نشریه پژوهش های اقلیم شناسی
پیاپی 49 (بهار 1401)

Seasonal forecasting has always been one of the challenges in forecasting Iran's diverse climate. In the last one or two decades, many efforts have been made to develop and improve the climate models of the restricted area and to minimize these challenges, but the problems and challenges still remain. Convective parameterization schemes are always one of the sources of error in regional climate models that have a significant impact on model outputs. Therefore, one of the most important issues in implementing the model is choosing the appropriate convective scheme from the existing schemes. One of the methods of forecasting precipitation in our country is the use of dynamical downscaling by RegCM model. Most of the studies that have been done for this purpose in the country so far have considered single convection schemes for the whole country, the results of which have not shown a significant improvement in rainfall forecasting.

In this study, a relatively new approach was adopted, so that convection schemes were selected appropriate to the climate of the region, and then the final forecast of the entire country by regional integration of each climate zone was presented. In this paper a relatively new perspective of the climatic zones of the regions, was used for optimum configuration of the RegCM4.5 model; The study area in this study is Iran, which includes 25 to 41 degrees north latitude and 47 to 63 degrees east longitude, but the model area ranges from 30 to 70 degrees east longitude and 10 to 55 degrees north latitude. It covers important geographical features, including mountains and seas. In this study, the output of the CFSv2 global climate model originating from November 1 in each year as the boundary condition data has been used and the CRU precipitation data has been used as reanalysis data to test the output of the RegCM model. Because CRU data are averaged monthly, they are suitable for studies that examine monthly averages. CRU data have already been used by various researchers in the country to validate the output of the RegCM model. After selecting the schemes of the planetary boundary layer and surface layer, the selection of the appropriate Cumulus Parametrization Schemas(CPS) was done based on Iran's climatic classification using the Demarten index. This method is the simplest and most common method for climate classification that precipitation and temperature variables are effective in calculating climate index, and precipitation and temperature data have also been used from the CRU database. For this purpose, Iran was first divided into seven very humid, humid, semi-humid, Mediterranean, semi-arid and arid climates based on the Demarten index, and each grid points of the study area were assigned the relevant climate index. The share of each climatic class in zoning was obtained as follows; Arid 32.4%, Semi-Arid 30.1%, Mediterranean 7.6%, Semi-humid 7.6%, Humid 10.5% and Highly humid 11.8%. The study period was 5 rainy seasons 2019-2014 (November to May) that the beginning of each simulation with the initial condition data on the first of November and its end at the end of May (as the end of the rainy season in the country) in each year. The horizontal resolution considered to be 30 km regional model, the planetary boundary layer schemes and the surface layer Holslag and BATS were considered, respectively. Kuo, Grell, Emanuel, Tiedtke and Kain convection schemes were tested during this period to achieve optimal configuration.

In the first stage, mean precipitation and its RMSE from individual and integrated schemas were calculated, but due to the fact that the Emanuel and Kain schemes did not rank higher in any of the model experiments in terms of climatic classes and have more errors than others, theywere removed from the configuration selection process. The results showed that in very humid, humid, semi-humid and humid climates the Tiedtke convection scheme, in the semi-arid regions the Grell scheme and in the arid areas of the Kuo scheme had the least bias compared to other convection schemes. Therefore, the seasonal forecast of the country was presented by combining regional schemas, the average bias of which was calculated at 0.45, 0.79, 1.01 and 0.69 mm in the integrated schemes of Tiedtke, Grell and Kuo, respectively. On the other hand, in addition to calculating the ability of different schemas to predict precipitation using the RMSE index, the area under the ROC curve was also calculated in three classes less than normal (BN), normal (NN) and more than normal (AN) for different climates. For this purpose, in each precipitation layer, the number of schemas that predicted precipitation in different climates and had the largest area under the curve compared to other schemes was extracted. ROC diagrams of different schemas showed that Tiedtke and Grell schemas have the highest ability to predict less than normal, normal and more than normal rainfall classes. The results showed that the regional integrated scheme (TGK: Tiedtke, Grell and Kuo) showed an improvement of 54 to 126% compared to the individual schemas. In general, it can be said that choosing the optimal configuration based on the idea of climate-based convection scheme can increase the performance of the RegCM4.5 regional model in seasonal precipitation forecast in Iran.

Although a study with a regional climatic zones perspective was not found on Iran, but some studies have found the Tiedtke scheme suitable for our country (Alizadeh Choubari et al., 1398), which with the findings of this study in which the Tiedke scheme for four of the six climates used in this study are considered appropriate. On the other hand, Zarrin and Dadashi (1399) used the Grell scheme to study the events of the partial rainfall in Iran by RegCM4 model, which in this study was found to be suitable for semi-arid climate. In addition, it was observed that in the study period of seven months, the most RMSE error occurred in April, which is the month of transition from cold to warm season.

