Vegetation greening reduces dust storm activity in northern China

Vegetation greening reduces dust storm activity in northern China | 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 Vegetation greening reduces dust storm activity in northern China Yiwen Wang, Peijun Shi, Cesar Azorin-Molina, Lorenzo Minola, Ziqi Lin, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9002280/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Dust storms represent a major environmental challenge in northern China, adversely affecting air quality, agricultural productivity, and energy supply. However, the drivers behind recent changes in dust storm activity remain poorly understood. By analyzing 39 years of dust storm observations (957 stations), remote sensing, and reanalysis data (1982–2020), we document a significant decline in annual dust storm frequency (− 0.049 day decade⁻¹; p < 0.05), most pronounced in northwestern China. Concurrently, vegetation cover expanded (annual NDVI increase: 0.100 decade⁻¹), exhibiting a strong negative correlation with dust activity (r = − 0.616; p < 0.01), Sensitivity experiments conducted with the physically-based Dust Emission Model (DuEMv1) further confirm that enhanced vegetation cover weakens dust activity, suggesting that vegetation greening plays a key role in suppressing dust storms. This vegetation-driven suppression provides a scalable strategy for dust-storm management in global drylands, demonstrating how ecosystem restoration can counteract environmental degradation at regional scales. Earth and environmental sciences/Climate sciences Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Natural hazards Dust storm frequency vegetation greening dust emission model Northern China Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Dust storms are meteorological phenomena common in arid and semi-arid regions where strong winds lift large amounts of dust, sand, and debris from dry soil into the atmosphere - are thus the results of strong surface winds acting on erodible dry surface. Dust storm events frequently occur in global arid and semi-arid regions and their surrounding areas 1 – 3 . Particles in dust storm are often mixed with high salt, bacteria, and metal contents, making it toxic for human health 4 – 6 . Furthermore, it can worsen air quality 7 , 8 , affecting socio-economic activities 9 and agricultural production 10 . Besides, dust storm associated strong winds can damage buildings and infrastructures 11 , posing a major threat to people and property. The three fundamental elements of dust storm formation are (i) high winds, (ii) abundant sources of sand and dust, and (iii) thermally unstable atmospheric stratification 12 . As the driving force of dust storm dynamics, wind plays a dominant role in dust emission and transport 2 , 13 , 14 . The loose and dry surface of the earth can provide abundant material conditions for the occurrence of dust storms. Dust storms in arid regions are primarily driven by the intense release of dust resulting from soil wind erosion 14 . Changes in local natural conditions, such as a decrease in precipitation, a reduction in vegetation cover, or a decline in soil moisture, can make the soil more vulnerable to erosion by strong near-surface winds, thereby increasing the likelihood of dust storm occurrences 13 , 15 . The occurrence of sandstorms is also affected by large-scale atmospheric circulation patterns and local weather systems, primarily affects the transport process of dust 16 , 17 . Furthermore, human activities also play a crucial role in either exacerbating or mitigating sandstorms 18 – 20 . For example, overgrazing, farmland expansion, and engineering construction, can exacerbate the risk of dust storms 21 , on the other hand, ecological engineering can effectively reduce dust emissions. 22 Previous studies have shown a significant reduction in global dust storm activity since the 1980s 23 .This has been observed especially across many arid and semi-arid regions and their surrounding areas, such as Central Asia 24 , North Africa 25 , Mongolia 10 , and Eastern Australia 10 , 26 , as well as northern China 27 , 28 . Dust storm events in northern China frequently occur during spring and winter months, due to strong winds and the rich sources of dust particles provided by dry land 29 . Long-distance dust transport plays a crucial role in the formation process of dust storms in northern China 30 : Mongolia is one of the primary dust source regions for northern China 31 . Weakened atmospheric circulation, evidenced by declined wind speed in northern China 32 , 33 , are possible causes for the reduction in dust storm activity 34 . However, a reversal of surface wind speeds has been documented in northern China since 1990 32,33 , and the frequency of dust storms in northern China has not exhibited a positive trend in recent decades. This contradicts the hypothesis that winds have drived the dust storm changes. China has implemented several ecological restoration programs that began in the early 1980s, such as returning grazing land to grassland and the three north shelterbelt programs (Cui et al., 2022; Fu et al., 2023). As a result, continued increase in vegetation cover, a phenomenon known as vegetation greening, has been detected over northern China since the last 3–4 decades 35 , 36 . The vegetation greening can regulate the threshold wind velocity of sand or soil movement, and can affect the probability of dust storms occurring. To date, it is not clear if the changes in surface vegetation cover may have offset the impacts of recently enhanced wind speeds on dust storm activity. For all these reasons, this study aims at: (i) investigating the variability in observed dust storm frequency in the recent past 4 decades (i.e., since 1982); (ii) revealing the relation between vegetation cover change and dust storm frequency variability; and (iii) quantifying the impact of vegetation change on dust emission using a physically-based dust emission model. Our study wants to investigate the key role of vegetation greening on dust storm dynamics and to offer new insights into dust storm predictions and preventions. Results DSF changes estimated from the station observations Figure 1 shows the annual, spring, and summer variability and trends in DSF anomalies for 1982–2020 over northern China. Annually, DSF observations significantly declined for the whole period (−0.049 days dec -1 , p < 0.05), and it reaches maximum in 1983 (3.3 days) and minimum in 2011(0.2 days). Note that after a continued downward trend from 1982 to 2011, the long-term decline was interrupted for several years between 2012 and 2020. When examining the DSF series of the three sub-regions, it can be seen that they all decreased significantly ( p < 0.05) during 1982-2020, with the strongest negative trend for NWC (-0.980 days dec -1 ), followed by CNC (-0.800 days dec -1 ) and NEC (-0.120 days dec -1 ). The DSF series in CNC are consistent with the regional mean, while the DSF series in NWC are always higher than the regional mean. Conversely, the DSF series in NEC are consistently lower than the regional mean. Seasonally, both spring and summer mean DSF decreased significantly ( p < 0.05). The spring DSF trend (-0.030 days dec -1 ) aligns closely with the annual one, while the summer DSF shows a more gradual reduction (-0.008 days dec -1 ). Figure 2 displays the spatial distribution of DSF trends over the northern China for 1982–2020. Annually, observed DSF declined across most of the study region, with the Inner Mongolia and northwestern China showing the strongest and most intensive negative trends (<−0.200 days dec -1 , p < 0.05). Seasonally, widely declined DSF are found in spring, and the trends are more pronounced in the northwestern and central parts, where the reductions in dust storm days are more significant. Summer DSF exhibited a similar declining trend pattern, especially in the northwestern region. Impact of vegetation greening on DSF changes Figure 3 displays the spatial distribution of NDVI from 1982 to 2020 in northern China: it represents a proxy for vegetation cover, with the changes in vegetation cover revealed by the differences in NDVI over the years. It clearly shows that in 1982 and 1990 the majority of the northern China had very low magnitudes of NDVI (< 0.4), and only a few (mainly eastern and southern) regions exhibited higher magnitudes. By 2000 the magnitude of NDVI had widely increased over the northern China, showing an evident vegetation greening pattern. Areas with higher values of NDVI (> 0.6) were located in the eastern and southern regions. In addition, the NDVI in the northwestern part have shown an evident increase. From 2000 to 2020 the NDVI values in northern China showed a slight increase, with higher magnitudes still concentrated in the eastern, southern, and northwestern regions. In 2020 the NDVI values in most areas of northern China exceed 0.8. Figure 4 shows the relation between annual mean NDVI and DSF across northern China from 1982 to 2020. NDVI have gradually increased during the study period, while DSF have significantly decreased at the same time. DSF and NDVI exhibited a significant negative correlation ( r = -0.616, p < 0.01). Note that NDVI remained persistently low ( 1 in most of the years). However, this relation reversed since 2000. When comparing the distribution of DSF trend with vegetation greening rate, the regions exhibiting more significant NDVI increases correspond to the areas with greater reductions in DSF (Figure 5). This is particularly true for the northwestern corner, east, and south part of the study area. This spatial correspondence indicates that vegetation greening may have played a key role in decreasing dust storm occurrences. Box-and-whisker plots in Figure 6 show DSF trends for station categories according to vegetation-greening rates. Such plot shows that the DSF trend may be negatively associated with the vegetation greening rate. That is, station groups with low and moderate vegetation greening rates have relatively weak negative trends of DSF, while the stronger declining DSF trends were found in station groups with the high and very high vegetation greening rates. These results indicate that DSF in the northern China could have been weakened by vegetation growth during 1982–2020. Impact of vegetation greening on DSF changes revealed by dust emission model simulations