جستجوی مقالات مرتبط با کلیدواژه « SDSM » در نشریات گروه « جغرافیا »

The aim of this study is simulation and forecasting of maximum temperature and heat waves in Urmia city from 2020 to 2050 in order to predict and reduce the negative effects of a sudden increase in temperature, which is simulated using SDSM software and CanESM2 model And is predicted using the most appropriate RCP scenario for the next 31 years. For this purpose, the maximum daily temperature data of Urmia from 1961 to 2005 were obtained from the Meteorological Organization and forecasted. The innovation of this study is using the least error RCP scenario for more accurate prediction. According to the results, RCP 8.5 scenario was selected as the least error scenario for forecasting. According to the results the average maximum temperature in Urmia will decrease from late winter to late spring compared to the base period and in mid-summer there will be a slight increase. In general, during the years 2020-2050, the maximum temperature trend of Urmia will be increasing. According to the results of the Baldi index, The heat waves will be short and maximum four days. One-day heat waves will have the highest frequency and will have a slight upward trend, Two, three and four day heat waves will have a decreasing trend. In general, short-term heat waves are more likely to occur than long-term heat waves. Also, since the detected heat waves often showed the highest frequency in autumn and winter, so the probability of this hazard occurring in cold seasons is higher than warm seasons.

IntroductionHeat waves are one of the most dangerous climate threats associated with global warming, affecting society, economy and environment (Kogenhof et al., 2015: 930). We can say that the first step to control or at least reduce the damage caused by climatic hazards, including heat waves, is to define, detect and identify their characteristics using scientific methods (Abbasnia et al., 2016: 25). Therefore the purpose of this study is Simulation and prediction of maximum temperature and heat waves Southwest of Iran (Case study: Ahvaz and Ilam cities)MethodologyIn the present study to identify heat waves, Maximum daily temperature data of each studied stations, from 1961 to 2005, were obtained from the Meteorological Organization and SDSM software and CanESM2 model and three RCP scenarios (2.6, 4.5 and 8.5) were used. Then using error measurement indicators the lowest risk scenario was selected for each city and the maximum temperature was predicted based on that scenario and using the Fumiaki index and through programming in MATLAB software, Days that had temperatures above +2 standard deviation or above average (NTD) and these conditions lasted for at least two days, were identified and selected as the day with heat waves. The Fumiaki index is obtained by relation 1:(1 (T(i,j) ) ̅=∑_(n=2020)^2050▒〖T(i,j,n)÷N〗 Where T (i, j, n) temperature of day i th from month j th in year n th, (T (i,j)) ̅ the average temperature of day i from month j. To eliminate the noise in the mean, a 9-day moving average filter was performed on these data three times and calculated by the following equation (Fujibi et al., 2007; Ismail Nejad et al. 2014).(2 ∆T=(i,j,n)=T(i,j,n)-T(I,J)Where ∆T= (i, j, n) absolute deviation of temperature from the average on day j th of the month i th, in year n th compared to the average temperature of the same day. In order to the values of temperature deviation of different times and places to be comparable at a certain time and place, it is necessary to standardize these absolute values of temperature deviation by means of temperature diffraction. Like day-to-day changes, diffuse T∆ at 31 days for each day is calculated by the following equation:(3 (σ^2(i,j) ) ̅=∑_(n=2021)^2050▒〖 ∑_(j-31)^(j+31)▒〖[∆T(i,j^',n)-(∆T(i,j^',n) ) ̅ ]^2÷31N 〗〗 The value (T (i,j)) ̅ is the average temperature deviation in 31 days that is calculated by the following equation:(∆T*(i,j)) ̅=∑_(n=2020)^2050▒∑_(j=j-31)^(j+31)▒〖T(i,j^',n)÷31N 〗 (4Finally, (NTD) is calculated by the following equation:(5 (i,j,n)=∆T(i,j,n)÷(σ(i,j)) ̅ x Where .Then days with temperatures +2 above average (NTD) and lasting at least two days, were selected as the day with the HW. (Ismail Nejad et al, 2014). Equation 6:(6 2 ≥ NTD (i+p) NTD (i+p-1) ≥ 2, NTD (i-1), NTD (i+1)… NTD (i), To evaluate the ability of CanESM2 model scenarios to predict the average maximum temperature for the next 31 years, the outputs of each scenario are averaged by the absolute error values (MAD), mean square error (MSE), root mean square error (RMSE) were compared and the most accurate and least error scenario was selected. Equation 7-9.(7 MAD= (∑_(t=1)^n▒〖〖|A〗_t-F_t |〗)/n (8 MSE= (∑_(t=1)^n▒〖〖(A_t-F_t)〗^2 |〗)/n(9 RMSE=√((∑_(t=1)^n▒〖(A_t-F_t)〗^2 )/n) Results and discussionAccording to the results, during the next 31 years (2020-2020) there will be an increasing trend in the average maximum temperature in both Ahvaz and Ilam stations, which will be the most increasing trend in Ahvaz. Results also showed during the years 2050-2020, the heat waves of these two cities were identified and divided into two categories: short -term and Long-term waves. (Heat waves lasting between 2 to 5 days as short-term heat waves and waves lasting 6 days or more, long-term heat waves). According to the results, in both cities, 2-day heat waves will have the highest frequency, which is predicted to be higher in Ahvaz than in Ilam. But three-day waves in Ilam more than Ahvaz and 4-day heat wave in Ahvaz more and 5-day heat wave in both Ahvaz and Ilam only one case is predicted. The highest frequency of 2-day heat waves in Ahvaz cities in the months related to spring was predicted in Ilam in summer. The 3-day heat waves in both cities will be in the fall. Ahvaz four-day heat waves in autumn, in Ilam in summer. Five-day heat waves, one of which was forecast in the fall at both stations. The highest frequency of this hazard in both stations was related to 2-day warm waves, which were predicted more in Ahvaz than in Ilam, and in terms of time of occurrence in Ahvaz in late winter and early spring, and in Ilam in late summer and early autumn. Most heat waves will be experienced. In general, the occurrence of heat waves in the predicted period in both cities will often occur in the cold seasons of the year.ConclusionThe purpose of this study was to use the CanESM2 model in simulating and predicting the maximum temperature and heat waves of Ahvaz and Ilam cities as selected cities in southwest of Iran. Based on the results of simulation of maximum temperature and forecast of heat waves during the years 2020-2050. The maximum temperature in both stations will have an increasing trend, which will be the highest increasing trend in Ahvaz. The maximum continuity of heat waves in both stations will be 5 days and therefore short-term, and the 2-day heat wave will have the highest frequency. The trend of two-day heat waves in Ahvaz will be increasing and decreasing in Ilam, but the 3-day, 4, and 5-day waves did not show special trend in any of the stations. Over the next 31 years, heat waves will be less continuous and often occur in the cold months of the year.