One of the significant impacts of climate change is the change in the type of precipitation that occurs in temperate and mountainous areas. Liquid or solid precipitation in form of frozen rain or snow can stop ground transportation, cause power outages, and determine the speed of the basin's response to floods and potential dangers to water resources in drainage basins especially in mountainous areas and, in particular, impose freshwater resources on rivers and glaciers. The type of precipitation can be just as important as the intensity and the amount of precipitation in the seasonal hydrological cycle and the health of ecosystems in high latitudes. The potential benefits and disadvantages of precipitation prediction depend on the form and intensity of precipitation, and the incorrect forecast of precipitation phase can cause problems in managing many areas, including water storage, air and soil moisture, land albedo, and surface currents. Changes in precipitation have more direct impact on society than changes in other meteorological elements. However, it is very difficult to determine the precipitation characteristics of an area due to its temporal and spatial fluctuations. Therefore, an important issue in modern hydrology is to determine the effects of climate change on the share of liquid and solid precipitation and its statistical distribution. Although many studies have been conducted so far to examine precipitation characteristics, changes in the precipitation phase have received less attention and studies in this field have often used daily data to detect and predict the precipitation phase. Atmospheric conditions on a scale of less than one day can also change the phase of precipitation. On the other hand, incorrect forecast of the precipitation phase can affect the environmental dimension of an area, especially its hydrological and climatic conditions. Relying on a specific element to detect the precipitation phase can lead to uncertainties due to the complexity of the precipitation process. Therefore, the present study was conducted to identify the precipitation phase in 19 stations in northwestern Iran with KSS model. The data used include the average of temperature, relative humidity, snowfall, and rain in two time intervals of 3 and 24 hours during the statistical period of 1951-2018. The model was executed for hourly and daily data and the accuracy of the model was evaluated with POD, CSI, PC, TSS, FAR, and FBI indices. The results were divided into two groups of solid and liquid precipitation and then the accuracy of the model was evaluated using 6 indices. Based on the indicators used, the model's performance in detecting both types of precipitation in the region is very high to the extent that the average evaluation of indicators in most cases is over 90%. The results of this study are consistent with the studies of Koistinen and Saltikoff, where the POD values for liquid and solid precipitation are 0.81 and 0.97 and Gjertsen & Ødegaard for solid and liquid precipitation are 0.97 and 0.84, respectively. Another important goal of this study was to compare the effect of time scale data on the accuracy of the model. The results showed that the use of 3-hour data in detection of the liquid phase of precipitation increases the performance of the model. Based on POD, CSI, PC, and TSS indicators, the model accuracy is 0.92 with hourly data and 0.85 with daily data. While the detection of the solid phase of precipitation in some cases offers contradictory results compared to liquid precipitation and the performance of the model with 24-hour data does not affect the accuracy of the model. Even in the POD index, a slight increase is seen compared to hourly data. The impact of data scale reaches its highest value with FAR index so that using daily data, -350 and 40% change is observed in detection of solid and liquid precipitation compared to the hourly data. On the other hand, the behavior of the model was different in some stations such as Sardasht and Khalkhal stations where the performance of the model was increased or decreased compared to the change of data time intervals, while no change was observed in Sarab station. Another result of the study shows that according to POD, FBI, and CSI indicators, the performance of the model in detecting hourly liquid rainfall is higher than solid hourly rainfall. Moreover, POD, FBI, and TSS metrics also showed an increase in the model's ability to detect solid daily precipitation relative to daily liquid precipitation. Because the study area is one of the areas affected by local masses and short time scale, its prevailing precipitation is in form of liquid precipitation in spring and autumn, while solid precipitation is more the result of migratory and permanent air masses in the region; therefore, based on the results of model evaluation, it can be concluded that the performance of the model in this area of Iran is quite acceptable.

The dust has affected most parts of Khorasan Razavi in July 1, 2014. The purpose of this study is ‎to simulate this event by RegCM model and validate its results with observation and remote sensing ‎data. For this purpose, horizontal visibility were prepared from the Iran Meteorological ‎Organization and AOD of Aqua and Merra-2 satellites from Giovanni web.‎ The result shows that the development of low heat pressure on Pakistan along with north and ‎northeast winds of 20 m/s in the east of the Razavi province leads to the transfer of dust to the ‎region. The HYSPLIT model and the output of the RegCM model show the dust source in ‎Turkmenistan and western Afghanistan.‎ The optical depth of aerosol obtained from the RegCM model is in good agreement with the ‎visibility in the east and south stations of the Razavi province, So that the correlation coefficient in ‎Gonabad was -0.98, Torbat Jam -0.66 and Sarakhs -0.61. In addition, the correlation coefficient of ‎the RegCM model and the Merra-2 optical thickness of aerosol obtained in Sarakhs and Torbat-e ‎Jam located in in the east of the province, including, were -0.45 and -0.78, respectively.‎ The result of comparing the time series showed the model output in three-hour intervals is in good ‎consistency with the visibility, the Aqua optical depth aerosol, and the Merra-2 optical thickness ‎aerosol in the east and south stations of the Razavi province. However, the RegCM model does not ‎estimate dust and has a time delay of 6 to 9 hours in the central and western stations.‎

The Gap winds are low-level winds associated with geomorphological features such as mountain passes, canyon valleys, and gaps Gap winds are usually significant when there is a considerable pressure gradient on two side of the gap. The Gap winds, in addition to intrinsic values for study, also have practical aspects, including in the field of wind energy use, architecture and development plans and plans, maritime transport (if they are located in the vicinity of water areas), air transport, operations Military. In the present study, for the first time in Iran, the mechanism of formation of Gap Lut wind in the form of climate has been studied and the characteristics of this wind have been introduced including hourly, daily, monthly and seasonal behavior. Due to the wind in the eastern part of the desert plain, Lut desert and Jazmourian plain, the name of this wind, which is known as mountain wind in Shahdad region, was named Koloja wind. Ko, from the beginning of Kavir letter, Lu, from Lut and Ja, from Jazmourian.The data studied in this study include three main categories, which are:First, for the initial identification of the characteristics of meteorological quantities and their statistical analysis, the data of 75 meteorological stations have been used in the country at intervals of 3 hours and in the period from the beginning of establishment until 2018.reanalysis data (Era-Interim) of European Center for Medium-Scale Atmospheric (ECMWF) were used to study, the meteorological parameters and structure of patterns like sea level pressure, geopotential height and temperature for standard pressure levels, relative and Specific humidity in low-troposphere, wind field, relative vorticity, wind, vertical velocity, convergence and cross section of the relevant quantities were studied by temporal and spatial intervals 3-hour and 0.125 degree resolution (in terms of latitude and longitude during the period of 1987-2019The study of 10 m surface wind pattern with a scale of 10 km in the eastern regions of Iran shows a northerly wind with a sharp and specific pattern in these areas. This north wind is divided into two branches, western and eastern, on the north of Khorasan. This wind on the eastern borders of Iran on the Sistan plain is known as the 120-day wind. The western branch of this wind, which blows on areas east of the desert plain, Lut desert, Jazmourian, is less known in Iranian sources. The monthly pattern of both branches of this north wind is relatively the same and their peak activity is in August. The strengthening of surface heating conditions, both in the field of sea and land, has caused a spatial expansion in August; Increase the altitude and relative integrity of these two winds. This wind also has a specific daily pattern. The peak of north wind strengthening in both branches is 12 and 18 UTC. The presence of a high-pressure belt is in the areas between 50 and 70 degrees north with a European high-pressure core in the north of the Caspian Sea and west of the Urals. This high-pressure belt, in contrast to the low-pressure belt prevailing in the subtropical region over the Middle East, causes wind conditions to blow from the north to the eastern regions of Iran. Existence of high Pamir-Hindu Kush-Himalayan mountain system in the east of the region, especially its western massif, Pamir on the one hand, and the existence of lower and of course important mountain systems such as North Khorasan, South Khorasan as a secondary induction plays an important role in channeling the north wind. And have a bias towards the northeastern regions of Iran. In the southern part of low-pressure belt, the presence of low desert zones as well as the warm water zone of the Oman-Persian Gulf has provided the conditions for the formation of a large low-pressure center on this water zone. In addition, on the flat and desert areas of the region, the increase in solar radiation (snsible heat flux) caused by short-wave solar radiation, which is accompanied by a deep high-altitude system in the middle and upper troposphere, leads to the formation of local independent low-pressure cells such as Gang, Rigistan. , Baluchistan, Sistan, Lut, Jazmourian.The mountainous region not only lead to the formation of high pressures affecting the wind and strengthen its intensity, but also the topographic arrangement of the eastern region of Iran in the form of parallel meridian mountain ranges on both sides of Lut plain, is the main cause of strong winds in Koloja. The combination of regional and local synoptic conditions along with the mountain walls on both sides of the flat desert area of Lut has provided the conditions for the Bernoulli effect and the creation of a wind chat during the day in this area.The direction of the wind is slightly variable, depending on the surface topography and the rough arrangement of the stations. Shahdad station can be considered the best place to study the wind conditions. Warm season rosewind show a stronger role of this wind in the hot season than the cold season at all stations. This issue has the role and effect of different spatial radiation pattern conditions as well as the uneven role in pressure distribution compared to cold season synoptic patterns. The windrose show the 24-hour variations of wind behavior of Koloja. The behavior of this wind shows that this wind is highly dependent on daily radiation and heating conditions. At night, especially at 3 UTC, in all control stations, especially the southern stations located in the desert and lowlands, the prevailing wind is the wind that blows from the surrounding highlands and does not indicate the direction corresponding to the Koloja wind. The core of the speed of this wind is at an altitude of 1500 meters above sea level. Depending on the height of the study area, this accelerating current is formed at an altitude of 1000 meters above the surface (low level jet stream).

