جستجوی مقالات مرتبط با کلیدواژه "temperature" در نشریات گروه "آبخیزداری، بیابان، محیط زیست، مرتع"

Many human civilizations have collapsed due to the climate. Historical and geographical evidence demonstrates that the majority of significant and stable civilizations were established in specific geographical-climatic areas with suitable biological conditions. Human civilizations have faced serious damages and collapses due to unfavorable climatic conditions. The existence of most human civilizations can be attributed to climatic conditions, with historical and geographic evidence, especially the special environmental condition of the world in the current situation. Countries that deal with biological, economic, social, and even political issues may not give special importance to this issue. The climate conditions around the world are constantly changing, and there will be severe problems in different dimensions in the not-too-distant future. Their existence could even be threatened by economic problems they will face. The world's population increase and energy demand in today's industrial world also result in damage to the planet's climate and atmosphere, as well as the effects of natural disasters. The human burden has not been spared and has been affected by many fundamental changes, of which the phenomenon of climate change is one of the clear examples. The most important indicators of climate change are temperature and precipitation. The change of each of these factors causes climate variability, which also has consequences on people's lives. The increase in greenhouse gases and suspended particles in the atmosphere, along with variations in the earth's surface, are among the most apparent consequences, and the damage caused by the world's industrial activities and human societies in the past two centuries. Different economic sectors, such as agriculture, industry, tourism, water, and health, are affected by this phenomenon. Based on what has been stated, it can be concluded that climate change will definitely occur in Iran. The agricultural sector will face significant challenges due to the occurrence of such things, given the climatic situation, hydrological characteristics, and limitations of the country's agricultural sector. It has created a unique climatic situation in Iran due to its location. The high-pressure rule in the subtropical zone causes the total amount of precipitation in Iran to be low.

The LARS-WG model is a model that is utilized for exponential micro-scale GCM models. This model is a great choice for generating random weather data, which can be used to generate rainfall, radiation, and maximum and minimum daily temperatures at a station for both current and future climate. In order to model meteorological variables, the LARS-WG model uses a complex statistical distribution. Modeling is based on the length of dry and wet periods, daily precipitation and radiation series, and semi-empirical distribution. Climatic parameters such as minimum temperature, maximum temperature, precipitation, and solar radiation are inputs for the LARS-WG model, which are all in the daily time frame. In this model, temperature is not taken into account when modeling radiation, and the sundial can be utilized instead. From the semi-empirical distribution of the rainfall for the month in question, and without taking into account the wet series or the amount of precipitation from the previous day, the amount of precipitation for one day is calculated. In this model, the temperature is estimated using Fourier series. The daily minimum and maximum temperatures are modeled as random processes using the average and standard deviation of the daily standards, which depend on whether the day is wet or dry. The mean and standard deviation of the seasonal temperature are simulated using the third-order Fourier series. Residue values are determined by subtracting the average values from the observed values, and the minimum and maximum data are utilized in time autocorrelation analysis. Minimum temperature, maximum temperature, precipitation, and radiation are the outputs of this model. LARS-WG model data generation involves three stages: calibrating, evaluating, and creating meteorological data. In the next step, using LARS-WG models under RCP 2.6, 4.5, and 8.5 scenarios and the micro-scale exponential LARS-WG generating model of Jiroft City climate changes during the planting period of each crop in the periods of 2011-2045, 2065-2046, 2066-2079 and 2080-2099. In the final stage, a positive mathematical programming model was utilized to investigate the impact of climate change scenarios on Jiroft City's planting patterns using predicted climate change results. The cultivation pattern of Jiroft city today and the predicted effects of climate change parameters during the periods 2011-2045, 2046-2065, 2066-2079, and 2080-2099 are examined in this section. The current cultivation pattern will be discussed in the previous step. A positive mathematical programming model and GAMS25 software have been employed in this regard. Separate information about the area under cultivation, production, and performance of selected agricultural products, as well as the consumption of inputs, for the agricultural year 2021-2022 is presented in the table.

