Impact of near-surface wind speed variability on wind erosion in the eastern agro-pastoral transitional zone of Northern China, 1982–2016

Zhang, Gangfeng; Azorín-Molina, César; Shi, Peijun; Lin, Degen; Guijarro, José Antonio; Kong, Feng; Chen, Deliang

doi:10.1016/j.agrformet.2019.02.039

Cited by 69 publications

(44 citation statements)

References 71 publications

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“…In addition to precipitation, SWS was the second crucial climatic factor influencing vegetation variation, since the wind speed was negatively correlated with soil moisture content, soil texture, and nutrient content [58][59][60]. A similar result indicated that the SWS has shown a downward trend in recent decades [61]. Therefore, the decreased SWS in recent decades had a positive effect on vegetation growth from a soil moisture perspective.…”

Section: The Ndvi Response To Climate Changementioning

confidence: 86%

Detecting Vegetation Variations and Main Drivers over the Agropastoral Ecotone of Northern China through the Ensemble Empirical Mode Decomposition Method

Xue

Zhang

et al. 2019

Remote Sensing

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Vegetation is the major component of the terrestrial ecosystem. Understanding both climate change and anthropogenically induced vegetation variation is essential for ecosystem management. In this study, we used an ensemble empirical mode decomposition (EEMD) method and a linear regression model to investigate spatiotemporal variations in the normalized difference vegetation index (NDVI) over the agropastoral ecotone of northern China (APENC) during the 1982–2015 period. A quantitative approach was proposed based on the residual trend (RESTREND) method to distinguish the effects of climatic (i.e., temperature (TEM), precipitation (PRE), total downward solar radiation (RAD), and near surface wind speed (SWS)) and anthropogenic effects on vegetation variations. The results showed that the NDVI exhibited a significant greening trend of 0.002 year−1 over the entire study period of 1982–2015 and that areas with monotonous greening dominated the entire APENC, occupying 40.97% of the region. A browning trend was also found in the central and northern parts of the APENC. PRE presented the highest spatial correlation with the NDVI and climate factors, suggesting that PRE was the most important factor affecting NDVI changes in the study area. In addition, the RESTREND results indicated that anthropogenic contributions dominated the vegetation variations in the APENC. Therefore, reusing farmland for grass and tree planting made a positive contribution to vegetation restoration, while deforestation, overgrazing, and the reclamation of grasslands were the opposite. In addition, with the continuous implementation of national ecological engineering programs such as the Grain to Green Program, positive human activity contributions to vegetation greening significantly increased. These results will support decision- and policy-making in the assessment and rehabilitation of ecosystems in the study region.

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Section: The Ndvi Response To Climate Changementioning

confidence: 86%

Detecting Vegetation Variations and Main Drivers over the Agropastoral Ecotone of Northern China through the Ensemble Empirical Mode Decomposition Method

Xue

Zhang

et al. 2019

Remote Sensing

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“…S2) since the early 1990s (Y. . As successfully applied previously (e.g., Azorin-Molina et al 2016;Shi et al 2019), the R package Climatol (Guijarro 2017; http://www.climatol.eu/; last accessed 1 November 2019) was used to perform quality control, relative homogenization, and missing data infilling on raw DMWS series. Climatol works with ''normalized'' values, and here normalizations were achieved by using the normal ratio method (dividing observed DMWS by the long-term averages of the DMWS series).…”

Section: B Quality Control and Homogenizationmentioning

confidence: 99%

“…where V g refers to geostrophic wind (m s 21 ), f is the Coriolis parameter (s 21 ), H is the geopotential height (m 2 s 22 ), and qH is the pressure difference associated with the distance qd (m). Further, vertical transport of atmospheric momentum changes was represented as vertical wind shear between 1000 and 850 hPa (Shi et al 2019) and atmospheric thermal stratification instability (index A, dimensionless; Zhang et al 2014). Vertical wind shear [Eq.…”

Section: Atmospheric Circulation Modes and Indicesmentioning

confidence: 99%

“…Moreover, after 3-5 decades of stilling, a stabilization or ''recovery'' of global average terrestrial winds commenced in 2013 (Tobin et al 2014) and has continued in the last years (Dunn et al 2016;Azorin-Molina et al 2017Zeng et al 2019). Regional studies have also documented this recent recovery of land surface wind speeds (Kim and Paik 2015;Azorin-Molina et al 2018b;Zhang et al 2019).…”

