Global databases provide complete source of required PRECIPITATION information for users. The aim of this study is to investigate the accuracy of the TRMM, NCEP and MERRA global PRECIPITATION databases in estimating the daily and monthly PRECIPITATION in the Balkhaluchai catchment area of Ardabil province. PRECIPITATION data of Nir station the three mentioned databases were extracted from 2002 to 2018. Mann-Kendall, Mann-Whitney and standard normal tests were used to check the trend and homogeneity of PRECIPITATION data. For three global PRECIPITATION databases, amount of R2, RMSE, NSE, RE and GMER were determined. The results of homogeneity test showed that all P-values were higher than 0.05 and the PRECIPITATION data were completely stochastic and homogeneous and no trends were observed in them. In both TRMM and NCEP databases, the R2 coefficient is good and is above 0.6. The MERRA database has had a weaker performance in this case. For the monthly period and the two TRMM and NCEP databases, the NSE index was higher than 0.5, which indicates the reliability of the PRECIPITATION data produced in these two stations. At the daily and monthly level, for all three global databases the GMER parameter values were obtained smaller than unity, which indicates that all global databases have overestimates. The overestimation of PRECIPITATION for the MERRA global database is much more severe than other two databases. MERRA database has a much weaker performance than NCEP and TRMM databases. In general, the TRMM database has a better efficiency than other databases.

The proportion of daily PRECIPITATION to the annual PRECIPITATION (PDPAP) is a criterion to recognize the intensity of daily PRECIPITATION. The high proportion demonstrates that it is possible for the annual PRECIPITATION to occur only in a few days. The high proportion reveals that having heavy and showery PRECIPITATION is normal. In contrast, the low proportion shows that there are moderate PRECIPITATION and more PRECIPITATION days. In the present research, data of the highest amount of daily PRECIPITATION of 46 Iranian synoptic stations from 1976 to 2005 were collected from the country meteorology organization to measure the proportion of daily maximum PRECIPITATION to annual PRECIPITATION. In the first, statistical methods including correlation tests, simple and multiple regressions, analysis of variance and the Friedman, nonparametric tests were used to measure the impact of independent factors of height, geographical longitude and latitude on that proportion and to judge the significance of the proportion changes of daily maximum PRECIPITATION to annual PRECIPITATION during the statistical period as well. Then, charts and maps were used for visual assessing and finally by using the Ward, s clustering method, the continental survey of the stations under study was presented based on the proportion of PRECIPITATION. Measuring the impact of geographical latitude on the proportion of daily maximum PRECIPITATION to annual PRECIPITATION of Iran showed that the proportion is increasing with latitude declining. Evaluating changes of the proportion of daily maximum PRECIPITATION to annual PRECIPITATION during the statistical period in the stations showed that those changes were significant only in the case of the Sanandaj station. Those changes of the proportion throughout Iran (46 station) made it clear that the first decade did not have any significant relationship with the second one, but changes in the third decade had increased in a significant way in comparison with the first one. The results also manifested that there was a significant relationship between the maximum amount of daily PRECIPITATION of the stations and their annual PRECIPITATION. Eventually, the continental survey of the stations illustrated 5 continental classes based on the proportion of daily maximum PRECIPITATION to annual PRECIPITATION in Iran.

In this paper 366 daily isohyet maps of Iran were created using Kriging method. Mean daily PRECIPITATION data for 333 synoptic stations has been used. Spatial resolution of these maps was 18Km*18Km. So 5214 pixels cover the country and temporal and spatial behavior of PRECIPITATION could be represented by a 5214*366 matrix. A cluster analysis applied on this matrix and eight different PRECIPITATION regions detected. The spatial configuration of these regions showed that amount of PRECIPITATION more or less depends to the topography but its timing is mainly controlled by regression and transgression of synoptic systems.

Different Climatic elements have movements and variation during time which must be recognized them. Some of these variations are regular or irregular patterns. The irregular variation is known as random changes. Thus it was used Random Changes models for measuring and Random changes seasonal and annual PRECIPITATION of Iran. According to measuring and forcast models all stations had random seasonal PRECIPITATION changes. Seasonal PRECIPITATION of Ahvas, Bandar Anzali, Tabriz and Yazd had change Models but other stations were change Models Annual PRECIPITATION all stations except Tabriz were under random changes effective.

