طیب رضیئی

Due to the significance of forests in both the natural and human environment, this study aims to investigate the impact of meteorological drought on oak forest dieback in Ilam province. Specifically, the study seeks to determine the relationship between Zagros Forest drought and droughts in this particular region. The analysis utilizes the Standard Precipitation Index (SPI) to identify the frequency of droughts during different time periods. The results indicate that the years 2007, 2008, 2011, 2015, and 2016 experienced the highest occurrence of droughts. Additionally, remote sensing data from MODIS images were employed to examine the trend in tree greenness (NDVI) from 2000 to 2016. The analysis reveals a significant correlation (R2 = 0.9999) between the greenness trend and the drought index (SPI). Moreover, a land survey of oak drying points and simulation using Landsat satellite images, with a 15×15 pixel output from GIS software, indicate that approximately 17,894 hectares of forests in the region experienced drying and destruction between 2000 and 2016. By combining the oak forest drying layer with the output layers derived from drought zoning, visual indicators were created, and statistical analysis was conducted for three 5-year time series. The results demonstrate a correlation coefficient of 96.6% and an explanation coefficient of R2 = 0.985 for the 2002-2006 time series, a correlation coefficient of 95.4% and an explanation coefficient of R2 = 0.980 for the 2007-2011 time series, and a correlation coefficient of 98.8% and an explanation coefficient of R2 = 0.995 for the 2012-2016 time series. These findings illustrate the influence of drought and its variations in terms of intensity and duration on oak forests in the Zagros region of Ilam. Based on the study results, it is predicted that if the drought persists with the same trend, approximately 1,118.4 hectares of oak forests in Ilam province will dry up and be destroyed annually.

Mineral Dust, the most important type of aerosol, has a significant direct and indirect role in weather and climate. In this case, it intend to investigate the capability and capability of MACC model validated by MODIS for detection of dust episodes in the Kurdistan province during 2003-2012. To achieve that, we analysis satellite and model data using Man-Kendall trend and statistical tests. The results of the temporal distribution indicated that the mean Aerosol Optical depth (AOD) in 2008 was 0.36 and its lowest was 29.04 for 2004. In addition, average AOD in menthioned year was 0.036, 0.335, 0.385, 0.377 and 0.3368 for the cities of Sanandaj, Saqez, Ghorveh, Kamyaran, Marivan, respectively. The spatial distribution of AOD average in different seasons showed that winter and autumn had the lowest amount and spring, and summer season had the highest AOD. AOD's monthly spatial distribution showed that high dust belonging to April-August period to covers completely interested area.The results of the Man-Kendall test showed that the area had a significant positive trend in the spring season throughout the province and the summer season in the east of the province. Therefore, the spring season in the area known Extreme Season and June 19, 2009 between the five days of the dust extreme is as an extreme episode with an average AOD of 1.16 and a horizontal visibility of less than two kilometers that it have the highest and most widespread mineral dust. In general, the results of the MACC with multidimensional approach showed that optical depth (AOD, DOD) is a more appropriate criterion than horizontal visibility in determining dust storm.

Heavy and consecutive rains at early April of 2018 led to severe floods in large parts of Iran, especially in the Karkheh basin, which was accompanied by huge damages. The average rainfall in the Karkhe dam basin for the event of April 4-7, 2018 was about 87 mm, and for the event of April 11-17, 2018, it was nearly 108 mm. For the flood management by the reservoir, estimation of the peak discharge and flood hydrograph is essential in order to predict the hydrological behavior of the basin. Rainfall-runoff models that are used to simulate flood hydrographs are one of the methods of estimating runoff and a suitable tool for investigating and evaluating hydrological processes, water resources, and flood management.

Since the estimation of peak discharge and flood hydrograph has great important to predict the hydrological behavior of the basin and also to take the necessary measures to reduce the flood risk, the present study was conducted by using HEC-HMS model to simulate the rainfall-runoff events during 2007-2018 in the Karkheh Basin .By using this model capabilities and the data from some hydrometric and meteorological stations in the basin, the volume and peak discharge of floods in that period were estimated. Because Seymareh dam impoundment has started since 2013; two separate basin models were developed and for running the model, 11 flood events were obtained then, the basin parameters were calibrated based on six events and the others were used for validation. In the process of developing the basin model, the SCS Curve Number method is used to calculate basin runoff losses and convert rainfall to runoff, the Clark Unit Hydrograph method and the Return flow method to calculate the base flow, the Muskingum method for hydrological routing, and the Weighted average method for spatial data analysis of rainfall. The Outlet Structure method was used for routing the reservoirs of Karkheh and Seymareh dams.

Comparing the initial simulation results of the model with the observed values at the outlet of the basin and some hydrometric stations of the basin showed that the hydrograph model overestimates the flow. Therefore, using the residual squaredsum objective function, basin parameters (CN, time concentration, storage coefficient, initial absorption, and recession constant) were calibrated. After calibration of parameters, the results showed that the calculated hydrographs were in good agreement with the Observational hydrographs in the Karkheh and Seymareh dams. Next, to check the accuracy and confirm the results, the model was validated by the five new rainfall events and to evaluate the efficiency of the model used in this stage, the Nash-Sutcliffe indices and the simulated variance coefficient were used.

Comparing the calculated results with the flood observational values (peak discharge) using the correlation coefficient (R2) showed that there is a relatively good agreement between simulation and observation in sub-basins 5, 2, 7, and 1 (0.92, 0.73, 0.73 and 0.70, respectively). Also, the model efficiency index values in the validation period for the Nash-Sutcliffe index (0.33-0.99) and simulation variance coefficient (0-0.73) for the outlet of sub-basins 9, 6, 5, 1, and 8 are favorable and the HEC-HMS model approximately can provide an acceptable estimation of the flood hydrograph. So, it can be well-analyzed how the way flood events are formed in the Karkheh basin. Also, the sensitivity analysis of the model parameters showed that the curve number parameter (CN) has a greater effect on the changes in the objective function than other basin parameters.

Monitoring of hydrological droughts is one of the basic needs of water resources management in watersheds, especially in the field of water agriculture. Drought is divided into three major groups: meteorology, agriculture and hydrology. Hydrologic drought can be studied in different ways. One of the common methods is the use of low flow indexes and threshold level approach.

In this research, the minimum flow indices (Q75, Q90 and Q95) extracted from the flow continuity curve and minimum flow series (10 and 30 days) as well as the amount of flow deficit for hydrological drought monitoring in the Caspian Sea Basin were investigated and evaluated. For this purpose, 40 hydrometric stations with 41-year statistics (1970-2011) were selected. In the next step, the data of the studied stations were evaluated in terms of homogeneity, independence and randomness. Then, with the help of hierarchical cluster analysis and step-by-step regression, hydrological homogenous areas were determined and regional analysis of these indicators was done.