The results indicate that the performance of selected products is significantly affected by the climatic parameters of temperature and precipitation. Also, by applying the forecast of climate variability in the cultivation pattern model of all selected crops in the periods 2011-2045, 2046-2065, 2066-2079 and 2080-2099 based on the noses of the HadCM3 model are affected by different climate scenarios. The improvement of agricultural productivity and climate change are both negative effects of this phenomenon. The findings of this investigation can be advantageous for agricultural planning and economic development in Hamadan province.

Long-term warming of the atmosphere, decrease in precipitation, drought and floods lead to the creation of climate change phenomena in natural and unnatural ecosystems. In addition, an increase in greenhouse gases, which cause the temperature to rise, also increases this phenomenon. Climate change affects different aspects of Mashhad city in terms of geographical location, tourism and tourism. In this study, using the CanESM2 model and the precipitation and temperature data of Mashhad station from 1961 to 2005, they were predicted in the near future from 2030 to 2060 and from 2070 to 2100 under RCP climate change scenarios. The results showed that the CanEMS2 model is in good agreement with the observational data and can be used to simulate the data in the future period. The simulation results show that the amount of precipitation will decrease in the near future, but not in the distant future. This parameter will also increase, but the temperature will increase in the future (near and far) and the phenomenon of global warming will increase. The results of this research can be used for environmental management and the services and industries that will lead to an increase in greenhouse gas emissions in Mashhad city can be used for proper policy and planning in order to reduce greenhouse gas emissions.

Sand covers around 20% of the world's drylands, and 97% of it is found in the sandy land of the arid region of the earth. Different landforms are created by the wind in these regions. Sand dunes are a landform that is mentioned in arid lands. Knowing the distribution of sand in these areas is crucial because wind erosion can cause a lot of damage. The significance of studying sand dunes lies in its impact on water and soil resources, plants and animals, human infrastructure, and roads. In Iran, various researchers and related organizations have reported various areas of the sand dunes. Different tools and methods have been utilized by these researchers, including aerial photos, topographic maps, satellite images, and field visits. This study was conducted to improve accuracy and update the existing maps due to the differences between the maps provided by previous researchers. By providing a new map of sand dunes distribution in Iran, more precise information can be obtained. Precipitation, temperature, evapotranspiration, and UNEP aridity index were used to calculate climate characteristics for Iran's sand dunes.

Remote sensing was used to export the sand dunes distribution map in this research using the Google earth engine system. The image collection of the OLI sensor of the Landsat 8 satellite was used. The process of image classification involved introducing 4000 points as training points. The land use map of Iran was exported after verifying and calculating the overall accuracy and kappa coefficient, which were 0.91 and 0.89, respectively. The map's sand dune class was imported into ArcMap. The maps of the distribution of sand dunes obtained from previous researchers were compared with the obtained map. The boundary of sand dunes was modified by drawing 1020 polygons using vision recognition. We aim was always to utilize the most up-to-date satellite images for visual interpretation. Due to differences in tools and study time, and the prepared maps based on different criteria, there are numerous differences between the maps in practice. The research utilized up-to-date satellite images and geological maps, topography, hand-planted forests, residential lands, agricultural lands, and land use as auxiliary maps. In the next step, the long-term climate maps prepared by Jamab's consulting engineers for the period of 1350 to 1390 were used to investigate climate characteristics such as temperature, precipitation, evaporation and transpiration and UNEP aridity index. The mentioned maps were interpolated and classified in ArcMap for this purpose. The distribution of the country's sand dunes was examined in each of the categories mentioned in the climatic indicators.