To verify our hypothesis that the rapid vegetation greening weakened DSF, two sensitivity experiments configured with the same settings and forcing but with different LAI data (i.e., 1982LAI and 2020LAI) were implemented using a physically based dust emission model (DuEM v1). Figure 7 presents the spatial distributions of dust emission simulated by DuEM v1 for 1982 (forced with 1982LAI) and 2020 (forced with 2020LAI), along with the difference between the two. Using actual 1982 data in the model, we obtained the 1982 baseline dust emission simulations (Figure 7a). The baseline simulation reveals the strongest dust emissions in northwestern China and the Inner Mongolia, which is consistent with observed dust storm frequency in northern China (Figure S1). This indicate that the model accurately simulates the spatiotemporal characteristics of dust emissions after comprehensively considering multiple influencing factors. We then conducted a sensitivity test by adjusting the vegetation coverage input. Specifically, the 1982 LAI data was replaced with 2020 LAI data to derive dust emissions under the 2020 vegetation-increased scenario (Figure 7b). The spatial pattern of DSF for the 2020LAI simulations resembles the spatial distribution of dust emission flux using the 1982LAI. This indicates that vegetation greening has not caused the change in the spatial distribution of dust emission in northern China. When considering the difference in dust emission between the two simulations (i.e., 2020LAI minus 1982LAI, Figure 7c), negative dust emission differences were found in most northwestern China and the Inner Mongolia ( p < 0.1). Overall, average dust emissions in 2020LAI have reduced by 1.887 g·m⁻²·yr -1 when compared to the one simulated in 1982LAI. This confirms that the decline in DSF was driven by vegetation greening (i.e., the vegetation cover changes). Mechanisms of vegetation greening affecting DSF To uncover the physical processes that could explain how the vegetation greening affects dust storm activity, we used the DuEM v1 model for sensitivity experiments by altering the LAI data (1982LAI and 2020LAI) and determining the threshold friction velocity for dust particles across northern China under differing vegetation conditions. The spatial distributions of threshold friction velocity under the 1982 and 2020 LAI scenarios are shown in Figure 8a and 8b, respectively. Overall, the two scenarios show a generally consistent spatial pattern across northern China. The 2020 scenario (Figure 8b) is characterized by higher threshold friction velocity values in the northeastern Inner Mongolia. The frequency distribution of the threshold friction velocity shifted markedly toward higher values under the 2020LAI scenario (Figure S2): this shows that the threshold friction velocity increased across most of the study area. Figure 8c visually illustrates the spatial differences in threshold friction velocity between the 2020 and 1982 vegetation scenarios. The results show that threshold friction velocity significantly increased across most parts of northern China, particularly in the northwest and northeastern Inner Mongolia ( p < 0.1). Overall, the increased vegetation coverage raised the average threshold friction velocity by 0.16 m·s -1 under the 2020 scenario compared to the 1982 scenario, strengthening surface resistance to dust emission across most of northern China. Discussion This study investigated the recent spatiotemporal variations in dust storm activity across northern China, a region that has experienced an increase in its vegetation cover according to the NDVI analysis from 1982 to 2020. Overall, our results revealed a significant decreasing trend in dust storm frequency (DSF). This finding is consistent with the weakening trend of dust storm activities 28 , 37 – 39 and near-surface wind speed 32 , 33 in northern China. However, a long-term negative trend is interrupted for a few years during 2012 to 2020, but then it continues. This means dust storm activity is not fully consistent with change in surface wind speed, which shows an earlier reversal since 1990s 32 . Therefore, other factors, such as vegetation cover changes, may have played a key role in regulating changes in dust storm activity. By comparing DSF trends with vegetation greening rates (i.e., vegetation cover increases), it appears that stations with fastest declines in DSF were mainly located in areas with the highest vegetation greening rates, while stations with the weakest negative DSF trends were placed over the areas that experienced the lowest vegetation greening rates. This indicates that rapid vegetation greening in northern China may be associated with the widely weakened DSF in northern China during 1982 to 2020. Existing studies also documented that NDVI has a direct negative impact on the normalized brightness temperature dust index (NBTDI 40 ). Notably, the northeastern region deviates from this pattern. Although the northeastern region experienced substantial vegetation increase, its decreasing trend in dust storm activity was weaker. This is primarily because the baseline frequency of dust storm activity in the northeastern region is low and it mainly regulated by the long-distance dust transport from dry land 41 . Note that some areas in the study region exhibit an extremely low baseline of vegetation cover, even a slight increase in vegetation produces a notable amplifying effect and greater dust storm suppression compared to more densely vegetated areas. DSF also declined in regions without a significant NDVI greening trend, which may be linked to factors like variations in large- to mesoscale atmospheric circulation. 42 As vegetation cover has widely increased over the northern China during 1982 to 2020, the difference between the simulated dust emission under the 1982LAI and 2020LAI forcing reflects to a large extent the impact of vegetation greening on DSF changes. The results clearly demonstrate that the simulated dust emission forced by the 2020LAI was much lower in those regions (e.g., northwestern part and Inner Mongolia) that experienced rapid vegetation greening, when compared to the simulation forced by the 1982LAI. This pattern is strongly consistent with the distribution of DSF changes based on the station observations and NDVI difference: it further confirms that rapid vegetation greening has weakened DSF over the northern China during the last decades. In particular, vegetation greening can impact DSF in two ways: (i) increased vegetation cover reduces surface wind speed ; and (ii) it raises the threshold wind speed for dust emission 43 , thereby decreasing dust emissions. Note that dust emission fluxes increased in most areas of the southern and eastern Mongolia in the simulation forced by the 2020LAI. This demonstrates that dust activity in Mongolia has increased in recent years 44 under the ongoing desertification and aeolian erosion in the southern Gobi Desert 45 . Therefore, the increase of dust emissions in Mongolia may also influence changes in dust storm activity within the study area by long-distance transport: this should be further investigated in future work. Although our study encompasses data from 957 stations that recorded dust storms, their non-uniform distribution across the study area leaves certain regions without coverage (e.g., western Inner Mongolia and Taklimakan Desert surroundings), potentially affecting the precision of our findings. Recent advances in consistent monitoring capabilities and high-resolution satellite retrievals have enabled widespread application of Dust Optical Depth (DOD) products for detecting dust storm activity 46 , 47 . These datasets are particularly valuable in regions with sparse station observations, where they can effectively address monitoring gaps in dust storm investigations. Therefore, future research should incorporate remote sensing products to enhance accuracy of dust storm activity. Besides, in the simulation of dust emission fluxes, this study employed a simplified parameterization scheme assuming homogeneous sandy soil textures throughout the research domain. Given the documented heterogeneity of real soil types in the region, this uniform soil representation inevitably introduces uncertainty. Future investigations should incorporate spatially-distributed soil variations to enhance simulation accuracy and improve modeling precision. Conclusion To conclude, this study found a significant decrease in DSF over northern China from 1982 to 2020. This decline occurred concurrently with a widespread increase in vegetation coverage across most of the study area, indicating a clear greening pattern. Notably, the stations exhibiting the largest negative DSF trends were primarily located in regions that simultaneously experienced the highest rates of vegetation greening, as reflected in NDVI changes. In contrast, areas with minimal vegetation greening showed no significant trends in DSF. Statistically, changes in vegetation coverage across northern China were significantly and negatively correlated with DSF (r = -0.616, p < 0.05), suggesting that the observed greening from 1982 to 2020 partly contributed to the regional DSF reduction. This causal link is further confirmed by sensitivity experiments conducted with a physically-based dust emission model. The experiments indicate that vegetation greening has led to a significant reduction in dust emissions across northern China, with a regional mean decrease of 1.887 g·m⁻²·yr⁻¹. The primary mechanism for this reduction is the raising of the threshold wind speed required to initiate dust lifting. Our results offer robust evidence that the pronounced vegetation greening across northern China has already curbed dust-storm activity, and underscore the potential of ecological restoration as a nature-based buffer against dust extremes in a warming climate. Methods Observations of dust storms This study used daily dust storm observations from 1 January 1982 to 31 December 2020, which were retrieved from the China Meteorological Administration (CMA; http://data.cma.cn/en, last accessed on 1 January 2026). Observers conduct manual observation and recording in accordance with the successive editions of the Ground Meteorological Observation Standards 48 issued by the China Meteorological Administration. They determine daily weather phenomena such as dust storms, blowing dust, or floating dust have occurred. A dust storm day is recorded when a meteorological station observes the occurrence of dust storm within the 24 hours in that day. The dataset consists of 957 stations covering northern China（Figure 9）, with the highest station (i.e., Wudaoliang) located at 4,214 m a.s.l (meters above sea level). Observations of daily dust storms observations were aggregated into monthly values. Reanalysis outputs In the dust emission model, to simulate the dust emission fluxes, outputs from ERA5 for 1982-2020 were used, in particular: (a) U and V wind speed components at surface, (b) snow cover; (c) volumetric soil water, 0-7cm. ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather, with data available from 1940 onwards 49 . ERA5 outputs are produced hourly at a horizontal resolution of 31 km: this dataset has been proven to be a reliable dataset for detailed research on climate change and variability 50,51 . ERA5 outputs were obtained from the Copernicus website (https://cds.climate.copernicus.eu/; last accessed on 1 January 2026). Remote sensing products The Normalized Difference Vegetation Index (NDVI) data were retrieved from the Earth Observing System Data and Information System Distributed Active Archive Center (EOSDIS-DAAC, https://www.earthdata.nasa.gov/data). This dataset comprises the Global Inventory Modeling and Mapping Studies – 3rd Generation Version 1.2 (GIMMS-3G+) data for the NDVI. The NDVI is derived from calibrated and corrected measurements based on Advanced Very High Resolution Radiometer (AVHRR) data, featuring a spatial resolution of 0.0833 degrees and providing northern China coverage from 1982 to 2020. The dataset integrates observations from multiple AVHRR sensors and accounts for various external influences, including calibration loss, orbital drift, and volcanic eruptions 52 . Given the extensive missing values in existing Leaf Area Index (LAI) products across northern China, we instead derived LAI from NDVI. Specifically, the LAI for 1982 and 2020 was estimated using the empirical NDVI–LAI relationship established by Qi et al. (2000). The function is expressed as: LAI = a × NDVI³ + b × NDVI² + c × NDVI + d where the coefficients are a = 18.99, b = -15.24, c = 6.124, and d = -0.352 53 . Dust emission model Dust emission is a key indicator characterizing dust activity and can be estimated through modeling 54 . This study used a physically-based dust emission model (DuEMv1 1,55 ), which incorporated near-surface meteorology and land surface conditions. The DuEMv1 model was developed on the assumption that vertical dust emission is driven primarily by saltation bombardment and aggregate disintegration. Particle saltation begins when the friction velocity, a measure of wind shear stress, exceeds the threshold friction velocity. Horizontal saltation flux scales with the cube of friction velocity. From the soil volume entrained by saltating grains, saltation efficacy—the ratio of vertical dust flux to horizontal saltation flux—is derived by accounting for inter-particle bond strength. Dust is emitted solely from exposed erodible surfaces, after masking non-erodible surfaces such as snow, vegetation, and water bodies. The model’s input variables include friction velocity, air density, soil moisture, snow cover fraction, and vegetation cover, among others. The model has performed well in comparisons with field observation data and has been widely applied in dust simulation studies at both regional 56,57 and global 58,59 scales. Input parameters required for the DuEMv1 experiments are presented in Table 1. Since the model requires near-surface wind speed as input, wind speed data at 10 m height were converted to 2m height using a conversion formula 60 . All data were resampled to a common horizontal resolution of 0.25° x 0.25° grid during the preprocessing phase to maintain spatial consistency in the calculations. A detailed description of dust emission model is illustrated in supplementary materials. In the model simulations, the soil type was uniformly set as sandy soil with the corresponding parameterization scheme. This setup was based on the actual condition that sandy soil is predominantly distributed across the study area. Table 1. Input parameters for DuEMv1 model Data types Temporal resolution Spatial resolution Period Data source Wind speed (at 10m) Hour 0.25° x 0.25° 1982 https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=download Soil moisture Day 0.25° x 0.25° 1982 https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=download Snow cover Hour 0.1° x 0.1° 1982 https://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=download NAVI 15days 0.0833° x 0.0833° 1982 and 2020 https://search.earthdata.nasa.gov Statistic methods and model experiment The dust storm activity is expressed here as dust storm frequency (DSF; in days). The regional mean dust storm frequency was defined as the total number of recorded dust storm days from all the stations in the study region, divided by the number of stations. Trends were computed using the Sen’s slope method 61 (in days per decade; hereafter day dec -1 ), and the multidecadal variability of dust storm frequency was shown using the 11-year Gaussian low-pass filter 62 . Mann–Kendall’s tau-b non-parametric correlation coefficient was applied to determine the statistical significance of the calculated trends 63,64 . To measure the degree of relation between DSF and NDVI , Pearson’s correlation coefficient was calculated 65 . Three p -level thresholds were used to determine the differences in the statistical significance of trends in DSF: (i) significant at p < 0.05, (ii) significant at p 0.05). We also assessed DSF changes as a function of vegetation greening rate across the study region. Here the vegetation greening rates of each station for 1982 to 2020 were calculated as the difference in LAI value as 2020 minus 1982. DSF from the 957 stations were classified into 5 groups with different vegetation greening rates being: (i) negative-vegetation greening rate (< 0); (ii) low-vegetation greening rate (0-2); (iii) moderate-vegetation greening rate (2-4); (iv) high-vegetation greening rate (4-6); and (v) very-high-vegetation greening rate (6-8). To quantify the contribution of vegetation greening to changes in dust storm frequency in northern China, two experiments were conducted using the DuEMv1 model. The baseline (“All”) experiment uses surface wind speed, vegetation cover, and soil moisture at 1982. In the sensitivity experiments, only LAI is allowed to vary from 1982 to 2020, while all other drivers remain fixed at their 1982 values. Note that for the sensitivity experiments with the other factors fixed as in 1982, the seasonal variations of these factors are still considered and set to the values as in 1982, but the interannual variations of these factors are excluded. By comparing these experiments, we identified the contributions of vegetation cover to the variations of dust activity in northern China. Declarations Author Contributions Statement G.Z. conceived and designed the research Y.W. performed the data analysis.Y.W. wrote the paper with the inputs of P.S, C.A., L.M, Z.L, W.L, H.M., and G.Z. All authors contributed to the interpretation of the results and approved the final manuscript. Competing Interests Statement The authors declare no competing interests Author Contribution G.Z. conceived and designed the research Y.W. performed the data analysis.Y.W. wrote the paper with the inputs of P.S, C.A., L.M, Z.L, W.L, H.M., and G.Z. All authors contributed to the interpretation of the results and approved the final manuscript. Acknowledgement This research was supported by the National Natural Science Foundation of China (42330502, 42101027), Qinghai Provincial Central Government-Guided Local Science and Technology Development Fund - Science and Technology Innovation Base Construction Project(2025ZY017) and Independent Research Project of State Key Laboratory of Earth Surface Processes and Resource Ecology at Beijing Normal University. C.A-M. acknowledges support from the GVA-PROMETEO Grant CIPROM/2023/38; CSIC-LINCGLOBAL Ref. 598 LINCG24042; and CSIC’s PTI-Clima. Data Availability This study used daily dust storm observations from 1 January 1982 to 31 December 2020, retrieved from the China Meteorological Administration (CMA: http://data.cma.cn/en, last accessed 1 January 2026). ERA5 reanalysis data were obtained from the Copernicus Climate Data Store (https://cds.climate.copernicus.eu/, last accessed 1 January 2026). 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Quantitative assessment of driving factors behind the ecological effects of the Beijing–Tianjin sandstorm source region, China during past 20 years. Ecological Frontiers . 45 , 1005-1016 (2025). Piao, S. L. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1 , 14-27 (2020). Guan, Q. et al. Dust Storms in Northern China: Long-Term Spatiotemporal Characteristics and Climate Controls. J. Clim. 30 , 6683-6700 (2017). Shao, T. et al. Characteristics and a mechanism of dust weather in Northern China. Clim. Dyn. 61 , 1591-1606 (2023). Gui, K. et al. Quantifying the contribution of local drivers to observed weakening of spring dust storm frequency over northern China (1982–2017). Sci. Total Environ. 894 , 164923 (2023). Wang, N. et al. Quantifying the influence of dominant factors on the long-term sandstorm weather - A case study in the Yellow River Basin during 2000–2021. Atmos. Res. 311 , 107717 (2024). Wang, X., Huang, J., Ji, M. & Higuchi, K. Variability of East Asia dust events and their long-term trend. Atmos. Environ. 42 , 3156-3165 (2008). Liu, Y., Xu, R. R., Ziegler, A. D. & Zeng, Z. Z. Stronger winds increase the sand-dust storm risk in northern China. Environmental Science-Atmospheres . 2 , 1259-1262 (2022). Pu, B. et al. Retrieving the global distribution of the threshold of wind erosion from satellite data and implementing it into the Geophysical Fluid Dynamics Laboratory land–atmosphere model (GFDL AM4.0/LM4.0). Atmos. Chem. Phys. , (2020). Cheng, X., Xu, Z., Yu, Y. & Zhang, X. Changes in frequency and possible causes of dust occurrence in northern China and Mongolia since 2001 revealed by remote sensing. Journal of Desert Research . 