the mountain top region is jointly regulated by topographic uplift and slope wind effects; in the left bank vegetated belt, vegetation roughness effects are the primary influence; in the Mingsha Mountain desert-edge region, the results reflect the dynamic coupling of strong winds and coarse particle sedimentation in mobile dunes; and in the open bare land of the right bank, a clear vertical sorting pattern of coarse and fine particles under strong winds is observed. These findings provide a deeper understanding of the typical vertical variation patterns of Aeolian sand grain size across different geomorphic units at the edge of the Kumtag Desert and offer scientific support for the optimization of regional ecological management measures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Vertical Sorting Characteristics\u003c/h2\u003e \u003cp\u003eTo further elucidate the vertical grain size sorting characteristics of Aeolian sand deposits in different geomorphic units at the edge of the Kumtag Desert, boxplot analyses of grain size data at various height layers were conducted for the mountain top, left bank vegetated belt, Mingsha Mountain desert-edge area, and right bank exposed floodplain regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe box-plots in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e depict layer-wise variations in grain size (\u0026micro;m).A Kruskal\u0026ndash;Wallis test followed by Dunn\u0026rsquo;s post-hoc pairwise comparison (α\u0026thinsp;=\u0026thinsp;0.05) indicates that only the surface layer H10 (0\u0026ndash;0.2 m) differs significantly between the left- and right-bank sites (p\u0026thinsp;=\u0026thinsp;0.018), while differences among deeper layers H30\u0026ndash;H90 are not significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.10).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the mountain top region (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3.2\u003c/span\u003ea; MT1), the dispersion of grain size data across different height layers was relatively small, and the median grain size gradually decreased with increasing height. The bottom layer H10 (0\u0026ndash;0.2 m) exhibited the highest median grain size and the widest grain size distribution range, indicating a dominance of coarse particles. As height increased, the concentration of grain size data became more pronounced, the box size gradually narrowed, and the gap between the upper and lower quartiles decreased, demonstrating a clear vertical grain size sorting effect and reflecting the slow upward fining trend of Aeolian sand under sparse vegetation conditions in the semi-fixed sandy land. By the top layer H90 (0.8\u0026ndash;1.0 m), the median grain size was distinctly finer.\u003c/p\u003e \u003cp\u003eIn the left bank vegetated belt region of the Danghe Reservoir (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3.2\u003c/span\u003eb; LB1\u0026ndash;LB4), the vertical sorting characteristics were more distinct. At the bottom layer H10 (0\u0026ndash;0.2 m), the median grain size was relatively large, and the interquartile range was wide, indicating a significant presence of coarse particles and a broad range of grain size fluctuations. With increasing height, the median grain size decreased markedly, the box height gradually shrank, and a clear shift toward finer particles was observed. By the top layer H90 (0.8\u0026ndash;1.0 m), grain size data were concentrated within a distinctly finer range, and the distribution became significantly more centralized, demonstrating that vegetation cover strongly promotes vertical grain size sorting.\u003c/p\u003e \u003cp\u003eIn the Mingsha Mountain desert-edge region (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3.2\u003c/span\u003ec; MS1), the vertical sorting of grain size exhibited a pronounced gradient. The bottom layer H10 (0\u0026ndash;0.2 m) had the largest median grain size and the widest distribution among all regions, reflecting the rapid near-surface deposition of coarse particles under the influence of strong winds at the desert margin. As height increased, the median grain size decreased rapidly, the distribution range narrowed significantly, and concentration improved. At middle to upper height layers H50\u0026ndash;H90 (0.4\u0026ndash;1.0 m), grain size data showed strong concentration trends with markedly smaller box sizes, indicating a clear upward fining trend, further confirming that vertical grain size sorting was strongest in the Mingsha Mountain region under strong wind conditions.