Based on the result, the updated area of Iran's sand dunes is 5.15 mha which includes the sand sheets and different forms of sand dunes such as barchans, star dunes and nebkas. Based on this, the provinces of Kerman, Sistan and Baluchistan, Isfahan, South Khorasan and Yazd respectively, with an area of about 1000000, 700000 and 590000 ha, have allocated the largest area of sand dunes in the country. There are some differences in the obtained results compared to the previous maps of sand dunes. In Dehlran city in Ilam province, some regions that were previously considered sand dunes have now become agricultural lands, for instance. The border modification resulted in a 366% increase in the area of Qaen city in South Khorasan province. According to climate data, sand dunes are distributed in three climates that are hyper- arid, arid, and semi-arid. The highest and lowest amount of precipitation can be found in Farashband city in Fars province and Hirmand city in Sistan and Baluchestan Province, respectively. The range of precipitation changes is ranging from 6 to 312 mm.  The hottest sand dunes are located in Shahrud city in Semnan province and the coldest sand dunes are located in Buin Zahra city in Qazvin Province. The temperature of sand dunes is in the ranges of 10 to 28.5 °C. the dominant thermal regime of sand dunes is thermic, which covers about 68% of the sand dunes in 8 provinces. Hyperthermic thermal regime dominates 31% of sand dunes in 7 provinces. Only about 1% of sand dunes have mesic thermal regime which are scattered in 3 provinces. The range of changes in evapotranspiration in the sand dunes of Iran is between 1500 to 4000 mm per year. Evaluating the UNEP aridity index showed that 89% of the sand seas are classified as hyper arid and 11% of them is in arid class. The result of this research by determining the location of sand seas and their climate classification, will help the administrators to locate appropriate areas for reclamation and wind erosion control projects, especially the biological project.

A relatively accurate prediction of the concentration of pollutants and environmental variables, short-term and long-term, is an important step in reducing the damage caused by poor air quality. In this study, first by spatiotemporal analysis and then, using prediction techniques, to predict the concentration of fine atmospheric particles (PM2.5), temperature and vegetation cover index (NDVI) on the trend of PM2.5 in a period of 5 years (2017-2022) was discussed at the level of Iran. The data of PM2.5 concentration, temperature and vegetation index were extracted based on MERRA-2, FLDAS and MODIS satellite models. In the five-year study period, a somewhat downward trend was observed for the air concentration of PM2.5. The results showed the lowest annual average concentration of fine atmospheric particles in 2019 and 2020. Also, a strong correlation between PM2.5 concentration and temperature was obtained. The highest average concentration of PM2.5 occurred in the northwest, west, and southwest of Iran. In the next step, in order to predict the future concentration of PM2.5 air particles, temperature and vegetation index, the Exponential Smoothing approach was used in the Python statistical library (Statsmodels) to model monthly time series. Evaluation of the models with two criteria of root mean square error (RMSE) and coefficient of determination (R2) was done to minimize the estimation error and find the most suitable model among the eleven predicted models. The results show that double exponential smoothing models are more suitable for predicting PM2.5 concentration and triple exponential smoothing models with Holt-Winter trend are more suitable for predicting temperature and NDVI data. This study can help public and private institutions to better understand economic, health and environmental condition affected by air pollution effects by predicting the period when air pollution levels may be particularly high.

Climate change can lead to changes in the frequency, intensity, and duration of extreme climate events in different parts of the world. The purpose of this research is to investigate temperature and precipitation extremes in Lorestan Province. The data used in this study included precipitation and the maximum and minimum daily temperature of nine synoptic stations in Lorestan Province during a 28-year (1990-2017) common period. The matrices of minimum and maximum temperatures and precipitation daily data for each station were prepared and used to compute the extreme climate indices (26 precipitation and temperature extreme indices based on the recommendation of CLIVAR \ CLL expert group) using the R programming software. The results of studying the trend of cold and hot extreme weather indicators during the period 1990-2017 in the province using the Mann-Kendall trend test showed that for all stations, the hot indices have increased and more cold indices have decreased. In different regions of the province, positive and negative trends of hot and cold indices with different intensities have occurred. The highest upward trend of the warm extreme indices has occurred for the hot night’s index. Among the cold indicators, the greatest decrease occurred for several frost days and cold days indices. The decreasing trend of ice days is significant for 45% of stations at 99% level and the decreasing trend of cold days for 77% of stations at different levels of 90, 95, and 99%. The results of the study of the frequency of occurrence and trend of precipitation extreme indicators in Lorestan province showed that the total rainfall of this province, like many regions of the country, has decreased. In contrast, the occurrence of maximum rainfall in addition to being significant in the province has an increasing trend during the period 2017-1990. These conditions can indicate an increase in the number of occurrences of heavy and short-term rainfall events and, shorten the period of the rainfall season in the region.

