جستجوی مقالات مرتبط با کلیدواژه "تغییراقلیم" در نشریات گروه "کشاورزی"

High temperature has negative effects on olive physiology, especially flowering, fruit growth and oil production. To identify olive cultivars/genotypes tolerant to heat stress, 12 olive cultivars and genotypes with four temperature treatments T1: (34 ± 1.5 ˚C), T2: (37 ± 1.5 ˚C), T3: (40 ± 1.5˚C) and T4: (43 ± 1.5 ˚C) were evaluated at Tarom olive research station, Iran, in 2016. The experiment was conducted as factorial arrangement in randomized complete block design with three replications. The results showed that the photosynthesis rate and water use efficiency decreased with increasing temperature. The results showed that the highest photosynthesis rate was obtained in T1 with 11.84 μmol m-2 s-1 and the lowest in T4 with 4.87 μmol m-2 s-1. In T4 temperature treatment, cv. Manzanilla maintained more than 50% and cv. Amin 44% of their photosynthesis efficiency compared to T1 temperature treatment. The of cultivar temperature × treatment interaction effect showed that the highest photosynthesis rate in T1 was 17.42 and 17.25 μmol.m-2s-1 in cv. Amin and cv. Deira, respectively. At temperatures > 40° C, there was a sharp decrease in the stomatal conductance of the leaves. Cv. Arbequina and Ozine genotype had the highest and lowest water use efficiency with 2.63 and 1.84 mmolCO2 molH2O-1, respectively. Also, the highest water use efficiency was obtained in KH15 genotype and cv. Deira in T1 temperature treatment. Due to the considerable decrease in photosynthesis rate at temperatures above > 40 °C, olive cultivation in areas with hot summers will cause significant reduction in fruit yield and in fruit oil content. 

The objective of this study is to evaluate the performance of the statistical downscaling models SDSM and LARS-WG in simulating minimum and maximum temperatures and precipitation at four stations (Urmia, Maku, Takab, and Mahabad) in West Azerbaijan province, with data from the periods 1987-2010 and 2020-2065.

The study used the statistical downscaling models SDSM and LARS-WG to simulate temperature and precipitation variables at the selected stations. The observed data for the period 1987-2010 and the forecasted data for 2020-2065 were compared in both models. Additionally, to validate the SDSM model, the simulated parameters were compared with NCEP data and real observed data.

The results showed that both models were more accurate in simulating temperature than precipitation. The SDSM model performed better in simulating daily temperature compared to LARS-WG, whereas the precipitation results from the LARS-WG model were slightly more accurate than those from the SDSM model. Additionally, the RMSE values for the SDSM and LARS-WG models for precipitation were 2.84 mm and 3.4 mm, respectively, while for maximum temperature, the RMSE values were 0.02°C and 0.29°C, respectively. 

Based on the results, the SDSM model demonstrated higher accuracy in simulating both precipitation and temperature compared to the LARS-WG model. This model can be considered a reliable tool for predicting future changes in temperature and precipitation.

The main goal of this research is to simulate the interaction of surface water and groundwater by creating a connection between surface water and groundwater models in the Lor plain under climate change conditions. In this regard, the effects of climate change on surface water and groundwater sources were investigated based on the sixth report of the inter-state commission using a WEAP-MODFLOW coupled integrated model. The changes in the water level of the aquifer and the amount of the dropdown in the groundwater level were evaluated under the reference scenario assuming the continuation of the current situation and climate change scenarios, and the number of fluctuations in the entire plain for the 27-year period of 2050-2023(September 2050) in all climate change scenarios based on a model. A hybrid model, composed of different models, was predicted. The results showed that the average dropdown in the groundwater level at the end of 27-year period of 2023-2050 will be about 11 meters if the current situation (observational scenario) continues. In this scenario, the maximum dropdown in the groundwater level will be 38.7 meters in a part of the central and southwestern areas of the plain. If the climatic parameters predicted by the hybrid model are used in the coupled model of surface water and groundwater, the average dropdown in the groundwater level in the scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP4-8.5 will be 9.8, 10, 10.18 and 10.83 meters, respectively. The maximum dropdown in these scenarios will be 34.5, 35.2, 35.5 and 38.2 meters, respectively.

