Changes of Pan Evaporation in the West of Iran

Water Resour Manage (2011) 25:97 111 DOI 10.1007/s11269-010-9689-6 Changes of Pan Evaporation in the West of Iran Hossein Tabari Safar Marofi Received: 20 January 2010 / Accepted: 11 June 2010 / Published online: 2 July 2010 Springer Science+Business Media B.V. 2010 Abstract Evaporation is an important component of the hydrological cycle and its change would be of great significance for water resources planning, irrigation control and agricultural production. The main purpose of this study was to investigate temporal variations in pan evaporation (E pan ) and the associated changes in maximum (T max ), mean (T mean ) and minimum (T min ) air temperatures and precipitation (P) for 12 stations in Hamedan province in western Iran for the period 1982 2003. Significant trends were identified using the Mann Kendall test, the Sen s slope estimator and the linear regression. Analysis of the E pan data revealed a significant increasing trend in 67% of the stations at the 95% and 99% confidence levels. To put the changes in perspective, the trend in E pan averaged over all 12 stations was (+)160 mm per decade. Trend analysis of the meteorological variables for examination of causal mechanisms for E pan changes showed warming trends in T max, T mean and T min series in almost all the stations, which were significant over half of the total stations. On the contrary, no significant changes in precipitation were found approximately at all of the stations. Furthermore, a moderate positive correlation was observed between E pan and T max,t mean and T min, while a inverse correlation was found between E pan and P data. The results indicated that the study area has become warmer and drier over the last 22 years, hence the evaporative demands of the atmosphere and thereby crop water requirements have increased. Keywords Trend analysis Temporal variations Class A pan evaporation Air temperature Precipitation H. Tabari (B) S. Marofi Department of Irrigation, Faculty of Agriculture, Bu-Ali Sina University, Hamedan, Iran e-mail: hosseintabari@gmail.com

98 H. Tabari, S. Marofi 1 Introduction Evaporation (E) is an important component of the hydrological cycle and influences the availability of water, particularly for agriculture (Burn and Hesch 2007). The measurement of E is difficult, hence the measurement of potential evaporation is often used instead (Jovanovic et al. 2008). In applications such as those in ecology, hydrology, agriculture and engineering, the potential evaporation is taken to be proportional to the rate at which water evaporates from a pan located at the surface, known as pan evaporation (E pan ). Pan evaporation has traditionally been used to represent the evaporative demand of the atmosphere when estimating crop water requirements (Roderick and Farquhar 2004). Pan evaporation will increase as the average air temperature near the surface increases. This expectation is based on an implicit assumption that, as the air temperature increases, everything else is held constant. That is, E pan would increase as the air at the surface warmed if there were no change in the vapour content of the air and wind speed were unchanged (Roderick and Farquhar 2004). The mechanisms causing the observed trends in E are not clearly understood. Although there is widespread agreement that global temperatures are increasing, there are many meteorological factors that can result in an increase or a decrease in evaporation (Burn and Hesch 2007). The majority of the studies conducted in the United States and the former Soviet Union (Peterson et al. 1995; Lawrimore and Peterson 2000; Golubev et al. 2001; Hobbins et al. 2004), Australia and New Zealand (Roderick and Farquhar 2002, 2004, 2005; Jovanovic et al. 2008) showed decline in E pan rates in the last decades. The similar decreasing E pan was also reported in Venezuela (Quintana-Gomez 1997), Canada (Burn and Hesch 2007) and Puerto Rico (Harmsen et al. 2004). In addition, the Second Assessment Report of the Intergovernmental Panel on Climate Change (IPCC 1996) described widespread decreases in E pan during the twentieth century using pan measurements from the former Soviet Union and the United States. In Asia,Xu etal. (2006) studied the spatial distribution and temporal trend of reference evapotranspiration (ET o ), E pan and pan coefficient in the Changjiang catchment in China during 1960 2000. They showed that there was a significant decreasing trend in both ET o and E pan, which was mainly caused by a significant decrease in the net total radiation and to a lesser extent by a significant decrease in the wind speed over the catchment. Besides, no temporal trends were detected for the pan coefficient. Wang et al. (2007) considered changes of E pan and ET o in the Yangtze River basin in China from 1961 to 2000. They found that both E pan and ET o decreased during the summer months contributing most to the total annual reduction. Jhajharia et al. (2009) analyzed the temporal characteristics of E pan trends under the humid conditions for 11 sites of northeast India. They concluded decreasing E pan trends mainly in pre-monsoon and monsoon seasons. The findings of this study suggested that mainly two parameters i.e. sunshine duration followed by wind speed strongly influenced E pan changes at various sites from different regions in different seasons. On the other hand, decreases of E pan have not been universal and increases of E pan have been reported in many parts of the world. Analysis of evaporation measurements at Bet Dagan in Israel s central coastal plain between 1964 and 1998 by Cohen et al. (2002) showed a small but statistically significant increase in screened Class A E pan, mainly in the dry, summer half of the year. Likewise, no changes were

