The Influence of Near-Surface Ground Features on Near-Surface Airflow

Abstract

Dust and sand storms occurring in northern China are strongly controlled by near-surface aerodynamics, yet the spatial heterogeneity of these processes remains poorly understood. We obtained field measurements of the wind above gobis, sandy surfaces, and dry lakebeds in the Hexi Corridor Desert and Heihe River Basin, and sandy surfaces in northern China. First, the slope of wind profile (a₁) reveals distinct drag reversal with increasing wind speed: under low winds, a₁ increases from sandy to dry lakebed to gobi surfaces, whereas under high winds, actively saltating sandy surfaces exhibit the highest a₁, surpassing gobi and dry lakebed. Second, the dynamic feedback between sediment transport and aerodynamics is clear: at below-threshold winds, friction velocity ($u_{*}$) and aerodynamic roughness length (z₀) are lowest for sand; however, as wind speed increases to initiate significant saltation, the sandy surface develops the highest $u_{*}$ and z₀, highlighting the dominant role of grain-borne roughness. Third, the focal height (z_f) shows regional disparity, varying by up to two orders of magnitude for both sandy and gobi surfaces, with a strong correlation to local gravel coverage. This work provides spatially explicit parameterizations of surface type, offering a physical basis for modeling dust emission and transport in northern China and similar arid regions globally. Such parameterizations are essential for developing reliable early warning systems and evidence-based land management strategies. These advances contribute directly to ecosystem sustainability and community resilience in vulnerable arid and semi-arid regions under climate change.

1. Introduction

Wind erosion results from the entrainment and transport of sediment particles, resulting in the formation and evolution of the world’s sand seas and the long-range transport of sediments from continents to oceans [1]. This transport has gradually become a key environmental issue around the world [2,3,4]. Both wind properties and surface properties affect wind erosion. For instance, wind velocity, turbulence, air density, and viscosity differed above different land surfaces, leading to spatial variation in sand transport [5]. The characteristics of the near-surface stratum, including soil moisture, vegetation, average particle size, and topography [6], also affect wind erosion of soil [7]. However, the transported sand also changes the wind field by absorbing some of the wind’s energy and increasing the surface roughness length [8,9,10]. Therefore, differences in the wind velocity above different surfaces and during different sand transport processes are a key issue in aeolian research.

Wind is the most important external dynamic factor during wind erosion, and it provides power for the emission and transport of aeolian sand. Wind velocity changes with height near the surface, and these changes are expressed as a wind profile, which reflects the changes in the vertical momentum flux and the magnitude of the shear stress with increasing height [1,11]. The spatial variation in wind profiles is affected by the existence of roughness elements (e.g., vegetation, gravel), and the length of the uniform surface over which the wind is blowing (i.e., the fetch), leading to wind velocity profiles with different shapes [12]. A number of equation forms have been used to describe the equilibrium wind velocity profile, such as power and logarithmic equations [13,14]. However, most of the research results were obtained in a wind tunnel [15], with relatively scarce field measurements [16].

The wind’s friction (shear) velocity ($u_{*}$) and aerodynamic roughness length (z₀) are two main parameters in aeolian research, and can usually be calculated from the wind velocity profiles using a logarithmic equation [17]. $u_{*}$ and z₀ are used to describe the effect of near-surface aerodynamic roughness on the wind. $u_{*}$ reflects the air’s power to entrain particles and can be used to represent the distribution of this power above different surfaces as well as the variation in the momentum flux at the surface [1]. Under the influence of different underlying surfaces, the wind’s friction velocity changes.

The aerodynamic parameters of wind above a surface reflect the surface properties, since a larger surface roughness generally creates a larger z₀. z₀ is an important parameter of the boundary layer because it represents the height where the wind velocity in the near-surface layer equals zero [18]. It is therefore a physical quantity that reflects the interaction between the underlying surface and the airflow [19]. Aerodynamic roughness has been used to assess the aerodynamic properties of sandy, vegetated, and snow-covered surfaces [20,21,22] and to determine how sand transport is affected by variations in surface roughness [18,23,24].

The $u_{*}$ and z₀ parameters obtained from wind profiles can be used to calculate the sediment transport rate (q) and the resulting total quantity of transported sediment [21]. Different types of surfaces affect the airflow and turbulence near the surface. Gobis (gravel deserts), dry lakebed surfaces, sandy surfaces, and other surface types are widely distributed in northern China [16]. Gobi and dry lakebed surfaces are often considered to be non-erodible surfaces, whereas sandy surfaces are clearly erodible. These different surface types can affect the near-surface wind velocity in different ways and, thus, affect the emission of dust. Our previous field study indicated that sand transport differed above gobis, sands, and dry lakebed surfaces [25], but it was unclear how the mechanisms differed among these three land surfaces. Unlike previous research that predominantly focused on sandy surfaces or wind tunnel simulations, this study provides systematic field measurements specifically targeting the aerodynamic characteristics of gobi and dry lakebed surfaces, which have been significantly understudied. Therefore, we designed the present study to analyze spatial variations in wind conditions above these surfaces and the effects on sand and dust emission and transport. Although we collected the wind data used in the present study during that previous research, the wind data in the present study does not overlap with the data in that previous manuscript.

Currently, a lack of data on both wind velocities in the field and surface roughness has limited our ability to validate models of sand emission and transport above gobis and dry lakebed surfaces. In the present field study, we used Windsonic sensors to measure the wind velocity profile above sand surfaces, dry lakebed surfaces, and gobis in the Hexi Corridor Desert of northern China and other deserts in northern China. Our main purpose was to quantify the spatial differences in wind velocity and provide field data to parameterize models of atmospheric conditions above these surfaces. Our results provide insights into the characteristics of the aerodynamic processes above different surfaces in northern China, and will have implications for similar surfaces around the world. Understanding the mechanisms that control the initiation of sand transport will provide important support for parameterizing wind-erosion models and will offer guidance for the prevention of wind erosion in these areas. Furthermore, the mechanistic insights of aeolian processes across diverse surface types and field-based parameterizations presented in this work provide essential scientific support for evidence-based decision-making. They enhance the predictive accuracy of dust emission and transport models, which is critical for developing early-warning systems and sustainable land management strategies to mitigate sand and dust storm impacts. Consequently, this work aids in reducing disaster risks, enhancing the resilience of ecosystems and supporting sustainable development in vulnerable drylands facing climate change.