Iran's location in the drylands belt has increased the frequency of dust storms in western parts of Iran, particularly the city of Kermanshah and negative environmental impacts. Arid lands, as one of the main sources of suspended dust in the atmosphere, face problems such as sandstorms, high levels of dust particles and reduced visibility, which are major climate problems in the country, especially in the border provinces. The purpose of the present study is to investigate the performance of a numerical model between the meteorological-chemical atmosphere scale called WRF-Chem model in the simulation of the concentration of suspended particles in Kermanshah region. By comparing the spatial distribution and concentration of suspended particles, meteorological parameters simulated by the model, and the available observational values for PM10 particles in Kermanshah, the efficiency of the WRF-Chem model was evaluated. The results of the simulation of PM10 particles for the studied days showed that the central and western deserts of Iraq, Syrian desert, Kuwait and northern Saudi Arabia are the main source of dust storm. Due to the logical correlation between dust particle emissions and temperature and relative humidity parameters, accurate estimation of these parameters is very effective in PM10 particle simulation accuracy. Based on the analysis of the PM10 variables, temperature and relative humidity and the plotted graphs and their comparisons, a favorable agreement was achieved between the simulated and measured values for PM10, temperature and relative humidity.

Changes in the climate system's components, and in particular the severity and frequency of extreme events, are more effective on society and the natural environment than changes in climate averages. Therefore, it is important to examine the variability of limit indices. To this end, this study used the temperature and precipitation data collected from six meteorological stations in Ahvaz, Khorramabad, Dezful, Kermanshah, Hamedan, and Sanandaj Karkheh synoptic basins to investigate the spatial and temporal trends of temperature and precipitation limit values, which were then measured using R-climdex software. Moreover, the trends of all ten precipitation indices and sixteen temperature indices were determined individually via the Mann-Kendall test. The study's results indicated that the trend of precipitation indices was decreasing in most of the watershed stations, leading to a significant decrease in the trend of precipitation indices on wet, heavy, and very heavy precipitation days, and consecutive wet and/or dry days. The results also suggested that temperature indices such as frost days, ice days, cold days, cold nights, and the range of daytime temperature variations revealed negative trends at most stations at 95% and 99% confidence levels. Furthermore, increasing trends were found for the indices of summer days, hot days, and hot nights at all stations studied at the catchment level.

This study used the data collected from six Karkheh watershed's synoptic stations, including Kermanshah, Khoramabad, Sanandaj, Hamedan, Dezful, and Ahvaz to study the spatial and temporal trends of temperature and rainfall in terms of maximum temperature, minimum temperature, and rainfall at a daily scale during a thirty-year period (1980-2010). It should be noted that the data such as quality control and their homogeneity was pre-processed using the RH Test program. Then, the data was converted to the R-climdex model format to identify extreme indices.The output of the model was used to determine the trend analysis using MATLAB2014 software, the Mann -Kendall test, and Sen's slop. It is worth mentioning that the output included ten precipitation extreme indices, including maximum 1-day precipitation amount, maximum 5-day precipitation amount, simple daily intensity index, the number of heavy precipitation days (over 10 mm), the number of very heavy precipitation days (greater than or equal to 10 mm), the maximum number of consecutive dry days (sum of precipitation less than 1 mm), the maximum number of consecutive wet days (the sum of precipitation rate is greater than or equal to 10 mm), days with a total precipitation rate of more than 95 percentile of rainy days (very wet days), days with precipitation rate greater than the 99th percentile of rainy days (very wet days), the ratio of precipitation to very wet days to the sum of rainfall rate. The output also included sixteen temperature indices including the number of below-freezing days, the number of summer days, the number of ice days, the number of increasing tropical nights, season length, the monthly maximum value of daily maximum temperature, the monthly minimum value of daily maximum temperature, the monthly maximum value of daily minimum temperature, the monthly minimum value of daily minimum temperature, cold nights, percentage of cold days, warm nights, warm days, heatwave duration index, cold wave duration index, and diurnal temperature range.

The results suggested a decreasing trend for precipitation indices in most of the studied stations. Accordingly, there found a significant decrease at a 99% confidence level for precipitation indices, including the number of consecutive wet and/or days and heavy and very heavy precipitation days at Kermanshah and Sanandaj stations, respectively. Moreover, the results indicated negative trends for extreme indices such as the number of below-freezing days in Khorramabad, Hamedan, and Sanandaj, cold days, and cold nights at Ahwaz, Sanandaj, and Hamedan, ice days at Hamedan and Kermanshah, and diurnal temperature range at Ahwaz and Sanandaj stations. Also, increasing trends were found for summer days, warm days, and warm nights in all the stations located at the watershed.

