فهرست مطالب سید حسین ثنایی نژاد

Snow studies drastically influences on managing water resources and preventing the dangers caused by floods. Moreover, the modeling of snow melt runoff is faced with the lack of snow density data in the basin due to many changes in the distribution of snow masses, and creating snow measurement stations at high altitudes are difficult and expensive work. Considering the issues, this study attempted to invent a new equation and use remote sensing techniques in a simpler and more physical way to determine the degree-day factor (α) and then the simulation was performed by defining its calculated values using classical and new methods along with the annual and seasonal values ​​of the recession coefficient (k) for the snow-melt runoff model (SRM). The outcomes were evaluated and compared. In order to evaluate the performance of the model and its parameters, simulations were performed for the calibration and validation periods for the water years 2011-2012 and 2012-2013, respectively, and the MODIS snow cover product (MOD10A1) was used to estimate the snow-covered area and to estimate the radiation values. NEO net radiation product (CERES-NETFLUX-E) was used. The results showed that the best method for simulating runoff in the calibration period (2011-2012) was the new degree-day factor calculation method and the two values of the seasonal recession coefficient with the coefficient of determination of 0.72 and the volume difference of 4.17. In the validation period (2012-2013), the runoff simulation method with the new degree-day factor method and an annual recession coefficient with a coefficient of determination of 0.51 and the volume difference of 4.38 provided the best results in terms of assessment of the model accuracy criteria.

Precipitation is one of the main components of the hydrological cycle. Rainfall distribution plays a significant role in the Earth’s energy balance and human access to water resources, but rainfall is not always promising for humans. Floods cause a lot of human and financial losses to people and infrastructure all over the world annually. Planning for the damage reduction must be done with some measurements. It is necessary to record the amount of rainfall in high resolution, but measuring precipitation in high spatial and temporal resolution is a costly process. Scientists have developed many models to estimate precipitation values. It is necessary to evaluate the output data of these models and their performance before using them. In this study, the performance of two precipitation estimation products, PERSIANN-CCS and PDIR-Now, was evaluated against precipitation recorded in all synoptic stations of Iran for the devastating flood events from 2017 to 2019. For comparing precipitation estimations with the ground data, 9 indices (PC, BIAS, FAR, POD, HSS, , ME, MAE & RMSE) were used. The results showed high variability of indicators in different events. On average, PERSIANN-CCS and PDIR-Now products have a coefficient of determination of 0.16 and 0.19 and RMSE of 9.38 and 12.78, respectively. The values are not desirable, even though the average of PC is high. Moreover, the results showed that the PERSIANN-CCS and PDIR-Now products do not perform well due to the statistical indexes for all stations, and further researches for better heavy rainfall estimation are needed.

The accurate separation of evapotranspiration components is one of the key gaps in evapotranspiration research. Knowing this variable as well as the mechanism of separating its components to determine the exact value of the components of the water balance equation in relation to planning and managing water resources, optimizing crop production, designing irrigation systems, evaluating crop performance, identifying plant stresses and the impact of drought, and also evaluating the effects of climate change is very important on the efficiency of water consumption. In this research, the efficiency of Two-Source Energy Balance (TSEB) model was evaluated to separate the components of this parameter. In this regard, the outputs of TSEB model were compared and evaluated with the outputs of the standard FAO-56 dual crop coefficient method in the corn field located in the agricultural research station of Ferdowsi University of Mashhad. For this purpose, four Landsat 8 satellite images were used between planting and harvesting corn plants in the spring and summer planting seasons of 2021. The results of this research showed that despite the closeness of two methods (TSEB and FAO-56 dual crop coefficient method with R2=0.94) in terms of total values of evapotranspiration, there is a big difference between the two methods in terms of detail components (R2=0.46 for transpiration and R2=0.75 for evaporation). This difference can be due to the overestimation of the transpiration amount and underestimation of the evaporation amount in the dual crop coefficient method, and because the FAO-56 dual crop coefficient method estimates transpiration and evaporation values and it can be associated with some error, it cannot be said for sure that the TSEB model is not accurate enough. Also, comparing the ratio of transpiration to evapotranspiration in this research (0.77) with the results of other researchers (0.75-0.88) showed that the outputs of the TSEB model are within the permissible range and provide reliable outputs.

 Drought analysis in agriculture can not only be achieved by measuring precipitation changes but also by using other parameters such as soil moisture. Due to the fact that soil moisture affects plant growth and yield, it is often considered for monitoring agricultural drought. Remote sensing data are often provided from three sources: microwave, visible and thermal. Most satellite soil moisture-based algorithms rely on passive microwave images, active microwaves, or a combination of data from several different sensors. Among the various remote sensing methods, the microwave electromagnetic spectrum has fewer physical limitations than other spectrum in measuring soil moisture. However, microwave soil moisture data often have very large pixel dimensions (more than 10 km), making it difficult to use them on a small scale.

 In this study, in order to calculate the agricultural drought index at the field-scale, AMSR2 Retrieval data were calibrated first using field moisture measurement data in the Neishabour plain during 2017 to 2019. During the research period, 560 soil samples (20 samples in 28 shifts) were collected and soil moisture was measured in the laboratory of the Department of Water Science and Engineering, Ferdowsi University of Mashhad. LPRM_AMSR2_ SOILM3_001 is one of the third level products of the AMSR2 sensor, which is produced on a daily basis with a spatial resolution of 25 × 25 km2. Land surface parameters including surface temperature, surface soil moisture and plant water availability were obtained by passive microwave data using the Land parameter Retrieval Method (LPRM). Then, by using Modis sensor images (NDVI and LST), linear downscaling equations were extracted. The dimensions of the AMSR2 images were reduced from 25 kilometers to 1000 meters using these equations. In next step, SMADI Agricultural Drought Index, which is a combination of vegetation characteristics, soil moisture and land surface temperature, was used to monitor agricultural drought at the field-scale. Statistical indicators such as coefficient of determination (R^2), mean absolute error (MAE) and root mean square error (RMSE) were also used to evaluate the statistical performance.

By visual analysis of the role of vegetation and land unevenness, it was found that these two factors affect the regression relationships extracted for calibration of remote sensing data. The RMSE and MAE values for the regression equations used in the calibration process were calculated in the range of 1.6 to 4%, which can be considered acceptable in comparison with the mean values of the soil moisture data (15 to 20). The results showed that changes in SMADI index in three land use zones including rainfed cultivation (R1), medium rangeland (R2) and poor rangeland (R3) have experienced a similar trend to precipitation changes, illustrating that precipitation is one of the most effective factors in major changes in SMADI agricultural drought index fluctuations. It was also observed that SMADI index changes with a delay of 1 to 8 days compared to the precipitation changes in all three zones. In all three zones, the SMADI index followed a similar trend to in-situ soil moisture changes. At mot 80% of the changes in SMADI-R1 index can be explained by in-situ SM-R1, and the rest of the changes were related to other environmental factors or measurement error. This decreases to 68% in the R3 zone. It should be noted that soil moisture monitoring can more accurately reflect the impact of environmental factors on the changes in agricultural drought index such as SMADI than other variables; because the rainfall recorded at the meteorological station does not necessarily occur uniformly throughout the study area. On the other hand, any amount of precipitation will not necessarily lead to an effective change in soil moisture storage. This also renders assessment of the performance of agricultural drought indicators difficult.

 Examination of statistical indices of coefficient of determination (R2), mean absolute error value (MAE) and root mean square error (RMSE) showed that the algorithm used in downscaling as well as estimating SMADI agricultural drought index is well able to reflect the interactions between precipitation, soil moisture, vegetation and changes in canopy temperature profile. This feature justifies and strengthens its application in agrometeorological analysis.

