Among the meteorological components precipitation is one of the complicated issues in hydrological process, due to its considerable changes over time and space. By increasing application of satellite-based technologies over the past decades, it is now easy to access high resolution precipitation products in most parts of the world. This research addressed the accuracy of European Center for Medium Range Weather Forecasts (ECMWF) reanalysis datasets for estimation of daily and monthly precipitation over the SefidRood catchment. Moreover, in order to better evaluating the performance of ECMWF dataset, the PERSIAN and TRMM datasets are also used. Findings on the daily time scale showed that the correlation coefficient between ground observation and ECMWF, PERSIAN and TRMM products was respectively about 0. 8, 0. 47 and 0. 32 and this proved the superiority of ECMWF for estimation of rainfall in daily time scale. On monthly time scale both ECMWF and PERSIAN products correlated very well with gauge measurements (CC statistic is more than 0. 9) but TRMM with the CC equal to 0. 57 correlated moderately with observations. According to the categorical verification statistics for SefidRood catchment, ECMWF yields better results compared with other satellite data sets, for detection of precipitation events on the basis of Probability of Detection (POD), Critical Success Index (CSI) and False Alarm Ratio (FAR). Therefore, in ungauged catchments or for hydrological modeling which requires an accurate precipitation dataset, using ECMWF dataset is suggested.

Rainfall is a crucial component of the hydrological cycle and plays a key role in water resource planning. Recent research has investigated the use of gridded data as a supplement to and replacement for traditional rain gauge measurements, particularly in areas with limited gauge coverage. Gridded precipitation data offering a structured method to represent precipitation patterns across large regions by dividing the data into grids. This enables more precise spatial analysis of precipitation distribution and variability. The study assessed the accuracy of six high-resolution gridded rainfall product estimates (ERA5, ERA-Interim, CMORPH, PERSIANN, PERSIANN-CDR, and PERSIANN-CCS) at 12 rain gauge stations in Iran at various time scales. Comparisons with rain gauge network data using statistical and graphical methods revealed that ERA5, ERA-Interim, and PERSIANN-CDR data outperformed other models on annual and monthly scales, so that the highest correlation coefficient in monthly scale was obtained by ERA5 model at Doroodzan station with correlation coefficient of 0.93. Also, the results on a daily scale indicate the appropriateness of the output data of the reanalysis models (ERA5, ERA-Interim) compared to other models in such a way that the lowest RMSE value in all stations except Sefidroud Dam is related to the reanalysis data and the lowest RMSE value is equal to with 0.78 mm at the Chahnimeh station and the highest value of the correlation coefficient equal to 0.63 corresponds to the Karaj dam rain gauge station; Also, in correctly detecting rainy and non-rainy days, ERA5 model has the most accuracy in all stations.

Precipitation, as one of the main components of the hydrological cycle, plays a significant role in the processes related to water resources management, environmental protection, and weather disaster management. Due to the lack and limited access to widespread rainfall data in the country, the use of global rainfall data obtained from remote sensing and modeling can be very useful in the analyzes required in the field of water resources management. This paper evaluates the accuracy of the latest global precipitation databases resulting from reanalysis and satellite data with high spatial resolution (ERA5-Land, GSMaP, IMERG, MSWEP and CHIRPS) in estimating precipitation at different time scales in Iran. For this purpose, rainfall data in 70 synoptic stations in the country were used for the period of 2000 to 2018, and the performance of the databases in question was investigated at daily, monthly and annual time scales, separately for 6 different climatic regions. The results showed that the rainfall estimation in these databases is more accurate for the rainiest regions of the country than the dry regions, and generally the accuracy of the rainfall estimation is higher in the rainy months of the year than in the dry period of the year. In general, the GSMaP database in estimating daily rainfall and the MSWEP database in estimating monthly and annual rainfall in Iran show better performance than other databases; However, the accuracy of each database depends on the desired time scale and the climatic region under investigation, which must be taken into account.