In order to investigate the characteristics of the minimum current in the Caspian Sea Basin, first, the continuous flow curve was drawn for each of the stations, and then, three indices Q75, Q90 and Q95 were calculated for each of the stations. For the spatial comparison of the minimum flow, the specific minimum discharge or qs (minimum discharge value divided by the area) was used. qs75 index varies between 0.0006 and 13 m3s-1per km2. The value of qs75 is less (drier) in the eastern parts and in the western parts of the region, the amount of dryness of the stream is less than other places. Examining the spatial distribution maps of these three indicators shows that the trend of their spatial changes is almost similar and they all indicate that the western regions of the Caspian Sea Basin are more humid than the eastern and central regions. In the next step, to examine the minimum flow indicators, a series of minimum flows of 10 and 30 days was prepared. By comparing distribution parameters with the help of scoring method, Log-Pearson type 3 distribution was selected as the best distribution in most stations. After choosing the most appropriate distribution, the values of the 10-day and 30-day minimum indices with different return periods were calculated. Examining the average indicators shows that the minimum discharge value of 10 days with a value equal to 0.01 m3s-1 in Vatana Station (12-035) located in the east of the basin and the highest with a value of 19.2 m3s-1, it is at Rudbar Station (17-034) in the western region of the basin. Regarding the average minimum discharge of 30 days, the lowest value is equal to 0.20 m3s-1 and the highest value is equal to 8.52 m3s-1in these two stations. In order to investigate the temporal changes of hydrological drought intensity, the annual time series of 10-day and 30-day low flow at each station were plotted in relation to the year of their occurrence, in order to determine the trend of changes in the drought situation in different years. Examining the time trend of the minimum flow indicators on the graphs, shows a decrease in the value of the indicators in recent years and a negative trend of the indicators. In other words, the graphs in almost all stations show hydrological droughts (reduction of minimum flow indicators) during recent years. In order to determine the length of minimum flow periods, 10 and 30 day moving averages of discharge were compared with Q90 index value in different stations. The results show that the persistence of drought in the central parts of the Caspian Sea Basin (Pulor, Razan, Karsang, Tange Lavij, Pol Zoghal and Zowat sub-basins) is more than the rest of the regions, these sub-basins are located in Mazandaran Province. The lowest duration of drought (between 22 and 25 days) is related to the sub-basins of Shalman, Pol-e-Sazman, Pashaki, Astana and Tutkabon in the eastern part of the Caspian Sea Basin and in Gilan Province. The eastern parts of the basin have also experienced a drought period between 28 and 30 days.

Results indicate that the years 1990 to 2010 have undergone severe and long droughts in most of the stations. The review of the spatial distribution of indexes shows better conditions in the western parts of the study area compared to the eastern sections in terms of dryness. However, the duration of hydrological droughts in the central study area is longer than in other parts of the basin. Investigating the time trend of the indexes also shows the increase in the frequency and duration of hydrological droughts in recent years. A comparison of different indexes shows that all of them have similar results in the region. The results of cluster analysis divided the area into three distinct homogenous units (in 0.01 significant level). The result of the regional analysis showed that in the eastern homogeneous region, the influencing factor on low flow indexes is elevation, while in the central and western regions, the drainage area and density have a greater impact.

The use of temperature and precipitation data from the NASA Earth Exchange Global Daily Downscaled Projections dataset (NEX-GDDP) is increasingly expanding as one of the products derived from the global climate models. Investigating the quality of this product’s precipitation data in Iran can help the researchers to consciously use them in hydrological and water resources practices. In this study, first, the degree of improvement of the monthly precipitation data of ACCESS1-0 model from the NEX-GDDP product was investigated against the corresponding GCM model as well as the observational data measured at stations located in eight homogeneous precipitation regions of Iran. Then the NEX-GDDP data was bias-corrected using the Quantile Mapping (QM) method through using the SSPLINE, QUANT, PTF, RQUANT and DIST functions. Comparison of monthly precipitation data of the selected models of the NEX-GDDP product with its raw GCM data showed that R, PBIAS, NSE, and KGE statistics have significantly improved respectively in 75%, 100%, 100%, and 88% of the studied stations. The correlation between the bias-corrected data and the observational data was also improved in 50% of the stations, the NSE and KGE were improved respectively in 75% and 62.5% of the stations, and PBIAS was improved in all stations. This study also showed that among the used bias correction functions, the RQUANT had the best performance.

In this study, the relationship between dieback of Persian oak trees and drought in Ilam province has been investigated. The main purpose of the research is to introduce a suitable model of relationship between oak dieback and drought. So from SPI and NDVI indicators and Moran index and statistical regression statistics and satellite images of Modis and Landsat have used to analyze the relationship between dieback of Ilam forests and happened drought in the region. The precipitation data of 93 rain gauge stations were analyzed during the statistical period and according to the dry coefficients of SPI index, drought zoning layers of Ilam province were prepared for two time series of 2000 to 2009 and 2010 to 2019. Greenery's raster layers were prepared from Modis satellite imagery for the mentioned time series. The results of analysis of Moran's statistical showed a significant correlation between the SPI index and the NDVI index in spatial dimensions. By a simple random method, 143 points of oak dieback with dimension of 30 m2, which each point was equivalent to a pixel-size, were recorded with a GPS device, and by simulating in satellite imagery, the droplet layer of oak dieback was extracted. Although linear regression between extracted oak dieback points with SPI and Moran statistical indicators was significant, but the relationship between NDVI index and Moran statistic has the effect of independent variable of drought trend in spatial and temporal dimensions on the dependent variable process of oak drought with spatial analysis. And nonlinear regression has a more appropriate and accurate statistical significance and explanation. So this method as desirable method has been introduced for analyzing of drought and oak dieback.

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.