Changes of Pan Evaporation in the West of Iran 99 found in total open water evaporation or ET o estimated with Penman s combined heat balance and aerodynamic equation because the decreases found in the radiation balance term were offset by increases in the aerodynamic term. da Silva (2004) analyzed time-series of eight climatic variables to ascertain the existence of climate variability in the northeast of Brazil. He indicated increasing trends for maximum (T max ), mean (T mean ) and minimum (T min ) temperatures, E pan,et o and aridity index, and decreasing trends for relative humidity and precipitation (P). Oguntunde et al. (2006) investigated trends and variability in hydroclimatology variables of the Volta River Basin in West Africa from 1901 to 2002 and found positive trends in E pan data. Stanhill and Möller (2008) analyzed evaporation measurements made at 16 sites in the British Isles for evidence of long-term changes. Four out of eight studied Irish Class A evaporation pan series between 1963 and 2005 showed significant linear trends, three of increasing and one of decreasing evaporation. Besides, five out of eight studied UK sunken evaporation tank series between 1885 and 1968 indicated statistically significant linear trends, two of increasing and three of decreasing evaporation. So far, several authors investigated the estimation of E pan and ET o in Iran (Tabari 2010; Tabari et al. 2010; Sabziparvar et al. 2010; Sabziparvar and Tabari 2010), but no comprehensive study has been carried out on the temporal trends in E pan and ET o time series. As the first attempt in Iran, the main aim of this study was to investigate temporal variations in annual E pan for 12 stations located in Hamedan province in western Iran during 1982 2003. Also, the influences of air temperature and precipitation on the temporal trends detected in E pan were analyzed. 2 Materials and Methods 2.1 Study Area and Data The study area is Hamedan province which is located in the west of Iran, at 47 45 Eto49 36 E longitude and 33 33 Nto35 38 N latitude, covering 19,368 km 2 of land area (Fig. 1). Hamedan is one of the mountainous provinces of Iran. The highest point in this province is the Alvand peak, 3,574 m high. The climate in the study region is semi-arid with mild summers and very cold winters. The mean annual rainfall is 320 mm. Winter precipitation is mainly snow, lasting some 6 to 8 months in the mountainous areas and 1 to 2 months on the plateau. The rest of the precipitation is provided by scarce spring and fall rains. Hamedan is one of the coldest provinces of Iran and its temperature may drop below 30 C on the coldest days. The mean monthly temperature in the study area varies from 5 C in January to 24 CinJuly, with an annual mean of 11 C. Data including maximum, mean and minimum air temperatures, precipitation and pan evaporation were collected from 12 stations for the period 1982 2003 (Table 1). The basic statistics for the 22-years of data set are summarized in Table 2. Long-term E pan data are available for a few stations in Iran. Only stations that currently record E pan and have at least 22 years of continuous E pan data were selected for this study. In order to increase the number of stations with data covering 22 years or more,

100 H. Tabari, S. Marofi Fig. 1 Geographic location of the study region and the stations one neighbouring station (Kangavar) was combined. The class A pan was chosen as the standard for measuring evaporation in Iran due to it being the international preference. The class A pan is a circular pan made of galvanized iron, with 121 cm diameter and 25.5 cm deep which is supported by a wood frame stand. 2.2 Trend Analysis A large number of tests can be used for trend detection in long time series of meteorological and hydrological records. In the present study, three tests including Mann Kendall, Sen s slope estimator and linear regression have been