2. Materials and Methods

2.1. Study Regions

Our study was conducted across six typical arid and semi-arid regions in northern China, including the central Hexi Corridor Desert (northwest of Jinta County, Gansu Province), the Heihe River Basin (near Ejina Banner), the central and southeastern Taklimakan Desert, and the Mu Us Sandy Land (Figure 1). Additionally, wind profile data from the Tengger Desert, Badain Jaran Desert and Ulan Buh Desert were incorporated from previously published literature to expand the regional coverage. Detailed characteristics of each study area are described below.

2.1.1. Hexi Corridor Desert

Located in the central Hexi Corridor (40°00′–40°17′ N, 98°14′–98°34′ E), this region has a temperate continental arid climate. The annual mean temperature is approximately 6.2 °C, and the annual mean precipitation is 50–150 mm, of which 90% of precipitation falls between June and August. The annual potential evaporation ranges from 1500 to 2500 mm, indicating extreme aridity [26]. Wind conditions are characterized by an annual mean wind speed of 2.8–4.6 m s⁻¹, with the strongest winds occurring in April and May, coinciding with the peak sandstorm season [27,28].

The dominant surface types include gobi, sandy, and dry lakebed (Figure 1b). Gobi areas are primarily distributed on alluvial fans of the Qilian Mountains and Beishan Mountains, covered with well-sorted gravel and sparse vegetation. Sandy surfaces consist of mobile dunes interspersed with scattered small shrubs, while dry lakebeds form hard crusts (physical and biological) via the evaporation of stagnant water, with sparse vegetation in low-lying areas and loose sediments from human activities (e.g., road construction) [28]. All observation sites were established on flat, level ground to avoid topographic interference with wind measurements.

2.1.2. Heihe River Basin

The study area in the Heihe River Basin is situated near Ejina Banner (41°40′–42°17′ N, 100°05′–101°01′ E, Figure 1c), sharing a temperate continental arid climate with the Hexi Corridor Desert but differing in key climatic parameters. The annual mean temperature is approximately 9.1 °C (higher than the Hexi Corridor Desert), the annual mean precipitation is only 37 mm (90% concentrated in June–August), and the annual potential evaporation averages 3841.51 mm—resulting in a drought index (potential evaporation/precipitation) exceeding 100 [29]. The annual mean wind speed is 4.4 m s⁻¹, with frequent sandstorms in winter and spring [30,31].

The dominant surface type in this region is gobi, with scattered dry lakebeds. Within the gobi areas, gravel particles are coarser and surface moisture is slightly higher; influenced by the terminal hydrological processes of the Heihe River, dry lakebeds exhibit more developed crusts [29]. Observation sites were established in open, flat areas to exclude non-representative surfaces such as oases and water bodies.

2.1.3. Taklimakan Desert

Two sub-regions were studied in the Taklimakan Desert—the largest shifting sand desert in China: the central desert (38°45′–38°46′ N, 83°55′–83°56′ E) and the southeastern desert (39°03′–39°16′ N, 88°51′–89°16′ E) (Figure 1a). The climate is temperate continental, extremely arid, with an annual mean temperature of 9.9–12.6 °C, an annual mean precipitation of 17.4–66.3 mm, and potential evaporation exceeding 3000 mm [32]. Despite lower overall wind speeds than the Hexi Corridor Desert and Heihe River Basin, the desert has high dune mobility (over 90% sandy surfaces) and frequent sand-related hazards.

Sandy surfaces (predominantly fine to medium sand) are mainly distributed in the desert hinterland, where complex longitudinal ridges—with secondary simple dunes superimposed on them—are the dominant dune types (vegetation coverage < 1%). In contrast, Gobi surfaces are limited to Piedmont zones along the desert margin, covered with 40–60% gravel (mostly weathered crusts from mountain erosion) [32]. Observation sites were placed in inter-dune depressions or open gobi areas to avoid wind disturbance from dune crests or slopes.

2.1.4. Mu Us Sandy Land

Located in the northwestern Ordos Basin (38°23′–38°28′ N, 109°27′–109°30′ E)—a transition zone between the Ordos Plateau and the Loess Plateau—this region has a temperate semi-arid continental climate, influenced by the East Asian monsoon. The annual mean temperature is 6–9 °C, the annual mean precipitation is 200–450 mm (concentrated in July–August), and the annual potential evaporation is 1800–2200 mm [33,34].

Sandy surfaces are the primary land cover, with dune types including mobile, semi-fixed, and fixed dunes (Figure 1d). Semi-fixed dunes account for 51.81% of the total dune area, covered with vegetation (e.g., Artemisia ordosica, Leymus chinensis) at 30–50% coverage [35]. Sandstorm intensity is lower than in deserts due to higher vegetation coverage, though dust events still occur in winter and spring. Observation sites were established in flat inter-dune areas to minimize interference from dense vegetation.

2.1.5. Literature Data Regions (Tengger Desert, Badain Jaran Desert, Ulan Buh Desert)

Wind profile data from the Tengger Desert, Badain Jaran Desert and Ulan Buh Desert were extracted from peer-reviewed literature to supplement field observations [36,37,38]. The Tengger Desert (37°33′–37°35′ N, 105°01′–105°03′ E) features a temperate continental arid climate, with an annual mean precipitation of less than 250 mm (concentrated in July–August) and an annual potential evaporation larger than 2500 mm [39]. The annual mean wind speed is 2–3 m s⁻¹, and is relatively higher from April to June and lower from September to February of the following year, with northwesterly winds dominating the region [40]. Its landscape is characterized by mobile and semi-fixed dunes interspersed with lake basins. The Badain Jaran Desert (41°19′–41°22′ N, 102°21′–102°27′ E) is characterized by a temperate continental, extremely arid climate. Its mean annual precipitation shows a distinct southeast-to-northwest gradient, decreasing from 120 mm in the southeast to 40 mm in the northwest, while mean annual evaporation varies, ranging from 100 mm on lake surfaces to 1000 mm on sand surfaces [41]. The desert is dominated by large sand mountains and inter-dune lakes: individual sand dunes reach heights of 200–400 m and lengths of 3–5 km, with lakes commonly distributed in the depressions between these dunes. In terms of wind regimes, the desert has a mean annual wind speed of 2.8–4.6 m s⁻¹ (with a south-to-north increasing trend), and the strongest wind events occur in April and May [42]. The Ulan Buh Desert (39°35′–39°40′ N, 106°17′–106°29′ E) has an annual precipitation of 100–150 mm, potential evaporation of 2200–2600 mm and a mean annual wind speed of 3.7 m s⁻¹, with aeolian activities as its primary natural hazard [43]; and its landscape consists of shifting, semi-fixed and fixed sand dunes accounting for 36.9%, 33.3% and 29.8%, respectively [32].

