واکاوی فضایی مجموع فراوانی سالانۀ بارش‌های شدید و خیلی ‏شدید ناحیۀ خزری

بارش عنصری اقلیمی با وردایی زمانی مکانی بسیار بالاست. از جلوه‏های وردایی بارش مقادیر فرین آن است که با پیامدهای محیطی– انسانی و به‏سبب تاثیرات گسترده در ساختارهای فیزیکی و انسانی در کانون مطالعات اقلیمی است. بنابراین، بررسی این نوع بارش‏ها در ناحیه‏ای که قطب کشاورزی است و، از طرفی، این بارش‏ها یکی از مخاطرات طبیعی است اهمیت دارد. به‏منظور واکاوی فضایی مجموع فراوانی سالانه بارش شدید (آستانه‏های صدک 9095، 9599) و خیلی ‏شدید (صدک 99 و بیشتر)، از داده‏های میان‏یابی‏شده بارش روزانه طی بازه 19662016 استفاده شد. برای شناسایی الگوی پراکنش مکانی از آماره موران و gi* استفاده شد. با توجه به مقدار نمایه موران کلی 9/0 (سطح اطمینان 99درصد)، الگوی فضایی برازنده بارش‏ها الگوی خوشه‏ای است. در گروه بارشی شدید، بیشتر الگوهای خوشه‏ای مثبت در بخش‏های مرکزی و غربی و بارش خیلی شدید بیشتر در بخش شرقی، مرکزی و ناخوشه‏ها در گروه اول و دوم بیشتر در بخش شرقی و در گروه سوم در بخش‏های مرکزی و جنوب‏ غربی ناحیه است. آزمون gi* فراوانی خوشه‏هایی با ارزش بالا و پایین را تایید می‏‏کند. در بررسی روابط مکانی با آماره دومتغیره موران، طول جغرافیایی و ارتفاعات البرز بیشترین تاثیر را در رخداد بارش‏ها دارند.

An analysis of spatial autocorrelation of sum annually frequency of heavy and very heavy precipitation occurrence in Caspian region

Precipitation is considered as one of the most important climate elements affecting different environmental aspects represented through several different behavioral form among of which is extreme precipitation. Extreme precipitation can occur in the form of flashfloods and draught with considerable negative consequences on humaneenvironmental. Precipitation extremes follow a geographical pattern like all other climate elements. Recognition of such patterns, specifically in those areas where people’s lives depend on precipitations, can determined the amount of success in environmental management as well as certainty in resources planning. Regarding high extreme precipitation in Iranian coastal region of Caspian Sea, especially in eastern parts, the recognition of spatial autocorrelation of such a phenomenon can facilitate environmental planning and the reduction of vulnerability and also increasing adaptability with such a disaster. Materials & Methods: Therefore, in order to analyze the autocorrelation of the sum frequency of annually extreme precipitations of under investigation region, the 9095,9599 and 99 percentile of precipitation for each pixel of the map is considered. Accordingly, 385 stations (synoptic, climatology, and rain gauge of Islamic Republic Organization of Meteorology, and rain gauge of the Ministry of Power) were studied during the time period covering 1966 to 2016. We have used spatial statistics techniques (global Moran index (1), local Moran (2), and Gi* index (3)) to analyze spatial autocorrelation features. Function(1) Function(2) Function(3) In order to investigate the relationship between spatial factors (latitude, longitude, slope and gradient) with the annually frequency of extreme precipitation, we first use the ARCGIS spatial analysis using the Caspian Elevation Digital Elevation Model (DEM) and By applying the following steps, and finally, by sampling for all pixel points that were found in the interpolation of daily rainfall data, calculated on the basis of the following steps: Extracting altitudes, slopes and geographic directions of the subsurface points from the digital elevation model and the slope and the direction of slope obtained during the extractionsample steps and then the connection of the descriptive table of the layers with the elevation, slope and geographic directions of the slope obtained for the subtropical points. In the last step, the relationship between the spatial factors and extreme precipitation frequency for each month was calculated using general Moran multivariate statistics z_k=[x_k(x_k ) ̅ ]/σ_k , z_l=[x_l(x_kl ) ̅ ]/σ_l Function(4) I_kl=(z_k w_zl)/n Results and Discussion: A study of spatial relationships in order to recognize spatial dispersion of spatial complications and spatial autocorrelation is one of the best methods for recognizing the spatial behavior of extreme. The aim of this study is to determine the spatial pattern of the total annual precipitation patterns 9095, 9599 and 99 percentile of precipitation using the spatial statistics techniques (global Moran index, local Moran, and Gi* index to analyze spatial autocorrelation features. Accordingly, 385 stations (synoptic, climatology, and rain gauge of Islamic Republic Organization of Meteorology, and rain gauge of the Ministry of Power) were studied during the time period covering 1966 to 2016.. In the study of spatial autocorrelations based on the index used in this study, it shows that the global Moran index is above 0.9, which indicates a statistical significance of this coefficient at a confidence level of 99%, therefore, the pattern governing this behavior The three groups are high based on the Moran profile of the cluster pattern. Frequency maps of the annual occurrence of extreme precipitation show that the highest occurrence of these precipitation is in the first order of the third group and then the second group of precipitation, and the second group of precipitation is less frequent in this respect, as well as the maximum nucleus of this precipitation In the first and second groups, in the central and western parts, and in the third group in the eastern regions, this also shows the extent of the third group's influence in this area Positive and negative autocorrelations spatial clusters have seen the impact of the Alborz Mountains Systems in different parts of the Caspian region, On the eve of the first and second threshold, most cluster patterns of positive autocorrelations in the central and western parts, on the third threshold The most positive autocorrelations is in the eastern and central parts of the Caspian region. Also, negative correlation patterns were observed in the first and second groups more in the eastern part and in the third group in the central and southwestern regions of the district. G* test approved the frequency of clusters with high and low values. Conclusions: In general, it can be said that the Caspian region is more affected by the rainfall of the third and the first group, which covers a large area of the region, especially in the western and central parts, and due to the frequency of occurrence of this type of rainfall in the area that occurs in the event of a flood Effective analysis of spatial dispersion and spatial relationships of this disorder can be effective in identifying areas where flooding is greater and being used to manage and plan environmental hazards to reduce vulnerability and increase adaptability