Changes of Pan Evaporation in the West of Iran 101 Table 1 Geographic characteristics of the stations used in the study Station name Station type Longitude (E) Latitude (N) Elevation (m a.s.l.) 1. Dargezin Climatological 49 01 35 21 1,870 2. Ekbatan Research 48 32 34 52 1,730 3. Ekbatan dam Evaporimeter 48 36 34 46 1,880 4. Ghahavand Raingauge 49 01 34 51 1,480 5. Kangavar Synoptic 47 59 34 30 1,468 6. Kheir-Abad Evaporimeter 48 32 34 28 1,740 7. Khomigan Evaporimeter 49 02 35 22 1,810 8. Khosro-Abad Evaporimeter 48 02 34 38 1,500 9. Malayer Synoptic 48 49 34 17 1,776 10. Nahavand Synoptic 48 24 34 09 1,685 11. Nozheh Synoptic 48 41 35 12 1,679 12. Varayeneh Evaporimeter 48 24 34 05 1,800 used for detection of trends. Brief descriptions of these statistical methods are as follows: 2.2.1 Linear Regression Model Simple linear regression is an important and commonly used parametric method for identifying monotonic trend in a time series. It is used to describe the relationship between one variable with another or other variables of interest. It is often performed to obtain the slope of hydrological and meteorological variables on time. Positive slope shows increasing trend while negative slope indicates negative trend. Regression has the advantage that it provides a measure of significance based on the hypothesis test on the slope and also gives the magnitude of the rate of change (Hirsch et al. 1991). The total change during the period under observation is obtained by multiplying the slope with the number of years. 2.2.2 Mann Kendall Test The Mann Kendall test is a non-parametric test for identifying trends in time series data. The test compares the relative magnitudes of sample data rather than the data Table 2 Mean values with standard deviation of the variables used in this study at different stations during 1982 2003 Station T max ( C) T mean ( C) T min ( C) P (mm) E pan (mm) Dargezin 18.2 ± 0.8 11.0 ± 1.0 4.0 ± 0.8 325.9 ± 101.6 1,554 ± 183 Ekbatan 19.1 ± 1.1 11.0 ± 1.1 2.9 ± 1.1 310.0 ± 71.4 1,550 ± 197 Ekbatan dam 18.3 ± 0.8 10.7 ± 0.9 3.1 ± 1.0 337.0 ± 79.8 1,807 ± 211 Ghahavand 19.7 ± 1.4 11.3 ± 1.2 2.9 ± 1.2 233.7 ± 51.4 1,585 ± 268 Kangavar 21.0 ± 1.1 13.3 ± 0.9 4.7 ± 1.1 401.6 ± 101.4 1,705 ± 365 Kheir-Abad 19.6 ± 1.3 12.8 ± 1.1 5.6 ± 0.9 312.2 ± 72.5 2,612 ± 313 Khomigan 17.8 ± 1.0 10.6 ± 2.0 4.3 ± 1.6 273.3 ± 69.9 1,933 ± 180 Khosro-Abad 21.8 ± 1.3 12.5 ± 1.1 2.7 ± 1.8 329.7 ± 84.0 2,262 ± 470 Malayer 20.0 ± 1.1 14.0 ± 1.5 6.0 ± 0.8 307.8 ± 75.4 1,996 ± 238 Nahavand 20.5 ± 0.9 13.5 ± 1.2 5.9 ± 1.1 421.7 ± 116.9 1,835 ± 250 Nozheh 19.3 ± 1.0 10.9 ± 0.9 2.5 ± 1.0 331.5 ± 74.0 1,429 ± 189 Varayeneh 19.8 ± 1.2 9.8 ± 1.8 0.1 ± 2.8 517.9 ± 142.4 1,878 ± 320

102 H. Tabari, S. Marofi values themselves (Gilbert 1987). One advantage of this test is that the data need not conform to any particular distribution. The second advantage of the test is its low sensitivity to abrupt breaks due to inhomogeneous time series (Jaagus 2006). Mann (1945) originally used this test and Kendall (1975) subsequently derived the test statistic distribution. According to this test, the null hypothesis H 0 states that the deseasonalized data (x 1,...,x n )isasampleofn independent and identically distributed random variables. The alternative hypothesis H 1 of a two-sided test is that the distributions of x k and x j are not identical for all k, j n with k = j. The test statistic S, which has mean zero and a variance computed by Eq. 3, iscalculated using Eqs. 1 and 2, and is asymptotically normal: S = n 1 n k=1 j=k+1 sgn ( x j x k ) (1) +1 if sgn ( ) x j x k = 0 if 1 if ( ) x j x k > 0 ( ) x j x k = 0 (2) ( ) x j x k < 0 Var (S) = [ n (n 1)(2n + 5) t 18 ] t (t 1)(2t + 5) The notation t is the extent of any given tie and denotes the summation over t all ties. In cases where the sample size n > 10, the standard normal variable Z is computed by using Eq. 4. S 1 if S > 0 Var (S) Z = 0 if S = 0 (4) S + 1 if S < 0 Var (S) Positive values of Z indicate increasing trends while negative values of Z show decreasing trends. When testing either increasing or decreasing monotonic trends at a α significance level, the null hypothesis was rejected for absolute value of Z greater than Z 1 α/2, obtained from the standard normal cumulative distribution tables (Partal and Kahya 2006; Modarres and da Silva 2007). In this research, significance levels of α = 0.01 and 0.05 were applied. 2.2.3 Sen s Slope Estimator If a linear trend is present in a time series, then the true slope (change per unit time) can be estimated by using a simple non-parametric procedure developed by Sen (1968). The slope estimates of N pairs of data are first computed by (3) Q i = x j x k j k for i = 1,..., N (5)

