كاربرد شاخص خشكسالي(CPEI) در تعيين متغيرهاي مناسب براي تحليل خشكسالي هاي ايران

Determination of Suitable Variables for Analysis of Droughts in Iran by Using CPEI Index

خشكسالي با استفاده از شاخص هاي متعددي مطالعه مي­شود. عنصر رايج در اكثر اين شاخص­ها، بارندگي است. اهميت بارش در تحليل و پيش بيني خشكسالي ها باعث شده است كه محققان شاخص هاي متعددي بر اساس متغيرهاي بارشي تعريف و ارائه نمايند. در پژوهش حاضر، شاخص متغيرهاي تاثيرگذار بارندگي (CPEI) براي بررسي خشكسالي هاي فصلي و سالانه ايران مورد استفاده قرار گرفته است. در اين شيوه، برخلاف شاخص هاي كمي مورد استفاده در مطالعه خشكسالي ها، خروجي ها به صورت مدل هاي رياضي و مقادير كمي ارائه نمي شود بلكه نتايج شاخص مذكور به صورت تعداد و عناوين متغيرهاي تاثيرگذار بارندگي هستند كه مي توانند در بررسي مطالعه خشكسالي در يك مكان مورد استفاده مدلسازان قرار بگيرد. در اين راستا، داده هاي بارش روزانه 40 ايستگاه سينوپتيك ايران طي دوره آماري 1980 تا 2009 مورد استفاده قرار گرفت. براساس روش هاي رياضي- آماري و مقادير همبستگي بين متغيرها ، تركيب بهينه­اي از متغيرهاي تاثيرگذار بارندگي براي هر ايستگاه تعيين و ميزان انطباق دوره هاي زماني و مكاني با شاخص CPEIبراي بررسي خشكسالي­ها و پايش آن ها در ايران به دست آمد. نقشه هاي پهنه بندي براساس تناسب ايستگاه­ها با شاخص CPEI تهيه شد. نتايج نشان داد كه در بيشتر ايستگاه ها شاخص مذكور قابل استفاده است و از شاخص CPEI براي تعيين دوره هاي خشك فصلي و سالانه در ايران مي توان در طراحي مدل­ها استفاده نمود. همچنين معلوم شد كه استفاده از اين روش براي مطالعه خشكسالي هاي فصل بهار و سالانه مناسب تر از بقيه دوره هاي زماني است. مناطق شمالي و شمالغرب، در دوره هاي سالانه و فصلي (به­ استثناي فصل بهار) با شاخص CPEI، هماهنگي كمتري نشان دادند.

Drought is one of the most important hazards that occur in all the earth especially in arid and semi-arid climates. Every year، about half of the earth’s surface experienced droughts and while drought is not a constant feature of any climate but occur more frequently in arid and semi-arid regions of the world. Although the occurrence of droughts cannot be prevented but by studying the nature and characteristics of droughts and also identify factors that affecting their occurrence useful information can be gained about drought and their destructive effects. The researches in recent years designed and proposed a lot of indices to study and analyze the droughts and today various characteristics such as intensity، duration، area and so on with these indices are studied. Many indices used by researches to analysis and identify properties of climatic droughts and dry periods. In these indices often the variables of precipitations، combination of precipitations and temperature، humidity or evaporation، crops yields and teleconnection climatic indices are used. In this study using the CPEI index and 30 years (1980-2009) daily rainfall data in 40 synoptic stations overall Iran، to analysis and assess of Iran droughts suitable variables detected. Four seasons and annual period is considered in this study. To determine the appropriate variables in the design of suitable models and modeling of drought to assess and predict droughts Otun in 2005 proposed CPEI index as Conjunctive Precipitation Effectiveness Index. He selected 10 conjunctive precipitation variables as ORS(Onset of Rainy Season)، CRS(Cessation of Rainy Season)، LRS(Length of Rainy Season)، TWD(The Total no of Wet Days)، TDS(Total no of Dry Spell)، TDW(Total no of Dry Days within a Wet Season)، TDY(Total no of Dry Days within a Year)، LDS(Length of the Dry Season)، MDL(Maximum Dry Spell Length within a Wet Season)، MAR(Mean Annual / Seasonal Rainfall Depth) and determined the relationships between variables in each synoptic stations and climatic regions. Since the units of measurement the rainfall variables are diverse، it is essential that the units be converted to a standard unit، in other words variables be standardized. The relationship between variables was determined by Pearson correlation coefficient. Finally، the right combination of precipitation variables for each station through the proposed formula Otun(2005) were determined. In the end، for each of the seasons and the annually period regionalization maps were prepared. All 40 synoptic stations were evaluated by Otun’s method (Aton، 2005). The results showed that 95 percent of stations in spring، 75 percent in fall، 57 percent in winter and 75 percent in annual period are compatible with used method. Thus، spring، fall and winter seasons and also annual period are compatible with above mentioned index. Among the used variables MAR، MDL، TDY and TDS which with respectively are as follows: total amount of precipitation in any period، the maximum duration of dry periods in a wet period، the total number of dry days in a wet period and the total number of dry period during wet period among the stations are more abundant. In annually period، in addition to the above mentioned variables، precipitation variable of LPS (length of dry period) also seen among some stations. Also، results showed that CPEI index can be used on most stations and climatic regions of Iran. It was also found that the spring compared the other seasons and annual period is more comparable on the base of CPEI index. Otun in 2010 used the CPEI index in semi-arid region of Nigeria and has achieved good results. The results of our study show good agreement with Otun’s work. The use of this index in the study of meteorology، climatology، agriculture and many environmental projects can be beneficial because in many of these fields of study، precipitation and its characteristics have an important role. In general we can say that in regions where CPEI index does not show a high proportion or set of variables are not enough it is better to use other indices such as SPI and RAI. The results obtained in similar climate zones such as Nigeria has shown that CPEI index has very good ability to identify and explain the precipitation effectiveness variables which can be used in modeling of droughts and dry periods. There are many similarities between combination of precipitation variables that identified by CPEI index for Iran and other regions of the world. Similarities، especially with respect to MAR، MDL، TDY and TDS are abundant.