The investigation of the trend of temperature indices in the Karkheh watershed during the period of 1980-2010 indicated an increase in the frequency of warm events, such as warm days and nights and growing season length, and a decrease in the frequency of cold events, such as cold days and nights, and the number of below-freezing and ice days. However, while most of the obtained results were consistent with the results found by the Intergovernmental Panel and other national and international studies, a completely unified pattern cannot be made for changing the indices across the basin. On the other hand, it can explicitly be stated that the minimum quantities of the minimum temperatures changed more than those of the maximum temperature rates. Moreover, the analysis of the precipitation indices revealed a decreasing and negative trend in all the studied stations.Furthermore, the comparison of the rainiest and the least rainy years showed that the range of precipitation fluctuations varied greatly from year to year and that the precipitation distribution was different at different stations. Therefore, considering large dispersion and low precipitation in most stations, a uniformed regional pattern cannot be offered for precipitation. The results also indicated that the temperature points increased with a positive trend and that the precipitation peaks represented a negative precipitation trend.

One of the most common and effective problems in long-term climate studies is the presence of gaps in the time series of various climatic and hydrological data. Therefore, the present study evaluates the accuracy of methods for infilling missing data of daily, monthly and annual temperature time series in the arid regions of Iran. For this purpose, the observed daily minimum, average and maximum temperature data for the period 1987-2014 measured at 73 synoptic stations distributed all over arid regions of Iran were used. Methods of readjustment used include: Normal ratio method, linear regression, multivariate regression and Inverse Distance Weighting (IDW). In this study, the capability of each mentioned methods for infilling missing data of daily, monthly and annual precipitation time series in the arid regions of the Iran was investigated, while the proportion of missing data varies from 5 to 50% of total data. In order to compare and evaluate the accuracy of the four mentioned methods three statistical indicators, namely the correlation coefficient (R), the Root Mean Square Error (RMSE) and Nash coefficient were used. The results showed that in general, each of the methods mentioned had different functionalities at a special level of readjustment and time scale. On annual and monthly scales, linear regression and normal ratio methods are the most accurate method in readjustment temperature data in the arid region of Iran. The correlation value between the readjustment and observational data at different levels reaches more than 0.95 using these methods. On the daily scale, there is no significant difference between the accuracy of the methods used in the readjustment of temperature data, and almost all four of these methods have appropriate accuracy because in all methods the correlation between readjustment and observed data is more than 90%. However, multivariate regression methods with an average correlation of 0.99 showed the most accurate performance in readjustment daily data at different levels of readjustment. Generally, each method should be used in accordance with the conditions, and therefore it is recommended to develop a software package for infilling missing data.

Surface temperature is an important management parameter at the time of planning in various environmental sciences including natural resources. But the uneven distribution and small number of terrestrial data stations is the incentive of using remote sensing data. The purpose of the present study was to investigate the emission correction method of vegetation and soil cover using the NDVI index on OLI and TIRS Landsat 8 satellite imagery. Data were taken in September 2018 in Golestan province. The results showed that NDVI and LST were inversely correlated with -0/96, with vegetation values ​​of R2 = 0/87 which can be used to obtain ground surface temperature. Also, the difference in determination of the maximum temperature obtained from the image, or LST calculated, is 2/9 ° C higher than the maximum data recorded in the synoptic and climatological ground stations in the region.

