Implications of surface properties for dust emission from gravel deserts (gobis) in the Hexi Corridor

Zhang, Zhengcai; Dong, Zhibao; Li, Jiyan; Qian, Guangqiang; Jiang, Chanwen

doi:10.1016/j.geoderma.2016.01.011

Cited by 53 publications

(60 citation statements)

References 34 publications

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“…The Hexi Corridor in Gansu is located between the Beishan Mountains and Qilian Mountains, and borders Kumtag Desert to the west, Badain Jaran Desert to the north, and Tengger Desert to the east. Besides the local SDS source, the Hexi Corridor experiences SDS transported from the west (Gobi and Kumtag Desert) and north (Gobi, Badain Jaran Desert and Tengger Desert) [61][62][63]. In Qinghai, the Qaidam Basin is surrounded by the Qilian and Altun Mountains to the north and the Kunlun Mountains to the south.…”

Section: Major Transportation Routes Of Sand and Dust Storms In The Tmentioning

confidence: 99%

Characterizing Sand and Dust Storms (SDS) Intensity in China Based on Meteorological Data

Cao

Zhang

et al. 2018

Sustainability

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Sand and dust storms (SDS) are global phenomena that significantly impact the socio-economy, human health, and the environment. The characterization of SDS intensity is a fundamental aspect of SDS issues and studies. In this study, a sand and dust storms index (SDSI) is developed to characterize SDS intensity by addressing the potential impacts of sand and dust storms on sensitive elements. Compared with other indices, SDSI includes four SDS-related components: SDS frequency, SDS visibility, SDS duration, and SDS wind speed. Using SDSI, this study characterizes the SDS intensity in the Three-North Forest Shelterbelt Program (TNFSP) region of China. The SDSI results show that high values of SDSI are mostly concentrated in southern Xinjiang, western and central Inner Mongolia, western and central Gansu, and northern Ningxia. By analyzing the SDSI components, over half of the stations experienced sand and dust storms no more than once per year on average. Most of the SDS events reduced horizontal visibility to less than 500 m, one-third of SDS events last more than two hours, and the wind speed of over half of the SDS events varied between 10-17 m/s. In comparison with SDS frequency, SDSI performs better in reflecting the spatial and temporal variation of SDS events. Therefore, instead of SDS frequency, SDSI can be applied to studies relevant to SDS intensity. Finally, five major SDS transportation routes were identified based on the surface prevailing wind direction, SDSI, and the existing literature. The SDS routes, combined with SDSI, could help governments and policy-makers cooperate on a regional level to combat SDS events more effectively.Keywords: sand and dust storms (SDS); sand and dust storms (SDS) index; sand and dust storms (SDS) intensity; sand and dust storms (SDS) routes

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Section: Major Transportation Routes Of Sand and Dust Storms In The Tmentioning

confidence: 99%

Characterizing Sand and Dust Storms (SDS) Intensity in China Based on Meteorological Data

Cao

Zhang

et al. 2018

Sustainability

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“…The question of which soil texture acts as the strongest dust source remains controversial in the literature (Wang et al, 2013; Zhang et al, ). Buschiazzo and Funk () pointed out that loamy soil was more resistant against wind erosion than sandy soil but had a greater potential for dust production because it contained a greater fraction of fine particles.…”

Section: Discussionmentioning

confidence: 99%

“…It not only causes degradation of valuable and nonrenewable soil resources but also releases a considerable amount of dust that negatively impacts the air environment and human health (Fryrear & Saleh, 1993). The dynamic process of wind erosion is controlled by the regional wind regime and by surface characteristics (Zhang, Dong, Li, Qian, & Jiang, 2016). Grain size is the fundamental property of any soils, sediments, and dust deposits (Blott & Pye, 2001).…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers believe that sandy soils with strong saltation bombardment are the main dust sources (Laurent, Marticorena, Bergametti, & Mei, 2006;Mei, Zhang, & Cao, 2004;Xuan, Liu, & Du, 2000), whereas some others proposed that soils with more fine particles were the main dust sources (Li et al, 2009). Therefore, it remains uncertain, which is the soil texture that releases more dust (Wang, Wang, Lang, Hua, & Wang, 2013;Zhang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Difference in wind erosion characteristics between loamy and sandy farmlands and the implications for soil dust emission potential