Hail is a natural disaster for all people, especially farmers. Usually in the insurance industry to calculate the risk of hail damage in each area, the frequency of rainfall (in terms of days) and the average damage are used, which is statistically significant. Hail is one of the phenomena connected with thunderstorms that occur in unstable atmospheres with high humidity and in the presence of strong winds and with mechanisms that increase instability, and these conditions are affected by Local topography and climatology of air masses. Therefore, in order to obtain an overview of the spatial and temporal distribution of hail damage on agricultural products in East Azerbaijan province and to investigate the differences between different parts of the region in terms of this type of rainfall It is better to identify and introduce the cause of possible differences between different areas and the conditions under which this rainfall occurred by achieving zoning of vulnerable areas in terms of hail damage. Thus, by considering and emphasizing the strategy of natural risk management program, which is considered as a potential and a very serious role in aggravating and increasing the damage caused by natural disasters in the region, it is possible to predict and deal with hail and lead To control the injuries caused by this phenomenon.

East Azerbaijan is located in northwestern Iran between 36˚47' N and 39˚ 40' N latitudes and between 45˚ 3' E and 48˚ 50' E longitudes. East Azerbaijan with an area of 45261.4 square kilometers is located in the northwestern corner of the Iranian plateau, which has agricultural lands of about 1320 thousand hectares, which includes 7.47 percent of the country's agricultural lands. In this study, to investigate and analyze the losses of the agricultural sector due to hail, the statistics of losses of the Agricultural Insurance Fund for the statistical period (2010-2019) were used. Due to the fact that in many cases when the hail phenomenon occurs, its very small area and the limited number of synoptic stations, the occurrence of this phenomenon can not be seen and recorded. So the area to be recorded includes only a small number of these events. Therefore, in order to assess the damage caused by hail in the study area, the days when hail caused damage were extracted and examined from the statistics of damage caused by the Agricultural Products Insurance Fund. Then, spatial statistics, hot spot index and ARC GIS software were used to identify areas vulnerable to hail.
discussion and

According to Table (3), we see that the damage caused by this phenomenon on horticultural products in East Azerbaijan province is an average of 123.5 hectares per year, with Bonab city having an average annual level of 568.1 hectares and Ahar with 491.2 hectares and Tark with 476.2 hectares are in the next ranks. But in terms of damage to crops, it was determined that Qara Aghaj with an annual average of 1143.9 hectares has the highest level of damage and Hashtrood with 826.6 hectares and Ahar with 369.1 hectares are in the next ranks.In terms of the level of damage to the total crop and horticultural products during the study period, it was found that in the province, on average, about 262 hectares of the province's area under cultivation are damaged annually due to this phenomenon. The highest level of damage is related to Qara Aghaj section with an annual average of 1159.7 hectares, which includes 14.7% of the total hail damage in the study area, and Hashtrood and Ahar with 1057.9 (13.4%) and 860.3 hectares (10.9%, respectively). ) Are in the next ranks. In order to identify areas vulnerable to hail, spatial statistics and spatial autocorrelation techniques were used, and in order to ensure areas with high and low value clusters, the * Gi index (Getis - Ard GE) was used. The results showed that in agriculture, the values of positive spatial correlation are concentrated in parts of the south of the province, which is the most vulnerable area in the study area, the central parts of Charavimaq and Shadian, and in the garden sector, the values of positive spatial correlation in Parts of the northwest and southwest of the province are concentrated, and among these, the most vulnerable area in the study area is the central and Yamchi Marand districts.