Changes in climate conditions and the frequency and intensity of extreme climate events like droughts and heatwaves have pronounced impacts on forest health and diversities around the world. Among the climate extremes and hazards, drought has the most influential impacts in forests and can adversely alter their density and spatial extent around the globe, particularly in mid-latitudes of both hemispheres. By investigating the impacts of droughts and diseases on forest trees, Loustau et al., (2006) stated that although most pathogens can resist water deficiency in drought periods in general droughts have no negative impact on forest diseases. By simulating historical long-lasting droughts in moderate climate forests, Borken et al., (2006) reported that a reduction in severe summer droughts significantly increases the CO2 conservation in forest soil. Hogg et al., (2008) investigated the impacts of regional droughts on the productivity, biomass and dieback of the Aspen forest of western Canada and concluded that the forest dieback and decline have a good correlation with annual relative humidity index which is related to short term droughts.In recent decades, oak forest dieback becomes one of the most important environmental challenges in western Iran. Climate change, pests, physiographic and anthropogenic factors were introduced as the main causes of Zagros forests dieback in the preliminary studies. The present study investigates the oak forest dieback in Lorestan province, Iran, concerning drought occurrences and their characteristics.The data used in this study include 1) field surveys using GPS, 2) MODIS satellite data for the period 2000-2017, and 3) monthly precipitation records of meteorological stations for the period 1980 - 2017. Using GPS, the coordinates of the areas encountered canopy level dieback were recorded by the authors through several field surveys. The monthly precipitation records of 16 meteorological stations well distributed over the study area were acquired from the meteorological organization of Iran. The 30 years of 1980 – 2017 was chosen as it has complete data records in all considered stations and meets the minimum record length required for SPI computation in the arid and semi-arid climates. For all considered stations, the SPI time series were computed for 3-, 6-, 9-, 12-, and 24- month time scales from which drought characteristics (i.e., drought frequency, duration, severity, and magnitude) were subsequently computed. The MODIS satellite images with a 16-day interval, totally accounting for 411 images during the eighteen years of 2000-2017 time period were also retrieved from its website and processed by ENVI software for computing NDVI index as the representative of forest health and freshness. The NDVI time series were then divided into three categories based on the density of the forest coverage, namely forest with low, medium, and maximum densities. The mean annual time series of NDVI were then correlated with the SPI time series of each time scale ended at each calendar month. The maps of the long-term mean of drought characteristics were also drawn to spatially analyze drought characteristics over the study area and assess their linkage with oak trees canopy level dieback hot spots over the region.The field study and the literature review showed that the dieback has occurred in Lorestan forests with different intensity and spatial extent, being not related to a specific land elevation, geographic location, and mountain slope and direction. The canopy level dieback of the oak trees was also found as a more common feature than the forest decline in the study area. Investigating the freshness of the forests represented by the Normalized Difference Vegetation Index (NDVI) time series derived from MODIS data showed that the first widespread forest dieback occurred in 2004 and then more intensified in 2008. The results show that the mean annual NDVI as a signal of forests trees health and freshness have the highest correlation with the SPI time series ended in all spring and summer months, particularly at 9- and 12- month time scales that showed the highest correlations with the mean annual NDVI time series. This relationship is relatively similar in most of the studied stations, more specifically in those located in or near the forest dieback hotspots that showed the highest correlation coefficients being significant at 5% significance level. The computed SPI time series also reveals that droughts are more frequent, long-lasting, and severe in the southwest, central, and northeast areas of Lorestan province where more hotspots of oak trees dieback were observed. Investigating the relationship between drought events and the spatial and temporal variations of NDVI time series indicates that a significant reduction in precipitation amount, as represented by SPI, is the main cause of oak trees canopy level dieback in the Lorestan forest, explaining 20% to 70% of the variation of the dieback phenomenon depending on the location over the province. The more widespread, severe and long-lasting droughts in the province were observed in 2004, 2005, and 2008 that coincide well with the years identified with the more pronounced oak trees canopy level dieback in the Lorestan province, as reported by literature and the local habitants.Based on the results achieved in this study, it can be concluded that the oak trees canopy level dieback occurred in many parts of the Lorestan oak forests rather than forest decline. It was also found that drought occurrences explain 20% to 70% of the variation of this phenomenon depending on geographical locations s and SPI time scales considered. Investigating the relationship between drought events and spatial and temporal variations of the NDVI showed that a significant reduction in precipitation amount, as represented by SPI, is one of the main causes of oak trees canopy level dieback in the Lorestan forest.

One of the important issues in recent years is the drying up of Zagros oak forests and since Zagros oak forest is one of the natural resources of Ilam province, this issue needs to be evaluated. The main purpose of this study is to analyze the spatial relationship between drought in Zagros forests and droughts in the area. For the spatial and temporal analysis of drought in Ilam province, Standard Precipitation Index (SPI) for the period 2000 to 2016 was used from the rainfall data of the province and for spatial analysis of oak forest drying relationship using spatial pattern distribution models, Moranchr(chr('39')39chr('39'))s spatial autocorrelation model. A systematic random sampling of 15 m2 of dried oak trees at the surface of the area was performed using GPS and simulated using Landsat satellite images with 15 m2 resolution. The results of Moran model showed that oak drought has a cluster pattern and the Moran index with SPI coefficient was 0.914532 in positive drought zone and 0.938461 in severe drought zone and 0.985233 in relatively dry zone and 996512 in near-normal drought zone. The mean Moran index was 0.956758 with a significant level of 0.00001 indicating the effect of drought on oak dryness.

One of the most important issues in the Zagros forests of Ilam is the dieback of oak trees, and the purpose of this research is to analyze the temporal-spatial analysis of the relationship between oak trees dieback and occurred droughts. In order to identify the dry areas of oak, using the method of determinationof. the training samples in geographic information system (GIS) and satellite images of modis were used. For temporal and spatial analysis of drought in Ilam province, NDVI index from MODIS satellite images in the statistical period of 2000 to 2018 was used. For  spatially analysis of relationship between oak forest dieback and droughts, Moran spatial autocorrelation model, one of the spatial pattern distribution models, was selected. Coincidentally, a 30-square-meter GPS device was used to find the coordinates of the dried droplets and the number of dried oak trees at each point, and a map of oak tree dieback distribution was prepared using GIS software. The results of combining drought zoning from NDVI index with oak dieback distribution map in two time series from 2000 to 2009 and 2010 to 2018 in the analysis of Moran index showed that oak dieback has a cluster pattern and the decline of oak trees is spreading in a mass. Also there was a significant relationship between drought trend in spatial and temporal dimensions and oak dieback in recent years in different parts of Ilam.

Dust is influenced by the interaction of the atmosphere-Earth system and, by changing radiation energy, atmospheric chemistry and physics, affects the climate of an area. Due to the necessity of the role of dust and its spatial-dynamic distribution in the atmosphere of a region, as well as the existence of advanced remote sensing techniques and modeling in the field of dust simulation, the present study attempts to compare, quantize and simulate dust using aerosol optical depth (AOD) of MODIS and MACC products. Pearson correlation showed that there is a significant relationship between the sensor and the model in the west of the interested area. The lowest correlation was observed over Hamadan province. The results showed that simulated dust was more than satellite observations. The annual dust revealed that dust have two active (2000-2010) and inactive from 2010 onwards. In addition, monthly distributions of Dust in MODIS and MACC showed that the interested area had the highest dust concentration in the months of April to August; it was high overlaps of AOD between MACC and MODIS in the wet months (December to March) than dry month (April to November). In general, the spatial distribution of dust in the study showed that the dust pattern in both the sensors and the model has increased from south to north in Kurdistan, Kermanshah and Ilam, but the spatial variation of dust in the MODIS is more regular than the MACC. In general, the spatial distribution of dust had the South-North trend and northward decreased

Drought is one of the natural disasters that may occur in any climate. In recent decades, Iran has been affected by severe droughts and its harmful effects in various sectors, such as agriculture, environment and water resources of the country. Today, vegetation indices, which are obtained through remote sensing technology, are used to identify and analyze agricultural droughts. Accordingly, the aim of this study was to investigate the effectiveness of NDVI, EVI and VCI vegetation indices in agricultural drought identification and analysis in Karkheh basin. In order to calculate these indices, MODIS sensor Images (Terra satellite, MOD13A2 product) were used during the 2000-2017 statistical period. The accuracy of these profiles was evaluated with the ZSI index calculated at 11 meteorological stations located in Karkheh basin for the statistical period of 2000-2017. The results showed that the changes of NDVI, EVI and VCI in the studied stations were approximately the same during the statistical period. Based on NDVI, EVI and VCI values, the lowest and highest vegetation cover was observed in 2000, dehno station and 2001, helilan-seymareh station, respectively. The ZSI survey showed that most stations Faced with droughts from 2000 to 2008, and the most severe drought occurred in 2008, nazarabad station. Then, in order to validation of the results, the vegetation indices with ZSI index were evaluated. Pearson correlation between mean vegetation indices of NDVI, EVI and VCI with mean ZSI was 0.766, 0.725 and 0.776, respectively, and all vegetation indices with ZSI index are significant at 0.99% confidence level. As seen, according to the results, the ZSI index confirms the results of NDVI, EVI, and VCI. So, according to the results, there is no conformity of meteorological and agricultural droughts in all years, Therefore, in addition to other precipitation, climate variables should also be considered.

