جستجوی مقالات مرتبط با کلیدواژه « ریزمقیاس نمایی » در نشریات گروه « جغرافیا »

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.

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.

The satellite-based precipitation products are one of the sources of rainfall estimation. Nonetheless, for usage in the local regions and, or for parameterizing of meteorological and hydrological models at basin scales, their spatial resolution is often coarse. Therefore, in this study, a downscaling– calibration method was developed for global precipitation measurement (GPM) satellite estimates (at 0.1° spatial resolution), for one year from 01/04/2014 to 31/03/2015, by considering the spatial heterogeneity of the relationship between precipitation and the environmental variables using the mixed geographically weighted regression (MGWR) model for Golestan province. In obtaining improved precipitation data with 1 km spatial resolution at an annual scale, the results showed that (1) the proposed method not only improved the spatial resolution of precipitation but also increased accuracy; (2) the downscaled and calibrated precipitation data (CC = 0.74, bias = 0.23) performed better than the original data (CC = 0.58, bias = 0.35) against ground observations.

Due to technical and financial limitations, it is not possible to simultaneously provide high spatial and temporal resolution by a sensor. There is always a trade-off between the spatial and temporal resolution of the sensors. For studies such as estimating evapotranspiration, land surface temperature with high temporal and spatial resolution is required; however, estimating actual evapotranspiration with high temporal and spatial resolution by a single sensor is not possible. Since high spatial and temporal resolution together increase the reliability of analyzing and extracting information from the image, so the best way to overcome this problem is to downscale images to high temporal and spatial resolutions. Downscaling is the process of converting images with low spatial resolution to images with high spatial resolution. So far, several methods have been proposed for downscaling. These methods differ for downscaling of the reflectance and thermal bands. Many studies that have been conducted so far on the actual evapotranspiration estimation, indicate the efficiency of SEBAL algorithm for this purpose. Therefore, in this study, in order to calculate the actual evapotranspiration, the SEBAL model was used and the products of different downscaling methods were given as input to this model. Assessing the accuracy of actual evapotranspiration ​​calculated using remote sensing data indicates the efficiency of products obtained from different methods. According to the studies conducted in this field, so far no study has been done on the combination of downscaled bands obtained from different downscaling methods applied on thermal data and non-thermal data in order to calculate the actual evapotranspiration. In this study, STARFM, ESTARFM and Regression algorithms were used to downscale the reflectance bands and SADFAT, Regression and Cokriging algorithms were used to downscale the thermal bands. Then the accuracy of the results was evaluated.

The study area is Amirkabir agro-industry located in the south of Khuzestan province, one of the seven companies for the development of sugarcane cultivation and ancillary industries (longitude 48.287100, and latitude 31.029696 degrees). The gross land area of this agro-industry is 15000 hectares and its net area is 12000 hectares which is divided into several 25-hectare plots. In this research, the images of MODIS located on Terra satellite and the images of OLI and TIRS sensors of Landsat 8 satellite were used. It is worth noting that the Landsat image for time 2 was used to evaluate the simulation results. The downscaling algorithms used in this research included STARFM, ESTARFM, and REGRESSION algorithms were applied on reflectance bands and SADFAT, Regression and Cokriging algorithms were used for thermal band downscaling. In order to conduct this research, first, various downscaling methods were applied on MODIS images to be downscaled to the images with Landsat spatial resolution. Then, using MODIS downscaled images, evapotranspiration values were calculated for different combinations of downscaled data using SEBAL method and the results were compared and evaluated with evapotranspiration obtained from Landsat images acquired at the same date as MODIS data.

In order to evaluate the results, the downscaled bands were visually and quantitatively compared with the corresponding bands of the Landsat image acquired on the same date. In order to compare these data quantitatively, the root mean square error (RMSE) and the coefficient of determination (R2) were used. According to the RMSEs, it can be concluded that the STARFM, ESTARFM, Regression, SADFAT and Cokriging downscaling algorithms all perform well. Among the methods applied to the reflectance bands, STARFM with the RMSE of 0.0180 had the best performance, followed by ESTARFM with the RMSE of 0.0186 and Regression with the RMSE of 0.0479. Among the methods applied to thermal bands, the SADFAT algorithm with the RMSE of 0.0224 had the best performance, followed by Cokriging with the RMSE of 0.0234 and Regression with the RMSE of 0.0464. It should be noted that the difference in outputs is very small, and given that the study area of ​​this study is a homogeneous area of ​​agricultural land cover including a single sugarcane crop. This issue can be the main reason for the close performance of downscaling methods and the high accuracy of their outputs. Moreover, according to the results obtained for evapotranspiration, ESTARFM / Regression, ESTARFM / SADFAT, STARFM / Regression and STARFM / SADFAT had the best performance with the lowest difference and the Regression / Cokriging method had the weakest performance, respectively.

