ali reza nikbakht shahbazi

Drought as a natural hazard significantly impacts various sectors such as agriculture and water resources and causes considerable damage to these sectors worldwide. Therefore, adaptation strategies should be taken to reduce drought damage, and in the meantime, planning and adaptation to drought conditions using drought forecasting is one of the most effective strategies. Due to the need for drought forecasting and the limited studies that evaluated drought indicators obtained from the rainfall forecast output from the North American Multi-Model Ensemble (NMME) in Iran. This study evaluated these models in four catchments of Karkheh, Karun, Heleh, and Hindijan-Jarahi for1982-2018.

In this study, the monthly output of different NMME ensembles were evaluated in the forecast leads of 0 to 9 months from 1982 to 2018, the SPI drought index was calculated. Comparison of these data with GPCC data was used for evaluation. Three quantitative criteria, including correlation coefficient, RMSE, and BIAS, were used for evaluation. Also, to integrate the existing models, two

a: Arithmetic mean between the existing models and B: Weighted average between the models have been evaluated by considering the correlation coefficient (CC) results. Also, two criteria (i.e., POD and FAR) and the quantitative statistical criterion (i.e., correlation coefficient) were used to evaluate the SPI drought index.

The results of the precipitation evaluation of the models showed that the integrated models have better performance than the individual models. In this integrated model, the weighted model also had better performance. Evaluation of spatial distribution of precipitation models also showed the excellent performance of NMME models in Karun and Hindijan-Jarahi catchments in the zero-month forecast lead and Hindijan-Jarahi catchments in the one-month forecast lead. The results of drought index evaluation showed that integrated models, although having better performance in precipitation forecasting, but in drought forecasting, the best performance belongs to NASA-GMAO-062012 and CFSv2 models. The results also showed that drought index forecasts in three and six-month periods have better performance than one month. Spatial distribution evaluation also showed that the models perform better in the southern basins. In general, it can be concluded that NMME models have good performance in predicting drought in some places and specific forecast leads, so they should be evaluated at each point before use.

The results of precipitation evaluation showed that, in general, integrating the output of dynamic models increases its proficiency, and integration in weighted mode (WeightedNMME) performs better than the non-weighted model (NMME). According to the zero-month forecast among individual models, the NASA-GMAO-062012 model has the most skills in terms of the correlation coefficient. However, in the one-month forecast lead in terms of the correlation coefficient, RMSE and BIAS, the best performance belongs to the CFSv2 model. Evaluation of drought indices showed that the model's performance could be different from their performance in predicting rainfall. WeightedNMME, for example, performed well in NASA-GMAO-062012 and CFSv2 months, although they performed well in predicting drought. The spatial evaluation also showed that the southern catchments perform better than other basins.

In the wake of global and regional climate change and heightened human activities, runoff from some rivers in the world, especially in the arid and semi-arid regions, has significantly decreased. During the past decades, due to the climate warming and the significant regional precipitation variation coupled with strong human activities such as the drastic agricultural and industrial development, soil and water conservation projects and water conservancy projects, more attention has been given to assess the impacts of climate change and human activities on runoff change. These variations in runoff have been an urgent challenge for water resources planning and management. In response to this challenge, one of the tasks for researchers is to identify the different roles of climate change and human activities on the nonstationarities in runoff. Both deterministic rainfall–runoff models and statistical methods have been proposed to assess the impacts of climate change or human activities on runoff. Climate change and human activities are the two main factors affecting runoff change in basins and the separating their impacts is of great importance for land use planning, water resources management and social, economic and political activities. Identifying and evaluating these factors and their contribution to reducing surface runoff is important for the sustainable development of water resources and for reviewing past and future calculations, plans and designs.