Recently, the increase in water demand and the pollution of water resources have threatened groundwater resources. The purpose of this research is to investigate the impact of the conservative scenarios of groundwater resources and its effect on the restriction of the aquifer volume reduction process using the system dynamics approach in Marvdasht-Kharameh plain.The current conditions of the aquifer during the statistical period of 2005-2018 were simulated and recalibrated according to the observed values of the aquifer volume. The scenarios have been applied in three ways to investigate the effect of climate change, changing the amount of water allocated to farmers from Doroudzan dam and changing the cultivation pattern under the scenarios SSP1-2.6 and SSP5-8.5 on the volume of the aquifer. In order to investigate the effect of climate change on Marvdasht-Kharameh aquifer in the period from 2021 to 2046, the rainfall data of GFDL-ESM4 model under two scenarios SSP1-2.6 and SSP5-8.5 were considered. The study of the impact of climate change and the release of the Darudzen dam showed that the volume and recharge of groundwater will decrease in the scenario SSP1-2.6 (50 and 49 million cubic meters respectively), and in the scenario SSP5-8.5 (54 and 67 million cubic meters respectively),. Also, by removing the rice crop from the cultivation pattern of Marvdasht-Kharameh plain in the future climate conditions under two scenarios SSP1-2.6 and SSP5-8.5, which will add 495 and 207 million cubic meters to the aquifer volume respectively, the greatest effect on increasing the aquifer volume will be achieved. It will have 30 and 50 percent reduction in the area under rice cultivation. Therefore, in order to manage and preserve groundwater resources, it is necessary to change the cultivation pattern and replace high consumption crops with low consumption crops.

Mangrove forests have an important role in carbon dioxide stabilization and modification of increasing greenhouse gases and the human effects of global warming. The main purpose of this study is to eliminate the information gap of carbon storage in the mangrove forests of Bushehr province. In this study, the total carbon storage and the top 1 m, and total soil depth of soil as well as the carbon stored in mangrove trees in the mangrove forest of Bushehr Province (Nayband and Melgonze) were evaluated. The amount of sediment carbon in sediment cores and the diameter of breast height and height of trees were measured in 12 stations in the area. Satellite imagery showed that the area of the Nayband and Melgonze mangrove forests is 141 and 14 ha, respectively, 73% of which is high-density forest. The results showed that the average carbon content of the top 1m of Nayband and Melgonze mangrove forests is 158 and 190 tons per hectare (t ha-1) respectively, which is within the range of the global average in arid mangroves. The amount of carbon in the top 1m of Bushehr mangrove soil was estimated at 25000 tons, 89% of which is sequestered in Nayband mangroves. Soil and mangrove trees in the area have sequestered 59500 tonnes of carbon (equivalent to 218 tonnes of CO2) 28500 tonnes of which is stored in soil and the rest (31000 tonnes) is sequestered by mangrove trees. Estimates show that if the mangrove forests of the area are completely deforested, more than 155000 tons of carbon dioxide will re-mineralize into the atmosphere. Evaluating the carbon content of the total soil depth enabled us to estimate the total carbon content of mangrove habitats in Bushehr province for the first time.

The sustainable availability of water resources and the qualitative and quantitative status of these resources are threatened by many natural and antropogenic factors, among which climate change plays an important role. Climate change can have profound effects on the hydrological cycle through changes in the amount and intensity of precipitation, evapotranspiration, soil moisture, and increasing temperature. On the other hand, the distribution of rainfall in different parts of the world will be uneven. So that some parts of the world may face a significant decrease in the amount and intensity of precipitation, as well as major changes in the timing of wet and dry seasons. Therefore, sufficient knowledge about the effects of climate change on hydrological processes and water resources will be of particular importance. In this research, as the first comprehensive study, the effect of future climate change on the water resources components of Neyshabur-Rookh watershed was investigated by a set of one hydrological model and six General Circulation Models under the RCP4.5 scenario.

The Neyshabur-Rookh watershed with an area of 9449 square kilometers is a sub-basin of Kavir-e Markazi-e Iran and a part of the Kalshoor Neyshabur watershed, which is located between of 58 degrees and 13 minutes and 59 degrees and 30 minutes and east longitude and 35 degrees and 40 minutes and 36 degrees and 39 minutes north latitude. The study area with an average altitude of 1549.6 m above sea level and average annual precipitation of 246.83 mm, a mean annual temperature of 13.3 Celsius has an arid to semi-arid climate. For hydrological simulation of the watershed using WetSpass-M model, maps of Digital Elevation Model (DEM), land-use, soil texture, slope, and distribution map of groundwater depth, Leaf Area Index (LAI), and climate data (rainfall, mean temperature, potential evapotranspiration, wind speed and the number of rainy days) per month in 1991-2017 period were used. Then the prepared model was calibrated and validated. The climatic data of six General Circulation Models (GCMs) under the RCP4.5 scenario (Representative Concentration Pathways) were downscaled using the Quantile Mapping Bias-Corrected method. The downscaled GCM models were ranked and weighted in each station according to results of the Leave one out cross validation method and utilized as an ensemble for projecting the near-future climatic conditions of the water resources components of the watershed. By importing the monthly maps of precipitation, average temperature and evapotranspiration in the period of 2026-2052 into the calibrated hydrological model, the hydrological response of watershed to near future climate change was determined and evaluated.