Background and Objectives: Flood is one of the deadliest natural disasters in the world, which causes enormous damages to human societies and changes in environment. In recent years, the possibility of floods in many parts of the world has been a source of concern for people and governments. This has caused the need for more experienced managers and flood engineers to reduce the flood impact on the society and environment. Flood forecasting is one of the most promising methods of flood risk management that provides valuable information for flood warning services and people and organizations in emergency situations will have enough time to prepare for flood control measures. Materials and Methods: In this study, standardized PRECIPITATION index (SPI) and standardized PRECIPITATION-evapotranspiration index (SPEI) in Darzikala and Sangadeh weather stations located in Kasilian basin, Mazandaran province were calculated to determine the wet period. Then, in order to explore the efficiency of SPI and SPEI in flood detection, the results were compared with river discharge time series in Valikbon gage station. The parameters for the diagnosis probability (POD), the False Warning Ratio (FAR) and the Critical Position Indicator (CSI) were used to assess the success rate of these indicators in flood detection at the stations. Results: The results showed that the maximum and minimum POD, CSI and FAR were 0. 89 and 0. 68, 0. 71 and 0. 61, 0. 32 and 0. 11, respectively, for SPI and SPET indices. The trend analysis of mentioned indices showed that the SPEI index was relatively better than SPI due to the use of temperature data and hence the calculation of evapotranspiration, especially at the end of spring and summer in the Kasilian basin which has rainfall– snow regime. Conclusion: Based on the POD, FAR and CSI statistics, it is suggested that due to the use of temperature data for snow melt, especially in spring and summer, the application of SPEI index is more suitable than SPI for flood forecasting in rainfall-snow regime. The SPI index is also an important indicator because of its ability in providing desirable results in flood forecasting with only one parameter and also due to this fact that the rainfall is the only accurate data with appropriate historical period in all watersheds and at least two organs in the country are collecting information about it.

Introduction Climate changes and global warming have caused changes in weather patterns and the occurrence of serious events in different regions of the earth. Therefore, it is necessary to predict destructive floods, especially in coastal areas, to announce flood warnings and control them. Satellite PRECIPITATION estimation systems often generate data with global coverage that can provide information in areas where data from other sources are not available. Satellite retrieval and estimation systems, rain gauges, and radar networks complement each other regarding coverage and rainfall monitoring capabilities. Satellite PRECIPITATION estimation systems often produce data with global coverage. One of the main advantages of satellite-based PRECIPITATION products is the ease of real-time data availability and high spatial-temporal resolution. Currently, various satellite PRECIPITATION products such as GPM, TRMM, GSMap, CMORPH, CHIRPS, etc. are available to the public. Materials and Methods In this study, the southwest coastline of Caspian Sea, Gilan province is considered as the study area and the accuracy of four satellite-based products namely CHIRPS, GPIM-IMERG, PERSIANN CDR, TRMM-3B42V7 is compared in order to estimate the heavy PRECIPITATION from 2017 to 2021. Estimates are performed using categorical indices including PC, CSI, BIAS, and HSS and statistical criteria of correlation coefficient (Corr) and normalized root mean square error (nRMSE). Results and Discussion Evaluations and statistical criteria are conducted by comparing estimated satellite PRECIPITATION with ground stations through definitive indicators as well as the height of each station and the average of all stations respectively. The results show that in the PC index in eight stations, all of which are located at an altitude of fewer than 40 m above sea level and on the Caspian coast, the product IMERG scores higher than other products. The weakest performance in the PC index at the threshold of more than 5 mm belongs to TRMM and PERSIANN-CDR, which have the lowest scores jointly in eight stations. According to the results obtained from CSI, IMERG product had better performance in all stations except the Manjil station. Additionally, evaluation of the height of stations and the results obtained from this index demonstrate the fact no relationship was found between station height and product performance. In the Bias quality index, in IMERG and CHIRPS products, the bias of each station is directly related to its height, so that in higher stations, rainfall is overestimated, and vice versa in lower altitudes, the PRECIPITATIONs have been underestimated less than the amount of observation. Conclusion The IMERG product has performed better than the other three products in all categorical indices and correlation criteria. Finally, to evaluate and analyze heavy PRECIPITATION in the coastal strip of Gilan province and studies related to PRECIPITATION estimation, it is suggested that the IMERG product be used as a priority. The analysis carried out in this study, from the collection of satellite PRECIPITATION bases in estimating heavy PRECIPITATION in coastal areas, reveals the need to study and investigate this issue as much as possible in future research.

Todays, satellite PRECIPITATION data with high spatial and temporal resolution is an appropriate alternative source for conducting various studies of meteorology and hydrology in areas with inappropriate distribution of stations. These data are very important for a country like Iran which is always faced with problems of water shortage and PRECIPITATION. Therefore, the main purpose of this study is to evaluate the accuracy of GPM and TRMM daily PRECIPITATION against the recorded daily PRECIPITATION data of selected stations in Iran. Statistical methods were used to achieve this purpose. The data used include daily PRECIPITATION data from meteorological stations and daily rainfall from latest GPM and TRMM products in the period 2015-2000 in Iran. Statistical analysis showed that the daily PRECIPITATION values of these sensors are not accurate in Iran, and the estimated PRECIPITATION error is significant for most stations. Comparison of rainfall estimates from TRMM and GPM showed that although there is a great similarity between the estimates of PRECIPITATION values of GPM and TRMM, however, GPM data are more accurate than TRMM data in Iran. In terms of location, the best accuracy of the GPM and TRMM databases were observed in west of Iran, especially along the Zagros mountains, and like many other databases, the lowest accuracy was observed in coastal areas, especially along the Caspian Sea. Comparison of total annual PRECIPITATION of the stations and the remote sensing databases showed that not only there is a difference between the estimates of PRECIPITATION, there is also a spatial difference between high and low PRECIPITATION cores.