An investigation on reference crop evapotranspiration trend from 1975 to 2005 in Iran

Authors


Abstract

Evapotranspiration is one of the most important factors in agriculture and the hydrological cycle that can be influenced by global warming and climatic changes. In this study, the trend of reference crop evapotranspiration (ET0) computed by penman Penman-Monteith equation and surveyed in 42 synoptic weather stations during last 3 decades (1975–2005). Nonparametric statistical test, Kendall's rank correlation (or τ test) was used to determine ET0 trends. Although, downward and upward trends were observed, increasing trends had more frequency. Spatial analysis of results indicated upward trends especially in the boundary parts of the country while, no significant trends were distributed in central parts. In addition, correlation analysis between ET0 trend and other climatic parameters trend (the trend of mean temperature, mean of minimum temperature, and mean of maximum temperature, mean of relative humidity, mean of wind speed and mean of sunshine duration) showed that wind is the most effective parameter on ET0. Iran losses more than 70% of annual precipitation by ET0. It is obvious that in this country where there are many limitations for water resources management, increase in ET0 could lead to more problems. Copyright © 2011 Royal Meteorological Society

1. Introduction

Climate change is one of the most important global challenges and has aroused the interest of researchers, scientists, planners and politicians due, in part, to a persistent increase of global warming, associated with the greenhouse effect (Valdez-Cepeda et al., 2003). It can be said that the reality of climatic changes, its causes and effects on human life and ecosystems are the main challenges for scientists. Since it is a pervasive element and affects ecosystems, even a slight change in the climate can affect other parts of the ecosystem with different levels of severity (Kousari et al., 2011). Studies have indicated that an increase in CO2 concentration in the atmosphere leads to global warming and intensifies the global hydrological cycle (Brutsaert and Parlange, 1998). During the study of (Georgios and Christodoulides, 2008), carried out during the period 1850–2007, it has been indicated that the temperature gradient was essentially 0 during the years 1850–1910. Then, there was an increase in the temperature gradient for the next 30 years. From about 1940–1980, the gradient was again 0. Finally, there was a definite increase of about 0.6 °C in the mean global temperature since 1980. Also, widespread changes in climatic conditions have been reported, with long-term trends observed in global average air temperature (e.g. IPCC, 2007), vapour pressure (e.g. Durre, 2009), precipitation (e.g. New et al., 2001), net radiation (e.g. Wild, 2009), and wind speed (e.g. McVicar, 2008).

Recent studies in climate change in Iran were focused mainly on long-term variability of temperature and precipitation. Modarres and Silva (2007) surveyed the time series of annual rainfall; the number of rainy days per year and monthly rainfall of 20 stations to assess the climatic variability in arid and semi-arid regions of Iran. They showed mixed trends of significantly increasing and decreasing rainfall only for Sabzevar and Zahedan stations by the Mann-Kendall test (Modarres and Silva, 2007). Kousari and Asadi Zarch (2011) showed that there is a significant upward trend in temperature parameter especially in mean minimum temperature in arid and semi-arid regions of Iran while there is considerable declining trend in the mean of relative humidity in these regions. In addition, Kousari et al. (2011) noted that most of the eastern and centrally located stations of Iran showed a decrease in relative humidity trend, while this condition was not recorded in Zagros region and northern part of Iran. The present results also showed that the upward trend of minimum air temperature had an effect in increasing the mean air temperature in the stations with temperature ascending trend. This effect of minimum temperature was significantly more than that of the maximum temperature, which could be the result of increasing the amount of greenhouse gases and the reflection of received thermal energies, from land through the night (Kousari et al., 2011).

Evapotranspiration as the third important climatic factor controls energy and mass exchange between terrestrial ecosystems and atmosphere (Chen et al., 2006) has, so far, received less attention in Iran. On the other hand, Iran receives about 240 mm of precipitation (annual average), but more than 72% of that is lost by evapotranspiration. Therefore, there are many problems regarding evapotranspiration in water resource management in Iran. Clearly, any changes in evapotranspiration would affect agricultural production and also water resource programming. Evapotranspiration plays a crucial role in the heat and mass fluxes of the global atmospheric system. Governed by a variety of climatic variables such as sunshine, temperature, wind and atmospheric humidity and its related effects on soil moisture and surface albedo, evapotranspiration should provide a sensitive tool to monitor the changes of energy and moisture transfer from the ground to the atmosphere (Chen et al., 2006).

