فهرست مطالب mahdi amirabadizadeh

Climate is a complex system that is affected by changes in climatic parameters. By predicting and examining the range of changes in meteorological parameters in the future, it is possible to adopt appropriate solutions to reduce the harmful effects of climate change. Using atmospheric general circulation models is the most reliable method. In this study, precipitation, maximum and minimum temperatures of five synoptic stations of Birjand, Qaen, Nehbandan, Ferdows and Tabas, for the base period of 1988 to 2005 as well as the outputs of six climate models of CanESM2, GFDL-CM3, CSIRO-MK3, MPI-ESM - LR, MIROC-ESM and GISS-ES-R, were collected under RCP8.5 and RCP4.5 emission scenarios for a 16-year period (2020-2035) and downscaled using the LARS-WG5.0 model. Then, using the RMSE and MAE statistical indices, the quality of the down-scale representation was evaluated. Afterwards, by calculating the climate classification indices of De Martonne and Amberger, the province was classified with the help of GIS software. De Martonne classification indicates that the climate of the province will not change in the near future compared to the base period while based on the classification of Amberger and under all six models and both scenarios, Birjand, Qaen and Ferdows cities are predicted to have temperate climate and Tabas city is expected have a hot and mild desert climate. For Nehbandan city, the GFDL-CM3, CSIRO-MK3 and GISS-ES-R models of the fifth report under the RCP4.5 scenario predicted a moderate climate and the rest of the large-scale models predicted a moderate desert climate.

Climate change has a significant impact on water resources and the environment, which is reflected in agriculture, society, and economy. The use of General Circulation Models (GCMs) with downscaling models is a method for assessing climate change. Considering the placement of South Khorasan Province and the city of Birjand in the arid region of Iran, population growth, industrial and mining development, and the pursuit of sustainable agriculture, it is essential to assess the effects of climate change on essential meteorological parameters. The objective of this study is to compare the performance of historical models NCEP and ECMWF in downscaling temperature parameters for the Birjand County and investigate the changes in this parameter until 2030 using the top model and the SSP245 scenario with the CanESM5 model.

In this research, to compare the performance of two GCMs, NCEP and ECMWF, in downscaling temperature parameters, daily temperature data from the Birjand synoptic station for the period from 1990 to 2021 were used as the baseline period. Additionally, to evaluate the performance of these two GCMs, the statistical downscaling model SDSM was utilized. To assess the performance of these two models, evaluation criteria such as NS, KGE, RMSE, and BR2 were employed.

To investigate and compare the performance of two GCMs, NCEP and ECMWF, daily average temperature data from the Birjand synoptic station were used from the January 1990 to the September 2021. The data from 1990 to the January 2008 were considered for calibration, and data from the January 2008 to the September 2015 were used for validation. Both NCEP and ECMWF models had 26 parameters, and for downscaling, the parameters with the highest correlation with observed temperature were selected among these 26 parameters using the R software and the HydroGof package. Additionally, evaluation criteria such as NS, RMSE, KGE, and BR2 were used to assess the models' performance in calibration and validation sections. The closeness of variance and mean values of time series generated by the NCEP and ECMWF models to the variance and mean values of observed time series in the entire simulation period was examined using F and T tests. The results of the calibration section showed that the two models, NCEP and ECMWF, exhibited similar performance since the values of evaluation criteria NS, RMSE, KGE, and BR2 for the ECMWF model were calculated as 0.69, 4.86, 0.85, and 0.7, respectively, and for the NCEP model, they were 0.70, 4.79, 0.85, and 0.7, respectively. Since box plots, mean values, and standard deviations have a high capability in deciding the degree of dispersion and similarity between two time series, box plots, mean values, and standard deviations of the generated time series and observed time series in the calibration and validation periods were used to assess the similarity and closeness of the time series. The results of the evaluation criteria in the validation section showed that the ECMWF model outperformed the NCEP model, with values of evaluation criteria NS, RMSE, KGE, and BR2 for the ECMWF model being calculated as 0.69, 4.9, 0.85, and 0.73, respectively, and for the NCEP model, 0.67, 5.3, 0.83, and 0.7, respectively. Overall, the results indicated that the ECMWF model had a better performance and was selected as the superior model. Therefore, to simulate and predict the average temperature parameter, the parameters mslp, P500, P5-f, P5-u, P850, and P8-u from the ECMWF model were used. Consequently, it is predicted that the average temperature will increase by approximately 3 degrees Celsius compared to the statistical baseline period in the next 8 years.