In this study, the contribution of the impacts of climate change and human activities on runoff reduction in Karkheh basin has been separated by hydrologic sensitivity analysis method. Hydrological sensitivity can be described as the percentage change in mean annual runoff in response to the change in mean annual precipitation and potential evapotranspiration. The Kaekheh basin is an important transboundary river basin whose environmental and economic significance, as well as future food security and hydropower production challenges, have been recognized nationally and internationally. The area of the Karkheh basin up to the Jelogir hydrometric station is approximately 40,000 square kilometers, of which about 55.5% are in mountainous areas and about 44.5% are plains and foothills. The common time period between the studied variables including daily streamflow in the hydrometric station and the air temperature and precipitation in the synoptic stations from 1990 to 2020 was considered. streamflow naturalization was done by adjusting agricultural water withdrawals, associated return flows and operational reservoir upstream of the Jelogir station. The Thiessen polygon method was used to estimate the average rainfall in the basin. Also, the arithmetic mean method was used to estimate the average air temperature and potential evapotranspiration at the basin. Hargreaves method was used to estimate potential evapotranspiration in synoptic stations. The trend in the annual values of precipitation, runoff, air temperature runoff and potential evapotranspiration from 1990 to 2020 was determined by Mann-Kendall test. Also, change points in the annual runoff were detected through Pettitt’s test and the precipitation–runoff double cumulative curve method. The Pettitt’s test is a non-parametric approach to determine the occurrence of a change point. It has been commonly used to detect changes in the hydrological series as well as climatic ones. The double cumulative curve is the plot of the accumulated values of one variable against the accumulated values of another related variable for a concurrent period.

The results show that runoff shows a decreasing trend at the significance level of 0.01, while no significant trend is observed in the amount of precipitation. The average annual air temperature and evapotranspiration show the potential of increasing trend at the significance level of 0.01. The rate of the annual runoff, potential evapotranspiration and air temperature trend is equal to -1.96, 2.71 and 0.062 mm per year, respectively, at a confidence level of 99%. The results show that the change-point in the annual runoff series based on the Pettitt’s test and the precipitation–runoff double cumulative curve method occurred in 1999. Therefore, the pre-change period and post-change period are before and after 1999, respectively. The average annual runoff in the post-change period has decreased by 34.4% compared to the pre-change period. The results of hydrologic sensitivity analysis method show that climate change and human activities have reduced runoff by 36.2% and 63.8% in the Karkheh basin, respectively. The results of this study can provide a reference for the development, utilization and management of the water resources and environment protection. The results of the present study can supply a reference to regional water resources management and planning. Water and soil conservation is not the sole purpose and means of development, at the same time, a practically possible proposition in term of increasing the produced runoff can be put forward for local managers to reasonably arrange the local actions, synthetically considering the sustainable development in the regional water resource and ecological environment.

The water-oriented development of countries calls for the enforcement of the regulations on the Clean Development Mechanism (CDM) through integrated water resources management (IWRM). The simulation of the most important drainage basin in the Middle East, which consists of six reservoir dams with a cumulative PV capacity of over 10500MW, was the goal of this study. The simulations of the groundwater and surface water resources were carried out separately by using the MODFLOW and WEAP models and coupling these models with a link file. The multi-objective function optimization process, which involved the maximization of the supply of hydropower and demand and minimization of aquifer drawdown, was carried out using the NSGA-II genetic algorithm. Afterward, all the system and policy components influencing the integrated water resources system were assessed. These components included inter-basin water transfer, combined use of water resources, variation of irrigation network efficiencies, and the effect of water shortage, According to the model results, the inter-basin water transfer with a scenario of 1.5 billion cubic meters increase will reduce about 10% of the average annual production of hydropower in the basin. Finally, the model showed the best scenario for attaining the CDM objectives.

In this research, the simulation and optimization models are integrated to apply the reservoir hedging policy. The simulation of the studied basin is executed using the WEAP model to conduct the system optimization and the multi-objective MOPSO model is utilized so that the first purpose is to maximize the percentage of supplying demands, while the second one is to minimize the violation of allowable capacities of the reservoir during the operation period. In this regard, the operation modeling from the reservoir was carried out based on the current condition for a 360-month period. Finally, by defining the optimized scenario and applying the reservoir hedging policy, the optimization of the operation from the reservoir is conducted and the results were compared with the outcomes of the reference scenario. In this study, by considering 24 decision variables including 12 hedging level variables and 12 hedging coefficient variables, the optimal answers were achieved after 1000 iterations. The results showed that the violation of the allowable capacities has not occurred in any periods, while in the reference scenario the reservoir level has reached the dead level in sequent months with more water shortage which might lead to the lack of water supply in such months and serious damages to the system. Due to the application of hedging policy in the optimized scenario, the percentage of supply in the critical months has increased between 20-35% compared to the reference scenario, which indicates a significant reduction in the failure rate in such months compared to the reference scenario.