Changes of Pan Evaporation in the West of Iran 103 where x j and x k are data values at times j and k( j > k), respectively. The median of these N values of Q i is Sen s estimator of slope. If N is odd, then Sen s estimator is computed by Q med = Q [(n+1)/2] (6) If N is even, then Sen s estimator is computed by Q med = 1 ) (Q 2 [N/2] + Q [(N+2)/2] (7) Finally, Q med is tested by a two-sided test at the 100(1 α)% confidence interval and the true slope may be obtained by the non-parametric test (Partal and Kahya 2006). In this work, the confidence interval was computed at two different confidence levels (α = 0.01 and α = 0.05) as follows: C α = Z 1 α/2 Var (S) (8) where Var(S) has been defined in Eq. 3, andz 1 α/2 is obtained from the standard normal distribution. Then, M 1 = (N C α )/2 and M 2 = (N + C α )/2 are computed. The lower and upper limits of the confidence interval, Q min and Q max,arethem1 th largest and the (M 1 + 1) th largestofthenordered slope estimates Q i.ifm 1 is not a whole number, the lower limit is interpolated. Correspondingly, if M 2 is not a whole number, the upper limit is interpolated (Salmi et al. 2002). 3 Results and Discussion 3.1 Trends in E pan Annual trends of E pan and their magnitude (in mm year 1 ) obtained by the Mann Kendall test, the Sen s slope estimator and the linear regression are given in Table 3. As shown, both positive and negative trends were observed in E pan series, which were mostly positive. Ten of the 12 stations showed increasing trends. Among the increasing trends, eight significant trends were detected at the 95% and 99% Table 3 Values of slope b of the linear regression analysis, values of statistics Z of the Mann Kendall test and values of statistics Q med of the Sen s slope estimator for annual E pan (1982 2003) a Trends statistically significant at the 99% confidence level b Trends statistically significant at the 95% confidence level Station Z Q med b (mm year 1 ) Dargezin 3.30 a 21.78 a 20.109 a Ekbatan 2.68 a 17.07 a 18.158 a Ekbatan dam 1.72 11.80 11.914 Ghahavand 0.99 9.69 9.572 Kangavar 2.09 b 26.60 b 25.255 b Kheir-Abad 2.09 b 21.71 b 26.286 a Khomigan 2.14 b 12.01 b 13.204 b Khosro-Abad 1.72 32.60 29.335 Malayer 1.13 11.74 7.0553 Nahavand 2.37 b 16.29 b 16.577 b Nozheh 2.96 a 19.76 a 19.217 a Varayeneh 3.81 a 41.80 a 39.300 a

104 H. Tabari, S. Marofi confidence levels. The observed increase in E pan, which is largely determined by available energy, could be caused by increases in temperature and/or net radiation over the last century. A change in temperature may be due to climate change, whereas a change in net radiation associated with a change in surface albedo may be due to historical land use change (Oguntunde et al. 2006). The significant increasing trends varied between (+)132 mm per decade in the Khomigan station and (+)393 mm per decade in the Varayeneh station. When averaged over all 12 stations, the trend in annual E pan rate for the period 1982 2003 was (+)160 mm per decade or to 9% of climatological mean, that is 1,845.7 mm. The positive trends of E pan series found in this study are in good agreement with results obtained for other territories in Brazil (da Silva 2004), Israel (Cohen et al. 2002) and West Africa (Oguntunde et al. 2006), but are different from E pan changes reported in China (Xu et al. 2006; Wang et al. 2007), India (Jhajharia et al. 2009), Australia (Roderick and Farquhar 2004; Jovanovic et al. 2008) and New Zealand (Roderick and Farquhar 2005) where decreasing trends have been identified. The rate of increasing trend in annual E pan obtained in this study is much smaller than that reported by da Silva (2004) when he analyzed climatic changes in the northeast of Brazil and detected a trend of 41.6% per decade. On the contrary, the magnitude found in this research is greater than those reported by Cohen et al. (2002) and Oguntunde et al. (2006) who investigated trends of E pan in Israel and West Africa, respectively. Cohen et al. (2002) defined an increasing trend of 2.17% per decade, while Oguntunde et al. (2006) detected a positive trend of 2 mm per decade for the period 1901 1969 and 18 mm per decade during 1970 2002. The comparison between the results of the parametric and non-parametric methods shows that the methods were greatly coincident. All of significant positive trends detected by the non-parametric tests were confirmed by the parametric method. 3.2 Meteorological Relations It is important to understand the causes of changes in E pan in order to make more robust predictions about future changes in the hydrological cycle. Pan evaporation is mainly a function of temperature, radiation, wind speed, humidity and precipitation. In this section, possible causes of the increase in E pan are discussed in view of the trends of the meteorological variables available in the study area including maximum, mean and minimum temperatures and precipitation. Correlations In order to identify the dominant variables associated with the changes in E pan in the study area, they are correlated with all the meteorological variables including maximum, mean and minimum temperatures and precipitation (Table 4). As shown, positive correlations between E pan and T max were found in almost all the stations. The positive correlations were significant at Dargezin, Ekbatan, Khomigan, Nahavand and Varayeneh stations. Likewise, E pan positively correlated with T mean in most of the stations, which were significant at Dargezin, Ekbatan, Khomigan, Nahavand and Nozheh stations. Furthermore, there were positive correlations between E pan and T min in the majority of the stations. The positive correlations were significant at Ekbatan, Khosro-Abad and Nozheh stations. Nevertheless, only one significant negative correlation was observed at Kangavar station. In general, pan