45 , 47-60 (2025). Kim, J., Dorjsuren, M., Zucca, C. & Purevjav, G. Mapping land degradation and sand and dust generation hotspots by spatiotemporal data fusion analysis: A case-study in the southern Gobi (Mongolia). Land Degrad. Dev. 34 , 1629-1647 (2023). Liang, P., Chen, B., Yang, X., Liu, Q. & Zhang, D. Revealing the dust transport processes of the mega dust storm event in 2021, northern China. Sci. Bull. 66 , (2021). Gkikas, A. et al. Quantification of the dust optical depth across spatiotemporal scales with the MIDAS global dataset (2003–2017). Atmos. Chem. Phys. 22 , 3553-3578 (2022). CMA, C. M. A. Ground Meteorological Observation Standard . (China Meteorological Press, 2003). Hersbach, H., Bell, B., Berrisford, P., Hirahara, S. & Thépaut, J. O. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. , (2020). Tarek, M., Brissette, F. & Arsenault, R. Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America. Hydrol. Earth Syst. Sci. 24 , 2527-2544 (2020). Minola, L. et al. The contribution of large-scale atmospheric circulation to variations of observed near-surface wind speed across Sweden since 1926. Clim. Change . 176 , 54 (2023). Recuero, L. et al. Mapping Periodic Patterns of Global Vegetation Based on Spectral Analysis of NDVI Time Series. Remote Sens. , (2019). Qi, J. et al. Leaf Area Index Estimates Using Remotely Sensed Data and BRDF Models in a Semiarid Region. Remote Sens. Environ. 73 , 18-30 (2000). Shao, Y. Physics and Modelling of Wind Erosion . (Springer Dordrecht, 2008). Shao, Y. Simplification of a dust emission scheme and comparison with data. Journal of Geophysical Research: Atmospheres . 109 , 2003JD004372 (2004). Kang, J., Yoon, S., Shao, Y. & Kim, S. Comparison of vertical dust flux by implementing three dust emission schemes in WRF/Chem. Journal of Geophysical Research . 116 , (2011). Hamidi, M., Kavianpour, M. R. & Shao, Y. Numerical simulation of dust events in the Middle East. Aeolian Res. 13 , 59-70 (2014). Klose, M. et al. Mineral dust cycle in the Multiscale Online Nonhydrostatic AtmospheRe CHemistry model (MONARCH) Version 2.0. Geosci. Model Dev. 14 , 6403-6444 (2021). Wu, C. et al. Description of Dust Emission Parameterization in CAS‐ESM2 and Its Simulation of Global Dust Cycle and East Asian Dust Events. J. Adv. Model. Earth Syst. 13 , (2021). Karnauskas, K. B., Lundquist, J. K. & Zhang, L. Southward shift of the global wind energy resource under high carbon dioxide emissions. Nat. Geosci. 11 , 38-43 (2018). Sen, P. K. Estimates of the Regression Coefficient Based on Kendall's Tau. J. Am. Stat. Assoc. 63(324) , (1968). Ding, R. et al. Estimating the limit of decadal-scale climate predictability using observational data. Clim. Dyn. , (2016). Kendall, M. G. Rank Correlation Methods. Br. J. Psychol. 25 , 86-91 (1990). Mann, H. B. Non-parametric tests against trend. Econometrica . 13 , 245 (1945). Pearson, K. Mathematical contributions to the theory of evolution.—On a form of spurious correlation which may arise when indices are used in the measurement of organs. Proceedings of the Royal Society of London . 60 , 489-498 (1897). Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Mar, 2026 Reviews received at journal 08 Mar, 2026 Reviewers agreed at journal 08 Mar, 2026 Reviewers agreed at journal 08 Mar, 2026 Reviewers agreed at journal 05 Mar, 2026 Reviewers agreed at journal 05 Mar, 2026 Reviews received at journal 04 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviewers invited by journal 03 Mar, 2026 Editor assigned by journal 03 Mar, 2026 Submission checks completed at journal 03 Mar, 2026 First submitted to journal 01 Mar, 2026 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-9002280","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":601527396,"identity":"d0d0211b-6766-48f8-b578-f2de147d5669","order_by":0,"name":"Yiwen Wang","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yiwen","middleName":"","lastName":"Wang","suffix":""},{"id":601527397,"identity":"f76f1dd9-3d05-4cd3-a6cd-e54f74caa0ba","order_by":1,"name":"Peijun Shi","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Peijun","middleName":"","lastName":"Shi","suffix":""},{"id":601527398,"identity":"d15cd10e-fde9-4653-b029-c9662c3e71f7","order_by":2,"name":"Cesar Azorin-Molina","email":"","orcid":"","institution":"Centro de Investigaciones sobre Desertificación, Consejo Superior de Investigaciones Científicas","correspondingAuthor":false,"prefix":"","firstName":"Cesar","middleName":"","lastName":"Azorin-Molina","suffix":""},{"id":601527399,"identity":"4c2201e6-3dfa-4592-a0e2-fc071ec1f89b","order_by":3,"name":"Lorenzo Minola","email":"","orcid":"","institution":"University of Turin","correspondingAuthor":false,"prefix":"","firstName":"Lorenzo","middleName":"","lastName":"Minola","suffix":""},{"id":601527400,"identity":"30baa7d2-71ad-457f-adfd-a5e2dd934591","order_by":4,"name":"Ziqi Lin","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Ziqi","middleName":"","lastName":"Lin","suffix":""},{"id":601527401,"identity":"d345be33-85cd-46bf-b86b-63526a5ab1b0","order_by":5,"name":"Wenxuan Li","email":"","orcid":"","institution":"Beijing Normal University","correspondingAuthor":false,"prefix":"","firstName":"Wenxuan","middleName":"","lastName":"Li","suffix":""},{"id":601527402,"identity":"aedb5976-c6ab-4c36-b55a-5fd084303804","order_by":6,"name":"Heng Ma","email":"","orcid":"","institution":"Ministry of Emergency Management of China","correspondingAuthor":false,"prefix":"","firstName":"Heng","middleName":"","lastName":"Ma","suffix":""},{"id":601527403,"identity":"45337772-d253-4414-b4a3-cc469e09b5de","order_by":7,"name":"Gangfeng Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYLCCCiDmlwAzmYnUcgaIJWeQrMXgBrFa5N3PHn5xoOKO3ebbvcckGCqsExvYzx7Aq8XwTF6axYEzz5K33TmXJsFwJj2xgScvAb+Whhwz449th5PNbuSYSTC2HU5skOAxwK+l/42ZwcF/h5ONZ4C0/CNCi7xEjvGDgw2H7QwkQFoaiNBiIPHGjOHAscMJEnfOGFskHEs3buPJIWBLf47xhwM1h+35Z/cY3vhQYy3bz36GgC0HGNhA8Z7YAOIlADEbXvUgWxoYmD8AaXtCCkfBKBgFo2AEAwBufkqnZFM7IAAAAABJRU5ErkJggg==","orcid":"","institution":"Beijing Normal University","correspondingAuthor":true,"prefix":"","firstName":"Gangfeng","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2026-03-01 14:53:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9002280/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9002280/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104101865,"identity":"8136a4b0-9a66-4115-8ba9-96a1c226a387","added_by":"auto","created_at":"2026-03-06 20:01:29","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2350619,"visible":true,"origin":"","legend":"\u003cp\u003eVariability of annual and seasonal mean dust storm frequency (days) in northern China and three sub-regions for 1982–2020. The 11-yr Gaussian low-pass filter (provided by the black dashed line) highlights multidecadal variability. The trend of mean dust storm frequency for the whole northern China are displayed in each plot.\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9002280/v1/c0e9e8b17f8bef473eb1a409.jpeg"},{"id":104101873,"identity":"0c888f8b-9c17-4f13-a8fc-04da18abf46b","added_by":"auto","created_at":"2026-03-06 20:01:29","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":28917786,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of sign, magnitude (in days dec\u003csup\u003e-1\u003c/sup\u003e), and significance of annual and seasonal trends of dust storm frequency over the northern China from 1982-2020.\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9002280/v1/9f9b66a0c3223cdd4ad0c402.jpeg"},{"id":104101866,"identity":"21d22c1f-b89e-4a97-b632-67fd55d8e32c","added_by":"auto","created_at":"2026-03-06 20:01:29","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":113600,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of NDVI in 1982, 1990, 2000, 2010, and 2020 in northern China\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9002280/v1/0844128aa55778e120f768ab.jpeg"},{"id":104403399,"identity":"391fc43c-afa4-4c89-9173-6f9d733e5caf","added_by":"auto","created_at":"2026-03-11 12:18:15","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":947594,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between dust storm frequency (DSF) and NDVI across northern China from 1982 to 2020.\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9002280/v1/0cf910d7a694cb06038f7264.jpeg"},{"id":104101871,"identity":"d256b419-f15e-406d-8373-bdfaa77cce06","added_by":"auto","created_at":"2026-03-06 20:01:29","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":15663085,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of NDVI difference between 1982 and 2020 (2020 minus 1982) across northern China. For the comparison with NDVI, trends and associated significance level of DSF (days dec\u003csup\u003e−1\u003c/sup\u003e) for 1982–2020 in each of the 957 stations are also displayed in the map.\u003c/p\u003e","description":"","filename":"image5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9002280/v1/f65d7b78913d9f898201a36f.jpeg"},{"id":104101868,"identity":"9739a34f-9722-4168-a8c2-bb4b447a4902","added_by":"auto","created_at":"2026-03-06 20:01:29","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":639174,"visible":true,"origin":"","legend":"\u003cp\u003eBox-and-whisker plots of DSF trends for station categories according to vegetation-greening rates across the northern China from 1982 to 2020. The median (red line), the 25th and 75th percentile range (boxes), and the 10th and 90th percentiles (whiskers) for each station category are provided. The horizontal axis represents five levels of vegetation greening rate: Negative (\u0026lt; 0), Low (0-2), Medium (2-4), High (4-6), and Very high (6-8).\u003c/p\u003e","description":"","filename":"image6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9002280/v1/ebe9b4c8dafa9df8fd39b34b.jpeg"},{"id":104101870,"identity":"1b9e143f-cbda-4406-99e0-73cb0f21daf3","added_by":"auto","created_at":"2026-03-06 20:01:29","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":4512238,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of dust emission flux in (a) 1982 using 1982 LAI data，(b) 1982 using 2020 LAI data, and (c) the discrepancy between the two simulations (b-a). Note that the upper right vertical legend is used for (a) and (b), and the low right vertical legend for (c). The black dot in the grid represents a statistically significant dust emission flux difference at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.1.