This study can be concluded as follows:All downscaling algorithms used in this research had an acceptable performance in simulating Landsat bands.Among the reflectance band-related downscaling methods, STARFM had the best performance, followed by ESTARFM and Regression, respectively.Among the thermal band-related downscaling methods, the SADFAT algorithm performed best, followed by Cokriging and Regression.The use of STARFM algorithm for reflectance bands and SADFAT algorithm for thermal bands in homogeneous areas is recommended.The difference between the different combinations of methods for estimating actual evapotranspiration is small.

The prediction of climactic changes is of great importance due to their destructive effects on aquatic, environmental, economic, and social resources. Accordingly, the purpose of this study was to predict the climactic changes of Kermanshah city using micro-scale general atmospheric circulation models accessible in LARS-WG6 (GFDL-CM3, MPI-ESM-MR, MIROC5) model in scenarios RCP4.5 and RCP8.5 for the 2020 to 2100 period based on the benchmark period of 1980-2010. In order to evaluate the data forecasted in LARS-WG model, the error rate of the observed and predicted data was addressed using R2, RMSE, MSE, and MAD criteria. The results showed that LARS-WG model had the needed capability to predict climactic data in future. In the secondary models, the MPI-ESM-MR model in scenario RCP4.5 showed a higher reliability rate compared to other secondary models evaluated in the study. Moreover, all models indicated increases in the average minimum and maximum temperature and forecasted changes in rainfall pattern in future periods in the studied area. Then, the SPI and De Martonne indices were calculated for all models. According to SPI index, all evaluated climactic models demonstrated that by the year 2100, the years with normal index would decrease while the years with dry conditions would increase. Moreover, based on De Martonne index, the GFDL model in RCP8.5 scenario estimated the climactic changes level more than other models, and predicted that dry and semi-dry years will be more than wet years. Contrarily, the MIRO model in RCP45 scenario acted more optimistically and predicted less climactic changes.

Today, climate change is considered a serious challenge to human societies and the environment and has caused anomalies in the Earth's climate system. According to scientists, an increase in the average global temperature is inevitable. In this study, the climatic elements of temperature and precipitation of Ahar synoptic station for the near future (2026-2045), middle future (2046-2065) and distant future (2066-2085) using the outputs of the CanESM2 climate change model. Based on RCP2.6, RCP4.5 and RCP8.5 with SDSM exponential microscale model and also the annual trend of these changes was investigated using Mann-Kendall test. Based on the output of the model, it was determined that in the near future, precipitation will occur in February and November, and in the next two periods, mid and distant, in October, the greatest decrease will occur, and for April, May and August, the precipitation will increase. On average, the minimum temperature will increase to 0.38 degrees Celsius, the average temperature to 0.52 degrees Celsius and the maximum temperature to 0.82 degrees Celsius. Based on the results of Mann-Kendall test, the annual trend of precipitation in the future will be decreasing, the average temperature in the three scenarios will have an increasing and significant trend and the temperature elements (minimum, medium and maximum temperature) in RCP8.5 will be increasing and significant.

Any changes in the concentrations of greenhouse gases are causing imbalance status in system components. However, these changes along whit their effects the future should be simulated. There are different methods for the use of climate models is the most reliable.Here in this research, climate change status in Dez river basin where a major basin for water and agricultural yields is studied. For this purpose, the PRECIS model was used. PRECIS is an exponential dynamics downscaling model used to estimate the temperature and precipitation rates for the period of 2070 to 2100 under A2 and B2 scenarios. According to the results of climate change assessment under scenario A2 for Dez river basin, precipitation would decrease up to 22% and up to 5 degrees centigrade would rise in average maximum and minimum temperature while concerning B2 scenario, a decrease in precipitation up to 33% and a rise in temperature rise up to 3°C are estimated.

As global warming and changes in global temperature are considered to be the most important instances of climate change in the present century, temperature can be introduced as an indicator reflecting the response and feedback of climate system to these changes. In this regard, climate forecasting is performed using "simulation" approach. Using atmospheric general circulation models such as RCPs and climate scenarios developed as their output is an accepted method of simulating climate variables, especially temperature. In each of these scenarios, radiative forcing changes at a certain rate until 2100. Downscaling is the main technique used in RCPs. Different methods are used for downscaling among which artificial neural network is more widely accepted due to its more accurate evaluations. 