Changes of Pan Evaporation in the West of Iran 105 Table 4 Results of Pearson s correlation between pan evaporation and the meteorological variables Station Tmax Tmean Tmin P r Equation r Equation r Equation r Equation Dargezin 0.465 a Epan = 99.112Tmax 0.506 a Epan = 98.587Tmean 0.345 Epan = 75.048Tmin 0.085 Epan = 0.1537 P 250.25 + 468.24 + 1252.7 + 1503.5 Ekbatan 0.581 a Epan = 103.27Tmax 0.516 a Epan = 98.293Tmean 0.498 a Epan = 83.833Tmin 0.243 Epan = 0.6687 P 423.3 + 466.58 + 1309.5 + 1756.9 Ekbatan dam 0.025 Epan = 6.2192Tmax 0.171 Epan = 40.685Tmean 0.320 Epan = 66.45Tmin 0.311 Epan = 0.821 P + 1693.3 + 2242.5 + 22014.5 + 2084 Ghahavand 0.026 Epan = 4.9321Tmax 0.139 Epan = 30.352Tmean 0.185 Epan = 41.6Tmin 0.117 Epan = 0.6086 P + 1682 + 1929.3 + 1705.8 + 1727.1 Kangavar 0.209 Epan = 66.346Tmax 0.239 Epan = 102.23Tmean 0.736 a Epan = 248.78Tmin 0.120 Epan = 0.4318 P + 314.34 + 347.26 + 2873.6 + 1878.9 Kheir-Abad 0.406 Epan = 101.43Tmax 0.150 Epan = 42.407Tmean 0.217 Epan = 75.112Tmin 0.315 Epan = 0.6087 P + 625.33 + 2068.1 + 2195.2 + 1716.9 Khomigan 0.474 a Epan = 84.916Tmax 0.416 a Epan = 38.133Tmean 0.387 Epan = 41.496Tmin 0.577 a Epan = 1.4836 P + 424.86 + 1527.5 + 1755.4 + 2339.1 Khosro-Abad 0.116 Epan = 40.906Tmax 0.074 Epan = 31.416Tmean 0.541 a Epan = 136.82Tmin 0.381 Epan = 2.1302 P + 1372.1 + 2655.5 + 1892.5 + 2964.8 Malayer 0.273 Epan = 57.789Tmax 0.005 Epan = 0.7264Tmean 0.208 Epan = 57.924Tmin 0.211 Epan = 0.664 P + 837.92 + 1986.2 + 1647.2 + 2200.7 Nahavand 0.451 a Epan = 119.33Tmax 0.628 a Epan = 127.64Tmean 0.351 Epan = 78.928Tmin 0.284 Epan = 0.6088 P 616.49 + 113.26 + 1368.6 + 2091.4 Nozheh 0.217 Epan = 40.109Tmax 0.423 a Epan = 88.693Tmean 0.515 a Epan = 91.927Tmin 0.324 Epan = 0.8287 P + 653.45 + 462.79 + 1202.2 + 1704.3 Varayeneh 0.581 a Epan = 150.64Tmax 0.281 Epan = 50.966Tmean 0.353 Epan = 101.46Tmin 0.546 a Epan = 1.2256 P 1112.4 + 1378.5 + 1279.4 + 2513.2 Average 0.314 0.232 0.181 0.222 Epan and P are in (mm/year); Tmax, Tmean and Tmin are in ( C) a Pearson s correlation statistically significant at the 95% confidence level

