The Effect of Climate Fluctuation on Frequency of Dust Storms in Iran

Authors

10.22052/deej.2018.7.21.11

Abstract

Introduction: Different regions have various dust production; and the increase of dust storms illustrates the arid ecosystem dominance in each region. Analyzing and identifying of dust storms and its association with climatic parameters is one of the crucial approaches to reduce the caused damage of this phenomenon. Since besides determining the portion of each climate variables in intensifying the circumstances, it also can play a fundamental role in priorities, macro management policies, and upstream rules in order to control and prevent dust particles.
 
Methods and Materials: In order to carry out this study hourly horizontal visibility data, World Meteorological Organization codes, hourly dust data, and also monthly meteorological and climatic data including maximum temperature, minimum temperature, average temperature, maximum wind speed, total rain, and soil temperature of different depth (5, 10, 20, 30, 50 cm) in 37 synoptic stations of the country were gathered through a longitudinal statistics of 25 years (1990-2014) and its trend was calculated by non-parametric statistics of Mann-Kendall and Sen’s Estimator tests. After the qualitative analysis of the stations’ statistics and elimination of defects, all the data were assessed via Run Test at the coefficient level of 95%. In order to analyze the effect of climate fluctuations on dust storms, beside the mentioned variables, draught variable of evaporation-rain index and standardized precipitation evapotranspiration index (SPEI) in seasonal period calculated and then Spearman test was applied for correlational analysis of climate elements and dust storms.
 
Results: The frequency outcomes of dusty days during a long-term study of 25 years indicates that Zabol, Abadan and Ahvaz orderly with 711, 401, and 321 days have the most dust storms. The results of Mann-Kendall show out of 37 under study weather stations, six stations of Ardebil, Birjand, Mashhad, Sabzevar, Yazd, and Semnan reported a descending trend. Five stations reported a meaningful growing trend with coefficient of 95% and 21 stations out of the 37 present stations have a growing coefficient of 99%. The result of Sen’s Estimator test shows the six stations which based on Mann-Kendall test had a descending trend, they don’t follow a trend in this method and orderly 11, 10, and 10 stations had a rising trend, significant rise at 95% coefficient level also at 99% coefficient level. Generally, in most cases the results of the two tests had the same coefficient level. The results of Spearman coefficient across the country shows that the frequency of dusty days with the maximum wind speed, SPEI scale, and maximum temperature among the mentioned climatic parameters in order 0.74, -0.57, and 0.48 had the highest coefficient and were all significantly coefficient at the 99% level. Among the rest temperature of different soil depths (5, 10, 30, 50 cm) in all 37 under study stations there is a direct relation with dust storms and were significant with 0.39 at the level of 95%. So, we can conclude the surface levels of soil (5 and 10 cm) have a crucial role in dust storms in different soil depths.
 
Discussion and Conclusion: The results of this study show, applying two methods of Mann-Kendall and Sen’s Estimator tests are effective for analyzing the long-term changes of dust storms. Generally, the results of Mann-Kendall tests show a rising trend of dust storms in west and south west and some parts of north west and south east of the country. The result of the Sen’s Estimator test says that Ardebil, Birjand, Mashhad, Sabzevar, Yazd, and Semnan don’t follow a trend. As far as the mentioned stations do not have regular dusty days, thus we can consider Mann-Kendall is not a very strong test because of presenting integer results in the stations which contain data of many zeros.  The results of correlation between climate elements and the frequency of the dust storm days showed that the highest correlation is related to the maximum wind speed variable with 0.74 as a strong lever for picking up the dust. There is a negative correlation between rain and evaporation-rain index and potential standardized precipitation evapotranspiration with dust phenomenon. This negative correlation is more tangible in SPEI and in most of the stations at the coefficient level of 95% and 99% significant. Thus, as there is more rain in the specified station, there are less dust days. All in all, after maximum wind speed, the SPEI index receives the highest correlation that justifies dust storms.  The results also show as we go deeper to the soil layers, the correlation between soil temperature and dust storms declines. The highest and lowest correlation exists in depth of 5.50 cm of soil with the registered numbers of 0.39 and 0.017. The results of this study are useful for recognizing the effect of climate years on the frequency of dust storm and restraining deserts.

