Analysis of seasonal precipitation trend based on different quantiles in northern Iran

Climatic events are often caused by extreme meteorological conditions. Therefore, analyzing the changes trend in the mean or median data does not provide considerable information and it is necessary to examine it in different parts of the data distribution. Quantile regression method is able to study the trend in different quantiles of climatic parameters. In this study, the seasonal changes trend was analyzed using the quantile regression method in different amounts of daily precipitation for different at Gorgan, Babolsar and Anzali stations located in northern Iran. First, the statistical period of 62 years (1959-2020) was used to study the trend and then this given reach was divided into two statistical periods of 31 years (1(1959-1989) and 2(1990-2020)) and then Quantile regression was performed for different quantiles of daily precipitation for all three statistical periods. The results show different trend patterns for each quantile of seasons. Increasing trends have often been related to the extreme upper quantile of precipitation (the quantile 0.99), which indicates an increase in extreme rainfall and flood events. The slopes of increasing and decreasing trend have been changed in different time periods and also extreme precipitation have increased in winter in recent decades. The trend of precipitation slopes in semi-humid areas (Gorgan station) and in humid parts (Anzali station) were exposed more than temperate in period 2 and period 1, respectively. In conclusion, the highest increasing trends of precipitation were observed in autumn and winter and the highest decreasing trends were observed in spring and summer.

Automated summary

This study analyzed the seasonal changes in precipitation using quantile regression method in different stations in northern Iran. The results showed different trend patterns for each season and extreme rainfall and flood events have been increasing.