The results of this study showed that the highest frequency of damaging hail occurred in May and the lowest frequency occurred in August. The results also showed that about 71% of the harmful hail in the study area occurred in the warm seasons, which coincides with the plant growing season in this area. In the period under review, the rainfall of harmful hail in East Azerbaijan province was on average between 09:00 and 15:00 (G.M.T) more than other hours, and in this 10-year period, the maximum rainfall occurred at 12:00.In the study of hot spots based on Gi* index, it was found that in agriculture and horticulture, high value amounts (positive spatial autocorrelation) are concentrated in parts of the south and northwest of the province, respectively. Examination of the total damage of agriculture and horticulture showed that high values (positive spatial correlation) are concentrated in parts of the south of the province, and the most vulnerable areas in the study are the central parts of Charavimaq, Shadian and Nazar Kahrizi. On the other hand, a region with less vulnerability in parts of the west of the province, especially the central parts of Osku, Khosrowshahr, Mamqan, Gogan and the suburbs of Azarshahr, corresponds to areas with a spatial distribution pattern with the highest significant negative spatial self-correlation and 99 Percentages (strong-cold-cold cluster) are concentrated. By examining vulnerable areas, we can point to the high area under cultivation in these areas, as well as the impact of local factors such as topography and altitude and external factors, including the entry of hail systems from the west and southwest of the province in its occurrence and intensification.

The consequences of climate change have led the international community to study more broadly that changes in natural resources, ecosystems, and populations will be affected by future climate change. Recent studies show that the global climate cycle will intensify. Climate change can have destructive effects on various sources such as water, forest, agriculture, etc. The first effects of climate change on atmospheric elements, especially temperature and precipitation, so it is essential to study the trend of climate change. The large scale model used in the present study is the CanESM2 model. Also, for exponential Downscaling in this research, 3 models of LARS -WG, SDSM and qmap have been used. The scenarios studied in the present study are two climatic scenarios RCP 4.5 and RCP 8.5. In this study, the performance of the introduced models as well as the temperature changes of the next period 2025-2056 in two synoptic stations of Rasht and Birjand have been investigated. The SDSM model combines linear regression and meteorological random generator, because the humidity variables and large-scale silicon pattern of the atmosphere are used linearly for local-scale meteorological generating parameters at single stations. The LARS-WG model is one of the most popular random weather data generator models and is used to generate daily series of rainfall values and minimum and maximum temperatures and sunny hours in a station under the conditions of basic and future climate conditions. This method is based on using random weather generators, which are offered based on the time series pattern and Fourier series. The dynamic bias correction method (BCSD) was first used to estimate the long-term components of hydrology and is now widely used in monthly climatic studies. By performing preprocessing operations on the minimum and maximum temperature data in the two stations analyzed, the results of the box diagram method showed that the studied data lacked outdated data. In addition, the results of data trend using the Mann-Kendall test for two parameters of minimum and maximum temperature in Birjand and Rasht stations show an increasing trend. According to these results, the SDSM model has a very high performance for predicting both stations’ minimum and maximum temperature parameters. The results of the LARS-WG model also show that the correlation between predicted data and daily observational data per month in both stations is perfect. The LARS model has a good performance in general, especially in predicting maximum temperature data, also the best performance of the LARS model According to the maximum temperature data in Birjand synoptic station. The worst performance is related to the minimum temperature data in this station. In addition, the results of the qmap model show that, in general, the best performance of qmap model is related to the simulation of the minimum temperature parameter for the next period in the Rasht station. The worst performance associated with the simulation of the minimum temperature parameter in the Birjand station. According to the results of the R2 index, this model has a good performance. Still, according to the NSE index results; this model's performance is not suitable for simulating the minimum temperature parameter for the next period. Comparison of annual maximum and minimum temperature changes in two synoptic stations of Birjand and Rasht shows that the parameters of minimum and maximum temperature in both stations will increase in the next period of 2056-2025 compared to the base period (1974-2005). Also, the temperature changes under the RCP 8.5 scenario are more than the rcp4.5 scenarios. In addition, the fluctuations of the minimum temperature parameter in the future period of Rasht station are more than Birjand station. In addition to these changes, the average maximum and minimum monthly temperatures in the period 2056-2025 compared to the base period (1974-2005) show that these changes are incremental for Birjand station and in Rasht station, except for the SDSM model in February, March, April, October, November and December and qmap in April, the changes are incremental. Changes in the average monthly minimum temperature of the next period in both stations are also an increase. However, in the SDSM model in April for Birjand station and in October, November, and December for Rasht station these changes decrease. However, according to the obtained results, in the period 2056-2025, the warming process is taking place in the study area.

Snow is a form of precipitation in hydrological cycle of mountain regions that plays an important role to supply drinking and agricultural water as the delay water resource during low-water seasons. Snow distribution is important to hydrological research of watersheds. The temporal and spatial distribution of snow cover has significant influence on snowmelt runoff. Knowing the extent of the snow is valuable information that provides insight as to the amount of water to be expected from snowmelt available for runoff and water supply. In this study there has been an attempt to investigate changes in snow cover of Koohrang watershed. However, the elevation effect on snow cover is also proposed. Koohrang’s catchment is one of sub-basins of northern Karoon with an area estimated over than 2700 km2 which is located in Chaharmahal-va- bakhtiyari Province of Iran. The permanent stream of the rivers; ultra-cold climate and high elevation are good and justifying evidences for solid precipitation in this area. Therefore, awareness of the amount of snow resources in this area is necessary in the storage of the equivalent amount of water, controlling seasonal floodwaters, anticipation of the river’s stream in supplying the required water of downstream land. This study aims to monitor the change of snow cover of the Koohrang watershed during 2010 to 2018 and to determine the association between snow cover and elevation zones.

Two groups of data including remote sensing and Geographic Information System (GSI) have been used in this study, And with respect to the subject, different techniques have been utilized. Moderate Resolution Imaging Spectroradiometer (MODIS) Satellite imagery within the statistical period of 2010-2018 was obtained. Then by calling those images into ENVI software, snow covered areas were differentiated from other areas, using difference index and NDSI ratios band 4 and 6; and also their maps were provided. In order to examine the amount of snow border of basin, with the use of the principle of environmental degradation and regression relationship and linear functions and the map, the daily 0°C (snow border) isothermal lines were defined. Furthermore, we investigate trends and variability of snow cover changes in the Koohrang watershed at different temporal scales (monthly and annual) and at different elevation zones between 1656 to 4074 m.