Data collected for the purpose of the present study include: 1) Daily maximum temperature recorded in Qazvin synoptic station during 1961-2005. These records were derived from Iran Meteorological Organization and used as an output for calibration, fitting, and finally selecting the best fit model for the observations, 2) Atmospheric observations including daily records of 26 atmospheric variables. These data were recorded by the United States National Centers for Environmental Predictions (NCEP) and the United States National Center for Atmospheric Research (NCAR) during 1961-2005 reference period and used as input or explanatory (predictor or independent) variables in the present study 3) Representative Concentration Pathway (RCP) extracted from atmospheric general circulation model (including the output of HadCM3 model) which is used to simulate 2006-2100 reference period.Artificial neural network model was used to downscale atmospheric data and simulate maximum temperature recorded in Qazvin synoptic station. Using Pearson correlation coefficient, the correlation between maximum temperature recorded in Qazvin synoptic station and each of the 26 atmospheric variables was estimated. Then, forward selection and backward deletion, percentage decrease index, and stepwise methods were used to preprocess the variables, select the most appropriate predictor variables (input variable in the network) and perform statistical downscaling. Following the selection of suitable explanatory variables in each of the above mentioned methods, selected variables were separately given as input to the network to reach a proper design for the neural network architecture and perform the final simulation. In other words, the artificial neural network model was fitted four times with different input variables. Then, number of neurons and network layers were determined, a suitable weight was assigned to each variable and the neural network was trained to reach the most appropriate architecture for the neural network. Finally, emission scenarios (RCP2.6, RCP4.5, and RCP8.5) were given as input to the selected architecture, and maximum temperature was simulated for 2006-2100 reference period. 

Appropriate explanatory variables were selected in the present study using four different preprocessing methods. Forward selection method with the lowest minimum mean square error (MMSE) of 6.7 and the highest correlation coefficient of 0.97 was selected as the most appropriate method. Therefore, variables obtained from this method, including average temperature near the surface, average pressure at sea level, and altitude at 500 and 850 hPa level, were selected as predictor variables entering the artificial neural network to simulate future temperature of the station. Finally, a neural network with 8 inputs, a hidden layer with 10 neurons and sigmoid transfer function, and an output layer with 1 neuron and Linear transfer function were confirmed using Levenberg-Marquardt algorithm. There were then used to simulate the future temperature of Qazvin synoptic station. The highest and the lowest temperature values were estimated for December with 9.9°C and January with 6.6°C, respectively. The lowest error rate also belonged to December (-1.5°C). Simulation results indicated that the highest increase in maximum temperature of Qazvin synoptic station in 2006-2100 reference period was observed in RCP8.5, RCP4.5 and RCP2.6 scenarios, respectively. The increasing trend in RCP8.5 scenario was estimated much higher than the base temperature. Moreover, the highest temperature increase (6.7°C) in RCP8.5 scenario belongs to June and the highest temperature decrease (3°C) in the optimistic scenario (RCP2.6) belongs to October.  

Selecting appropriate explanatory variables is an important step in the process of simulating future temperature. Various methods of variables selection, statistical downscaling and artificial neural network model were used to estimate and simulate temperature parameter. Then, RCP 2.6, RCP4.5, and RCP8.5 scenarios were simulated for the 2006-2100 reference period. Maximum temperature of Qazvin synoptic station in the simulated RCP scenarios (belonging to the reference period) was compared with maximum temperature in 1961-2005 period. Results indicate that the highest temperature increase in Qazvin station occurs in the pessimistic scenario (RCP8.5). The increasing trend of temperature begins with RCP2.6 scenario and reaches its highest level in RCP8.5 scenario. In these three scenarios, summer temperature of the next 94 years may increase at a higher rate as compared to other seasons in Qazvin. This means that not only Iran is located in an arid region, but also its temperature will be increasing in the future. Since temperature and precipitation in different parts of the world are considered to be among the most important indicators of climate change and global warming, various models designed to forecast and simulate these phenomena and the future climate suggest an increase in temperature during the coming decades.

The investigations show that the increase in greenhouse gasses has led to an increase in the average temperature of the earth’s atmosphere and, consequently the global warming in recent years. Moreover, the rise of greenhouse gasses has also caused changes in other climatic variables such as precipitation. Given the fact that climate change will bring about considerable effects on the water resources, the prediction of the climatic condition and water resources of a region is one of the planning prerequisites for the economic and social development of every country. Therefore, considering the climatic condition and volume of water resources in a country in the future is inevitable. Since the initial effects of climate change are exerted on the meteorological variables of precipitation and temperature, and any change in these variables results in disorder in many hydrological phenomena, it is essential to precisely examine the future changes of these parameters and their simulation methods. Hence, this study was conducted to evaluate the uncertainty of 20 AOGCM models under two emission scenarios of RCP2.6 and RCP8.5 in the two statistical periods (1961-2006) and (2006-2018) in the drainage basin of Karaj River. The changes in the parameters of minimum temperature, maximum temperature, and precipitation in the period of (2020-2040) compared to the baseline period were also investigated.