Snow cover is the most widely distributed and most dynamic component of the cryosphere, and its significant seasonal and annual variability have notable influences on the global water circulation and surface energy balance. Ground observations represent the most direct and reliable snow depth data source. Direct measurements are generally not used to monitor snow depth at hemispheric scales due to the inadequate number of measurements and the sparse distribution of measurements in remote regions, mountains, and high elevation areas. Instead, the method examined in this study is the use of reanalysis data. The aim of the current study is to evaluate the performance of the ERA5 and MERRA2 reanalysis datasets in estimating snow depth in Northwestern Iran. The evaluation has been done using snow depth data from meteorological stations throughout the study area. The area examined in this study includes the provinces of Ardabil, East Azerbaijan and West Azerbaijan, which are located in the range of 44 to 51 degrees in longitude and 35.7 to 40 degrees in latitude. This area was selected for two primary reasons: firstly, the mountainous terrain of the region that results in a higher amount of snowfall, and secondly, there is an abundance of observational data available for estimation purposes. To conduct this study, monthly mean snow depth data from the ERA5 and MERRA2 reanalysis databases for the period 1981 to 2020 were used. The spatial resolution of the ERA5 data is 0.25 x 0.25 degrees. The MERRA2 data, on the other hand, has a spatial resolution of 0.625 x 0.5. degrees Since the data from MERRA2 and ERA5 have different spatial resolutions, to compare these two databases, a regridding method is used to equalize their spatial resolutions. The ERA5 data is regridded to MERRA2 spatial resolution using nearest-neighbor interpolation. The results of this study showed that by examining the spatial and temporal distribution, the ERA5 database underestimates the mean value of snow depth over the entire study area. On the other hand, the MERRA2 database overestimated the average snow depth at most stations and had more errors than the ERA5 database. According to the ERA5 reanalysis database, the highest snow depth over a 40-year period has occured in February, while for the MERRA2 database, it has occurred in January. In the ERA5 database, the estimated snow depth values were consistent with the observations only in February, while in other months there was a discrepancy between the snow depth estimated by ERA5 and the observations. As the station's height and latitude increase, the error increases and the underestimation of snow depth from ERA5 also increases, but in the MERRA2 dataset, there is no significant relationship between the height of the station and latitude with bias. The analysis of the results of the present study shows that the ERA5 database is more accurate than MERRA2 for studying the spatial and temporal distribution of snow depth in the northwestern region of Iran. Of course, in the areas with high snow cover, the MERRA2 data estimates values are closer to observations.

The purpose of this research is to identify changes in the temperature trend in the western half of Iran. For this purpose, monthly temperature data of 15 synoptic stations were collected during 1960-2010. Quality control was applied on these data by applying Pettit, SNHT, Buishand and Von Neumann’ s tests. Later data Simulated and compared with reanalysis data such as ERA-Interim, ERA-20C, NCEP and CMIP5 models (RCP8. 5 for the period 1960-2100). Trends were calculated by the Mean Kendall test and the Sen’ s estimator (95 % confidence level). Based on the results obtained from all models, a significant positive trend was observed in spring, summer and autumn, and only in winter according to ERA-Interim. Based on CMIP5 results for the period 2050-2100 values between 2 and 4 ° C/100 achieved, which is lower than the results of other models for the period 1979-2010. Considering the CMIP5 models and their overall average in the study area, an increase in annual temperature (7 ° C /100) for the second half of the 21st century was confirmed.