\u003c/p\u003e","description":"","filename":"image7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9002280/v1/3daa5937c6d517e2f1c10a81.jpeg"},{"id":104404208,"identity":"9874b973-fad0-4c55-9053-3cb14844c7b2","added_by":"auto","created_at":"2026-03-11 12:19:50","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":777081,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of threshold friction velocity for dust emission in (a) 1982 using 1982 LAI data，(b) 1982 using 2020 LAI data, and (c) the discrepancy between the two simulations (b-a). Note that the upper right vertical legend is used for (a) and (b), and the low right vertical legend for (c). The black dot in the grid represents a statistically significant threshold friction velocity for dust difference at\u003cem\u003e p\u003c/em\u003e \u0026lt; 0.1.\u003c/p\u003e","description":"","filename":"image8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9002280/v1/379e589c4b31c1a1d5908975.jpeg"},{"id":104101874,"identity":"cc34bbd8-11d5-4aa6-8802-3fe9211ca478","added_by":"auto","created_at":"2026-03-06 20:01:29","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":22476469,"visible":true,"origin":"","legend":"\u003cp\u003eTerrain map and the distribution of 957 meteorological stations in northern China. Stations are classified into three zones: (i) Northeast China (NEC; Heilongjiang Provinces, Jilin Provinces, Liaoning Provinces); (ii) Central part of Northern China (CNC; Beijing, Tianjin, Hebei Provinces, the Inner Mongolia Autonomous Region, Shanxi Province); and (iii) Northwest China (NWC; Gansu Provinces, Shaanxi Provinces, Qinghai Provinces, the Ningxia Hui Autonomous Region, the Xinjiang Uygur Autonomous Region). The solid red line shows the border of northern China and the demarcation of the three regions. The illustrated map shows the location of study sites in northern China (shaded yellow) with all China (both country borders and internal provincial borders are shown). The areas with elevation below 0 m a.s.l. are Turpan Basin and Ayding Lake.\u003c/p\u003e","description":"","filename":"image9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9002280/v1/521f6b847943babba042c276.jpeg"},{"id":104408716,"identity":"61d3b1fb-ed46-40d3-a6d3-baf1db7f021a","added_by":"auto","created_at":"2026-03-11 12:43:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":77176340,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9002280/v1/b867aafd-3abb-4710-bb96-79cace7d373f.pdf"},{"id":104404121,"identity":"91771a79-5e55-42c1-9744-6c92bcd1c8a6","added_by":"auto","created_at":"2026-03-11 12:19:42","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":228760,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-9002280/v1/fd00e210c168822334f91992.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Vegetation greening reduces dust storm activity in northern China","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDust storms are meteorological phenomena common in arid and semi-arid regions where strong winds lift large amounts of dust, sand, and debris from dry soil into the atmosphere - are thus the results of strong surface winds acting on erodible dry surface. Dust storm events frequently occur in global arid and semi-arid regions and their surrounding areas \u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Particles in dust storm are often mixed with high salt, bacteria, and metal contents, making it toxic for human health \u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Furthermore, it can worsen air quality \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, affecting socio-economic activities \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and agricultural production \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Besides, dust storm associated strong winds can damage buildings and infrastructures \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, posing a major threat to people and property.\u003c/p\u003e \u003cp\u003eThe three fundamental elements of dust storm formation are (i) high winds, (ii) abundant sources of sand and dust, and (iii) thermally unstable atmospheric stratification \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. As the driving force of dust storm dynamics, wind plays a dominant role in dust emission and transport \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The loose and dry surface of the earth can provide abundant material conditions for the occurrence of dust storms. Dust storms in arid regions are primarily driven by the intense release of dust resulting from soil wind erosion \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Changes in local natural conditions, such as a decrease in precipitation, a reduction in vegetation cover, or a decline in soil moisture, can make the soil more vulnerable to erosion by strong near-surface winds, thereby increasing the likelihood of dust storm occurrences \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. The occurrence of sandstorms is also affected by large-scale atmospheric circulation patterns and local weather systems, primarily affects the transport process of dust \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Furthermore, human activities also play a crucial role in either exacerbating or mitigating sandstorms \u003csup\u003e\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. For example, overgrazing, farmland expansion, and engineering construction, can exacerbate the risk of dust storms\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, on the other hand, ecological engineering can effectively reduce dust emissions.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePrevious studies have shown a significant reduction in global dust storm activity since the 1980s \u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.This has been observed especially across many arid and semi-arid regions and their surrounding areas, such as Central Asia \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, North Africa\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, Mongolia\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, and Eastern Australia \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, as well as northern China \u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Dust storm events in northern China frequently occur during spring and winter months, due to strong winds and the rich sources of dust particles provided by dry land \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Long-distance dust transport plays a crucial role in the formation process of dust storms in northern China \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e: Mongolia is one of the primary dust source regions for northern China \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Weakened atmospheric circulation, evidenced by declined wind speed in northern China \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, are possible causes for the reduction in dust storm activity \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. However, a reversal of surface wind speeds has been documented in northern China since 1990 \u003csup\u003e32,33\u003c/sup\u003e, and the frequency of dust storms in northern China has not exhibited a positive trend in recent decades. This contradicts the hypothesis that winds have drived the dust storm changes.\u003c/p\u003e \u003cp\u003eChina has implemented several ecological restoration programs that began in the early 1980s, such as returning grazing land to grassland and the three north shelterbelt programs (Cui et al., 2022; Fu et al., 2023). As a result, continued increase in vegetation cover, a phenomenon known as vegetation greening, has been detected over northern China since the last 3\u0026ndash;4 decades \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The vegetation greening can regulate the threshold wind velocity of sand or soil movement, and can affect the probability of dust storms occurring. To date, it is not clear if the changes in surface vegetation cover may have offset the impacts of recently enhanced wind speeds on dust storm activity.\u003c/p\u003e \u003cp\u003eFor all these reasons, this study aims at: (i) investigating the variability in observed dust storm frequency in the recent past 4 decades (i.e., since 1982); (ii) revealing the relation between vegetation cover change and dust storm frequency variability; and (iii) quantifying the impact of vegetation change on dust emission using a physically-based dust emission model. Our study wants to investigate the key role of vegetation greening on dust storm dynamics and to offer new insights into dust storm predictions and preventions.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDSF changes estimated from the station observations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 shows the annual, spring, and summer variability and trends in DSF anomalies for 1982\u0026ndash;2020 over northern China. Annually, DSF observations significantly declined for the whole period (\u0026minus;0.049 days dec\u003csup\u003e-1\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05), and it reaches maximum in 1983 (3.3 days) and minimum in 2011(0.2 days). Note that after a continued downward trend from 1982 to 2011, the long-term decline was interrupted for several years between 2012 and 2020. When examining the DSF series of the three sub-regions, it can be seen that they all decreased significantly (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05) during 1982-2020, with the strongest negative trend for NWC (-0.980 days dec\u003csup\u003e-1\u003c/sup\u003e), followed by CNC (-0.800 days dec\u003csup\u003e-1\u003c/sup\u003e) and NEC (-0.120 days dec\u003csup\u003e-1\u003c/sup\u003e). The DSF series in CNC are consistent with the regional mean, while the DSF series in NWC are always higher than the regional mean. \u0026nbsp;Conversely, the DSF series in NEC are consistently lower than the regional mean. Seasonally, both spring and summer mean DSF decreased significantly (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05). The spring DSF trend (-0.030 days dec\u003csup\u003e-1\u003c/sup\u003e) aligns closely with the annual one, while the summer DSF shows a more gradual reduction (-0.008 days dec\u003csup\u003e-1\u003c/sup\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 2 displays the spatial distribution of DSF trends over the northern China for 1982\u0026ndash;2020. Annually, observed DSF declined across most of the study region, with the Inner Mongolia and northwestern China showing the strongest and most intensive negative trends (\u0026lt;\u0026minus;0.200 days dec\u003csup\u003e-1\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Seasonally, widely declined DSF are found in spring, and the trends are more pronounced in the northwestern and central parts, where the reductions in dust storm days are more significant. Summer DSF exhibited a similar declining trend pattern, especially in the northwestern region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact of vegetation greening on DSF changes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 3 displays the spatial distribution of NDVI from 1982 to 2020 in northern China: it represents a proxy for vegetation cover, with the changes in vegetation cover revealed by the differences in NDVI over the years. It clearly shows that in 1982 and 1990 the majority of the northern China had very low magnitudes of NDVI (\u0026lt; 0.4), and only a few (mainly eastern and southern) regions exhibited higher magnitudes. By 2000 the magnitude of NDVI had widely increased over the northern China, showing an evident vegetation greening pattern. Areas with higher values of NDVI (\u0026gt; 0.6) were located in the eastern and southern regions. In addition, the NDVI in the northwestern part have shown an evident increase. From 2000 to 2020 the NDVI values in northern China showed a slight increase, with higher magnitudes still concentrated in the eastern, southern, and northwestern regions. In 2020 the NDVI values in most areas of northern China exceed 0.8.