At first, the downscaling of the parameters of minimum temperature, maximum temperature, and precipitation was carried out using the two statistical periods of (1961-2006) and (2006-2018) and the large-scale NCEP variables as the inputs for the models of Artificial Neural Networks (ANN), LARS-WG, SDSM, and Change Factor method to determine the model’s error and evaluate its performance. For this purpose, the features and functions available in the programming environments of MATLAB, LARS-WG, and SDSM5.5.2 were employed after data normalization. The bootstrap confidence interval method and statistical criteria such as coefficient of determination (R2), Nash–Sutcliffe coefficient (NS), percent bias (PBIAS), and PRS were used to evaluate the performance of the models. Due to the suitable range of each one of statistical evaluation coefficients being within the confidence interval defined by the bootstrap method in the period of (1961-2006), the most appropriate climatic models were chosen for this from the selected AOGCM climatic model of the fifth report. In order to improve the accuracy of the chosen models in the period of (2006-2018) under the scenarios of RCP2.6 and RCP8.5, the most appropriate model for each AOGCM scenario was selected using the statistical indexes and bootstrap confidence interval method. In the next step, the chosen models of each climatic scenario were weighted using the MOTP approach for the final evaluation and selection of the model with the lowest uncertainty. Each one of these weights indicates the capability of each model in simulating the variables of minimum temperature, maximum temperature, and precipitation of the intended month, which was conducted in both scenarios of RCP2.6 and RCP8.5. The weights are presented as percentages, and the highest weight is 100, which is also considered the highest score for each model. The model chosen in this step conducts the downscaling and simulation of statistical parameters with lower uncertainty compared to the other AOGCM models and the downscaling models investigated in this study. In this stage, the confidence interval of the model (MPI-ESM-LR downscaled by the LARS-WG model) was calculated by the bootstrap method to ensure the efficiency of the chosen model. In order to analyze the accuracy of the model in downscaling the climatic elements, the number of station-months within the confidence interval was taken into account. In the last step, the climatic parameters predicted by the chosen optimum model (the model with the lowest uncertainty) in the future period (2020-2040) were compared with those in the baseline period (1999-2018). The changes in the precipitation, minimum temperature, and maximum temperature in the region were also investigated.

In the period of (1961-2006), among 20 AOGCM climatic models of the fifth report, the nine models of MIROC-ESM, CESM1-WACCM, CSIROC-MK3-6-0, EC-EARTH, GISS-E2-H, GISS-E2-R, MIROC-ESM-CHEM, MPI-ESM-LR, and MPI-ESM-MR were selected as the optimum climatic models in the drainage basin of Karaj according to the suitable range of each one of statistical evaluation coefficients for the models being within the confidence interval defined by the bootstrap method. More detailed investigations on the nine selected models in the (2006-2018) period under the RCP2.6 and RCP8.5 emission scenarios showed that the MPI-ESM-LR climatic model simulates and downscales the maximum temperature parameter in the two scenarios of RCP8.5 and RCP2.6 and the precipitation in the RCP8.5 scenario with the lowest uncertainty using the LARS-WG model. The GISS-E2-R2 model has similar performances on the minimum temperature in the RC8.5 scenario. Moreover, the EC-ERTH model simulates and downscales the precipitation with the lowest uncertainty. The three chosen models were weighted using the MOTP approach for the final evaluation and selection of the best model. The results showed that in most of the months, the highest weight percent in the simulation of climatic variables belongs to the MPI-ESM-LR model. This model is more capable of simulating the precipitation, minimum, and maximum temperatures in both scenarios of RCP8.5 and RCP2.6. Eventually, the confidence interval of the MPI-ESM-LR model (downscaled by the LARS-WG model) was calculated by the bootstrap model to ensure the efficiency of the chosen model. According to the results, it was found that among the 72 station-months, the average maximum temperatures in 62 cases were within the confidence interval.Furthermore, the monthly analysis of average and variance of the minimum temperature, maximum temperature, and precipitation showed that in most of the months, these parameters are within the confidence interval. This shows the high accuracy and low uncertainty of the selected model. In the next step, the parameters of minimum temperature, maximum temperature, and precipitation in the period of (2020-2040) were simulated and downscaled by the chosen model. The evaluation of results in this section showed that the minimum temperature would have a growing trend until 2040. The slope of the average minimum temperature curve in the studied statistical period under the RCP2.6 and RCP8.5 scenarios will increase by 0.02% and 0.08%, respectively. The most significant growth of this parameter was estimated to be +0.8ºC and +0.16ºC under the RCP2.6 and RCP8.5 scenarios in September and March, respectively. The precipitation in the RCP2.6 scenario has nor decreasing neither increasing trend. The statistics of Sen Test in the evaluated confidence intervals indicated that the average precipitation had decreased by almost 0.38 mm every year. The annual average in the RCP8.5 scenario has a significant decreasing trend with a slope of -1.31 mm in the low confidence interval (α = 0.1). The most considerable growth and reduction with the values of +0.45 and +0.63 in the slope of precipitation curve in this scenario are seen in July and October, respectively. This parameter has also a decreasing trend in the very high level of (α = 0.001) for the average maximum temperature in both scenarios. Based on the obtained results, this climatic parameter has a significant decreasing trend in the studied confidence intervals in most of the months. The largest reduction of this parameter under the RCP2.6 and RCP8.5 scenarios will happen in March and June by 0.20ºC and 0.14ºC.

