Precipitation is one of the most important climatic elements. It is one of the main components of the global water cycle which has influences on socioeconomic development, environmental planning, and life on the Earth. Proper knowledge of the spatial and temporal distribution of precipitation cannot be acquired unless there is a good distribution of meteorological stations. Despite the importance of precipitation, rain gauge datasets for hydroclimate applications, due to some limitations, such as the lack of long data records, the distribution and density of meteorological stations, and late updating, particularly in the case of complex terrains like those in Iran, has been a huge challenge both spatially and temporally. Gridded precipitation datasets are good alternatives for the monitoring of precipitation characteristics over regions where gaugebased observations are sparse or nonexistent. In the current study, the tempo-spatial accuracy of the era-5 database was evaluated in the estimation of precipitation over Iran during the statistical period of 1998-2019. For this purpose, the precipitation data of era-5 base with a spatial resolution of 0.25 degrees of arc was used in comparison with the data of meteorological stations. In order to evaluate the accuracy of the rainfall estimation of era-5, diffrent statistical methods including correlation coefficient (R), percentage bias (PBias), root mean square error (RMSE), probability of detection (POD), false alarm ratio (FAR), success threshold index (CSI) and Taylor diagram have been used. The findings showed that the era-5 database shows the spatial distribution pattern of precipitation in Iran well. Area-averaged correlation between the era-5 database and gaugebased observations is more than 0.9. There is a high agreement between the time series of precipitation values estimated by era-5 and the precipitation values observed on meteorological stations in most regions of Iran. The amount of precipitation estimated by the database in the rainy months is more than dry months. This overestimation is high in the high precipitation regions than in other areas of the country (PBias >100%). While in the dry interior regions of the country, the database has underestimated the amount of precipitation (PBias <100%). The RMSE values are more than 100 mm in January, February, March, April and December in the high precipitation regions on the Zagros heights in the west of the country and the southwest of the Caspian Sea. The RMSE value is less than 20 mm in most of the months of the year in the dry regions. The database correctly detects rainy days and dry days in the rainy months of the year over the rainy areas.    The accuracy of detecting rainy days in the months of January to April, November and December is more than 0.9, and the rate of misdiagnosis of dry days in the mentioned months is less than 0.3. In general, the accuracy of detecting rainy and dry days reaches more than 0.6 in the high precipitation regions of the country.

An increase in the temperature of the earth is one of the consequences of climate change, the effects of which on different countries and sectors are different according to the type of climate and the extent of their aridity and water areas. The effects of global warming on the biosphere and human life, especially sea surface temperature, play an important role in the earth's climate. Considering the importance of the topic and the fact that the process of climate change undoubtedly affects different systems of the earth, the present research was conducted with the aim of investigating the process of changes in the temperature of the Mazandaran Sea basin. In this regard, the analyzed monthly data of era-Interim with resolution of 0.25 x 0.25 was used. Due to the normality of the data, the linear regression test was used to calculate the trend. The obtained results showed that except for April, in other months of the region, the increasing trend prevails over the decreasing trend. During the months of July, August and October, the area of increasing trend and during the month of April, the area of decreasing trend has the largest width. Increasing trends have been observed mainly in the southeast, southwest and center of the basin. A decreasing trend has also occurred in the north of the Caspian Sea. In the month of April, the northern half of the basin within the borders of Russia has completely decreased. The results of the slope of temperature changes showed that the highest amount of temperature increase occurred in October and in the northern areas of the basin within the borders of Russia. In these areas, the temperature has increased between 0.07 and 0.1 degrees Celsius.

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.

Purpose: Due to the great damage that Corona has inflicted on the tourism industry, neglecting the future of tourism marketing can pose a serious challenge to this industry and economy and have irreparable consequences. The purpose of this study is to identify key factors, drivers and future scenarios of the tourism marketing.Method: The present study is applied-developmental in terms of purpose, descriptive-analytical in nature and mixed (qualitative-quantitative) in terms of methodology. The method of this research is scenario writing using MicMac software to identify key drivers and scenarios with Wizard scenario software.Findings: First, the future trends and meta-trends of post-corona tourism marketing were identified, of which 41 key trends and 8 meta-trends were identified. According to experts, 19 key factors in the future of tourism marketing in post- corona were identified using the cross-impact analysis and questionnaire completion. Then, using the cross-impact analysis questionnaire and with the help of the Wizard scenario software, two groups of favorable and unfavorable scenarios were identified.Conclusion: Based on the research results, there are two favorable scenarios and two unfavorable scenarios that can help marketing and tourism policy-makers and decision-makers to take into account the effective factors and scenarios proposed for proper planning of industry in the post-Corona era.