\u003c/p\u003e\n\u003cp\u003eFigure 4 shows the relation between annual mean NDVI and DSF across northern China from 1982 to 2020. NDVI have gradually increased during the study period, while DSF have significantly decreased at the same time. DSF and NDVI exhibited a significant negative correlation (\u003cem\u003er\u0026nbsp;\u003c/em\u003e= -0.616, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). Note that NDVI remained persistently low (\u0026lt; 0.45) during 1982 to 2000, while DSF stay high value (\u0026gt; 1 in most of the years). However, this relation reversed since 2000. When comparing the distribution of DSF trend with vegetation greening rate, the regions exhibiting more significant NDVI increases correspond to the areas with greater reductions in DSF (Figure 5). This is particularly true for the northwestern corner, east, and south part of the study area. This spatial correspondence indicates that vegetation greening may have played a key role in decreasing dust storm occurrences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBox-and-whisker plots in Figure 6 show DSF trends for station categories according to vegetation-greening rates. Such plot shows that the DSF trend may be negatively associated with the vegetation greening rate. That is, station groups with low and moderate vegetation greening rates have relatively weak negative trends of DSF, while the stronger declining DSF trends were found in station groups with the high and very high vegetation greening rates. These results indicate that DSF in the northern China could have been weakened by vegetation growth during 1982\u0026ndash;2020.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact of vegetation greening on DSF changes revealed by dust emission model simulations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo verify our hypothesis that the rapid vegetation greening weakened DSF, two sensitivity experiments configured with the same settings and forcing but with different LAI data (i.e., 1982LAI and 2020LAI) were implemented using a physically based dust emission model (DuEM v1). Figure 7 presents the spatial distributions of dust emission simulated by DuEM v1 for 1982 (forced with 1982LAI) and 2020 (forced with 2020LAI), along with the difference between the two. \u0026nbsp;Using actual 1982 data in the model, we obtained the 1982 baseline dust emission simulations (Figure 7a). The baseline simulation reveals the strongest dust emissions in northwestern China and the Inner Mongolia, which is consistent with observed dust storm frequency in northern China (Figure S1). This indicate that the model accurately simulates the spatiotemporal characteristics of dust emissions after comprehensively considering multiple influencing factors.\u003c/p\u003e\n\u003cp\u003eWe then conducted a sensitivity test by adjusting the vegetation coverage input. Specifically, the 1982 LAI data was replaced with 2020 LAI data to derive dust emissions under the 2020 vegetation-increased scenario (Figure 7b). The spatial\u0026nbsp;pattern of DSF for the 2020LAI simulations resembles the spatial distribution of dust emission flux using the 1982LAI. This indicates that vegetation greening has not caused the change in\u0026nbsp;the spatial distribution of dust emission in northern China. When considering the difference in dust emission between the two simulations (i.e., 2020LAI\u0026nbsp;minus 1982LAI,\u0026nbsp;Figure 7c), negative dust emission differences were found in most northwestern China and the Inner Mongolia (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.1). Overall, average dust emissions in 2020LAI have reduced by 1.887 g\u0026middot;m⁻\u0026sup2;\u0026middot;yr\u003csup\u003e-1\u003c/sup\u003e when compared to the one simulated in 1982LAI. This confirms that the decline in DSF was driven by vegetation greening (i.e., the vegetation cover changes).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMechanisms of vegetation greening affecting DSF\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo uncover the physical processes that could explain how the vegetation greening affects dust storm activity, we used the DuEM v1 model for sensitivity experiments by altering the LAI data (1982LAI and 2020LAI) and determining the threshold friction velocity for dust particles across northern China under differing vegetation conditions.\u0026nbsp;The spatial distributions of threshold friction velocity under the 1982 and 2020 LAI scenarios are shown in Figure 8a and 8b, respectively. Overall, the two scenarios show a generally consistent spatial pattern across northern China. The 2020 scenario (Figure 8b) is characterized by higher threshold friction velocity values in the northeastern Inner Mongolia.\u003c/p\u003e\n\u003cp\u003eThe frequency distribution of the threshold friction velocity shifted markedly toward higher values under the 2020LAI scenario (Figure S2): this shows that the threshold friction velocity increased across most of the study area.\u0026nbsp;Figure 8c\u0026nbsp;visually illustrates the spatial differences in threshold friction velocity between the 2020 and 1982 vegetation scenarios.\u0026nbsp;The results show that threshold friction velocity significantly increased across most parts of northern China, particularly in the northwest and northeastern Inner Mongolia\u0026nbsp;(\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.1). Overall, the increased vegetation coverage raised the average threshold friction velocity by 0.16 m\u0026middot;s\u003csup\u003e-1\u003c/sup\u003e under the 2020 scenario compared to the 1982 scenario, strengthening surface resistance to dust emission across most of northern China.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated the recent spatiotemporal variations in dust storm activity across northern China, a region that has experienced an increase in its vegetation cover according to the NDVI analysis from 1982 to 2020. Overall, our results revealed a significant decreasing trend in dust storm frequency (DSF). This finding is consistent with the weakening trend of dust storm activities \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e and near-surface wind speed \u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e in northern China. However, a long-term negative trend is interrupted for a few years during 2012 to 2020, but then it continues. This means dust storm activity is not fully consistent with change in surface wind speed, which shows an earlier reversal since 1990s\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Therefore, other factors, such as vegetation cover changes, may have played a key role in regulating changes in dust storm activity.\u003c/p\u003e \u003cp\u003eBy comparing DSF trends with vegetation greening rates (i.e., vegetation cover increases), it appears that stations with fastest declines in DSF were mainly located in areas with the highest vegetation greening rates, while stations with the weakest negative DSF trends were placed over the areas that experienced the lowest vegetation greening rates. This indicates that rapid vegetation greening in northern China may be associated with the widely weakened DSF in northern China during 1982 to 2020. Existing studies also documented that NDVI has a direct negative impact on the normalized brightness temperature dust index (NBTDI \u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e). Notably, the northeastern region deviates from this pattern. Although the northeastern region experienced substantial vegetation increase, its decreasing trend in dust storm activity was weaker. This is primarily because the baseline frequency of dust storm activity in the northeastern region is low and it mainly regulated by the long-distance dust transport from dry land \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Note that some areas in the study region exhibit an extremely low baseline of vegetation cover, even a slight increase in vegetation produces a notable amplifying effect and greater dust storm suppression compared to more densely vegetated areas. DSF also declined in regions without a significant NDVI greening trend, which may be linked to factors like variations in large- to mesoscale atmospheric circulation.