saeed movahedi

Water surface temperature in open seas and lakes is known as one of the indicators for measuring oceanographic and meteorological characteristics. In the present study, the data from the AVHRR sensor (1989-2019) have been used to analyze the temporal and spatial changes in water surface temperature of the Persian Gulf and Oman Sea. Average (monthly), seasonal, and annual temperatures were obtained from daily data in ArcGIS 10.8 software. Then, the trend of water temperature changes in the Persian Gulf and the Oman Sea compared to the Caspian Sea was investigated. The results of the water surface temperature data analysis showed that the water temperature in the Persian Gulf and the Oman Sea decreased in the northern part of the Persian Gulf in the spring, but in the summer, the temperature decreased in the central part of the Caspian Sea. In autumn and winter, the temperature drop is affected by the latitude. While the southern parts of the Caspian Sea, especially the southeast, are the hottest areas, the Middle Caspian has a lower temperature than the rest of the regions. In the spring season, the temperature decreased in the Middle Caspian, and an increase in temperature was observed towards the upper and lower latitudes. While in the autumn season, a decrease in the water surface temperature was observed in the Persian Gulf and in the winter, in the Oman Sea. The monthly temperature trends for the Persian Gulf and the Oman Sea are increasing for 12 months. In the Caspian Sea, the trend of temperature changes in January and December was decreasing and increasing in the rest of the months.

The decrease in the level of Lake Urmia is evidence of climate change and anthropogenicity. This decrease in level has led to an increase in salt area, salt storms and salinization of groundwater. It is one of the major environmental challenges in northwestern Iran. Fluctuations in lake level, decrease in water level of plains and decrease in river discharge are evidences of hydrological changes in Urmia Lake basin. The present study tries to provide a clear picture of the water changes of Urmia Lake during the last three decades. Hydrological data and images of Landsat satellite for Urmia Lake basin in the period 1984-2017 were studied using remote sensing and statistical methods. The classification of satellite images was performed using the maximum likelihood method. According to the results, the highest decrease in the area of the lake between 2001 and 2013 happened. Also, the analysis of the results showed that along with the very important role of global warming on the water volume of Lake Urmia, humans have been able to be one of the most important regional factors in creating the challenge of Urmia Lake. In fact, the water problems of Urmia Lake, especially after 2001, are a combination of climatic and anthropogenic factors.

Introduction:

Climate change will affect all sectors of the economy to some extent, but the agricultural sector may be the most sensitive and most vulnerable part because agricultural products are highly dependent on climate resources, And according to scientific evidence, future climate change, especially the combined effects of elevated temperatures and elevated atmospheric CO2 concentrations, will have a significant impact on crops (droughts, floods, frosts) on crops( Chiottioud ,1995). The general effects of climate change on crop development vary depending on the plant and study area (Rawlins, 1991), and commenting on the response of different species to climate change requires case studies.

area of study The study area includes west and northwest of the country Uncovering Climate Change in Past Times Climatic data of maximum temperature, minimum temperature, 30-year historical rainfall (1988-2008) were obtained from 23 stations in the west and northwest of the country And Using two nonparametric tests Mann-Kendall and Estimator slop sen, the trend of precipitation and temperature changes was investigated in order to detect climate change phenomenon in the region. Generating climate scenarios in future periods To assess future climate change in the west and northwest of the country, the CCSM4 general circulation model under RCP4.5 scenario is one of a number of new RCP emission scenarios that the Climate Change Intervention Board will develop in its Fifth Assessment Report (AR5) as representative of the linesVarious concentrations of greenhouse gases have been used Predictive model of wheat phenology and yield unctional and phenological data for three years (90-93) with 40 climatic parameters (Table 2) from seven stations (Zanjan, Arak, Sararood, Maragheh, Ghamloo, Ardabil and Orumieh) containing performance and phenology data Was prepared Then, using these data, performance and phenology in the baseline and future period were predicted through simple linear regression, multiple regression. The results consisted of 20 regression models The best model was selected based on R-squard index using RMSE.