106 H. Tabari, S. Marofi evaporation had significant positive correlations with maximum, mean and minimum temperatures in the majority of the stations. This reveals dependence of temperature and E pan in the study area. Besides, there is evidence of a week inverse relationship between E pan and precipitation in ten stations, but the correlation coefficient was not significantly different from zero in the majority of the stations. When averaged over all 12 stations, negative regression coefficient of 0.22 was obtained between E pan and precipitation. Jovanovic et al. (2008) found a very strong inverse correlation between E pan and P (correlation coefficient of 0.81) in Australia. Overall, the strongest correlation was found between E pan and T max (average correlation coefficient of 0.31) in this study. Jovanovic et al. (2008) also reported correlation coefficient of 0.53 between E pan and temperature. Mean temperature appears to be the second most dominant variable influencing E pan over all stations (average correlation coefficient of 0.23), although there are no meaningful differences between average correlation coefficients obtained for the meteorological variables. In addition, a correlation coefficient of 0.18 was found between T min and E pan when averaged over all 12 stations. Figure 2 also shows time series of the meteorological variables and their relationships with E pan series in the study area. It is clear that the pattern of recent rapid warming is reflected in the E pan changes. Combined influences of the meteorological variables on E pan were also investigated in this study. The average values of the E pan and meteorological variables were calculated for each year (from 1982 to 2003) over the 12 stations. Then, multiple linear regression (MLR) was applied for evaluating the relationship between E pan and all of the meteorological variables. In the MLR method, the E pan variable was defined as the dependent one and T max,t mean,t min and P were considered as independent. The results showed that there was a strong correlation (r = 0.65, p value = 0.041) between these variables and E pan indicating that the combined influences of the meteorological variables on E pan are much more than the influences of each variable separately. Trends Results of the three statistical tests on T max,t mean,t min and P series are given in Tables 5, 6, 7 and 8.AsshowninTable5, all trend signals in annual T max were positive indicating a warming climate. The non-parametric tests (Mann Kendall and Sen s slope estimator) detected significant trends at Ekbatan, Kangavar, Kheir-Abad and Varayeneh stations, while the parametric method (linear regression) identified six significant trends. Significant increasing trend rates in T max lay in the range of (+)0.631 C per decade in the Nahavand station to (+)1.295 C per decade in the Kheir-Abad station. Analysis of T mean series indicated positive trends in almost all the stations (Table 6). The Mann Kendall test, the Sen s slope estimator and the linear regression method detected four, three and six significant increasing trends, respectively. The significant increasing trends ranged between (+)0.715 C per decade in the Nozheh station and (+)1.426 C per decade in the Khomigan station. Hasanean (2001) also found a significant positive trend in T mean at the 99% confidence level for Jerusalem and Tripoli stations when investigated trends in T mean series at eight meteorological stations in the East Mediterranean. Similar to the T mean series, 11 warming trends were found in T min data (Table 7). Among the warming trends, seven significant trends were identified by

Changes of Pan Evaporation in the West of Iran 107 Fig. 2 Time series plots of the meteorological variables and their relationships with E pan series in Hamedan province 2300 2100 Pan evaporation Maximum temperature 22 20.75 Epan (mm) 1900 19.5 Tmax ( o C) 1700 18.25 1500 1982 1986 1990 1994 1998 2002 17 2300 2100 Pan evaporation Mean temperature 14 12.75 Epan (mm) 1900 11.5 Tmean ( o C) 1700 10.25 1500 1982 1986 1990 1994 1998 2002 9 2300 2100 Pan evaporation Minimum temperature 6.5 5.25 Epan (mm) 1900 4 Tmin ( o C) 1700 2.75 1500 1982 1986 1990 1994 1998 2002 1.5 2300 2100 Pan evaporation Precipitation 600 500 Epan (mm) 1900 1700 400 300 P (mm) 1500 1982 1986 1990 1994 1998 2002 200