\u003c/p\u003e \u003cp\u003eIn the right bank exposed floodplain region of the Danghe Reservoir (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e3.2\u003c/span\u003ed; RB1), the vertical grain size sorting was the most pronounced and the changes were the most dramatic. Near the ground surface H10 (0\u0026ndash;0.2 m), both the median grain size and the distribution range reached their maximum values, with the box height being wide and extending significantly toward the coarse particle end, indicating a strong dominance of coarse particles. As height increased, the median grain size decreased rapidly, and the interquartile range also narrowed significantly. By the top layer H90 (0.8\u0026ndash;1.0 m), the grain sizes were concentrated within an extremely fine range, reflecting the strong influence of aerodynamic forces on grain size sorting in the open bare floodplain environment, with the most distinct vertical grain size gradient observed.\u003c/p\u003e \u003cp\u003eIn summary, the mountain top region exhibited a slow vertical fining process under conditions of sparse vegetation and micro-topography; the left bank vegetated belt significantly enhanced vertical grain size sorting and increased data concentration; the Mingsha Mountain desert-edge region displayed a typical pattern of rapid coarse particle deposition and fine particle suspension under strong wind conditions; and the right bank exposed floodplain region, characterized by open terrain and lack of vegetation, showed the most pronounced vertical grain size sorting effect. These sorting characteristics further confirm that aeolian dynamics, vegetation cover, and topographic conditions exert significant influences on the vertical distribution of Aeolian sand grain size across different geomorphic units.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Vertical Percentage Composition\u003c/h2\u003e \u003cp\u003eTo further clarify the vertical distribution structures of Aeolian sand grain size components at different height layers within each geomorphic unit, this study classified particle sizes based on coarseness and fineness, and conducted a detailed analysis using percentage-stacked bar charts for the mountain top, left bank vegetated belt, Mingsha Mountain desert-edge area, and right bank exposed floodplain regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e).Height codes used in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e are H10\u0026thinsp;=\u0026thinsp;0\u0026ndash;0.2 m, H30\u0026thinsp;=\u0026thinsp;0.2\u0026ndash;0.4 m, H50\u0026thinsp;=\u0026thinsp;0.4\u0026ndash;0.6 m, H70\u0026thinsp;=\u0026thinsp;0.6\u0026ndash;0.8 m, and H90\u0026thinsp;=\u0026thinsp;0.8\u0026ndash;1.0 m.\u003c/p\u003e \u003cp\u003eIn the mountain-top region (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3.3\u003c/span\u003ea; MT1), the grain-size composition at all height layers was dominated by medium-sized particles (63\u0026ndash;250 \u0026micro;m), accounting for more than half (approximately 70\u0026ndash;80%) across all layers, with relatively minor vertical variation. The proportion of coarse particles (\u0026gt;\u0026thinsp;250 \u0026micro;m) was low, gradually decreasing from about 20% at the bottom layer H10 (0\u0026ndash;0.2 m) to approximately 8% at the top layer H70 (0.6\u0026ndash;0.8 m). Conversely, the proportion of fine particles (\u0026lt;\u0026thinsp;63 \u0026micro;m) steadily increased from about 10% at the bottom to around 20% at the top. This stable grain-size structure reflects a moderate vertical sorting process under the semi-fixed sandy-land conditions at the mountain top.\u003c/p\u003e \u003cp\u003eIn the left-bank vegetated belt (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3.3\u003c/span\u003eb; LB1\u0026ndash;LB4), the vertical percentage-stacked structure of grain-size composition was more distinct, exhibiting a pronounced vertical gradient effect. At the near-surface layer H10 (0\u0026ndash;0.2 m), the proportion of coarse particles was the highest, close to 40%, indicating effective interception of coarse particles by vegetation. With increasing height, the proportion of coarse particles sharply decreased, dropping to only about 5% at the top layer H70 (0.6\u0026ndash;0.8 m). Meanwhile, medium-sized particles progressively became dominant, increasing from approximately 50% at the bottom to over 65% at mid to upper layers. Fine particles also increased significantly with height, reaching around 30% at the top, reflecting the strong promoting effect of vegetation cover on the resuspension and upward transport of fine particles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the Mingsha-Mountain desert-edge region (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3.3\u003c/span\u003ec; MS1), the stacking structure reflected the typical vertical grain-size sorting characteristics under strong wind conditions at the desert margin. At the near-surface layer H10 (0\u0026ndash;0.2 m), coarse particles accounted for more than half (approximately 55%), the highest among the four regions. As height increased, the proportion of coarse particles decreased rapidly, while medium-sized particles gradually became dominant (reaching up to about 60%), and fine particles rapidly increased at the top layer H70 (0.6\u0026ndash;0.8 m) to nearly 30%. This pattern clearly illustrates the process of rapid coarse-particle deposition and upward migration of fine particles under strong wind conditions in the Mingsha-Mountain region.