To check the consensus, the average temperature data was obtained from the Meteorological Department of Ardabil Province and the days with temperature below -10°C during the statistical period (1355-1400) were selected as cold days, then the trend of the time series of the frequency of freezing days with the least square error method in the model Linear regression was determined and its results showed a decreasing trend from 2010 onwards. Finally, for the simultaneous analysis of atmospheric factors, 25 January 2016 was selected as a representative through the ward hierarchical clustering method. Next, the data of the upper levels of the atmosphere were received from the website (NCEP/NCAR) and synoptic maps were drawn in Gardes. The results of the analysis of these maps showed that the Siberian thermal high pressure system played an effective role in transferring cold air from northern latitudes to the studied area. In addition to this phenomenon, the location of the study area in front of the European Ridge has resulted in negative air circulation conditions, which resulted in the strengthening of downward currents and a decrease in air temperature. Hyfran-plus statistical model was used to estimate the return periods of cold waves, and the result showed that the stationarity test (Wald-Wolfowitz) and the normal statistical distribution were more accurate in determining the return periods of cold waves. And the results of the Chi-Squared test also showed that it is possible to ensure the extraction of the results at a significant level of 5%. Finally, in order to predict changes in the temperature parameter, using the CanESM2 general circulation model, under three scenarios RCP2.6, RCP4.5, and RCP8.5, the future perspective of the period (2020-2050) was examined. Its results showed that the model had the necessary ability to simulate the average temperature.

Changes in erosion and sedimentation of the basin are one of the most important factors that affect different parts of human life and natural life. it is very necessary to receive these changes quantitatively, which mainly take place under temperature fluctuations and climate changes in different regions, in order to be more prepared to deal with its negative consequences. In this research, erosion and sedimentation changes in Hajiler watershed were investigated and predicted using GeoWEPP and SWAT models. Based on this, first, by using the data of the current situation of the Ahar synoptic station and using the SDSM model, the changes of the statistical period2020-2040 in three scenarios RCP2.6-RCP4.5-RCP8.5 were investigated, then simulation and prediction of erosion changes was carried out. and sedimentation was done under the influence of climate change by using popular models. The output of the SDSM model indicates an increase in temperature and a decrease in rainfall for the basin until 2040.And the analysis of the simulation results of the sedimentation rate of the models showed that in the studied basin, the GeoWEPP with the selection of the domain method has a suitable level in estimating the sedimentation rate compared to observational statistics. The final model was chosen to predict the amount of sediment in the mentioned period of the basin. Using the downscaled results of the atmospheric general circulation model, the sediment changes in the statistical period of 2020-2040 under the above mentioned three scenarios were estimated as -1.97, 4.45, and 2.98, respectively.