In this study, the Spatio-temporal performance of 5 global gridded precipitation datasets including GPCC V8, CHIRPS V2, ECMWF ERA5, NASA MERRA2, and PERSIANN-CDR (PCDR) in drought monitoring has been evaluated. For this purpose, the standardized precipitation index (SPI) and precipitation information of 13 synoptic stations of the Meteorological Organization of Iran during the thirty years of 1987-2016 has been used. Comparisons were carried out based on performance indices include correlation, mean square root error (RMSE), Nash-Sutcliffe efficiency coefficient, and modified agreement index (MAI) as well as drought detection accuracy metrics including False Alarm Ratio (FAR), probability of detection (POD) and the Critical Success Index (CSI). The results showed that GPCC, ERA5, PCDR datasets had a strong agreement with SPI observations so that they showed the drought trends and situations well and their R2 with observational SPI was

The subsidence of the plains structure can be directly due to the drop in groundwater level and the destruction of aquifer alluvial tissue. In this study, in the Izeh plain, the MODFLOW mathematical model was performed by extracting a raster map of saturated aquifer subsidence in the ten-year period from 2008 to 2019 and the forecast period for three climatic scenarios between 2019 and 2029. The subsidence model was developed by developing the SUB package in the limited numerical code structure structure of MODFLOW. The quantitative groundwater flow model was calibrated using PEST automation code and sensitivity analysis. The results show that the mathematical model used to simulate Izeh aquifer has a relative relative error NRMSE, which confirms the ideal modeling after reviewing the validation process. An examination of the vertical changes in the structure of the land showed that in the long term of 20 years, with the climatic assumptions raised, the aquifer level will occur a total of up to 1.5 meters and a subsidence of at least 0.9 meters. If the aquifer alluvial structure is destroyed, this amount may be less than these figures, but in return, it is equal to the conditions without returning the groundwater aquifer feed.

In this study, hourly values ​​of precipitation and air temperature in Karoon 4 basin in southwestern of Iran were simulated with WRF numerical model to evaluate the accuracy of the model for flood prediction. The flood event was selected in March 2016. For global boundary conditions of the model, global data with a resolution of 0.75 were used. The WRF model was implemented using eight different configurations, including a convective schema, two planetary boundary layer schemes, two microphysical schemes, a surface layer schema, and two shortwave radiation schemes to obtain a suitable configuration for simulating temperature and precipitation. The results showed that in the simulation of hourly precipitation, the combination of MYJ boundary layer design with other micro-physics and short-ray projections yields better results than YSU schema. The best values of IOA (matching coefficient) between observed and estimated precipitation of the model was 0.77, 0.76, 0.74 and 0.52 in Shahrekord, Saman, Koohrang and Lordegan, respectively, by combining MYJ boundary layer, Lin physics and Dudhia short radiation. While in simulating hourly air temperature, the YSU boundary layer schema combination with other schema showed better performance. So that with this combination, the heighest conformity coefficient (0.47) was obtained between the estimated and observed hourly temperature. The estimated rainfall adjusted by MYJ boundary layer configuration, Lin physics, GODDARD short radiation and MYJLG has performed better prediction for peak dischage than the other schemas, so that the lowest Nash coefficient and RMSE were -0.293 and 37 respectively, with this combination. Therefore, the combination of MYJ boundary layer schema, Lin cloud microphysics schema, and GODDARD radiation schema appear to be the best for estimation of precipitation and temperature and consequently for prediction of the March 2016 flood in the Karun 4 basin.