The results indicate that based on the evaluation criteria, the ECMWF model performs relatively better in estimating the average temperature of Birjand County compared to the NCEP model. Moreover, the analysis of box plots, mean values, and standard deviations of the generated time series in the calibration and validation sections showed that both models produced similar patterns of dispersion, minimum, maximum, and mean values compared to the observed time series. However, the ECMWF model exhibited relatively better performance in terms of mean and variance values of the generated data on a monthly basis in the calibration and validation periods. As a result, the ECMWF model was selected as the superior model for simulating and predicting the average temperature of Birjand County for the years 2022 to 2030 under the SSP245 emission scenario using the CanESM5 model. The predicted results indicate that the average temperature of Birjand County is expected to increase by approximately 3 degrees Celsius compared to the statistical baseline period.

General Circulation Models are one of the important and practical models in regional scale climate change studies. Since the simulation of climate parameters in these models is done on a large spatial and temporal scale, their output is scaled down using different methods. In this study, the results of three downscaling models SDSM, LARS-WG and ANN has been compared in the climatic parameters of daily rainfall and minimum and maximum temperature in two stations of Taybad (hot and dry) and Qouchan (cold and moderate) in Khorasan Razavi province. For this purpose, the 20-year period of rainfall and minimum and maximum temperature data of the stations between 1986-2005 was used. To compare the accuracy of the models, the indicators of coefficient R2, RMSE and MAE were used. The results showed that LARS-WG and SDSM models have the least amount of error in simulating minimum and maximum daily temperature. The average MAE of the SDSM model error for the minimum temperature parameter in the two stations of Taybad and Qouchan is 0.61 and 0.71. The error value of the mentioned parameter in the LARS-WG model is 0.65 and 0.43 in the two stations of Taybad and Qouchan. On the other hand, in the ANN model, the absolute mean of the minimum temperature error is 1.78 in Taybad station and 1.73 in Qouchan station. Finally, SDSM and LARS-WG models have higher accuracy in simulating daily temperature climatic parameters and especially the minimum temperature parameter in Taybad and Qouchan stations compared to ANN model.

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.

Today, the increase in the concentration of greenhouse gases in the atmosphere causes changes in the components of water resources on a global and regional scale. On the other hand, forecasting the river flow in the future period is considered a basic tool in water resources management. The rainfall-runoff process is completely non-linear, and it is completely random in terms of time and place. Therefore, it is not possible to easily describe its mechanism using simple models. The IHACRES hydrological model is used to simulate precipitation and runoff in large basins and most weather conditions, and it is also an integrated and conceptual model. The main advantage of this model compared to other rainfall-runoff simulation models is the acceptable accuracy of the results along with the use of minimal input data and its simple structure. The aim of this research is to compare the performance of IHACRES models and the fuzzy neural inference system in the simulation of the outflow of the catchment of Kurit Dam in Golshan Tabas and its forecast in the near future under the RCP4.5 scenario and the large-scale GISS-E2-R model.

In this research, the performance of IHACRES models and Adaptive Neuro-Fuzzy Inference System (ANFIS) in forecasting the runoff was investigated. Model performance comparison of these models introduced the superior model. To simulate the future climate, the downscaling LARS-WG5.0 model was used under the RCP4.5 scenario. The temperature and precipitation data of the output of the large-scale model of the fifth GISS-E2-R report of the American country were extracted from the site. The precipitation and temperature data from 2022 to 2051 were downscaled and then the discharge values for the mentioned years were estimated with the superior model. The input data to the IHACRES rainfall-runoff simulation models and the ANFIS model include rainfall and temperature on a monthly scale. In this research, 60% of observed precipitation, temperature, and runoff data were used for training and the remaining 40% of data were used for validation of ANFIS and IHACRES models. After calibration of the two models, evaluation was done using RMSE and NS indices.