Basic wind and sediment characteristics of all study sites (field observations and literature data) are summarized in

Table 1. Wind conditions and soil properties at the measurement sites shown in Figure 1 and Figure S1.

Location	Sites	U 1 (m s−1)	umax (m s−1)	Mz (µm)	σ (φ Units)	Sk (φ Units)	K (φ Units)	${\mathit{u}}_{*\mathbf{t}}$ (m s−1)	C (%)	Crust
Hexi Corridor Desert	S1	8.30	13.7	–	–	–	–	–	–	no
	S2	6.02	13.01	150	−0.93	−0.33	1.36	0.39	–	no
	S3	6.87	13.28	200	−0.76	−0.04	0.93	0.23	–	no
	D1	9.75	19.29	60	−1.71	−0.20	1.13	0.14	–	yes
	D2	6.64	13.67	30	−1.78	−0.27	0.98	0.11	–	yes
	G1	8.63	14.99	–	–	–	–	0.45	56	no
	G2	9.65	17.62	170	−1.02	−0.05	1.17	0.40	66	no
	G3	10.41	19.79	160	−1.43	−0.22	1.35	0.39	60	no
Heihe River Basin	D3	4.69	8.05	140	1.62	−0.24	1.18	0.06	–	yes
	G4	10.54	15.98	140	1.17	−0.24	1.64	0.37	54	no
	G5	12.93	19.62	–	–	–	–	–	–	no
	G6	10.26	15.19	200	−1.26	−0.19	1.47	0.43	39	no
	G7	7.57	10.33	–	–	–	–	–	–	no
	G8	11.57	17.94	–	–	–	–	–	–	no
	G9	13.21	21.31	180	1.59	−0.20	2.10	0.41	61	no
Taklimakan Desert	G10	6.58	15.97	–	–	–	–	–	–	no
	S5	7.26	11.32	148	–	–	–	0.24	–	no
Mu Us Desert	S4	8.80	17.42	185	–	–	–	0.20	–	no
	S4v	5.89	13.66	–	–	–	–	0.20	–	no
Ulan Buhe Desert [38]	S6	–	–	204	–	–	–	–	–	no
Tengger desert [36]	S7	–	–	217	–	–	–	0.45	–	no
Badain Jaran Desert [37]	S8	–	–	248	–	–	–	0.22	–	no
Dunhuang [44]	S9	8.5	–	100–300	–	–	–	0.5	–	no
	G11	8.5	–	100–300	–	–	–	0.6–0.8	10	no

¹ u represents mean wind velocity at a height of 2 m, u_max represents the maximum free-stream wind velocity during the measurements, M_z represents the mean particle size, σ represents the surface sediment sorting; Sk represents the surface sediment skewness, K represents the surface sediment kurtosis; $u_{*\mathrm{t}}$ represents the threshold friction (shear) velocity for particle entrainment, and C represents the gravel cover. —represents no data. Sites: G, gobi; S, sand; D, dry lakebed. S1 to S5 are sandy surfaces, D1 to D3 are dry lakebed surfaces, and G1 to G11 are gobi (gravel) surfaces. The data of S6, S7, S8 are, respectively, from Li et al. [38], from Zhang et al. [36], and from He et al. [37]. The data of S9 and G11 from Liu et al. [44]. Marked with “–” where values are not provided in the original table due to data unavailability in the literature and the absence of field measurements.

.

2.2. Wind Velocity Measurements

Wind velocity and direction were measured using Windsonic sensors (Figure 2; Gill Instruments Limited, Lymington, UK), and data were stored in CR6 dataloggers (Campbell Scientific Inc., Logan, UT, USA). Data were recorded at 1 s intervals and stored as 1 min averages. As detailed in

Table 2. Comparison of Wind Velocity Observation Instruments and Parameters Across Study Regions.

Location	Period	Height (m)	Sampling Frequency	Instrument Accuracy (Wind Speed/Wind Direction)	Recording Format
Hexi Corridor Desert	10 January 2021–14 January 2021	0.3, 0.9, 1.3, 2.0	1 s	±2%/±3%	1 min average
Heihe River Basin	02 May 2022–29 May 2022	0.3, 0.4, 1.3, 2.0	1 s	±2%/±3%	1 min average
Taklimakan Desert	12 April 2024–13 April 2024	0.2, 0.5, 1.0, 1.5, 2.0	1 s	±2%/±3%	1 min average
Mu Us Desert	28 March 2024	0.2, 0.5, 1.0, 1.5, 2.0	1 s	±2%/±3%	1 min average
Ulan Buhe Desert [38]	–	0.2, 0.5, 1.0, 2.0	10 s	–	5 min average
Tengger desert [36]	–	1.0, 2.0, 4.0, 8.0	1 s	–	1 min average
Badain Jaran Desert [37]	28 March 2010–01 April 2010	0.2, 0.5, 1.0, 2.0	1 s	–	1 min average
Dunhuang gobi [44]	23 August 2009, and 26 May 2009	0.2, 0.35, 0.5, 0.65, 1, 2	–	–	1 min average

Marked with “–” where values are not provided in the original table.

, these measurements were conducted across multiple arid and semi-arid regions (Hexi Corridor Desert, Heihe River Basin, Mu Us Desert, and Taklimakan Desert) with standardized instrument accuracy (±2% for wind speed, ±3% for wind direction) and sampling protocols, while data from literature-based regions (Ulan Buhe Desert, Tengger desert, and Badain Jaran Desert,) exhibited inconsistent instrumentation parameters. For analysis, all records with diagnostic flags indicating sensor malfunction, icing, or other instrument errors were removed. To focus exclusively on dust-generating conditions, data were restricted to periods identified as dust-influenced weather. The only data corresponding to high free-stream wind velocity (u > 4 m s⁻¹) were retained [45].

