تحلیل فضایی مخاطرات محیطی

تحلیل فضایی مخاطرات محیطی

تحلیل فضایی مخاطره‌ی توفان‌های تندری بهاره‌ی ایران

نویسندگان
دانشگاه تربیت مدرس
چکیده
درپژوهش حاضر، داده‌‌های ماهانهی فراوانی وقوع توفان‌‌های تندری 25 ایستگاه سینوپتیک کشور در طی بازهی زمانی 51 ساله از 1960-2010 با استفاده از روش‌‌های تحلیل خوشه‌ای سلسلهمراتبی وارد شد و روش زمین‌آمار کریجینگ به منظور طبقه‌بندی و شناسایی مکان‌‌های اصلی رخداد مخاطرهی توفان‌‌های تندری فصل بهار در ایران تحلیل شد. پس از محاسبه فراوانی وقوع توفان‌‌های تندری فصل بهار و بررسی ویژگی‌‌های آماری مربوط به آن، تابع توزیع احتمالاتی مناسب با داده‌‌های توفان‌‌های تندری برازش داده شدو سپس، با استفاده از تحلیل خوشه‌ای به تقسیم‌بندی مناطق گوناگون به گروه‌‌های همگون و با استفاده از روش کریجینگ به پهنه‌بندی توفان‌‌های تندری اقدام گردید. پس از بررسی فراسنج‌‌های آمار توصیفی توفان‌‌های تندری بهاره، مشخص شد که توزیع احتمال فراوانی وقوع داده‌‌های توفان‌‌های تندری بهارهی ایران، مانند بیشتر متغیرهای تصادفی گسسته از توزیع احتمال ویبول سه پارامتری تبعیت می‌کند. براساس نتایج حاصل از تحلیل خوشه‌ای مناطق گوناگون کشور به پنج ناحیه همگن مجزا مشتمل بر نواحی شمالی، میانی، شمال شرقی؛ نواحی مرکزی و شرقی؛ شمال غرب؛ غرب و نیمهی جنوبی با روند مشابه خوشه‌بندی گردید. پس از انجام پهنه‌بندی مشخص شد که کانون‌‌های اصلی رخداد این پدیده بیشتر در نواحی شمال غرب و غرب کشور متمرکز شده است.
کلیدواژه‌ها

عنوان مقاله English

The Spatial Analysis of Hazard of Spring Thunderstorms in Iran

نویسندگان English

Yosef Ghavidel Rahimi
Parasto Baghebanan
Manuchehr Farajzadeh
چکیده English

Thunderstorm is one of the most severe atmospheric disturbances in the world and also in Iran, which is characterized by rapid upward movements, abundant moisture, and climatic instability. Since this phenomenon is usually accompanied with hail, lightning, heavy rain, flood and severe winds, it can cause irreparable damage to the environment. Investigation of spring thunderstorms has a great significance regarding the irreparable damages can cause by them and also because of the higher frequency of this phenomenon in the spring and the necessity for preparedness and disaster mitigation actions. To identify the locations of the major thunderstorm risk areas, the entire country with an area of 1648195 square kilometers, which is located between the 25°-40° north latitude and 44°-63° east longitude is considered. Spatial distribution of the occurrence of hazardous spring thunderstorms was analyzed using a series of monthly thunderstorm frequency data obtained from 25 synoptic stations over a 51-year-long period (1960-2010). Ward's hierarchical clustering and Kriging methods were used for statistical analysis. Initially, total number of thunderstorms in April, May and June were considered as the frequency of occurrence of thunderstorm in different stations in the spring. Measure of central tendency and dispersion which consists of the sum, minimum, maximum, range and coefficient of variation, standard deviation, and skewness were used to clarify the changes of thunderstorms and to determine the spatial and temporal climatic distribution of spring thunderstorms. An appropriate probability distribution function was chosen to determine the distributions of the data. Due to the large volume of data and the uneven distribution of stations, cluster analysis and kriging methods were used to classify different regions into homogeneous groups for zoning and spatial analysis of spring thunderstorms, respectively. The statistical characteristics of spring thunderstorms were reviewed and fitted with a 3-parameter Weibull distribution. Regions considered for this study were classified in four separate clusters according to the simultaneity of thunderstorms in the spring. After zoning, it was found that the highest rates of thunderstorm took place in the northwest and west of country. The northeast of Iran has the second highest number of thunderstorm occurrence. The least number of thunderstorm event had happened in the central and southern half of the country. According to the descriptive statistics parameters, maximum number of thunderstorms occurred in May.. Based on the results of the cluster analysis, there is a similar trend in the central and eastern regions, the rest of the country was clustered into five distinct homogeneous regions, including the northwestern, western, southern, northern, central northern and northeastern regions. Zoning results indicate that the highest number of the occurrence of this phenomenon in the country is concentrated in the northwestern and western regions. Higher frequency of occurrence of thunderstorms in the northwestern and western regions may be attributed to local topographic conditions like high mountains, orientation of the terrain, solar radiation on slopes and existence instability conditions, hillside convection, the presence of water resources and specific climatic conditions in these areas. In addition, as a result of a continuous surface obtained by the method of interpolation with the least amount of systematic error and also the use of correlation functions for recognizing the spatial structure of the data and estimating the model error when using the Kriging method, the weights are chosen in order to have a more optimized interpolation function. Also the cluster analysis may significantly reduce the volume of operation without affecting the results and will help in finding a real band due to more appropriate classification of different geographic areas with greater spatial homogeneity and minimal variance within the group. Based on the results of the spatial analysis, it is clear that Kriging and Ward cluster analysis methods are appropriate for thunderstorm zoning and classification of different regions according to occurrence of thunderstorm, respectively.