The issue of global warming, climate change and drought are among the major challenges facing the world today, which can cause widespread fluctuations in the Earth's climate. Areas with Mediterranean and semi-arid climates are highly dependent on temperature and precipitation and as a result are affected by climate change. Iran is one of the countries in the semi-arid and arid regions of the world that is more sensitive to climate change. Global warming due to increasing greenhouse gas concentrations and land use change has caused obvious changes in Iran's climatic parameters, including increasing temperature, decreasing rainfall and increasing the incidence of destructive atmospheric-climatic phenomena in the country. This research has been carried out with the aim of detecting future changes in temperature and precipitation in Liqvan watershed. For this purpose, statistical Downscaling of SDSM model and CanESM2 model under RCP2.6, RCP4.5 and RCP8.5 climate change scenarios on long-term data of Tabriz Synoptic Station (1951-2020) has been used. The results show that in four periods of 20 years (2020-2100) and based on the three existing scenarios, the temperature will increase and the precipitation will decrease. This temperature increase for the minimum temperature will sometimes be up to 14.35 ° C (January with RCP8.5 scenario and time period (2100-2081). Examination of rainfall in four periods and three scenarios shows that the amount of rainfall in October, November, December and April will increase and in the remaining months will decrease.In the present study, climatic parameters of temperature and precipitation were simulated using multiple linear model of SDSM and general circulation models of barley using data of Tabriz city for Liqvan watershed. In this research, the output of canESM2 model under scenarios RCP8.5, RCP4.5, RCP2.6 has been used for future periods.The results showed that temperature data correlated better with observational data (compared to rainfall data), this is because temperature variability is less than rainfall and temperature is a parameter with a normal possible distribution. One of the reasons for the decrease in rainfall correlation is that different factors affect rainfall and on the other hand, rainfall is a discrete variable. These results are consistent with the results of Sajjad Khan et al. (2006), Sarvar et al. (2010) and Nouri and Alam (2014). Therefore, solving the correlation problem in the development of future climate change models should be considered.Also, the results of this research are in line with the results of most researchers such as trainee et al. (2009), Sajjad Khan et al. (2006), Sarvar et al. (2010), Mino et al. (2012), Chima et al. (2013), Dehghanipour et al. (2011). And Shakeri et al. (2021) agree that the SDSM model has a good ability to small-scale temperature and precipitation data.Climate change can cause temporal and spatial changes in climate variables. The characteristics of these variables can have detrimental effects on ecosystem components. According to the results, it was found that during the 21st century, temperature is increasing and precipitation is decreasing.In Tabriz station, in general, precipitation will decrease in the three scenarios studied and in only one scenario and for the period 2100-2080, there will be an increase in precipitation. Also, rainfall will generally increase in winter and the rest of the seasons will decrease. These results are consistent with the results of Golmohammadi and Masah Bavani (2011) which introduced the period 2069-2040 as a period with increased rainfall in Qarasu basin but with the results of Rajabi and Shabanloo (2012) using the SDSM model in Kermanshah region in Western Iran has considered the period 2070-2070 to be drier, there is a difference, so that according to the results of the present study at Tabriz station, from May to September, we will have a decrease in rainfall for all periods and scenarios. Figures 8 and 9 show the changes in monthly and annual rainfall in the RCP scenarios and the four periods compared to the base period, which show a decrease in precipitation in most months except October, November and December compared to the base period.Changes in the average minimum temperature of Tabriz station in all months except October, November and December will increase in future periods. Figures 11 and 12 show the monthly and annual minimum temperature changes in the RCP scenarios and the four periods compared to the base period, which show an increase in the minimum temperature compared to the observation period, which is more than the other scenarios in RCP 8.5. .

Due to climate changes, precipitation forecasting and precipitation estimation, one of the most important climatic parameters in the field of water resources management, is of particular importance. Therefore, in this research, the application of SDSM model in precipitation estimation was investigated. In this research, the data related to Ahvaz, Abadan and Dezful synoptic stations were used. The forecast time frame for the future period (climate scenarios) is also 30 years between 2031 and 2060. The outputs of the HadCM3 model, under the A2 and B2 scenarios and the CanECM2 model, under the RCP26, RCP45 and RCP85 scenarios, were micro scaled by applying the SDSM statistical exponential micro scale model in the prediction of the precipitation parameter, also using statistical and graphical methods. Micro scaled and basic data were analyzed and then calibrated. Base period modeling was done with higher accuracy in CanESM2 data compared to HadCM3 data. The results indicated that in the coming years, the total rainfall will increase in all three stations and the maximum amount of rainfall will decrease in all three stations. According to the modeling results, it seems that the climate of Khuzestan will have a wetted winter climate and drier summers in the near future.

Heat waves are one of the most important weather disasters, and their destructive environmental effects require knowledge and forecast. The purpose of this study is to forecast the future conditions of heat waves in Ardabil, Tabriz and Urmia during the years 2020-2050 using SDSM downscaler and Baldy index. For this purpose, the maximum daily temperature data of selected cities from 1961 to 2005 after preparation were entered into SDSM software and analyzed with CanESM2 model with 2٫6, 8٫5and RCP 4٫8 scenarios and with the output of the model, the maximum temperature trend of the three stations during the next 30 years was investigated and their heat waves were Forecasted using the Baldy index. According to the results of the error measurement of the model, the simulations performed had acceptable accuracy. Due to the fact that each of the cities showed the least error in one scenario, so the heat wave prediction of each station was done using the least error scenario. Based on the results the average maximum temperature will decrease from late winter to late spring compared to the base period and will increase slightly in mid-summer. In general, we will see a decrease in the average maximum temperature in Ardabil and an increasing trend of the mentioned parameter in Tabriz and Urmia cities during the years 2020-2050. Also, the study of the predicted heat waves in the next thirty years showed that one-day waves in Ardabil and Urmia will be a slight increase and in Tabriz will be almost without trend. Two-day heat waves will decrease in Tabriz and Urmia and will increase in Ardabil. The trend of three-day heat waves in Ardabil and Urmia will be decreasing and increasing in Tabrizthe highest continuity of heat waves was four days, which was forecast in Urmia. The highest frequency of two- and three-day heat waves in all three cities will be in the cold seasons of the year.

