عنوان مقاله [English]
نویسندگان [English]چکیده [English]
Evapotranspiration and precipitation are the most important climatic variables for revealing the climate change and temporal-spatial patterns of variables influencing the eco-hydrological processes, which control the evolution of the surface ecosystem. This type of inquiry is fundamental to understand the coupling between ecosystem dynamics and the water cycle, in particular in arid and semi-arid environments in Iran. The goal of this study was to investigate the temporal trends on reference Evapotranspiration (ET0) and annual precipitation (P) time series over semi-arid region of the Urmia Lake basin, located in the northwest of Iran. For this purpose, meteorological observations collected from five high quality meteorological sites over a 30-year period (1976–2005) were used and statistically significant ET0 trends in annual time scale were detected using the nonparametric Mann-Kendall (MK) and Sequential Mann-Kendall (SMK) tests at the 5% significance level. The ET0 monthly data were prepared using a pre-validated solar radiation hybrid model to estimate monthly Rn values required in the original FAO56 model. In this study, we developed the Hybrid-PMF56 model to improve both Rn and ET0 estimates. Furthermore, to eliminate the effect of serial correlation on the test results, the Trend Free Pre-Whitening (TFPW) approach was applied.
The analysis was based on monthly meteorological data at five synoptic stations located in the basin consist of Urmia, Tabriz, Khoy, Saghez, and Mahabad during 1976-2005. At first step, monthly ET0 rates at each station were estimated using a newly developed model, known as PMF56-Hybrid. Then the non-parametric Mann-Kendall and the Theil-Sen slope tests were applied. In addition, the sequential Mann-Kendall test was used to detect abrupt changes in the time series. The Mann–Kendall test requires time series to be serially independent. The presence of serial correlation in the time series makes trend tests too liberal, i.e. the null hypothesis trend is rejected too frequently, speci?cally if there is positive serial correlation. For this, von Storch (1995) suggests that the time series should be ‘pre-whitened’, i.e. eliminate the effect of serial correlation before applying the Mann–Kendall test. This study incorporates this suggestion. The data must be serially independent in the case of the non-parametric tests. According to previous studies, the existence of serial correlation will increase the probability for significant trend detection. This leads to a disproportionate rejection of the null hypothesis of non-trend, whereas the null hypothesis is actually true. Therefore, the influence of serial correlation must be eliminated
Annual trend analysis on precipitation data showed that there is a decreasing trend at all stations except Mahabad. Furthermore, annual ET0 data at the 5% level in the same period did not show any significant increasing trend. The Theil-Sen test results showed the highest positive slope rate at Khoy station (3.44 mm/year). In addition, the minimum and maximum of the Sen-slope rates were 4.2 and 2.88 mm/year at Khoy and Tabriz stations, respectively. The Sequential Mann-Kendall test revealed that Khoy and Tabriz stations faced an abrupt change with a decreasing trend in annual precipitation data but non-significant at %5 except at Khoy station. For the PMF56-Hybrid based ET0 time series data, abrupt changes are not significant at all stations during 1976-2005. As shown, the majority of the annual precipitation series was characterized by negative trend which were mostly insigni?cant. In addition, the signi?cant decreasing trend was only observed at Sanandaj at the rate of -42.61 mm /decade which began in 1975 (Table 5).
In this study, temporal trends in ET0 and precipitation data for five synoptic stations located in the Urmia Lake basin were analyzed. The results showed an insignificant trend in ET0 time series at all stations. At seasonal time scale, all stations have increased trend which only at Tabriz and Saghez station during autumn; we found a significant increasing trend. The initiative coupled model of Hybrid radiation and FAO56 methods, was applied in the study area to improve ET0 estimations. Furthermore, pre-whitening was applied to eliminate the effect of serial correlation on the Mann–Kendall test. The results indicated that the climatic data were serially correlated showing the data were not independent. The main sources for abrupt changes in climate data include station relocations, changes in observation times, methods used to calculate daily means, changes in gauged locations, and changes in instruments, increased urbanized area and global warming. Another factor that could lead to climate changes is the change in atmospheric circulation. The results also suggest the need for further investigation on compressive trend analysis of different climate variables which help to understand the major causes of climate change in this semi-arid region.