The results show a decreasing trend of changes in snow cover in the region during the study period. The most decreasing trend among the months was related to February. October and March are on the second and third places. The average snow cover of the region during this period is 440 km2 (equal to 34%). 2010 and 2014 years showed the lowest and highest snow cover, respectively. The highest snow cover among the months of the year belongs to January with an average of 1132.3 km2 (equal to 88.6%). The results also indicate that there is a direct relationship between the elevation and the quantity of snow cover and its persistence. So the highest percentage of snow cover was related to higher elevation zones. The average percentage of snow cover in elevation zones of 1 to 6 were 10, 22, 32, 40, 48 and 58%, respectively.

The results of this study can be very useful and practical for planning water resources in the region, especially in the warm seasons. The trend analysis on the alternation of snow cover during the 9 years period from 2010 to 2018 has shown some important results. If the decreasing trend of the snow cover in February and March as observed in this study continue, this may result in significant changes in the river flows and water resources in the region, particularly in spring. This would have implications for aquatic ecosystems that depend on the seasonal melt water pulse, for irrigation dependent agriculture and, last but not least, for water resources in the densely populated downstream areas. Some of the trends observed in the snow cover changes can be explained by the high correlations observed between the snow cover and the observed temperature. It should be noted here that not only snow covered areas but also the snow depth and snow water equivalent will impact the snow melting, which subsequently influence the river flow regimes and water resources availability. Therefore, future studies should attempt to consider these factors for a more comprehensive assessment of the impact of Koohrang snow on the river flow regimes.

Flood is one of the natural that causes many damages each year and always has been an attraction for experts in the field of hydrology. According to the Intergovernmental Panel on Climate Change (IPCC), the temperature of the Earth's atmosphere will increase in the next century, and one of the major impacts is the increasing in climatic extremes, such as droughts and floods. The intensity of the rainfall has a logical limit that is known as Probable Maximum Precipitation (PMP). The probable maximum precipitation is the highest rainfall that occurs over a specified period in a given area due to climatic and topographic conditions. Due to the climatic conditions in the Urmia Lake basin and mountainous area, as well as the significant changes in rainfall and temperature in recent years in the basin, it is necessary to forecast and control flood beforehand. Therefore, the aim of this research is estimating of the homogeneous climatic regions in terms of the characteristics and calculates the probable maximum precipitation for those regions and determining the maximum annual flood and flood points in the basin.

The probable precipitation is investigated by using synoptic stations data in Urmia Lake basin.. Two statistical and synoptic methods are used for comprehensive study over the region. In method, atmospheric information of upper layers such as, relative humidity, temperature, storms, wind, dew point have been used. In statistical methods, the probable maximum precipitation is calculated according to the climatic characteristics of the area. The statistical methods presented in this study are Hershfield and the maximum probability methods. By definition of the maximum probability method, the maximum probable precipitation is obtained by maximizing the maximum 24-hour precipitation at each station. By using probabilistic distribution, the probable distribution of the maximum 24-hour precipitation rates of the station is fitted and the best probability distribution for these data is determined.In this method, by using probabilistic distribution, the probable distribution of the maximum 24-hour precipitation rates of the station is fitted and the best probability distribution is determined for these data.

In the Hershfield statistical method, the frequency factor K and the probable maximum precipitation for24-hour were calculated for each station. According to the results, the probable maximum precipitation rates of the stations vary from 35 mm for Sarab to 89 mm for Oshnavieh. In the maximum probability method, the most suitable probability distribution is fitted to the maximum precipitation data for 24-hour of each station. The purpose is to use the fitted distribution to obtain the maximum values of the maximum rainfall. According to the results of this method, the probable maximum precipitation values vary from 45 mm for Sarab to 134 mm for Oshnavieh. In addition the spatial pattern of probable maximum precipitation is presented by the values obtained from each station. In the method of estimating the maximum probable rainfall for 24-hour, first the storms were identified and maximized in Urmia Lake basin. In order to determine the maximum rainfall for 24-hour, the amount of precipitation occurred on the corresponding date at the same station is multiplied to maximizing factor. Maximum of these values in total dates is concerned the probable maximum precipitation for each stations. According to the results, the probability maximum precipitation is 118.8 mm, which can be considered as the maximum probable rainfall of the basin.

The results show the consistency of statistical and synoptic methods. The maximum probability has the best estimation than Hershfield and synoptic methods. Distribution of probable maximum precipitation obtained from the statistical methods and the results of the probable maximum precipitation of the stations, shows that the eastern basin of Urmia Lake has a lower probable maximum precipitationthan the other areas. The maximum amount of probable maximum precipitation is in the southwest of the basin in Saqqez, Mahabad and Oshnavieh.The maximum probable precipitation is obtained 102 mm by storm estimation method calculated for Tabriz station where located in Aji Chai basin for the 100-year continuity period. This reserch is consistent with the previous studies (Azizi and Hanafi (2010)) which used synoptic methods to estimate the maximum probable precipitation in Aji Chai basin and showed the maximum probable 24-hour precipitation 84.5 and 103.9 mm respectively for the 50 and 100 year continuity periods.

On February 1-6, 2014, a large snowstorm occurred in Iran, especially in the northern regions of the country. Snowfall in northern Iran was unprecedented in the last 30 years and caused extensive damage. Synoptic study of snowstorm phenomenon showed that there is a surface high pressure system in the north and a dynamic low pressure system in the east and southeast of Iran in the sea level pressure map and a deep through at the level of 500 hPa. The 925hpa wind map shows that the north and northwest winds throughout Iran cause cold air to enter from higher elevations. At 300hpa, the core of a western jet is located in central Iran and there is a northern jet north of Iran's borders. Maps of sea surface pressure anomalies show an unprecedented increase in pressure in northern Iran, as well as surface temperature anomalies show a sharp decrease in temperature throughout Iran, especially in the northeast. Examination of cross-sectional area of wind, temperature and relative humidity showed that in addition to large-scale and thermodynamic factors, the presence of a cold front in eastern Iran is another reason for rainfall in this region. Comparison of the output of the WRF model with the maps related to the mentioned date shows that the model simulates the synoptic pattern of the region in this sample well, although the intensity of the systems is slightly less than the real value. Examining the precipitation output of the model, it can be concluded that the model has calculated the height of snow and the extent of the areas covered by it much less than the actual values, but the main error is in determining the type of precipitation.