Dinpashoh (2006) estimated the reference crop evapotranspiration (ET0) in different regions of Iran. This study focused mainly on the prediction of ET0 of seven months of active crop growth season (April–October) and three low data demanded ET0 Prediction methods were selected. These were: (1) adjusted Thornthwaite method hereafter referred to as ATW (Pereira and Pruitt, 2004), (2) Hargreaves method adjusted in 1985, hereafter referred to as HG-1985 (Hargreaves and Allen, 2003), (3) Linacremethod (Linacre, 1977). Results showed that long-term mean annual ET0 varies from 830 mm to over 3627 mm across the country. The lowest monthly and yearly ET0 belonged to the Caspian Sea shoreline, but the highest ET0 belonged to the central and southeast parts of Iran. Some ET0-related studies in Iran mainly focused on ET0 estimation for a limited area and/or on a single station in a short time period. From these studies it can be referred to the works of (Haghighat-Joo, 2003; Hasan-Bagloee and Maghsodi, 2003; DehghaniSanij et al., 2004; Dinpashoh, 2006).

Dinpashoh et al. (2011) examined the trends in ET0 on monthly and annual time scales in Iran. ET0 was estimated using the globally accepted Food and Agriculture Organization (FAO) Penman Monteith method (FAO-56 PM) over the 16 weather stations located in the different regions of Iran. Results showed that both statistically significant increasing and decreasing trends were observed in the annual and monthly ET0. The increasing trends in ET0 were more pronounced than the decreasing trends.

Tabari et al. (2011) surveyed spatial and temporal variations of ET0 in arid and semi-arid regions where water resources are limited in 21 synoptic stations. Analysis of the impacts of meteorological variables on the temporal trends of ET0 indicated that the increasing trend of ET0 was most likely due to a significant increase in minimum air temperature, while decreasing trend of ET0 was mainly caused by a significant decrease in wind speed at the sites where increasing ET0 trends were statistically significant.

In the Tibetan Plateau as a whole, potential evapotranspiration (PET) has decreased in all seasons, the average annual evapotranspiration rate decreased by 13.1 mm/decade or 2.0% of the annual total (Chen et al., 2006). Roderick and Farquhar (2002) reported that as the average global temperature increases, it is generally expected that the air will become drier and that evaporation from terrestrial water bodies will increase. Paradoxically, observations over the past 50 years show the reverse. They showed that decrease in evaporation is consistent with what one would expect from the observed large and widespread decreases in sunlight resulting from increasing cloud coverage and aerosol concentration (Roderick and Farquhar, 2002). For example, the USA, former Soviet Union, India, China, Australia, New Zealand and Canada all show declines in pan evaporation (Roderick et al., 2009).

(Peterson et al., 1995) reported that pan evaporation had, on average, declined in the United States, former Soviet Union and parts of Asia from the 1950s to the early 1990s. Several subsequent studies have largely confirmed these trends. At individual sites, both increases and decreases in pan evaporation are commonly found, e.g. (Chattopadhyay and Hulme, 1997; Roderick and Farquhar, 2004).

Under conditions of global warming and climatic changes, the survey of ET0 trend in different parts of Iran is very important. Therefore, in this study, the temporal and spatial trend of ET0 has been investigated in 42 synoptic stations for different parts of Iran. Monthly, seasonal and annual trends of ET0, which were calculated by Penman-Monteith equation within a 31-year duration, were analysed by τ Kendall test. In addition, the impacts of other climatic parameters trend, which are effective on ET0, have been surveyed. In fact, in this work, a considerable number of weather stations (42 stations) has been used for ET0 trend analysis compared to previous researches. Furthermore, analysing the ET0 trend across the all parts of country over a long time period has been considered.