In-situ observations underlie a wide range of planning, applied studies and modeling in various fields and sciences. Using this data in studies and planning without ensuring the accuracy and homogeneity of them can lead to uncertainty in the results. The major problems that researchers face are the poor data quality, missing data, outliers and in-homogeneity in time series. Therefore, in this paper, the minimum and maximum daily temperature series and daily rainfall series were analyzed at 37 weather stations in Iran for outliers and homogeneity over the period 1959-2018. In this regard, the World Meteorological Organization in cooperation with the Climatology Commission has provided instructions for data homogenization (e.g. WMO/TD Document No. 1186, Guidelines on climate metadata and homogenization; WMO Document No. 1203, WMO Guidelines on the Calculation of Climate Normal).     The main steps in data homogenization are: Metadata analysis and data quality control; Creating a reference series; Detection of break points; Data correction.     To do this, in the initial clustering, according to the previous activities and studies in this field which have mostly used empirical and quantitative methods, including principal components and cluster analysis, Iran was divided into 5 clusters based on the climatic characteristics. After initial clustering, the daily maximum and minimum temperatures and daily rainfall series were statistically analyzed using SPSS software and the percentage of missing data was determined for each station. Then, Climatol package in R software was used to study outliers, in-homogeneity and homogenization. In each cluster, the series are re-clustered based on the variability of desired parameter, and for each station, the other stations with similar variability belonging to that cluster are considered as reference stations.     Based on this algorithm, first the desired series is estimated and standardized by reference series using type (II) regression method. After estimating the series, the standardized anomaly series are calculated, in which the difference between the observed and estimated values is calculated. For detecting outliers, two steps were followed. Original data corresponding to the standardized anomalies greater than the prescribed thresholds were detected as outliers. In the second step, in order to confirm the outliers, the detected outliers in the first step were compared with the values of the days before and after for temperature series. If they differed significantly, they would be accepted as outliers and deleted. For the precipitation series, the atmospheric condition of the desired dates would be checked. For detection of in-homogeneity, the standard normal homogeneity test (SNHT) was performed on the monthly series. If the SNHT test statistic was greater than the prescribed threshold, the series was split at the point of the maximum SNHT and all the data before the break point were transferred to a new series with the same geographic coordinates. This process was repeated until all series were homogeneous. If break points were confirmed by metadata, they would then be accepted as non-climatic breaks. Finally, all the missing data in every homogenous series are estimated using same estimation procedure. The only difference is that the fragments of series are used as references. Given the large number of missing and suspicious data in 1959, we considered the beginning of the statistical period from 1960. Investigations have shown that on some dates, all stations in a cluster lack data, possibly due to glitches in the MESSIR-CLIM system of Meteorological Organization through which data is received. In such cases, the average data of the days before and after the mentioned stations was used to estimate the data on that dates. MESSIR-CLIM is the database of IRIMO including climatic database management system that is based on PostgreSQL. The main functions of the system receive and store all kinds of weather and climatic data. The system is able to collect and process massive amounts of information and provide meteorological products (such as charts, maps, tables, and reports).     For the maximum temperature in the stations of the Caspian region (Cluster 4), 18 dates and for the minimum temperature, 13 dates in the mountainous areas (Cluster 5), in all the cluster stations were missing data.     The maximum and minimum temperature and daily precipitation series for 37 weather stations of Iran have an average of 5%, 7% and 2% missing values, respectively. In the 60-year time series (1959-2018) after deleting the 1959 data, the percentage of missing data at maximum and minimum daily temperature and daily precipitation decreased by an average of about 0.3%. In this time series, excluding 1959 data, 7 outliers were detected for the maximum temperature parameter. For the minimum temperature, this number reached 7 and for the precipitation parameter, 8 outliers were identified. In 8 cases, due to the lack of atmospheric data on the desired dates, it was not possible to make a definitive judgment about the accuracy of precipitation data outliers. In terms of daily temperature series, with the exception of Tabas station, out of 36 stations, 16 stations were homogeneous and 20 stations had one or two or three breakpoints. For the precipitation parameter, 5 in-homogeneous stations were identified.    Unfortunately, due to the lack of a comprehensive metadata bank, there was no definitive reason for many of these fractures at some stations.

In-situ observations underlies a wide range of planning, applied studies and modeling in various fields and sciences, and using this data in studies and planning without ensuring the accuracy and homogeneity of them, can lead to uncertainty in the results. Major problems that researchers face are the poor quality data, missing data, outliers and in-homogeneity in time series. Inappropriate co-locating of stations, human errors in reading and recording data, errors in measuring equipment, changes in measurement tools, different methods of observation, non maintenance and calibration of equipment, constructions around the stations, changes in the type of instruments and sensors for atmospheric parameters measurements and station relocation during the statistical period are problems that affect the accuracy and homogeneity of the meteorological data. Therefore, in this paper, the minimum and maximum daily temperature series and daily rainfall series at 134 weather stations in Iran were analyzed for outliers and homogeneity over the period 1989-2018. First, Iran was divided into 5 clusters based on climatic characteristics. After clustering, the daily maximum and minimum temperatures and daily rainfall data were statistically analyzed using SPSS software and the percentage of missing data was determined separately for each station. Then, Climatol package in R software was used to study outliers, in-homogeneity and homogenization. In each cluster, the series are re-clustered based on the needed parameter, and for each station, the other stations belonging to that cluster are considered as reference stations. Based on this algorithm, first the desired series is estimated and standardized by reference series by type (II) regression method. After estimating the series, the standardized anomaly series is calculated, in which the difference between the observed and estimated values is calculated. For detecting outliers, two steps were followed. Original data corresponding to standardized anomalies that were greater than the prescribed thresholds, were detected as outliers. In the second step, in order to ensure the correct detection of the outliers, for temperature series the detected outliers in the first step were compared with the values of the days before and after. If they differed significantly, they would be accepted as outliers and deleted. For the precipitation series, the atmospheric condition of the desired dates were checked. For detection of in-homogeneity, the standard normal homogeneity test (SNHT) was performed on the monthly series. If the SNHT test statistic was greater than the prescribed threshold, the series was split at the point of maximum SNHT and all the data before the break were transferred to a new series with the same geographic coordinates. This process was repeated until all series were homogeneous. If break points were confirmed by metadata, they would then be accepted as non-climatic breaks. Finally, all the missing data in all homogeneous series and subseries infilled with the same data estimation procedure using only the reference of their own other fragments. The maximum, minimum temperaturs and precipitation series for 134 weather stations of Iran have an average 3%, 4% and 2% missing values, respectively. In this time series, 63 outliers were detected for the maximum temperature parameter that 53 of them were related to the Geophysics station of the University of Tehran. For the minimum temperature, this number reached 50 that 11 of them belong to the Geophysics station and for the precipitation parameter 13 outliers were identified that 5 of them are related to the Geophysics station. For the daily temperature series (excluding Geophysics station), 89 stations were homogeneous and 44 stations had one or two break points, and for the precipitation series, 15 stations were identified as in-homogeneous.

Soil moisture is of particular importance in the study of water resources and agriculture. The microwave electromagnetic spectrum does not have the physical limitations of other radiometric spectral in measuring soil moisture, but they often have very large pixel dimensions (more than 10 km). In this study, in order to apply soil moisture remote sensing data at the farm scale, using field soil moisture measurement data in the Nishabour plain during 2017-2019, calibration of AMSR2 retrieval data was performed. It turned out that altitude and vegetation changes are among the key factors affecting calibration accuracy. Based on the theory of thermal inertia and with the help of MODIS sensor images, the interactions of the average daily soil moisture and the daily surface temperature difference between the two MODIS and AMSR2 sensors were investigated. Using it, downscaling linear relationships of soil moisture images were obtained to convert the image dimensions from 25 km to 1000 m. Validation of R2 statistical indices with a minimum of 0.73 and a maximum of 0.84, MAE and RMSE with a range of 1.6 to 4, showed that the algorithm used in the downscaling is well able to reflect the interactions between rainfall, soil moisture, vegetation and changes in canopy temperature profile and this feature reinforces its application in agrometeorological studies.

