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.

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.

⎯ As a long-term water deficit condition, drought is a challenging issue in the management of water resources and has been known as a costly and less known natural disaster. Monitoring and predicting droughts, especially accurate determination of their beginning and duration are crucial in management of water resources and planning for mitigating the damaging effects of drought. In this study, the droughts in the southwestern region of Asia (Iran, Afghanistan, Pakistan, and Turkmenistan) were evaluated using the joint deficit index (JDI). Data of monthly and annual precipitation of 1392 downscaled rain gauge stations (by using the Bias Correction Spatial Disaggregation (BCSD method) within the statistical period of 1971-2014 were employed to calculate JDI. The results indicated that in recent years, the number of dry months in the studied region (especially in humid regions of Iran) has significantly increased, such that across all regions in Iran, the percentage of dry months has reached over 50%. The results also showed that in addition to scientific description of the general drought condition, JDI is also able to specify the time of beginning of droughts as well as long-term droughts, allowing investigation of the drought condition on a monthly scale. The results of investigating the trend of changes in the JDI values in the studied region revealed that the variations in these values have decreased on annual scale in the studied region. The extent of reduction in JDI and the increase in the number of dry months within the statistical period of 1971-2014 have been significant (at level of 5%) in Iran, suggesting increased drought in Iran, especially during winter. The values of monthly and annual precipitation in the studied region have been descending, where among the studied countries, Iran has experienced the maximum extent of reduction in precipitation.

In the present study, a method based on the conditional density of vine copulas was used to drought monitoring and predicting the rainfall deficiency signature for a 60‐day duration in Dashband, sub‐basin of Lake Urmia basin. The annual rainfall and rainfall deficiency signatures at 10‐, 30‐ and 60‐day durations were considered as variables. D‐, C‐ and R‐vine copulas were used to represent the dependence among the variables, finding that D‐vine copula results to be more accurate for the case of interest. We found that, if the rainfall is less than the long‐term mean in the region, the rainfall deficiency signature for near future can be estimated by acceptable accuracy. Moreover, the results of the conditional probability analysis of rainfall deficiency signature for a 60‐day duration respect to the other variables showed that, on average, the probability of the occurrence of rainfall deficiency signature of 250 mm compared to the long‐term mean in the study area is more than 50% per year. The results showed that the proposed approach may facilitate the meteorological drought management in the considered sub‐basin.

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