Climate change impacts on climate variables are among the challenges of large cities. In this regard, General Circulation Models (GCMs) are among the most reliable tools for assessment of the future climate variables. In 5th assessment report on climate change, the Intergovernmental Panel on Climate Change (IPCC) has applied the Coupled Model Intercomparison Project, Phase 5 (CMIP5) models. These models use scenarios called Representative Concentration Pathway (RCP). In current study, the assessment of the climate variables of Tehran, Iran, under climate change impacts was addressed. The preceding studies for Tehran show they need to be updated with newer scenarios and more downscaling models. Furthermore, there is merit in using more synoptic stations due to the vastness of Tehran. Given these findings, the current study focuses on two main objectives. The first objective is to assess the climate variables under the RCP scenarios in Tehran for 2021-2040. To this end, the eight CMIP5 models under RCP2.6, RCP4.5 and RCP8.5 were used. Accordingly, seven climate variables including mean Temperature (Tmean), maximum Temperature (Tmax), minimum Temperature (Tmin), precipitation, relative humidity, mean Wind speed (Wmean) and the sunshine hours were used and simulated for baseline period (1989-2018) and then assessed for future period. In the Second objective, for downscaling the CMIP5s, in addition to use of the Statistical DownScaling Model (SDSM), Fuzzy logic was also applied for downscaling. Accordingly, the Fuzzy DownScaling Model (FDSM) was generated and the performances of FDSM and SDSM were analyzed.

In this study, to assess the Tehran climate variables under RCP scenarios, the multi-model ensemble were applied to reduce the CMIP5’s uncertainties. Accordingly, the eight CMIP5s including CanESM2, CNRM-CM5, CSIRO-Mk3.6, FGOALS-g2, GFDL-CM3, HadGEM2-ES, MIROC-ESM-CHEM and MPI-ESM-MR were used. Given the uncertainty caused by the different outputs of the eight CMIP5s, the weighted means of the models’ outputs were used to calculate the daily climate variables for future (according to the ability of the models in simulating the baseline period). For this purpose, first, the CMIP5s were downscaled. In this context, the SDSM software (version 5.3.5) was used and also FDSM was generated. Then the performances of FDSM and SDSM were analyzed. On this basis, the superior downscaling models were selected using the comparison of simulation results and the statistical indicators of R2, RMSE, NSE and MAE. Accordingly, the CMIP5’s outputs were downscaled using the superior downscaling models and then the daily values of each climate variable were calculated. In the calibration and validation of the downscaling models at baseline period, the predictors were selected from the daily data of the National Center for Environmental Prediction (NCEP) using correlation test in SDSM software. Furthermore, in developing the FDSM, the Fuzzy C-Means Clustering process was applied, to determine the Fuzzy Membership Functions and the relevant Fuzzy Rules. By using the structure obtained by clustering, the FDSM was built as a Mamdani Fuzzy Inference System. In this context, the FDSM was developed in MATLAB software using the trial and error process.

By correlation test in SDSM software, the predictors were selected for the SDSM and FDSM models. Accordingly, the SDSM and FDSM were developed using the daily climate variable and the selected predictors. The performance analysis of both downscaling models (based on the statistical indicators of R2, RMSE, NSE and MAE and the comparison of simulation results in baseline period) demonstrate very good quality and performance for all the daily Tehran climate variables. Therefore, the Fuzzy approach has an appropriate capability in simulating and downscaling the climate variables. In addition, neither model has absolute superiority over the other in downscaling. However, it appears that with a slight margin, the FDSM had a better performance for Tmean, Tmax and Tmin, and SDSM had a better performance for precipitation, relative humidity, Wmean and the sunshine. Accordingly these models were chosen as the superior downscaling models. The results of future period show the increasing trend of annual changes in Tmean and Tmax, precipitation and the Wmean. The maximum increase of annual average in Tmean and Tmax and the Wmean among all scenarios will be in the order of 1.29oC, 1.57oC and 0.8m/s (for RCP8.5) and also the maximum increases of annual average precipitation will be 10mm (for RCP2.6). Furthermore, the month long-term averages of Tmean and Tmax in all three scenarios show significant increases in summer. For precipitation, relative stability in summer, and increases in winter and early spring are projected, but the changes in Tmin, relative humidity and sunshine indicate relative stability.