2. Study area

Iran, a 1648000 square kilometres area, is located in the southwest of the Middle East. Climatically, in most parts of the country, four seasons are usually experienced and in general, one year can be divided into two seasons, warm and cold. Iran has different aspects such as, geographical and topographical, and therefore, the climate. Iran is surrounded by two mountain ranges, namely Alborz to the north and Zagros to the west, and the highest point of the country is located within the Alborz mountain range with an elevation of 5628 metres above mean sea level. These mountains avoid Mediterranean moisture-bearing systems to cross through this region to the east. The Zagros mountain range is responsible for the major portion of rain-producing air masses that enter the region from the west and northwest, with relatively high amounts of rainfall (Sadeghi et al., 2002). The climate in this region is defined as sub-tropical with hot and dry weather in the summer. The main cause of annual rainfall variability in Iran is the changing position of synoptic systems and year-to-year variation in the number of cyclones passing through the region (Modarres and Silva, 2007).

In general, the climate in Iran is arid and semi-arid, except the north and west mountainous parts of the country. In most parts of Iran, summer is warm-arid and winter is cold, while the internal part bears continental climate. Temperature range (maximum and minimum) in most parts of the country is about 22–26 °C. A considerable part of precipitation occurs in the period between November and May, and then the warm-dry season prevails. The mean precipitation in Iran is about 240 mm, most of which occurs in the plains between the northern Alborz mountain chain and the Caspian Sea side, and Zagros at the heights of about 1800 and 480 mm, respectively. Toward the east and centre of Iran, the precipitation decreases up to 100 mm or even less.

Locations of 42 synoptic weather stations are displayed on the map of Iran (Figure 1). These weather stations have a good distribution with a suitable period of weather data (31 years), which can support ET0 trends studies from two aspects; spatially and temporally in recent decades. Table I indicates the general characteristics of (42 surveyed stations) such as elevation, latitude and longitude, and also the average of ET0 of each station.

Figure 1.

The spatial distribution of 42 selected synoptic stations in at Iran. This figure is available in colour online at wileyonlinelibrary.com/journal/joc

Table I. General characteristic of surveyed 42 synoptic stations
Station nameX coordinateY coordinateElevation (m)Average of annual PET (mm)Average of annual precipitation (mm)Precipitation(mm)/ PET(mm)Zone
Abadan48.2530.376.62217.8169.80.077Arid
Abadeh52.6731.1820301485.6143.30.096Arid
Ahvaz48.6731.3322.51989.7240.90.121Arid
Arak49.7734.117081222322.60.264Semi-arid
Ardebil48.2838.251332908.4303.90.334Semi-arid
Babolsar52.6536.72− 21886.9943.11.063humid
Bam58.3529.11066.92040.359.30.029Hyper-arid
Bandar Abbas56.3727.22981865.5152.90.082Arid
Bandar Anzali49.4737.47− 26.21737.71853.502.21humid
Bandar Lenge54.8326.5322.71681.5205.60.118Arid
Birjand59.232.8714911690.8172.40.103Arid
Bushehr50.8328.981961575.3277.20.164Arid
Chabahar60.6225.2881394.6117.50.075Arid
Esfahan51.6732.621550.41469.2126.50.091Arid
Fasa53.6828.971288.31222.9316.50.215Semi-arid
Ghazvin50.0536.251279.2924.1329.50.269Semi-arid
Gorgan54.2736.851331332.4568.40.615Sub-humid
Hamedan48.7235.21679.72073.4323.10.243Semi-arid
Iranshahr60.727.2591.11768.4112.40.054Arid
Jask57.7725.635.21130.51390.079Arid
Kashan51.4533.98982.31667.71370.121Arid
Kerman56.9730.2517531394.6142.10.085Arid
Kermanshah47.1534.351318.61329.9431.40.309Semi-arid
Khoramabad48.2833.431147.8948500.30.376Semi-arid
Khoy44.9738.5511031271.9283.50.299Semi-arid
Mashhad59.6336.27999.21737.72710.213Semi-arid
Orumieh45.0837.531315.91026.36340.280.34Semi-arid
Rasht49.637.25− 6.9795.913671.718humid
Sabzevar57.7236.2977.61615205.10.127Arid
Sanandaj4735.331373.41278.6461.80.361Semi-arid
Semnan53.5535.581130.81308.8145.60.111Arid
Shahrekord50.8532.282048.91132.9335.40.296Semi-arid
Shahrud54.9536.421345.31274.3162.60.128Arid
Shiraz52.629.5314841640.53480.212Semi-arid
Tabas56.9233.67111638.788.30.054Arid
Tabriz46.2838.0813611338.52660.199Arid
Tehran51.3235.681190.81577.1245.50.156Arid
Torbat Heydarieh59.2235.271450.81355.8288.10.213Semi-arid
Yazd54.2831.91237.21742.764.40.037Arid
Zabol61.4831.03489.22813.562.60.022Hyper-arid
Zahedan60.8829.4713701870.275.30.04Arid
Zanjan48.4836.6816631136295.20.26Semi-arid