\u003c/p\u003e \u003cp\u003eIn the right-bank exposed floodplain of the Danghe Reservoir (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e3.3\u003c/span\u003ed; RB1), the vertical stacking characteristics of grain-size composition were particularly pronounced. At the bottom layer H10 (0\u0026ndash;0.2 m), the proportion of coarse particles was extremely high, reaching about 65%, the highest among the four regions. With increasing height, the proportion of coarse particles decreased rapidly and almost disappeared at the top layer H70 (0.6\u0026ndash;0.8 m), while fine particles gradually became dominant, exceeding 60% at H70. This indicates that under strong wind conditions, the exposed floodplain environment exhibited an extremely significant vertical sorting effect between coarse and fine particles.\u003c/p\u003e \u003cp\u003eIn summary, differences in the percentage-stacked grain size distributions across geomorphic units revealed that the mountain top semi-fixed sandy land exhibited a moderate and stable vertical grain size structure; the left bank vegetated belt showed enhanced vertical sorting due to the vegetation interception effect; the Mingsha Mountain region reflected a typical pattern of rapid coarse particle deposition and fine particle suspension at the desert edge; and the right bank exposed floodplain displayed the strongest vertical grain size differentiation effect. These differences profoundly demonstrate the critical influence of geomorphic conditions, vegetation cover, and aeolian dynamics on the vertical grain size distribution structures of Aeolian sand deposits.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe vertical distribution characteristics of Aeolian sand grain size are not only controlled by regional aeolian dynamics but are also closely related to vegetation coverage, surface roughness, and topographic features of different geomorphic units [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The results presented in Chap.\u0026nbsp;3 demonstrated that the vertical grain size distributions in the semi-fixed sandy land at the mountain top, the vegetated belt on the left bank of the Danghe Reservoir, the desert-edge area of Mingsha Mountain, and the exposed floodplain on the right bank of the Danghe Reservoir exhibited significant differences. For methodological consistency, results are limited to grain-size\u0026ndash;height diagnostics; no numerical estimates of T, D, or \u003cem\u003ev\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e are reported.\u003c/p\u003e \u003cp\u003eThis chapter will further focus on the key features of vertical grain size variations across the different geomorphic units. Through comparative analysis of mean grain size, linear fitting, and visualization using grain size\u0026ndash;height heatmaps, the intrinsic relationships between Aeolian sand transport processes and grain size sorting mechanisms under varying geomorphic conditions will be deeply explored. The aim is to provide theoretical support for the optimization of regional Aeolian sand control strategies and ecological protection measures.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Mean Grain-Size Trends by Height\u003c/h2\u003e \u003cp\u003eDuring the processes of Aeolian sand transport and deposition, the vertical differentiation of particle grain size not only reflects the interactions between aeolian dynamics and surface characteristics but also reveals the differences in depositional dynamics among various geomorphic units [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further understand the seolian sand sedimentation patterns in the Danghe Reservoir and its surrounding areas, Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e illustrates the variation trends of median grain size (D50) at different height layers within 0\u0026ndash;1.0 m across four typical geomorphic units: the semi-fixed sandy land at the mountain top (MT1), the vegetated belt on the left bank (LB1\u0026ndash;LB4), the desert-edge area of Mingsha Mountain (MS1), and the exposed floodplain on the right bank (RB1). Height codes are H10\u0026thinsp;=\u0026thinsp;0\u0026ndash;0.2 m, H30\u0026thinsp;=\u0026thinsp;0.2\u0026ndash;0.4 m, H50\u0026thinsp;=\u0026thinsp;0.4\u0026ndash;0.6 m, H70\u0026thinsp;=\u0026thinsp;0.6\u0026ndash;0.8 m, and H90\u0026thinsp;=\u0026thinsp;0.8\u0026ndash;1.0 m.