Mechanisms driving surface wind speed increases in an ecologically fragile region of Northwest China: Insights from circulation anomalies and geographical detector analysis
WANG Yongliang*(), ZHANG Weijiang
School of Civil and Hydraulic Engineering, Ningxia University, Yinchuan 750021, China
The Shaanxi-Gansu-Ningxia (SGN) border region, as a typical transitional zone between the East Asian monsoon and the westerlies, features a fragile ecological environment. In this study, the spatiotemporal evolution and multiscale driving mechanisms of surface winds in this ecologically fragile transition zone were investigated by integrating trend analysis, empirical orthogonal function (EOF) decomposition, and geographic detector method. The results indicated that from 1980 to 2022, the annual mean surface wind speeds in the SGN border region significantly increased, with a linear growth rate of 0.003 m/(s•a). However, the anomaly series revealed a clear interdecadal transition: surface wind speed anomalies were predominantly negative from 1980 to 1999 and shifted to persistently positive and increasing anomalies after 2000. Consistent strengthening was observed in summer, autumn, and winter, with the most pronounced increase occurring in autumn. The spatial distribution generally followed a pattern of higher values in the northwest and lower values in the southeast. Spring presented the strongest surface wind speeds and the most extensive areas with high values. EOF analysis revealed two dominant spatial modes: the first mode (variance contribution>73.40%) reflected regionally consistent changes, and its temporal coefficients increased continuously, corresponding to the overall strengthening trend of surface wind speed; and the second mode exhibited an east-west dipole oscillation pattern, dominated by interannual fluctuations. The geographic detector results revealed that fractional vegetation cover (FVC), temperature, and topographic elevation were key factors influencing the spatial differentiation of surface wind speeds, with all the factors exhibiting enhanced interactive effects—especially the synergistic effect between vegetation cover and temperature. Background circulation analysis indicated that enhanced westerlies and decreased geopotential height in the mid- to upper-troposphere provided favourable dynamic conditions for increased surface wind speeds. This study advances the understanding of surface wind speed changes in climate transition zones, providing a scientific basis for regional wind energy planning, ecological protection, and wind erosion control.
Received: 16 December 2025
Published: 30 June 2026
Writing - original draft preparation: WANG Yongliang; Visualization: WANG Yongliang; Software: WANG Yongliang; Methodology: WANG Yongliang, ZHANG Weijiang; Formal analysis: WANG Yongliang; Data curation: WANG Yongliang; Writing - review & editing: WANG Yongliang; Supervision: ZHANG Weijiang; Funding acquisition: ZHANG Weijiang. All authors approved the manuscript.
WANG Yongliang, ZHANG Weijiang. Mechanisms driving surface wind speed increases in an ecologically fragile region of Northwest China: Insights from circulation anomalies and geographical detector analysis. Journal of Arid Land, 2026, 18(6): 928-948.
Table S1 Classification thresholds for continuous factors
Interaction criteria
Interaction type
q(X1∩X2)<Min(q(X1), q(X2))
Nonlinear weakening
Min(q(X1), q(X2))<q(X1∩X2)<Max(q(X1), q(X2))
Univariate weakening
q(X1∩X2)>Max(q(X1), q(X2))
Bivariate enhancement
q(X1∩X2)=q(X1)+q(X2)
Independent
q(X1∩X2)>q(X1)+q(X2)
Nonlinear enhancement
Table 1 Criteria of the interaction types
Fig. 2Spatial (a-e) and percentage (f) distribution of the annual and seasonal surface wind speeds in the study area from 1980 to 2022
Fig. 3Temporal variation in the annual (a), spring (b), summer (c), autumn (d), and winter (e) surface wind speeds in the study area from 1980 to 2022
Fig. 4Spatial distribution (a) and temporal coefficient (b) of the first eigenvector field in the study area from 1980 to 2022. EOF1, first mode of empirical orthogonal function; PC1, first principal component (temporal coefficient). The black dashed line represents the 5 a moving average of the PC1 temporal coefficient. The value in the lower-left corner denotes the variance contribution of the first mode.
Fig. 5Spatial distribution (a) and temporal coefficient of the second eigenvector field in the study area from 1980 to 2022. EOF2, second mode of empirical orthogonal function; PC2, second principal component (temporal coefficient). The black dashed line represents the 5 a moving average of the PC2 temporal coefficient. The value in the lower-left corner denotes the variance contribution of the second mode.
Fig. 6q value of each factor to surface wind speed in the Shaanxi-Gansu-Ningxia (SGN) border region. FVC, fractional vegetation cover; CLCD, China Land Cover Dataset.
Fig. 7Ecological detection in the SGN border region. In the matrix, 'Y' indicates a statistically significant difference (P<0.05) between the effects of the two factors on surface wind speeds, whereas 'N' indicates no significant difference.
Fig. 8Interaction detection between factor-pairs in the SGN border region. The error bars represent the 95% confidence intervals of the q values based on 1000 bootstrap resamples.
Fig. 9Composite differences in the annual (a), spring (b), summer (c), autumn (d), and winter (e) 500-hPa geopotential height and horizontal winds in the SGN border region from 2000-2022 minus 1980-1999. Stippling represents the statistical significance of the 500-hPa geopotential height exceeding the 90% confidence level. The light green vectors refer to the 500-hPa horizontal wind differences exceeding the 90% confidence level.
Fig. 10Composite differences in the annual (a), spring (b), summer (c), autumn (d), and winter (e) 200-hPa geopotential height and horizontal winds in the SGN border region from 2000-2022 minus 1980-1999. Stippling represents the statistical significance of the 200-hPa geopotential height exceeding the 90% confidence level. The light green vectors refer to the 200-hPa horizontal wind differences exceeding the 90% confidence level. In panel e (winter), the white areas indicate that the absolute difference in 200-hPa geopotential height between the two periods is near zero, implying little to no change in the mean geopotential height.
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