According to Asadi Zarch et al. (2011), the precipitation and potential evapotranspiration were used for classifying the bioclimatic aridity in a globally comparable way. In mathematical terms, UNESCO (1979) used an aridity/humidity classification system based on average annual precipitation (P) divided by the average annual potential evapotranspiration (PET). On the basis of this classification, Table I demonstrates the type of climatic conditions, introduced for each experimental station. As can be seen in this table, the frequencies of stations, which are located in arid and semi-arid zone, have considerable frequency.

3. Materials and methods

3.1. Data collection and database formation

In the present study, the climatic data collected in 42 synoptic weather stations from Meteorological Organization of Iran (http://www.weather.ir) was used. The average of minimum temperature and average of maximum temperature, sunshine data, mean of relative humidity data and wind data, of the present analysis was collected. Since according to the Penman-Monteith equation, there is a variety of data, 42 synoptic stations were selected, because they had adequate data with sufficient period of climatic time series (1975–2005).

3.2. Evapotranspiration computation

Evapotranspiration can be measured or estimated using several methods. According to Chen et al, 2006, the Penman-Monteith method is the most reliable way to estimate PET under various climates (Jensen et al., 1990), as it reflects changes in all meteorological factors affecting evaporation and plant transpiration. (Jensen et al., 1990) have proposed the term ‘reference evapotranspiration’ instead of PET to underline this meaning. PET does not directly give an indication of actual evapotranspiration rates that are governed by characteristics of soil (soil type, infiltration capacity), relief (slope, exposition and relief form), plant (vegetation type, soil cover, LAI, rooting depth) and climate (precipitation amount and intensity, PET and the temporal distribution of both variables). One main advantage of the concept of PET is that it provides a standardized value that allows comparing evaporative environments under different climatic settings. This concept has been developed by the Food and Agriculture Organization of the United Nations (FAO) during the last decades (Doorenbos and Pruitt, 1977; Doorenbos and Kassam, 1986; Smith, 1992; Allen et al., 1998) and has been applied on a global scale to land use studies (Fischer et al., 2000).

Reference evapotranspiration was estimated using the FAO Penman-Monteith equation (Allen et al., 1998):

equation image(1)

where the ET0 is reference evapotranspiration using the PM-56 method (mm day−1), Rn is the daily net radiation (MJ m−2 day−1), G is the daily soil heat flux (MJ m−2 day−1), Ta is the mean daily air temperature at a height of 2 m ( °C), U2 is the daily mean wind speed at a height of 2 m (m s−1), es is the saturation vapour pressure (kPa), ea is the actual vapour pressure (kPa), Δ is the slope of the saturation vapour pressure versus the air temperature curve (kPa °C−1), and g is the psychrometric constant (kPa °C−1). In this study according to Rahimikhoob, 2010, the daily values of Δ, Rn, es, and ea were calculated using the equations (for albedo, α = 0.23 for green vegetation surface) given by Allen et al. (1998). The soil heat flux (G) was assumed to be zero over the calculation time step period (24 h; Allen et al., 1998). The measured relative humidity, T max and T min values were used to calculate ea and es. The daily solar radiation (Rs) was calculated using the Angstrom formula, which relates solar radiation to extraterrestrial radiation and relative sunshine duration. Equation 39 in Allen et al. (1998) was used to calculate the net outgoing long-wave radiation (Rahimikhoob, 2010).

The calculated ET0 data is organized in monthly, seasonal (winter, spring, summer and autumn), annual and finally in time series statement. It should be mentioned that in time series statement the data is continuous, while in the other time scale, each set of data which is disconnect to the other sets of data every month or season lie after or before the same month or the same season in the sequences.

3.3. Data processing

The mathematical and statistical analysis of for this study was performed in the MATLAB environmental programming software. The trend of calculated ET0 from Penman-Monteith equation during the time period between 1975 and 2005 was analysed using Kendall-τ statistics following the methods of (Zhenmei et al., 2008); (Chmielewski and Rötzer, 2002; Wang et al., 2008).

