Evaluation of CHIRPS precipitation data in the analysis of precipitation characteristics trends in different climatic regions of Iran

Nowadays precipitation distribution and rainfall rate are directly affected by climate change and, natural disaster, drought, agricultural product efficiency in every basin (Sharafati 2019). It is necessary to access studies related to climate change to be prepared against disadvantageous consequences. Precipitation trend studies are one of the conventional methods for evaluating climate change in specified time series on studied basins. For this purpose, parametric and non-parametric methods were presented by researchers to assess trends in datasets. Man-Kendall test is one of the most suitable methods among non-parametric trend analysis, especially in hydraulic data sets trend assessment. Access to reliable and collated precipitation datasets is a prerequisite for study and evaluate climate change but there is not precipitation data in some rain gauge stations in Iran, or unreliable observe datasets are available. Gridded precipitation data series are sort of main data sets known as “gridded” cause of their special distribution. These datasets are raster data information that showing rainfall depth or other climate data sets in certain points. The importance of gridded precipitation data is noticeable in areas without reliable observation data. and annual precipitation trends were evaluated and reported decreasing rainfall trends after 1990 in Iran.In this research, GPCC,CHIRPS data sets were evaluated to find out the performance of GPCC,CHIRPS datasets in trend analysis. At first, daily precipitation observes data of 68 synoptic stations were collected, Then GPCC,CHIRPS satellite-based precipitation of rainfall depth and rainy days were collected. Specification and description of GPCC,CHIRPS collected gridded data from 1997 to 2017 were used in this research. As far as the gridded data set was produced in network format, It was necessary to interpolate precipitation data to approach to 68 station correct data. The interpolated rainfall depth and rainy days were calculated with the inverse distance weighting method. Rainy days and rainfall depth of data sets trend was derived in all 68 stations and 12month of the year and the annual trend also calculated with the Mann-kendal test. Finally Gridded data sets trend and observation data sets trend were used to compute POD,TSS parameters in two parts; rainy days and rainfall depth. Finally for every 68 stations. The examination results of the trend of rainfall depth of GPCC datasets and observational data showed that the highest agreement in the process of GPCC data with observational data was observed in 3 stations with moderate compliance (POD parameter more than 60%) in low rainfall areas with an average rainfall of 60 mm. Also, the highest rate (TSS between 45 to 60%) was observed in the G1 region on the eastern edge of the country.The examination results of the correlation of CHIRPS data with observational data showed, the highest POD parameter was observed in the southern margin of the Zagros as well as the Persian Gulf and the Caspian Sea (ie 100%). The lowest amount was seen in the Oman Sea and north of the Alborz mountain range. Also the TSS parameter showed, with the exception of one station in the east of the country, almost all stations are located along the Zagros Mountains with 100% compliance. The lowest TSS parameter is also consistent with the POD results, meaning that the lowest TSS value is located on the shores of the Oman Sea and north of the Alborz mountain range.The examination results of the trend of rainy days of GPCC datasets and observational data showed that the total of 22 stations have a POD parameter of more than 80%. Most of these stations are located in mountainous areas with average rainfall and temperate climate. For 16 stations out of a total of 68 stations, the POD parameter was calculated to be more than 65%. Most of these stations are located at low altitudes and foothills. The TSS parameter result showed that almost all stations with TSS are 100% located in the G1 area. The lowest TSS parameters are located in stations in the northern and eastern parts of the country.The examination results of the correlation of CHIRPS data with observational data showed, most of the POD parameters, except for one station in the center of the country, the other stations are located along the Alborz mountain range. Also, areas with the lowest TSS parameter are located in the southeastern margin of the country. Most of the TSS parameters are located in the central regions of the Zagros Mountains and the northeastern part of Alborz. In this study, it was tried to determine the trend of precipitation data related to GPCC,CHIRPS gridded data in accordance with observational data.The results of rainfall dephts trend with both POD and TSS parameters on CHIRPS satellite data in comparison with ground precipitation data showed that in 28% of stations the precipitation trend is completely consistent with the observed precipitation data, which in practice ranks these data in average to acceptable rank. In terms of compliance. While the result of high compliance of GPCC data with observational data was observed in only about 8% of stations, which indicates a very low correlation of these data with observational data.GPCC data also did not have good results in examining the results of the adaptation of the number of rainy days, so that only 1.5% of the stations were in complete agreement with the observational data, which, like the results of examining the trend of rainfall depth, has very little conformity. Also, the results of the trend of the number of rainy days of CHIRPS satellite data in comparison with ground precipitation data showed that in 33% of stations, the precipitation trend is more than 80% consistent with the observed precipitation data. As a result, the trend compliance in these data is at an acceptable level.Therefore, according to the results of this study, it can be concluded that the use of GPCC data for routing in the replacement of observational data is not highly recommended. Used safely and reliably.