In this study, the temporal and spatial variation of snow depth over the mountainous region of Zagros, in the western Iran, for the period 1979–2010 was investigated for the cold season when the probability of snow occurrences was high. For this purpose, daily gridded snow depth data relative to Era-Interim/land were retrieved from the European Centre for Medium-Range Weather Forecasts (ECMWF) and used for spatiotemporal analysis of snow in the region. Furthermore, monthly maximum, minimum and mean air temperature relative to the weather stations distributed over the region were also used to investigate the relationship between snow depth and air temperature variability in the region. In each grid point, the rate of temporal changes in the snow depth was estimated using the Sen’s slope estimator, while the modified Mann-Kendall Test was applied to assess if the change identified was statistically significant. The results showed that in almost all of the studied months, especially February and March, the snow depth was significantly reduced in the region, which was statistically significant at 5% significant level. Unlike the observed statistically significant decreasing trend in the depth snow in the region, a significant increase in the maximum, minimum and average temperature was observed for all the studied months and the stations. The result suggested that the observed decrease in the snow depth in the region was related to the increasing trend in the temperature during the study period, which could be attributed to the global warming and climate change.

In this study, the possibility of snow estimation using the snow-rain phase separation models that usually integrated into the snowmelt models or in the numerical weather prediction (NWP) models are evaluated in the Zagros region, Iran. For this purpose, daily precipitation (rain and snow) and temperature from 36 weather stations distributed over Zagros and having data records over the 1951-2015 period were used. The performances of the snow-rain phase separation models in accurately predicting the phase of precipitation at the studied stations were evaluated through constructing contingency tables between the predictions and observations and eventually evaluating the result using a set of statistical skill scores. A statistically significant relationship was found between the phase of precipitation predicted by the models and the snow occurrences observed at all selected stations. The USACE, Pipes and Quick, Hyperbolic tangent function and Kienzle were found as the best models while the Mccabe and Wolock model and the Brown model based on the maximum and minimum of temperature resulted in the weaker predictions. The snow-rain separation temperature threshold at the selected stations varies from -1.7 to 5 degree Celsius, nonetheless, it it is between 0 and 2 degree Celsius in more than 75% of stations. It was also found that the temperature range within which both snow and rain can occur simultaneously varies in all considered stations, but in most of the stations it is between 9 and 13 degrees Celsius.

Delineation of precipitation regimes is very important for large countries such as Iran which is characterized with complex topography and different climates. The very rare previous studies on precipitation regimes of Iran have used very limited and unevenly scattered stations across the country; thus making it necessary to identify the most realistic precipitation regimes for Iran using as much as available stations. Henco, the data of 155 synoptic stations with relatively regular distribution over Iran; mostly having full data records for the common period of 1990 to 2014, were used for identifying the updated precipitation regimes for the country. For each station, the percentage of monthly precipitation in relation to total annual precipitation was computed for all the time period and the mean of the time period was considered for the analysis. A principal Component Analysis (PCA) was applied to the inter-stations correlations matrix (155×12) that is composed of 155 stations and 12 mean monthly percentage of precipitation for each station. The computed Kaiser-Meyer-Olkin measure of sampling adequacy for the considered matrix with the value of 0.79 indicates that the considered matrix is approximately meritorious for a PCA application. The first 5 leading significant PCs were considered for further analysis based on the Scree plot and the sampling errors of the PCs (North et al., 1982). The retained PCs were then rotated using varimax orthogonal and promax oblique criterion. The PC scores of both rotated solutions and un-rotated solution were separately used as input for Cluster Analysis (CA) to partition the considered stations into distinctive clusters. Moreover, all agglomerative CA methods as well as K-means CA were examined to find out the most appropriate method for partitioning the data. The cophenetic correlation coefficient was used to measure how well the hierarchical dendrogram of a given CA candidate represents the relationships within the input data. The results indicate that all the clustering approaches well represented the inherent structure of the input data, but the Ward method was selected as the most appropriate method since it resulted in much realistic clusters that well matched the topographic and geographical features of the country. The correct number of clusters was also selected based on the Silhouette index (Rousseeuw, 1987) that measures how well objects lie within their cluster, and which ones are merely somewhere in between clusters. The average silhouette width provides an evaluation of clustering validity, and might be used to select an ‘appropriate’ number of clusters. Computing the index for a set of predefined cluster numbers (2 to 15 clusters) suggests that 9 clusters is the most appropriate cluster number that better represents the inherent structure of the data. As such, all 155 stations were classified into five clusters applying Ward CA method on the 5 leading un-rotated PC scores. However, the 8th cluster that grouped stations from two distant areas into a single cluster was subjectively partitioned into 2 distinctive clusters to better represent the precipitation regimes of these two areas. Moreover, the 5 leading varimax rotated PC scores were also mapped to present spatial variability of seasonal precipitation across the country.
The maps of varimax rotated PC scores well represent areas characterized with seasonal precipitation maximum. For example, summer precipitation in the coastal areas of the Caspian Sea and south eastern Iran are presented by the rotated PC score 1 while the rotated PC score 2 points to the spring precipitation maxima in north western Iran. By taking into account the seasonal displacement of maximum precipitation across the country in the Ward clustering, the identified clusters labeled with its core geographic position or the season of the maximum precipitation well portrait the Iranian precipitation regimes. The Caspian Sea precipitation regime is the most humid precipitation regime in the country with relatively well distributed precipitation during the year that maximizes in autumn. The northern and southern Azerbayjan in northwestern Iran are represented by two distinct precipitation regimes, both being characterized with relatively uniform precipitation distribution during the year but getting their maximum precipitation in a different month of the spring. The south-eastern monsoon precipitation regime featured south-eastern Iran where summer monsoon precipitation has a considerable contribution in annual total precipitation. Similarly, the southeastern coastal precipitation regime characterizing coastal areas of Oman Sea that benefits from summer monsoon but with a lesser magnitude and duration. The western mountainous regime is characterized with a precipitation regime spanning from October to May that maximizes in March. The south-western precipitation regime that encompasses south-western and southern Iran along the Persian Gulf is characterized with a winter rainy season that maximizes in January. Central-eastern and central-northeastern Iran also exhibit two distinct precipitation regimes, both getting their maximum proportion of precipitation in winter but the rainy season is much shorter in central-eastern Iran. And finally, central Alborz is characterized with a precipitation regime in which summer precipitation is relatively high.