3.3.1. Trend test with Kendall's τ-test

The main methodology of the present study was time series analysis through the application of nonparametric statistical test, Kendall's rank correlation (or τ-test), which was based on the proportionate number of subsequent observations that exceeded a particular value. The τ-test is commonly used to assess the significance of trends in hydro-meteorological time series (Kendall and Stuart, 1973). For a sequence of x1, x2,…, xn; the standard procedure is to determine the number of times, say p, within all pairs of observations (xi, xj; j > i). As xj is greater than xi; the ordered (i, j) subsets are (i = 1, j = 2, 3,…, n), (i = 2, j = 3, 4,…, n),…, (i = n − 1, j = n), where n is the record length of dataset. A rising trend is seen where the succeeding values are greater than the preceding ones, and p is given by (n − 1)+ (n − 2)+ …+ 1, which is the sum of an arithmetic progression, given by n(n − 1)/4. If the observations are totally reversed, then p = 0 and, hence, it follows the equation given below for a trend-free series

equation image(2)

The test is based on the statistics of τ

equation image(3)

For a random sequence,

equation image(4)
equation image(5)

The test defines the standard normal variant N as

equation image(6)

N converges rapidly to a standard normal distribution as the n increases. At a specified level of significance of a, standard N* value can be obtained from the table of standard normal distribution. If Nj > Na/2, a positive N indicates an increasing trend in the time series, while a negative N indicates a decreasing trend. Statistical significance was declared at p < 0.1 (Zhenmei et al., 2008).

3.3.2. Correlation of ET0 with other climatic parameters

In order to analyse the main causes of ET0 trend changes based on other meteorological variables trend, according to Xu et al. (2006), the temporal trend of other meteorological variables (mean, maximum and minimum air temperature, relative humidity, wind speed and sunshine duration) have been computed with Kendall's τ-test. Then, the correlation between ET0 with other climatic trend has been surveyed. Therefore, the undernoted steps have been followed.

  • 1.The trend of mean, maximum and minimum air temperature, relative humidity, wind speed and sunshine duration computed based on the with Kendall's τ-test for different time scales (monthly, seasonal, annual and continues time series). Therefore, 17 matrixes, with 42 rows, which represent climatic stations, and 7 columns, which represent the trend of each meteorological variables trend, were surveyed. Generally, 17 matrixes formed, 12, 4, 1 and 1 for monthly, seasonal, annual, and continues time series, respectively. Table IV, shows an example of these matrixes just for annual time scale. Of course, the additional two last columns show the aridity classification of the surveyed stations based on UNESCO (1979).
  • 2.In this step, the stations with significant trend concerning ET0, (both downward and upward trends) separated. For example, the gray rows in the Table IV show the stations with significant ET0 trend.
  • 3.The correlation of the trend of ET0 computed with other meteorological variables trend, in the rows, are separated in step 2. Owing to computation of this correlation, r correlation coefficient is used, explain as:
    equation image(7)
    r correlation coefficient varied continually from − 1 to 1. A − 1 indicates full correlation between two surveyed parameters that have inverse relation with each other. The 0 and values around the 0 show no relation and correlation between two parameters. 1 and around 1 amounts show the full correlation with direct relation between two surveyed parameters.As results of this step, 17 correlation coefficients matrixes for various time scales formed. Table V, shows an example of these matrixes just for annual time scales. First row and column of this matrix indicates ET0 trend correlation coefficient with other meteorological variables trend. Therefore, for all analysed time scales, the first row data selected and presented in Table VI. Each row of this table displays ET0 trend correlation coefficients with other climatic parameters trends for particular time scale.

4. Results

Table II indicates the Results of the application of the Kendall's rank correlation test (N parameter) on monthly ET0 for all surveyed synoptic stations. Table III indicates these results in seasonality, annually and continues time series time scales. In these tables, * indicates the significant changes regarding the τ-test both downward and upward trend (N parameter less than − 1.645 and more than 1.645, respectively, and α< 0.1). In addition, the data in Table III have been used for spatial analysis of ET0 trend. Figures 2–7 indicate the amounts of ET0 trends across all parts of the country in different time scales (annual, seasonal and continues time series). A survey of the mentioned tables exhibited existence of downward, upward and no significant trends. Nevertheless, the numbers of upward trends are more considerable than the downward types. In monthly investigation in Table II, it can be found that some stations such as Arak, Bandar Abbas, Bushehr, Jask, Sanandaj and Shiraz do not have a significant trend in all months. To some extent, these conditions can be observed in Table III in the case of seasonal trend analysis and for mentioned stations.