Due to the low spatial resolution and simplification of atmosphere general circulation models compared to regional and short-term models, the outputs of these models cannot represent an accurate approximation of climatic conditions of the study area. Due to time-consuming and lack of economic efficiency in the use of dynamic models, there has been increased public attention to the use of statistical downscaling methods. One of the most useful and common methods in statistical downscaling is SDSM. For this purpose, the first, the atmospheric parameters in the region over the past 32 years (1984-2015) was investigated by the Mann-Kendall test. The results showed that changes in temperature and rainfall data have a significant trend. Then, by down-scaling, the data using SDSM software, the daily parameters of maximum, minimum, average temperature and rainfall of Haraz river basin in Mazandaran province were simulated according to RCP2.6 (optimistic) and RCP8.5 (pessimistic) scenarios and CanESM2 Climatic Model from the new CMIP5 series in three periods of twenty years from 2020 to 2079. Finally, under the RCP8.5 critical scenario, until the end of the prediction period, the maximum, minimum and average temperature of the basin will be increased 0.91, 1.13 and 0.96o C, respectively and precipitation decreased 15.1% (7.4 mm) compared to the base period.

This research was carried out to evaluate the effect of climate change on changes in cold stress on potato plants in tropical regions of Kerman province. In this study, changes in the occurrence of cold stress in the observation data of three stations of Jiroft, Kahnouj and Manoajan during the period 1981-2005 and the period 2011-2100 were considered as the future period. The future course data, using of the general circulation model CanESM2 under rcp26, rcp45 and rcp85scenarios, using were quantified SDSM software.the climatic indices used to evaluate the changes in cold stress in potatoes were determined by the tolerance thresholds of potatoes and based on the average of long-term climate statistics and was evaluated its probability of occurrence during the growth period. In the Jiroft station the probability of occurrence of premature and late temperature below 5℃ respectively than 83% and 63% decrease. At Kahnoj and Manoajan stations, the probability of occurrence of premature and late temperature below 5℃ in the future period decreased and increased, respectively.The beginning and the end of the incidence of cold stresses have been set at the beginning and end of the cold season.The statistical results indicate an increasing the days of cold stress (5≤T≤0) at the Jiroft station during the periods 2011-2040 and the Manojan Station in the period 2071-2100 compared to the previous period . At the Kahnouj station, decreases the frequency of days with cold stress. However, the days with freezing stress in Jiroft, Kahnoj and Manojan stations in the next climate periods are showing a significant increase.The results indicate that the average date of occurrence of early stages is 10 days earlier and the late stages are 14 days later than the average long-term climate in the study area.

Evapotranspiration is one of the most important parts of the water cycle (Boegh and Soegaard 2004). Precise prediction of actual evapotranspiration () is essential for various fields, such as agriculture, water resource management, irrigation planning and plant growth modeling. Therefore, accurate determination of actual evapotranspiration has always been a major concern of experts in these fields. Due to the limited number of weather stations and the fact that collecting ground information is both time consuming and expensive, remote sensing and satellite imagerycan be a suitable tool in determination of actual evapotranspiration (Brisco et al., 2014). Satellite productions are usually divided into images with low, medium and high spatial resolution (Rao et al., 2017). Surface energy balance is a method usually used in combination withremotely sensed spatial data for estimation. Information collected from various sources, such as remotely sensedimageries and meteorological data, are used in this method. The present studyinvestigatesspatial distribution on different scales (from field- to regional-) using remotely sensed imagerieswithdifferent spatial and temporal resolution. TheSurface Energy Balance System (SEBS) is one of the most important methods used for the estimation of in remotely sensed images (Ochege et al., 2019). This model needs thermal maps produced using satellite images. Daily maps produced with RS are usually very large, and their pixelsize is usually so large that it can provide the spatial diversity found in the basins with respect to the errors (Mahour et al., 2017).

In order to estimate the actual evapotranspirationin satellite images collected from Zayanderud basin,the effects of Co-Kriging downscaling of surface temperature (LST) were investigated in June 2017 using two different methods.To reach this aim, we first applied a co-kriging downscaling method to a low-power LST product collected from MODIS at 1000 meters. Then based on the results and using the SEBS system, the daily  was obtained from images with average spatial resolution (250 m).In the second method, map produced usinghigh resoultion satellite imageswas downscaled to medium resolution (250 m). For both methods, 250 m resolutionMODIS NDVI products were used as co-variables.Then, validation was performed using Landsat-8 imagery, and land surface temperature was extracted from its thermal bands. SEBS algorithm was used to determine in Landsat 8 30-meter resolutionimagery. Accuracy of measurements wasexamined based on a comparison between down scaledLST and maps (250 meterresolution).