Identification of temperature regimes is very crucial for a better management of energy resources, recreations and truisms as well as for adequately determining agricultural calendars in different parts of the country. Few attempts have been made to adumbrate the Iranian temperature regimes; thus it is necessary to identify the most realistic temperature regimes of Iran using as many available stations across the country as possible. For this purposes, 155 Iranian synoptic stations with a relatively regular distribution across the country, mostly having full data records for the common period of 1990 to 2014, were used for the identification of the temperature regimes. In all stations, the average of the mean monthly temperature was computed in the mentioned period and further employed for the analysis. A principal Component Analysis (PCA) was applied to the inter-stations correlations matrix (155×12) composed of 155 stations and 12 mean monthly temperature values for each station. The computed Kaiser-Meyer-Olkin (KMO) measure of the sampling adequacy indicated that the matrix with the KMO value of 0.87 is useful for a PCA application. The first three leading PCs were considered for further analysis based on the scree plot and the sampling errors of the PCs (North et al., 1982). The remaining PCs were then rotated using varimax orthogonal criterion. The PC scores of both rotated and un-rotated solutions were separately used as inputs for Cluster Analysis (CA) to partition the considered stations into distinctive clusters. Moreover, all agglomerative CA methods as well as K-means CA were examined so as to find out the most appropriate method for partitioning the data. The cophenetic correlation coefficient was employed to measure how well the hierarchical dendrogram of a given CA represents the relationships within the input data. The results indicated that all the clustering approaches properly represented the inherent structure of the input data, yet the Ward method was selected as the most appropriate method since it resulted in much realistic clusters that quite perfectly matched the topographic and geographical features of the country. The correct number of clusters was also selected based on the Silhouette index (Rousseeuw, 1987) that measures how well objects lie within their cluster, and which ones are merely somewhere in between the clusters. The average silhouette width provides an evaluation of clustering validity, and might be used to select an ‘appropriate’ number of clusters. Computing the index for a set of predefined cluster numbers (2 to 15 clusters), it was observed that six is the most appropriate number for clusters for a better representation of the inherent structure of the data. As such, all 155 stations were classified into six clusters applying Ward CA method on the three leading un-rotated PC scores.
The maps of varimax rotated PC scores properly represented areas characterized with seasonal temperature variability. The first and second varimax rotated PC sores respectively display the winter and summer temperature variability across the country. Applying Ward clustering on un-rotated PC scores resulted in seven distinct clusters that appropriately specified the Iranian temperature regimes. It was found that the western and northern mountainous areas have a mountainous temperature regime whereas the central-eastern Iran hosting the Iranian deserts has a plain temperature regime which is considerably warmer than the earlier one. The third temperature regime includes stations scattered across the mountainous region, all of which are characterized with a very high elevation, hence having the coldest winters and the coolest summers in the country. The hot temperature regime which is the warmest temperature regime in the country belonged to the southwest and certain parts of the south. The stations located in the coastal areas of the northern and southern Iran respectively have the Caspian coastal, Persian Gulf coastal and Oman Sea coastal temperature regimes, all with the lowest annual temperature ranges in the country. Four out of the seven Iranian temperature regimes are continental but the two mountainous temperature regimes are the most continental regimes in the country due to their wider temperature ranges. The results conduce to a better management of energy resources, recreations and tourisms as well as an optimal determination of agricultural calendars in different parts of the country.

Climate classification has a long history dating back to the ancient Greek scientists and philosophers, but the first quantitative classification of world climates was presented by the German scientist Wladimir Köppen (1846–1940) in 1900 (Kottek et al, 2006). Being trained as a plant physiologist and realizing that plants are indicators for many climatic elements; Köppen (1900) established a climate classification system which uses monthly temperature and precipitation to define boundaries of different climate types around the world, i.e., linking climate and natural vegetation. This system has been further developed (e.g. Köppen and Geiger, 1930; Stern et al., 2000) and widely used by geographers and climatologists around the world. Although there have been many efforts to find alternative ways to classify the climate, the Köppen system remains one of the most widely used climate classification systems. In this research, monthly precipitation and temperature of 155 Iranian synoptic weather stations with relatively regular distribution over the country were used to provide an updated map of climate classification for Iran which is one of the largest countries with diverse climates in the world. Missing values of the used data were estimated and replaced using inverse distance weighed method. Monthly averages of precipitation and temperature for the considered time period (1990-2014) were then interpolated at a network of grids with 0.1 spatial resolutions using ordinary Kriging method. Subsequently, the climate types of the used stations as well as of the predefined grid points were determined using Köppen-Geiger classification method (Kottek et al, 2006; Chen and Chen, 2013). Additionally, following Rubel and Kottek (2010), monthly mean temperature of the Climatic Research Unit (CRU) of the University of East Anglia and monthly total precipitation of the Global Precipitation Climatology Centre (GPCC), both covering 1901-2014 time period and having 0.5 spatial resolution were used for computing Köppen-Geiger climate classification for different time sections of present time, in order to examine if the Iranian climate types have experienced any shift due to global climate change.
Based on the observational data for 1990-2014 time section Iran composes of 9 climate types out of 31 possible Köppen-Geiger climate types. Most parts of central, eastern and southern Iran is characterized with BWh and BWk climate types. The coastal areas of the Caspian Sea and most parts of mountainous areas of Zagros and Alborz in west and north of Iran have moderate climate type (Csa). However, the eastern slope of Zagros and southern slope of Alborz that are connected to the central arid and semi-arid climate of central Iran are distinguished with BSk climate. The southern parts of Zagros region is mostly dominated by BSh climate. Dsa and Dsb climate types are found in some parts of mountainous areas of Zagros and Alborz, while Csb and Cfa are the localized climate types that can be found in coastal areas of the Caspian Sea. Using CRU and GPCC datasets for 1951-2000 time section the same climate types were found for Iran although the sources of the data and its spatial and temporal resolution differs from that of observational data. The identified climate types in this study using observational data are in agreement with those of Kottek et al. (2006) and Chen and Chen (2013) for Iran. The identified climate types for different time sections of 1901-1925, 1926-1950, 1951-1975, 1976-2000 and 1990-2014 revealed that some Iranian climate types were not stable during these five time periods. Comparison of climate classification using observational data for 1990-2014 with those of gridded datasets for 1901-1925, 1926-1950, 1951-1975, 1976-2000 and 1990-2014 revealed that Dfa and Dfb climate types have disappeared from Iran in the map of 1990-2014 climate classification, suggesting that the number of Iranian climate types have decreased from 11 to 9 in most recent years. It was found that the area of BWk climate in central-eastern Iran is continuously retreated by time and it replaced by BWh climate. The Ds climate types were found to be very vulnerable to change and shift. It was also found that the Dsb climate type tends to shift into Dsa climate types in recent years. Most importantly, it was observed that the Ds climate types in western Iran tend to be replaced by Csa climate type. However, the obvious shift from Ds or Csa climate types into BSk climate type is observed in northwestern Iran. This result indicates a rapid and widespread desertification in northwestern Iran due to global climate change.