In this study, two main objectives including the assessment of the climate variables under the RCP scenarios in Tehran for 2021-2040 and also the performance analysis of Fuzzy logic in downscaling were addressed. The performance analysis of FDSM and SDSM demonstrated the high performance of both models and the appropriate ability of the Fuzzy approach in downscaling the Tehran climate variables. Therefore, taking the Fuzzy approach for downscaling has a technical justification. In this context, the application of the Mamdani Fuzzy Inference System and the Fuzzy C-Means Clustering increases the accuracy and quality of the results at different conditions. According to the results, the annual changes in Tmean, Tmax and the Wmean at all the three RCP scenarios will have an increasing trend, while precipitation will also (marginally) increase. However, the other variables will have a relative stability. From the point of view of monthly changes, there were noticeable increases in the long-term means of Tmean and Tmax in the future period during the months of July, August and September (i.e. summer season). As regards to precipitation, a relatively stable trend was observed in comparison with the baseline during the warm months of future period, but during winter and in particular at the beginning of the spring of the 2021–2040 period, there will be more precipitation at different months of the year than the 1989–2018 period.

Pistachio, like many subtropical fruit trees, need a cold period in their annual cycle to allow the buds to bloom naturally after the right conditions are in place. There are several models to calculate the chilling needs of pistachio, of which the chilling hours model, Utah and Utah positive are the most important of these models. The studied geographical area is Yazd-Ardakan plain located in Yazd province.

In this study, according to statistical tests based on meteorological variables, the chilling hours model was selected for modeling. To conduct this research, three-hour temperature data of Yazd Synoptic Station during the statistical period of 1367-1396 were used to model and estimate the total monthly chilling hours of pistachio and The daily temperature data of this station during the statistical period of 1961-2005 were used for the SDSM model and the monthly temperature statistics of the years 1385-1397 were used to evaluate the downscaling data of the CanESM2 model under different RCP scenarios and finally modeling for the years 1400 -1429 was done.

Results indicate that there is a significant correlation between monthly cumulative hours of temperatures between zero and 7.2 ° C and monthly temperature parameters such as mean minimum temperature, mean maximum temperature and mean monthly temperature, which in the absence of data hourly temperature can be used to model and determine monthly cumulative hours.

Findings show that the chilling needs of Kalle-Ghuchi, Owhadi and Ahmad- Aghaei species will be met in the coming years and Akbari and Fandoghi species will not be met.

The consequences of climate change have led the international community to study more broadly that changes in natural resources, ecosystems, and populations will be affected by future climate change. Recent studies show that the global climate cycle will intensify. Climate change can have destructive effects on various sources such as water, forest, agriculture, etc. The first effects of climate change on atmospheric elements, especially temperature and precipitation, so it is essential to study the trend of climate change. The large scale model used in the present study is the CanESM2 model. Also, for exponential Downscaling in this research, 3 models of LARS -WG, SDSM and qmap have been used. The scenarios studied in the present study are two climatic scenarios RCP 4.5 and RCP 8.5. In this study, the performance of the introduced models as well as the temperature changes of the next period 2025-2056 in two synoptic stations of Rasht and Birjand have been investigated. The SDSM model combines linear regression and meteorological random generator, because the humidity variables and large-scale silicon pattern of the atmosphere are used linearly for local-scale meteorological generating parameters at single stations. The LARS-WG model is one of the most popular random weather data generator models and is used to generate daily series of rainfall values and minimum and maximum temperatures and sunny hours in a station under the conditions of basic and future climate conditions. This method is based on using random weather generators, which are offered based on the time series pattern and Fourier series. The dynamic bias correction method (BCSD) was first used to estimate the long-term components of hydrology and is now widely used in monthly climatic studies. By performing preprocessing operations on the minimum and maximum temperature data in the two stations analyzed, the results of the box diagram method showed that the studied data lacked outdated data. In addition, the results of data trend using the Mann-Kendall test for two parameters of minimum and maximum temperature in Birjand and Rasht stations show an increasing trend. According to these results, the SDSM model has a very high performance for predicting both stations’ minimum and maximum temperature parameters. The results of the LARS-WG model also show that the correlation between predicted data and daily observational data per month in both stations is perfect. The LARS model has a good performance in general, especially in predicting maximum temperature data, also the best performance of the LARS model According to the maximum temperature data in Birjand synoptic station. The worst performance is related to the minimum temperature data in this station. In addition, the results of the qmap model show that, in general, the best performance of qmap model is related to the simulation of the minimum temperature parameter for the next period in the Rasht station. The worst performance associated with the simulation of the minimum temperature parameter in the Birjand station. According to the results of the R2 index, this model has a good performance. Still, according to the NSE index results; this model's performance is not suitable for simulating the minimum temperature parameter for the next period. Comparison of annual maximum and minimum temperature changes in two synoptic stations of Birjand and Rasht shows that the parameters of minimum and maximum temperature in both stations will increase in the next period of 2056-2025 compared to the base period (1974-2005). Also, the temperature changes under the RCP 8.5 scenario are more than the rcp4.5 scenarios. In addition, the fluctuations of the minimum temperature parameter in the future period of Rasht station are more than Birjand station. In addition to these changes, the average maximum and minimum monthly temperatures in the period 2056-2025 compared to the base period (1974-2005) show that these changes are incremental for Birjand station and in Rasht station, except for the SDSM model in February, March, April, October, November and December and qmap in April, the changes are incremental. Changes in the average monthly minimum temperature of the next period in both stations are also an increase. However, in the SDSM model in April for Birjand station and in October, November, and December for Rasht station these changes decrease. However, according to the obtained results, in the period 2056-2025, the warming process is taking place in the study area.

