مدل‌سازی رابطه طوفان‌های گردوغبار با متغیرهای حدی و متوسط دما در نیمه غربی کشور

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران.

2 دانشجوی دکتری تخصصی مهندسی منابع آب،گروه مهندسی آبیاری و آبادانی ،دانشگاه تهران،کرج،ایران

3 گروه مهندسی آبیاری و آبادانی،پردیس کشاورزی و منابع طبیعی دانشگاه تهران،کرج،ایران

4 استادیار، پژوهشکده اقلیم شناسی

چکیده

افزایش وقوع طوفان‌های گردوغبار در چند سال اخیر در غرب و جنوب غرب ایران، اهمیت پیش‌بینی و ارتباط این پدیده با نوسانات اقلیمی را دوچندان کرده است. هدف از این پژوهش، بررسی شدت همبستگی و مدل‌سازی رابطه فراوانی روزهای همراه با طوفان گردوغبار (FDSD) با متغیرهای حدی و متوسط دما در نیمه غربی کشور می‌باشد. بدین منظور از داده‌های ساعتی گردوغبار و کدهای سازمان جهانی هواشناسی و همچنین داده‌های اقلیمی شامل دمای متوسط، دمای بیشینه و دمای کمینه در مقیاس ماهانه با طول دوره آماری 25 ساله (2014-1990) در 26 ایستگاه سینوپتیک واقع در نیمه غربی کشور استفاده شد. برای ارتباط­سنجی فراوانی روزهای همراه با طوفان گردوغبار با متغیرهای حدی و متوسط دما از ضرایب همبستگی پیرسون و اسپیرمن و همچنین روش رگرسیون خطی چندمتغیره در نرم‌افزار SPSS استفاده شد. به منظور تحلیل همبستگی، نقشه پهنه‌بندی ضرایب با روش IDW در نرم‌افزار ArcGIS تهیه شد. نتایج نشان داد که بالاترین ضریب همبستگی با شاخص FDSD مربوط به متغیر دمای بیشینه در ایستگاه آبادان با مقدار 875/0 و دمای کمینه در ایستگاه اهواز با مقدار 893/0 بود. همچنین با افزایش شاخص FDSD، مقادیر ضریب همبستگی افزایش یافت؛ به نحوی که در ایستگاه‌های آبادان و اهواز که به ترتیب با 401 و 321 روز در بازه زمانی 25 ساله، رکورددار بیشترین تعداد روزهای همراه با طوفان گردوغبار بودند، بالاترین ضرایب همبستگی بین متغیرهای حدی و متوسط دما با شاخص FDSD مشاهده شد. مدل‌سازی رگرسیون چند متغیره بین گردوغبار و پارامترهای مختلف دما در نیمه غربی کشور نیز نشان داد که تأثیر متغیرهای حدی دما در وقایع گردوغبار بیشتر از دمای متوسط است. مدل‌های رگرسیونی نیز نشان می‌دهند که در بهترین حالت، متغیرهای حدی دما در آبادان 2/81 درصد و در بندرماهشهر 3/79 درصد  از تغییرات شاخص FDSD را تبیین می‌کنند.

کلیدواژه‌ها


عنوان مقاله [English]

Modeling the Relationship between Dust Storms and Extreme and Average Temperature Variables in the Western Half of Iran

نویسندگان [English]

  • Masoud Pourgholam-Amiji 1
  • Mohamad ansarighojghar 2
  • Shahab Araghinejad 3
  • Iman Babaeian 4
1 Department of Irrigation and Reclamation Engineering, Faculty of Agriculture Engineering and Technology, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran.
2 Phd Candidate of Water Resources Engineering, Department of Irrigation and Reclamation Engineering, University of Tehran, Karaj, Iran.
3 University of Tehran
4 Assistant Prof. CRI
چکیده [English]

Introduction: The increase of dust storms occurrence in recent years in western and southwestern Iran has doubled the importance of prediction and communication of this phenomenon with climate variations. 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. The purpose of this study is to model the relationship of Frequency of Dust Stormy Days (FDSD) with extreme and average temperature variables in the western half of the country.
 