Land degradation is a multifaceted phenomenon, which is caused by various variables, including climate, land use changes and socio-human activities. In order to investigate the effects of climatic parameters on land degradation in five second degree watersheds (South Baluchistan, Bandar Abbas - Sedij, Kal - Mehran, Hillah and Mond) located in the entire Persian Gulf and Oman Sea Watershed, observational data E32 of synoptic stations were used in the mentioned catchment areas for of 31- year period (1988-2019). The IDW algorithm was used to map the climatic parameters. The results of the change detection showed that the trend of temperature class changes of 25– 27.5 follows an increasing rate of 19.03%, and the precipitation class is less than 150 mm in the region. The region is also facing an increasing trend of 17.3%. The trend of the evaporation parameter is such that the 2500-2750 and 300-3250 mm classes with the changes of -5.4, 8.3 percent, respectively, have the most decreasing and increasing effects. Moreover, the wind speed classes of less than 2 and 3-4 meters per second with changes of 5.7 and -7.5 percent show the highest increase and decrease respectively, based on the findings of the regression model, there is a significant relationship at the 0.05% level between the climatic variables (precipitation, temperature, evaporation and wind speed) on one hands and the vegetation index and salinity and the precipitation parameter on the others show the greatest effect. Considering that the four mentioned climatic variables explain 47.6% and 40.5% of the changes in the dependent variable of vegetation index and salinity, respectively, it can be concluded that part of the changes in vegetation and salinity are due to the conditions. As the climate prevails in the region, the poor vegetation and salinity were constantly fluctuating during the study period; consequently, the process of degradation followed an increasing and decreasing rate. Therefore, being aware of the effects of climatic parameters on the fluctuation of vegetation and salinity indices in a long period of 31 years, it is possible to make the necessary predictions for the optimal management of natural resources, especially during droughts. This enables the concerned authorities to control the development stages of land degradation in the coastal catchment areas of ​​the Persian Gulf and the Sea of ​​Oman.

In recent decades, the increase in temperature has caused widespread climate change all over the world, especially in arid and semi-arid regions of Iran and Yazd province. The purpose of this study is to project climate change in Yazd province using the CanESM2 model and new emission scenarios (RCP) during 2021-2050 (near future) and 2051-2080 (distant future) and to study the trend of changes in the baseline period using the Mann-Kendall test. The statistical indices of R2, RMSE, and NSE were used to evaluate the performance of the CanESM2 model. According to the results, the model had an appropriate performance for projecting precipitation and temperature in the future period and was classified into good and very good classes in terms of capability. Investigating the trend of the annual change of temperature and precipitation in the baseline period showed that the temperature had an increasing trend under most scenarios and stations, while the trend of the change of precipitation was no significant. The results of temperature changes in Yazd province indicated an increase of 2.2, 1.2, and 2.5 °C during 2021-2050 and 2.28, 3.19, and 4.77°C during 2051-2080 under RCP2, RCP4.5, and RCP8.5 scenarios, respectively. Changes in precipitation in Yazad province during the winter season showed a decrease in precipitation by 32, 26, and 34% during 2021-2050 and by 32, 32, and 5% during 2051-2080 under RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively.

Any change in the concentration of greenhouse gases will upset the balance between the components of the climate system. But, the change in the concentration of these gases and how they will affect in the future is unknown. To study the effects of climate change on different systems in the future, climate variables must first be simulated under changes in greenhouse gases (climate scenarios). There are several ways to do this, the most reliable of which is the use of climatic models. AOGCMs can simulate climate parameters globally in large scale, while these may not be suitable for small scales. One of the most important downscaling methods is dynamic methods that are based on increasing the resolution and analysis of planetary climate models. Here, in this research, climate change status in Karkheh River Basin where a major basin for water and agricultural yields is studied. For this purpose, the PRECIS model was used. PRECIS is an exponential dynamics downscaling model used to estimate the temperature and precipitation rates for the period of 2070 to 2100 under A2 and B2 scenarios. According to the results of climate change assessment under scenario A2, precipitation would increase up to 11% and up to five degree centigrade would rise in average maximum and minimum temperature while concerning B2 scenario, an increase in precipitation up to 7% and a rise in temperature rise up to three degree centigrade are estimated. However, under both the scenarios, despite, the fall’s precipitation is higher than the winter’s precipitation.

Regardless of rangeland plant production, it is not possible to plan and manage livestock and rangeland. Attention to rangeland forage production is essential for efficient and effective rangeland management. This importance is determined by identifying the relationship between forage production of plants and climatic variables which affect them in the field. For this purpose, the annual forage production values of rangeland index species were measured in Omidabad Eastern site during the years of 1998-2008 for 10 years in the national plan for rangeland assessment of different climatic zones and their re-measurement during the years of 2017-2018 for two years by cutting and weighing in 60 random plots of two square meters along four 300-meter tracts. The amount of rainfall and monthly temperature in this time interval were also calculated using the data of Abadeh meteorological station in all months separately each year. The amount of annual dry forage production of each species as a dependent variable and the amount of rainfall and temperature calculated as independent variables and relationships between them were investigated using a linear multivariate regression program in SPSS software. The results showed that rainfall, winter, and fall temperatures had a significant effect on species production and spring rains as well as the temperature of the growing season, in April and May, alone did not affect the forage production of the species and based on the results research, with the availability of rainfall and temperature data, the annual forage production of the studied species and the annual production of the coming years can be estimated with high accuracy.