An accurate estimation of precipitation is important and necessary for flood simulation, drought monitoring and water resources management. Currently, most parts of the world are suffering from the lack of the rain gauge observations and the spatial coverage of ground observations aren’ t enough and continues. One of the most important precipitation datasets is the model-based precipitation datasets, by which the satellite techniques, the general circulation models (GCMs) and the land surface models (LSMs) are integrated to provide high temporal and high resolution datasets for all parts of the world. This datasets can compensate the lack of adequate ground observation gauges or can be considered as an alternative for ground observations, especially in ungauged regions. In this research the accuracy of the most important reanalysis datasets, called ECMWF, for estimation of daily and monthly precipitation over the SefidRood watershed for the time period of 2000-2008 was investigated. In addition, for better assessment of the proposed precipitation datasets, TRMM dataset was used. Findings on daily and monthly time scales, show that the correlation coefficient (CC) between observed and ECMWF dataset is so remarkable, especially in south, central and west parts of the study area. For instance, the CC values of the average precipitation of ECMWF data versus gauge datasets in both daily and monthly time steps were estimated to be about 0. 83, 0. 94, respectively, while the CC values for TRMM dataset versus gauge datasets were estimated to be 0. 32 and 0. 57, respectively. In contrast to reanalysed datasets, one of the most important weakness of the precipitation datasets such as TRMM is that they estimate the rainfall only based on the cloud thickness and its available water. Moreover, according to the categorical verification statistics in both time spans, ECMWF due to having low value of false alarm ratio (FAR) and high values for accuracy and probability of detection (POD) yields acceptable results over the SefidRood watershed. SefidRood watershed is a large scale region and contains different climate and topographical conditions and hence the results of this research can be used as an appropriate guidance for other similar areas. Based on the findings in this study it’ s highly recommended for using this rainfall dataset as one of the best alternatives for ground observations, especially in data sparse regions that accessing to ground datasets is so hard or almost impossible.

Hydrological modeling needs climate data with high spatial accuracy especially in low-rainfall areas where usually there is a no-good observational network. Currently, the lack of climate observational data can somehow be overcome using gridded global databases. CRU and NCEP CFSR databases are amongst the most prestigious gridded databases which were evaluated in this study by using SWAT model for Maharlu Lake basin. Statistical index comparisons in 33 years (1980-2013) showed the accuracy of the two datasets with Nash Sutcliffe efficiency index as approximately 0.91 in monthly scale. The modeling was then conducted by SWAT for all three datasets. The modeling results showed that databases could have high accuracy in rainfall runoff modeling from which the CRU database indicated better runoff simulation compared to NCEP CFSR databases. Coefficients of determination and Nash Sutcliffe efficiency in each calibration and validation periods represented average values above 0.7 for observed datasets, 0.65 for CRU, and 0.6 for NCEP CFSR.

Estimating air temperature plays an important role in many water and energy balance calculations, hydrological modeling, meteorological and agricultural studies. Changes in air temperature influence on plant growth and many other components at the interface between earth surface and atmosphere. The most common sources for air temperature time series are meteorological stations. However, meteorological networks are sparse in complex terrains, such as mountains. This is mainly due to difficulties with the installation and maintenance of the stations. The air temperature can also be calculated using climate model and reanalysis datasets. The purpose of this study was to introduce two meteorological reanalysis databases: the European Center for Medium-Range Weather Forecasts (ECMWF) and Modern-Era Retrospective Analysis for Research and Applications (MERRA), and evaluation of their performance in estimating daily maximum, minimum and average air temperature. In this regard, maximum, minimum and average air temperature data at daily scale for a period of 14 years from 2003 to 2016 (5114 days) were obtained from 12 temperature measurement stations in Helleh river basin area in south of Iran and the Persian Gulf coasts. The elevation correction and downscaling temperature based on modeled lapse rate are used for correcting two meteorological reanalysis datasets. The correlation coefficient (CC), mean error (ME) and squared mean of errors (RMSE) were used to evaluate the presented datasets. The results showed that the compliance rate of reanalysis datasets in all parameters of maximum, minimum, and average air temperature are appropriate, but the ECMWF-ERA-Interim version dataset is much better than the MERRA version 2 dataset. The correlation coefficients for all parameters of maximum, minimum and average air temperature are more than 0. 9. Also, the performance of both datasets in estimating the average air temperature at daily scale is better than the maximum and minimum air temperature at daily scale. Both databases are also underestimated in estimating maximum temperature data and overestimated in estimating minimum data. The average air temperature at daily scale is estimated slightly warmer (0. 4° C) from the ECMWF-ERA-Interim version dataset, while the MERRA dataset of version 2 estimates the mean of air temperature colder (-0. 5° C). Finally, the use of daily air temperature parameters (maximum, minimum and average) of the ECMWF ERA-Interim dataset is more preferable than MERRA version 2 dataset. Considering the proper performance of reanalysis datasets and using their advantages, we suggest evaluating other meteorological parameters.