\u003c/p\u003e \u003cp\u003eFigure 4\u0026thinsp;\u0026minus;\u0026thinsp;1 presents the variation trends of the mean grain size (D50) of Aeolian sand deposits with height within the 0\u0026ndash;1.0 m vertical profiles across four different geomorphic units: the mountain top, the left bank vegetated belt, the desert-edge area of Mingsha Mountain, and the exposed floodplain on the right bank of the Danghe Reservoir. Overall, the grain size variations in each geomorphic unit were influenced by aeolian dynamic sorting, displaying nonlinear fluctuations. However, notable differences in variation direction and amplitude were observed among the units, reflecting the combined effects of wind strength, surface roughness, and vegetation resistance.\u003c/p\u003e \u003cp\u003eMountain top (MT1). Grain size remained relatively stable at the bottom layer H10 (0\u0026ndash;0.2 m) but increased sharply in the middle to upper layers (H30\u0026ndash;H70), reaching a peak at H70. A similar mid- to upper-layer coarsening phenomenon has also been observed in the Taklamakan Desert, where Chen et al. noted that approximately 0.2 meters height represents the transition from saltation-dominated to suspension-dominated transport around an 80-meter aeolian flux tower [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLeft bank vegetated belt (LB1\u0026ndash;LB4). The mean grain size was largest at the bottom layer H10 and gradually decreased with increasing height, but a distinct secondary peak appeared at the middle height layer H50 before declining again. This \u0026ldquo;bimodal\u0026rdquo; variation pattern is consistent with findings by van der Wal (2000) in coastal dune vegetation restoration areas, where it was attributed to the combined effects of coarse particle retention and fine particle uplift induced by vegetation[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMingsha-Mountain desert edge (MS1). D50 exhibited a nearly monotonous decreasing trend, with grain size progressively fining as height increased, except for a slight increase at H70. Similar vertical fining trends are widely observed in active dune areas. For instance, Wang et al. found that on gobi barelands, grain size continuously decreased above 0.3 meters, which aligns with the environmental conditions of low surface roughness and high shear stress typical of dune surfaces [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRight-bank bare floodplain (RB1). The amplitude of mean grain size variation was the smallest, showing a gentle pattern characterized by slightly coarser particles at mid-layers (H50) and finer particles at the top (H70/H90 if present). Such a weak convex trend has also been recorded in surface-crusted environments in moist settings. For example, Lancaster et al. observed that in moist intertidal zones, coarse particles concentrated around 0.3 meters height, with noticeable changes only under intensified wind conditions [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall, these different vertical trends indicate that the vertical grain size sorting of Aeolian sediments is strongly regulated by local aeolian dynamics, surface characteristics, and vegetation conditions [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Vegetation coverage can both intercept coarse particles at lower layers and alter turbulent structures to promote fine particle uplift, forming the \u0026ldquo;bimodal\u0026rdquo; grain size structure observed in this study and also by van der Wal et al. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Conversely, on exposed and dry dune slopes, the gradual settling of heavy particles typically results in a monotonous fining trend, consistent with wind tunnel experiments and field observations [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe weak variation observed in the exposed floodplain could be attributed to localized surface moisture or crusting effects, which inhibit saltation transport and cause coarse particles to accumulate at mid-heights, similar to observations in moist beach environments [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary, the vertical grain size variation patterns revealed in this study are highly consistent with previous understandings of vertical grain size differentiation in Aeolian deposits, such as those described by Qian and Dong for sedimentation in floodplain areas of China, where Aeolian sand grain size distributions were found to be governed by the coupling of multiple factors [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Additionally, the identification of secondary grain size peaks at mid-layers further supports the importance of the mixed bed collision-splash mechanism as proposed in numerical simulations [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].. These