1 Introduction Snow is a vital component of the Earth's climate system because of its interaction with the energy flux and surface moisture on a local to global scale. This parameter significantly increases the relationships with radiation at higher latitudes. Analyzing changes in the amount of snow is essential for the assessment of the impacts of climate variability of a region. Snow cover has major effects on surface albedo and energy balance, and represents a major storage of water. The snow pack strongly influences the overlying air, the underlying ground, and the atmosphere downstream. Snow cover duration influences the growing season of the vegetation at high altitudes. A shortening snow season enhances soil warming due to increased solar absorption. While the importance of information on mountain snowpack is clear, obtaining these measures remains challenging. This is in part because snow depth and snow water equivalent (SWE) are both spatially and temporally variable, and mountain regions are generally difficult to access. Snow depth is one of the key variables for understanding the relationship between hydrological cycles. The flow of many rivers, especially during the warm period of the year, is mainly due to snow accumulation, which varies depending on the amount of snow melting in the time series. As mentioned, snow is an important hydrologic variable and acts as a water source in many parts of the world, especially Iran. In Iran, mountainous regions act as water suppliers for arid and semi-arid areas around them, and the coincidence of these conditions is one of the most important reasons for the creation of aqueducts in the country. This study, using the ECMWF data base of the era Interim, evaluates the trend and slope trend of snow depth (SD) in northern Iran. The achievements of this research can be useful for studies on climate change, water resources, flood, and agriculture. As a step toward addressing this challenge, we evaluated Methods to increase the efficiency of snow surveys and to enhance remotely derived estimates. 2 Materials and Methods In this study eleven districts of North Khorasan, Golestan, Mazandaran, Gilan, Tehran, Alborz, Qazvin, Zanjan, Ardebil, East Azarbaijan and West Azarbaijan have been studied. Interim was produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The database is available as an hourly basis since 1979. In this study, the spatial resolution of 0. 125 × 0. 125 degrees arc for the period 1980-2016 was used. Non-parametric Mann-Kendall and Sen's Slope methods were used to evaluate the trend and trend slope of snow depth. 3 Result and Discussion The assessment of the depth of snow in northern Iran in January shows that only 0. 063% of the northern zone of the country has a significant increase in the level. These areas are more in the northwest of Iran on the border with Turkey, areas with no significant trend. An increase of 3. 82% of the total study region has come from this month. These areas are located in the North Khorasan Provinceand near Bojnurd. Areas with increasing trend at 0. 05, 0. 01 and 0. 001 levels have not been observed in northern zone of the country. The northwest regions of Iran on the border between Iran and Turkey, which show an increasing depth of snow, can be attributed to climate change affecting the systems leading to northwest Iran, with snow depth rising. January showed the lowest amount of snow depth for me-Kendall in winter. In this month, the maximum declined trend was 5. 58 and the average trend was-14. 3. Also, the average slope of the calculated trend in January was 0. 03. This indicates that the depth of the snow with a negative slope of 0. 07 cm is decreasing. 4 Conclusion The results show that snow depth in the north of Iran in winter is more than 96% of the studied area with decreasing trend. The significant decrease trend at the level of 0. 001 in the winter is the maximum trend, and from January to March, the size of areas under the territory of this level increase the meaning of the trend, so that in January, February and March, respectively, 47. 99, 56. 08, 71. 82 percent of the area of the northern zone of Iran has fallen into a declining trend at a probability level of 99. 99 percent. Winter season of the Iran regions in the northwest and east, the increasing snow depth was observed that this trend is not incremental but significant. The maximum decreasing trend is snow depth in the provinces of Tehran, Qazvin, Zanjan and East Azarbaijan. In end of April areas with no significant decreasing trend with more than 51% of the same areas. The pattern of snow depth in the spring follows the same pattern in the winter. The average slope of the trend has also declined in line with the trend slowdown in April. On the contrary, the decreasing trend in autumn is based on the statistical results obtained in the study period. Snowfall increases in autumn in October and November, unlike other months in the northern regions of Tehran and southern Mazandaran province, especially in the central Alborz region.