Parameters uncertainty of rainfall-runoff models are the main sources of uncertainty in real time flood forecasting. In this paper, the Monte Carlo method is used to estimate the uncertainty of the forecasted flood hydrograph due to uncertainty in the calibration parameters of the rainfall-runoff model in Dez Basin in southwestern Iran. Precipitation and air temperature were predicted using Weather Research and Forecasting (WRF) model. The HEC-HMS hydrological model was used to forecast the flood hydrograph corresponding to the predicted precipitation and air temperature. The SCS-CN, Clark Unit Hydrograph, and Muskingum-Cung methods were used to model losses, transform and flood routing, respectively. The results show that the best scheme in WRF model is MYJLG to predict hourly precipitation and air temperature in Dez Basin. Therefore, the MYJ boundary layer scheme, Lin cloud microphysics scheme and GODDARD radiant scheme have the best performance in flood forecasting in Dez basin. In addition, the results of this study show that considering the simultaneous uncertainty in all parameters, the uncertainty in peak discharge of the forecasted flood hydrograph is higher than the uncertainty in the volume of the hydrograph. So that the uncertainty in peak discharge and the volume of forecasted flood hydrograph due to the uncertainty of all parameters are equal to 32.5 and 21.2%, respectively. Thus, with the lack of flood forecasting and warning based on risk, quantifying uncertainty has provided additional information about forecasts that will help decision makers make better decisions.

Knowledge on the natural ability of basins is one of fundamental needs to optimal utilization of runoff. Thus, rainfall-runoff simulation in basins is of utmost importance. Continuous simulation of rainfall-runoff in Maroun basin performed using Artificial Neural Networks (ANNs) in order to evaluate the ability and accuracy of ANN for runoff estimation. Considering the fact that the number of rainy days per year less than sunny days, so runoff is caused by two different mechanisms. In continuous rainfall time and a few days later, runoff mainly is from high discharge and low base time. But on most days when there is no rainfall, baseflow has low discharge and long base time .Thus, in this research a double criterion model of rainfall-runoff includes model on rainy days and non rainy days were examined. Also efficient input variables on runoff in the Maroun basin are determined using the partial mutual information (PMI) algorithm. Comparison of statistical criteria between the single criterion model and double criterion model indicated that the double criterion model were more accurate. Therefore, the Nash-Sutcliff coefficient of single criterion model and double criterion model for test stage of network were 0.86 and 0.94 respectively.

Drought prediction is an important item in realm of hydrometeorology and hydrology, and selection of suitable meteorological variables for drought prediction is a goal in recent studies. In this paper, suitable feature selection is investigated with application of Mutual Information (MI) on the predictor’s time series and the well-known statistical machine learning methods, Support Vector Machine (SVM), is proposed to predict drought class based on Standardized Precipitation Index (SPI) in some seasonal scale scenario in the main watersheds of Tehran. In current study, ground weather temperature (at 300, 500, 700 and 850 mi bar) and geopotential height (at 300, 500, 700 and 850 mi bar) was applied in prediction models based on data from 1975 to 2005 in the main watershed of Tehran. Regarding to the amount of predictors, suitable feature selection is investigated with application of Mutual Information (MI) on the predictor’s time series and target time series and the well-known statistical machine learning methods, support vector machine (SVM), is applied to predict SPI class. One of the important issue in this research is use of different variables, for example regarding to selected data points, the effective regions on Tehran precipitation are southern, southwestern and northwestern of Iran in spring, northern and northwestern in autumn and northwestern and western in winter. SVM depicted accurate results in classification and prediction of SPI and it is suitable and applicable. The predicted SPI in winter and autumn are more accurate than the other scenarios.

