تفکیک نقش عوامل درونی و بیرونی در وردایی دهه‌ای بارش سالانۀ ایران‌زمین طی چهار دهۀ اخیر (1394 1355)

طی سدۀ گذشته، اقلیم به گونه‏های مختلفی تغییر یافته است. این روند تغییرات احتمالاً برای آینده ادامه خواهد داشت. یکی‏از جلوه‏های تغییر اقلیم در وردایی رفتار دهه‏ای بارش قابل ردیابی است. به‏منظور ردیابی وردایی بارش ایران طی چهار دهۀ اخیر (1355 1394) و تفکیک تغییرپذیری متاثر از مختصات (موقعیت) جغرافیایی (طول جغرافیایی و عرض جغرافیایی) و توپوگرافی (ارتفاع، جهت، و میزان شیب دامنه‏ها)، به‏عنوان عوامل درونی موثر بر بارش، از الگوی رگرسیون خطی چندمتغیره استفاده شد. بدین ترتیب، بارش متاثر از مختصات جغرافیایی عوامل توپوگرافیک برای هر دهه از میانگین بارش دهه‏ای تفکیک شد و الگوی مکانی بارش حاصل از عوامل بیرونی اقلیم بارشی به‏دست آمد. بنابراین، تغییرات دهه به دهۀ بارش در ارتباط با عوامل بیرونی مورد مداقه قرار گرفت. نتایج نشان داد طی چهار دهۀ اخیر تاثیر عوامل بیرونی موثر بر بارندگی ایران موجب کاهش پهنه‏های بارشی شده‏اند؛ به‏طوری‏که از اولین دهۀ مورد بررسی (1355 1364) به سمت دهۀ انتهایی (1385 1394) سهم عوامل بیرونی در وردایی بارش افزون‏تر شده‏ است. بعد از دهۀ دوم تاثیر عوامل بیرونی حتی نسبت به میانگین کل افزایش داشته‏ است؛ به‏طوری‏که در دهۀ اول 54.4 درصد و در دهۀ آخر نزدیک به 60 درصد از گسترۀ کشور با وردایی بارش حاصل از عوامل بیرونی مواجه بوده ‏است.

A Discrimination of Roles of Internal and External Factors on the Decadal Variation of Annual Precipitation in Iran over Recent Four Decades (1975 2016)