February, March, September, and the year are at 99 percent confidence levels, while January, June, July and August are at 95 percent confidence levels. As well as total rainfall, both the upward and downward trends have a significant and decreasing trend at 95% confidence level only in January and March. Changes in temperature and rainfall in the coming period The results of climate change assessment at each of the stations in the future climate show that the mean maximum temperature in the future climate has increased at 14 stations compared to the previous climate and decreased at the other stations. The mean minimum temperature in the future climate has increased in all the stations except for Ghamloo and Sanandaj stations compared to the previous climate. Average temperature also increased at all stations except Ahar, Zarineh, Sarab and Ghomloo stations in all stations compared to past climates Average mean precipitation in all stations excluding sarpolzahab station in future climates It increases with the past climate Impact of Climate Change on Phenology nder the climate change, the length of the flowering stage of the wheat in the future climate will be shorter than in the previous climate, so that the flowering stage length in all the studied stations with the exception of Zarrineh station in the next period (2039-2018 ) Is shorter than the average long-term flowering stage of wheat. he mean flowering stage duration in the basal climate is 136 days, whereas the mean flowering stage duration in the future climate is 118 days, ie the average flowering stage duration in the future climate is 18 days short. Increasingly, the shortening of the flowering stage in future climates is due to the increase in average temperature in May and April and average maximum temperature in December. Impact of Climate Change on PerformanceUnder climate change, wheat grain yields will increase in future climates than in the past, so that at all stations with the exception of Mahabad station, wheat grain yield is higher than the long-term average in the past. verage wheat yield in the past climate was 1863.3 kg / ha but in the future climate it would be 2529.9 kg / ha. Wheat yields are up 35 percent due to the favorable future climate in the region.

tudies show that precipitation in the west and northwest region of the country during the past period has been decreasing and the temperature is increasing in most months of the year. And in the coming period the temperature in all months of the year until the end of 2039 shows an increase of 2.5 to 3.5 ° C. The flowering period is also 136 days for the previous period but 118 days for the next period shortening of flowering stage of wheat plant is due to increase in average temperature in May and April and average maximum temperature in December as well as increase in precipitation in December and February. Comparison of wheat grain yields of the current and past periods showed that wheat grain yields will increase by 35% in the future, due to the increase in average March and April temperatures and average January and March minimum temperatures. It also saw an increase in the average precipitation of February and March in the next period compared to the previous period.

Lake Urmia is the largest interior lake in Iran. This lake, as an effective ecosystem unit in northwest of Iran, highly contribute in the ecology of the region. The water level of Lake Urmia has dropped between 6 and 7.40 meters that has reduced the area of water zone. This reduction has leaded to increase in salty marsh, since1995. By May 2017, the water level was 3.11 m lower than the ecological balance. Expansion of salt marshes is due to successive droughts and an increase in water harvesting from the surface and ground water sources. This phenomenon can promote dust and salt storm occurrence, and lead to the death of the lake ecosystem, compulsory migration and population health threats of more than 10 million people. In order to optimal long-term planning and management of the available water resources, a better understanding of the temporal and spatial variations of water balance components (especially actual evapotranspiration, surface runoff, and groundwater recharge) is essential. A review of various research findings around the world shows that the WetSpass-M model is a suitable model for the spatial simulation of surface runoff, actual evapotranspiration and groundwater recharge in basins. The main aim of this paper was to analyze spatial distribution of annual components of hydrology cycle in the basin of Lake Urmia, using WetSpass-M model during 25 years (1992-2017). The method adopted in this study was analytic. Climatic, hydrological and land use data were applied in this analysis. Climatic and hydrological data were provided from meteorological stations, hydrometric station information and observed wells respectively. In this study, the satellite images and field studies were applied to determine land use. These images were downloaded from the site of EarthExplorer. The original Wet Spass-M model is a quasi-steady state spatially distributed water balance model scripted in Avenue and used to predict hydrological processes at seasonal and annual time steps. Since the model is a distributed one, the water balance computation is performed at a raster-cell level. Individual raster water balance in this model was obtained by summing up independent water balances for the vegetated, bare soil, open- water, and impervious fraction of each raster cell. The total water balance of the given area was thus calculated as the summation of the water balance of each raster cell. Precipitation was taken as the starting point for the computation of the water balance by each of the above mentioned components of a raster cell. The rest of the processes (interception, runoff, evapotranspiration and recharge) follow in an orderly manner. To validate the results of Wet Spass-M model, the coefficients of Nash-Sutcliffe Efficiency and RMSE-Observations Standard Deviation Ratio were used. The basin of Lake Urmia has been affected by many climate and human changes that have caused Lake Urmia Crisis. During the study period, runoff declined but temperature and evaporation increased. Land use has also changed widely. These changes included dry farming and rangelands convert to settlement and increase in area of irrigated farming. About 3043 km2 of lake area has been reduced and added to salt marsh. The average depth of ground water has decreased by 7.4 meters. The analysis of simulation results indicated that Wets pass-M model works properly to simulate hydrological water budget components in the Urmia Lake Basin. According to the results, in 1992, the highest runoff occurred in the western part of the basin with the highest rainfall, and all groundwater in the southern and western parts of the basin was well recharge. In the year 2017 most of the runoff and groundwater recharge was confined to the southwestern part of the basin. In 2017, Lake Urmia experienced higher evapotranspiration than the year 1992. In 1992, 58.42% of the basin precipitation was spent on evapotranspiration, 7.20% for surface runoff, 31.18% for groundwater recharge and 3.2% for interception. In the year 2017, these changed to 55.49, 1.55, 39.77 and 3.19%, respectively. Among the simulated components during the study period, the runoff has the highest coefficient of variation and the lowest groundwater recharge. Also during the 25-year statistical period, eastern parts of Urmia Lake (including: Ajab Shir, Azarshahr, Maragheh and Shiramin) had the highest coefficient of variation in all studied components. The southwestern parts of the basin were in better condition.