One of the most fundamental processes, which influence climate and weather, both global and local scales that a fact which gives it the status of agriculture, is Evapotranspiration (ET). In irrigation and water resources management, ecosystem modelers, environmental assessment and solar energy system, accurate assessment of evapotranspiration is essential. Potential evapotranspiration (ET) has commonly applied to calculate the actual evapotranspiration, which was difficult to estimate by lysimeter measurement and water balance approach under field conditions. Until now, many methods have reported to estimating ET, however, due to the availability of the observed data, it is difficult to choose the optimal method. In this study, to determination of the best potential evapotranspiration method for the Khorasan Razavi Province, three temperature-based methods, Hargreaves–Samani (HS), Hamon (HAM) and Linacre (LIN) and five radiation-based methods, Jensen-Haise (JH), Makkink (MAK), McGuinness and Bordne (MB), Abtew (ABT), and Priestley–Taylor (PT), were compared with PM at yearly scale, using long-term (11-67 years) data from 13 meteorology stations. Indicators, viz. The correlation coefficient (CC), Relative bias (BIAS), normalized root mean squared error (NRMSE) and Relative error(Re) were used to evaluate the performance of ET estimations by the above-mentioned eight methods. The results showed that the performance of the methods in ET estimation varied among regions; ETLIN overestimated ET, while others underestimated. In Dargaz and Golmakan, ETHS yielded similar estimations to that of ETPM, while, in Torbate-jam and Khaf, ETLIN showed better performances. Also in Mashhad and Gonabad, ETHAM, in Neyshabour, ETJH and in Torbat-e Heydarieh, ETABT showed better performances. But in Fariman, Kashmar, Sabzevar, Sarakhs and Quchan, all methods showed poor performance. It indicated that ETPM is acceptable for ET simulation for the yearly timescale in these areas.    ​

One of the most important and main components of ecosystems is net primary production, which is an important index for assessing the ecosystems performance in the face of environmental changes. To this end, with regards to the importance of the subject, in this study, to quantify the climate change impacts on ecosystems, NPP values in Jiroft plain was simulated in two periods (2001- 2015and 2016-2030) using the BIOME-BGC model. To assess change in climatic parameters in future, LARS-WG 6 downscaling model was used. After ensuring the capability of the LARS-WG model to create climatic data, climatic variables were simulated in 2016-2030 under the RCP 4.5 scenario. NPP values in 2001-2015 were simulated using the BIOME-BGC model and validated with NPP data derived from Modis images (MOD17A3) that the results showed high accuracy of the model to simulate NPP. After ensuring the model accuracy, NPP was simulated under precipitation and temperature data in future (2016-2030). The results indicate an increase in precipitation, minimum and maximum temperature in the future period (2016-2030) compared with the baseline period (2001-2015). Also, according to the results, NPP value in future has increased in all biomes that this increase is due to increase in precipitation. There is the highest NPP value in the northern and western parts of the region that is related to biome 4 (with agricultural vegetation), biome 5 and 2 (with rangeland vegetation), respectively, and the lowest NPP value is related to the southern parts of the study area.