A Discrimination of Roles of Internal and External Factors on the Decadal Variation of Annual Precipitation in Iran over Recent Four Decades (19752016)IntroductionAccording to Intergovernmental Panel on Climate Change (IPCC) the Earth’s climate has been changed during recent century (IPCC, 2007). These changes may continues for the next century. The changes have happened in two ways, log term change (trend) and variation (in form of Oscillations, phases, shifts, and persistence) (Asakereh,2017). Moreover, some of these changes are due to internal factors of a region, whilst some of them result from external culprits of a given region. The distinguish between these two groups of factors is an important scientific effort to understand the changes mechanisms governing them. The emerge of climate changes and climate variation can be traced by investigation of some sensitive climate elements. One of those chaotic elements is precipitation which experiences changes in different tempospatial scales (Goudi, 1994). Most of these changes, specially the trends, are studied in global (Todorov, 1985; Vining and Griffiths, 1985; Diaz et al. 1989) and national (Askari and Rahimzadeh, 2003; Asakereh,2004; Zahedi et al., 2007; Katiraie , 2007; Mohamadi, 2012; Ekhtesasi et al. 2015; Nazeri Tahrudi, 2016) scals. In the climatology literature (Singh et al. 1995; Ghayur and Masoodian, 1996; Glazirin, 1997; Mojarrad and Moradifar, 2003; Asakereh, 2004; Raziei and Azizi, 2009; Asakereh and Seifipour, 2013) spatial changes of precipitation was attributed to spatial coordination (longitude and latitude) and topographic features (elevation, slope magnitude and aspect). The temporal changes in precipitation in association with these topospatial factors has not been considered in details and in proper ways in climate researches. In current study we put the spotlight on the decadal variation in relation with the topospatial features as a representative of climate change and as a vital context of research. Accordingly, a regression model is adopted so as quantify the effects of topo spatial factors effecting variability of precipitation over recent four decades.Materials and methodsIn order to achieve the aim of current study, two dataset were adopted; a national precipitation dataset, Esfezari, and Digital Elevation Model (DEM):The third version of Esfezari dataset is result from Kriging interpolation of daily record of 2188 synoptic, climatologic, and rain gauge stations for 46 years (19702016) with 10 km resolution. The 16801 daily maps with 167×205 pixels were created accordingly. Consequently, the dataset include dimensions of 167×205×16801. The DEM data with 10 meters spatial resolution was adopted from The U.S. Geological Survey which was provided by &Astet& satellite images. This dataset was applied to extract the topographic features (elevation, slope and its aspect) for Iran. To start with, the annual precipitation for the entire under investigation period (19702016) was analyzed according to abovementioned data. In the second place, the topospatial distribution of precipitation for four decades ended up to 20072016 was compared in an analyzing effort by using linear and nonlinear correlation. In this stage, according to Law of Parsimony, it is revealed that linear correlation illustrate the relation between precipitation and topo spatial factors in proper way. Moreover, A multivariate linear regression fitted on the five topospatial elements for every under study decade so as to detect precipitation accordingly. The regression model can be expressed as follow: where refers to annual precipitation in th pixel which is detected by the topo spatial factors and considered the internal cause precipitation. and are latitude and longitude, respectively. In the above regression model, , and are elevation, slop, and aspect of slop, respectively. Consequently, the external cause precipitation is the model residual. Finally, the descriptive characteristics of precipitation maps of internalcause and external cause were analyzed. Results and discussionGeneral Features of Iran PrecipitationThe general feature of precipitation over Iran shows a decreasing trends from west to east and from north to south. The coast of Caspian Sea and the summits of Zagros Mountain chain receive the highest values of precipitation. The spatial average of annual precipitation is about 250.5 mm. The strongest relationship between precipitation and topospatial factors is related to longitude in negative way. This feature is reflected in eastward decreasing of precipitation. The determine coefficients for latitude and elevation are 13% and 4.5%, respectively.The variation of effects of internal and external factors In spite of the stability of the determine coefficients of all regression models for all decades, some of topo spatial factors have noticeable variation. This feature refers to the fact that increasing in the effect of one factor may decrease the effect of other one(s). The models, however, illustrate a stable feature for precipitation over all four under study area during four decades. Consequently, the external factors are the main culprits in decadal changes of annual precipitation. Our finding showed that in the first decade the ratio of factors was 52.38% which increased gradually to 54.08% , 58.44%, and 59.72% among of four under the rest decades (Table 1). This refer to the fact that the changes in variation of precipitation is in association of global changes.Table 1: The areas under the effects of variation due to internal and external factors (%)Priod The percent of the country due to effects internal externalFirst decade 47.62 52.38Second decade 45.92 54.08Third decade 41.56 58.44Forth decade 40.28 59.72Entire period 44.38 55.62ConclusionThe climate and precipitation climatology of Iran is effected by internal and external factors, due to geographic features of the country. The internal factors include spatial (latitude and longitude), and topographic (elevation, slop and its aspect) features. In current study, in order to discriminate internal and external characteristics which effects precipitation variation we fitted a multivariate linear regression on internal factors to separate them out and distinct external factors. Accordingly, our finding revealed that the ratio of external factors in variability of precipitation increased from the first decade (52.38%) to the last decade (59.72%). This result is in line with previous studies (Khodadi et al. 2013; Farajzadeh and Ahmadian, 2014; Darand, 2015; Karimi et al., 2018). The most influenced area from the variations of external factors are internal parts rather than marginal parts of the country. Keywords: Iran, Precipitation, Climate Change, Decadal Variation, Precipitation Variability