The behavior of daily changes in temperature is not straightforward. We first drew the curve of this variable on a normal day. It can be seen that the distribution of this variable was not normal. The curve of this variable was a skewed curve to the right. Therefore, the equal coefficients could be used only as approximation for estimating daily average temperature. Climatic conditions of the meteorological stations were also another parameter to be considered. This research presents a new method for estimating daily average of temperature in three climatic regions of Iran. The patterns for the sample stations in each climatic region were presented separately.

E. Eccel (2012) developed algorithms to simulate the relative humidity of the minimum daily temperature in 23 weather stations in the ALP region of Italy. In this research, the base pattern was calibrated by temperature and precipitation measurement. Ephrath, et al. (1996) developed a method for the calculation of diurnal patterns of air temperature, wind speed, global radiation and relative humidity from available daily data. During the day, air temperature was calculated by:  (1)   (2)     where S (t): Dimensionless function of time, DL: Day Length h, LSH: the time of maximum solar high h, ta: Current air Temperature, P: the delay in air Tmax with respect to LSH h. Farzandi, et al. (2012) presented more accurate patterns for estimating daily relative humidity from humidity of Iranian local standard hours and daily precipitation variables, the minimum, maximum and average daily temperature in coastal regions. The purpose was to present linear and nonlinear patterns of daily relative humidity separately for different months (12 patterns) and annually in coastal regions (the Caspian Sea, the Persian Gulf, and the Oman Sea). Rezaee-Pazhand, et al. (2008) introduced new patterns for estimating daily average temperature in arid and semiarid regions of Iran. Final pattern has interception and new coefficients for estimate daily average of temperature. (3)   Veleva, et al. (1996) showed that the atmospheric temperature-humidity complex (T-HC) of sites located in a tropical humid climate cannot be well characterized by annual average values. Better information is given by the systematic study of daily changes of temperature (T) and relative humidity (RH), which can be modeled with linear and parabolic functions. Farzandi et al. (2011) divided Iran into three climatic clusters used in the present work. First a classification which provides climatological clustering. This clustering was used the data of annual relative humidity, temperature, precipitation, altitude, range of temperature, evaporation and three indices of De Martonne, Ivanov and Thornthwaite. Iran was partitioned into three clusters i.e. coastal areas, mountainous range and arid and semi-arid zone. Several clustering methods were used and around method was found to be the best. Cophenetic correlation coefficient and Silhouette width were validation indices. Homogeneity and Heterogeneity tests for each cluster were done by L-moments. The “R”, software packages were used for clustering and validation testes. Finally clustering map of Iran was prepared using “GIS”. The data of 149 synoptic stations were used for this analysis. Systematic sampling was done to select sample stations. The linear regression model was fitted after screening and data preparation. A model was presented for estimating daily average of temperature in each climatic region and sampling stations in each cluster. The best models were presented by reviewing the required statistics and analyzing the residuals. The calibration and comparison of the presented patterns in this paper with commonly applied models were undertaken to calculate the mean squared error. “SPSS.22” software was used for analysis.

The coefficient of determination (R2) and the Fisher statistics show that the patterns have a good ability to estimate the daily average of temperature. The daily average temperature pattern confirmed an interception in the equations. Standardized coefficients showed that predictor variables were not weighted in all of the patterns. The average values of the residuals in each pattern was zero. According to the graphs, stabilization of variance can be seen based on the residual on each pattern in each cluster. The mean squared error  is a measure of the applicability of patterns. The accuracy of the estimating daily average temperature by the recommended models in three climates was confirmed by calculating the mean squared error. The proposed patterns of this study had less error than common patterns.  Thus, the patterns have a good ability to estimate daily average temperature.

The maximum temperature in calculating daily average of temperature is more effective than the minimum temperature. The standardized coefficient (Beta) of the daily average temperature patterns in coastal cluster was 48.2% for the minimum temperature and 51.8% for the maximum temperature. The largest influence of the maximum temperature was 63.1% in mountainous cluster for estimating daily average temperature. Range of the interception in the equations was from -1.735 to 0.26. The independent assumption of the residual was confirmed with the acceptable value of Durbin-Watson statistics. The average of the residuals in each patterns was zero. According to the graphs stabilization of variance can be seen based on the residual on the each pattern in each cluster. The proposed patterns were calculated according to mathematical principles but the common patterns did not consider these mathematical principles. The mean squared error (MSE) of the proposed patterns are less than common patterns. Therefore, the patterns presented in this study are more powerful than common patterns. The largest difference between the proposed patterns and the common patterns for estimate the daily average of temperature was 24% in mountainous cluster. Climatic clustering was done for states.  The monthly and annual average temperature can be reliably estimated by using the data of sample stations in each state. These findings can be used to estimate daily, monthly and annual average of relative humidity in three climates and sample stations. In addition, one can employ the method for estimating daily, monthly and annual average of relative humidity and temperature based on around climatological clustering of Iran and other stations. Annual relative humidity, temperature, precipitation, altitude, range of temperature, evaporation can also be applied to estimate daily, monthly and annual average of temperature and relative humidity more accurately.

Due to the importance of precipitation in various aspects of human life, precipitation data are largely applicable in different fields of study. Therefore, accurate measurement of precipitation is considered to be crucialin various fields such as agriculture, water resources, and industrymanagement. Due to the problems related to generalization of point precipitation to regional precipitation, alternative methods have been proposed forthe measurement of this variable. In many cases, short reference period, inadequate density of stations and poor quality of data collected from precipitation measurement networks have challenged the analysis of this climate variable. In order to overcome these problems, it is necessary to identify alternative sources, evaluate and use them to estimate the amount of precipitation. The present study primarily seeks to evaluate precipitation data from the TMPA and provide calibration data for arid, semi-arid, Mediterranean, humid, and very humid regions of Iran on a monthly scale.

In the present study, monthly precipitation data of 15 synoptic stations in 5 regions of Iran (arid, semi-arid, Mediterranean, humid and very humid) were selected as reference data and monthly precipitation data from the TMPA (3B43-v7) were corrected based on them. To ensure reliability of results and reduce errors,stations were selectedrandomly from 15 separate provinces with different topographic conditions. A 20-year reference period (1998-2017) was selected for the study. Collected satellite data have a monthly temporal resolution and a spatial resolution of 0.25 degrees covering 50th parallel south to 50th parallel north. Table 1 shows features of the selected stations and their corresponding pixels. Pre-processing included quality control, homogeneity test, and data accuracy test. Usinga long-term reference period of 20 years, different statistical criteria to evaluate satellite data and a correction relationindependent from ground data are among the advantages of this research. In this study, a more efficient method is used to determine errors and one of the most modern methods of calibration is also used. Followingthe application of log transformation and multiplicative model, monthly C parameter was calculated to rectify satellite data collected from different climates. Results were evaluated using R2 (Coefficient of Determination), MBE, MAE and RMSE.

Findings indicated that the distribution of initial data obtained from TMPA satellite in a monthly scale is similar to the distribution of pattern obtained from ground data (due to a correlation of above 75% (R2>0.6)). Satellite data collected from arid areas are usually overestimated, while data collected from humid areas are generally underestimated. However, determination coefficients (R2) of different climates show a strong correlation between these two sources of data. The initial TMPA data have estimated the monthly precipitation of Bam, Piranshahr and Abali stations with the least amount of error. The highest level of errors were obtained from Marivan, Bandar Anzali, and Koohrang stations. In other words, the highest level of errors have occurred in the very humid region. Calibration of TMPA data collected from the 5 different climates indicated that correction of TMPA monthly data would improve valuesestimated from satellite images. Mean bias error (MBE) was reduced by 88.7, 95.3, 68.4, 38.4 and 63.9 percentin arid, semi-arid, Mediterranean, humid and very humid climates, respectively. Values of the correction parameter (C) in the arid climate indicate that a reduction factor has been applied to rectify satellite data collected in each month of the year. In the semi-arid climate, reduction factorswere obtained for each months of the year. A reduction factor is also required to rectify data collected in the warmest months of the year (June, July, and August) in the Mediterranean climate. Due to the low precipitation of these months, overestimation seems reasonable in these areas. A reduction factor should also be applied in the humid climate for 6 months of spring and summer.Considering the precipitation rate in these areas, decreasing precipitation rate in these seasonsresults in overestimation and error. Due to the significant precipitationrate in the cold months of the year (autumn and winter), decreasing factorand underestimation are expected to occur. In the very humid climate, a reduction factor should be appliedin the warmest months of the year (June, July, and August). Due to the low precipitation rate of these months and higherfrequency of cloudy days, overestimation will be reasonablein these areas. Due to underestimationin the coldest months of the year (autumn and winter), coefficients higher than one must be corrected.

