Sand and Dust Storm Risk Assessment in Arid Central Asia: Implications for the Environment, Society, and Agriculture

Abstract

This study aimed to assess sand and dust storm (SDS) risks in arid Central Asia during 2001–2021 from a multisectoral (environment, society, and agriculture) and comprehensive perspective on the Google Earth Engine (GEE) platform. The results show that the areas with moderate or greater SDS risk accounted for 18.75% of the total area of arid Central Asia. The high SDS risk areas are mainly concentrated in the oases around the desert and are most widely distributed in spring and summer. The SDS risk in the oasis area of southern Xinjiang increased significantly, while the SDS risk in the northeastern Aral Sea region and the Kazakh hilly region decreased significantly over the 21 years. Khwarazm of Uzbekistan, located in the Amu Darya River Delta, is the administrative district with the highest comprehensive risk of sandstorms, and the Balkan State of Turkmenistan and Kashi City and Zepu County in China are the administrative districts with the highest multisectoral risk of sandstorms. The results of this study provide a complete picture of SDS risks in the arid Central Asia region and will provide some guidance to policymakers and local authorities in SDS risk mitigation.

References

[] AlonsoJA, LamataMT. Consistency in the analytic hierarchy process: A new approach. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2006, 14(4): 445-459

[] AmatulliG, McInerneyD, SethiT, StroblP, DomischS. Geomorpho90m, empirical evaluation and accuracy assessment of global high-resolution geomorphometric layers. Scientific Data, 2020, 7(1): 1-18

[] Bahtebay, J., F. Zhang, M. Ariken, N.W. Chan, and M.L. Tan. 2021. Evaluation of the coordinated development of urbanization-resources-environment from the incremental perspective of Xinjiang, China. Journal of Cleaner Production 325: Article 129309.

[] Boloorani, A.D., S.N. Shorabeh, N. Neysani Samany, A. Mousivand, Y. Kazemi, N. Jaafarzadeh, A. Zahedi, and J. Rabiei. 2021. Vulnerability mapping and risk analysis of sand and dust storms in Ahvaz, Iran. Environmental Pollution 279: Article 116859.

[] Broomandi, P., K. Mohammadpour, D.G. Kaskaoutis, A. Fathian, S.F. Abdullaev, V.A. Maslov, A. Nikfal, A. Jahanbakhshi, et al. 2023. A synoptic- and remote sensing-based analysis of a severe dust storm event over Central Asia. Aerosol and Air Quality Research 23(2): Article 220309.

[] ChenX, BaiJ, LiX, LuoG, LiJ, LiBL. Changes in land use/land cover and ecosystem services in Central Asia during 1990–2009. Current Opinion in Environmental Sustainability, 2013, 5(1): 116-127

[] Chen, J., M. Gao, S. Cheng, W. Hou, M. Song, X. Liu, and Y. Liu. 2022. Global 1 km × 1 km gridded revised real gross domestic product and electricity consumption during 1992–2019 based on calibrated nighttime light data. Scientific Data 9(1): Article 202.

[] CuiP, PengJ, ShiP, TangH, OuyangC, ZouQ, LiuL, LiC, LeiY. Scientific challenges of research on natural hazards and disaster risk. Geography and Sustainability, 2021, 2(3): 216-223

[] Didan, K. 2015. MOD13A1 MODIS/Terra Vegetation Indices 16-Day L3 Global 500m SIN Grid V006. Data set. NASA EOSDIS Land Processes DAAC. Sioux Falls, SD: USGS Earth Resources Observation and Science (EROS) Center.

[] Friedl, M., and D. Sulla-Menashe. 2015. MCD12C1 MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 0.05Deg CMG V006. Hampton, VA: NASA Langley Research Center.

[] GilbertRO. Statistical methods for environmental pollution monitoring, 1987 Hoboken, NJ John Wiley & Sons

[] Gilbert, M., G. Nicolas, G. Cinardi, T.P. Van Boeckel, S.O. Vanwambeke, G.R.W. Wint, and T.P. Robinson. 2018. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Scientific Data 5(1): Article 180227.

[] Gkeka-Serpetsidaki, P., and T. Tsoutsos. 2022. A methodological framework for optimal siting of offshore wind farms: A case study on the island of Crete. Energy 239: Article 122296.

[] GMAO (Global Modeling and Assimilation Office). 2015. tavg1_2d_aer_Nx: 2d, 1-Hourly, Time-Averaged, Single-Level, Assimilation, Aerosol Diagnostics V5. 12.4. Greenbelt, MD: NASA Goddard Space Flight Center (NASA/GSFC).

[] Goepel, K.D. 2018. Implementation of an online software tool for the analytic hierarchy process (AHP-OS). International Journal of the Analytic Hierarchy Process 10(3). https://doi.org/10.13033/ijahp.v10i3.590.

[] GP-STAR. 2022. GP-STAR – UN-SPIDER Knowledge Portal. https://www.un-spider.org/network/gp-star-brochure. Accessed 10 Aug 2023.

[] Hengl., T. 2018. Sand content in % (kg / kg) at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution (Version v02). Zenodo, 24 December 2018.

[] International Union for Conservation of Nature and Network. 2015. Gridded species distribution: Global mammal richness grids, 2015 release. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC).

[] IshizakaA, LabibA. Analytic hierarchy process and expert choice: Benefits and limitations. OR Insight, 2009, 22(4): 201-220

[] Kraemer, R., A.V. Prishchepov, D. Müller, T. Kuemmerle, V.C. Radeloff, A. Dara, A. Terekhov, and M. Frühauf. 2015. Long-term agricultural land-cover change and potential for cropland expansion in the former Virgin Lands area of Kazakhstan. Environmental Research Letters 10(5): Article 054012.