In this study, First, the snowstorm from 1 February 1 to 6 February, 2014 is evaluated synoptically and thermodynamically by using ERA5 data with an accuracy of 0.5 degrees. The European Climate Forecast Center (ECMWF) presents its forecasts at 37 pressure levels from surface (1000 hPa) to 1 hPa. ERA-Interm re-analysis data was replaced by ERA4 data, which provides a new, higher-quality atmospheric quantity analysis (Di et al., 2011; Francis et al., 2019). In this study, the mean sea level pressure, geopotential height at the level of 500 hPa, 850 hPa and 925 hPa along with zonal and meridional winds at several levels with a spatial resolution of 0.5 degrees were used.Also, snowfall data, maximum and minimum temperatures of several synoptic stations located in northern Iran were obtained from their SYNOP reports, which well indicate the severity of the storm and its extent. Then, in order to simulate this event, the WRF model with a horizontal accuracy of 30 km and 30 vertical levels was implemented from 00 UTC on February 1 to 00 UTC on February 6, 2014. For the initial and boundary conditions of the model, GFS analysis data were used with an accuracy of 0.5 degrees.

In February 2014, between 1 and 6 February, heavy snowfall affected most parts of Iran, especially the northern regions of the country. Investigation of the Earth's surface synoptic map showed that a high-pressure system is located in the north and a dynamic low-pressure system is located in the east and southeast of Iran, which over time strengthens the surface high pressure and its tabs extend to central Iran and push back the low pressure. Also, the existence of an occluded front is obvious in the north of Afghanistan, followed by a cold front in eastern Iran. At the level of 500 hPa, there is a deep trough that moved cold air from higher latitudes and high surface pressure that causes this cold air to fall to the ground. On the other hand, if the humidity has risen to the mid-levels of the atmosphere, the necessary moisture will Provide for rainfall. The 925hPa wind map shows the north and northwest winds blew throughout Iran, which cause that cold air entered from higher elevations.The existence of a cold front in the east of Iran was confirmed in the synoptic map of 850hPa level and the intersection of geopotential and isothermal height bands, as well as the cross-sectional area of the meridional wind, temperature and relative humidity components. The change of wind direction from south to north wind, extreme temperature gradient, slope of isothermal lines and extreme relative humidity gradient are all signs of the presence of the front that has been observed in eastern Iran. Therefore, it can be concluded that in addition to large-scale and thermodynamic factors, the presence of a cold front can also be another reason for existence of precipitation.Comparing the output of the WRF model with the maps related to the mentioned date shows that the model has simulated well the synoptic pattern of the region in this case. The output of the model in the ground map has obtained the surface high pressure in the north of the country and low pressure located in the eastern and southern half of the country and has shown the pressure gradient in the center of Iran well. The model also shows the low-altitude and high-altitude geopotential centers of different compression levels similar to the real maps, and the well has a level of 500hpa and even the tilt of its axis to the east. The comparison of the maps shows that the intensity of surface high pressure, high altitude of 850 hPa and 500 hPa is slightly less than the real value. The WRF model obtained the height of snow and the extent of the areas covered by it far less than the values reported from the stations. By examining the amount of rainfall obtained by it, it can be concluded that the main error of the model is in calculating the type of precipitation that maybe is in result of error in calculating the temperature

Greenhouse gases emission cause the rising average temperature of the Earth and has disturbed the global and local water cycle (IPCC, 2007). Precipitation is one of the most important climatic variables, which has been affected spatiotemporally by climate change. Its effects are not uniformly influenced the terrsitrial areas. Changes in the number of rainy days, extreme statitics of precipitation (and their variation of mean and standard deviation), etc. are those reported consequences of climate change over the world. The aim of this study is not to analyze the significances of climate change on the precipitation patterns and its extreme behavior, and which stations would be behaved in the projected future climate change scenarios farther from their historical pattern. The implemented methods are briefly explained in the following.

To assess the effects of climate change on distribution of precipitation in Iran, the downscaled precipitation over the network of 288 rain gauge stations have been adopted from Pahlavan et al. (2018), which are scattered over different areas/provinces on Iran. They used CanESM2 GCM model and statistically downscaled the precipitation values to project future climate with three different scenarios according to the various GHGs emission levels. The outputs of the current research are used to investigate the effects of climate change on precipitation distributions and their extreme values. To achieve the goal, three different steps have been performed. In the first step, the stationarity of precipitation was examined both in the historical and projected future scenarios via the Mann-Kendal test (Kendall, 1948; Mann, 1945). In the second step, the deviation of precipitation distributions in each scenario from their historical periods was determined. To assess the issue, three divergence methods were performed which are known in the literature as Energy Distance (Székely & Rizzo, 2013), Kolmogorov Smirnov test (Massey, 1951), and Jenson-Shannon divergence (Fuglede & Topsoe, 2004). Finally, the annual maximum precipitation values (in each period and scenarios) have analyzed via GEV distribution to examine how would be distributions of the extreme values of climate change scenarios in Iran. In the follow, the results are described in brief.

The results of stationarity analysis showed that in the historical period, there are some stations (5.5% of them) with non-stationary behavior. As reported in the previous report (Kottek et al., 2006), these stations are located in warm and dry areas, as well. The stationarity tests of the projected future scenarios show the share of non-stationary stations increases as well as the RCPs. According to the results, the portion of non-stationarity stations of the projected climate change scenarios (2.6, 4.5, and 8.5 RCPs) are increased up to 13%, 22%, and 56% of the whole stations, respectively.In the next step, three divergence metrics have been used to evaluate the future climate scenarios, and the results showed that the stations in the northeast and southwest of Zagros Mountains are more diverged from their historical distributions. Comparing the historical and future scenarios of climate change, this is worthwhile to mention that with increasing the GHGs level, the deviations of precipitation patterns grow up. Calibrating the GEV distribution over the historical and evaluation of different return period values, positive trends of precipitation statistics (mean, mode, and range of extreme values) are obviously detected.