2.3. Data Analysis

The relationship between wind velocity and height can be expressed as a log-linear function under conditions of neutral atmospheric stability [46,47,48]:

$$u=a_{1}ln\left(z\right)+a_2$$

(1)

where u represents the wind velocity at height z (2 m in the present study) and a₁ and a₂ are regression coefficients.

$u_{*}$ and z₀ can be calculated by the gradient method or the wind profile method, which produce similar results [25]. We chose the wind profile method:

$$z_0=\mathrm{e}\mathrm{x}\mathrm{p}[-a_2/a_1]$$

(2)

$$u_{*}=k{a}_1$$

(3)

where k is Von Karman’s constant (0.4).

To compare the wind speed profiles over different underlying surfaces, the wind speeds at different heights and the wind speed at the highest layer are normalized according to the following equation:

$${u_i}^{\prime }=u_i/u_{2\mathrm{m}}$$

(4)

where i denotes the i-th layer from bottom to top (I = 1, 2, 3, 4, 5), u_i is the wind speed at the i-th layer (m s⁻¹), u represents the wind velocity at height z (2 m), and u_i′ is the dimensionless wind speed at the i-th layer after normalization.

The relationships between u and $u_{*}$ and between z₀ and $u_{*}$ were fitted according to the method of Ho et al. [48]:

$$z_0=z_{\mathrm{f}} \mathrm{e}\mathrm{x}\mathrm{p}[-u_f k/u_{*}]$$

(5)

where b₁, b₂, c₁ and c₂ are regression coefficients and u_f and z_f are the height and velocity (respectively) at a “focus” point defined as the position where the wind velocity is independent of the friction velocity.

Shao et al. [49] formulated the fluid threshold friction velocity ($u_{*\mathrm{t}}$) as:

$$u_{*\mathrm{t}}=\sqrt{d_1({\sigma }_{p}gd+{\displaystyle \frac{d_2}{\rho d}})}$$

(6)

where d is the mean diameter of the erodible grains (mm), g is the acceleration due to gravity (=9.8 m s⁻²), ρ is the air density (=1.29 kg m⁻³), and σ_p is the particle-to-air density ratio (=2208.3). The coefficients d₁ = 1.23 × 10⁻² and d₂ = 3 × 10⁻⁴ kg s⁻² were estimated by fitting to the wind-tunnel data of Shao and Lu [50].

Bagnold [17] formulated the fluid threshold friction velocity for particle entrainment as:

$$u_{*\mathrm{t}}=A\sqrt{({\sigma }_p-1)gd}$$

(7)

where A is an empirical constant taken as 0.11 based on the more recent data of Shao et al. [50] and Han et al. [48], σ_p is the particle-to-air density ratio, d is the mean diameter of the erodible grains, and g is the acceleration due to gravity. Data were analyzed by origin 9.0 and matlab 2016a.

To evaluate the goodness of fit of these equations with the empirical data, we used the root-mean-square error (RMSE).

3. Results

3.1. Wind Profiles

To further explain the effect of the land surface on the wind profiles, we compared representative wind profiles for free-stream wind velocities of 5, 9, 11 m s⁻¹ under the prevailing wind direction (Figure 3 and Figure 4;

Table 3. Statistics of Wind Speed and Direction for Different Surface Properties (gobi, sandy and dry lakebed surfaces) in the Study Area During the Observation Period.

Location	G1		G2		G3		G4
Wind speed	WS	WD	WS	WD	WS	WD	WS	WD
u = 5 m s−1	5.32 ± 0.46	E *, SE	5.49 ± 0.51	E, SE	5.41 ± 0.23	E, ESE	5.51 ± 0.33	W
u = 9 m s−1	8.97 ± 0.59	NE, E, SE	9.03 ± 0.58	E, SE	9.06 ± 0.57	E, SE	9.00 ± 0.56	W, NW
u = 11 m s−1	10.79 ± 0.54	NE, E, SE	10.89 ± 0.59	E, SE	10.97 ± 0.58	E, SE	11.02 ± 0.57	W, NW
Wind speed	G5		G6		G7		G8
u = 5 m s−1	–	–	5.57 ± 0.28	W	5.51 ± 0.30	N, NE	–	–
u = 9 m s−1	9.49 ± 0.42	NW	9.04 ± 0.54	W, NW	8.84 ± 0.54	N, NE	9.18 ± 0.57	N, NW
u = 11 m s−1	11.10 ± 0.52	NW	11.06 ± 0.57	W, NW	10.19 ± 0.15	NE	11.02 ± 0.58	N, NW
Wind speed	G9		G10		G11		S1
u = 5 m s−1	–	–	4.92 ± 0.55	N, NE	–	E	5.30 ± 0.47	E, SE
u = 9 m s−1	9.22 ± 0.56	W, NW	9.02 ± 0.58	NE	7.66	E	8.90 ± 0.56	E, SE
u = 11 m s−1	11.01 ± 0.56	W, NW	10.94 ± 0.56	NE	10.37	E	10.76 ± 0.50	E, SE
Wind speed	S2		S3		S4		S4V
u = 5 m s−1	5.07 ± 0.56	NE, E	5.15 ± 0.54	E, SE	5.17 ± 0.56	N, NW	5.01 ± 0.57	W, NW
u = 9 m s−1	8.86 ± 0.55	NE, E	8.91 ± 0.56	E, SE	9.01 ± 0.58	NW, N	8.74 ± 0.53	W, NW
u = 11 m s−1	10.58 ± 0.45	E, ENE	10.66 ± 0.32	E, SE	10.97 ± 0.56	NW, N	10.66 ± 0.53	W, NW
Wind speed	S5		S6		S7		S8
u = 5 m s−1	5.52 ± 0.42	NE, E	5.93 ± 0.28	NW	5.05	NW	5.24	NW
u = 9 m s−1	8.78 ± 0.52	NE, E	8.92 ± 0.26	NW	9.39	NW	–	NW
u = 11 m s−1	10.51 ± 0.43	NE, E	–	NW	10.95	NW	11.05	NW
Wind speed	S9		D1		D2		D3
u = 5 m s−1	–	E	5.55 ± 0.34	E, ENE	5.01 ± 0.57	E, SE	4.72 ± 0.51	SW, W
u = 9 m s−1	7.45	E	8.98 ± 0.56	E, SE	8.86 ± 0.57	E, NE	8.05	SW
u = 11 m s−1	–	E	10.87 ± 0.53	E, SE	10.73 ± 0.57	E, ENE	–	–

* E = East, ENE = East-Northeast, NW = Northwest, etc. Marked with “–” where values are not provided in the original table.