Investigation of linear correlation between precipitation and temperature with observed runoff showed that there is a significant relationship between precipitation and runoff in low and high monthly precipitation, but changes in observed runoff and temperature do not have a significant correlation (at the 95% confidence level). The evaluation criteria of RMSE and NS in the validation section of the IHACRES model were calculated as 0.1 and 0.95 respectively, and for the ANFIS model as 0.05 and 0.52 respectively, which shows the superiority of the IHACRES model over the fuzzy neural inference system model at this stage. In order to complete this section, as explained in the research method section, the LARS-WG5.0 software was used for downscaling of precipitation and temperature output of the large-scale model GISS-E2-R in the near future (2022-2051) under the RCP4.5 scenario.  Extracting the long-term monthly average showed that the average values of precipitation and temperature in the future horizon are equal to 17.38 mm and 21.7 °C, which, compared to the same values in the base period, shows that an increase of 5.45 mm in the average monthly precipitation and 2.9 °C an increase in the average temperature will occur. It can also be expected that this change in the values of climatic parameters will cause changes in the runoff caused by precipitation in the future. After simulating the monthly rainfall and temperature data from 2022 to 2051 by the LARS-WG5.0 downscaling model, these data were given as input to the IHACRES model to simulate the discharge by this model for the near future time period. Based on the results of flow simulation by the superior model (IHACRES model), the flow rate in the river will increase in the near future horizon.

In this research, In order to evaluate the IHACRES models and the adaptive neuro-fuzzy inference system, the available data (2013-2018) were divided based on the ratio of 60% to 40% for calibration (2013-2016) and validation (2016-2018). The validation result showed that the IHACRES model was more capable than the neural-fuzzy inference system model in simulating the observed runoff at Mavder station. The American GISS-E2-R large-scale model and the RCP4.5 scenario was used as input of hydrological model to simulate the runoff in the near future horizon. The results of statistical downscaling by LARS-WG from 2022 to 2051 showed an increase of 2.9 °C in temperature and an increase of 5.45 mm in total monthly precipitation. The output of the downscaling model was used as the input to the superior IHACRES model to simulate the runoff in the near future horizon for the investigated watershed.

Changes in temperature and rainfall have a large impact on the quantity and quality of water resources, In this research, the effects of climate change on the development of special Qanats in Khusf city (Giw, Mazhan and Vahad Abad) were investigated. For this purpose, minimum and maximum temperature and precipitation were firstly estimated in the two near future (2030-2060) and far future (2070-2100) using the CANESM2 large scale model and two emission scenarios of RCP4.5 and RCP8.5. These data were then quantified by two downscaling models of LARS-WG and SDSM. Using rainfall data for the upcoming period, Discharge value for 3 Qanats were estimated for future periods. The results showed that the minimum and maximum temperatures in the RCP8.5 scenario would be higher than RCP4.5 in the results of both downscaling methods and will increase for both future periods. Rainfall also has slight variations for two scenarios of RCP8.5 and 4.5 for the upcoming period and the base period. Also, comparing two methods of downscaling, the LARS-WG model estimates the temperature changes less than the SDSM model and less changes in rainfall. The results of Qanat change in the upcoming period also showed that in all qanats, the discharge average for the coming period is less than the base period. Also, the RCP4.5 scenario compared to RCP8.5 in two methods of LARS-WG and SDSM is estimated to be greater. Given the changes in discharge in the future, management strategy can be adopted to deal with possible droughts.

Nowadays؛ climate change and its effects creates a major crisis on the management of water resources. On the other hand؛ since groundwater is one of the most important sources of water supply؛ analyzing the impact of climate change on groundwater in the upcoming period will be necessary. The goal of current study is investigation of the effects of climate change scenarios for the balance of the groundwater level؛ case study on the subterranean water level in the field of Birjand South Khorasan province. In line with this goal in order to evaluate the effects of climate change؛ 26 parameters from the output of the Canadian General Circulation Models (CGCM3.1 AR5) under the emission scenario RCP4.5 was used as predictors. As well as؛ the SDSM model was applied for downscaling the climate data to generate the regional daily scenarios. The Artificial neural Network Fuzzy Integrated System (ANFIS) was used to investigate the changes in the balance of underground water. The results o؛ optimal and intelligent management of groundwater resources and improving the consumption pattern seems necessary.