In the present study, mean LST equals 3/312 K (SD = 1.74) and average daily equals 12.5 mm / day (SD = 0.86). In the downscaling phase, the relationship between LST parameters and and vegetation index(as a co-variable)was investigated.Moreover, to investigate the relation betweenhigh resolution variables and NDVI, we re-sampled LST and   variables from a 1000 mresolution to 250 mresolution.In250 mresolution, there is a negative linear relation (r=-0.85) between LST and NDVI, but the relation betweenand NDVI is positive (r = 0.80). Thus, lower LST (> 305k) indicates more vegetation (NDVI >0.3) inthe region, while higher LST results in lower NDVI or lack of vegetation. As a result, more vegetation can be observed in regions with higher(12 mm/day). Results indicated that the difference between average  downscaled-SEBS (12.56 mm/day) and reference  (13.11 mm/day) is negligible. The RMSE between the reference and the downscaled  equaled 1.66 mm/day (r = 0.73), and RMSE between the reference LST and the downscaled LST equaled4.36 K (r = 0.78). Thus,values obtained from two downscaling methods were similar, but the  obtained from downscaled LST showed a higher spatial variation. Therefore, LST has greatly influenced the production of maps using remotely sensing images, and Co-Kriging downscaling has been useful for providing daily  maps with intermediate spatial resolution.

Evapotranspiration downscaling using the co-kriging method is not significantly different from the SEBS product and the results are similar. The results of -SEBS method isalso acceptable, but the  derived from the SEBS algorithm is more variable due to the LST downscaling.

Studying and identifying the climate variabilities occurring in different regions, may give insight toward possible future climate variabilities.  Using available climate models as well as downscaling, is a way to recognize the possible variabilities of climate components of future. In the present study, the precipitation and runoff of Rood Zard Basin were downscaled and simulated for the time period of 2006-2100. For this purpose, the RCP output scenarios of the CanESM2 model were utilized for 1975-2005. For downscaling the precipitation and runoff of Rood Zard Basin, the daily precipitation data of Baghmalek and the runoff data of the Mashin station and artificial neural network method were used. The mean sea-level pressure, the geopotential height at 500 hPa, and the mean temperature at ground level were all selected as the predictive variables, using correlation and partial correlation calculations, as well as the backward variable elimination method. The verification of the design was carried out by the RMSE and R2 indexes. Finally, the network architecture was selected through the Bayesian Regularization algorithm along with three hidden layers as the optimal network. The results show that annual precipitation have decrease trends in future 95 years. revealed that the precipitation increased in the hot months of the year and decreased in the cold months. In other words, the increase of local rainfalls due to the temperature rise is most probable in future periods. The runoff would decrease in the cold months and increase in the warm months regardless of the temperature and vegetation impact. Climate change is the main phenomenon affecting the climate and the human environment as well as environmental phenomena (such as droughts and wetness years, water resources, sea level changes, temperature alterations, changes in the behavior of climate elements, and many other phenomena). Investigating many phenomena of the past decades revealed that the planet earth's climate is changing. Compared to the previous time periods, the  results of the previous studies indicated that the climate variablity trend has become faster in the past 150 years. To fully understand the climate, all the units involved in its formation should be evaluated simultaneously. For this purpose, models may be helpful to some extent. Modeling is the process of creating a model that can provide the structure and function of systems. One of these methods is GCM in which the climate is simulated. These models are developed based on different climate scenarios aiming to simulate the impact of greenhouse gases on the earth's climate. Moreover, they are able to simulate and predict the future climate of the earth. These models create various time series of climate variables with relatively large networking. However, they are not suitable for direct use in the studies relating to the local climate variability. Thus, researchers have designed suitable downscaling methods to gain the climate data on a local scale. One of these methods is the statistical downscaling.

In the present study, the precipitation and runoff of the Rood Zard Basin are downscaled based on the RCP climate scenarios. RCPs are new emission stimulant scenarios which are used as the input of CMIP5 climate models and are based on the fifth report of IPCC. Scenarios are important parts of climate simulations that allow the researchers to study the long-term outcomes of the current decisions. In the RCP scenarios, 26 atmospheric parameters were considered for future simulations. Each of these has a relatively high connection to environmental elements. The selection of the most optimal parameter for expressing the relationship between weather conditions and the environmental characteristics depends on the type of environmental parameters. To select the appropriate parameters, the correlation and partial correlation calculations and the Backward Variable Elimination methods were applied.For downscaling, the BOX_019X_44Y data were acquired from the Environment website of Canada. The data were analyzed through calculating the correlation coefficients, partial correlation and also the Backward Variable Elimination method. The results revealed that 3 variables including the Mean Sea Level Pressure, the geopotential height at 500 hPa, and the mean temperature at ground level had an acceptable correlation with the precipitation at the Baghmalek station and omitting other variables created a lower missing variance.Downscaling was carried out based on the artificial neural network model with the Bayesian Regularization algorithm. Artificial neural networks are the patterns for processing data which are produced by imitating the neural network of the human brain. In recent decades, this method has been recognized as a useful and reliable tool for modeling complex maps existing between different variables. Artificial neural networks are able to pick up a system’s hidden behavior through available data. Each network has three layers: the input layer, the hidden layer, and the output layer. The input layer is, in fact, a layer used for producing the data given to the network as an input. The output layer includes values that are simulated by the network. The hidden layer is the place of analyzing the data. Unusually, the number of chosen neurons in this layer is obtained through trial and error.