Materials and methods: For this purpose, the hourly data of dust and codes of the World Meteorological Organization, as well as climatic data including average temperature, maximum temperature and minimum temperature on a monthly scale with a statistical period of 25 years (1990-2014) in 26 synoptic stations located in the western half of the country were used. After reviewing and controlling the quality of station statistics and eliminating statistical deficiencies, the homogeneity of the data was evaluated using the Run Test and the randomness of the data was accepted at a 95% confidence level. The Pearson and Spearman correlation coefficients as well as a multivariate linear regression method were used in communicate the frequency of days associated with dust storm with extreme and average temperature variables. In this study, the observational values of the frequency of days with dust storm were considered as dependent variables and the average temperature data and cardinal temperature variables were considered as independent variables. In order to analyze the correlation, the zoning map of the coefficients was prepared by IDW method in ArcGIS software.
 
Results and discussion: The results showed that the highest correlation coefficient with FDSD index was related to the maximum temperature variable in Abadan station with 0.875 and the minimum temperature in Ahvaz station with 0.893. Also, with increasing FDSD index, correlation coefficient values increasedat Abadan and Ahvaz stations, the stations which had the highest number of dust days with 401 and 321 days, respectively, during the 25-year period. Multivariate regression modeling between FDSD and different temperature parameters in the western half of the country showed that the most important factors influencing dust events are the extreme temperature variables.
In all 26 stations studied, there is a positive correlation between the minimum temperature and the frequency of days with dust storm, but this correlation is more significant in some stations and in Dehloran, Ilam, Kermanshah, Safi Abad and Sanandaj stations at 95% confidence level and also in Hamedan (Airport), Islamabad Gharb, Abadan, Ahvaz, Bandar Mahshahr and Bostan stations were significant at 99% confidence level. Meanwhile, the highest Spearman correlation coefficient between different temperature parameters and Frequency of Dust Stormy Days (FDSD) is related to the maximum temperature variable in Ahvaz, Abadan, Bostan and Bandar Mahshahr stations with correlation coefficients of 0.59, 0.57, 0.53 and 0.51, respectively, have been registered. The highest Pearson correlation coefficient between temperature parameters and frequency of days with dust storm is related to the minimum and maximum temperature variables, which were recorded in Ahvaz and Abadan stations with a correlation coefficient of 0.893 and 0.875, respectively. Regression models show that, in the best case scenario, the temperature variables of 81.2% (Abadan) and 79.3% (Bandar Mahshahr) determine the changes in the FDSD index.
 
Conclusion: The cardinal temperature variables are known as an important and influential factor in the formation of dust storms because increasing the values of Cardinal temperature parameters leads to excessive evaporation from the soil surface, which can provide a source of particles for the occurrence of such dust storms. It should be noted that the average temperature variable can also have an important effect on increasing dust events, but compared to the cardinal temperature variables, its effect is much less. The results of this study can be useful in managing the issues caused by dust storms and in the combating plans to desertification in the study regions. Also, the results of this research can be a new guide for predicting and modeling the phenomenon of dust storms in the country.

کلیدواژه‌ها [English]

  • Critical Areas
  • FDSD Index
  • Correlation Coefficient
  • ArcGIS Software
  • Multivariate Linear Regression
  1.  