WetSpass-M was calibrated by changing the calibration parameters in five hydrometric stations and the compared measured and simulated streamflow. The values of four evaluation criteria NS, R2, MB, and RMSE indicated the good performance of the model during the calibration and validation process. By predicting climatic parameters in near future and preparing and importing maps of monthly precipitation, mean temperature, and evapotranspiration to WetSpass-M, the hydrologic simulation of the watershed was done in the 2026-2052 period. The results indicated that the mean annual temperature and precipitation would be respectively increased by 4.66% and 1.21°C under RCP4.5 in the near-future period compared to the baseline period. The average temperature will increase in all months so that the most changes will occur in September and the least changes will occur in March. The rainfall of the watershed will increase in March, April, May, October, and December and will decrease in the rest of the months. The highest and lowest rainfall changes will happen in April and August, respectively. The analysis of the components of water resources in the near future shows that annual total runoff, groundwater recharge, and actual evapotranspiration will increase by 5.9%, 14.85%, and 1.42% compared to the base period, and annual direct runoff and interception will decrease by 15.15% and 3.54%, respectively.

Considering the importance and major role of the Neyshabur watershed in the economy of agricultural products of Razavi Khorasan province, the results of this research will be of great help to the managers and policymakers of the country's water resources management in order to make appropriate decisions with the aim of reducing the effects of climate change on the water resources of the Neyshabur-Rookh Basin.

The impact of dust storms on the Sistan and Baluchistan region is one of the most important current issues in this region. The current research is an attempt to investigate the effects of climate change on the trend of changes in dust storms in Sistan and Baluchistan province in the future perspective (until the end of 2100) compared to the base climate period (2010-2020). The study method consists of two parts, namely, analyzing the trend of dust storm events changes from the past to the present and projection the future situation. In the first step, using the multiple linear regression method, the correlation and relationship between the variable of days with dust with other climatic parameters (temperature, rainfall, relative humidity, and wind speed (during the statistical period of 2010-2020 was obtained. Then, the modeled data of the CMIP6 global model in three scenarios and 8 decades from 2020 to 2100 were used as inputs of significant regression relationships during the period 2010 to 2100 to investigate the future status of dust storms. Analysis of the results showed that, the trend of dust event change based on three scenarios of optimistic, moderate, and pessimistic in Sistan and Baluchestan provinces is increasing. As expected, the least dusty days are seen based on the optimistic scenario (SSP1-2.6). The dust trends based on the middle (SSP3-7.0) and pessimistic (SSP5-8.5) scenarios have almost the same trend and are increasing. Therefore, we can expect to have more frequent dust events in the future as climate change continues. As a result, the necessary investments for adaptation measures and mitigation against the harmful effects of dust need to be put on the agenda from now on.

One of the challenges issues of the 21st century is climate change. One of its effects could be a change in climatic parameters that will have a great impact on the planet's climate. Temperature and precipitation are the two most important elements for describing climate that their changes also alter the climatic structure of any region. It is very important to study the temperature and precipitation trends in different time and regions. Therefore, in this study, the effect of climate change on temperature and precipitation in Bandar Abbas was investigated using four General Circulation Models (GCM) in the period 2020-2020. Moreover, LARS-WG6 statistical model was used for exponential microscale and two emission scenarios RCP8.5 and RCP4.5. The results of evaluation models by Correlation Coefficient (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) showed that LARS-WG6 software is a appropriate tool for reproducing precipitation and temperature for the future. The highest correlation coefficient is belonged to the minimum temperature in GFDL-CM3 models (0.92), MIROC5 (0.98) and the maximum temperature of GFDL-CM3 model (0.95). The highest and lowest correlation belonged to the minimum temperature and precipitation, respectively. The forecasting results of temperature and precipitation except for a few cases show that the maximum and minimum of monthly temperatures will increase in all four models and two scenarios. Highest increase in minimum and maximum temperature is determined in June, HadGEM2-ES model, RCP4.5 scenario (3.65 ° C) and January, HadGEM2-ES model, RCP4.5 scenario (2.48 ° C), respectively. This increase is repeated in the annual and seasonal average.  The average of minimum and maximum annual temperatures will increase by 1 and 0.86 ° C, respectively. The results of the models generally predicted an increase in precipitation in most cases. The GFDL-CM3 forecast the biggest increase in the RCP4.5 scenario (25.82%) in March. Forecast data also showed an increase in annual and seasonal rainfall. The RCP4.5 scenario recorded more increase in precipitation than RCP8.5 scenario. According to these results, Bandar Abbas will face an increase in rainfall and temperature in the future. This increase in temperature and rainfall can have devastating consequences in various sectors such as agriculture, tourism, water source, environment and health. As a result of events such as heat stress and devastating floods, damage to plants and animals, the spread of diseases caused by these changes, as well as reduced visits to tourist areas such as Hengam Island are some of the consequences that Bandar Abbas will face in the next twenty years. Therefore, the results of this study can be useful in recognition and solving problems related to climate change. According to the results of this study, managers in different sectors can assume the necessary strategies to adapt and reduce the consequence effects of climate change.