In this study, the patterns of precipitation distributions over Iran both in historical and future climate change scenarios have been analyzed. The results of trend analysis via Mann-Kendal test showed the same increasing trends of precipitation and GHGs level. So, the divergence methods were implemented to analyze the distance between rainfall distributions. The results showed some stations are more sensitive than the others and have more divergence from historical distributions. The extreme values of the recorded precipitation also analyzed using GEV distribution showed for a certain return period, the carbon emission level is directly correlated with the means and standard deviations of the extreme values. In conclusion, the results of this study showed that the increasing the level of emitted GHGs forces the statistical distribution to behaved more chaotic than the historical period. This makes the extreme values higher and more frequent than before. To further investigations, detection and attribution on Iran can be used to reflect the reality of national climate change and variabities. Also, considering the potential of climate change and climate classes, future (and probable) climate change patterns can be examined using the results of decreasing temperature and evaporation scales.

Flood is one of the most damaging natural disasters that is always accompanied by economic losses and in some cases human casualties and with the highest relative frequency of natural disasters in the world (around 40%) causing extensive and even homelessness, and there have been a lot of migration. Compared to other countries in the world due to environmental diversity, Iran has a high rank in the crisis caused by natural disasters, of the 40 natural disasters in the world, 31 have occurred in Iran. According to the FAO report, Iran ranks 10th in the world in terms of talent and potential for natural disasters. Statistics show that not only has the number and severity of floods been increasing in recent years, but economic, social and environmental damage is also increasing. Population growth is the occupation of floodplains and inappropriate land use, including causes of flooding. Therefore, damage reduction and flood control are important priorities and require more attention and resource management to ensure sustainable development. In addition, severe climate change in the form of global warming has led to changes in temperature and precipitation patterns and climate change in most parts of the world. In this regard, human activities such as deforestation and grazing by livestock have destroyed the vegetation of forests, rangelands, and reduced the appearance of water intake in these areas, with little rainfall and large amounts of runoff. In order to prevent or reduce flood damage in one area, it is necessary to anticipate severe rainfall by taking specific measures and establishing a local flood alert and risk management system. The area reduced the severity of floods and their damages. In this regard, using synoptic analysis on meteorological maps, it is possible to identify the patterns leading to floods and to predict them before they occur and to minimize the damages. Therefore, in this study, the statistical and synoptic analysis of the flood system in the south and south east of Iran in January, 2020 was investigated. This study focuses on environmental circulation analysis method, so that synoptic patterns of this phenomenon are identified based on flood event. For the purpose of statistical and synoptical analysis of precipitation system in the south and south east of Iran on January 11-13, 2020, statistical data of precipitation were first obtained during the activity of this system. Then to investigate atmospheric patterns and their behavior, synoptic maps of sea level pressure, geopotential of 500 hPa, relative humidity of surface and level of 500 hPa, wind velocity at 850hPa, 700 hPa and 300 hPa,Omega and columnar precipitable water were analyzed from two days before the flood to one day after the flood. In this way, the synoptic pattern of the flood system was analyzed for four consecutive days at different levels of the atmosphere. Statistical analysis of the rainfall received from 11 to 13 January shows that during this period there was a wave of precipitation throughout the country which resulted in floods and floods in many areas, especially southern and southeastern areas of the country. According to the statistics analysis, the average rainfall in the mentioned period was recorded at 8.4 mm, which is an increase of about 4 mm compared to the same period (4.5 mm) Give. The highest rainfall occurred in Sistan and Baluchestan province in the period of 31.5 mm which is approximately 15 times its long-term average. After that, Gilan and South Khorasan province had the second and third highest rainfall with 20.7 and 15.9 mm, respectively. Also, the average rainfall during the operation of the system was 8.4 mm. The zoning of the percentage of precipitation deviation received in the system compared to the similar long-term average also shows that most of the changes are in the eastern regions of the country, especially in the southeast of the Iran, reaching more than 500%. According to recorded rainfall data in the south and southeast synoptic stations of the country, the highest cumulative precipitation within 24 hours was related to the two Qeshm (coastal) and Rudan stations in Hormozgan province with 176 and 222.8 mm, respectively. Precipitation is the highest 24-hour record during this system. These two stations had an increase in precipitation of 167.7 and 82.8 mm, respectively. Synoptic analysis also showed that on the day of precipitation (January 11st) a deep trough with north-south direction continued from the Caspian Sea to the southern part of the Arabian Sea, extending southward into the south and south-east areas of the trough region. The country should be positioned at the front of the trough and below the positive horizontal rotation zone, providing for the extreme instability and flooding in the area. Also on this day, the rule of a low-pressure center in south and south-east of Iran and two high-pressure centers on Afghanistan and eastern Turkey caused a strong pressure gradient in east and northwest of Iran. Due to the geopotential elevation map of Level 500 hPa this day, the low-pressure area and lower-level convergence deployments, especially the low-pressure convergence located in southeastern Iran and the high-pressure over Afghanistan and Pakistan in front of the deep middle slope and slope High pressure between low pressure and high pressure stations located in the region causes large volumes of moisture and heat to be transmitted to the lower surfaces, especially from the Indian Ocean and Oman Sea to poison.

Water demands will increase in the next 50 years due to 40 to 50 percent population growth and the expansion of industries and cities, According to the World Water Association report. This demand will be significant for countries with a water deficits. Water demand for Iran by 2025 will increase by 110% of extractable water resources. With the current climate of the country, access to this volume of water seems impossible. One way to reduce reliance on water resources is to collect rainwater. Rainwater harvesting systems have been adopted in many parts of the world, especially in arid and semi-arid regions, as a practical way to minimize the risk of drought. Because rainwater can be easily collected without special tools and can be used for non-drinking demands. In this study rainfalls that cause to creation of runoff from autumn to late spring in Ahvaz city were investigated.