Daily precipitation records of Mehrabad synoptic station based in Tehran, for the period 1951–2013 was used to identify moderate to heavy cold weather precipitation events in the mainly rainy season of Iran which starts in October and ends in May. Mehrabad is one of the oldest stations in the country that holds the longest and most complete precipitation records available in the country with very few missing values; thus being suitable for identifying the types of precipitation events for the region and the associated atmospheric circulations. Following the Iranian Meteorological Organization definition, we identified moderate and heavy precipitation events for Tehran Province as the events for which total daily precipitation ranges from 5 to 20 mm and from 20 to 50 mm, respectively; but being characterized with anomalous cold weather conditions. This screening approach has resulted in a set of 133 days of moderate to heavy precipitation events featured with cold weather conditions, which is adequate for implementing a multivariate analysis. The 500 hPa geopotential height and relative vorticity, sea level pressure (SLP), 850 hPa wind field and advection of specific humidity at 00 UTC over the time period considered (October–May), covering a large geographical domain centred on Iran (20°E–70°E, 20°N–55°N) with a 2.5° latitude × 2.5° longitude spatial resolution were retrieved from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis archive (Kalnay et al., 1996; Kistler et al., 2001).
In the present study the S- and T-mode Principal Component Analysis (PCA) were used for classifying the 500 hPa atmospheric circulations associated with the 133 precipitation events. The S-mode PCA with correlation as a similarity measure was used as a data reduction tool and pre-processor of K-means clustering method, while a T-mode PCA with correlation as a similarity measure was employed to classify 500 hPa atmospheric circulations independently. Based on the scree plot (Cattel, 1966) and the sampling errors of the eigenvalues (North et al., 1982) five and six PCs were retained for, respectively, for the S- and T-mode PCA applications. The retained PCs were orthogonally and obliquely rotated using varimax and promax criteria, respectively. For an S-mode PCA, we used varimax rotated PC scores as input for K-means clustering, resulting in 5 circulation types (CTs). But applying a T-mode PCA coupled with varimax (promax) rotation classified all the considered days into six CTs. The skills of K-means clustering and un-rotated, varimax and promax rotated T-mode PCA in classifying atmospheric circulations were examined using some indicators measuring the separability and equability of the identified groups of each classification method. The results suggest that the obliquely rotated T-mode PCA outperforms both K-means clustering and orthogonally rotated T-mode PCA in classifying atmospheric circulations.
Each of the six CTs identified are capable of producing significant precipitation in Tehran, but all cases of heavy daily precipitation above 40 mm belong to the CT1 and CT2. Although various forms of tilt in mid-tropospheric geopotential trough are observed among the CTs, but the dominant tilting is that of the northeast–southwest direction, indicating the anti-cyclonic wave breaking. Except CT5, the CTs are associated with a dipolar structure in surface temperature anomaly consisting of a pair of negative and positive anomalies to the west and east of the country, respectively.

The impacts of potential climate change on the surface climate variables can be appraised through the projections of the global circulation onto the target variables, considering that the observed climate shifts are commonly associated with the changing patterns of the general circulations (Fraedrich et al, 2001). Since a combination of climate variables are utilized to develop climate classification schemes, they are useful for validating the outputs of the general circulation models (GCMs). The Köppen climate classification (Köppen, 1936), as the widely used climate classification method, is well suited for simultaneously validating the temperature and precipitation model outputs, considering that it takes into account both precipitation and the near-surface air temperature as the major input variables, as well as their annual cycles and linkage with the natural vegetation patterns (Kalvova et al, 2003). Therefore, such a climate classification allows for an outlook on the possible future shifts in the climate zones under a changing climate. Many researchers applied the Köppen climate classification to the general circulation model outputs in order to assess the shifts in the climate zones caused by the foreseen climate changes, represented by the GCMs (Fraedrich et al, 2001; Diaz and Eischeid, 2007; De Castro et al, 2007; Ruble and Kottek, 2010; Chen and Chen, 2013; Chan et al, 2016; Engelbrecht and Engelbrecht, 2016). The present work aimed at investigating the shifts in the Iranian climate zones induced by the possible climate changes in the 21st century. Monthly total precipitation of the Global Precipitation Climatology Centre (GPCC) and the mean monthly temperature of the Climatic Research Unit (CRU) of the University of East Anglia, both having 0.5 degree spatial resolution, were used for computing present time (1951-2000 time period) Köppen-Geiger climate classification for Iran. Following Rubel and Kottek (2010), the global temperature and precipitation projections corresponding to the period 2001 to 2100 were also taken from the Tyndall Centre for Climate Change Research dataset, TYNSC2.03 (Mitchell et al., 2004), to compute Köppen-Geiger climate classification for the 21st century. The TYNSC2.03 consists of a total of 20 GCM runs, combining 4 possible future worlds of emission scenarios (A2, B1, B2, A1F1) described by SRES (ARNELL et al., 2004) with 5 state-of-the-art climate models, namely the Hadley Centre Coupled Model Version 3 (HadCM3), the National Center for Atmospheric Research-Parallel Climate Model (NCARPCM), the Second Generation Coupled Global Climate Model (CGCM2), the Industrial Research Organization-Climate Model Version 2 (CSIRO2) and the European Centre Model Hamburg Version 4 (ECHam4). The Köppen-Geiger climate types of the present time were computed on the basis of the GPCC precipitation and CRU temperature datasets for 1951-2000 and 1976-2000 time periods. The 21st century Köppen-Geiger climate types were further computed for 1901-1925, 1926-1950, 1951-975 and 1976-2100 time sections of the TYNSC2.03 datasets.
The comparison of the climate classifications of 1951-2000 and 1976-2000 time periods highlighted certain signals of change in the Iranian climate zones in the latter half of the 20th century. The most obvious changes were the tendency of northwestern Iran to a drier climate and the extensive retreatment of BWk climate type in the central and eastern Iran in favor of BWh climate type in the last quartile of the 20th century. Shifts and changes in the climate zones of Iran were more profoundly observed in the climate classification maps of the 21st century, particularly in the final quarter of the century. Except for A2 and A1F1 pessimistic scenarios which showed the maximum climate shifts in Iran, all the scenarios considered in this study more or less agree in displaying moderate changes in the climate zones of Iran. In general, based on the pessimistic scenarios, northwestern Iran is extremely susceptible to an extensive climate shift in the future. Nevertheless, all the scenarios indicate that northwestern Iran tends to have a much drier and warmer climate in the second half of the 21st century. Moreover, the Csb climate type, currently the main climate type of most parts of the Zagros mountainous areas of western Iran, will be replaced by Bsk and Bsh climate types at the end of the 21st century. Obviously, the BWk climate type will disappear from the country at the end of the century due to the widespread invasion of the BWh climate type in the central and eastern Iran, indicating an anticipated widespread desertification in almost all parts of the country, particularly in the northwest of Iran, under a changing climate.