Climate change is one of the greatest challenges facing humanity in the 21st century. Therefore, prediction of climate change is very important to predict the future situation and to consider the necessary measures to adjust and adapt to climate change. Therefore, in this study, climate change was predicted in the Dez watershed. For this purpose, the data of two global models HadGEM2 and CanESM2 were used under three scenarios RCP2.6, RCP4.5 and RCP8.5 and the application of two downscaling models of LARS-WG and SDSM and the climate changes in the next three periods compared to the base period (1989-2018) were examined. The results showed that both downscaling models have good accuracy in simulating climate change in the study watershed. The results of prediction the studied models also showed that in the future periods the amount of precipitation in the watershed will change between -6.3 to 15.7% compared to the base period. The most decreasing and increasing changes will be related to the eastern and southwestern areas of the watershed, respectively. Also, the maximum temperature of the watershed will fluctuate between 1.3 to 3.9 oC and the minimum temperature will fluctuate between 1.5 to 3.5 oC. The highest and lowest changes will be related to the southeastern and northwestern areas of the watershed, respectively. Therefore, due to the increase in temperature and precipitation, as well as the mountainous nature of the watershed under study, it is necessary to consider flood control and containment and management strategies.

Temperature change in the future, which is very important in terms of agriculture, development and health, can be one of the consequences of climate change and global warming. Knowing how it can be a significant help to the agricultural sector and development issues and managers decisions. One of the effects of climate change is the increase in temperature in some parts of the world, including Iran, so it is necessary to study this phenomenon. The purpose of this study is to know the rate of temperature change in the next period (2015-2073) compared to the base period (1987-2015) in stations of East Azerbaijan province. In this study, the trend of temperature changes in Maragheh, Miyaneh, Jolfa and Tabriz synoptic stations with the help of SDSM statistical microscale exponential model under three scenarios of optimistic, intermediate and pessimistic radiation induction during the next two periods (2016-2044) and the middle future (2073-2045) has been examined. For this purpose, the observational data of the mean maximum and minimum temperature in the base period have been entered as input to the model. The output results of the exponential microscale model showed that temperature changes in future periods have an increasing trend in all studied stations. For example, in the Tabriz synoptic station, in the pessimistic scenario, the average temperature will increase by 4.17 ° C in the future, and in the optimistic scenario, the increase will be 1.85 ° C. According to the results of this study, the highest temperature increase in the middle pessimistic scenario compared to the base period for the middle synoptic station by 4.51 ° C and the lowest temperature increase in the near future medium scenario at Maragheh station by 1.31 ° C has been observed. It is suggested that politicians and planners pay attention to this increase in temperature in the fields of urban architecture, agriculture, energy management, etc.

Climate change is a phenomenon that has affected natural ecosystems and all aspects of human life in recent years. Therefore, identifying and predicting climate change can greatly help manage it and reduce its harmful effects. The purpose of the present study is to identify whether or not occurrence of climate change by extreme indices including TXx and TNn during 1961-2015. In addition, CanESM2 model use under three scenarios to predict future climatic scenarios and the data were processed using SDSM model to detect the temperature of the studied stations. To this aim, the maximum and minimum daily temperature data of Sanandej and Saqez synoptic stations were used. Mann-Kendall test use to detect the trend of temperature extreme indices and Sen’s estimator applied to magnitude of trend. Subsequently, climate forecasts were carried out under three scenarios of RCP2.6, RCP4.5 and RCP8.5 of CanESM2 model and two extreme indices were obtained in 2020-2050. The results showed that both of extreme indices had a significant trend and the TNn have a significant trend in Saghez during the baseline period. In the case of the generated scenarios, only RCP2.6 was meaningful in two Indices for Sanandej and in TXx have a significant trend for Saghez. The results showed that in the case study, the highest maximum and the lowest minimum temperature will increase under three scenarios over the next 30 years by one percent compared to the average of baseline period. It should be noted that this increase will occur over a period of 30-year and will certainly increase in the longer term.

Rising global warming is one of the greatest challenges facing humanity in the 21st century. Therefore, it is very important to predict the maximum temperatures in order to know the amount of changes and, as a result, to take the necessary measures to adapt and moderate the adverse effects caused by it. Therefore, in this study, maximum temperatures were predicted in three provinces of Kurdistan, Kermanshah and Ilam in the west of the country. For this purpose, the data of two global models HadGEM2 and CanESM2 were used under three scenarios: RCP2.6, RCP4.5 and RCP8.5, as well as two microscale models of LARS-WG and SDSM, and changes in maximum temperatures on a monthly and annual basis in the future 2050-2021) compared to the base period (2018-1989) were examined in 17 meteorological stations. MAE, MSE, RMSE and R2 indices were used to calibrate and validate SDSM and LARS-WG models. The results showed that both models have a high ability to simulate the maximum temperatures of the study area. However, the SDSM model is more accurate than the LARS-WG model, with the lowest and highest accuracy for Bijar and Tazehabad stations with RMSE of 0.02 and 0.18, respectively. The results of maximum temperature forecasting also showed that according to both models, the maximum temperature in the future period will increase compared to the base period, which is the average of the studied models between 0.8 to 1.9 degrees Celsius in the region. Will be studied. The highest rate is estimated based on the RCP8.5 scenario. Spatially, the most changes are related to the northern and eastern areas of the study area and the least changes are related to the western areas of the study area.