Predicting the effect of climate change on the distribution of valuable and endangered plant species is essential for their conservation and management. In this study, the MaxEnt model and 10 environmental variables were used to predict the current and future distribution of E. amoenum and E. italicum in response to climate change. Also, to predict the effect of climate change in the future (the 2050s and 2070s), two climate scenarios of RCP 2.6 and RCP8.5 were used under the CCSM4 general circulation model. Evaluating the accuracy of the models based on the AUC index indicates their excellent performance (<0.9). Resutls  of this study reveal that the most important variables affecting the distribution of E. amoenum species are slope (38.8%), annual temperature range (11.5%), and precipitation in the driest season (31.9%) are. Also, the solar radiation (37.6%), slope (36.4%), and the average temperature of the coldest season (11.5%) are the most important environmental factors affecting the distribution of E. italicum. In addition, the results show that the distribution of the studied species will decrease in response to climate change and under RCP2.6 and RCP8.5 in the 2050s and 2070s. Therefore, the results of this study emphasize the need to develop  conservation strategies to prevent the extinction of these species.

Suitable prediction of climatic parameters is considerably important for adapting to climate change and mitigating vulnerabilities in local scales. In current research, for climate change assessment, the CanESM2 model in the 5th IPCC report and Statistical down scaling model (SDSM) was used under three scenarios: RCP2.6, RCP4.5 and RCP8.5. The input data were minimum and maximum temperature and solar radiation of Gharakheil synoptic station in Ghaemshahr and NCEP data (National Environmental Prediction Center) for the base period (1984-2005). Sunshine hours were converted to solar radiation using adjusted Angstrom formula for climate condition of the north of Iran. Future climate scenarios were then developed for early-future (2011–2040), medium-future (2041-2070) and late-future (2071-2100). The results showed that during these periods, solar radiation, maximum temperature and minimum temperature will increase by 4-9 %, 5-24% and 8-34% compared to the base period. The largest increases (5.30c and 4.20c for maximum and minimum temperatures respectively, and 1.3 Mj/m2 for solar radiation) will occur during late future period under RCP8.5 scenario. Since temperature and radiation are the main affecting factors for crop growth and yield simulation, the planning is essential in order to choose the appropriate planting date to adapt to climate change in the study area

Accurate estimation of potential evapotranspiration is crucial in crop water use determination especially under climate change conditions. In this study, the changes of potential evapotranspiration values estimated using Hargreaves-Samani and Thornthwaite methods were investigated during the baseline period, 1986–2018. For future projection, the outputs of the general circulation model (CanESM2) under RCP2.6, RCP4.5 and RCP8.5 scenarios were downscaled using SDSM statistical model during near and far future periods (2025-2050 and 2075-2100). The simulation results showed a rise in temperature in future periods and all scenarios. The results showed that ETp will increase from 2025 to 2100 under three scenarios, except for the Thornthwaite method estimations in March. In the RCP2.6 scenario, the highest increase in ETp in the Thornthwaite method was obtained in July, equal to 55 mm. This value was calculated in the Hargreaves-Samani method equal to 1.63 mm in April. In the RCP4.5 scenario and near future period, the projected rise in ETp values by Thornthwaite method in month of July is 54.96 mm and Hargreaves-Samani method for month of January was 1.45 mm. These values for the same month and methods, in case of RCP8.5 scenario, were 40.34 and 1.72 mm, respectively. According to the results, the lowest increase in ETp will occur in the RCP4.5 scenario between years 2025 and 2050 and in the RCP8.5 scenario between years 2075 and 2100.