The lack of reliable and updated precipitation datasets is the most important limiting factor in studying many climatological and hydrological topics including climate change and temporal variability of precipitation in many data sparse areas around the globe. This is particularly valid for Iran that encompasses vast deserts and un-settled hyper-arid climate areas (central-eastern Iran) that hinders establishing an adequate network of rain-gauge stations required for climatological studies. Similarly, the high elevation areas of mountainous regions of western and northern Iran suffer from limited representative stations. Using the gridded or reanalysis precipitation datasets could be one of the possible solutions to overcome this obstacle; knowing that the representativeness of these datasets has been already proved for many different parts of the world. Amongst many available gridded precipitation datasets are the Global Precipitation Climatology Center (GPCC) and the Tropical Rainfall Measuring Mission (TRMM) that have been widely used in many researches; indicating their accurate estimation of precipitation values and intra-annual variation for the regions studied. The reanalysis precipitation dataset which is a product of the Numerical Weather Prediction (NWP) models is an alternative source of precipitation data that is widely used in the literature and many authors have pointed to the relatively accurate precipitation prediction of reanalysis for many parts of the word. The two widely used reanalysis datasets are NCEP/NCAR and different products of ECMWF, namely ERA-15, ERA-40 and ERA-Interim reanalysis. ERA-Interim which is used in the present study is produced at T255 spectral resolution (about 80 km) and covers the period from January 1979 to present, with product updates at approximately 1 month delay from real-time (Dee et al., 2011; Balsamo et al., 2015). The ERA-Interim atmospheric reanalysis is built upon a consistent assimilation of an extensive set of observations (typically tens of millions daily) distributed worldwide (from satellite remote sensing, in situ, radio sounding, profilers, etc.). To develop the reanalysis, the analysis step combines the observations with a prior estimate of the atmospheric state (first-guess fields) produced with a global forecast model in a statistically optimal manner (Balsamo et al., 2015).
The representativeness and performance of ERA-Interim in forecasting precipitation amount at 45 Iranian synoptic stations distributed across the country is herein examined. Spatial resolution of ERA-Interim dataset used in this study is 0.125 × 0.125 in latitude and longitude. For each station, the closest grid point of ERA-Interim to the station coordinates was chosen for a statistical comparison analysis. To evaluate the performance of the considered dataset when compared to the observed precipitation records at the considered locations we have used R squared, the Nash–Sutcliffe model efficiency coefficient (EF), RMSE, Bias, B slope of the regression and the standardized RMSE indicators. The performance of the dataset was also graphically represented through scatter plots of the established regression between ERA-Interim and observation at the selected stations. The results of the statistical indicators were represented through plotting the indicators over the map of Iran to ease displaying spatial tendency of the indicators and explaining the possible geographical role in controlling the spatial variation of the indicators. The results indicate that the ERA-Interim performs well in majority of the studied stations with strong correlation coefficient. However, it was found that the ERA-Interim underestimates precipitation in most of the stations located in the coastal areas of the Caspian Sea as well as in some stations along the Persian Gulf and the Oman Sea, suggesting that ERA-Interim is somewhat inefficient in adequately forecasting precipitation in the coastal areas; very likely due to not properly taking into account the complex topography of the region in its model parameterization or not being able to adequately differentiate between land and sea characteristics for the stations very close to the sea. It should be noted that the ERA-Interim is less efficient in accurately forecasting extreme precipitation in the Caspian Sea region. Nevertheless, we found very high agreement between observations and ERA-Interim in this region when some extreme precipitation events were excluded from the analysis. Contrarily, the results suggest an over-estimation for most of the stations located in northwestern and northeastern mountainous areas of the country; once again due to perhaps improper representation of topography of these regions in the model.

Delineation of homogeneous precipitation sub-regions featured with different time variabilities is very important for large countries such as Iran, which are characterized by complex topography and different climates. Very rare efforts have been devoted to identify modes of monthly precipitation variability in Iran and delineating sub-regions having different temporal variabilities of precipitation. On the other hand, most studies of precipitation regionalization in Iran have used very limited and unevenly scattered stations across the country; thus making it necessary to identify the most realistic precipitation sub-regions for Iran using almost all available stations. As such, 155 synoptic stations with relatively regular distribution over Iran, mostly having full data records for the 25 years common period of 1990–2014, were used for identifying an updated precipitation regionalization of the country. The cubic root transformed monthly precipitation of the considered stations were used as input for an S-mode principal component analysis (PCA) applied to the inter-stations correlation matrix (300×155) that is composed of 155 stations and 300 cubic root transformed monthly precipitation. The computed Kaiser–Meyer–Olkin measure of sampling adequacy for the considered matrix with a value of 0.98 indicates that the considered matrix is marvelous for a PCA application. The first five leading significant PCs accounting for approximately 80% of total variance of the dataset were considered for further analysis based on the Scree plot and the sampling errors of the PCs (North et al., 1982). To better characterize the underlying spatial structure of the considered data matrix, the retained PCs were then rotated using varimax orthogonal criteria. The five leading varimax rotated loadings were mapped to present spatial modes of monthly precipitation variability across the country and precipitation sub-regions borders were delineated using the maximum loading value approach (Comrie and Glenn, 1998; Miller and Goodrich, 2007; Chen et al., 2009).
The maps of varimax rotated loadings well represent areas characterized by different modes of precipitation variability and regimes. The five precipitation sub-regions identified using maximum loading values of the varimax rotated components are the Caspian Sea region, the northwestern, the western, the central-eastern, and the central-northeastern of the country. The Caspian Sea region featured with maximum precipitation in autumn and relatively regular distribution of precipitation throughout the year includes the coastal areas of the Caspian Sea and the northern faces of the Alborz Mountain in northern Iran. The north-western sub-region is distinguished from the rest of the country for its identical precipitation regime characterized by maximum precipitation in spring and relatively uniform precipitation all over the year. The three remained precipitation sub-regions of Iran are characterized with a much shorter rainy season, which maximizes in the winter time. The western sub-region encompasses mountainous areas of western Iran as well as the lowlands of the southwestern country. The central-eastern sub-region differs from the western sub-region due to its shorter rainy season and much lower precipitation values in all of the months, but similarly, its maximum precipitation occurs in January. Finally, the central-northeastern precipitation sub-region receives its maximum precipitation in March as opposed to the two aforementioned sub-regions which peak in January. The independence of the identified precipitation sub-regions was examined by applying the Kolmogorov–Smirnov non-parametric test to the regional anomalies of annual precipitation series; the result proved that all the sub-regions are statistically different at 99% confidence level. The identified precipitation sub-regions can serve as a tool for a better water resources management in the country.