). The results in Figure 3a–e illustrated that the vertical wind profiles (relating height z to free-stream velocity u) exhibited distinct surface-type dependence and wind-speed sensitivity across gobi (a,b), sandy (c), and dry lakebed (d) surfaces in Chinese arid regions. At u = 5 m s⁻¹ (no sand transport), gobi surfaces (a) showed the steepest profile slopes (a₁ = 0.58 to 0.75), followed by dry lakebed (a₁ = 0.63 to 0.65, in the Hexi Corridor Desert and Heihe River Basin) and sandy surfaces (a₁ = 0.32 to 0.88), indicating stronger aerodynamic resistance for gobi and dry lakebed surfaces. At u = 9 m s⁻¹ (significant sand transport), sandy surfaces showed notable profile flattening with a₁ ranging 0.64 to 1.52 due to saltation-induced turbulence, while gobi profiles remained relatively stable (0.68 to 1.35), and dry lakebed surfaces in the Hexi Corridor Desert and Heihe River Basin had intermediate a₁ (0.67 to 1.1). At u = 11 m s⁻¹ (intense sand transport), sandy surfaces (a₁ = 0.85 to 1.90) exhibited notable profile flattening due to saltation-induced turbulence, while gobi profiles (a₁ = 1.06 to 1.59) remained relatively stable, and dry lakebed surfaces in the Hexi Corridor Desert and Heihe River Basin showed a high a₁ (1.46). Within the same region, gobi surfaces exhibited the largest wind profile slope (a₁ = 0.75), with sandy and dry lakebed surfaces in the Hexi Corridor Desert and Heihe River Basin showing smaller slopes (a₁ = 0.64, and 0.65) at low wind speeds (u = 5 m s⁻¹). Conversely, sandy surfaces had the largest slope (a₁ = 1.73), whereas gobi and dry lakebed surfaces in the Hexi Corridor Desert and Heihe River Basin displayed smaller slopes (a₁ = 1.59, 1.46).

These findings indicated that at low wind speeds, wind profile coefficients were dominated by the surface roughness elements of gobi and dry lakebed surfaces in the Hexi Corridor Desert and Heihe River Basin, while at high wind speeds, sandy surface properties exerted a more prominent influence on these coefficients. Collectively, these results highlight that wind profile characteristics are co-governed by surface roughness and aeolian transport intensity, providing critical insights for parameterizing atmospheric boundary layers and sand transport models in arid regions.

3.2. Variation in Aerodynamic Roughness Length (z₀) with Friction Velocity ($u_{*}$)

z₀ and $u_{*}$ were obtained from the wind velocity profiles (Equations (2) and (3), Figure 4), and z₀ and $u_{*}$ exhibited distinct variations across surface types (gobi, sand, dry lakebed) and free-stream wind speeds (5, 9, 11 m s⁻¹). With increasing wind speed, $u_{*}$ consistently escalates. Similarly, z₀ over sandy and gobi surfaces augments with increasing free-stream wind speed.

For $u_{*}$, at low wind speed (5 m s⁻¹), dry lakebed surface in the Hexi Corridor Desert and Heihe River Basin showed the smallest (0.19 ± 0.01 m s⁻¹ [mean ± SD]), while gobi and sand surface had comparable values (0.23 ± 0.003 and 0.24 ± 0.07 m s⁻¹, respectively) (Figure 5a). At 9 m s⁻¹, $u_{*}$ was smallest for the dry lakebed surfaces in the Hexi Corridor Desert and Heihe River Basin (0.32 ± 0.06 m s⁻¹), followed by gobi surfaces (0.41 ± 0.07 m s⁻¹), and sandy surfaces (0.46 ± 0.12 m s⁻¹) (Figure 5b). At high wind speed (11 m s⁻¹), sandy surfaces and gobi surfaces showed comparable (0.58 ± 0.14 and 0.53 ± 0.08 m s⁻¹), slightly higher than dry lakebed surfaces in the Hexi Corridor Desert and Heihe River Basin (0.46 ± 0.06 m s⁻¹) (Figure 5c). For z₀, sandy surfaces consistently had the largest z₀ across all wind speeds, with values increasing from 1.52 ± 2.09 × 10⁻³ m (5 m s⁻¹) to 4.55 ± 4.34 × 10⁻³ m (11 m s⁻¹) (Figure 5d–f). Gobi showed moderate, gradually increasing with 0.72 ± 0.56 × 10⁻³ m (5 m s⁻¹) to 1.33 ± 1.39 × 10⁻³ m (11 m s⁻¹) (Figure 5d–f). Dry lakebed exhibited the lowest at 9 m s⁻¹ (z₀ = 0.10 ± 0.35 × 10⁻³ m) in the Hexi Corridor Desert and Heihe River Basin but increased to 0.25 ± 0.20 × 10⁻³ m at 11 m s⁻¹ (Figure 5e,f).