Figure 2.

Spatial distribution of upward, downward and non-significant trend of ET0 in winter season. This figure is available in colour online at wileyonlinelibrary.com/journal/joc

Figure 3.

Spatial distribution of upward, downward and non-significant trend of ET0 in spring season. This figure is available in colour online at wileyonlinelibrary.com/journal/joc

Figure 4.

Spatial distribution of upward, downward and non-significant trend of ET0 in summer season. This figure is available in colour online at wileyonlinelibrary.com/journal/joc

Figure 5.

Spatial distribution of upward, downward and non-significant trend of ET0 in autumn season. This figure is available in colour online at wileyonlinelibrary.com/journal/joc

Figure 6.

Spatial distribution of upward, downward and non-significant trend of ET0 in annual time scale. This figure is available in colour online at wileyonlinelibrary.com/journal/joc

Figure 7.

Spatial distribution of upward, downward and non-significant trend of ET0 in time series time scale. This figure is available in colour online at wileyonlinelibrary.com/journal/joc

Table II. Results of the application of the Kendall's rank correlation test on monthly ET0 data for all synoptic stationsThumbnail image of
Table III. Results of the application of the Kendall's rank correlation test on seasonality, annually and time series ET0 data for all synoptic stationsThumbnail image of

In the winter season, based on the Table III, 20 stations indicated positive monthly trends and just 2 stations (Shahrud and Birjand) showed the negative trend. This information has been exhibited in Figure 2. This figure illustrates that non-significant trends have been distributed in central parts of Iran. Beside the borderlines of neighbourhood countries there are significant trends particularly positive significant trend. In the spring season, 17 positive significant values and 5 negative trends can be found. The spatial trends of ET0 in spring season have been shown in Figure 3. Same as the winter, the significant trends are located near the boundary of Iran.

In the summer season, 14 significant upward trends and 8 significant downward trends can be seen. The distribution of significant and non-significant trends for this season have been shown in Figure 4. This figure shows more significant trends compared to other seasons. Especially, northwest of country has non-significant trends while, to some extent, for other seasons, this parts of Iran has positive significant trends.

Results in Table III for autumn season showed that there are 21 significant positive values against 4 significant negative values. Figure 5 exhibits the distribution of ET0 trends in different parts of the country. Most of the positive significant trends are located in boundary parts while the non-significant trends, and also negative trends, are located in internal parts of Iran. It should be mentioned that beside the coastline of the Persian Gulf, for many time scales and also for autumn, a negative trend of ET0 could be observed. Among them for the Bandar-Lenge station, the downward significant trend could be seen.

Regarding annual and time series, there are 20 and 14 significant positive values and 5 and 3 significant negative values, respectively. Figure 6 shows the spatial trend of ET0 across the country for the annual time scale. It could be observed that the upward trend was extended in the north, southeast toward centre, northeast, west and northwest of Iran. Figure 7 shows the spatial analysis of ET0 trend in continuous time series. Not the same as in the annual map, but to a weaker extent, the upward trends are located in the southeast, and to some extent in the west of Iran. In this map, only in Birjand, Chabahar and Shahrud stations, the downward trend can be seen.

Figures 8 and 9 show the time series of annual ET0 of some surveyed stations (according to UNESCO (1979), Abadan and Tehran located in arid zone, Arak and Shiraz in semi-arid climatic status, Bam and Zabol at Hyper-arid zone and Babolsar and Bandar anzali for humid climatic conditions). These figures and stations cover the main types of climatic conditions in Iran. Generally, they indicate that in many stations after the 1980s, and for some stations especially after the 1990s, there is an increasing trend of ET0 after those periods. Nevertheless, it could be found that before the mentioned perids (1980s and 1990s), there is a downward trend.

Figure 8.

Time series of annual ET0 (mm) in Abadan, Abadeh, Ahvaz, Arak, Ardebil and Babolsar synoptic stations. This figure is available in colour online at wileyonlinelibrary.com/journal/joc

Figure 9.