[] Li, J., Q. He, X. Ge, and A. abbas. 2022. Spatiotemporal distribution of aerosols over the Tibet Plateau and Tarim Basin (1980–2020). Journal of Cleaner Production 374: Article 133958.

[] Li, Y., S. Kang, X. Zhang, C. Li, J. Chen, X. Qin, L. Shao, and L. Tian. 2023. Dust dominates the summer melting of glacier ablation zones on the northeastern Tibetan Plateau. Science of the Total Environment 856: Article 159214.

[] Liu, J., J. Ding, M. Rexiding, X. Li, J. Zhang, S. Ran, Q. Bao, and X. Ge. 2021. Characteristics of dust aerosols and identification of dust sources in Xinjiang, China. Atmospheric Environment 262: Article 118651.

[] Meijer, J.R., M.A.J. Huijbregts, K.C.G.J. Schotten, and A.M. Schipper. 2018. Global patterns of current and future road infrastructure. Environmental Research Letters 13(6): Article 064006.

[] MiddletonN, TozerP, TozerB. Sand and dust storms: Underrated natural hazards. Disasters, 2019, 43(2): 390-409

[] Middleton, N., and U. Kang. 2017. Sand and dust storms: Impact mitigation. Sustainability 9(6): Article 1053.

[] Middleton, N. 2020. Health in dust belt cities and beyond – An essay by Nick Middleton. BMJ 371: Article m3089.

[] Muñoz Sabater, J. 2019. ERA5-Land hourly data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). Reading, UK: European Centre for Medium-Range Weather Forecasts (ECMWF).

[] Pesaresi, M., and S. Freire. 2016. GHS Settlement grid following the REGIO model 2014 in application to GHSL Landsat and CIESIN GPW v4-multitemporal (1975-1990-2000-2015). Ispra, Italy: Joint Research Centre (JRC), European Commission.

[] Pi, H., N.P. Webb, J. Lei, and S. Li. 2022. Soil loss and PM10 emissions from agricultural fields in the Junggar Basin over the past six decades. Journal of Soil and Water Conservation 77(2): Article 113.

[] PoulterB, CiaisP, HodsonE, LischkeH, MaignanF, PlummerS, ZimmermannNE. Plant functional type mapping for Earth system models. Geoscientific Model Development, 2011, 4(4): 993-1010

[] Shepherd, G., E. Terradellas, A. Baklanov, U. Kang, W. Sprigg, S. Nickovic, A. Darvishi Boloorani, A. Al-Dousari, et al. 2016. Global assessment of sand and dust storms. Nairobi: United Nations Environment Programme (UNEP).

[] ShiP. On the theory of disaster research and its practice. Journal of Nanjing University No., 1991, 11: 37-42 (in Chinese)

[] ShiP, KaspersonR. World atlas of natural disaster risk, 2015 Berlin Springer

[] ShiP, ShuaiJ, ChenW, LuL. Study on large-scale disaster risk assessment and risk transfer models. International Journal of Disaster Risk Science, 2010, 1(2): 1-8

[] SivakumarMVK. SivakumarMVK, MothaRP, DasHP. Impacts of sand storms/dust storms on agriculture. Natural disasters and extreme events in agriculture: Impacts and mitigation, 2005 Berlin Springer 159-177

[] Takeuchi, N., and Z. Li. 2008. Characteristics of surface dust on Ürümqi Glacier No. 1 in the Tien Shan Mountains, China. Arctic, Antarctic, and Alpine Research 40(4): 744–750.

[] Tatem, A.J. 2017. WorldPop, open data for spatial demography. Scientific Data 4(1): Article 170004.

[] UNCCD (United Nations Convention to Combat Desertification). 2022. Sand and dust storms compendium: Information and guidance on assessing and addressing the risks. Bonn, Germany: UNCCD.

[] Wang, W., A. Samat, Y. Ge, L. Ma, A. Tuheti, S. Zou, and J. Abuduwaili. 2020. Quantitative soil wind erosion potential mapping for Central Asia using the Google Earth Engine Platform. Remote Sensing 12(20): Article 3430.

[] Wang, W., A. Samat, J. Abuduwaili, Y. Ge, P. De Maeyer, and T. Van de Voorde. 2022. Temporal characterization of sand and dust storm activity and its climatic and terrestrial drivers in the Aral Sea region. Atmospheric Research 275: Article 106242.

[] WeissDJ, NelsonA, Vargas-RuizCA, GligorićK, BavadekarS, GabrilovichE, Bertozzi-VillaA, RozierJ, et al. . Global maps of travel time to healthcare facilities. Nature Medicine, 2020, 26(12): 1835-1838

[] WiggsGFS, O’HaraSL, WegerdtJ, Van Der MeerJ, SmallI, HubbardR. The dynamics and characteristics of aeolian dust in dryland Central Asia: Possible impacts on human exposure and respiratory health in the Aral Sea basin. The Geographical Journal, 2003, 169(2): 142-157

[] YangH, ZhangX, ZhaoF, WangJ, ShiP, LiuL. ShiP, KaspersonR. Mapping sand-dust storm risk of the world. World atlas of natural disaster risk, 2015 Berlin Springer 115-150

[] Zhang, X.-X., C. Claiborn, J.-Q. Lei, J. Vaughan, S.-X. Wu, S.-Y. Li, L.-Y. Liu, Z.-F. Wang, et al. 2020. Aeolian dust in Central Asia: Spatial distribution and temporal variability. Atmospheric Environment 238: Article 117734.

[] ZhengL, XiaZ, XuJ, ChenY, YangH, LiD. Exploring annual lake dynamics in Xinjiang (China): Spatiotemporal features and driving climate factors from 2000 to 2019. Climatic Change, 2021, 166(3): 1-20