In order to downscale neural network using the output RCP scenarios of the CanESM2 model, the daily precipitation data in the Baghmalek station during a time period of 30 years (1975-2005) were chosen as the base statistical period. After the selection of atmospheric high-scale variables, these variables were introduced into the neural network as input. The precipitation was considered as the target and the network was designed using algorithms and numerous hidden layers. Finally, the network designed with the Bayesian regularization and 3 hidden layers were chosen as the optimal network. As mentioned earlier, the artificial neural network was used for downscaling. Moreover, the daily precipitation data were simulated for the statistical period of 2006-2100. Linear regression was applied for simulating the runoff for the aforementioned period. The daily runoff, as well, was estimated for this period. The results demonstrated that the estimated monthly precipitation rate from November to December in the future 95-year period has decreased. Likewise, the simulated precipitation rates from January to November were higher than the monthly precipitation rates in the base period. Therefore, it can be concluded that the precipitation decreased in the cold months and increased in the hot months.  Additionally, the runoff in the base period from January to May was less than the observed runoff and it was more than the observed runoff from June to December. This was due to the fact that only precipitation was used as an independent variable for modeling; whilst, the runoff was affected by other factors such as springs water in addition to the rainfall. From November to May, the estimated monthly rates of runoff for the next 95 years were reduced. Moreover, from November to October, the simulated runoff rates were more than the monthly runoff rates in the base period. Accordingly, it can be concluded that the runoff decreased in the cold season and increased in the hot season, as well. The increase in the precipitation and runoff rates in the hot season could be due to the rise in the local rainfalls. In other words, an increase in the local rainfalls due to global warming was probable in future periods.

Sustainable development of agriculture is facing with several challenges such as climate change resulting change in the pattern of weather parameters as well as crop water requirements. In this research, due to the important role of agricultural activities whitin Urmia Lake Basin in the production of various agricultural products such as wheat, the possibility of supplying the plant's water requirement from green water and drought and evapotranspiration (ET) changes under climate change were investigated. Using the SDSM model and baseline meteorological data (1967-2015) of Tabriz and Urmia stations, meteorological data for the future period (2016-2100) was projected under RCP2.6 and RCP8.5 scenarios. Reference ET and the severity of drought were assessed by using the FAO-Penman-Monteith equation and the SPI drought index, respectively. The SDSM model had a relatively good performance and accuracy in estimating rainfall and ET in the study area. The findings of the study indicated an increase of 63 and 3% in the average rainfall of the future period for Tabriz and Urmia stations, respectively, and a reduction in severity of drought compared with baseline period. Under climate change, the average of annual ET of Tabriz and Urmia stationswill be decreased by 3 and 1.5%, respectively, and the amount of green water will be increased.