Based on the results, the model used to correct precipitation in all 5 climates have reduced errors in precipitation measurement. However, this improvement was more obvious in arid and semi-arid climates. Sincea large part of Iran havean arid and semiarid climate, this calibration model is highly recommended. In addition, the final correction model does not depend on ground data and thus, applying the calibration modelto areas other than the specified stations will also be useful.

Rainfall always has been considered as one of the most important variables for estimating the balance of watershed. The evaluation and correction of precipitation data as a supplement to the ground data in Iran, which it most regions is located in arid and semi-arid zone, is necessary. In this study, the precipitation data from 6 synoptic stations in 3 climate of Iran (extra arid, arid and semi-arid) were selected as the basis for the period of 20 years (1998-2017). The satellite monthly precipitation data (TMPA-3B43) was corrected with the multiplicative model, and results were evaluated by using R, MBE, MAE, and RSME indices. Based on uncorrected results, the correlation coefficient (R) varied from 0.79 for Garmsar station to 0.95 for Ramhormoz station. In all three climates, satellite data had overestimated values. After applying the multiplicative model, a seasonal parameter has been obtained for satellite data correction. All evaluation indices, especially the MBE, were significantly reduced after the correction. The lowest values for this error in the corresponding pixels of Garmsar, Boshrouyeh, Saveh stations were reduced to 0.1(spring), 0.4(autumn), and 0.7(winter) respectively. According to the results of the correction parameter in all three studied climates, the highest overestimate was observed in the autumn season. Also, this correction coefficient was 0.64, 0.67, and 0.79 for Qazvin(semi-arid), Garmsar(extra-arid), and Saveh(arid) stations, respectively. Based on the obtained results, the calibration model can be used to correct the seasonal data of the remote sensing in dry regions.

Achieving satellite images with high simultaneously spatial-temporal resolution has been one of the serious challenges faced by researchers in the field of remote sensing and its applications. In recent years, researchers have made serious efforts to solve the problem. In this study, producing Landsat like land surface temperature images with less than 16 day temporal resolution and over different land covers, using spatio-temporal image fusion algorithm (STI-FM) and MODIS Land surface temperature images, was investigated. The STI-FM technique consist of two main steps. First establishing a linear relationship between two consecutive MODIS LST images acquired at time 1 and time 2; then utilizing the above mentioned relationship as a function of a Landsat-8 LST image acquired at time 1 in order to predict a synthetic Landsat -8 LST image at time 2. The results showed strong linear relationship between the two consecutive MODIS images at times 1 and 2 (R2 in the range 0.85-0.95). The synthetic LST images were evaluated qualitatively and quantitatively and it was found that there is a high visual and strong agreements with the actual Landsat-8 LST images over different land covers. For example R2 and RMSE values were ranged 0.74-0.94 and 1.44-2.52, respectively.

Drought is a very complex natural phenomenon which changes with time and space. Spatial and temporal variations of drought are analyzed separately. Geostatistical methods can be used for spatiotemporal analyses to find related spatial and temporal pattern changes. These methods, which use the spatio-temporal data, considering the spatial position of the data relative to each other, also take into account their temporal dependence. If needed, they can estimate values of their variable at any location and any time. Moreover, the drought spatial variations in the studied region can be drawn at every desired period. On the other hand, it is expected that intervening of the time dimension in the equations of these methods, as compared to the purely spatial methods, provide more precision in estimating the values of drought indices, which is studied in this research.

Monthly rainfall data of 48 stations in the northeast of Iran for the period of 1981-2012 were used in this study. The SPI drought index is calculated for the 12-month time scale. Data were divided into two groups of training data from 1981-2011 and experimental data of 2012. After analyzing the data regarding their stationarity and isotropic assumptions, the spatiotemporal data were formed and their spatiotemporal empirical variogram was drawn. Furthermore, the purely spatial and temporal variograms for the zero space and time steps were also drawn. Then, four models of the spatiotemporal variogram functions were applied on the training data. The performance of these models was tested and compared by estimating the parameters of the model based on the Square Error (MSE). Moreover, three-dimensional fitted variograms were drawn for different models. Mean The best spatiotemporal variogram model was selected by comparing the models prediction with experimental data using the Mean Square Prediction Error (MSPE). Using spatiotemporal kriging method, the predicted values of experimental data were interpolated ​​and that of the observed values ​​were interpolated by kriging method. Cross validation on experimental data was also performed using RMSE, MAE, ME and COR. Then spatiotemporal and purely spatial variogram models were investigated and compared.

The results showed that the 12-month SPI index had no spatial trend but had a decreasing trend against the time. Hence, a simple regression equation was used for fitting the trend of the data. After detrending the data, the SPI index values were considered as the dependent variable, while the time was taken as the independent variable. On the other hand, drawing the variogram in different directions (0°, 45°, 90°, and 135°) had no significant effect relative to each other, and the hypothesis of isotropic state was accepted. The plots of purely spatial and temporal variograms showed that the spherical variogram for space and the linear variogram for the time would have the best fitting. The empirical 3-D and 2-D spatiotemporal variograms of the training data were plotted. The empirical 3-D variogram showed that the data had reached to its temporal sill in a 1-year time lag, and had reached to its spatial sill, in about 25-kilometers, which are in conformity with the purely spatial and temporal variograms. The comparison of different variogram functions showed that the MSE values of the separable, metric, product-sum and sum-metric models were 0.00139, 0.00295, 0.00111, and 0.00112, respectively, the last two of which had fewer errors. Drawing the spatiotemporal variogram of these functions showed that the spatiotemporal variogram of product-sum and sum-metric models have more similarity to the sample one. Regarding the selection of the best model, the MSPE statistics of the product-sum and sum-metric models were 0.281 and 0.389, respectively. Therefore, the product-sum model could be selected as the best model. The least rate of errors was found in the exponential variogram model for space, and in the linear variogram for the time. The parameters of the nugget effect, partial sill and range for the spatial variogram would be 0.00, 0.063, and 5.78, and for the temporal variogram would be 0.00, 0.635, and 1.044, respectively. After predicting values of 12-month SPI in 2012 by the product-sum variogram model and adding the values of the trend, they were interpolated by using the spatiotemporal kriging, and the observed values were interpolated by the use of kriging. The obtained plot from the predicted values had great similarity with that of the observed values, which indicates the appropriate capability of the model in predicting the unobserved values. The cross-validation of different spatiotemporal and the spatial models with 25 and 47 neighborhoods showed that the performance of the models had no significant differences relative to each other, and they also had no better performance relative to the purely spatial model.

The results of this study showed that the product-sum model had a better performance among different spatiotemporal variogram models in predicting the 12-month SPI values of 2012. However, the performances of different spatiotemporal models were quite close to each other. There is no significant difference that could be observed between spatiotemporal and purely spatial models. Also, it is proposed to use the dynamic spatiotemporal models and the results to be compared with the classical models.

Drought is one of the most complex and dangerous natural disasters that changes both in space and time. Global warming has intensified such extreme events in recent years. Thus, the use of drought indices that consider both the effects of precipitation and temperature, as well as the use of joint spatio-temporal methods, which are the extensions of spatial statistics, can probably lead to better drought monitoring and thereby increasing the accuracy of predictions. The data correlation structure is determined by the spatio-temporal covariance functions in these methods. The aim of this study is to use and compare a number of spatio-temporal variograms for predicting and spatio-temporal mapping of drought by using the 12- month SPEI index.