\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eAs vegetation cover has widely increased over the northern China during 1982 to 2020, the difference between the simulated dust emission under the 1982LAI and 2020LAI forcing reflects to a large extent the impact of vegetation greening on DSF changes. The results clearly demonstrate that the simulated dust emission forced by the 2020LAI was much lower in those regions (e.g., northwestern part and Inner Mongolia) that experienced rapid vegetation greening, when compared to the simulation forced by the 1982LAI. This pattern is strongly consistent with the distribution of DSF changes based on the station observations and NDVI difference: it further confirms that rapid vegetation greening has weakened DSF over the northern China during the last decades. In particular, vegetation greening can impact DSF in two ways: (i) increased vegetation cover reduces surface wind speed ; and (ii) it raises the threshold wind speed for dust emission\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e, thereby decreasing dust emissions. Note that dust emission fluxes increased in most areas of the southern and eastern Mongolia in the simulation forced by the 2020LAI. This demonstrates that dust activity in Mongolia has increased in recent years \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e under the ongoing desertification and aeolian erosion in the southern Gobi Desert \u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Therefore, the increase of dust emissions in Mongolia may also influence changes in dust storm activity within the study area by long-distance transport: this should be further investigated in future work.\u003c/p\u003e \u003cp\u003eAlthough our study encompasses data from 957 stations that recorded dust storms, their non-uniform distribution across the study area leaves certain regions without coverage (e.g., western Inner Mongolia and Taklimakan Desert surroundings), potentially affecting the precision of our findings. Recent advances in consistent monitoring capabilities and high-resolution satellite retrievals have enabled widespread application of Dust Optical Depth (DOD) products for detecting dust storm activity \u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. These datasets are particularly valuable in regions with sparse station observations, where they can effectively address monitoring gaps in dust storm investigations. Therefore, future research should incorporate remote sensing products to enhance accuracy of dust storm activity. Besides, in the simulation of dust emission fluxes, this study employed a simplified parameterization scheme assuming homogeneous sandy soil textures throughout the research domain. Given the documented heterogeneity of real soil types in the region, this uniform soil representation inevitably introduces uncertainty. Future investigations should incorporate spatially-distributed soil variations to enhance simulation accuracy and improve modeling precision.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTo conclude, this study found a significant decrease in DSF over northern China from 1982 to 2020. This decline occurred concurrently with a widespread increase in vegetation coverage across most of the study area, indicating a clear greening pattern. Notably, the stations exhibiting the largest negative DSF trends were primarily located in regions that simultaneously experienced the highest rates of vegetation greening, as reflected in NDVI changes. In contrast, areas with minimal vegetation greening showed no significant trends in DSF. Statistically, changes in vegetation coverage across northern China were significantly and negatively correlated with DSF (r = -0.616, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that the observed greening from 1982 to 2020 partly contributed to the regional DSF reduction. This causal link is further confirmed by sensitivity experiments conducted with a physically-based dust emission model. The experiments indicate that vegetation greening has led to a significant reduction in dust emissions across northern China, with a regional mean decrease of 1.887 g\u0026middot;m⁻\u0026sup2;\u0026middot;yr⁻\u0026sup1;. The primary mechanism for this reduction is the raising of the threshold wind speed required to initiate dust lifting. Our results offer robust evidence that the pronounced vegetation greening across northern China has already curbed dust-storm activity, and underscore the potential of ecological restoration as a nature-based buffer against dust extremes in a warming climate.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eObservations of dust storms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used daily dust storm observations from 1 January 1982 to 31 December 2020, which were retrieved from the China Meteorological Administration (CMA; http://data.cma.cn/en, last accessed on 1 January 2026). Observers conduct manual observation and recording in accordance with the successive editions of the Ground Meteorological Observation Standards\u003csup\u003e48\u003c/sup\u003e issued by the China Meteorological Administration. They determine daily weather phenomena such as dust storms, blowing dust, or floating dust have occurred. A dust storm day is recorded when a meteorological station observes the occurrence of dust storm within the 24 hours in that day. The dataset consists of 957 stations covering northern China（Figure 9）, with the highest station (i.e., Wudaoliang) located at 4,214 m a.s.l (meters above sea level). Observations of daily dust storms observations were aggregated into monthly values.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReanalysis outputs\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the dust emission model, to simulate the dust emission fluxes, outputs from ERA5 for 1982-2020 were used, in particular: (a) U and V wind speed components at surface, (b) snow cover; (c) volumetric soil water, 0-7cm. ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather, with data available from 1940 onwards \u003csup\u003e49\u003c/sup\u003e. ERA5 outputs are produced hourly at a horizontal resolution of 31 km: this dataset has been proven to be a reliable dataset for detailed research on climate change and variability \u003csup\u003e50,51\u003c/sup\u003e. ERA5 outputs were obtained from the Copernicus website (https://cds.climate.copernicus.eu/; last accessed on 1 January 2026).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRemote sensing products\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Normalized Difference Vegetation Index (NDVI) data were retrieved from the Earth Observing System Data and Information System Distributed Active Archive Center (EOSDIS-DAAC, https://www.earthdata.nasa.gov/data). This dataset comprises the Global Inventory Modeling and Mapping Studies \u0026ndash; 3rd Generation Version 1.2 (GIMMS-3G+) data for the NDVI. The NDVI is derived from calibrated and corrected measurements based on Advanced Very High Resolution Radiometer (AVHRR) data, featuring a spatial resolution of 0.0833 degrees and providing northern China coverage from 1982 to 2020. The dataset integrates observations from multiple AVHRR sensors and accounts for various external influences, including calibration loss, orbital drift, and volcanic eruptions \u003csup\u003e52\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eGiven the extensive missing values in existing Leaf Area Index (LAI) products across northern China, we instead derived LAI from NDVI. Specifically, the LAI for 1982 and 2020 was estimated using the empirical NDVI\u0026ndash;LAI relationship established by Qi et al. (2000). The function is expressed as:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLAI = a \u0026times; NDVI\u0026sup3; + b \u0026times; NDVI\u0026sup2; + c \u0026times; NDVI + d\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ewhere the coefficients are a = 18.99, b = -15.24, c = 6.124, and d = -0.352 \u003csup\u003e53\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDust emission model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDust emission is a key indicator characterizing dust activity and can be estimated through modeling \u003csup\u003e54\u003c/sup\u003e. This study used a physically-based dust emission model (DuEMv1\u003csup\u003e1,55\u003c/sup\u003e), which incorporated near-surface meteorology and land surface conditions. The DuEMv1 model was developed on the assumption that vertical dust emission is driven primarily by saltation bombardment and aggregate disintegration. Particle saltation begins when the friction velocity, a measure of wind shear stress, exceeds the threshold friction velocity. Horizontal saltation flux scales with the cube of friction velocity. From the soil volume entrained by saltating grains, saltation efficacy\u0026mdash;the ratio of vertical dust flux to horizontal saltation flux\u0026mdash;is derived by accounting for inter-particle bond strength. Dust is emitted solely from exposed erodible surfaces, after masking non-erodible surfaces such as snow, vegetation, and water bodies. The model\u0026rsquo;s input variables include friction velocity, air density, soil moisture, snow cover fraction, and vegetation cover, among others. The model has performed well in comparisons with field observation data and has been widely applied in dust simulation studies at both regional \u003csup\u003e56,57\u003c/sup\u003e and global \u003csup\u003e58,59\u003c/sup\u003e scales. Input parameters required for the DuEMv1 experiments are presented in Table 1. Since the model requires near-surface wind speed as input, wind speed data at 10 m height were converted to 2m height using a conversion formula \u003csup\u003e60\u003c/sup\u003e. All data were resampled to a common horizontal resolution of\u0026nbsp;0.25\u0026deg; x 0.25\u0026deg;\u0026nbsp;grid during the preprocessing phase to maintain spatial consistency in the calculations.