The long-term trends in temperature over Iran were examined over 34 synoptic stations during a 50 year period on seasonal and annual time scales. Two methods, a modified version of the Mann-Kendall test by eliminating the effect of all significant autocorrelation co-efficients and the regional Kendall test, were used in trend identification. The results revealed that the temperature had experienced significant positive trends in autumn, spring and especially summer over the study area. On an annual time scale and in the winter, the highest increasing trends were observed at stations located in the southern and southeastern parts of Iran. For regional analysis of trends, the stations were divided into five clusters based on the K-means clustering method and the silhouette index. Subsequently, the regional trend of temperature was analysed on seasonal and annual time scales using the regional Kendall test. The results of the regional Kendall test also indicated the rising trends in temperature during the last 50 years throughout the country on both seasonal and annual time scales. After verifying the presence of an increasing trend in the temperature time series, the non-parametric Pettitt test was used to detect the change points in the annual and seasonal time scales. The results showed that the change point of average temperature began from the summer of 1972 (Sabzevar station) and continued until the summer of 1998 (Zahedan station). The most frequent change point occurrence was between the years 1986 and 1994.

The complex hydrological events such as storm, flood and drought are often characterized by a number of correlated random variables. Copulas can model the dependence structure independently of the marginal distribution functions and provide multivariate distributions with different margins and the dependence structure. In this study, the conditional behavior of two signatures was investigated by analyzing the joint signatures of groundwater level deficiency and rainfall deficiency in Naqadeh sub-basin in Lake Urmia Basin using copula functions. The study results of joint changes in the two signatures showed that a 90–135 mm reduction in rainfall in the area increased groundwater level between 1.2 and 1.7 m. The study results of the conditional density of bivariate copulas in the estimation of groundwater level deficiency values by reducing rainfall showed that changes in values of rainfall deficiency signature in the sub-basin led to the generation of probability curves of groundwater level deficiency signature. Regarding the maximum groundwater level deficiency produced, the relationship between changes in rainfall deficiency and groundwater level deficiency was calculated in order to estimate the groundwater level deficiency signature values. The conditional density function presented will be an alternative method to the conditional return period.

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The prediction of global climate change using the values recorded in a statistical period requires a precise method that can accurately identify the fluctuations of these changes. By patterning these changes, the parameter values for the years or future periods are predicted, or the statistical gap can be eliminated. In this research, meteorological data of six stations in different climates of Iran were used to model and estimate the values of the dew point temperature (DPT). The stations studied are Ahvaz, Urmia, Kerman, Gorgan, Rasht, and Babolsar. In order to estimate the DPT values, support vector regression was used, and to optimize the parameters of the support vector regression model, the ant colony algorithm was used. In this study, four different input patterns of meteorological data have been investigated as input of the support vector regression model. Pattern I with seven inputs (monthly minimum, maximum, and average air temperatures, monthly precipitation, saturation vapor pressure, actual vapor pressure and relative humidity), Pattern II with three inputs (monthly average air temperature, saturation vapor pressure, and actual vapor pressure), Pattern III with two inputs (monthly minimum and maximum air temperatures), and Pattern IV with an input (monthly average air temperature) were used. It is recommended that if the number of inputs in the model is small, the model will be more user-friendly. Based on the results of analyzing different patterns, it can be concluded, that Pattern III is the suitable pattern for estimating DPT values at the stations studied in different climates of Iran based on the three criteria of root mean square error (RMSE), Nash–Sutcliffe model efficiency coefficient (NSE), and coefficient of determination (R2). Overall, the results showed that the selected pattern increases the accuracy of the model by up to 24% compared to the conventional model.