Inappropriate distribution of precipitation measurement stations has led to the use of gridded precipitation datasets, consisting of satellites, reanalysis and ground-based precipitation datasets in recent years. In this study, one of the important precipitation products named era5 has been evaluated in Ardabil province. The observation data were first interpolated during the 2004– 2014 statistical period and compared with era5 data based on daily, monthly and annual time scales. Evaluations were performed using RMSE, correlation coefficient and contingency table indices consisting of POD, FAR, CSI and POFD. The results showed that the correlation coefficient for era5 at the daily time scale was above 0. 75 for most of the cells and RMSE was below 3 mm. Also, the correlation coefficient for monthly time scale was above 0. 8 and the RMSE was below 20 mm in most of the cells. Evaluation using contingency table indices showed that POD index in the studied cells ranged from 0. 7 to 0. 85, FAR ranged from 0 to 0. 25, POFD ranged from 0. 1 to 0. 2 and the CSI was in the range of 0. 4 to 0. 5. Precipitation values of both precipitation sources were classified into 6 classes using Ward cluster analysis method. The results of k-means method and Wilkes-Lambda model confirmed the classification accuracy and the difference between the means of the clusters. In general, it can be concluded that the era5 precipitation product at both daily and monthly time scales can be used as an appropriate alternative to data scarce regions after bias correction.

Introduction The wind is the horizontal displacement of air that is less than one meter per second. The wind is a dynamic phenomenon and has three main characteristics: intensity, direction, and frequency. Therefore, knowledge of wind characteristics in every area of importance is remarkable. The effects of global warming on temperature and precipitation at the global level over the past decades, many studies were considered,However, relatively little attention to climate change is wind speed. Wind speed changes can affect the energy of storms, shipping industries, as well as soil moisture, evaporation, and water resources,and it may even affect the evolution of dry and semi-arid environments. Also, a lot of research on wind and meteorology has shown that the performance of wind turbines is sensitive to climate change. Possible changes to the future wind regime have been widely considered under changing weather conditions,under global warming, the intensity and frequency of wind events are expected to change at the end of this century. Materials and methods The study area in this study of the eastern strip of Iran includes four provinces of Khorasan Razavi, South Khorasan, Kerman, and Sistan and Baluchestan. The study used wind speed data at a height of 10 meters, 10 synoptic stations with a daily statistical period of 2015-1985, which has 30 years of data,In choosing this station, in addition to proper distribution in the region, an attempt was made to select more stations in the station to be affected by the 120-day winds of Sistan. In this study, 10-meter daily wind speed data of the era-Interim version with a resolution of 0. 125 × 0. 125 degrees period of 1980-2015 were used,for the study area, 3772 pixels with an inter-pixel distance of about 12. 5 km have been obtained. To evaluate the performance of simulated data against observational data,There are several indicators used in this study from the Root mean squared error (RMSE), Mean bias error (MBE), mean absolute error (MAE), and the coefficient of determination (R2). The non-parametric Man-Kendall method was used to investigate the trend of wind speed changes in research. Results and discussion The ECMWF era-Interim version has a high and good performance for wind speed. The results showed that the output of the mentioned base in all the studied stations is on average between 0. 722 and 0. 984. RMSE, MBE and MAE characteristics in Zahedan, Khash and Saravan stations are less than m / s1,In other words, the wind speed of ECMWF base in these three stations has the highest performance of the 11 stations studied. The monthly statistical assessment of wind speed in selected stations in eastern Iran during the statistical period studied (1985-1985) showed that the average wind speed is 3. 56 m / s. The relationship between wind speed with negative altitude and positive longitude is significant at the level of 0. 05. Also, the relationship between latitude and wind speed showed that this relationship is negative in the cold months of the year and positive in the warm months of the year. The average wind speed fluctuates greatly during the 30-year statistical period. The average wind speed varies between 2. 82 and 4. 57 m / s. The minimum and maximum wind speeds were calculated in December and July, respectively. The average 30-year wind speed at selected stations in eastern Iran was calculated to be 2 m / s. The maximum wind speed in eastern Iran has many fluctuations,autumn showed the lowest statistical value in terms of maximum wind speed,In December, the maximum wind speed was calculated to be 3. 98 m / s. The maximum wind speed is increasing in all the studied months,From a statistical significance level, all the studied months except January, which, despite being increasing, are,But statistically, it is not significant at the level of 0. 05 and 0. 01,Other studied wind speed studies have a significant incremental trend at α,= 0. 01. The average wind speed in the study area is negative in 7 months (January, April, May, July, August, October and December) from the negative years and in 5 months (February, March, June, September and November). The maximum wind speed is January 4. 42, February 4. 86 and March 5. 02 m / s. The next area to be obtained in the form of a fertile area in winter,Zabol is also the center of Iran's border with Afghanistan in the border areas of South Khorasan Province. The wind speed trend is positive at the time of onset (June, 0. 79), the 120-day wind is positive and negative at the time of termination (October,-0. 15). Conclusion The average wind speed in the study area (Khorasan Razavi and South Khorasan, Kerman and Sistan and Baluchestan provinces) during the long-term statistical period of 30 years (2015-1985) is 3. 56 m / s,The minimum and maximum wind speeds are obtained in July and December, respectively,The reason for the increase in wind speed in July is due to the 120-day wind activity in Sistan, which started in June. The average wind speed in the study area is negative in 7 months (January, April, May, July, August, October, and December) from the negative year and in 5 months (February, March, June, September, and November). Investigation of wind speed process using non-parametric Man-Kendall (M-K) test,It showed that the wind speed trend in eastern Iran in the first month of June (June) 120-day winds showed an increasing trend (Z score of the Man-Kendall test 0. 795) and in the last month (October) it decreased (-0. 1152). ). Also, in July, when the wind speed is maximum, the average trend in the study area with a score of Z, 0. 242-is decreasing. Pearson correlation test showed that the relationship between wind speed and topography in the study area was statistically significant at 0. 05,In contrast, the relationship between longitude and wind speed is significant in all studied moles at the alpha level of 0. 05. In contrast, longitude and altitude in the study area did not show a uniform relationship between latitude and wind speed, this relationship is reversed for the cold months of the year and directly for the warm months. In October alone, the relationship between wind speed and latitude is not significant.