In this research, the monthly rainfall and temperature data of 48 stations in the northeast of Iran during the statistical period of 1981-2012 were used to calculate the SPEI index in a 12-month time scale. The exploratory analysis of the data was studied in terms of stationarity and isotropy assumptions. The data were divided into two groups of training and experimental data of 2012. The separable, metric, sum-metric and product-sum spatio-temporal covariance functions were fitted to determine the best combination of spherical, linear and exponential variograms for each of the spatial and temporal variograms on training data. The best model was selected using the MSE and MSPE statistical criteria, and the required parameters were estimated. Finally, using spatio-temporal kriging, the experimental data were predicted, mapped, and compared with the map of the observed values. Cross-validation of spatio-temporal and purely spatial models was done via COR, ME, MAE and RMSE statistical criteria by using 25 and 47 neighborhoods.

The test of the stationarity of spatio-temporal data showed the spatial stationary. Drawing of the average time series data showed a decreasing trend, which was modeled by a simple regression with the use of SPEI index values as dependent variable and time as an explanatory variable, and the data were detrended. The spatial variogram in four directions of 0°, 45°, 90° and 135° did not show a significant difference between the four variograms and the assumption of isotropy was therefore accepted. The separable, metric, sum-metric and product-sum models were used to determine the correlation structure of data. The comparison of models by means of MSE criteria showed that product-sum and sum-metric models have less error as compared with the other two models. Comparison of these two models in the prediction of unobserved values selected the product-sum model as the better model with the linear variogram for both the space and time via the MSPE criteria. After estimating the model parameters and using spatio-temporal kriging, the SPEI values were predicted for the experimental data and their spatio-temporal maps were plotted. The similarity of the map of the predicted values and that of observed values indicated the good performance of the model in predicting the unobserved values. Cross-validation of spatio-temporal and purely spatial models also showed that the performances of various models were very close to each other.

The results of this study showed that the product-sum spatio-temporal covariance model has a good ability to predict the unobserved values as compared to other models, and with the aid of these models, the values of the desired variable can be predicted in any spatial location and at any time scale. Also, cross-validation of the models showed that the different spatio-temporal and purely spatial models do not differ significantly from one another, and the precision of the models have not increased as compared to the purely spatial state.

Climate change and global warming has increased climatic unfavorable events that could reduce crop yields and endanger food security. 13 agro-climatic indices which are based on the outputs of CMIP5 models and RCP emission scenarios, were used to investigate the effect of climate change on areas at risk of adverse events. Occurrence probability of heat stresses will be increased during the flowering and grain-filling for early and late cultivars at the end of the century so that these stresses will become the dominant adverse events in all areas. The occurrence probability of at least one adverse event is more than 20 and 90 percent for early and late cultivars, respectively in all areas for the baseline conditions, and this probability is expected to increase by more than 40 and 94 percent for early and late cultivars, respectively under future climate scenarios. Proportional to the reduction of water stress for different emission scenarios, the probability of simultaneous occurrence of heat and water stress at the flowering stage will decrease in future as compared to the baseline. In future, areas where are at risk of at least two adverse event occurrences will increase further, as compare to those where are at risk of at least one adverse event occurrence. Toward the end of the century, more areas will be at risk of at least one and two adverse event occurrences.

Adverse and extreme  agro climatic events will disrupt food production and these  changes are expected to increase in the world. The wheat is Iran's dominant diet, especially in the form of bread. It is important as a food product that has an impact on food security. Climate change can affect wheat production in major areas of rainfed wheat production in Iran, with social and economic consequences. Therefore, it is important for policy makers and scientists  to evaluate the effects of climate change on the agricultural sector and food security. Crop models cannot take into account the effects of severe weather events (such as heavy rainfall, heat stresses) on the final yield of the crop. It could be useful to  utilize  agro climatic indices to provide more comprehensive projections of the impact of climate change on  agro climatic conditions. The purpose of this study was  evaluating the  probability of occurrence of adverse and extreme  agro climatic events at different stages of wheat development using  agro climatic indices.

The focus of this study is on main areas of rainfed wheat production in Iran (Kurdistan, Kermanshah, Golestan, Zanjan, Hamedan, and Ardebil provinces). According to the latest statistics and information from the Ministry of Agricultural Jihad, more than 55% of wheat production  achieve in these areas. The evaluations are based on the outputs of seven CMIP5 models and RCP8.5 and RCO2.6 emission scenarios for the period 2045-2065 and 2080-2100.  The equidistant quintile-based mapping method (EDCDF) was applied to bias correct the outputs of CMIP5 models .The proposed method of Allen et al. (1998) was  utilized to estimate daily crop evapotranspiration, soil moisture and relative reduction in crop yield under soil water shortage to describe the major adverse conditions for wheat production;  the set of 13 indicators was used to cover the major causes of low yields of winter wheat.

 The average temperature during the growing season will be increased by 3.1 °C for the late cultivar and RCP8.5 scenario during the period 2080-2100 compared to the baseline. The appropriate sowing dates will occur later for all scenarios relative to the baseline and shift to late autumn. Due to the increased average temperature during the growth period, anthesis and maturity dates will occur earlier relative to the baseline and subsequently the average growth period for all scenarios is shorter than the baseline. Average total crop evapotranspiration (ETc) during the growing season will be reduced in most stations. The average relative reduction in crop yield (YD) and the average total effective solar radiation will be more favorable than the baseline.  Thus, it can be said that these crop yield indicators are better than the baseline. However, increasing frequency of adverse events will be undesirable and the most unsettling possibility is the increase in the likelihood of occurrence of at least one, two and three adverse events during the growing season that can be extremely unfavorable climatic conditions for the production of wheat. The close connection between the likelihood of adverse events  and the duration of growth period (such as moisture and heat stresses) is obvious so that the longer growth period,  is more likely to be exposed to high temperatures and moisture stresses. An early cultivar will be a more suitable cultivar for sowing compare to late and medium-ripening cultivar which can change future climate conditions in favor of rainfed wheat production in most areas, especially cold regions.

In this study, the probability of occurrence of adverse and extreme  agro climatic events during the growing season of wheat was determined, which is usually not well considered in crop models. However, it is well known that the impacts of such extreme events can be substantial. The results of this study showed that, despite high uncertainty in the climate projections within CMIP5 models, the probability of occurrence of at least one (or more) adverse event during the growth period for each cultivar will increase compared to the baseline for the same cultivar. So that, the longer growth period, the greater likelihood of occurrence of at least one (or more) adverse event.

1

Land Surface Temperature (LST) is an important parameter in controlling surface heat and water exchange with the atmosphere (Li, Tang, Wu, Ren, Yan, Wan, Trigo, & Sobrino, 2013). Remote sensing images are now one of the most important data sources for estimating LST (Hengl, Heuvelink, Perˇcec Tadi´c, & Pebesma, 2012). But the presence of pollutants in the air, cloudiness and failure of the sensors result in the huge data loss, which is called image gaps. So far, several methods have been proposed to estimate the missing values. These methods are divided into three categories: spatial, temporal or spatio-temporal. Time-based methods for estimating missing data are mathematical calculations, the most famous of which are Savitzky and Golay filters (1964). In addition to simple solutions, more sophisticated methods such as Brooks, Thomas, Wynne, and Coulston )2012) and harmonic analysis of Zhou, Jia, and Menenti )2015) were also introduced and implemented on various remote sensing products. Simple interpolation methods such as the nearest neighborhood, SP-Line method, and Inverse Distance Weighing (IDW) are spatial methods. In most of these algorithms, the weighted average is used. Compared to the simple interpolation methods, the approaches that utilize auxiliary data are more of a researcher's interest. Chen, Zhu, Vogelmann, Gao, and Jin (2011) proposed a neighborhood Similar Pixel Interpolation method (NSPI). Zhu, Liu, and Chen (2012) presented the improved NSPI method named Geostatistical Neighborhood Similarity Pixel Interpolation using geostatistics. Gerber, de Jong, Schaepman, Schaepman-Strub, and Furrer (2018) proposed a spatio-temporal algorithm based on subsetting method for estimating the missing values of MODIS NDVI data. The estimation results are influenced by factors such as the structure of the algorithm, the used scenarios, the type of variable, and the region of study. Finding the right fitting image on cloudy days or providing a long time series are also among the most important factors influencing the results of these methods Kandasamy, Baret, Verger, Neveux, and Weiss (2013). To solve this problem, time series from other satellites are mostly used. These images may differ with the target images in terms of spectral structure and have negative effect on the algorithms performance. Only a few researches like Jang, Kang, Kim, Lee, Kim, Kim, Hirata (2010) have used numerical prediction models outputs in producing continuous spatial-temporal remote sensing data. They concluded that the outputs of numerical prediction models could be used on cloudy days.
The aim of this study is to evaluate the approach proposed by Gerber, de Jong, Schaepman, Schaepman-Strub, and Furrer (2018) to reconstruct MODIS LST images and also to study the feasibility of using MM5 model outputs as an auxiliary data for estimating missing values of the images.
2