Today, it is accepted that the occurrence of any change in the climatic system is important in water and soil resources management. Climate fluctuations have had irreparable effects on water and soil resources of Khorasan Razavi area, especially in Mashhad. Hence, the attitude towards the future is considered as one of the essential requirements for metropolitan and regional management. In order to provide an outlook from future changes of extreme events, especially precipitation, the output of models of atmosphere general circulation (HadCM3, CNCM3, NCCCSM) are used based on A1B, and A2 scenarios under the LARS-WG model for the two upcoming periods of 2011-2030 and 2046-2065. The studied rainfall indices in this research include PRCPTOT, R10mm, R20mm, R95p, R99p, RX1day, RX5day, and SDII. The prediction of changes in the extreme events caused by global warming and climate change is very important in assessing the potential impacts of climate change on different sectors, such as water, agriculture, and management of urban water collection systems. Since the city of Mashhad has a density of large urban population and is known as a semi-industrial region in which the effects of climate parameters on different parts of the urban and industrial community are important, economic development and sustainable living conditions in the future years depend on the ability to manage the risks associated with extreme events. In this study, the magnitude of extreme values change in the predicted rainfall of Mashhad station was investigated for the two periods of 2011-2030 and 2046-2065 using simulated data through three general circulation models (HadCM3, CNCM3, NCCCSM) under the two scenarios of A2 and A1B, and was downscaled in the station scale by LARS-WG Model (Baseline Statistical Period 2014-2016). It was obtained from the reduction of the uncertainty of the average of calculated indices for the three models. Finally, the percentage rate and the amount of index change were calculated. In this research, data quality control was performed using a software package called RClimDex. Also, the homogeneity of the data used was done by using RHtests_dlyPrcp software package under R programming language. To investigate the ability of HadCM3, NCCCSM, and CNCM3 models in the simulation of weather data, especially rainfall, correlation coefficient was used between the monthly rainfall data observed and simulation data of the three models during the base period of 1966-2014. The results showed that there was a relationship between the two series of data with 99% confidence. Despite the low amount of correlation coefficient between the observed and simulated data, the significance test of this coefficient showed that there is a relationship between the two series of data with 99 certainty. The mean values, variance and standard deviation of climate variables can be compared using T and F tests in surveying the ability of LARS-WG model in simulation of climatic data. The climatic parameters of precipitation were firstly calculated using 53 years of monitored data in Mashhad station (2014-2016) using the semi-experimental distribution in the LARS-WG model. Then, the model was performed to generate 80 years of data based on the obtained parameters based on the data series observed at the station. This operation was performed several times by changing the random number to obtain acceptable statistical results. The results of t-test for this station showed that there was no significant difference in the significance level of 0.05 between the mean of simulated rainfall and its actual value, and, correlation coefficients, bias, and mean of absolute error were also calculated for this station in the monthly series of observed and simulated data. Consequently, a comparison was made between the mean values, the standard deviation, and the maximum of monthly rainfall of the two observational and simulated series. This study aims to present an outlook of the events and investigate the effect of changes in greenhouse gas rates based on A1B and A2 scenarios on the mentioned indices in terms of percent and the amount of their change relative to the basic period. The results showed an average of five-day precipitation and rainfall intensity during the 2011-2030 period will probably increase under the A2 scenario. Also, a more share of the total annual precipitation will belong to the occurrence of torrential and floody rains, i.e., precipitations over the percentile of 95 and 99 basic period. According to the results, the increase of these indices means an increase in the frequency of flood occurrence and its severity, especially during the upcoming period of 2011-2030. However, the probability of decreasing precipitation intensity and indices of maximum five-day rainfall are predicted during the period from 2046 to 2065. Due to the importance of the subject, it is recommended that the authorities pave the way for further studies such as the use of other methods of downscaling under new scenarios in this field, since such results are necessary in long-term planning in the urban services sector.

Statistical downscaling methods to predict climate variables such as temperature, because of the importance of these factors in the planning and environmental management have been used extensively. In this study, the performance of statistical downscaling model (SDSM) is the predicted temperature parameters studied. The data used in this study includes data on the minimum, maximum and average synoptic stations Mashhad, Shiraz and Ramsar data NCEP data model HadCM3 (the third generation model of global climate scenario A2 and B2) for the base period ( 1990-1961 AD). The first 15 years of data (1975-1961) for calibration and the second 15 years (1976-1990) were used to evaluate the performance of the model. To help HadCM3 data (A2) and HadCM3 (B2), temperatures for three terms (2039-2010), (2069-2040) and (2099-2070) predicted and compared with the base period Statistical criteria to evaluate the performance of the model, such as the mean absolute error and root mean square error model results HadCM3, indicated that this model in cold semi-arid to arid region of desert cold of higher performance and acceptable accuracy for predicting the temperature is.The results for scenario A2 B2 represents the temperature rise in the months of June to August for each station.

Satellites acquire data in low, medium, and high spatial resolutions. Freely-available high temporal resolution images are often acquired in medium (or low) spatial resolution and high spatial resolution images usually suffer from a low temporal resolution or from high costs. Moreover, high spatial resolution images are prevented to use in modeling of processes such as evapotranspiration due to the lack of thermal bands. Evapotranspiration mapping with a high spatial and temporal resolutions have been always one of the main subjects in the field of remote sensing. Daily evapotranspiration mapping with a 30 meter spatial resolution is the aim of current study. The case study of the research is Amir- Kabir agro-industrial farms. For this purpose, among 36 bands of MODIS image, those being more spectrally similar to Landsat bands were selected. Then, SADFAT and STARFM algorithms were applied on Landsat 8 and MODIS images to simulate visible and infrared bands with daily temporal resolution and 30-m spatial resolution. Afterward, the simulated bands were used as input for SEBAL algorithm to calculate actual evapotranspiration. Comparing the results with the actual evapotranspiration derived from FAO-Penman-Monteith equation indicated a RMSE of 2.53 mm/day and R2 of 0.69. Also, an RMSE of 0.68 mm/day and R2 of 0.94 were derived when the actual evapotranspiration derived from the downscaled bands were compared with that derived from the Landsat-8 bands. Accordingly, these results showed the efficient performance of the downscaling framework proposed in this study.