One of the most important challenges of human being in recent years is water crisis and drought. So evaluating the situation of drought is very important in optimized water resource management. In current research, the data of precipitation for two future periods of 2025-2045 and 2065-2085 produced using results of 5 general circulation models of atmosphere (Csiromk3.6, Gfdlcm2, GISS-E2-R, Ipsl, and MIROC-ESM) and 4 scenarios (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5). Next step was downscaling which carried out using WG-LARS model. The intensity of drought identified using Standard Precipitation Index (SPI) using different time periods (3, 12 and 48 months) for Zabol and Shiraz zones. Results of SPI index showed that the intensity of drought in 12 and 48 months periods has higher drought intensity in comparison with 3 months. Also results showed that drought in Zabol and Shiraz was higher due to results of ESM-MIROC model and RCP 8.5 Scenario. Comparison of scenarios also shows that in both study areas, the RCP 8.5 scenario in both future periods shows a drought in the average range compared to the baseline period.

Climate change is one of the most important factors that will affect different parts of human life on earth and will be adverse effects on environmental resources, economic, social and especially water resources. Increasing concentration greenhouse gases in recent decades and increased temperature caused by it, has led to significant changes in meteorological elements. One of the strategies to mitigate climate change effects is to assess its effects on precipitation and temperature in each region. So, climate change effects on temperature and precipitation variables in Birjand Plain have been studied in this research using CanESM2 AR5 model under emissions scenarios 2.6, 4.5 and 8.5 RCPs. Due to the low resolution of general circulation models, SDSM downscaling model was used and climate changes in precipitation and temperature were simulated for future periods (2020-2049). Model simulation results indicate a rise in temperature in future period, so that maximum temperature rise (1.89°C) and the lowest rainfall (0.07 mm) was observed in June. Results can be used in planning water resources management and agricultural sector.

The solar energy is the source of energy on the planet, one of the most important sources of clean energy, especially in Iran. The correct estimation of solar radiation is considered one of the important parameters in simulating plant growth and the estimation of evapotranspiration is very important. Measuring the intensity of solar radiation, although it has a relatively long history in Iran, due to the high cost, most of the stations in the country are not equipped with a radiation meter, and this defect is even seen in developing countries. Since the prediction of solar radiation intensity in across Iran daily range with GeneXproTools software has not been done so far, due to the wide range of different climates in different regions of the country, this research has been carried out across Iran. Therefore, the ability of the simulation model of GeneXproTools software to predict solar radiation based on altitude, longitude, latitude, precipitation, relative humidity, maximum temperature, minimum temperature, average temperature and solar radiation on a daily basis in 31 stations over Iran. The period from 2007 to 2016 has been taken. Iran with an average annual rainfall of 241 mm is located in dry and semi-arid latitudes of the planet, between the two meridians of 44⁰ and 64⁰ in the east, and two circuits 25⁰ and 40⁰ in the north. About 94.84% of its surface is located in arid and semi-arid regions with low atmospheric rainfall and high evapotranspiration. Meteorological stations were selected based on climatic variation in this research. In this way, the study stations were divided into six submergence classification systems, hyper-arid, arid, semi-arid, Mediterranean, humid and very humid (A). Gene expression planning (GEP) is one of the newest methods of artificial intelligence. This is the generalized method of genetic algorithm (GA), which was based on Darwin's theory and invented by Ferreira in 1999 (Roshgar and Mirhidaryan, 2014). In order to compare the results of the gene expression algorithm were defined in the prediction of solar radiation, 8 scenarios are considered based on the parameters affecting solar radiation such as altitude (m), longitude (degree), latitude (degree), precipitation (mm) relative humidity (%), maximum temperature (°C), minimum temperature (°C) and average temperature (°C). The main objective of this research is to select the best model for predicting daily average solar radiation in Iran using meteorological parameters. The number of training and testing data for the GEP model is presented in Table 1. Among the data collected, 80% of the data were used for training (total 8000 data, 6400 data from every parameter for every 31 stations) for the model. The experiment was performed for 20% of the data (a total of 1600 data from every parameter for each 31 stations) for the model.
 