TThe amount of runoff entering the dam’s reservoirs are continuously affected by climatic parameters, which influenced by the climate change phenomenon. In this study, the climate change parameters were obtained based on CANESM2 climate model using the SDSM4.2 statistical downscaling model. Then the rainfall-runoff process was simulated by ANFIS model with Sugeno structure and subtractive clustering at the entrance of Golestan dam reservoir in climate change conditions. Finally, the Improved Multi-Objective Whale Optimization Algorithm (MOIWOA) which is a combination of Whale V (WOA) and the Differential Evolution (DE) is used to extract the optimal operation rules of Golestan Dam Reservoir in Golestan province. The results of the uncertainty analysis indicated that the simulation results of the climate change period were in the 95% confidence band - in both calibration and validation phases. Also optimization of Golestan reservoir in baseline (March 2005-September 2018) and climate change (April 2021-October 2033) periods showed that the vulnerability changes in the baseline and climate changes are in the range of 18-45% and 10-39%, respectively, and the reliability ranges over 52 to 89.5% and 28 to 90%, respectively, in both baseline and climate change phases. And for 80% reliability, the baseline and climate change conditionschr('39') vulnerability are obtained as 31% and 27%, respectively. Comparison of the optimal rules derived from the baseline conditions with the optimized ones from the climate change showed that the plan water demand is met by 80% reliability index. In addition, the release volume in climate change conditions is higher than its baseline one, which can be due to the increased volume of water demand in climate change conditions. On the other hand, comparing the performance of the reservoir to meet the irrigation demands of downstream land at the Pareto point (80% reliability) in terms of baseline and climate change also suggests a greater adaptation of reservoir release to demand in climate change period.

Investigation of the potential impacts of climate change on hydropower generation and reservoir operation at the basin is a necessary issue. In this research, the effect of climate change on the generation of hydroelectric energy in the Karun 4 dam was investigated. For this purpose, after collecting the required data, temperature and precipitation variables were generated using general circulation models (GCM) under RCP4.5 and RCP8.5 climate scenarios for the upcoming period (2029-2050). Then, the temperature and precipitation values were downscaled for the future period using the changes factor method. The generated time series were used as inputs for the IHACRES rainfall-runoff model and the runoff were simulated under different scenarios. Then the effect of runoff variation under considered scenarios in the amount of generating hydroelectric energy was investigated. Climate predictions indicate an increase of 1.72 and 2.4° C in the long-term average annual temperature, and a reduction in the annual precipitation of 5% and 14% under the RCP 4.5 and RCP 8.5 scenarios, respectively, in the period of 2029-2050. The results of rainfall-runoff modeling indicate that the average annual inflow into the dam reservoir will be decreased 21% and 34% under the scenarios RCP 4.5 and RCP 8.5, respectively. In order to investigate the effect of runoff reduction on reservoir status and hydroelectric generation, the reservoir of Karun 4 Dam was simulated with WEAP software. The results showed that the generated hydroelectric energy by the Karun 4 Dam in the upcoming period (2029-2050) will be decreased by 17% and 31% under RCP 4.5 and RCP 8.5 scenarios, respectively.