The study area is North Khorasan, Khorasan Razavi, and Southern Khorasan provinces, northeast of Iran, located between 55 to 61 degrees east and 30 to 38 degrees north. The total area of the region is 313,000 square kilometers and the overall climate is semi-arid to dry Ahmadian, Sheibani, Araqi, Shirmohammadi, and Mojarad (2001).
Two types of data were used in this study. 1- LST images, which are produced by Level 3 MODIS (MOD11A2) with a spatial resolution of one square kilometer and a time interval of 8 days. 2- MM5 model Output data, which were images with spatial resolution of 0.5 × 0.5 degrees for the period of 2000-2010. The data was prepared based on the latitude and longitude of the study area from NASA and NOAA internet pages. In the present study, the Subset-Predict Algorithm (SPA) proposed by Gerber et al. (2018) was selected as the basic method. They used the spatio-temporal approach to estimate the missing values of remote sensing images. This approach  is suitable for a data set with a four-dimensional array structure. In this approach, the missing values are predicted in two main steps: 1. Subset, 2. Forecasting lost values based on subsets. This approach was implemented for MODIS LST images. MATLAB software was used to do this. The inputs of the algorithm were an original image and a series of auxiliary images. In this research, two types of tests were designed to estimate missing values. The first test was performed using time series information of LST MODIS and the second test using MM5 output data. In this study, the Root Mean Square Error (RMSE), Mean Difference (AD), and Determination Coefficient (R2) were used to evaluate the performance of the SPA approach in two different situations.
3

The results of simulations show that the SPA method has a good accuracy. The average value of the obtained error is 1.487 degrees Celsius. Meanwhile, Kilibarda, Hengl, Heuvelink, Gräler, Pebesma, Perčec Tadić, and Bajat (2014) reported a mean error of ± 2.5 degrees Celsius in reconstruction of LST images of 2011. Implementing SPA algorithm with the MM5 outputs is less accurate than test 1. This can be due to the uncertainty of the MM5 model in predicting the surface temperature. The visual test of the images showed that the spatial pattern of the LST trend was preserved in estimating the missing values, and the algorithm did not impose artificial pattern on the images. This algorithm has been able to easily reset missing values in most places by maintaining a spatial pattern on the edges and also inside the gaps. Only in the quartile section of the right and above the gap area, the temperature pattern was different from the original LST image. Moreover, in this case, using the MM5 model output, the spatial pattern reflects the temperature trend better than the remote sensing time series.
The spatial distribution map of the mean error in the gap region showed that in most pixels, the error value is in the range of 0 to 2 degrees Celsius. Also, an error of more than 6 degrees Celsius is seen in a small number of pixels in the center of the gap. This may be due to the structure of the method and the subsetting in the neighborhoods or because of the extreme changes in the topography in the area. In contrast to the approach proposed by Chan and Shen (2001), SPA method was accurately simulating missing values at both edges. Given that the maximum error location is in both the center of the gap center, there is likely to be a structural problem in the SPA algorithm that needs further investigation.
Validation of the method revealed that test 1 RMSE value is less than test 2. This means that the accuracy of the algorithm is greater in test 1. According to the AD index, in both cases, the SPA algorithm underestimated the missing values. Also, the implementation of the algorithm with the test 1 scenarios with the correlation coefficient was 0.8% more than test 2.
4

In this study, the spatio-temporal SPA method proposed by Gerber et al. (2018) was used to estimate the missing values of MODIS LST and image reconstruction in the years 2000-2010 in the north east of Iran. The results of the SPA method in both cases showed that the chosen method was able to accurately estimate the missing values. They also showed that the obtained error values were within the acceptable range in the temperature data (Ferguson & Wood, 2010). Implementing the SPA algorithm was also less accurate than test 1 with the help of MM5 output maps. The algorithm did not impose an artificial pattern on the images. This algorithm has been able to retrieve missing values by maintaining a spatial pattern on the edges and also inside the gap. On the contrary, many of the existing documentation methods, such as Chen et al. (2004), can't estimate all of the missing pixels.
Error spatial distribution maps show that the highest simulation error relates to a number of pixels in the central region of the gap. The results of this study showed that in the absence of sufficient information for temperature in a region, data from the MM5 model can be used to fill in the missing data pixels and maintain the spatio-temporal continuity of the remote sensing images.

This research aims to assess the potential impacts of climate change on water resource in the headwater basin of Salman Farsi dam in Fars province using Soil and Water Assessment Tool (SWAT). Climate projections from three Global Circulation Models (GCMs) under two Representative Concentration Pathways (RCP2.6 and RCP8.5) during future period (2020-2050) were incorporated into the calibrated SWAT model and changes in simulated runoff, blue water, green water flow, and green water storage were compared to the baseline period (1978-2008). SWAT model efficiency in simulating hydrological response of the basin was confirmed by performance evaluation indices in Tang-e-Karzin hydrometric station which is located at the outlet of basin in the calibration (NSE=0.77, R2=0.80) and validation (NSE=0.57, R2=0.67) periods. The GCMs projections indicate increasing the frequency of severe precipitation events and reducing precipitation events with low intensity in the future as compared to the baseline period, and also increasing the minimum and maximum temperature in the basin. These changes lead to an annual increase in runoff and blue water due to increase in extreme events and flood producing potential of the basin, as well as an annual increase in green water flow due to increase in temperature and precipitation over future.The findings of this research can be helpful for water resource managers and policymakers to provide management plans and strategies to encountering climate change.