Due to the importance of climate change and the effects it can have on runoff, developing a suitable model for simulating the present and future conditions of the catchment areas is of great importance (Rajabi et al, 2012). Nowadays, the LARS-WG and SDSM models are used to downscale environmental parameters in climate change studies nowadays. Studies show that the SDSM model has less uncertainty and a more complex simulation process, and the LARS-WG model with simpler process and faster performance is more efficient (Aghashahi et al., 2012). Considering that many of Iran's watersheds lack hydrometric stations, it is of great importance to use those methods that can estimate the amount of runoff obtained from the rainfall. Therefore, the present study aimed to investigate the role of climate change in estimating runoff from the Silakhor-Rahimabad basin of Lorestan using a rainfall-runoff model (SIMHYD).

Silakhor-Rahimabad basin is in the catchment area of Dez Dam in Borujerd, Lorestan Province, which is located between N 33º 45¢ and 34º 7¢ and E 48º 29¢ and 48º 57¢. First, the baseline data including observation data of minimum temperature, maximum temperature, precipitation and sunshine during the period 1990-2014, and rainfall-runoff data including evaporation, rainfall and flow data, which were available, were received from the General Department of Aerology and Regional Water Authority of Lorestan Province, respectively. In this study, the daily data from Borujerd metrological synoptic station were used as the basis because they were complete and the elevation of the station was equal to the average of other stations’ elevations. For the evapotranspiration variable, the mean daily data from Rahimabad, Borujerd, and Silakhor stations were used, the mean daily data from the six rain-sensing stations for the rainfall variable. Moreover, Rahimabad hydrometric station at the outlet was used as the base station to observe the runoff variation in the basin.

The results of the evaluation of criteria show that the LARS-WG model has a good ability to simulate rainfall parameters, minimum temperature, and maximum station. The simulated precipitation is in good agreement with the observed values (Table 1 and 2). After assuring the ability of the LARS-WG model to produce the rainfall data, minimum and maximum temperatures of Silakhor-Rahimabad Basin, the output of the HADCM3 model was downscaled under the scenarios A2 and B1, the parameters were predicted and compared with their values in the period 1990-2014 (Figures 2, 3 and 4). The results of the LARS-WG model indicate a decreasing trend of precipitation and temperature rise under both scenarios A2 and B1 for the period 2046-2065. The average amounts of annual rainfall predicted under the scenarios A2 and B1 are 451.445 and 4.420 mm, respectively. If the annual rainfall is 453.8 mm in the base period, the study area will observe a decrease in precipitation from 51.0 to 20.7 percent. The results obtained in the SDSM model under the two climate scenarios A2 and B2 for the future period indicate that the average air temperature is increasing and in the period 2050, the monthly average temperature, compared to the 1990-2014 period, increases in most months of the year. Also, rainfall has a decreasing trend in this period (Figure 5). Investigations show that the model efficiency has a direct correlation with the recharge coefficient, infiltration coefficient and soil moisture storage capacity. The effects of climate change on runoff are presented in Figures 9-16. In the period 2046-2065, the amount of runoff in the studied basin will decrease compared to the 1990-2014 period. This decrease in runoff rate can be attributed to the increase in temperature, followed by an increase in evaporation and a decrease in rainfall. Regarding the study of temperature and rainfall for the future period and monthly runoff, it is observed that the amount of runoff will decrease in the future period.

In recent decades, the increase in greenhouse gases and thereby, the rise in temperature, have made Earth’s climate system imbalanced and caused massive climate change in most parts of the planet. Therefore, it seems necessary to apply climate predictions in national macro plans, especially in relation to natural disasters. The results indicate the decrease in precipitation and temperature rise in both SDSM and LARS-WG models. Also, in the present study, the SDSM model showed more variations than the LARS-WG model. Finally, the results obtained in both statistical downscaling models indicate the decreased amount of runoff in the studied basin is in the future period. In the study of the effect of climate change on runoff in the studied area, according to the values of Nash-Sutcliff coefficient and the coefficient of determination obtained at the calibration step (0.63 and 0.779, respectively) and the verification step (0.61 and 0.61, respectively), it is observed that the SIMHYD model has acceptable performance in the studied basin. These results are consistent with the findings of Aghashahi et al. (2012), Rajabi et al. (2013), Zolgharnein et al. (2013), Zhang et al. (2014).