    1. Abdolshahnejad, M., Khosravi, H., Nazari Samani, A. A., Zehtabian, G. R. & Alambaigi, M. (2020). Determining the Conceptual Framework of Dust Risk Based on Evaluating Resilience (Case Study: Southwest of Iran). Strategic Research Journal of Agricultural Sciences and Natural Resources, 5(1), 33-44.
    2. 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.
    3. Amgalan, G., Liu, G. R., Kuo, T. H., & Tang-Huang, L. (2017). Correlation between dust events in Mongolia and surface wind and precipitation. TAO: Terrestrial, Atmospheric and Oceanic Sciences, 28(1), 2.
    4. 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.
    5. Ansari Ghojghar, M. & Araghinejad, Sh. (2017). Investigating the Reaction of Temperature extremes Variables of against the Frequency of Days with Dust Storms (Case Study: Lorestan Province), 5th International Congress of Civil Engineering, Architecture and Urban Development, Shahid Beheshti University of Medical Sciences, Tehran, 26-29 December.
    6. Ansari Ghojghar, M., Pourgholam-Amiji, M., Bazrafshan, J., Liaghat, A., & Araghinejad, Sh. (2020). Performance Evaluation of Statistical, Fuzzy and Perceptron Neural Network Models in Forecasting Dust Storms in Critical Regions in Iran. Iranian Journal of Soil and Water Research (Articles in Press).
    7. Araghinejad, S. (2013). Data-driven modeling: using MATLAB® in water resources and environmental engineering (Vol. 67). Springer Science & Business Media.
    8. Araghinejad, Sh., Ansari Ghojghar, M., Pourgholam-Amiji. M., Liaghat, A., & Bazrafshan, J. (2019). The Effect of Climate Fluctuation on Frequency of Dust Storms in Iran. Desert Ecosystem Engineering Journal, 7(21), 13-32.
    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. Chok, N. S. (2010). Pearson's versus Spearman's and Kendall's correlation coefficients for continuous data (Doctoral dissertation, University of Pittsburgh).
    11. Fengmei, Y., & Chongyi, E. (2010). Correlation analysis between sand-dust events and meteorological factors in Shapotou, Northern China. Environmental Earth Sciences, 59(6), 1359-1365.
    12. Fotouhi Firoozabad, F. & Malekinejad, H. (2020). Analysis and Zonation of Maximum 24-Hour Rainfall of Iran Using Wakeby Distribution and Geostatistic Technique. Desert Management, 7(14), 75-92.
    13. Ghorbani, S. & Moddress, R. (2019). Modelling the Relationship between the Frequency of Dust Storms and Climatic Variables in the Summer Time in Desert Areas of Iran. Journal of Water and Soil Science, 23(3), 125-140.
    14. Goudie, A. S., & Middleton, N. J. (2006). Desert dust in the global system. Springer Science & Business Media.
    15. Hahnenberger, M., & Nicoll, K. (2014). Geomorphic and land cover identification of dust sources in the eastern Great Basin of Utah, USA. Geomorphology, 204, 657-672.
    16. Herweijer, C., Seager, R., Cook, E. R., & Emile-Geay, J. (2007). North American droughts of the last millennium from a gridded network of tree-ring data. Journal of Climate, 20(7), 1353-1376.
    17. 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. Journal of Spatial Analysis Environmental Hazards, 3(4), 101-119.
    18. 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.
    19. 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, 19(71), 69-80.
    20. Mohammadi, G, H., (2015). Analysis of Atmospheric Mechanisms in Dust Transport over West of Iran. Ph.D. thesis, Tabriz University, 142 pp.
    21. 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.
    22. Pearson, K. (1897). 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(359-367), 489-498.
    23. Pourgholam-Amiji, M., Ansari Ghojghar, M., Khoshravesh, M. & Liaghat, A. (2020). Trends of Soil Salinity Changes and Its Relation to Climate Variables. Water Management in Agriculture, 6(2), 77-90.
    24. Seiler, M. C., & Seiler, F. A. (1989). Numerical recipes in C: the art of scientific computing. Risk Analysis, 9(3), 415-416.
    25. Shaker Sureh, F. & Asadi, E. (2019). Meteorological and hydrological drought communication in Salmas Plain. Desert Ecosystem Engineering Journal, 8(22), 89-100.
    26. Shojaeezadeh, K., Derijani, R. & Heidari, F. (2013). Investigating the Relationship between Climate and Dust Phenomena (Case Study: Mahshahr City), 2nd International Conference on Environmental Hazards, 29 October.
    27. 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).
    28. 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.
    29. Wang, X., Zhou, Z., & Dong, Z. (2006). Control of dust emissions by geomorphic conditions, wind environments and land use in northern China: An examination based on dust storm frequency from 1960 to 2003. Geomorphology, 81(3-4), 292-308.
    30. Xiao, F., Zhou, C., & Liao, Y. (2008). Dust storms evolution in Taklimakan Desert and its correlation with climatic parameters. Journal of Geographical Sciences, 18(4), 415-424.
    31. Xu, X., Levy, J. K., Zhaohui, L., & Hong, C. (2006). An investigation of sand–dust storm events and land surface characteristics in China using NOAA NDVI data. Global and Planetary Change, 52 (1-4), 182-196.
    32. Xu, X., Levy, J. K., Zhaohui, L., & Hong, C. (2006). An investigation of sand–dust storm events and land surface characteristics in China using NOAA NDVI data. Global and Planetary Change, 52(1-4), 182-196.
    33. Yarmoradi, Z., Nasiri, B., Mohammadi, Gh. H., & Karampour, M. (2018). Trend analysis of dusty day’s frequency in Eastern arts o Iran associated with Climate Fluctuations. Desert Ecosystem Engineering Journal, 7(18), 1-14.
    34. 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.