Keywords

References

1. Alizadeh-Choobari, O., Sturman, A., Zawar-Reza, P., 2014. A global satellite view of the seasonal distribution of mineral dust and its correlation with atmospheric circulation. Dynamics of Atmospheres and Oceans, 68, 20-34. 2. Allen, R. G., Pereira, L. S., Raes, D., Smith, M., 1998. Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56. Fao, Rome, 300(9), D05109. 3. Amgalan, G., Liu, G-R., Lin, T-H., Kuo, T-H., 2017. Correlation between dust events in Mongolia and surface wind and precipitation, Terr. Journal of Atmospheric & Ocean Science 28 (1), 23-32. 4. Ansari Ghojghar, M. Araghinejad, Sh., 2018. Investigating the effect of wind speed on the frequency of days with dust storms (Case study: Lorestan province). The fourth national conference on wind erosion and dust storms. Yazd. 5. Araghinejad, S., 2013. Data-driven modeling: using MATLAB® in water resources and environmental engineering (Vol. 67). Springer Science & Business Media, 292 pp. 6. Azizi, Gh., Shamsipour, A. A., Miri, M., Safarrad, T., 2012. Dust analysis in southwestern Iran, Journal of Environmental Studies, 38(3), 123-134. 7. Bazrafshan, J., Khalili, A., 2013. Spatial analysis of drought over Iran during 1963-2003. Desert, 18, 63-71. 8. Cannarozzo, M., Noto, L. V., Viola, F., 2006. Spatial distribution of rainfall trends in Sicily (1921–2000). Physics and Chemistry of the Earth, Parts A/B/C, 31(18), 1201-1211. 9. Cao, R., Jiang, W., Yuan, L., Wang, W., Lv, Z., Chen, Z., 2014. Inter-annual variations in vegetation and their response to climatic factors in the upper catchments of the Yellow River from 2000 to 2010. Journal of Geographical Sciences, 24(6), 963-979. 10. Colditz, R. R., Ressl, R. A., Bonilla-Moheno, M., 2015. Trends in 15-year MODIS NDVI time series for Mexico. In analysis of multitemporal remote sensing images (Multi-Temp), 2015 8th International Workshop on the (pp. 1-4). IEEE. 11. Farajzadeh Asl, M., Alizadeh, Kh., 2011. Spatial Analysis of Dust storm in Iran. The Journal of Spatial Planning 15 (1), 65-84. (In Persian) 12. Goudie, A. S., Middleton, N. J., 2006. Desert dust in the global system. Springer Science & Business Media. 13. Goudie, A., 2014. Review Desert dust and human health disorders. Journal of Environment International 63 (3), 101-113. 14. Guhathakurta, P., Menon, P., Mazumdar, A. B., Sreejith, O. P., 2010. Changes in extreme rainfall events and flood risk in India during the last century. National Climatic Centre, Research Report 3, 1-20. 15. Hahnenberger, M., Nikoul, K., 2014. Geomorphic and land cover identification of dust sources in the eastern Great Basin of Utah, U.S.A. Journal of Geomorphology 204 (2), 657-672. 16. Hamed, K. H., Rao, A. R., 1998. A modified Mann-Kendall trend test for autocorrelated data. Journal of hydrology, 204(1-4), 182-196. 17. Hamzeh Hossein, N., Fattahi, I., Zoldehdi, M., Ghaffarian, P., Ranjbar, A., 2016. Synoptic and Dynamic Analysis of Dust and its Simulation in Southwest of Iran in the summer of 2005. Spatial Analysis of Environmental Hazards 1, 102-91. (in Persian) 18. Herweijer, C., Seager, R., Cook, K., Geay, E., 2013. North American Droughts of the Last Millennium from a Gridded Network of Tree- Ring Dates, Lamont-Doherty Earth Observatory. Drying Technology: An International Journal, 13(15), 134-142. 19. Hosking, J. R., 1990. L-moments: analysis and estimation of distributions using linear combinations of order statistics. Journal of the royal statistical society. Series B (Methodological), 105-124. 20. Jiang, W., Yuan, L., Wang, W., Cao, R., Zhang, Y., Shen, W., 2015. Spatio-temporal analysis of vegetation variation in the Yellow River Basin. Ecological Indicators, 51, 117-126. 21. Kang, L., Huang, J., Chen, S., Wang, X., 2016. Long-term trends of dust events over Tibetan Plateau during 1961–2010. Atmospheric Environment, 125, 188-198. 22. Karegar, M. E., Bodagh Jamali, J., Ranjbar Saadat Abadi, A., Moeenoddini, M., Goshtasb, H., 2017. Simulation and Numerical Analysis of severe dust storms Iran East. Jsaeh, 3 (4), 101-119. (in persian) 23. Kim, D., Chin, M., Kemp, E. M., Tao, Z., Peters-Lidard, C. D., Ginoux, P., 2017. Development of high-resolution dynamic dust source function-A case study with a strong dust storm in a regional model. Atmospheric environment, 159, 11-25. 