findings enhance the understanding of Aeolian sediment budgets and grain size dynamics in the Danghe Reservoir region and lay a solid foundation for future research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Height\u0026ndash;Grain-Size Linear Fits\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e4.2\u003c/span\u003e presents ordinary-least-squares fits between median grain size (D50) and height (h) within 0\u0026ndash;1.0 m for the four geomorphic units: mountain top (MT1), left-bank vegetated belt (LB1\u0026ndash;LB4, aggregated), Mingsha-Mountain desert edge (MS1), and right-bank bare floodplain (RB1). Height codes: H10\u0026thinsp;=\u0026thinsp;0\u0026ndash;0.2 m, H30\u0026thinsp;=\u0026thinsp;0.2\u0026ndash;0.4 m, H50\u0026thinsp;=\u0026thinsp;0.4\u0026ndash;0.6 m, H70\u0026thinsp;=\u0026thinsp;0.6\u0026ndash;0.8 m, H90\u0026thinsp;=\u0026thinsp;0.8\u0026ndash;1.0 m. For the left bank, site-level series from LB1\u0026ndash;LB4 were combined (mean of layer medians) before fitting to ensure naming consistency.\u003c/p\u003e \u003cp\u003eMechanistic interpretation. Negative slopes signal dominance of settling over mixing for coarse grains under strong winds (MS1), whereas weak or slightly positive slopes indicate topographic speed-up or boundary-condition damping (MT1, RB1). Vegetation increases the magnitude of the negative slope by simultaneously trapping coarse grains at H10 and enhancing mixing for fines above the canopy (LB1\u0026ndash;LB4). Within the convection\u0026ndash;diffusion lens, slope magnitude scales with \u003cem\u003eK\u003c/em\u003e\u003csub\u003e\u003cem\u003ez\u003c/em\u003e\u003c/sub\u003e/\u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003eₛ\u003c/em\u003e\u003c/sub\u003e for fines, while R\u003csup\u003e2\u003c/sup\u003e reflects the temporal steadiness of the local wind/roughness state [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll four units exhibit statistically significant linear relationships (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), but the slope sign and coefficients of determination (R\u003csup\u003e2\u003c/sup\u003e) differ, reflecting the coupled controls of regional wind regime, surface roughness, and vegetation resistance [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMountain top (MT1). A weak positive slope with the lowest R\u003csup\u003e2\u003c/sup\u003e indicates that slight topographic undulations and wind direction shear led to localized coarse particle retention in the middle to upper layers, thereby weakening the typical \u0026ldquo;finer with height\u0026rdquo; trend. This characteristic corresponds well with the redeposition peak height observed by Qian et al. (2019) on ridges in the Qaidam Basin [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLeft-bank vegetated belt (LB1\u0026ndash;LB4). A moderate negative slope and moderate R\u003csup\u003e2\u003c/sup\u003e reflect strong interception of coarse particles near H10 by vegetation and vegetation-induced perturbations to near-surface turbulence that promote upward transport of fines, yielding a rapid fining trend [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMingsha-Mountain desert edge (MS1). Both the most negative slope and the highest R\u003csup\u003e2\u003c/sup\u003e among units reveal a typical strong-wind sorting mechanism: rapid near-surface deposition of coarse particles and efficient upward transport of fine particles. The slope of \u0026minus;\u0026thinsp;0.62 mm\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e obtained by Chen et al. (2015) from an 80-meter flux tower in the Taklamakan Desert closely matches the slope of \u0026minus;\u0026thinsp;0.59 mm\u0026middot;m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e found in this study [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRight-bank bare floodplain (RB1). Although the slope is negative, the R\u003csup\u003e2\u003c/sup\u003e is relatively low, implying that the D50\u0026ndash;height relation is weakened by surface-moisture crusting and/or low roughness under a relatively stable wind field. A similar weak convex behavior was recorded for intertidal seolian deposition [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall, the differences in linear fitting parameters among the geomorphic units further confirm that topographic undulations determine the locations of near-surface shear velocity transitions, vegetation cover enhances grain size sorting by altering turbulent pulsation structures, and strong wind zones exhibit clearer coarse\u0026ndash;fine shear stratification. This regional comparison is highly consistent with both domestic and international field observations and provides reliable quantitative boundary conditions for subsequent heatmap clustering analyses and numerical simulations [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study quantified the vertical grain-size structure of seolian sand across four geomorphic units\u0026mdash;MT1 hill-crest, LB1\u0026ndash;LB4 left-bank vegetated belt, MS1 Mingsha-Mountain desert edge, and RB1 right-bank bare floodplain\u0026mdash;using five-layer trapping (H10\u0026ndash;H90), grain-size statistics, and linear fits.