Abstract
This study estimated spatial variability of precipitation in the monthly and annual scales in Iran for the period of 1975 to 2014 in 140 stations using kriging interpolation methods. In precipitation variability analysis three procedures were used: Mann-Kendall test, Sen's slope estimator and spatial trend patterns. Results show that there are both increasing and decreasing trends in monthly precipitation in Iran. Based on the magnitude of the significant trend, there are three patterns of the significant trend (average, lower and upper) in the monthly precipitation of Iran that vary from -0.0785 mm/month in October to 0.1536 mm/month in November. As a result, in January, February, March, May, October, and December, the magnitude of negative trends and in April and November the random and positive patterns were estimated in the precipitation in 140 stations, respectively. Key words: Spatial variations, trend variations, spatial variability, Mann-Kendall, precipitation.
Highlights
Precipitation is an important element to analyze the environmental process
The distribution of point pattern (39042 points) on annual precipitation using ordinary Kriging (OK) is presented in Figure 2, which shows a mean of 237.2 mm, with a minimum of 52.06 mm, with a maximum of 1757.8 mm with a standard deviation of 178.09 mm and a variations coefficient of 75.07% in the precipitation using OK across Iran’, it must be taken into account that the precipitation was not concentrated in the latitudes and longitudes
The distribution of point pattern (39042 points) on annual precipitation using universal kriging (UK) is presented in Figure 4, which shows a mean of 237.23 mm, with a minimum of 52.07 mm, with a maximum of 1757.82 mm, and with a standard deviation of 178.08 mm and with a variations coefficient of 75.07% in the precipitation using UK across Iran’; it must be taken into account that the precipitation was similar to OK in the latitudes and longitudes
Summary
Precipitation is an important element to analyze the environmental process. There are various spatialtemporal methods which were employed for classifying simulations in climatic series. The trend pattern and its spatial simulations of precipitation is an important aspect in the analysis of climatic and environmental simulation patterns. The spatial variability and trend pattern of precipitation (Zilli et al, 2017) are important property in the regionalization of climatic condition. The trend patterns and its spatial simulations of precipitation (Guan et al, 2017) was considered by various aspects due to several benefits over spatial methods. In spite of mentioned aspects, the effectiveness of the spatial variability in the precipitation series (Kutiel and Türkeş, 2017) on the results of trend spatial method has been
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