24. McKee, T. B., Doesken, N. J., Kleist, J., 1993. The relationship of drought frequency and duration to time scales. In Proceedings of the 8th Conference on Applied Climatology, American Meteorological Society, Boston, 17, 179-183. 25. Mehrabi, Sh., Soltani, S., Jafari, R., 2015. Investigating the Relationship between Climatic Parameters and the Exposure of Greenhouses (Case Study: Khuzestan Province). Journal of Water and Soil Science (Journal of Science and Technology of Agriculture and Natural Resources), 19(71): 69-80. 26. Mohammadi, G, H., 2015. Analysis of Atmospheric Mechanisms in Dust Transport over West of Iran. Ph.D. thesis, Tabriz University, 142pp. (in Persian) 27. Mohammadkhan, sh., 2017. Status and trends of dust storms in Iran from 1364 to 1384. Pasture and Watershed, Iranian Natural Resources Journal, 70(2), 495-514.‎ (persian) 28. O’Loingsigh, T., McTainsh, G. H., Tews, E. K., Strong, C. L., Leys, J. F., Shinkfield, P., Tapper, N. J., 2014. The Dust Storm Index (DSI): a method for monitoring broadscale wind erosion using meteorological records. Aeolian Research, 12, 29-40. 29. Press, V., Teukolsky, F., 1992. Numerical Recipes in C: The Art of Scientific Computing (2nd Ed.). Journal of Simulation 31 (1), 640. 30. Rafiei Majoomerd, Z., Yazdani, M., Rahimi, M., 2017. Trend analysis of number of dusty days in Iran. Arid Biome, 6(2), 11-23. (In Persian) 31. Rashki, A., Kaskaoutis, D. G., Goudie, A. S., Kahn, R. A., 2013. Dryness of ephemeral lakes and consequences for dust activity: the case of the Hamoun drainage basin, southeastern Iran. Science of the Total Environment, 463, 552-564. 32. Sari Sarraf, B., Rasouli1, A. A., Mohammadi, GH. H., Hoseini Sadr, A., 2016. Long-term trends of seasonal dusty day characteristics West Iran. Arab Journal Geoscience, 9(10), 563-573. 33. Shao, Y., Wyrwoll, K. H., Chappell, A., Huang, J., Lin, Z., McTainsh, G. H., Yoon, S., 2011. Dust cycle: An emerging core theme in Earth system science. Aeolian Research, 2(4), 181-204. 34. Shong Chok, N., 2010. Pearson’s Versus Spearman’s and Kendal’s Correlation Coefficients for Continuous Data. M.Sc. thesis, University Of Pittsburgh, 43pp. 35. Tan, M., Li, X., Xin, L., 2014. Intensity of dust storms in China from 1980 to 2007: A new definition. Atmospheric environment, 85, 215-222. 36. Tanarhte, M., Hadjinicolaou, P., Lelieveld, J., 2012. Intercomparison of temperature and precipitation data sets based on observations in the Mediterranean and the Middle East. Journal of Geophysical Research: Atmospheres, 117 (D12). 37. Tavoosi, T., Zahraei, A., 2014. Sistan and Balouchestan Province Based on Extrapolation of Time Series Curves. Journal of Management System, 1, 139-157. (In Persian) 38. Thornthwaite, C. W., 1948. An approach toward a rational classification of climate. Geographical review, 38(1), 55-94. 39. Uzan, L., Egert, S., Alpert, P., 2018. New insights into the vertical structure of the September 2015 dust storm employing eight ceilometers and auxiliary measurements over Israel. Atmospheric Chemistry & Physics, 18(5), 3203-3221. 40. Vicente-Serrano, S. M., Beguería, S., López-Moreno, J. I., 2010. A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. Journal of climate, 23(7), 1696-1718. 41. Yarmoradi, Z., Nasiri, B., Mohammadi, Gh. H., Karampour, M., 2018. Trend analysis of dusty day’s frequency in Eastern parts of Iran associated with Climate Fluctuations. Desert Ecosystem Engineering Journal, 7(18), 1-14. (In Persian) 42. Yue, S., Pilon, P., Cavadias, G., 2002. Power of the Mann–Kendall and Spearman's rho tests for detecting monotonic trends in hydrological series. Journal of hydrology, 259(1-4), 254-271. 43. Zanganeh, M., 2014. Climatological Analysis of Dust Storms in Iran. Applied Climatology 1(1), 1-12. (In Persian) 44. Zeinali, B., 2016. Investigation of frequency changes trend of days with dust storms in western half of Iran. Journal of Natural Environment hazards 5(7), 100-87. (In Persian)
Desert Ecosystem Engineering
Volume 7, Issue 21
February 2019
Pages 13-32

Files

Share

How to cite

Statistics