Section: Introductionmentioning

confidence: 99%

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Variability of Daily Maximum Wind Speed across China, 1975–2016: An Examination of Likely Causes

et al. 2020

Self Cite

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Assessing change in daily maximum wind speed and its likely causes is crucial for many applications such as wind power generation and wind disaster risk governance. Multidecadal variability of observed near-surface daily maximum wind speed (DMWS) from 778 stations over China is analyzed for 1975–2016. A robust homogenization protocol using the R package Climatol was applied to the DMWS observations. The homogenized dataset displayed a significant (p < 0.05) declining trend of −0.038 m s−1 decade−1 for all China annually, with decreases in winter (−0.355 m s−1 decade−1, p < 0.05) and autumn (−0.108 m s−1 decade−1; p < 0.05) and increases in summer (+0.272 m s−1 decade−1, p < 0.05) along with a weak recovery in spring (+0.032 m s−1 decade−1; p > 0.10); that is, DMWS declined during the cold semester (October–March) and increased during the warm semester (April–September). Correlation analysis of the Arctic Oscillation, the Southern Oscillation, and the west Pacific modes exhibited significant correlation with DMWS variability, unveiling their complementarity in modulating DMWS. Further, we explored potential physical processes relating to the atmospheric circulation changes and their impacts on DMWS and found that 1) overall weakened horizontal airflow [large-scale mean horizontal pressure gradient (from −0.24 to +0.02 hPa decade−1) and geostrophic wind speed (from −0.6 to +0.6 m s−1 decade−1)], 2) widely decreased atmospheric vertical momentum transport [atmospheric stratification thermal instability (from −3 to +1.5 decade−1) and vertical wind shear (from −0.4 to +0.2 m s−1 decade−1)], and 3) decreased extratropical cyclones frequency (from −0.3 to 0 month decade−1) are likely causes of DMWS change.

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“…By analyzing the observations at 822 surface weather stations, Vautard et al (2010) showed that the surface wind speed has been reduced by 5%-15% over almost all continents in the Northern Hemisphere (NH) midlatitudes over . A recent study also showed a decrease in surface wind speeds and wind power potential in most of NH over the past four decades (Tian et al 2019) exerting adverse impacts on windgenerated electricity (Sherman et al 2017) and the dispersion of air pollutants (Li et al 2019b), reducing evaporation (McVicar et al 2012), and affecting soil erosion (Zhang et al 2019). However, the mechanisms related to the observed wind stilling are not well understood (Wu et al 2018, Zeng et al 2018.…”

Section: Introductionmentioning

confidence: 99%

Urbanization has stronger impacts than regional climate change on wind stilling: a lesson from South Korea

Chen

Jeong

Park

et al. 2020

Environ. Res. Lett.

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Wind stilling has been observed in many regions across the Northern Hemisphere; however, the related mechanisms are not well understood. Analyses of the wind speed variations in South Korea during 1993-2015 in this study reveal that the annual-mean surface wind speeds at rural stations have increased by up to 0.41 m s −1 decade −1 , while those at urban stations have decreased by up to −0.63 m s −1 decade −1 . The local wind speed variations are found to be negatively correlated with the population density at the corresponding observation sites. Gustiness analyses show the increase in local surface roughness due to urbanization can explain the observed negative wind speed trends at urban stations as the urbanization effect overwhelms the positive wind speed trend due to climate change. The observed negative wind speed trend in urban areas are not found in the regional climate model simulations in the Coordinated Regional Climate Downscaling Experiment-East Asia (CORDEX-EA) as these models do not take into account the impact of urbanization on wind variations during the period. This study suggests that urbanization can play an important role in the recent wind stilling in rapidly developing regions such as South Korea. Our results suggest that future climate projections in CORDEX-EA may overestimate wind speeds in urban areas, and that future regional climate projections need to consider the effects of urbanization for a more accurate projection of wind speeds.

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Impact of near-surface wind speed variability on wind erosion in the eastern agro-pastoral transitional zone of Northern China, 1982–2016

Cited by 69 publications

References 71 publications

Detecting Vegetation Variations and Main Drivers over the Agropastoral Ecotone of Northern China through the Ensemble Empirical Mode Decomposition Method

Detecting Vegetation Variations and Main Drivers over the Agropastoral Ecotone of Northern China through the Ensemble Empirical Mode Decomposition Method

Variability of Daily Maximum Wind Speed across China, 1975–2016: An Examination of Likely Causes

Urbanization has stronger impacts than regional climate change on wind stilling: a lesson from South Korea

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