\u0026nbsp;A detailed description of dust emission model is illustrated in supplementary materials. In the model simulations, the soil type was uniformly set as sandy soil with the corresponding parameterization scheme. This setup was based on the actual condition that sandy soil is predominantly distributed across the study area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eInput parameters for DuEMv1 model\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"112%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eData types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eTemporal resolution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eSpatial resolution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003ePeriod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003eData source\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eWind speed\u003c/p\u003e\n \u003cp\u003e(at 10m)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eHour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.25\u0026deg; x 0.25\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003ehttps://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=download\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eSoil moisture\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eDay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.25\u0026deg; x 0.25\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003ehttps://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=download\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eSnow cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eHour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.1\u0026deg; x 0.1\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003ehttps://cds.climate.copernicus.eu/datasets/reanalysis-era5-land?tab=download\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eNAVI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e15days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003e0.0833\u0026deg; x 0.0833\u0026deg;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1982 and 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003ehttps://search.earthdata.nasa.gov\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eStatistic methods and model experiment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dust storm activity is expressed here as dust storm frequency (DSF; in days). The regional mean dust storm frequency was defined as the total number of recorded dust storm days from all the stations in the study region, divided by the number of stations. Trends were computed using the Sen\u0026rsquo;s slope method\u003csup\u003e61\u003c/sup\u003e (in days per decade; hereafter day dec\u003csup\u003e-1\u003c/sup\u003e), and the multidecadal variability of dust storm frequency was shown using the 11-year Gaussian low-pass filter\u003csup\u003e62\u003c/sup\u003e.\u0026nbsp;Mann\u0026ndash;Kendall\u0026rsquo;s tau-b non-parametric correlation coefficient was applied to determine the statistical significance of the calculated trends\u003csup\u003e63,64\u003c/sup\u003e. To measure the degree of relation between DSF and NDVI , Pearson\u0026rsquo;s correlation coefficient was calculated\u003csup\u003e65\u003c/sup\u003e. Three \u003cem\u003ep\u003c/em\u003e-level thresholds were used to determine the differences in the statistical significance of trends in DSF: (i) significant at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, (ii) significant at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.10, and (iii) not-significant at \u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). We also assessed DSF changes as a function of vegetation greening rate across the study region. Here the vegetation greening rates of each station for 1982 to 2020 were calculated as the difference in LAI value as 2020 minus 1982. DSF from the 957 stations were classified into 5 groups with different vegetation greening rates being: (i) negative-vegetation greening rate (\u0026lt; 0); (ii) low-vegetation greening rate (0-2); (iii) moderate-vegetation greening rate (2-4); (iv) high-vegetation greening rate (4-6); and (v) very-high-vegetation greening rate (6-8).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo quantify the contribution of vegetation greening to changes in dust storm frequency in northern China, two experiments were conducted using the DuEMv1 model. The baseline (\u0026ldquo;All\u0026rdquo;) experiment uses surface wind speed, vegetation cover, and soil moisture at 1982. In the sensitivity experiments, only LAI is allowed to vary from 1982 to 2020, while all other drivers remain fixed at their 1982 values. Note that for the sensitivity experiments with the other factors fixed as in 1982, the seasonal variations of these factors are still considered and set to the values as in 1982, but the interannual variations of these factors are excluded. By comparing these experiments, we identified the contributions of vegetation cover to the variations of dust activity in northern China.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contributions Statement\u003c/h2\u003e\n\u003cp\u003eG.Z. conceived and designed the research Y.W. performed the data analysis.Y.W. wrote the paper with the inputs of P.S, C.A., L.M, Z.L, W.L, H.M., and G.Z. All authors contributed to the interpretation of the results and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests Statement\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eG.Z. conceived and designed the research Y.W. performed the data analysis.Y.W. wrote the paper with the inputs of P.S, C.A., L.M, Z.L, W.L, H.M., and G.Z. All authors contributed to the interpretation of the results and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eThis research was supported by the National Natural Science Foundation of China (42330502, 42101027), Qinghai Provincial Central Government-Guided Local Science and Technology Development Fund - Science and Technology Innovation Base Construction Project(2025ZY017) and Independent Research Project of State Key Laboratory of Earth Surface Processes and Resource Ecology at Beijing Normal University. C.A-M. acknowledges support from the GVA-PROMETEO Grant CIPROM/2023/38; CSIC-LINCGLOBAL Ref. 598 LINCG24042; and CSIC\u0026rsquo;s PTI-Clima.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThis study used daily dust storm observations from 1 January 1982 to 31 December 2020, retrieved from the China Meteorological Administration (CMA: http://data.cma.cn/en, last accessed 1 January 2026). ERA5 reanalysis data were obtained from the Copernicus Climate Data Store (https://cds.climate.copernicus.eu/, last accessed 1 January 2026). The Normalized Difference Vegetation Index (NDVI) data were retrieved from the NASA Earth Observing System Data and Information System Distributed Active Archive Center (EOSDIS-DAAC: https://www.earthdata.nasa.gov/data).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWu, C., Lin, Z., Shao, Y., Liu, X. \u0026amp; Li, Y. Drivers of recent decline in dust activity over East Asia. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, (2022).\u003c/li\u003e\n\u003cli\u003eWang, F. et al. 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Mathematical contributions to the theory of evolution.\u0026mdash;On a form of spurious correlation which may arise when indices are used in the measurement of organs. \u003cem\u003eProceedings of the Royal Society of London\u003c/em\u003e. \u003cstrong\u003e60\u003c/strong\u003e, 489-498 (1897).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Natural Hazards](https://www.nature.com/npjnathazards/)","snPcode":"44304","submissionUrl":"https://submission.springernature.com/new-submission/44304/3","title":"npj Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Dust storm frequency, vegetation greening, dust emission model, Northern China","lastPublishedDoi":"10.21203/rs.3.rs-9002280/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9002280/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDust storms represent a major environmental challenge in northern China, adversely affecting air quality, agricultural productivity, and energy supply. However, the drivers behind recent changes in dust storm activity remain poorly understood. By analyzing 39 years of dust storm observations (957 stations), remote sensing, and reanalysis data (1982\u0026ndash;2020), we document a significant decline in annual dust storm frequency (\u0026minus;\u0026thinsp;0.049 day decade⁻\u0026sup1;; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), most pronounced in northwestern China. Concurrently, vegetation cover expanded (annual NDVI increase: 0.100 decade⁻\u0026sup1;), exhibiting a strong negative correlation with dust activity (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.616; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), Sensitivity experiments conducted with the physically-based Dust Emission Model (DuEMv1) further confirm that enhanced vegetation cover weakens dust activity, suggesting that vegetation greening plays a key role in suppressing dust storms. This vegetation-driven suppression provides a scalable strategy for dust-storm management in global drylands, demonstrating how ecosystem restoration can counteract environmental degradation at regional scales.\u003c/p\u003e","manuscriptTitle":"Vegetation greening reduces dust storm activity in northern China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-06 20:01:07","doi":"10.21203/rs.3.rs-9002280/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-08T22:47:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-08T18:14:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"114365460058247780796643484593722567833","date":"2026-03-08T12:35:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144038052143566192269142017540085428759","date":"2026-03-08T07:56:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78468432668415058042702374677917461207","date":"2026-03-05T11:28:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4679208143673742913967944161294081875","date":"2026-03-05T08:00:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-04T19:07:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"324700915726228450177993905383296183917","date":"2026-03-04T15:43:12+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-03T09:42:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-03T08:02:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-03T07:48:49+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Natural Hazards","date":"2026-03-01T14:47:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Natural Hazards](https://www.nature.com/npjnathazards/)","snPcode":"44304","submissionUrl":"https://submission.springernature.com/new-submission/44304/3","title":"npj Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"63979a7a-d1b4-4518-a21c-649f292240ed","owner":[],"postedDate":"March 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64014714,"name":"Earth and environmental sciences/Climate sciences"},{"id":64014715,"name":"Biological sciences/Ecology"},{"id":64014716,"name":"Earth and environmental sciences/Ecology"},{"id":64014717,"name":"Earth and environmental sciences/Environmental sciences"},{"id":64014718,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2026-05-04T10:39:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-06 20:01:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9002280","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9002280","identity":"rs-9002280","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}