Table 1. Training and testing data for study stations for the GEP model


 
The performance of the GEP model by evaluating the best fitness fittings, namely RMSE, MAE, NSE and R2, which at best (Best Mode) of the values of best fitness RMSE, MAE, NSE and R2were 1000, 0, 0, 1, and 1, respectively. Comparing the results of model assessment statistics in different scenarios, it was found that in all scenarios, c scenario due to consideration of latitude (degree) parameters, precipitation (mm), average relative humidity (%), maximum temperature (°C), minimum temperature (°C), average temperature (°C), and average solar radiation (MJ.m-2.d-1), the desired prediction has presented. The best model for this study is R2 for training and data testing of 0.72 and 0.73, respectively. The GEP model shows the optimal output model as a tree, and also the equation derived from this structure. Since the four genes in this study are composed, each gene has a subtree and its own equation, which ultimately results from the final equation bond function. The final equation is a relation 1.
Rs= SUB ET1+ SUB ET2 + SUB ET3 + SUB ET4 = sin((((((2RH)-cos(Tmax))-cos(Tmean))-p)/altitude)) + Tmean + sin(altitude) + ((((altitude + 7.30)-(2 Tmean))-p)/((sin(-8.41)+ 5.76)+atan(Tmin)))) From the results of the analysis, it was found that the predicted values ​​are well adapted to the values ​​measured in the Gene Expression Programming Model (GEP). In the GEP model, the e scenario is due to the fact that it does not consider altitude (degree), longitude (degree), latitude (degree), precipitation (mm), relative humidity (%), maximum temperature (°C) and minimum temperature (°C), with a lower correlation coefficient and more error and less efficiency. In this model, in the c scenario, with the addition of average temperature, and solar radiation parameters, a more favorable estimation of solar radiation is obtained. The results of the c and e scenarios are very close together, but the e scenario is weaker in the prediction of solar radiation, and the best model in this study is c scenario. More precise results will be obtained when latitude (degree), precipitation (mm), relative humidity (%), maximum temperature (°C), minimum temperature (°C), average temperature (°C) and solar radiation (MJ.m-2.d-1) are predicted for solar radiation. Also, the criteria for assessment under c scenario are higher with R2= 0.72, RMSE = 3.59, MAE = 2.82, and NSE = 0.72 in the education section of other scenarios. In general, the GEP method has the most accurate results in estimating daily solar radiation in Iran.

Climate change affects the hydrological processes through changes in temperature, precipitation, and other climate variables. One of the most important consequences of climate change is its impact on agricultural water consumption, which can seriously address water resources management. The purpose of this study was to investigate the effects of climate change on daily evapotranspirationin different climates across Iran. For this purpose, the minimum, and maximum temperature, as well as precipitation, and sunshine hours were simulated for the period 2016-2036 on the A1B, A2 and B1a scenarios using the HadCM3 general circulation model and weather generator (LARS-WG 5.5) on daily time scale. The results indicated increase in temperature and evapotranspiration and decrease in precipitation in different climates. In another section of this study, by comparing the empirical equations, it was concluded that Hargreaves method with high R2, fair NS, and lower values of RMSE and MAE compared to other methods, can be an appropriate alternative for the FAO Penman-Monteith method on daily scale. For example, the results for Birjand station were R2 = 0.59, NS = 0.53, RMSE 6.84 and MAE = 6.01. Finally, Hargreaves- Samani and the Makkink methods had the least accuracy in estimating the reference evapotranspiration. Therefore, Hargreaves method can be selected as the best alternative method for estimating the reference evapotranspiration on a daily basis.