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(1)\u003c/b\u003e Unit-specific vertical trends. All profiles exhibit upward fining, but with distinct signatures: MS1 shows quasi-monotonic fining with ΔD50\u0026thinsp;\u0026asymp;\u0026thinsp;121 \u0026micro;m and R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.91; the left bank (LB1\u0026ndash;LB4) presents a bimodal pattern with secondary mid-layer coarsening (overall R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.78); RB1 displays mid-layer coarsening and weak linearity (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.35); MT1 shows a mild upper-layer coarsening shoulder superimposed on the general fining trend.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(2)\u003c/b\u003e Component stacking. Heat-map/stack analyses indicate fines (\u0026lt;\u0026thinsp;63 \u0026micro;m) increase upward by 18% \u0026plusmn; 6% per 0.2 m, while coarse fractions (\u0026gt;\u0026thinsp;250 \u0026micro;m) concentrate within H10 (0\u0026ndash;0.2 m), defining a near-surface control layer for sand-control design.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(3)\u003c/b\u003e Vertical distribution shape (qualitative). With height the grain-size distributions narrow and the fine-end dominance increases, most pronounced at MS1; the left bank retains mid-layer mixing consistent with vegetation-induced bimodality, while RB1 is coarse-rich near H10 and rapidly fine-dominated aloft.\u003c/p\u003e \u003cp\u003e \u003cb\u003e(4)\u003c/b\u003e Between-unit differences. Nonparametric tests (Kruskal\u0026ndash;Wallis with Dunn\u0026rsquo;s post-hoc) show a significant difference between left- and right-bank sites only at H10 (p\u0026thinsp;=\u0026thinsp;0.018); deeper layers are not significantly different (p\u0026thinsp;\u0026gt;\u0026thinsp;0.10).\u003c/p\u003e \u003cp\u003e \u003cb\u003e(5)\u003c/b\u003e Mechanistic implications. A size-dependent convection\u0026ndash;diffusion framework, constrained by the observed D50\u0026ndash;height relations, supports the view that wind regime, vegetation roughness, and topographic acceleration jointly control vertical stratification through differential settling (\u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003es\u003c/em\u003e\u003c/sub\u003e) and turbulent diffusion (\u003cem\u003eK\u003c/em\u003e\u003csub\u003e\u003cem\u003ez\u003c/em\u003e\u003c/sub\u003e). These parameters provide actionable bounds for optimizing windbreak height, checkerboard spacing, and sand-fence layouts in desert\u0026ndash;oasis ecotones, with priority intervention in the 0\u0026ndash;0.4 m layer.\u003c/p\u003e \u003cp\u003eLimitations and outlook.The study uses one site per non-left-bank unit and lacks fully co-located, high-frequency wind/roughness measurements; seasonal extremes are under-represented. Future work should extend multi-site, multi-season observations and pair trapping with in situ turbulence/roughness sensors or wind-tunnel tests to achieve process-level replication and strengthen model calibration.\u003c/p\u003e \u003cp\u003eFuture work. We will extend observations to multi-season and extreme-event periods, add co-located wind/turbulence and surface-roughness measurements, and broaden spatial replication across additional left/right-bank, dune-edge, and hill-crest sites. Controlled experiments and numerical modeling will be used to isolate vegetation/topography effects and inform sand-control design, and the harmonized LB/RB/MS/MT (H10\u0026ndash;H90) datasets will be released to support reproducibility.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China, Regional Fund: Study on the Mechanism of Heavy Metal Migration and Transformation in Alpine Inland Rivers Due to Cascading Dam Construction (Grant No. 52169015); China\u0026rsquo;s National Key Research and Development Program: Targeted Depolymerization and Enhanced Pretreatment Technology for Diversified Biomass Alcohol Feedstocks (Grant No. 2022YFB4201901-1); Gansu Provincial Major Science and Technology Project: Key Technology R\u0026amp;D for Large-Scale Disposal and Recycling of Plateau Summer Vegetable Waste (24ZDNA004).\u003c/p\u003e \u003cp\u003eThis work was supported by Incubation Program of Excellent Doctoral Dissertation-Lanzhou University of Technology.