The agricultural sector plays a vital role in the national economy and food production in Iran. In the meantime, due to the country's particular climate and the disproportionate distribution of time and place of precipitation, water cultivation is the main driver of food production. In arid areas such as Khuzestan province, nearly one hundred percent of agricultural production comes from water cultivation. According to the Research Center of the Iran Parliament, in order to meet the food needs until 2030, based on approximate 2600 kcal/day more than 150 billion cubic meters of annual water will be required that is not available in water cart of Iran. In at globalizing world, where problems increase with the effect of warming and climate changes, it seems that decrease in usable freshwater bodies will pose a serious problem. This study intends to investigate climate change impacts on evapotranspiration (ET) of agricultural crop in Khouzestan province in future periods. The importance and urgency of the subject and the limited research done in this regard, the local and regional effects of climate fluctuations on the evapotranspiration of agricultural products are of particular importance. These changes are of great importance, especially in fertile and agricultural areas such as Khuzestan. In this research, climate change impact on precipitation and evapotranspiration of agricultural products in future periods was investigated. In order to investigate climate change impact on ET, the CanESM2 atmospheric general circulation model (GCM) data under two RCP scenarios (IPCC-AR5) were used. In order to downscale CanESM2 model data, SDSM analysis software was used. The data used in this study include precipitation, minimum and maximum temperature and daily average temperature. To determine the feasibility of future periods meteorological data production of SDSM model, calibration and verification was performed for the base periods (1990-2005). Minimum and maximum temperature and precipitation estimated by SDSM method by CanESM2 data in three future periods: 2030s, 2060s and 2090s and compared with historical data. The strategic agricultural products such as wheat, barley, rice, corn and sugar cane were selected for this study. Potential evapotranspiration of these products were used during the cultivation period. According to the fifth report of the Intergovernmental Panel on Climate Change (IPCC) using CanESM2 model under RCP scenarios, meteorological data in the upcoming periods were simulated, the average periods potential evapotranspiration were calculated with information generated in the upcoming periods. Results showed that temperature on average increases about 4°C in all selected scenarios, this increase is higher in case of RCP8.5 compared to RCP4.5, and precipitation reduce in 2060s and 2090s. Studied Crops ET under RCP4.5 compared to baseline is always less than RCP8.5 scenario. For rice and corn, the average ET increase in 2060s and 2090s is higher than the others. It can be concluded that studied crop ET will increase in future periods, therefore we should consider that there will be lack of water and less crop production in Khouzestan province. We should change our cultivation periods and cultivate more effective crops in order to increase water efficiency and decrease potential evapotranspiration in some area of Khouzestan province.

Agricultural sector has a vital role in the national economy and food production in Iran, so that about 27 percent of the country's gross domestic product and 22 percent of the workforce are related to this sector. Due to special climatic condition of Iran and poor temporal and spatial distribution of rainfall, irrigated farming is the main form in food production. Despite, somehow, the same dry and irrigated cultivation in the country, the highest production is obtained through irrigation, so that during the last 5 years almost always close to 90 percent of total Iran agricultural production has been by irrigated cultivation. In order to meet the food needs until the year 1410, based on approximate 2600 kcal/day, more than 150 billion cubic meters of annual water will be required that is not available in water cart of Iran. In a globalizing world, where problems increase with the effect of warming and climate changes, it is thought that a decrease in usable freshwater bodies will pose a serious problem. Allan presented the term virtual water for the first time. In the last decade, this concept was paid attention by most researchers of water industry. The term virtual water content (VWC) connects water, food, and trade together and is the opposite of water efficiency. This study intends to investigate climate change impacts on the virtual water content of agricultural crop in Khouzestan province in future periods. The strategic products and production whose data is available such as wheat, barley, rice, corn and sugar cane were selected for this study. Potential evapotranspiration of these products were used during the cultivation periods. The average crop production of each city was taken from ministry of Agricultural affairs and each crop virtual water content was calculated. Wheat and barley are planted both in irrigated and rainfed lands in Khouzestan. The cultivation of rice, maize, and sugar cane are just in irrigated form. The virtual water for each crop was calculated separately. In order to investigate climate change impact on VWC, the CanESM2 atmospheric general circulation model (GCM) data under two RCP scenarios (IPCC-AR5) were used. In order to downscale CanESM2 model data, SDSM analysis software was used. The data used in this study include precipitation, minimum and maximum temperature, daily average temperature, and daily evaporation. To determine the feasibility of future periods meteorological data production of SDSM model, calibration and verification were performed for the base periods. Minimum and maximum temperature and precipitation estimated by SDSM method using CanESM2 data in three future periods: 2030s, 2060s and 2090s and compared with historical data. Results showed that temperature on average increased in all selected scenarios, this increase is higher in the case of RCP8.5 compared to RCP4.5, as precipitation will reduce in 2060s and 2090s. Highest increase of average temperature in Khouzestan is about 4.6° C in 2090s under RCP8.5 and decrease in temperature in 2030s is about 1° C under RCP4.5. The maximum and minimum temperature variation compared to the monitoring period under two RCP scenarios from June to November have increasing trends in 2060s. Precipitation under RCP8.5 for most months on average is declining and the sharpest decline occurs in November, while most months are associated with increased precipitation under RCP4.5. The minimum and maximum temperatures will decrease and increase, respectively, in most months of the year in 2090s, and precipitation has a sharp decrease compared to the observed period under RCP8.5, and a decrease will occur in January, February, and December, although precipitation will increase from August to October. Crop virtual water under RCP4.5 compared to baseline is always less than that of RCP8.5 scenario. Wheat, barley and sugar cane VWC increase compared to RCP4.5. There is 22.5% difference for wheat VWC between two RCP scenarios in 2030s compared to baseline period. For barley, there is 17.5% difference between the two RCP scenarios in 2090s compared to baseline period. For rice, there is 18.2% difference between the two RCP scenarios in 2090s compared to baseline period.