Within the same region, $u_{*}$ exhibited a wind speed-dependent hierarchical pattern, driven by differential surface erodibility. As wind speeds increased to 5 to 9 m s⁻¹, the gobi surfaces displayed the highest $u_{*}$, while the sandy and dry lakebed surfaces showed comparable but lower $u_{*}$. When wind speed increased to 11 m s⁻¹, this pattern shifted that $u_{*}$ over sandy and gobi surfaces became higher than over the dry lakebed surfaces, with a frequent order of gobi surfaces > sandy surfaces > dry lakebed surfaces. Notably, this wind speed-dependent hierarchy of $u_{*}$ was also observed in other regions, such as the Taklamakan and Dunhuang deserts. For z₀, it likewise showed surface-specific dependence on wind speed, in a manner similar to that observed for friction velocity. At wind speeds of 5 to 9 m s⁻¹, z₀ decreased in the order gobi surfaces > sandy surfaces > dry lakebed surfaces. At 11 m s⁻¹, this order reversed, with the sandy surface exhibiting the highest z₀, followed by the gobi, while the dry lakebed remained the lowest z₀. A similar wind-speed dependency of z₀ was identified in the Taklamakan and Dunhuang deserts, where gobi surfaces showed higher z₀ than sandy surfaces at 5 to 9 m s⁻¹, but sandy surfaces exceeded gobi at 11 m s⁻¹.

For shifting sandy surfaces across distinct deserts, the mean $u_{*}$ relative to the Hexi Corridor Desert exhibited significant inter-desert differences and wind speed dependence. At 5 m s⁻¹, the Mu Us (1.21) and Badain Jaran (1.14) deserts had the highest ratios (>1.0), while the Ulan Buhe (0.47) and Tengger (0.49) deserts showed the lowest (<0.6); intermediate values were observed for Dunhuang (0.74) and Taklimakan (0.74) (Figure 5a). At 9 m s⁻¹, the ratios ranged from 0.47 (Dunhuang, lowest) to 1.09 (Mu Us, highest), with Ulan Buhe (0.61), Tengger (0.67), Taklimakan (0.75), and Badain Jaran (0.97) intermediate (Figure 5b). At 11 m s⁻¹, the Mu Us Desert remained the highest (1.06), while the Ulan Buhe Desert maintained the lowest ratio (0.49), with other deserts (Tengger, 0.61; Badain Jaran, 0.84; Taklimakan, 0.83) showing moderate values (Figure 5c).

3.3. Relationships Between Aerodynamic Roughness Length (z₀) and Friction Velocity ($u_{*}$)

The z₀ values varied across three surface types (gobi, sandy, dry lakebed) and different northern Chinese deserts, yet universally exhibited an exponential relationship with $u_{*}$ at all sites (Equation (5), Figure 6,

Table 4. Coefficients for the relationships between friction velocity ($u_{*}$) and aerodynamic roughness length (z₀).

Location	G1–3	G4–9	G10	D1–2	D3	S1–3
zf 1	0.013	0.006	0.001	0.008	0.003	0.047
uf 1	1.756	3.226	1.334	3.018	1.074	3.329
R2	0.551	0.711	0.351	0.314	0.428	0.906
RMSE	0.002	0.000	0.000	0.004	0.000	0.001
Location	S4	S4V	S5	S7	S8	-
zf 1	0.015	0.025	0.001	0.001	0.006	-
uf 1	1.430	1.288	0.893	2.729	0.353	-
R2	0.435	0.981	0.647	0.837	0.541	-
RMSE	0.002	0.001	0.000	0.000	0.001	-

¹ z_f and u_f represent the focus points for height and wind velocity in Equation (5) for the graphs in Figure 6. RMSE, root-mean-square error. p < 0.01 for all regressions. Site types: G, gobi; S, sand; D, dry lakebed.

) with different coefficients. z₀ increased with $u_{*}$ and that reached an asymptote range from 10⁻⁴ to 10⁻² m among the gobi, dry lakebed in the Hexi Corridor Desert, Heihe River Basin and sandy surfaces (Figure 6), indicating surface-specific sediment transport dynamics.

Spatially, z_f values (Figure 6a, Table 4) varied as follows: gobi surfaces showed 1.3 × 10⁻² m (Hexi Corridor), 6 × 10⁻³ m (Heihe River Basin), and 1 × 10⁻³ m (Taklimakan); dry lakebeds registered 8 × 10⁻³ m (Hexi Corridor Desert) and 3 × 10⁻³ (Heihe River Basin); while sandy surfaces in the Mu Us and Hexi Deserts ((1.5 to 4.7) × 10⁻² m) exceeded those in the Tengger, Badain Jaran, and Taklimakan Deserts ((1 to 6) × 10⁻³ m) (Figure 6b, Table 4). The u_f values also exhibited distinct regional and surface-type dependencies (Figure 6, Table 4). Within the Hexi Corridor, sandy surfaces (3.33 m s⁻¹) and dry lakebeds (3.02 m s⁻¹) had higher u_f than gobi surfaces (1.76 m s⁻¹). However, in the Taklimakan Desert, sandy surfaces (0.89 m s⁻¹) showed lower u_f than their gobi counterparts (1.33 m s⁻¹). In contrast, dry lakebeds in the Heihe River Basin (1.07 m s⁻¹) had lower u_f than local gobi surfaces (3.23 m s⁻¹), though Heihe gobi exceeded some sandy and dry lakebed sites. Notably, mean u_f over shifting sands in other northern Chinese deserts (0.35 to 2.73 m s⁻¹) was lower than that in Hexi Corridor sandy surfaces (3.33 m s⁻¹), collectively highlighting surface-type and regional controls on near-surface turbulent dynamics.

Mechanistically, z₀ increased with $u_{*}$ and stabilized at higher $u_{*}$ for gobi and dry lakebed surfaces (Figure 6a), driven by enhanced momentum exchange from gravel and crust. Sandy surfaces (Figure 6b) showed analogous z₀–$u_{*}$ growth but at lower magnitudes, attributed to mobile fine sand grains and the erosibility of the surface type.

4. Discussion

Saltation above sandy surfaces is the most important form of aeolian transport [17]. Both the aerodynamic roughness length and the wind’s friction velocity are affected by the characteristics of the sandy surface, including the average particle size and the grain-size distribution, as well as by characteristics of the wind regime, including the free-stream wind velocity and turbulence. For non-sandy sites, surface properties such as the vegetation and gravel cover and the presence or absence of soil crusts are important. The combinations of these surface and wind factors ultimately control sediment transport.

4.1. The Effect of Surface Properties on Wind Profiles

The morphological differences in wind profiles are inherently governed by the combined effects of surface roughness properties and sediment supply potential [17,51]. Thus, surface attributes and wind speed synergistically modulate the variations in wind profile coefficients [21].

