Deepak Jhajharia

Effect of Short-Term and Long-Term Persistence on Identification of Temporal Trends

In this study, the trends in precipitation in the northwest (NW) of Iran were identified using the four different versions of the Mann-Kendall method, i.e., the conventional Mann-Kendall method (MK1); the Mann-Kendall method following the removal of the effect of significant lag-1 autocorrelation (MK2); the Mann-Kendall method after the removal of the effect of all significant autocorrelation coefficients (MK3); and the Mann-Kendall method by considering the Hurst coefficient (MK4). Identification of trends was carried out on different time scales (monthly, seasonal, and annual) using the precipitation data of 50 years from 1955 to 2004 of the sixteen stations selected from the NW region of Iran. The Theil-Sen method was used to estimate the slopes of trend lines of precipitation series. Results showed that: (1) on a monthly time scale, the statistically significant Z-statistics were negative for all but one (July) month; and the strongest negative (positive) precipitation trend-line slope among all the negative (positive) cases was found to be −0.89ð0.38Þ mm=year at Bijar (Kermanshah) station in NW Iran; (2) on a seasonal time scale, the median of trend-line slopes was found to be negative in all four seasons; the winter and spring seasons precipitation series witnessed negative trends for almost all the stations using all four different versions of the MK test; and in the summer and autumn seasons, both upward and downward trends were observed for most of the sites of NW Iran; (3) in an annual time scale, all stations had witnessed negative trends using both the MK1 and the MK4 tests. However, application of the MK4 instead of the MK1 reduced the absolute value of the Z-statistic for most of the time series. The strongest negative annual trend-line slope was −4.04 mm=year at Bijar station. Therefore, the observed decreases in precipitation in NW Iran in the recent half of the past century may have serious implications for water resources management under the warming climate with probably a higher rate of the population growth and the higher consumption of freshwater as a result of the rise in standards of living of the population of NW Iran. DOI: 10.1061/(ASCE)HE.1943-5584.0000819.

Development and application of a new index for analyzing temperature concentration for Iran’s climate

Spatiotemporal variation of temperature and precipitation helps in classification of type of climate of each region and affects the availability of access of drinking water to the inhabitants in the region. A new index that is called as the temperature concentration index (TCI) was developed in the present study to investigate the concentration of temperature in Iran based on the two known inspiring patterns of Gibbs–Martin (Am Sociol Rev 27:667–677, 1962) and Oliver (Prof Geogr 32:300–309, 1980). The developed index was classified based on three methods, including the Oliver’ classification (1980), the coefficient of variation of temperature data, and coefficient of variation of TCI values. The results show that all the three classification methods represent almost similar classes for the TCI. After the classification of the proposed TCI, two indices of the precipitation concentration index (PCI) and the TCI were applied to the long-term analysis of precipitation and temperature of thirty-four synoptic stations of Iran during the 50-year period (1961–2010). Based on the TCI, highly irregular distributions in temperature were observed during the winter season in the northwest of Iran. In the annual timescale, the calculated TCI values indicated the uniform temperature pattern in the borders of the Persian Gulf, the Gulf of Oman, and the coast of the Caspian Sea. The comparison between the two indices, the PCI and the TCI, reveal that these two indices are inversely related to each other; i.e., reduction in one index will be along with an increase in another index. The irregular distribution of temperature implies higher changes in temperature, and so the conditions for higher precipitation will be provided. The PCI index represents a uniform distribution of the precipitation over the selected stations of Iran.