The results of most climate models, under diffusion scenarios (RCPs), indicate an increase in global temperature by the end of the 21st century, compared with the period 1850-1900 (IPCC, 2014). Considering the perceptible effects of climate change on meteorological parameters and then water resources, simulation and prediction of runoff in watersheds is extremely important. When it is necessary to simulate only the flow at the outlet of the watershed, the conceptual rainfall-runoff models (such as the IHACRES model) are preferred; because, they require less computational effort and input data, as well as provide a good response. A review of several studies on the effects of climate change on different systems in future periods shows that although many sources of uncertainty affect the final results, usually these sources of uncertainty are ignored in the calculations, which reduces the accuracy of the results. In some studies, despite the attempt to consider the uncertainty of AOGCM models in the calculations, all the AOGCM models have been applied with the same weight. Also, in most studies on climate change, limited types (less than 10) of climate models has been used. Therefore in the present study, 23 climate models were used to predict future climatic conditions, in order to reduce the uncertainty of these models. The superior model was selected using the weighting method. Then, using the IHACRES model, the effects of climate change on the Shirin (Azam Jareh) river runoff, with emphasize on the uncertainty of AOGCM models was studied in the forthcoming period of 2020-2040. Shirin River (Azam Jareh) is one of the main tributaries of Dalaki River, which originates about 70 km from the southern slopes of Arjan Plain and its tributaries include Sarkhoon and Tang-E- Gachi. The average long-term flow of Shirin River (Azam Jareh) is 11.3 m3/s and the average annual precipitation is 761 mm. In order to quantify the uncertainty in this study, the output of 23 AOGCM models was evaluated, using weighting method and calculation of performance criteria. The best selected models were GFDL-ESM2G, GISS-E2-R, MPI-ESM-LR, MPI-ESM-MR and MRI-CGCM3. Then, their outputs were obtained under three scenarios of RCP2.6, RCP4.5 and RCP8.5. According to the IPCC recommendation, the historical period of 1975 to 2005 was used to calibrate GCM models. In order to mitigate the uncertainty in estimating, the changes of climate variables due to the climate change were weighed by the Mean Observed Temperature Precipitation (MOTP) method, developed by Massah Bovani (2006). Then, the performance criteria were calculated. In the MOTP weighting method, AOGCM models are weighted based on the deviation of mean simulated temperature or rainfall in the base period from the average of the observed data. In this study, the precipitation was used to investigate the uncertainty, due to its importance and effect on runoff. The inputs of LARS-WG model were the time series of observational data of temperature and precipitation of the Shiraz station, along with the output of climate models for every month. With the help of this generator, a 30-year time series of temperature and precipitation data was generated for this station. After this stage, it was possible to use the output of climate change models to predict the runoff in Shirin River Basin (Azam Jareh), using the IHACRES rainfall-runoff model. In order to reduce the uncertainty in AOGCM models, weights were calculated to observe the accuracy of the models in estimating the precipitation parameter. Also, the performance criteria were calculated in order to evaluate the performance of the models. The results showed that different GCM models had different accuracy in predicting precipitation. The MPI-ESM-MR and MIROC-ESM-CHEM models with the weight of 0.166 and 0.010 had the highest and lowest accuracy in estimating precipitation, respectively. Comparing the evaluated performance criteria of each model with the observed data of corresponding precipitation values of the same month, it can be concluded that the use of several different models reduced uncertainty and significantly increased the accuracy of climate forecasts. Comparison of the results of climate change forecast in the future period of 2020-2040 with the base period indicated an increase in temperature and a decrease in precipitation. Performance evaluation of IHACRES model showed that the coefficient of determination (R2) for the calibration and validation period were 0.74 and 0.69, respectively. The corresponding Nash-Sutcliffe coefficients were 0.7 and 0.52, respectively. These results indicated the acceptable performance of the model in rainfall-runoff simulation. Based on the simulations performed, the future outlook of mean annual Shirin River (Azam Jareh) runoff in the period of 2020-2040 indicates a decrease in runoff under all three RCP scenarios. The minimum in runoff was due to the RCP2.6 scenario with 682 mm3 (much less than 3000 mm3 of the base period). Also, the results of the average monthly runoff during the period 2020-2040, compared with the base period, showed a decrease of runoff in the most of months (except May, June, July and August). The highest forecasted runoff reduction, under all three scenarios were belong to January, February, March, April, September, November and October. In general, it can be concluded that climate change would reduce the runoff volume of Shirin River (Azam Jareh) during the future period 2020-2040.

Agriculture is directly affected by climate conditions and changes; therefore, it is essential to understand the effects of climate change on agricultural water resources in order to adapt negative effects on sustainable crop production. In this study using three scenarios; RCP2.6, RCP4.5 and RCP8.5 of the HadGEM2-ES climate model, the future climate data (precipitation, minimum and maximum temperatures) for two periods (2020-2039 and 2040-2059) were forecasted under climate change and based on a 20-years basic period (1985-2005) climate data using LARS-WG software. The base-period evapotranspiration was estimated using Hargreaves-Samani method and they were converted to Fao-Penman-Monteith method by Coefficient (G) in order to improve accyracy. According to this, irrigation requirement of rice (most important and major crop in Gilan province) and evapotranspiration were compared between the past and future. Results declared that the average annual maximum and minimum temperatures for the periods of (2020-2039) and (2040-2059) in comparison to the base period, will increase 2.2 and 2.1, 3.2 and 2.8 ˚C respectively. Also, the average monthly rainfall under all three scenarios will increase 11.8 and 13.6 millimeters for the proposed future periods, respectively. In addition, the net irrigation changes of rice for the both proposed periods will increase 0-31% and 0-45%, respectively. The trend of evapotranspiration changes is ascending and the range of changes during the two periods will be 0.4%-13.6% and 6.1%-27.6%, respectively. This method demonstrated the outlook of climate change impacts on irrigation to the farmers. The results of this study suggest that it is necessary to understand climate change impacts on agriculture, for improved agricultural management planning. The results of this study highlight the need to pay more attention to the effects of climate change on reliable agricultural management and planning.