  Temperature and precipitation are two of the main variables in meteorology and climatology. These are basic inputs in water resource management. The length of the statistical period plays a pivotal role in the accurate analysis of these variables. Observation data at Iran's first synoptic station from 1330 (1951) is available at the Iranian Meteorological Organization website The historical monthly precipitation and temperature of five stations in Iran is available since 1880 with missing data. These data measured by the Embassy of the United States and Britain from the Qajar period and recorded in World Weather records books. These synoptic stations include Mashhad, Isfahan, Tehran, Bushehr, and Jask. The monthly missing data were predominantly recorded during World War II (1941-1949). Unfortunately, these data have missing. Therefore, the accuracy of simulating these variables is very important.  The current research aimed to predict the missing values of monthly temperature and precipitation in Mashhad station. The stations in the neighboring countries were selected due to the distance to Mashhad, relationship, and completeness of data since 1880, as the predictive variables. Monthly precipitation of Ashgabat from Tajikistan and Sarakhs, Kooshkah, Bayram Ali, Kerki and Repetek from Turkmenistan were selected as an independent variable in the making of Missing Rainfall in Mashhad. Also, the temperature of Ashgabat, Bayram Ali, Gudan, Sarakhs, and Tajan were selected to restore the monthly temperature of the Mashhad station. This research has fitted ten multiple regression models to monthly rainfall of Mashhad station and has fitted 6 multiple regression to the monthly temperature of Mashhad. then the parameters of these patterns are optimized by genetic and Ant Colony algorithm. Also, the Artificial Neural Network (MLP) model and Support vector regression have been selected and implemented in order to simulate monthly precipitation and temperature data of Mashhad. In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables when the focus is on the relationship between a dependent variable and one or more independent variables (or 'predictors'). Genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio-inspired operators such as mutation, crossover, and selection. Ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. This algorithm is a member of the ant colony algorithms family, in swarm intelligence methods, and it constitutes some metaheuristic optimizations. Artificial neural networks are one of the main tools used in machine learning. As the “neural” part of their name suggests, they are brain-inspired systems which are intended to replicate the way that we humans learn. Neural networks consist of input and output layers, as well as (in most cases) a hidden layer consisting of units that transform the input into something that the output layer can use. They are excellent tools for finding patterns which are far too complex or numerous for a human programmer to extract and teach the machine to recognize. In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). At the first stage, several multiple regressions were fitted to monthly precipitation (with coefficients ranging from 0.63 to 0.81) and six patterns for monthly temperature (0.986-0.993). Afterward, GA and ACO were applied to improve the accuracy of the selected regression models by optimizing their parameters. At the next stage, ANN and SVR were used to estimate the monthly missing values separately. Finally, the results of the previous stages were compared using the root mean square error (RMSE), and the optimal models were applied to determine the missing values of monthly temperature and precipitation of Mashhad. The results showed that the Genetic Algorithm and Ant Colony increase the accuracy of the estimation of missing rainfall data significantly more than the previous methods. The lowest error criterion (RMSE) between regression patterns is 9.8 millimeters. By genetic algorithm, this criterion is reduced to 2.56 mm, and by ant colony algorithm to 2.559. Comparison of the above methods in restoration temperature and precipitation shows that evolutionary methods (GA and ACO) are the best for estimating the missing monthly precipitation and machine learning methods (ANN and SVR) are the best to imputation missing data of monthly temperature.

The purpose of this study is to use a semi-distributed VIC model to estimate monthly runoff, actual evapotranspiration and soil moisture in the Neyshabour basin for the period of 1985 to 2014. Results of sensitivity analysis showed that in the mechanism of simulating runoff, the VIC model is more sensitive to infiltration capacity parameter and second soil layer. First, the VIC model was calibrated using a simulated runoff data comparison with the basin outlet for the years 1995-1993. The results indicate the high performance of the model in simulating the flow of the basin outlet. So that the values of R2 and NSE were 0.85 and 0.99, respectively. Also, the VIC model was verified with runoff data of Hossein Abad Jangal station and simulated evapotranspiration with SWAT and SEBAL algorithm. Values of R2 and NSE for runoff were 0.8 and 0.9. The results indicate that the simulated VIC model is more compatible with SWAT model data, and the values of R2 and NSE are 0.76 and 0.7, respectively. According to the obtained results, the VIC model can be used to simulate the variables required in water resources and meteorological studies.

The aim of this study is investigation the application of operation scenarios on Golestan dam to reduce the inundated area and the flood damages at downstream of dam. So considering confidence interval, the inflow hydrographs for Golestan dam were calculated for 4 return periods (25, 50, 100, 200 year). Then these hydrographs were routed through Puls method in order to obtain the outflow hydrographs passing through dam spillway. Also, the integrated of Geographical Information System (GIS) and the HEC-RAS hydraulic model were used to produce a flood map for different floods. Finally, the damage to land around the river was estimated in each scenario. The results showed that assigning a flood control volumes in Golestan dam is effective only for floods whit peak discharge up to 2000 m3/s. And as the flood peak discharge increases, the effect of dam on flood controlling decreases, even if you keep the dam empty. So that, by the application of operation scenarios, flood inundated area can be reduced to 100% in the floods whit peak discharge up to 500 m3/s.

Achieving sustainable practices of mitigation and adaptation to climate change requires accurate projections of climate change in each region. In this regard, Coupled Model Inter-comparison Project (CMIP) over the past 20 years has shown a good performance. Therefore, new CMIP5 climate models are expected to be bases for many climate change studies. These models use a new set of emission scenarios called Representative Concentration Pathway (RCP) to project climate change. Climate change is expected to impact wheat production and food security in Iran. So far, no study has not been conducted to regionally project climate change based on new CMIP5 models and RCP scenarios over the major wheat-producing areas in Iran. Our objective was to evaluate the performance of CMIP5 climate models in simulating temperature and precipitation in these areas. In addition, different combinations of climate models were evaluated to select appropriate models in these areas. According to the latest data, nearly 60% of rainfed wheat is produced within our study area. The mean monthly temperature and precipitation data were provided by Meteorological Organization of Iran for synoptic stations. Period of 1975-2005 was considered as a historical period (baseline period). We evaluated outputs from 21 GCMs from CMIP5 climate models for monthly values of total precipitation and mean surface air temperature. One in ten ensembles of each GCM model was evaluated as available. We used model outputs for two emission scenarios i.e. RCP-2.6 and RCP-8.5, for the future periods of 2045–2065 and 2080-2100 to project temperature and precipitation changes. We assigned the models into two groups, high resolution (models less than 2° latitude/longitude, high-re; 11 models) and low resolution (models greater than 2° latitude/longitude, low-re, 10 models). Output GCM models were used for a grid in which recorded data are available. We applied the equidistant quintile-based mapping method (EDCDF) to correct bias of monthly precipitation and temperature simulated by models in the historical period (1975-2005) and, then in the future periods. We also used the root mean square error (RMSE), the coefficient of correlation and the skill scores (SS) to evaluate the model performance.
Result and Average of all ensembles of an individual model outperformed the other ensembles in simulating the historical climate. This superiority is largely caused by the cancellation of offsetting errors in individual ensembles of a model, and also reduces the effects of natural internal climate variability in simulations. Taylor diagram showed, contrary to a simulation of temperature, simulations of precipitation have great variability than observations and the standard deviation of simulated precipitation values was less than that of observations for most used models. The models simulated temperature much better than precipitation across the region. Contrary to precipitation, the simulated temperature did not show a significant difference among the models. Several combinations of models resulted in an improvement in precipitation and temperature simulations. Therefore, a combination of models can be used in regional climate change assessment studies. The models performance for simulating the historical climate was evaluated based on skill score (SS) and Δ (the Euclidian distance from perfect skill, point (1, 1, 1, . . . , 1)). Many different combinations of 21 GCM models were evaluated, which combination of 7 models as selected models yielded a lower Δ and higher skill scores. For multimodal ensemble (MME) mean (All, high-re, low-re and Selected, models) Δ value was less than that for individual models. SS values in the simulation of precipitation were more than -3 for 75% of models during the high precipitation months. Uncertainty in the simulation of precipitation during the low precipitation months was more than that of high precipitation months and it was even much more in southern areas (especially in August and September). Uncertainties in temperature and precipitation changes projections were affected by the scenario, the time period and models selected. All models showed biases indicating the fact that direct use of such models in climate change studies (without bias correction) is not recommendable. Although the use of statistical methods for bias correction resulted in a significant reduction of nonsystematic biases, systematic biases were not considerably influenced. Precipitation will increase in northern areas toward the end of the century and a higher reduction in precipitation is anticipated in the southern areas. The average, long-term (2080–2100) temperature increase was 5.5°C under RCP-8.5. Further, temperature increase will be greater in the southern regions. Performance of 21 GCMs from CMIP5 climate models were evaluated in major rainfed wheat-production areas in Iran and temperature and precipitation changes were projected under RCP-2.6 and RCP-8.5. Taking into account all GCM’s initial conditions (if they are available) leads to a better performance. Simulations of models exhibited biases, so models output must be corrected before they can be used in regional climate change assessment studies. Although bias correction resulted in a significant reduction of nonsystematic biases, systematic biases were not significantly affected. The MME (All, high-re, low-re and Selected, models) consistently outperformed individual models for both precipitation and temperature suggesting that a smaller group of models can be used in regional climate change assessment. We recognized a subset of 21 models (7 selected models) based on performance that combination of them can provide the best performance and plausible future projections.