Precipitation is a major component of the hydrological cycle, which has significant spatial and temporal vriations. The lack of suitable data for this parameter causes a problem in hydrological forecasts. Since satellite data provides a new solution for estimating rainfall with spatial and temporal variation, this study evaluated the accuracy of some of these data types at the upstream of the Maroon Dam, including highresolution spatial data consist of era-Interim, CHIRPS and PERSIANN-CDR on daily, monthly and annual timescales. With respect to evaluation, gridded precipitation data and observational data from 2003 to 2014 were considered. The results showed that estimation of the annual rainfall data of the gridded precipitation models was underestimated so that the estimated average annual precipitation was evaluated less than the mean annual observational precipitation. In the estimation of monthly precipitation with regards to the Nash-Sutcliff coefficient at Dehno, Idenak and Margoon stations, the eraInterim model and at the Ghale-Raeesi station CHIRPS model indicated the best performance compared to other models. In the daily rainfall estimation, like the monthly rainfall, the best estimate at the Idenak station was the era-Interim model, which had an NSE of 0. 63 and the best estimate of precipitation in all stations was by era-Interim. era-Interim had the best performance from the 3 gridded models in the correct detection of rainy days. The best performance of this model was in determining the correct rain days with POD = 0. 53 at Idenak station.

In modern times many militant atheist thinkers and activists have tried to promote the idea that religions, as well as religious ways of life, are one of the main, if not the main source of evil in the social arena. Some other non-believer scholars, while taking a respectful approach towards religions and religious people, maintaining that it is more rational for people and communities to adopt a non-religious outlook on life and become members of the community of non-believers. In this paper, I do not take issue with the militant non-believers. The reason is that their approach to religion is so ideologically driven that it leaves not much room for proper rational discussions; it only invites some polemical replies. My aim here is, instead, to enter into a dialogue with those non-believer scholars who view religion in a measured and rational way. My intention is to critically assess the claims of this latter group of scholars and explain why, contrary to what they suggest, certain interpretations of religion, and in particular, the Islamic faith, can provide them with better alternatives than their atheist outlooks. The arguments of this paper are mostly in reply to the views expressed by Professor Shearmur in his paper on critical rationalism and religion.