Climate change and global warming are the greatest challenges that humanities have ever faced. Temperature variations, quantity and pattern of precipitation are the important potential impacts of climate change which may affect water resources and drought conditions. Mann-Kendall trend test and Pttitt homogeneous test were respectively used to detect trend variation and abrupt changes in the time series.The SPEI drought index which is based on precipitation and temperature data, is not only designed in a way to consider both precipitation and potential evapotranspiration (PET) but also has the capacity to include the effects of temperature variability on drought assessment. Therefore, the objet of this study is to investigate the trend and the detection of change points in seasonal series of SPEI drought index in Iran, and for this purpose the non-parametric Mann-Kendall and Pettitt statistical tests has been used. In the first step, the SPEI data, calculated based on monthly rainfall and evapotranspiration is extracted from those parts of the SPEI world database covering Iran, and are about 624 points of the 0.5° geographic network were separated from the entire data and stored in the Excel format. Then, the 50 years seasonal times series of SPEI data during the period of 1969-2018 were made for different seasons of the year. Finally, the nonparametric Mann-Kendall trend and Pettitt change point detection tests were performed on the data, and the results were analyzed. The results of using the Pettitt test in 624 points from Iran showed that in between the series of SPEI drought index in the spring, summer, autumn, and winter, 11, 31, 8, and 15 percent of points had significant change point, respectively, and their spatial location are the decreasing change points in the eastern and north-west in spring, northernwest, northern east, middle part and southern east in winter. Meanwhile in summer and autumn seasons the increasing and decreasing change points are in dispersed points from the southwest, and the south and northeast and the increasing change points in the middle parts of Iran, and parts of the south and north of the country, respectively. Also, the results of using Mann-kendall test showed a significant decreasing trend in 28 percent of points for spring, a significant increasing and decreasing trend in 5 and 25 percent of points for the summer season, a significant decreasing trend in 18 percent of points for the autumn, and also the existence of significant increasing and decreasing trends is in 1 and 15 percent of points, respectively in winter. The spatial location of these points for the four studied seasons was in the east, northwest and south of the country, half east, southwest and scattered parts from the south of the country, the central parts of the country, as well as the northeast and northwest, respectively. Result of using Mann-Kendall and Pettitt statistical tests indicated that climate change has occurred in parts of the country, varies from one season to another in such a way that in east and northwest parts of the country in spring and in north-west and south-east parts of the country in winter, more dry conditions are visible but in autumn, more humid condition were observed in central parts of the country. According to the fact that most of the country's precipitation is in winter and spring, this drying trend will cause a hazard in reducing water resources. The results also indicate that in most of the cases that a change-point exist in the drought index, a trend is also observed in data series based on Mann-Kendall trend test.

The frequency and intensity of extreme rainfalls will increase over many areas of the globe due to climate change. So, it is required to revise result of such studies based on the climate change scenarios. One of the most effective tools in such studies is Weather Generators, including LARS-WG. While GCMs predict future changes in the various characteristics of precipitation, usually in downscaling using LARS-WG, just changes of monthly averages are considered. In this paper, the future climate change impact on extreme precipitation in Gorgan and Khoramabad stations are assessed; while, the results of two methods of applying just change in averages (simple method) or applying changes in various characteristics of precipitation (complete method) in downscaling are compared. For future, CanESM2 outputs under RCP2.6, RCP4.5, and RCP8.5 scenarios for 2036-2065 period were used. The results showed that for climate change impact assessment on extreme rainfalls, additional to change in averages, change in other precipitation characteristics should be considered. Because the results of the two methods are different. In Gorgan, for example, the annual maximum daily rainfall with a return period of 15 years in the future will increase by 16 to 21 percent according to the more complete method, but between 37 and 49 percent according to the simpler method. Based on the complete, Intensity of the extreme rainfalls at both stations will increase in the future. This increase will be between 23% and 30% for the 2-year return period and between 25% and 29% for the 30-year return period.

Zarrineh Roud basin plays an important role in supplying water to Lake Urmia and the region's drinking and agricultural needs. Agricultural development, water transfer outside the basin and climate change have reduced the flow into Lake Urmia. In this study, we investigate the simultaneous impact of climate change and managers' s policies on the availability of water resources in the Zarrineh Roud basin. For this purpose, first using AOGCM model and SDSM downscaling model and considering two A2 and B2 emission scenarios, the minimum temperature, maximum and rainfall data for 2070 to 2084 are reduced and then basin's charge was calculated by using SWAT model. The results showed an average increased in water and temperature stress to 6/29 and 5/27 Centigrade degree and an average decrease in annual discharge into the lake under A2 and B2 emission scenarios. In the next step, a comprehensive dynamic system model of the zarrineh Roud basin was developed by taking into account the set of factors affecting it, and the impact of inland and out of case study basin on water resources was investigated after validation and calibration of model. The results indicate a conflict between the agricultural sector and the environmental need of Lake Urmia that increased production and agricultural levels would reduce the inflow into Lake Urmia.