Evapotranspiration is one of the important components of water balance which is measured and estimated by several methods. Since these methods mainly involve point-by-point measuring and requiring a large amount of grounded data, so they have limitations. In this study, potential evapotranspiration for 8 days in 2014, 2015 and 2016 was estimated using the Priestley-Taylor method and remote sensing technique in Fariman area in Khorasan Razavi province using Landsat 8 Operational Land Imager (OLI). To determine the accuracy of the estimates, the results of this study were compared with the FAO Penman-Monteith method (the reference method for estimating potential evapotranspiration). Comparison of the obtained results by the Priestley-Taylor method with the FAO Penman-Monteith method showed that the R2 and Root Mean Square Error (RMSE) are 0.91 and 0.78 mm/d, respectively. This result indicates that the high accuracy of this method in estimating potential evapotranspiration in a semi-arid climate.

Iran located in arid and semi-arid area has many parts with high susceptibility to drought. The average rainfall of Iran is less than a third of the average annual rainfall in the world which has an inappropriate spatial -temporal distribution. Meteorological indices can be used for drought monitoring calculated by situations data. One of the main problems for applying these indices in Iran is inappropriate distribution of station and lack of data. In other hand, remote sensing technology is able to extract data from remote area. In this study in order to analyze drought risk in Khorasan Razavi province Synthesis Drought Index (SDI),”between 2001 to 2010” was applied. Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Precipitation Condition Index (PCI) through Principal Component Analysis (PCA) were measured for estimating SDI. For assessing the accuracy of SDI, correlation between these indicators and SPI (3, 6, 9-month) have been studied during the growing season, comparison was also done between total annual rainfall and long-term average rainfall in 10 synoptic stations in the studied area, as well as correlation between two indices VCI and SDI with the yield of rainfed wheat and barley. The results indicated that drought has been occurred in years 2001, 2002, 2006 and 2008 in the Khorasn Razavi province. The result of validation showed high correlation between SDI and SPI. Also the results showed that the Synthesis Drought Index in addition to monitor meteorological drought with ability of enhanced spatial resolution could be applied for identifying agricultural drought.

1. Precise estimates of rainfall in areas with complex geographical features in the field of
climatology, agricultural meteorology and hydrology is very important. TRMM satellite
is the first international effort to measure rainfall from space reliably (Smith, 2007).
Another set of data that has become available in recent years is the output of numerical
prediction models. Akter and Islam (2007) used MM5 model for weather prediction
especially for rainfall in Bangladesh. They compared MM5 outputs with 3B42RT
production of TRMM, rain gage and radar data and concluded that MM5 is reliable for
rainfall prediction. Ochoa et al. (2014) compared 3B42 product of TRMM with
simulated rainfall data by WRF model. Their results showed that TRMM data is more
applicable for presenting spatial distribution of annual rainfall. In addition to the
methods of statistical comparison, the similarity algorithm (Herzfeld & Merriam, 1990)
was also used in this study. This algorithm compares a large number of data
simultaneously, which can be in the form of maps or models output. In Iran, very few
studies have compared the output of numerical prediction models with TRMM products
of rainfall. The aim of this study was to evaluate and compare the rainfall data using
similarity algorithm for different locations and time periods in order to fill a gap in the
space-time data.
2. Material and The study area consisted of North Khorasan, Khorasan Razavi and South Khorasan
provinces in North East of Iran, which is geographically located between the longitudes
of 55 to 61 degrees and latitudes of 30 to 38 degrees. The climate of the area is arid and
semi arid. Total area is approximately 313000 square kilometers. In this study, three
types of data were used. Ground-based observations used from synoptic and rain-gauge
stations of Meteorology Organization. The seventh series products of TRMM 3B42 sensor containing three hours TRMM rainfall data with a spatial resolution of 0.25
degree were downloaded for free from the site of NASA. MM5 model outputs which
were in the form of images with a spatial resolution of 0.5× 0.5 degrees for the period of
2000-2010 were also obtained from NASA and NOAA .In this study, KED as a
geostatistical method was used to interpolate rainfall. For running geostatistics
algorithms, GS and ArcGIS software were used. Similarity algorithm was executed
for each grid point map and the similarity values were derived. After standardization by
calculating the similarity value for the entire study area, F network model for similar
map was created. In similarity algorithm, closest values to zero indicate a good
similarity between the input maps in a specific location and higher values indicate
weaker similarity. Standardization algorithms, similarity and analytical software
programming in MATLAB were performed for each grid point of the map.
3. RMSE values for MM5 model were higher in the warm months. The highest RMSE
values were obtained in late spring and early summer. This result proved that in the
summer, rainfall was predicted less accurately than in the cold months in winter. RMSE
values for TRMM showed a reverse pattern with MM5 model output. Maximum
amount of RMSE for TRMM was obtained in January with 14 mm per month. The
reason for this may be because microwave energy scattering from frozen ice on the
ground. The scattering from rain or frozen rain in the atmosphere is similar. Similarity
values in the area were scattered with uniform distribution that represents the least
significant inter-annual variation is cold seasons. For the warm seasons, in the south and
north of the area, similarity values vary from 1 to 2. Results showed that inter-annual
variations of rainfall in warm seasons and in central areas is high. One of the reasons for
these results can be errors in the observed data.
By examining the time series of TRMM images using similarity algorithm, we found
that in the cold season, the south zone of the study area had similarity values 0.05 to 0.1
with a uniform distribution of values. However, higher similarity values were obtained
for the northern and central areas where the distribution of similarity values was not
uniform.
Due to these facts, it can be concluded that rainfall production of TRMM data was
relatively good in the cold season in south and relatively week in north and central parts
of the region. In the warm season the least amount of similarity could be seen in the
northeast part of the study area. But generally, TRMM estimated rainfall fairly in the
warm season.
4. The validation results of MM5 model rainfall and TRMM monthly rainfall images
showed that the model predicted rainfall amounts in the cold months better than in the
warm months. However unlike the MM5 model, remote sensing images had the highest
error in cold months. The reason was the presence of snow and ice on the ground in the
cold months of winter. Considering inter-annual and seasonal changes, it became clear
that there is much difference between inter-annual remote sensing image changes and the actual amounts of rainfall (KED). Nevertheless the model inter-annual changes were
consistent with real data. Inter-annual changes of the model and the station data (KED)
were higher in cold season.
KED methods also retained spatial variability of rainfall as well as remote sensing data
and model output. The estimates, especially above 1500 meters in the central regions,
had low precision in the products. The results showed that in the absence of adequate
rain gages in the region, MM5 output model and TRMM data could be used to fill the
gaps.

In recent years, there is an increasing concern in weather and climate extremes, since they may cause serious disasters to human society and nature and seem to more sensitive to climate change than the mean value. The rise in greenhouse gas emissions arising from increased industrialization and urbanization has, in recent years, contributed significantly to global warming. This global warming leads to the change of climatic extreme index and increases the intensity and frequency of occurrence of extreme climate events. Extreme events are relatively rare, unpredictable and often brief but they are highly destructive. Many studies show that climate extremes cause significant damage to crop growth and final yields, and the various frequencies and intensities of extreme events result in differing degrees of soil erosion and flooding. Climate variability is largely influenced by temperature change, which is particularly important through its role in the global climate system and energy cycles. Analyses of observed temperature in many regions of the world have already shown some important changes in the extremes. To predict future change in extremes understanding the recent past is essential. The objective of this study was to investigate the spatial and temporal distribution of temperature extremes in Khorasan Razavi province during 1990–2015 based on 15 climate indices proposed by the Expert Team on Climate Change Detection and Indices and to detect the effect of climate change