The aim of this research is an analysis of tempo-spatial variations of Cloud cover at Iran. To achieve the goal, Cloud Fraction parameter has been used from cloud product of MODIS sensor on Terra satellite at level 2 and version 6 (MOD06) in the period of 2000 to 2013. Given that, the Available parameters in level 2 MODIS cloud products have not geographical coordinate’s regular network, At first, the Cloud Fraction parameter data were transferred to a regular network, due to this process, Climate analysis on the data is possible, Base on new database the results showed that In annual scale, the trend variations in cloud cover increase at a rate of 0.02% per annual (2% per century) in the country. Cloud cover in the seasonal scale showed that the decreasing trend occurs in autumn and increasing trend in other seasons. Cloud cover in the monthly scale, in July, October, December, and January decreased and there is an increasing trend in other months. The spatial distribution of cloud cover in the long-term annual trend showed that there is decreasing trend in the North East, North West and sporadically in the central regions and an increasing trend in the South, Southeast, and East of the country. Spatial distribution of seasonal trend showed that most parts of the country have been with the positive trend in autumn and the negative trend in winter.

  All studies in the field of climate change impact assessment needs climate data with different spatial and temporal scales. The lack of temperature and precipitation data with high spatial resolution is a major limitation to analyzing future climate change. In addition, the output of the models has error that needs to be corrected; otherwise they will make a significant bias for assessing effect of climate change. Therefore, identifying the best regional climate model for downscale the global climate models is essential to better understanding of climate conditions in the local and regional scale. In the last few years, using various regional climate models in order to produce a multi-member set of the downscaled data in the CMIP5 project by World Climate Research Program (WCRP) in action with Coordinated Regional climate Downscaling Experiment (CORDEX) with the aim of producing regional climate change forecasts, was established as an input to researches on the impacts of climate change and ways for adaptation to it. The main objective of this research is accuracy evaluation of different model outputs of the CORDEX project with different domain and resolution in Iran. In the CORDEX project there are two domain that covering Iran. These two domains are North Africa-Middle East (CORDEX-MNA) including latitude of 7° S to 45° N and longitude 27° W to 76° E and South Asia (CORDEX-WAS) that includes latitude 13° S to 44° N and longitude is 27° E to 107° E (Figure 1). To do this research, first daily output of precipitation, maximum and minimum temperatures in the period of 1990-2005 for three regional climate models with a special resolution of 0.22 ° and 0.44 ° that performed by three international meteorology institutes, available at ESGF web site (Table 1). Daily observation data that recorded in 304 synoptic stations in Iran for the three variables were collected from Iran Meteorology Organization and transferred to a matrix with 3044×5844 dimensions. Then, several scripts were written in the MATLAB software for extract the model data in the domain of Iran and compare output model and observational data with two conditions. The first condition is in the output models resolution of 0.44° (spatiotemporal matrix with dimensions of 5844×740), the observation station should have a distance of less than 25 km, and the next condition is in the resolution of 0.22 ° (spatiotemporal matrix with dimensions of 5844×3218) should have a distance of less than 12 km. The difference between observation data and its corresponding estimated cell were investigated with statistical method such as Mean Error (ME), Pearson Correlation Coefficient, Root Mean Square Error (RMSE) and Standard Deviation (SD). Also, we were used Box-Whisker plots and Taylor Diagram to find the best regional climate model.