Two sites were considered for this research. The first was located in the department of parks and green space organization in the south of Ahvaz with the roof area of 115.6 m2, and the second was in the north of Ahvaz town, with the roof area of 35 m2. The roof of both sites had Waterproof insulation levels. The roof runoff of these sites was transferred by gravity from the outlet point to the water storage tanks through a pipe.The tank volume was designed for the first point with a capacity of 2100 liters of polyethylene and the second point with a capacity of 220 liters of plastic (polypropylene). Reservoir volume design was calculated based on 3 components: the average maximum rainfall of the region (December), roof level and runoff coefficient of 0.7. Tanks were installed and calibrated under the outlet pipes of the respective roofs. The volume of water collected at midnight of every day during the test period (in case of rain) was read and the tank was emptied for sampling the next day. On days when the rainfall was more than the volume of the tank design; To prevent the tank from overflowing, the tank water was drained and recorded during the rain. Sampling was started from October 2018 to June 2019. Rainfall data was received from Ahvaz Synoptic Station. The calculation of the roof area of the residential units was based on the urban area houses calculated and was compared with the area of the roofs obtained from Google Earth.

The rainfall statistics reported in Ahvaz by the Meteorological Organization of Khuzestan Province showed that from October 2017 to June 2018, there were 61 rainy days. There were 18 rain events in autumn, 28 events in the winter, and 15 ones in spring. The average runoff coefficient in Ahvaz city was obtained respectively 0.759, 0.711, and 0.797, for autumn, winter, and spring seasons. The runoff coefficient for autumn, winter, and spring seasons were reported 0.66, 0.69, and 0.62 in Mashhad, and 0.75, 0.76, and 0.69, in Noor city, respectively. This difference can be due to the difference in rainfall regime, the type of roof insulation, the slope of the roof, and so on. Also, the results showed that with the extraction of rainwater from the roof surfaces of Ahvaz in the months of October to May, respectively, 2.24, 8.4, 21.2, 14.85, 13.65, 9.17, 7.44, and 4.03% of the domestic needs (except for drinking and cooking), 16.85, 63, 100, 100, 100, 68.84, 55.77 and 30.2% of the public needs, 8.43, 31.5, 79.61, 54.68, 51.19, 34.42, 27.88 and 15.10% of the commercial and industrial needs and 1.06, 4.48, 13.17, 8.88, 7.66, 5.05, 4.3 and 1.88% of the greenhouse needs can be supplied.

The results showed that in the city of Ahvaz, 75% of the rainfall has led to runoff production. The runoffs that can be extracted from the roofs were the most share in December and winter months and the last one in June, July August, and September in Ahvaz. The highest amount of rainfall in Ahvaz, which did not lead to runoff, was 0.2 mm. In other words, rainfall that is less than this amount does not lead to runoff. Also, the lowest amount of rainfall in Ahvaz, which has led to runoff, is 0.3 mm. Therefore, the probability of the threshold of runoff in Ahvaz is 0.3 mm. In other words, rainfall that is more than this leads to runoff production. Also, the results obtained from this study showed that in December, January, and February, water that can be extracted from the roof surface, in addition to providing 100% of the city's general consumption needs in these 3 months, can save 416.14 million liters, Which will be usable in other months. Due to the lack of measurement of quality parameters, the collected water is not recommended for drinking.

With the accelerating rate for the development of human communities in form of urban and rural residential areas, necessity of forecasting and modeling various aspects of natural and human hazards, ensuring associated with controlling the risk of various hazards and other management measures in order to reducing their harmful effects among urban and regional planners and managers has been increased. The flood phenomenon is the one of the hazardous disaster that dictates mortal and economic losses on many people every year all around the world within rural and urban habitats. With regarding intensifies and frequencies of flood events, the global and scientific associations concerns have been increased about its consequences and damages.So far in current study an efficient and different method for analyzing this natural hazard have been developed and proposed. First, based on a literature review and consultation with experts and scholars aware of the particular circumstances of the under studied area, the required data and information were gathered from various resources such as Iran National Cartographic Center, Tehran province water and wastewater company, the satellite remotely sensed imageries and Geological Survey & Mineral Explorations of Iran (GSI) and after preparing and carrying out the required reforms, they were entered into environment of Geographic information system (GIS). The approach of this study is based on using the spatial analyst functions and tools within GIS for manipulating the data of hydrometric and rain gauges stations, the remotely sensed satellite imagery and thematic layers in the bed of Artificial Neural Network (ANN) algorithm for spatial modeling of flood in the basin of Kan river located in the west northern part of Tehran province. In the next step for achieve the goal of study, the optimal architecture of neural network has been designed and developed based on trial and error and some of the recommended relationships developed in the earlier studies. Using seven inputs layers including the thematic layers of land slope, curvature, accumulated flow, Normalized Differential Vegetation Index (NDVI), geology units, soil classes and the daily precipitation data and one target layer including recharge values of stations within under studied area along with eight and sixteen neurons dedicated to the first and second hidden layers respectively and at last one neuron for the desired output that shows the daily discharge of stations, the spatial modeling of flood incidence were performed. The designed methodology were applied for forecasting and simulating the daily recharge of hydrometric stations then based on the highest recorded accuracy and using the inter-neurons weights between the first two layers of the accomplished network that were applied for weighting overlay of thematic layers, the map of flooding potential was created. The produced map indicates the susceptibility of lands within Kan basin to flood event that generally the northern areas and lands around Kan river have the highest potential for flood hazard. The resulted relative importance for input layers from running Artificial Neural Network were indicate input layer of the daily precipitation data has the highest weight that followed by vegetation layer input for weighting overlay and assessing the flooding potential in form of the continuous surface within the studied area. The main reason of that is the more dynamic and variability of the above mentioned inputs and so their more importance in incidence of flood in comparison with the other input layers. The parameters of R2, RMSE and MAE were computed for the evaluation of the accomplished method efficiency and accuracy. The algorithm of artificial neural network in forecasting daily discharge with values of 0.82, 0.13 and 0.18 and maximum discharge with values of 0.84, 0.12 and 0.16 for R2, RMSE and MAE respectively reached acceptable outputs. Generally the applied ANN method in assessing the maximum discharge that has a very high correlation with flood occurrence had the more accurate results. The results of this study can be applied in two type for spatial modeling of flooding consist of discrete and continuous forms for the future environmental planning in regional scale as a possibility for improving crisis and environment hazards management. Generally the current study demonstrated that the joint application of the GIS spatial analyst functions and tools with artificial network have the high capability for predicting the potential of natural hazards occurrence like flooding.