کلیدواژه‌ها English

thunderstorm
Climatic hazards
Zoning
Spatial analysis
Iran
Adelekan, I.O. 1998. Spatio-Temporal variation in thunderstorm rainfall over Nigeria. International Journal of Climatology, 18: 1273-1284.
Allen, j.t. and karoly, d.j. 2013. A climatology of Australian severe thunderstorm environments 1979–2011: inter-annual variability and ENSO influence. International Journal of Climatology, 34: 81-97.
Bently, M.; Mote, T. and Thebpany, R. 2002. Using Land Sat to identify thunderstorm change in agricultural region. Bulletin of.American Meteorological Society, 83(3):363-376.
Bielec- Baskowska, Z. and Lupikasza, E. 2009. Long-term precipitation variability on thunderstorm days in Poland (1951–2000). Atmospheric Research, 93(1): 506-515.
Bielec, Z. 2001, Long-term variability of thunderstorms and thunderstorm precipitation occurrence in Cracow. Poland, in the period 1896–1995. Atmospheric Research, 56( 1–4): 161-170.
Changton, S,A. 2001. Thunderstorm rainfall in the conterminous united states, Bulletin of.American Meteorological Society, 82(9): 1925-1940.
Dai, A. 2001. Global Precipitation and Thunderstorm Frequencies. Part I: Seasonal and Interannual Variations. Journal of climate, 14 (6): 1092-1111.
Dai, A. 2001. Global Precipitation and Thunderstorm Frequencies. Part II: Diurnal Variations. Journal of climate, 14 (6): 1112-1128.
Easterling, D. R. 1989. Regionalization of thunderstorm rainfall in the contiguous U.S. International Journal of Climatology, 9: 579-567.
Easterling, R. 2003, Trends in U.S. climate during the twentieth century. Consequences, 2: 3-2.
Florin Necula, M. 2010. Recent changes in Thunderstorm activity in Vaslui. Present environment and sustainable development, 4: 407-414.
Geiby, A.; Sen,N.; Puranik,D. and Karekar, R. 2005. Thunderstorm identification from AMSU-B data using and artificial neural network. Meteorological Application, 10 (4): 329-336.
Kunz, M.; Sander,J. and Kottmeier, Ch. 2009. Recent trends of thunderstorm and hailstorm frequency and their relation to atmospheric characteristics in southwest Germany. International journal of climatology, 29 (15): 2283–2297.
Nastos, P.T.; Matsangouras,I.T. and Chronis, T.G. 2014. Spatio-temporal analysis of lightning activity over Greece - Preliminary results derived from the recent state precision lightning network . Atmospheric Research, 144:207-217.
Olafasson, H. and et al. 2004. Seasonal and interannual variability of thunderstorm in Island and origion of air masses in the storm.
Osmar Pinto, Jr. 2015. Thunderstorm climatology of Brazil: Enso and Tropical Atlantic. International journal of climatology, 35:871-878. DOI: 10.1002/joc.4022.
Rasuly, A.A. 1996. Temporal and spatial study of thunderstorm rainfall in the greater Sydney region. Doctor of Philosophy Thesis. school of Geoscience. university of Wollongong, Australia (new south wales).
Villarini, G. and Smith,J. A. 2013. Spatial and temporal variability of cloud-to-ground lightning over the continental U.S. during the period 1995–2010. Atmospheric Research, 124:137-148.