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.W.: Conceptualization, Methodology, Investigation, Data curation, Formal analysis, Writing \u0026ndash; original draft. Y.W. (Yu): Conceptualization, Methodology, Investigation, Formal analysis, Writing \u0026ndash; review \u0026amp; editing, Supervision. Y.Z.: Investigation, Data curation, Writing \u0026ndash; review \u0026amp; editing. K.L.: Investigation, Data curation, Writing \u0026ndash; review \u0026amp; editing. T.-f.L.: Investigation, Data curation, Writing \u0026ndash; review \u0026amp; editing. M.T.: Investigation. W.R.: Investigation. X.Z.: Investigation. F.H.: Investigation. J.Z.: Investigation. Y.H.: Investigation. Y.W. (Yinuo): Investigation. E.Q.: Investigation. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors gratefully acknowledge the colleagues and students from the College of Energy and Power Engineering, Lanzhou University of Technology, for their assistance with field sampling, hydrological monitoring, and laboratory analyses. We also thank the local hydrological and environmental agencies in the Danghe River Basin for providing long-term observational data and logistical support. The authors have reviewed and edited the output and take full responsibility for the content of this publication.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eGrain-size and trapped aeolian sand datasets were deposited into the Zenodo repository under DOI https://doi.org/10.5281/zenodo.18019777 and are available at the following URL: https://doi.org/10.5281/zenodo.18019777.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang, T. Aeolian desertification and its control in northern China. \u003cem\u003eInt. Soil. Water Conserv. Res. 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[Internet]\u003c/em\u003e. \u003cb\u003e32\u003c/b\u003e, 144\u0026ndash;153. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.aeolia.2018.02.004\u003c/span\u003e\u003cspan address=\"10.1016/j.aeolia.2018.02.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Aeolian sand, Desert edge, Vertical distribution, Sediment grain size","lastPublishedDoi":"10.21203/rs.3.rs-8396723/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8396723/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe characterize height-resolved grain-size stratification in captured (trapped) aeolian sand across four geomorphic units at the edge of the Kumtag Desert, and assess the roles of wind forcing, vegetation roughness, and topographic effects. From 2023 to 2025, seven five-layer QN-JSY collectors were deployed at a hill-crest semi-fixed sand area (MT1), the vegetated left bank of the Danghe Reservoir (LB1\u0026ndash;LB4), the Mingsha Mountain desert edge (MS1), and the bare right-bank floodplain (RB1), yielding n\u0026thinsp;=\u0026thinsp;420 samples. Grain-size distributions were measured (Mastersizer 2000) and analyzed using D50, layer-wise fractions, heatmap profiles, and D50\u0026ndash;height fits with nonparametric between-unit tests. Grain size generally fines with height, with unit-specific signatures: MS1 shows quasi-monotonic fining (ΔD50\u0026thinsp;\u0026asymp;\u0026thinsp;121 \u0026micro;m; R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.91), the left bank exhibits a vegetation-linked bimodal structure (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.78), RB1 shows mid-layer coarsening with weak linearity (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.35), and MT1 presents a mild upper-layer coarsening shoulder. Fine fractions (\u0026lt;\u0026thinsp;63 \u0026micro;m) increase upward, whereas coarse fractions (\u0026gt;\u0026thinsp;250 \u0026micro;m) concentrate near H10, highlighting a near-surface control layer relevant to sand-control design. These results support a size-dependent convection\u0026ndash;diffusion perspective for interpreting vertical sorting across contrasting desert\u0026ndash;oasis settings.\u003c/p\u003e","manuscriptTitle":"Vertical and Spatiotemporal Variations in Grain Size of Aeolian Sand Deposits at the Edge of the Kumtag Desert","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-20 09:47:58","doi":"10.21203/rs.3.rs-8396723/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"40645f62-146c-4c38-a1b6-436bf1cbd1dd","owner":[],"postedDate":"March 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64735514,"name":"Biological sciences/Ecology"},{"id":64735515,"name":"Earth and environmental sciences/Ecology"},{"id":64735516,"name":"Earth and environmental sciences/Environmental sciences"},{"id":64735517,"name":"Earth and environmental sciences/Solid earth sciences"}],"tags":[],"updatedAt":"2026-04-28T20:24:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-20 09:47:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8396723","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8396723","identity":"rs-8396723","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}