In this study, Normalized wind profiles (u/u_2m) revealed distinct morphological patterns among surface types (Figure 4), and the mean wind profile coefficient a₁ and Constant term a₂ exhibited marked disparities among gobi, dry lakebed in the Hexi Corridor Desert and Heihe River Basin, and sandy surfaces (Figure 3). Within the same region, the mean wind profile coefficient a1 on sandy surfaces (a₁ = 0.64) was significantly smaller than that on dry lakebed (a₁ = 0.65) and gobi surfaces (a₁ = 0.75) at low wind speeds, whereas this pattern was completely reversed at high wind speeds, with sandy surfaces displaying the largest slopes (a₁ = 1.73) and gobi, dry lakebed surfaces showing smaller slopes (a₁ = 1.59, and 1.46, respectively). This characteristic aligns closely with surface properties: sandy surfaces feature small roughness elements and abundant sediment supply (Figure 7), whereas gobi and dry lakebed surfaces are dominated by gravel and physical crusts, respectively, thus constraining sediment availability [52]. These observed behaviors of wind profiles can be further elucidated from the perspective of the threshold friction velocity governing the transition between “static roughness–dominated” and “dynamic roughness-dominated” regimes. The shift from static dominance—where surface roughness elements such as rocks, vegetation, and crusts dominate momentum dissipation—to dynamic dominance—driven by sand grains and the bedforms such as ripples—depends on whether wind speed exceeds a threshold (Figure 7). For surfaces like gobi and dry lakebeds, which have a larger static roughness length (z₀) and limited sediment availability, the threshold friction velocity required to initiate sand movement is relatively high. This means that wind energy must first overcome the substantial drag imposed by static roughness elements before sediment transport can be initiated. Consequently, over a wide range of wind speeds, the morphology of their wind profiles (i.e., the coefficient a₁) changes relatively gradually, as reflected in the modest increase in a₁ from low to high wind speeds (e.g., for gobi surfaces, a₁ rises from 0.75 to 1.59). In contrast, homogeneous sandy surfaces exhibit low static roughness and abundant sediment supply, and once wind speed surpasses this lower threshold, sand grains are rapidly mobilized, forming ripples and significantly altering the effective surface roughness, thereby transitioning the surface into a dynamic roughness-dominated regime. This explains the sharp increase in a₁ observed on sandy surfaces at high wind speeds (e.g., from 0.64 to 1.73).

At the regional scale, the wind profile slopes of sandy surfaces exhibited distinct patterns with wind speed. The slopes decreased from west to east: relatively large in the Mu Us Sandy Land (0.85 at 5 m s⁻¹ and 1.85 at 11 m s⁻¹), smaller in the Ulan Buh and Tengger Deserts (0.32, 0.33 at 5 m s⁻¹ and 0.85, 1.07 at 11 m s⁻¹), larger again in the Badain Jaran Desert and Hexi Corridor (0.77, 0.64 at 5 m s⁻¹ and 1.45, 1.73 at 11 m s⁻¹), and smallest in the Taklamakan Desert (0.51 at 5 m s⁻¹ and 1.44 at 11 m s⁻¹) with no and intense sand transport. This spatial variability is closely linked to adjustments in surface roughness induced by higher vegetation coverage in the eastern regions [53].

This finding aligns with Bagnold’s [17] classic saltation theory and modern aeolian dynamics research, which emphasize that surface properties (roughness, sediment supply) regulate the vertical structure of aeolian processes, thereby shaping wind profile morphology [6]. In turn, wind profile changes feed back on aeolian transport intensity and patterns, collectively forming a coupled surface–airflow–sediment system in arid regions. Such insights are critical for improving parameterizations of atmospheric boundary layers and sand transport models in arid and semi-arid zones.

4.2. The Relationships Between $u_{*}$ and z₀

Field measurements of the friction velocity ($u_{*}$) and aerodynamic roughness length (z₀) over three typical surfaces in northern China, including sand, gobi (gravel desert), and dry lakebed, revealed distinct and non-linear responses under varying wind regimes. The overall trend shows that both $u_{*}$ and z₀ increase with wind speed (Figure 6), with z₀ on the order of magnitude ranging from 10⁻² to 10⁻³ m. More importantly, the relative magnitudes of these parameters between surfaces shift significantly as a function of sediment transport intensity, indicating a transition from static to dynamic roughness control.

At low wind speeds (5 to 9 m s⁻¹), the difference in $u_{*}$ and z₀ was determined by the inherent physical structure of the surface. The physical hard surface crust of the dry lakebed inhibits particle entrainment [54,55] and results in the lowest $u_{*}$ and z₀. The loose sand grains and small sand ripples of shifting sand surfaces form a medium $u_{*}$ and z₀, while the immobile gravel of the gobi surfaces constituted a high inherent $u_{*}$ and z₀ [56,57]. When the wind speed exceeds 9 m s⁻¹, the aerodynamic behavior of surfaces is primarily governed by sediment transport dynamics [58]. Sand surfaces undergo full sand lifting at high wind speeds, with the accumulation of saltating sand grains forming dynamic sand ripples, and the interaction between the aeolian flow and the airflow intensifies turbulent disturbances, leading to an explosive increase in effective $u_{*}$ and z₀. In contrast, the gravel of the gobi is stable and cannot form continuous dynamic roughness elements, resulting in limited growth in effective roughness. The dry lakebed surfaces remains the smoothest due to the dominant suspension of fine particles after sand lifting, maintaining the lowest $u_{*}$ and z₀.

We obtained z_f (Bagnold’s “focus point”) by analyzing the wind profiles above the three land types and different deserts, which means that the heights where the point was no longer affected by the free-stream velocity [17]. Essentially, z_f quantitatively characterizes the experimental relationship between $u_{*}$ and z₀ under aeolian transport conditions. It can be understood as a “reference height” separating the saltation layer from the overlying airflow, directly reflecting the thickness of the sand-transport layer.

