پديد آورندگان :
برزو، فرزانه نويسنده دانشجوي دكتري اقليم‌شناسي، دانشگاه رازي كرمانشاه , , عزيزي، قاسم نويسنده ,
كليدواژه :
احتمال وقوع , ايران , بارش سنگين , شاخص
چكيده فارسي :
هدف اين پژوهش به‌دست دادن رابطه‌اي ساده و صحيح براي محاسبه بارش سنگين است كه در همه مناطق ايران، حتي مكان‌هاي با فقر داده‌اي كاربرد داشته باشد. به‌دليل دشواري دسترسي به آمارهاي روزانه، اين روش متكي بر شاخص‌هاي ماهانه است. براي تعيين بارش‌هاي سنگين از آمار روزانه بارش پنجاه سال اخير (1961 تا 2011) چهل ايستگاه سينوپتيكي استفاده شد. مقدار بارش سنگين با استفاده از داده‌هاي روزانه، ماهانه و سالانه محاسبه و با يكديگر مقايسه شد. از داده‌هاي سالانه براي تعيين شاخص بارش سنگين ايران و از بارشي كه احتمال وقوع آن، ميان بارش‌هاي ثبت‌شده پنج درصد بود، به‌مثابه معيار اوليه استفاده شد. با استفاده از روش خوشه‌بندي، مولفه‌هاي موثر بر بارش سنگين ايران شناسايي شد. در تحليل عاملي از يازده مولفه بارشي، دو مولفه ميانگين مجموع بارش سالانه و تعداد روزهاي بارشي يك ميلي‌متر، درمجموع 86 درصد اثر را پوشش مي‌دهد. رابطه نهايي از نسبت ميانگين مجموع بارش سالانه به ميانگين تعداد روزهاي بارشي يك ميلي‌متر و بيشتر همراه يك ضريب عددي تشكيل شد. براي تعيين ضريب عددي، ايران به هفت گروه تقسيم شد و براي هر گروه، ضريب جداگانه به‌دست آمد. بارش سنگين محاسبه‌شده با معيار اوليه همبستگي زيادي (997/0) نشان داد. درنهايت، نقشه پهنه‌بندي ضريب عددي و بارش سنگين براي محاسبه مقدار بارش سنگين هر نقطه ايران با نرم‌افزار GISرسم شد.
چكيده لاتين :
Extended abstract
Introduction
Selecting the clear and transparent index for the precipitation using long-term homogeneous data is an important point for the researchers. Several investigations have led to different indexes for heavy rainfall. In some cases the specific amount of precipitation was used for heavy rainfall (Rahimzadeh 2005, Masoodian 2008 and and kamiguchi et al 2006) e.g. Alijani (2002) has suggested the precipitation more than 30 milimeter. Some investigators have used the percentage of daily precipitation as a heavy rainfall index (Mohammadi and Masoodian 2010) e.g. Easterling et al (2003) used the greatest annual 5-day total precipitation amount and the percent of annual precipitation, due to all 24-h rainfall totals exceeding the 95th percentile of daily amounts. A series of international workshops have introduced a set of indecators to show the effect of climate change on extreme events (Folland et al. 1999, Nicholla and Murray 1999, Manton et al. 2001). some investigators used several indecators as an index for heavy rainfall (Seibert et al 2005, Haylock et al 2006, Haylock and nicholis 2000, Osborn and Hulme2002, Simonov et al 2007 , Vaidya and Kulkarni 2007, Campins et al 2006,Paddock and et al 2008, Kysely and Picek, 2007, Bukantis et al 2010, Schmidli et al 2002) e.g. H?nsel and Matcshullat (2009) to study monthly trends of daily heavy precipitation indicators used 22 heavy precipitation indicators (HPI) that may be classified in to the four groups “A”, “I”, “F” and “M”. “A” stands for average precipitation indicators like monthly precipitation totals and number of wet days. “I” comprises indicators measuring the precipitation intensity, like the SDPI (Simple Daily Precipitation Index) or the percentage of precipitation above the 95th percentile. The frequency of heavy precipitation events is studied by indicators in class “F”, while category “M” includes indicators of heavy precipitation events magnitude. zhang et al ( 2001) proved that annual and seasonal time series of heavy event frequency are obtained by counting the number of exceedances per year. Characteristics of the intensity of heavy precipitation events are investigated examining the 90th percentiles of daily precipitation, the annual maximum daily value, and the 20-yr return values. Based on the results, uses of percentile indecators are more common compared with threshold indicators and in some studies both indicators have been used. It seems that the use of heavy rainfall at some degree is depends on the geographical characteristics of the rainfall region. The natural ecosystems adapt themselves with the annual precipitation and extreme events in every region through the time. So the amount of precipitation that shows the heavy rainfall in a dry station, in a humid station can be recognized as normal. This study tries to find a simple method for indicating the heavy rainfall with regard to monthly trends based on daily data.
Data and Methods
In regarding to determine an index for heavy precipitation, data of daily precipitation for 40 stations with synchronized meteorological data which are distributed homogeneously throughout the country in periods (1961-2011) were used. The probability (1, 5, 10, 20 and 50%) for the entire period of rainy days was calculated using the Weibul equation. A very high percentage of daily precipitation values were obtained with the test 1 percentage, so the occurrence of five percent of daily precipitation was used as an index. The relationship between the ratio of the total mean annual precipitation (mm) and No. of days with precipitation equal to or greater than one millimeter with a numerical coefficient which may provide the best indicator for the heavy rainfall. Factor analysis of these two components can be selected from among the eleven factors of rainfall data includes a total of 86 percent. Finally, the isohyet map was plotted using the numerical index by GIS so the heavy rainfall could be calculated for each part of Iran.
Results and Discussion
To determine the appropriate numerical factor in Iran, all the stations are classified in to seven groups using K means cluster analyses, because of the different geographical characteristics and rainfall patterns. The average total annual rainfall was used to classify the groups. Then the numerical coefficient was calculated for each group separately.
Conclusion
According to the proposed heavy rainfall index, the isohyet map was plotted. Using the isoline map of numerical coefficient calculated for each station or related area in order to estimate heavy rainfall. The average error between the proposed index and the five percent probability of daily precipitation is 0.07. Only in Ardebil, Urumia, Dezful and chabahar port the error is more than one milimeter. There is no error in Ahvaz and Isfahan i.e. the proposed index is equal to the five percent probability of daily precipitation. The comparison between the heavy rainfall isohyet map and the total average annual precipitation and the No. of days with precipitation equal to or greater than one milimeter in Iran shows the same distribution.
Key words: heavy rainfall” indicator”probability” Iran