108 H. Tabari, S. Marofi Table 5 Values of slope b of the linear regression analysis, values of statistics Z of the Mann Kendall test and values of statistics Q med of the Sen s slope estimator for annual mean of T max (1982 2003) a Trends statistically significant at the 95% confidence level b Trends statistically significant at the 99% confidence level Station Z Q med b( C year 1 ) Dargezin 1.16 0.046 0.0445 Ekbatan 2.55 a 0.087 a 0.1006 b Ekbatan dam 0.71 0.021 0.0244 Ghahavand 1.41 0.045 0.0782 Kangavar 2.68 b 0.116 a 0.1101 b Kheir-Abad 3.17 b 0.118 b 0.1295 b Khomigan 0.76 0.020 0.0333 Khosro-Abad 1.27 0.055 0.0675 Malayer 1.64 0.057 0.0746 a Nahavand 1.58 0.057 0.0631 a Nozheh 0.59 0.025 0.0405 Varayeneh 3.11 b 0.114 b 0.1185 b the statistical tests. The magnitude of significant positive trends in annual T min varied from (+)1.086 C per decade in the Nozheh station to (+)1.360 C per decade in the Ekbatan station. The minimum, mean and maximum temperatures show, in general, a similar warming pattern, although the magnitude of the increasing trends in T min data was higher than that in T max. This is coincident with results of Salinger and Griffiths (2001) that investigated trends in New Zealand daily temperature and rainfall extremes. The positive trends of T max and T min series found in this research match the findings of Turkes and Sumer (2004) and Smadi (2006) for Turkey and Jordan, respectively. The rate of increasing trends in annual T max,t mean and T min obtained in this study is greater than that reported by da Silva (2004) that investigated climatic variability in the northeast of Brazil. As shown in Table 8, the majority of the stations exhibited decreasing trends in P time series. Only one significant trend of (+)44.837 mm per decade was observed in the Ghahavand station (99% confidence level). Roderick and Farquhar (2004) also pointed out that the trend in precipitation of Australia for 1970 2002 when averaged over all sites was not statistically significant. Besides, the other study carried out by Roderick and Farquhar (2005) in New Zealand indicated that there were very few stations showing statistically significant changes in precipitation. Furthermore, no significant changes in precipitation were found by Cohen et al. (2002) in Israel. The Table 6 Values of slope b of the linear regression analysis, values of statistics Z of the Mann Kendall test and values of statistics Q med of the Sen s slope estimator for annual mean of T mean (1982 2003) a Trends statistically significant at the 99% confidence level b Trends statistically significant at the 95% confidence level Station Z Q med b( C year 1 ) Dargezin 1.64 0.064 0.0725 Ekbatan 3.53 a 0.106 a 0.1232 a Ekbatan dam 2.46 b 0.073 b 0.0789 a Ghahavand 2.89 a 0.107 a 0.1177 a Kangavar 0.82 0.027 0.0135 Kheir-Abad 0.68 0.020 0.0200 Khomigan 1.69 0.087 0.1426 b Khosro-Abad 0.08 0.000 0.0049 Malayer 0.20 0.013 0.0282 Nahavand 1.47 0.047 0.0824 b Nozheh 2.15 b 0.055 0.0715 b Varayeneh 0.31 0.025 0.0056

Changes of Pan Evaporation in the West of Iran 109 Table 7 Values of slope b of the linear regression analysis, values of statistics Z of the Mann Kendall test and values of statistics Q med of the Sen s slope estimator for annual mean of T min (1982 2003) a Trends statistically significant at the 99% confidence level b Trends statistically significant at the 95% confidence level Station Z Q med b( C year 1 ) Dargezin 1.41 0.047 0.0427 Ekbatan 4.21 a 0.120 a 0.1360 a Ekbatan dam 3.42 a 0.115 a 0.1120 a Ghahavand 3.02 a 0.109 a 0.1167 a Kangavar 0.93 0.050 0.0692 Kheir-Abad 1.05 0.025 0.0445 Khomigan 0.54 0.025 0.0390 Khosro-Abad 2.45 b 0.122 b 0.1290 b Malayer 0.88 0.025 0.0395 Nahavand 3.85 a 0.075 a 0.1125 a Nozheh 3.14 a 0.120 a 0.1086 a Varayeneh 3.85 a 0.075 a 0.1125 a absence of any major trends in precipitation is not surprising given the large year-toyear variability that is typical of precipitation records (Roderick and Farquhar 2004). Overall, the study area has experienced a rapid warming over the 1982 2003 period. One expected consequence of this warming is that the air near the surface should be drier, which should result in an increase in the rate of evaporation from terrestrial open water bodies (Roderick and Farquhar 2002). The main factors associated with increasing E pan are temperature variables (min, mean and max). Besides, increasing E pan was not strongly related to P changes. In other words, the change of pan evaporation is not very sensitive to changes in precipitation. The concurrent occurrences of significant increasing trends in E pan and significant positive trends in T max,t mean and T min were found at Ekbatan station. Likewise, the concurrent occurrences of significant positive trends in E pan and significant warming trends in T mean and T min were observed at Nozheh station. Furthermore, the concurrent occurrences of significant upward trends in E pan and significant increasing trends in T max and T min were detected at Varayeneh station. The significant increasing temperature and pan evaporation together with decreasing precipitation can be expected to have led to a marked increase in aridity. In other words, Hamedan province has become more arid over the last 22 years, not because precipitation has changed, but rather because evaporation, and hence the atmospheric demand for water, has increased. These results support the suggestion of Smit et al. (1988) that Table 8 Values of slope b of the linear regression analysis, values of statistics Z of the Mann Kendall test and values of statistics Q med of the Sen s slope estimator for annual P (1982 2003) a Trends statistically significant at the 99% confidence level b Trends statistically significant at the 95% confidence level Station Z Q med B (mm year 1 ) Dargezin 0.11 0.850 2.8020 Ekbatan 0.67 1.775 1.1209 Ekbatan dam 0.45 2.100 1.2896 Ghahavand 2.65 a 4.300 a 4.4837 a Kangavar 0.68 1.300 1.7198 Kheir-Abad 0.37 1.058 1.0112 Khomigan 0.48 0.941 0.3477 Khosro-Abad 0.62 1.169 1.7293 Malayer 0.00 0.433 2.1691 Nahavand 0.34 1.146 1.4379 Nozheh 1.10 3.489 3.3021 Varayeneh 1.83 9.292 9.2357