Increasing the concentration of greenhouse gases has contributed to significant changes in hydrological cycle. To study the effect of climate change on precipitation and temperature, general circulation models and downscaling are the best methods for estimating these effects. In this research, the capability of linear and data-driven method for measuring the magnitude of rainfall and temperature at three synoptic stations of Birjand, Tabas and Qaen were investigated. The indexes of estimating the coefficient of determination, the efficiency of Nash-Sutcliff and the mean absolute error were used. The large-scale data in this study relates to the CanESM2 Canadian Fifth Report. The results of downscaling of precipitation by three models at three stations in Birjand, Tabas and Qaen showed that the NARX model had a weak in the average daily rainfall at the Qaen Station in the low rainfall months of the year. In the rainy months, SDSM and NARX models have been more capable at Birjand and Tabas stations. The SDSM model showed over estimation at Birjand station in the first six months of the year and NAR and NARX showed under estimation for maximum temperature. The results showed that both SDSM and NARX downscaling methods have a more acceptable performance than the NAR over the whole year. The predicted results for the near future horizons of 2021-49 showed that, while decreasing the amount of annual precipitation, the rainfall time also changes to the end of winter and early spring. The maximum and minimum temperatures will increase on this horizon.

Soil moisture is an important factor in hydrological processes. So in this study, uncertainty AOGCM models were investigated in estimation of soil moisture by SWAP model for future period as 2099-2080. The climatology data were produced by ten of AOGCM models and two of emissions scenarios (A2 and B1) and had been downscaled by LARS_WG model and was used in SWAP model. The results showed that INMCM3 and NCARPCM models had the most and lowest amount of soil moisture during weeks of post-growth. The uncertainty of yearly soil moisture showed that INMCM3 model had the most of uncertainty band for A2 and B1 scenarios and GISS-ER model and CGCM3T47 model had the lowest uncertainty band for A2 and B1 scenario, respectively. Also, it was determined by comparison of soil depth's moisture that depth of 60 cm would have more soil moisture than depth of 30 cm.

This study conducted to assess the impacts of climate changes on hydrological regime of Langat River Basin using the Soil and Water Assessment Tool (SWAT). The Statistical Downscaling Model (SDSM) applied as a practical tool to bridge the spatial difference between GCM grid-box scale and sub-grid box scale in the Langat River Basin as a tropical area. The calibrated and validated SDSM was applied to project the possible scenarios in two future periods (2030s and 2080s) of meteorological variables. The SDSM predicts increasing mean monthly precipitation and maximum temperature during the two future periods. The Sequential Uncertainty Fitting (SUFI-2) algorithm was used for sensitivity analysis and calibration of the SWAT model in simulating of observed average monthly streamflow. The observed data matched reasonably well with the simulated flows with respect to Nash-Sutcliffe coefficient, PBIAS, RSR, and R2 with values of 0.62, 5.7, 0.61, 0.63 during calibration process (1997–2004) and 0.55, 3.5, 0.67, 0.56 during validation process (2005-2011), respectively. The projected results of monthly streamflow revealed that the average annual streamflow may increase about 71% and 108% in future mid-century and significantly for end century period, respectively along with increase in surface runoff.

Cardiovascular diseases are the leading cause of morbidity and mortality worldwide. Increased histamine level in the cardiac tissue has deleterious effects after ischemia-reperfusion occurrence. Regarding the existence of histamine type 2 receptors in the heart and their decisive role in inflammation، in the present study the cardioprotective effects of ranitidine in the factors effective gainst LV functions and the extent of the infarcted area were studied. Thirty-two male Balb/C mice were divided into 4 equal groups. The control group received saline; the others were administered 400 mg/kg ranitidine، ip. Group RE just on the test day; R14 and R28 groups for 14 and 28 consecutive days، respectively. Then، the hearts of the subjects were isolated and subjected to Langendorff-perfusion with induced global ischemia for 30 minutes followed by reperfusion of 60 minutes. Left ventricular systolic (LVSP)، and end diastolic pressures (LVEDP)، heart rate (HR) and coronary flow (CF) were measured، and left ventricular developed pressure (LVDP= LVSP-LVEDP) and contractility (+dp/dt) calculated. Myocardial infarct size was determined by triphenyltetrazolium chloride staining. Ranitidine can significantly increase LVSP; and decrease infarct size especially in R28 compared to the control group (P<0. 01). It was found that ranitidine of 400 mg/kg dose، esp. during chronic phase can be cardioprotective against ischemia-reperfusion damage through improvement of cardiac performance.