Time series of annual ET0 (mm) in Bam, Bandar Abbas, Bandar Anzali, Bandar Lenge, Birjand and Bushehr synoptic stations. This figure is available in colour online at wileyonlinelibrary.com/journal/joc

Results about the correlation coefficient between ET0 trend and other meteorological variables have been indicated in Tables IV, V and VI. Table IV exhibits the results of r correlation coefficient (correlation of ET0 trend with other climatic parameter's trends) in annual time scale. According to this table, an abundance of the significant ET0 trends (both upward and downward trends) are considerable in the arid and semi-arid synoptic stations for annual time scale, respectively. Also, it can be observed that all stations with significant upward or downward ET0 trends show significant trends in regard to wind speed except Tabas and Babolsar (these stations are located in humid regions).

Table IV. The results of r correlation coefficient (correlation of ET0 trend with other climatic parameter's trends in annual time scale)Thumbnail image of
Table V. Correlation coefficient matrix for the significant trends in regard to surveyed meteorological variables (annual time scale)Thumbnail image of
Table VI. Correlation coefficients between ET0 trend and other surveyed climatic parameters trends in different time scales
Time scaleMean air temperatureMinimum air temperatureMaximum air temperatureRelative humidityWind speedSunshine duration
January0.430.350.36− 0.400.910.48
February0.180.220.080.070.69− 0.14
March0.580.300.57− 0.710.730.27
April0.690.390.70− 0.620.880.48
May0.36− 0.140.48− 0.310.890.46
June0.11− 0.110.27− 0.430.940.54
July0.440.220.40− 0.630.960.47
August0.360.290.27− 0.370.940.27
September0.05− 0.210.17− 0.400.930.36
October0.200.110.40− 0.200.930.42
November− 0.030.11− 0.08− 0.280.960.58
December− 0.21− 0.31− 0.11− 0.040.950.13
Winter0.570.650.470.010.910.65
Spring0.370.260.29− 0.780.850.21
Summer− 0.02− 0.03− 0.04− 0.380.870.32
Autumn− 0.22− 0.02− 0.42− 0.130.980.39
Annual− 0.15− 0.09− 0.16− 0.210.910.32
Time series0.120.150.000.120.520.36

Correlation coefficients matrix for the significant trends in regard to surveyed meteorological variables just for annual time scale have been shown in Table V. Furthermore, the Correlation coefficients between ET0 trend and other surveyed climatic parameters trends in different time scales have been exhibited in Table VI. As it can be observed in Table VI, for all the time scales, the wind speed trend has more correlation coefficient with ET0 trend. In other words, the direct and strong correlation coefficient between ET0 trend and also wind speed trend are obvious. It can be seen that to some extent, the trend of ET0 has direct relation with wind and sunshine duration, while the trend of ET0 has inverse relation with relative humidity trend. These results, according to Dinpashoh et al. (2011), confirm that wind speed was found to be the most important and dominant variable influencing the rate of ET0 over almost entire Iran.

From the other side, for other parameters, results vary from one time scale to another. Generally speaking, in March, April, May, June and July, the correlation coefficients between ET0 and other climatic variables are considerable. Therefore, for each time scale, the priority of the meteorological variable trend correlation with ET0 trend are different, except the wind speed. For example, in regard to annual time scale, the wind speed, sunshine duration, relative humidity, maximum, mean and minimum air temperature have more correlation coefficients, respectively, while for continuous time series time scale, wind speed, sunshine duration, relative humidity, minimum, mean and maximum air temperature have more coefficient trend with ET0 trend, respectively.

Figure 10 shows the box and whisker plots of annual ET0 values in all surveyed stations for the 1975–2005 analysis period. According to this figure, minimum and maximum of average ET0 can be observed in 1992 and 2001, respectively. In addition, outliers of ET0 have been seen in this figure. These outliers are generally related to some specific climatic stations, particularly Zabol station, that are located in the southeast of Iran. Furthermore, these box plots show that the varieties of ET0 in Iran are between less than 1000 mm and more than 3000 mm, annually.

Figure 10.

