Journal of Spatial Analysis

Precipitation is one of the important aspects of the Earth’s climate that has both spatial and temporal variations. Understanding the behavior of this element and analyzing its spatial and temporal variation is importantwhich can lead to a comprehensive and detailed planning for water resource management and agriculture. Geostatistical techniques and spatial autocorrelation analysis are the most widely used techniques in the field of the spatial continuity. Spatial autocorrelation analysis is applied to help researchers understand the spatial patterns in the area.

      The purpose of this study is to identify the heavy precipitation spatial patterns in Guilan Province. For this purpose, the 6- hourly sea level pressure of the network from  0 to 120 Easter longitude and 0 to 80 Northern latitude with 2.5×2.5 degrees spatial resolution were obtained from the National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) for the period 1979-2010. The daily precipitation data of 21 stations were obtained from the Islamic Republic of Iran Meteorological Organization and Ministry of Energy.

      Guilan province is one of the most humid regions in the country. The heavy rain days were selected as days when more than 30 percent of the all stations had daily rain amount more the 95^th percentile. As a result, 321 days were selected as heavy and widespread rainy days. By using principal component analysis these 321 days were reduced to 9 factors. These factors then were subject to cluster analysis with Ward method and resulted in three surface pressure patterns of heavy rainy days. Within the resulted pressure patterns by using local geostatistical techniques we identified the heavy rain spots and their spatial orientation. These spatial methods include Kriging,  Geostatistical Analysis, and Anselin local Moran index.

According to the results of this research, the first pattern was characterized with a high pressure over northern part of the Black Sea causing the highest Variance of heavy rainfalls. The second pattern is identified as a low pressure on the Black Sea. But the third pattern showed a precipitation distribution with low variation caused by the Siberian high-pressure. The results of Spatial Statistics techniques indicated that heavy rains were clustered in all there patterns. The clusters of heavy rains were localized mostly over the coastal areas and some over the central regions. The clusters of the western high-pressure patterns penetrated somewhat inside the province, while clusters of the Siberian high pressures was located on the shoreline of the province. The precipitation of western migratory high-pressures was heavier than of the Siberian high-pressure. The results of the standard deviation ellipse showed that heavy rain clusters were oriented in the east-west direction and were nonhomogeneous. While the ones oriented in the south east direction were more homogeneous and clustered. Because of this arrangement, the entry of moisture from the Caspian Sea is relatively concentrated on the East or North East. Because of the concentration of heavy rains in the central areas of the coast, the risks of floods and soil erosion is very high in these areas. This study showed that contrary to the popular belief, the heavy rains of Guilan were produced by western systems and the role of the Siberian high pressure is less important and is limited only to the coastline.