Knowledge of the watershed natural ability to produce runoff is an essential requirement toward optimal planning for runoff utilization. The aim of this paper is to evaluate HEC-HMS model using Soil Moisture Accounting (SMA) with continuous rainfall-runoff simulation in watershed and estimate the base flow contribution from direct runoff in Abolabbas watershed. It is possible to manage and plan water resources utilization by separating baseflow contribution and direct runoff from total runoff. SMA model use a series of storage layers including interception, canopy storage, soil storage profiles (upper layer storage and tension storage), and groundwater storage consider water loss in the watershed. Model calibration was performed using daily precipitation, evapotranspiration, temperature and river flow from 2001 to 2009 data and model verification was performed using 2010 to 2015 data. Results showed that HEC-HMS model with SMA have well consistently in simulation of daily runoff in wet and dry periods in Abolabbas watershed. Results showed that base flow contribution effect on total watershed runoff is more than direct runoff, in which the average percentage of total direct runoff and base flow runoff are 9.05 and 90.95 percent respectively. The results showed that on average, the percentage of runoff coefficient during the statistical period in Abolabbas watershed was 6.6 percent.

Increase greenhouse gases in the Earth's atmosphere have led to imbalances in the phenomenon of climate over the past decades, defined as Climate Change. Studies show that climate change can have negative effects on water resources, agriculture, environment, health, industry and economy. Global warming and climate change is happening and changing weather and climate volatility is associated with greater risk of damage. Since increasing the likelihood of future climate change could have devastating consequences for human societies, it is essential to examine the drought situation in the future periods in this area. For climate change effects on various resources in the future, climatic variables affected by greenhouse gases should be determined. Different techniques are available to simulate the future climatic variables under climate change effects; the most reliable data is atmospheric general circulation models. GCM models are three-dimensional models of the physical relationships that govern the atmosphere, crysphere, biosphere and hydrosphere. One of the weaknesses of GCM models is large spatial and temporal scales of the climatic variables. Therefore variables regarding hydrological and water resources studies are not sufficiently accurate. It should be downscaled by various techniques. Since different methods are available for downscaling, the uncertainty associated with these methods must be investigated. Various uncertainties affect the final outcome runoff simulation in a basin under the impact of climate change. The credibility of the results by ignoring any of these uncertainties would be reduced.
In this study, the GCM models uncertainty, methods of downscaling climate models and the SRES emission scenarios over the period 2069-2040 on Dez Ulya basin runoff were examined. For this purpose, the simulated temperature and precipitation of 10 GCM models, including BCM2.0, CGCM3T63, CNRMCM3, CSIROMK3.0, GFDLCM2.0, GISS-ER, HADCM3, INMCM3.0, IPSLCM4, MIROC3.2MEDRES, with two downscaling methods (Change factor and statistical using LARS-WG software) and three emission scenarios (A1B and A2 and B1) and artificial neural network model were used to simulate rainfall-runoff model. LARS-WG (Long Ashton Research Station Weather Generator) is a stochastic weather generator which can be used for the simulation of weather data at a single site, under both current and future climate conditions. These data are in the form of daily time-series for suitahle climate variables, namely, precipitation (mm), maximum and minimum temperature (°C) and solar radiation (MJm-2day-1). Stochastic weather generators were originally developed for two main 1) To provide means of simulating synthetic weather time-series with statistical characteristics corresponding to the observed statistics at a site, but which were long enough to be used in an assessment of risk in hydrological or agricultural applications.2) To provide means of extending the simulation of weather time-series to unobserved locations, through the interpolation of the weather generator parameters obtained from running the models at neighboring sites. It is worth noting that a stochastic weather generator is not a predictive tool that can be used in weather forecasting, but is simply a means of generating time-series of synthetic weather statistically ‘identical’ to the observations. New interest in local stochastic weather simulation has arisen as a result of climate change studies. At present, output from global climate models (GCMs) is of insufficient spatial and temporal resolution and reliability to be used directly in impact models. A stochastic weather generator, however, can serve as a computationally inexpensive tool to produce multiple-year climate change scenarios at the daily time scale which incorporate changes in both mean climate and in climate variability. It utilizes semi-empirical distributions for the lengths of wet and dry day series, daily precipitation and daily solar radiation. The rainfall-runoff models for the base period (2000-1971) has been calibrated and verified, then by downscaling of ten GCM-AR4 climate models for the study area and take into account each of them separately for rainfall-runoff models, changes of runoff in the period 2069-2040 under the three scenarios (A1B and A2 and B1) were determined.
Results from downscaling models showed that the rainfall for some models increase and others decrease in the future, compared to the base periods. Changing factors in downscaling method showed more decrease than statistical method. Results showed that the percentage change in long-term monthly simulated runoff for the two downscaling methods is about 5.11 percent, while a decreasing trend in the future compared to the base runoff was seen. Runoff simulation scenarios relative to each other in different months had the same difference. The results showed uncertainty in climate models used in this study is more than of uncertainty according to downscaling methods and emission scenarios.