Wang

Zhou

et al. 2018

Land Degrad Dev

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Dust emission depends directly and indirectly on soil particle distribution, but little information is available that examines dust emission potential and its relationship with wind erosion for different soils. This study aimed to elucidate the difference in wind erosion characteristics between loamy farmland (LF) and sandy farmland (SF) and discuss the influence of this difference on dust emission potential. Field observations were conducted during 2012–2016 in Bashang district, North China. With increasing friction wind speed, the horizontal mass flux increased exponentially in LF but obeyed the power function in SF. The horizontal mass flux was higher in SF than in LF, but the gap narrowed from 766 times at friction wind speed of 0.3 m s−1 to 50 times at 0.6 m s−1. The dry aggregate size distribution of sediment in LF was finer than in SF. Fine particle content (diameter < 0.05 mm) in windblown sediment was much higher in LF (31.39%) than in SF (5.92%), which was influenced by parent soils. With height increasing, the mass percentage of sediment decreased more rapidly in SF than in LF, with 86% and 66% of sediment clustering at height 0–20 cm, respectively. Because the horizontal mass flux and dust content in sediment both increased more quickly in LF than in SF with increasing friction wind speed, the dust emission potential was larger in LF than in SF under strong winds. The adverse effect of dust emission in loamy soils on atmospheric pollution should receive more attention in strong wind erosion events.

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“…Moreover, multitemporal remote sensing datasets are considerably valuable for change detection analysis. Importantly, Synthetic Aperture Radar (SAR) systems are proficient resources for determining soil surface roughness because there is a high correlation between the surface roughness and SAR backscattering coefficient (Prigent et al, 2005;Srivastava et al, 2008;MirMazloumi and Sahebi, 2016;Zhang et al, 2016;Alexakis et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Investigation of Potential Dust Sources Using Sentinel-1 and Neural Network: A Case Study From Bandar-E Emam-Omidiye

Khoshnoud

Mirmazloumi

Amani³

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Aeolian erosion is a serious environmental threat that damages soils. Dust storms are one example of the consequences of aeolian erosion in dry and semi-arid areas across the world. In this regard, soil surface roughness is an important parameter for monitoring climate changes on the Earth and modelling aeolian erosion. Synthetic Aperture Radar (SAR) systems are valuable resources for estimating soil surface roughness. In arid soils, SAR backscatter is sensitive to the soil surface roughness at higher frequencies and higher incident angles. Based on these facts and lack of studies in the field of dust and erosion using remote sensing methods, an Artificial Neural Network (ANN) along with Sentinel-1 images in two polarizations (VV and VH) were initially applied to estimate surface roughness for the first time in Bandar-e Emam-Omidiye, Khuzestan, Iran. Subsequently, the results were used to investigate potential dust sources. The parameters used to train the ANN included the radar backscatter coefficient, incident angle, and in-situ roughness. The training accuracy of the proposed ANN was relatively high with an RMSE of 0.8821 and RMSE=0.8804 for VH and VV polarizations respectively. These data were subsequently used to identify areas prone to dust. The results obtained from the investigation of 25 stations located in areas with five different land covers indicated accurately that locals on clay flats (RMSE=1.08) are the most prone to aeolian erosion in the form of dust.

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Implications of surface properties for dust emission from gravel deserts (gobis) in the Hexi Corridor

Cited by 53 publications

References 34 publications

Characterizing Sand and Dust Storms (SDS) Intensity in China Based on Meteorological Data

Characterizing Sand and Dust Storms (SDS) Intensity in China Based on Meteorological Data

Difference in wind erosion characteristics between loamy and sandy farmlands and the implications for soil dust emission potential

Investigation of Potential Dust Sources Using Sentinel-1 and Neural Network: A Case Study From Bandar-E Emam-Omidiye

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