110 H. Tabari, S. Marofi mid-latitude regions such as the mid-western USA, southern Europe and Asia are becoming warmer and drier. 4 Conclusions In this study, we analyzed changes of observed E pan and the associated variations in T max,t mean,t min and P data for 12 stations in Hamedan province in western Iran from 1982 to 2003. Trend analysis was carried out by the Mann Kendall test, the Sen s slope estimator and the linear regression method. Significantly increasing E pan was observed in 67% of the stations at the 95% and 99% confidence levels. Likewise, the significant positive trends in E pan ranged from (+)132 mm per decade in the Khomigan station to (+)393 mm per decade in the Verayeneh station. Analysis of relations between E pan and the meteorological variables indicated that E pan has significant positive correlations with T max,t mean and T min. The concurrent occurrences of significant increasing trends in E pan and significant positive trends in T max,t mean or T min were found at Ekbatan, Kangavar, Kheir-Abad, Nahavand, Nozheh and Varayeneh stations. In contrast, concurrent occurrence of significant positive trends in E pan and significant decreasing trends in P were not observed. Due to lack of wind speed, relative humidity and radiation data, their relations to pan evaporation changes were not investigated in this study. It is also recommended to evaluate the relations in other areas in Iran with similar climatic conditions, provided that the data are available. The results of this research revealed that the study area has become more arid in recent years. The findings of this study need to be verified in other climatic conditions of Iran especially in arid climates where evaporation changes are crucial for estimating crop water requirements. Acknowledgements Special thanks are due to the different people who collected the required data at 12 mentioned sites. The authors are grateful to the anonymous reviewers whose suggestions significantly contributed to improve the work. References Burn DH, Hesch NM (2007) Trends in evaporation for the Canadian Prairies. J Hydrol 336:61 73 Cohen S, Ianetz A, Stanhill G (2002) Evaporative climate change at Bet Dagan, Israel, 1964 1998. Agric For Meteorol 111:83 91 da Silva VPR (2004) On climate variability in Northeast of Brazil. J Arid Environ 58:575 596 Gilbert RO (1987) Statistical methods for environmental pollution monitoring. Van Nostrand Reinhold, New York Golubev VS, Lawrimore JH, Groisman PY, Speranskaya NA, Zhuravin SA, Menne MJ, Peterson TC, Malone RW (2001) Evaporation changes over the contiguous United States and the former USSR: a reassessment. Geophys Res Lett 28:2665 2668 Harmsen EW, Gonzalez-Perez A, Winter A (2004) Re-evaluation of pan evaporation coefficients at seven locations in Puerto Rico. J Agric Univ P R 88:109 122 Hasanean HM (2001) Fluctuations of surface air temperature in the Eastern Mediterranean. Theor Appl Climatol 68:75 87 Hirsch RM, Alexander RB, Smith RA (1991) Selection of methods for the detection and estimation of trends in water quality. Water Resour Res 27:803 814 Hobbins MT, Ramirez JA, Brown TC (2004) Trends in pan evaporation and actual evapotranspiration across the conterminous US: paradoxical or complementary? Geophys Res Lett 31(13):L13503. doi:10.1029=2004gl019846

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