Result and The precipitation accuracy of regional climate models output presented by different meteorological institutes (Table 1) was evaluated with observational data in two domain, CORDEX-MENA and CORDEX-WAS, in Iran (Fig. 4). The calculation of the outputs mean error of different models showed that none of the models have a suitable estimation of precipitation values in research domain. The HadRM3P model shows the lowest RMSE calculated with comparing to observational data for the maximum temperature across Iran except the central parts. However, for the minimum temperature RegCM4.1 model shows the lowest difference with compare to observation data in most parts of the research domain. For annual precipitation using the Box-Whisker plot, which compares the correlation coefficients between the observed data and the corresponding cells in the northern and southern half of Iran, in general, none of the models have an accurate estimate of precipitation in Iran (Fig. 8a). This plot for different models showed that the outputs of the HadRM3P and RegCM4.1 models, respectively, for maximum and minimum temperatures in most cells, have more than 0.8, correlation coefficient (Fig. 8b and c). The correlation of rainfall data shows that most models in the central and mountainous regions of Iran do not have high correlation coefficient with observational data. Spatial distribution of correlation between maximum temperature model outputs and observational data in Iran shows that the two HadRM3P and RCA4-WAS0.44 models have a strong correlation coefficient, respectively and changes in the correlation coefficient in the HadRM3P model are low in both the northern half and the southern half of Iran. The RegCM4.1 model had the stronger correlation in the northern half in compare to the southern parts of Iran. Also, the mean difference of estimated model output with observation data of this variable in the whole of Iran is less than 1°C and this model is the most appropriate model among the available models for minimum temperature in Iran

Global climate change is relatively depending on human activities which have important impacts on the environment. So the climate change is a very important issue in the recent age. Climate change is the impact on human lives so the climate change increase their temperature and also is very important global problem and monitoring of the climate conditions in early region is a good solution for combating the national housing regarding to climate change large floods and other disasters. There are different climate models and in the CORDEX projects there are regional climate were included in some domains. The main objective of the present work is to evaluate CORDEX-WAS with a spatial resolution of 50 kilometer compared with observational data over Isfahan province. At the end we are evaluating the creation the variation of the minimum and maximum temperature during 2017 -2050 according to model outputs for three models with 2, RCP4.5 and RCP8.5 emissions scenarios.

Knowledge of past climate needs long-term and accurate climatic data for future planning and predicting. In this study, we reconstructed the average maximum temperature in spring and minimum temperatures in fall and winter by applying the width of annual ringsofQuercusPersica through multiple- regression. With this goal in mind, two growth heights were selected in Dena Forests and 40 growth samples from 20 bases in two geographical directions of southwest and northeast were extracted at breast height and measured with AutoCAD software with an accuracy of 3 microns. After cross dating stage, to eliminate non-climate effects, all climatic parameters and tree rings time series were standardized. The calculated Residual Chronology (RES) was calibrated with climatic variations of the period 1881-2011 and positive and significant correlation with the width of growth rings was confirmed. Based on the relations and correlation between the calculated chronology and joint statistical climatic data the reconstruction of annual rainfall was performed and it was found that the average rainfall of the last three decades had a 4 percent increase in comparison to the average rainfall of the last century.