Spatially, z_f over shifting sand surfaces showed distinct patterns, which z_f was significantly higher in the Mu Us and Hexi Corridors deserts ((1.5–4.7) × 10⁻² m) compared to the Tengger, Badain Jaran, and Taklamakan deserts ((1–6) × 10⁻³ m). These values are consistent with z_f = (9 ± 8) × 10⁻³ m measured by Ho et al. [48] on eroding beds and z_f = 8 × 10⁻³ m (d = 190 μm) by Ralaiarisoa et al. [59]. For gobi surfaces, z_f exhibited significant regional variations, which 1 × 10⁻³ m (Taklamakan), 1.3 × 10⁻² m (Hexi Corridor, gravel coverage of 56–60%) and 6 × 10⁻³ m (Heihe River Basin, gravel coverage of 40–60%), reflecting the regulatory effect of gravel coverage on saltation layer height. Over dry lakebed surfaces, z_f was 8 × 10⁻³ m (Hexi Corridor) and 3 × 10⁻³ m (Heihe River Basin). Although influenced by surface crusts, these values were still higher than z_f ≈ 7.77 × 10⁻⁶ m measured by Ho et al. [48] on rigid wind tunnel beds, consistent with the absence of perfectly rigid surfaces in natural field conditions. The friction velocity u_f at the focus height was closely coupled with height z_f. Lower u_f values were observed over gobi surfaces (Hexi Corridor and Taklamakan, 1.76 m s⁻¹ and 0.27 m s^−1, respectively). Higher u_f values were found over shifting sandy surfaces (average 0.35–2.73 m s⁻¹ in other deserts of northern China). Intermediate u_f values characterized dry lakebeds (Hexi Corridor, 2.99 m s⁻¹). Collectively, the high z_f and u_f values over shifting sands indicate a thicker saltation layer and greater potential for particle entrainment. In contrast, gobi surfaces are more prone to forming an intense collision layer under high wind speeds [60], while the z_f and u_f characteristics of dry lakebeds suggest some similarity in their particle entrainment mechanisms to gobi surfaces [61].

4.3. Applicability and Limitations of the Parameterization Scheme

The parameterization scheme developed in this study, particularly the focus point model characterized by z_f and u_f parameters, demonstrates robust performance under specific boundary conditions. The scheme is primarily applicable to arid and semi-arid regions with the following constraints: wind speeds ranging from 5 to 12 m s⁻¹, sediment transport intensities corresponding to saltation-dominated regimes, and surfaces with gravel coverage between 30% and 70% for gobi environments.

For cross-regional application, three core factors require particular attention and potential adjustment: First, surface sediment characteristics, especially the grain size distribution and the presence of physical or biological crusts, significantly influence the z_f-u_f relationship. Regions with finer sediment sizes or higher clay content may exhibit reduced saltation layer thickness. Second, vegetation coverage, which was generally low (<10%) in our study areas, must be considered, as higher vegetation density would substantially alter the aerodynamic roughness and sediment availability. Third, climatic factors, including precipitation frequency and wind regime characteristics (e.g., seasonal variability, dominant wind directions), may necessitate calibration of the threshold friction velocities.

The scheme shows limited applicability in densely vegetated areas, coastal environments with high humidity, or regions experiencing frequent precipitation events where soil moisture dramatically affects sediment transport thresholds. Additionally, surfaces with extreme gravel coverage (>80% or <20%) may require parameter adjustments as the model was calibrated primarily for moderate gravel coverage conditions. We recommend that future applications in new regions incorporate local measurements of surface properties and wind characteristics to validate and, if necessary, recalibrate the z_f and u parameters presented herein.

5. Conclusions

Wind velocity and surface characteristics jointly determine sand transport dynamics, as quantified by parameters ($u_{*}$ and z₀) derived from field observations over different surfaces. Our study yields the following key findings:

The slope of the wind profile highlights the transition in surface drag. Under low winds, sandy surfaces exhibited the lowest drag, compared to dry lakebed and gobi. This order reversed under high winds, with the sandy surface showing the highest drag, followed by gobi and dry lakebed.

The response of $u_{*}$ and z₀ to increasing wind speed is strongly surface-dependent. Below the transport threshold, gobi surfaces exhibited the highest $u_{*}$ and z₀. Once transport was initiated, $u_{*}$ and z₀ over sandy surfaces increased sharply, eventually surpassing other surfaces. In contrast, $u_{*}$ responded significantly to wind speed over gobi and sandy surfaces but showed limited sensitivity over dry lakebed. Notably, z₀ increased substantially with wind speed over sandy surfaces, while remaining relatively stable over gobi and dry lakebed, highlighting the role of particle movement in modifying surface roughness.

Focal height (z_f) and its associated friction velocity (u_f) displayed distinct spatial patterns linked to regional surface properties. For sandy surfaces, z_f was significantly greater in the Mu Us Sandy Land and Hexi Corridor than in other deserts. For gobi surfaces, z_f correlated strongly with surface gravel coverage. The corresponding u_f averaged 0.35–3.33 m s⁻¹ for sandy surfaces, 0.27–3.23 m s⁻¹ for gobi, and 1.07–2.99 m s⁻¹ for dry lakebed, underscoring the spatial heterogeneity controlled by local surface characteristics.

The region-specific parameterizations of $u_{*}$ and z₀ provided herein offer crucial field-based constraints for improving the accuracy of aeolian transport and dust emission models at regional scales. The aerodynamic parameters obtained in this study provide a critical scientific basis for the design of windbreak and sand fixation projects in arid regions. The parameters z_f and u_f offer theoretical support for determining the height of sand barriers. In addition to advancing wind-blown sediment dynamics, this work contributes directly to environmental sustainability. Our region-specific parameterizations improve the accuracy of dust emission models, which is critical for developing reliable early-warning systems for sand and dust storms. Effective mitigation of these hazards is essential to protect agricultural productivity, infrastructure, and public health in arid regions. Thus, our findings provide a scientific foundation for evidence-based land management strategies that support ecological security and sustainable development in arid and semi-arid regions worldwide under changing climatic and human activities.

To advance predictive capabilities, future efforts should focus on integrating these aerodynamic parameters with concurrent, high-resolution measurements of sand and dust flux across the diverse dryland landscapes. Furthermore, establishing a unified observation network to systematically quantify the dynamic feedback between wind regimes, surface properties, and sediment mobility will be essential for understanding and forecasting the response of dryland atmospheres to climatic and anthropogenic changes.