Variations in frequency and intensity of drought have substantial impact on water resources and environment, which in turn are reflected on agriculture, society, and economy. The aim of this study is to investigate the effect of climate change on meteorological drought in Urmia. For this purpose, using rainfall data, the drought of the base period (1986-2005) and the future periods (2031-2050 and 2051-2070) were calculated for 3, 6, 12 and 24-months’ time scales in Urmia. Precipitation for future periods were determined using the CanESM2 Fifth Report Model and three Scenarios (RCP2.6, RCP4.5 and RCP8.5) and were downscaled using the SDSM model. Then the trend values of SPI in baseline and future periods were investigated using Mann-Kendall test. The study of precipitation changes showed that in the first future period, based on two scenarios RCP2.6 and RCP8.5, the average precipitation will increase, but in the second future period there is a very small decrease in precipitation. Also, SPI values on a long-term time scales indicate a higher severity of drought and among the studied scenarios, RCP8.5 shows more severe drought intensity than the base period compared to other scenarios. The results of trend analysis also show significant changes in SPI values in the reference period and future periods based on the RCP8.5 scenario.

This study predicted amount of precipitation and temperature by using atmospheric General Circulation Models, simulation of climate in the periods 2070-2099 and 2020-2049 in Babolsar, Gorgan, Ramsar, Qazvin, Rasht, Zanjan and Tehran synoptic stations. In order to prepare climate scenario for the future was used of outputs from general circulation model HADCM3, A2 and B2 scenario. General circulation models have a low precision; further the small-scale model of the SDSM was alternated. And then the method of Domarten for the determination of future climatic were used. The results of the prediction of climate parameters indicated that the model well simulated climate parameters. Results of precipitations at all stations in the study period 2020- 2049 compared with the period 1979-2008 and showing an increasing trend in all stations; the period 2070-2099 compared to observation periods and results show the increasing in precipitation; but with decreasing trend with the periods was 2020-2049. In the 2070-2099 and 2020 – 2049 periods, maximum, minimum and maximum temperature relative to the observation period 1979-2008 has increased. The results of the climate determination by Domarten method has showed climate in the Babolsar, Qazvin, Ramsar and Rasht station compared to climate observed to 1979-2008 in future periods will not change. In the Gorgan station at periods 2070-2099 under the A2 scenario climate from Semi-arid will change to arid climate. In the Zanjan station at periods 2020-2049 under the A2 scenario climate from Semi-arid to Mediterranean climate and at periods 2070-2099 climate from Mediterranean will change to Semi-arid climate.

Temperature and precipitation are among the most important climatic elements in the study of climate change due to significant temporal and spatial changes, and the projection of their changes is agreat importance in environmental planning and hazards. Therefore, in this study, future projection of temperature and precipitation changes in the southern cost ofcaspian sea was investigated. For this purpose, the data of CanESM2 model was used under three scenarios (RCP2.6, RCP 4.5 and RCP 8.5) using the SDSM downscaling model the temperature and precipitation changes in three different periods (2021-2050, 2051-2080- and 2081-2100) were examined in relation to the basic period (1989-2018). To calibration and validation the SDSM model, stationary observation data and NCEP data were used, as well as MAE, MSE, RMSE and R2 indicators. The results showed that the SDSM model has the ability to simulate temperature and precipitation changes in the study. Based on the results of the CanESM2 model output, the minimum and maximum temperatures will be increase in all studied periods and each period compared to the previous period, the average amount for the minimum temperature is 2 oC and for the maximum temperature, it will be 2.2 oC  compared to the base period. Most of the temperature changes are related to the western regions of the study area. The projection of precipitation changes also shows that except ofGorgan station in other stations study, the amount of precipitation will decrease in all future periods compared to the base period, the average of which will be during the next period (2021-2100),Will be equal to 3.6%. Most of its changes are related to the central and western areas of the study area. Also, the RCP8.5 scenario showed the most changes in temperature and precipitation.

Precipitation, as one of the most important elements of the hydrological cycle, is affected by climate change and this can lead to new conditions in water resources.In the present study, to monitor and simulation of Precipitation conditions in the southern coast of the Caspian Sea (Gilan and Mazandaran provinces), daily precipitation data of Astara, Rasht, Bandar Anzali, Ramsar, Gharakhil, Noshahr and Babolsar synoptic stations for base period 1986- 2015 from the Meteorological Organization and daily data for CanECM2 model with three scenarios RCP2.6, RCP4.5 and RCP8.5 from the Canadian Climatological Site was obtained. SDSM version 3.5 was used to simulate precipitation changes over the future period (2021-2050). After doing model calibration and validation, the downscaling of precipitation data was performed for the future period (2021-2050). Then, intensity, duration and frequency extreme indices of precipitation for the base and future periods were calculated. The results of three scenarios showed that the average annual precipitation in the study area increased, on average, by 20 to 70 mm as compared to base period (1986-2015). In addition, the results of analysis of precipitation indices showed an increase of precipitation with more than one mm in all weather stations and an increase of precipitation with more than 10 mm in most of the weather stations, excluding Babolsar and Gharakhil, in the future period 2021-2050 in comparison with the base period. However, the precipitation more than 20 mm was similar in the future and base period with a little change. The results revealed that a decrease in R95p and R99p indices at Ramsar and Nowshahr stations and an increase in the western and eastern parts will occurred in the future. Moreover, a decrease in the dry period length will happened in the future as compared to the base period. The extreme precipitation values are expected to be high for the region, which will increase the average annual precipitation.