This study investigates and analyzes the decades of change in climatic zones in Karkheh, Karoun, and Zohreh - Jarahi watersheds. For this purpose, the database of temperatures and precipitation networks during the period 1976-2015, with a spatial resolution of 15 x 15 km, formatted in the conical Lambert image system, was used. In fact, because these two elements are the most important climatic drivers in the study area and the changes of other elements in these two elements are more visible, they have been used to study and analyze the climate change zone. After establishing the database, cluster analysis was used to identify climatic regions, and the audit analysis was used to validate it. Cluster analysis was performed on 52 indices of temperature and precipitation (monthly and annual average, coefficient of variation of temperature, and precipitation monthly and annual changes) during different periods, and climatic zones and their changes were analyzed. Based on the results, three climatic zones including humid and moderate zone, hot and dry zone, and very hot and dry zone were identified in the study area. The results showed that in recent periods, the humid area, in addition to having spatial displacement, its area reduced so that in the fifth period, the area reached less than five percent. Also, the hot and dry zone, stretched as a strip from the northwest to the southwest of the study area, had less spatial displacement, unlike the humid area, while its area increased in recent periods. On the other hand, the area of the very hot and dry zone has increased towards recent periods. Spatial and temporal increase of hot and dry climates and very hot and dry climates and decrease of the humid zone has been able to lead to the development and intensification of dust source, especially downstream of the studied watersheds.

The purpose of this study was to investigate the effect of climate change on the monthly discharge of Karoon Catchment as the largest basin in the country. In this study, five hydrometric stations (Bamdgeh, Telezang, Gharmaleh, Gotvand and Dezful) and three synoptic stations (Ahwaz, Dezful and Masjed Soleiman) were considered. Using the SDSM software, NCEP data and large-scale data from the general circulation model (HadCM3 for temperature and CgCM3 for water discharge) were scaled parameters under two climate scenarios A1B and A2 in the Karun Basin. Then, the climate change data and the output of the microscale model were applied to the SPSS 19 and Minitab 17 to predict the significance of water discharge for future climate courses (2020-2070) be simulated. Results of climate change analysis showed that under different scenarios, monthly air temperature in the scenario A1B increases by 1.60°C and in the scenario A2 1.58°C, but the average annual rate of stations in the scenario A1B is 19.82 m S-1 in size and 16.27 m S-1 in the A2 scenario. The modified Kendall and age tests were used to identify seasonal and semi-annual seasonal time series trends. Results also showed that under climate scenarios of discharge in spring and first half of the year, there was no significant trend at 95% of confidence, but in other seasons of the second half of the year, there was a significant decrease.

Any changes in the climate system affect on the access and management of natural resources such as water and soil. Temperature and precipitation are the key elements of climate for studying their trend can be important for atmospheric scientists, environmental managers and planners in the field of hydrology, agriculture, environment and so on. In this study, the trend of climate fluctuations was investigated using the ERA-Interim data. In the first step, calculating RMSE, MAE and Pearson correlation coefficient, the accuracy monthly data of ERA-Interim was studied using synoptic data of Yazd city. The monthly data with the least error and the highest correlation were selected to analyze the changes trend. Then, the selected data including the temperature of two meters above ground level, the soil surface temperature in depth of 1-7 cm, soil temperature in depth of 7-28 cm and synoptic monthly means precipitation were modified by the TerrSet software using the test Mann Kendall. The results showed that the northern and central western parts of the study area had a decreasing trend of precipitation variation at a level of 5% and in the rest of the areas changes are not significant. Also, studying the trend of temperature variables showed that soil temperature changes in two meters above ground level, in depths of 1-7 and 8-28 cm was significant at 1% level in all of study area. The change in the Z test of Mann Kendall was greater than 4.8 everywhere.

Results of assessment of the future climate change impacts is associated with some uncertainties. Considering the range of uncertainties increases reliability of the results. In this study, climate change impacts on daily precipitation, maximum and minimum temperature of Sira basin are assessed using LARS-WG model, for 2036-65 period. Accordingly, uncertainty of new emissions scenarios (RCP2.6، RCP4.5، RCP6 و RCP8.5) of HadGEM2 and climatic variability are investigated. Climate variability is a major source of uncertainty which is ignored in most previous studies. Based on the results, for precipitation, the range of the future scenarios have considerable overlaps with the historical ranges. So, it cannot certainly be concluded that whether precipitation increase or decrease in the future. But in the mean of the possible scenarios, it is expected that annual precipitation will change between -9% to +1% in the future. About temperature, the range of the future temperature scenarios have no overlaps with the corresponding historical ranges, and there is a grate certainty for temperature increasing in most of the months, and change in the seasonal cycle of temperature. In the mean of the possible scenarios, it is expected that average temperature will rise between 2.1 to 3 ºC in the future.