It is accepted that the global annual average temperature has been increased during recent decades. The climate of Iran is also affected by this global warming. Some studies indicated the increasing of mean annual temperature of Iran between 3.5-4.5○C by 2050. Crop phenology is directly related to temperature, so climate change could significantly change the phenology and yield of crops. Modelling phenology is a way to simulate the timing of phenological stages based on climatic factors. Amongst models, WOFOST is known as a powerful model for simulation of phenological stages and yield of wheat. In this study climate warming of Iran was evidenced. Based on this fact, trends of potential grain yield and time of flowering and maturity of wheat were studied in different climatic zones of Iran during 1992-2012. It is expected that understanding the thermal induced of phenological changes, can lead us to better field management decision making.

Weather data of 18 cities from 4 climatic zones of Iran (northern warm and humid (Zone 1), southern warm and dry (Zone 2), temperate (Zone 3) and cold and high elevation (Zone 4)) were analyzed during 1992-2012 and the long-term trends of air temperature were detected by linear regression. The crop growth simulation model WOFOST, was used to simulate the time of flowering and maturity and also the potential grain yield of winter wheat. Calibration and validation of model was conducted in 4 selected cities from each zone using statistical measures. The consequences of temperature on duration of grain filling period and also yield of wheat were determined.

Results indicated that the mean annual air temperature was significantly increased in all cities during 1992-2012. The highest increasing rate (regression line slope) belonged to Ardabil (0.159○C.y-1) and the lowest rate was observed in Pars Abad Moghan (0.06○C.y-1). Validation of WOFOST in 4 selected cities from each climatic zone, showed the perfect ability of model in simulating the flowering and maturity time of wheat. Regression analysis showed that the grain filling period was increased in accordance with temperature rise in zone 1 and 4 whereas it was shortened in zone 2 and 3. Grain yield showed the same trends predicted for grain filling period in different climates. Slop of linear regression between temperature and yield was significant except in Gorgan (Zone 1). In all cities the relation between grain filling period and yield was direct and positive.

Although temperature has increased in all climatic zones of Iran, flowering and maturity time and grain yield of wheat has showed different responses. There was a nonsignificant slope of regression line in the north humid climate, which means that the humidity of northern part of Iran (south of Caspian Sea) enhanced stability for this area. Annual mean temperature in southern warm and dry zone of Iran ranged between 24.3-27.9 ○C that could be stressful for wheat, and hence resulted in shorter grain filling period and less grain yield. It seems that in temperate zones (such as Mashhad) increasing temperature caused yield reduction due to faster GDD accumulation and lack of time to complete remobilization of photosynthetic materials. On the other hand, in cold areas increasing temperature could reduce the risk of cold stress in flowering time, resulting in longer grain filling period and higher grain yield. According to WOFOST simulation results, during 20 years study (1992-2012), mean potential yield of wheat increased 6.25 and 11.42 percent in northern warm and humid (Zone 1) and cold and high elevation (Zone 4), respectively and decreased 12.17 and 13.11 percent in southern warm and dry (Zone 2) and temperate (Zone 3), respectively. Total mean potential yield of wheat, by consideration of proportion of each zone in total wheat production of Iran, showed reduction of 1.8% and 3.28% during 1992-2001 period (10 years) and 1992-2012 period (20 years), respectively.          

Drought is one of the natural destructive phenomena which cause a lot of damage to the affected area. In this study, outputs of Fifth climate change report were used to investigate changes in precipitation and drought intensity at Birjand Synoptic Station during the next two periods. Precipitation data for two near future periods (2015-2045) and far future period (2045-2075) against base period (1975-2005) were downscaled using two scenarios RCP4.5 and RCP 8.5 using the LARS-WG model. Then the SPI values were determined for six time scales 1,3,6,12,24 and 48 months. The results showed that in most models, precipitation changes for both the near and far future periods would not be much higher than the base period. However, precipitation will drop further in the near future (2045-2075) than in the near future (2015-2045). Among the GCM models, the CanESM2 model shows the highest change over the base period and the RCP8.5 scenario estimates a lower rainfall than the RCP4.5 scenario. Also, in comparing the GCM models, NorESM1-M estimates the number of years with more severe drought. The results also showed that in future periods, as the SPI time period increases from short to long intervals, SPI values will show more severe drought conditions. Models uncertainty analysis also showed that the highest uncertainty value among models for long-term and short-term SPIs was related to NorESM1-M and Mpi-esm-mr models, respectively. The overall results also indicate an increase in severity and duration of drought in the future (especially the distant future).