The lack of reliable and updated precipitation datasets is the most important limitation in the study of many climatological and hydrological subjects, including climate change and temporal variability of precipitation in many data sparse areas around the globe. This is particularly valid for Iran where vast areas of central-eastern country that host the Iranian deserts, suffer from an inadequate network of rain-gage stations, required for climatological studies. The highlands of the mountainous regions of western and northern Iran have the same problem and limited representative stations are available for high elevation areas of these regions. One of solution to overcome this obstacle is to use available gridded precipitation datasets that have proved their representativeness for many different parts of the world. Among many available precipitation datasets are the Global Precipitation Climatology Center (GPCC) and theTropical Rainfall Measuring Mission (TRMM) that have been widely used in many researches, indicating their accurate estimation of precipitation values and intera-annual variation for the regions studied. The GPCC is a gage based dataset that is routinely creating through interpolation of worldwide precipitation stations combined with satellite records, whereas the TRMM is a purely remote sensed data developed by joint collaboration between NASA and the Japan Aerospace Exploration Agency (JAXA). The representativeness and performance of the GPCC and TRMM-3B43V7 precipitation datasets in estimating precipitation amounts at the locations of 46 Iranian synoptic stations distributed across the country is herein examined. Spatial resolutions of TRMM-3B43V7 and GPCC datasets used in this study are respectively 0.25 × 0.25 and 0.5 × 0.5 latitude and longitude. For each station, the closest grid point of each of the datasets to the station coordinates were chosen for statistically comparison analysis. To evaluate the performance of these datasets in comparison with the observed precipitation records at the considered locations we have used R squared, the Nash–Sutcliffe model efficiency coefficient, RMSE, Bias, B slope of the regression and the standardized RMSE indicators. The performances of the datasets were also graphically represented through scatter plots of the established regression between the observation and each of the two used datasets. The results of the statistical indicators were represented through plotting the indicators over the map of Iran to ease revealing spatial tendency of the indicators and explaining the possible geographical role in controlling the spatial variation of the indicators. The results revealed that both GPCC and TRMM-3B43V7 perform well in majority of the studied stations with strong correlation coefficients. However, it was found that the TRMM-3B43V7 underestimates precipitation in some stations located in the coastal areas of the Caspian Sea as well as in some stations along the Persian Gulf and the Oman seas, indicating that TRMM-3B43V7 is somewhat inefficient in adequately estimating precipitation in the coastal areas; which is very likely due to being unable to remove the effect of sea atmosphere interaction in stations nearby the seas. Contrarily, in some locations mostly situated in northwestern and northeastern mountainous areas of the country the TRMM-3B43V7 moderately over estimates the observed precipitation. Similarly, the GPCC well estimates precipitation in almost all stations with very high correlation coefficient and Nash–Sutcliffe model efficiency coefficient. Similar to TRMM-3B43V7, again it was found that the GPCC underestimates precipitation in most stations located along the coastal areas of the Caspian Sea. As for TRMM-3B43V7, the over-estimations of GPCC are mostly observed in northwestern Iran which is very likely due to not incorporating enough stations from high elevation areas of western Iran by the GPCC. On the whole, the results indicate that both datasets perform well in most locations of Iran and can be confidentially used in climatological and hydrological studies with or without the observation data. The results also indicate that the GPCC perform better in areas that share a denser network of stations with GPCC and vice versa. However, the very good results achieved with TRMM-3B43V7 that are completely independent from the observation indicates a promising future in having much improved remotely sensed precipitation records that well match the observed precipitation in very remote areas having no rain gages.

In this research, the target area were regionlized into few distinctive homoginious sub-regions by applying principal component alalysis to the SPI time series at 3-, 6- and 12-months time scales and the resultant PC scores were considered as the regional SPI time series for drought forecasting using time series modellingineach identified sub-region. The probability of occurences of dry, normal and wet events were also predicted for all the considered stations using Markov chain model and the results were spatially mapped and analysed. The expected drught numebr and drught length of the prediceted drought events were also estimated and mapped to spatially display their results in order to ease their spatial variability comparrison. Furthermore, different time series models were fitted to the Regional SPI series (PC scores) to identify the best fitted model for each region in order to use for drought forcasting. The result shows that the ARMA is the best fitted model for SPI time series at 3- and 6-months time scales while for the 12-months time scales the SARIMA model is the best fitted model. Using the identified models the magnitude of the SPI was forcasted for the leading times. The result shows that the time series models can favorably forcast SPI values for three months ahead, wherease the predicted results for more than three months ahead is not reasonably accurate.

Drought characteristics were investigated in arid and semi-arid regions of eastern and central Iran using monthly precipitation records of 69 synoptic and meteorological weather stations for the period 1975-2005. After quality control of the data records, the the ُStandardized Precipitation Index (SPI) was computed at 3, 6 and 12 month time scales for all considered stations. The target area was regionlized into a few distinctive homoginious sub-regions by applying principal component alalysis and Varimax rotation to the SPI time series computed for 3-, 6- and 12-months time scales, i. e., the target area is composed of two homeginous sub-regions based on SPI at 3- and 12-months time scales, while it classified into 3 sub-regions considerng 6-months time scale. Drought charcteristics (severity, duration and frequency) were also identified for all considered stations and time scales and their respective spatial variability were analysed. The results indicate that drought events with different types of severity are more frequent in northern part of the studied area, while south-eastern part of the region is prone to severe to extreme drought events. The results also suggest that the target area experienced wide spread droughts in 23. 5% of the years alaysed. In such years, the ratio of stations hited by severe to exterme droughts are lesser than the number of stations affected by mild to moderate drought events, indicating that the more severe drought the lesser areal extent.

Drought charachteristics including severity, duration and magnitude and the associated spatio-temporal variability in Karkheh basin was investigated using Standardized precipitation index (SPI) for the period 1965-2000. The results showed that the time variability of SPI series strongly co-vary accros the basin resulting in simultaniusely occurring dry and wet spells in the considered stations though the they may differ regarding to drought severity. Regarding to SPI on 3 month time scale it was found that most of the stations has experienced dry spells in 1966-67, 1970-71, 1973, 1977-78, 1984, 1991, 1995 and 1998-2000 which were very important for the basin regarding agricultural point of view. Though the short dry spells most often occur simultaneously in all stations when shorter SPI time scales are considered, but they are less concordance regarding the initiation and termination of drought when SPI time scale increased. The results also indicate that the basin has experienced between 20 and 23 dry spells among which some last between 9 and 11 months. A survey on SPI series at 12 month time scale also showed that the basin experienced between 7 to 10 long lasting drought events. The most frequent drought events in October (between 11 and 14 cases) were occurred in southeastern and wetern parts of the basin. In November, the northern, northwestern and southern parts of the basin are the regions that frequently hitted by drought ranging between 20 and 24 events. In April and May the northern part of the basin has experienced the most frequent drought events. Overall, it can be concluded that the northen and southern parts of the basin are the more prone areas to frequent drought events.