This study aimed at investigating climate change impacts on precipitation and temperature variations in the watershed of Karoon 3 Dam using LARS-WG statistical model in the future. For this purpose, the climate data of 10 atmospheric general circulation models (GCM) were used under three distinict emission senarios in the watershed of Karoon 3 Dam. The precipitation and temperature parameters of the study area were simulated for 2011-2099 using the data of 1980 - 2007 in six synoptic stations. First, mean monthly precipitation was calculated using daily data by Inverse Distance Weighted interpolation method. In order to investigate six months’ precipitation variation, the summation of 6, 12, and 24 months’ precipitation were calculated.The model calibration and verification was evaluated using LRAS-WG5 model for the base year (2007-1980) in order to determine the production feasibility and simulation of meteorological data in future periods. Using atmospheric GCM and the IPCC climate change scenarios, the simulation and production of meteorological data was performed for future periods. The monthly precipitation and minimum and maximum temperature difference were analyzed. The research showed thatdifference between monthly maximum and minimum temperature decreases under climate change in spring and summer. Spring precipitations increase while summer and autumn precipitations decrease. The most increase of precipitation takes place in winter and in January. Moreover, the results showed that the frequency of normal and wet years with respect to the long-term average rainfall in the area was more in B1 and A2 emissions scenarios compared with A1B.

In various researches, implementation of meteorological parameters in drought prediction is studied. In the current work, meteorological drought classes based on Standardized Precipitation Index (SPI) for six seasonal scenarios (autumn, winter, spring, autumn + winter, winter +spring, and autumn + winter + spring) and meteorological predictors contained ground and sea surface temperature, weather temperature (at 300, 500, 700 and 850 mi bar) and geopotential height (at 300, 500, 700 and 850 mi bar) wide of North (0, 60) and East (0, 90) was applied in prediction models based on data from 1975 to 2005. In these models, temporal range of meteorological predictors is between October to April month on the same predicted SPI. SPI was calculated based on mean precipitation at seasonal time scale in the main watershed of Tehran (Taleghan, Mamloo) by Inverse Weighted Distance method. The well known statistical supervised machine learning method, support vector machine (SVM), is applied to predict SPI. Regarding to selected data points, the effective regions on Tehran precipitation are southern, southwestern and northwestern of Iran in spring, northern and northwestern in autumn and northwestern and western in winter. SVM depicted accurate results in prediction of SPI, spatially prediction of SPI in all scenarios, and it can be proposed as a very suitable statistical learning method in investigating of nonlinear behavior of meteorological phenomena with a short samples. The predicted SPI in spring and autumn are more accurate than the other scenarios.