تحلیل روند تغییرات زمانی و مکانی دوره های خشکسالی و ترسالی شمال غرب کشور بر اساس دو شاخص خشکسالی Spi و Rai

روند تغییرات بارندگی در یک منطقه می تواند نقش بسزایی در تحلیل تغییرات زمانی و مکانی خشکسالی داشته باشد. در این مطالعه با استفاده از داده های بارندگی 39 ایستگاه سینوپتیک واقع در شمال غرب کشور (2010-1986)، ابتدا سری های زمانی دوره های خشکسالی و ترسالی بر اساس دو شاخص خشکسالی بارش استاندارد شده (spi) و ناهنجاری بارندگی (rai) تعیین گردیده، سپس به بررسی روند تغییرات دوره های خشکسالی و ترسالی بر اساس سری مقادیر spi و rai با استفاده از آزمون ناپارامتریک من کندال با حذف اثر معنی داری تمامی ضرایب خود همبستگی با تاخیرهای مختلف پرداخته شده است. نتایج علاوه بر تاکید حساسیت روش من کندال به ضرایب خود همبستگی معنی دار با تاخیرهای مختلف در تعیین روند دوره‏های خشکسالی و ترسالی، بیانگر وجود روند منفی (نزولی) در اکثر ایستگاه ها و وجود روند منفی معنی دار (در سطح اطمینان 95%) در ایستگاه های غرب منطقه مطالعاتی می باشد. همچنین شیب خط روند در اکثر ایستگاه های مطالعاتی منفی بوده و بیشترین شیب منفی بر اساس شاخص های مذکور در ایستگاه مراغه مشاهده گردید. در نهایت، نتایج نشان داد که می توان از هر دو شاخص خشکسالی spi و rai صرفاً برای تعیین روند تغییرات دوره های خشکسالی و ترسالی به دلیل همبستگی بالای دو شاخص خشکسالی فوق در ارزیابی و تعیین روند تغییرات خشکسالی و ترسالی، استفاده کرد.

Identification of Trend in Spatial and Temporal Dry and Wet Periods in Northwest of Iran Based on SPI and RAI Indices

Introduction: Droughts are natural extreme phenomena, which frequently occur around the world. This phenomenon can occur in any region, but its effects will be more severe in arid and semiarid regions. Several studies have highlighted the increasing of droughts trend around the world. The majority of studies in assessing the trend of time series are based on basic MannKendall or Spearman 's methods and no serious attention has been paid to the impact of autocorrelation coefficient on time series. However, limited numbers of studies have included the lag1 autocorrelation coefficient and its impacts on the time series trend. The aim of this study was to investigate the trend of dry and wet periods in northwest of Iran using MannKendall trend test with removing all significant autocorrelations coefficients based on SPI and RAI drought indices. Materials and Methods: Study area has a region of 334,000 square kilometers, with wet, arid and semiarid climate, located in the northwest of Iran. The rainfall data were collected from 39 synoptic stations with average rainfall of 146 mm as the minimum of Gom station, and the highest annual rainfall of 1687 mm, in the Bandaranzali station. In this study, Standardized Precipitation Index (SPI) and Rainfall Anomaly Index (RAI) were used for trend analysis of dry and wet periods. SPI was developed by McKee et al. in 1993 to determine and monitor droughts. This index is able to determine the wet and dry situations for a specific time scale for each location using rainfall data. RAI index was developed by Van Rooy in 1965 to calculate the deviation of rainfall from the normal amount of rainfall and it evaluates monthly or annual rainfall on a linear scale resulting from a data series. Then, correlation coefficients of time series of these drought indices with different lags were determined for check the dependence or independence of the SPI and RAI values. Finally, based on dependence or independence of the time series values, trend analysis of wet and dry periods was conducted in different stations using one of the basic or modified MannKendall tests. Also, the magnitude of the trends was derived from the Theil Sen’s slope estimator. Results and Discussion: Time series of SPI and RAI drought indices for a given annual rainfall as an example for three stations of Marivan, Gom and Maku show that during 1991 to 1994 and from 2002 to 2007 are in wet period and during 1987 to 1990 and 1998 to 2001 are in the dry period. It is clearly show that, dry and wet periods in RAI index are more severe than SPI. Comparison the correlation between Lag1 autocorrelation coefficients values of SPI and RAI time series and Lag1 autocorrelation coefficients of annual rainfall data indicate that these correlations are high and about 0.97 and 0.99, respectively. This difference is due to the different classification of SPI and RAI drought indices. The results of trend analysis indicate a decreasing trend in most of stations. Also, MannKendall statistic has been declining while eliminating the effect of all significant correlation coefficients of dry and wet periods. This result in both SPI and RAI indices are similar and have a high correlation with R = 0.99. According to results, west of the study area have a significant decreasing (negative) trend. The spatial distribution of dry and wet periods showed that the difference between MannKendall statistics of SPI and RAI indices is minimal. Also, The results show that, the slope of the trend line based on the SPI and RAI drought indices is negative in most of stations and correlation between these two indices in determining the slope of the trend line is high. But, this correlation compared with the trend statistics of SPI and RAI time series is less. Conclusions: In this study, first the time series of SPI and RAI time series based on annual precipitation and common quantitative classification of mentioned two drought indices were determined. Then, trends of dry and wet periods of selected stations in northwest of Iran were evaluated based on these indices using the MannKendall trend test with removing all significant autocorrelation coefficients. The results from this study indicate that using MannKendall test with removing all significant autocorrelation coefficients effects are essential in assessing trend in time series. Although, according to various studies available in the literature, SPI is known as more accurate than RAI in drought mitigation, but according the results of this study, can solely be used both RAI and SPI index for trend detection.