Box and whisker plots of annual ET0 values for the 1975–2005 analysis period. Note: The line inside the boxes represents the median and the upper and lower lines of the boxes indicate the 75 and 25% percentile, respectively. Furthermore, the upper and lower part of the whiskers indicates the respective maximum and minimum values of the ET0. Outliers are data with values beyond the ends of the whiskers. Outliers are displayed with a red + sign. This figure is available in colour online at wileyonlinelibrary.com/journal/joc

5. Discussion

Tables II and III, besides maps of different timescales, indicate that the frequency of upward significant trend of ET0 is more than the downward type. Furthermore, the boundary parts of Iran in most time scales have upward trends in ET0. To some extent, the west, northwest and southeast of the country had considerable upward ET0 trends, while in the most central parts of the country, there was no significant trend in ET0. It is important to note that the northern and western parts of country have important roles in agricultural production. On the other hand, agricultural lands in Iran have the highest consumption of water resources. Therefore, the upward trend for ET0 in this region can affect consumption of water resources especially for farmlands.

In addition, correlation analysis between ET0 trend and other climatic parameters trend showed the wind is one of the most important parameters which affect ET0. It could be said that the changes in the wind have affected ET0 trend excessively. In addition, other studies illustrated that wind is the main meteorological at parameter for ET0 changes. Bandyopadhayay et al. (2009) reported that relative humidity and wind speed are the two main meteorological parameters responsible for ET0 decreases in India. Also, The net radiation followed by wind speed were the main two variables responsible for the observed decreasing trends in ET0 in the Yangtze River catchment (Xu et al., 2006). Thomas (2000) reported sunshine duration is the main parameter, which affects PET in South China. However, in northwest, central and northeast China, wind speed, relative humidity and maximum temperature are the most important factors. Also, wind was found to be most strongly associated with PET in all but one (winter) season at Hami in China, the station with the highest absolute annual PET decrease (Thomas, 2000).

Considering global warming and climate changes, the upward trends of evapotranspiration were anticipated, while the declining trend results are reported in many parts of the world (Peterson et al., 1995; Roderick and Farquhar, 2002; Roderick and Farquhar, 2002; Chen et al., 2006). At individual sites, both increasing and decreasing trends in pan evaporation are commonly found (e.g. Chattopadhyay and Hulme, 1997; Roderick and Farquhar, 2004). According to the results of this study, in many cases (temporally and spatially), the significant upward trends are more considerable than downward trends. On the other hand, the increasing trend in air temperature over the last decades is affected by the global warming as reported across most parts of the globe (e.g. Brunetti et al., 2000; da Silva, 2004; Kousari and Asadi Zarhc, 2011; Kousari et al., 2011; Tabari and Hosseinzadeh Talaee, 2011). After 1980, there was considerable change in temperature (Georgios and Christodoulides, 2008). Therefore, the surveyed ET0 trend in this period can lead us to better results in the effects of climate changes. It could be assumed that probably global warming has affected ET0 changes in Iran.

Global dimming and brightening are also other concerns related to ET0 changes. According to the review study of Wild (2009) on global dimming and brightening, Surface Solar Radiation (SSR) shows significant reduction over the period (roughly) 1960s–1980s based on surface observations (dimming), while between 1980s and 2000s, there are significant increases in SSR in many regions of the world (brightening). In many studies, global dimming has been determined as one of the main important parameters that affects the ET0 and evaporation changes. The observed reductions in pan evaporation in the Northern Hemisphere are broadly consistent with observed reductions in solar radiation at the Earth's surface in the period up to 1990 (Stanhill and Cohen, 2001; Roderick and Farquhar, 2002). Some of this solar dimming is simulated by global climate models (GCMs) that include increases in anthropogenic aerosols (Liepert et al., 2004), suggesting that anthropogenic aerosols probably contributed to the reductions in pan evaporation, at least in the Northern Hemisphere (Rotstayn et al., 2006). Anyway, it is anticipated that with increase in global dimming, ET0 would decrease. The study duration of this research is between 1975 and 2005, when the global dimming had significant decreasing trend. Therefore, the global dimming can be assumed as another effective parameter on ET0 changes in Iran. In addition, the figures which indicate the ET0 time series in the stations with upward trends, show an increasing trend in ET0 after the 1980s. These results are in good agreement with Wild (2009) about the occurrence of global brightening, while the downward trend of ET0 before this time could be related to global dimming.

In this study, the trend of ET0 were surveyed and investigated. It is essential to investigate more details about the dates and amounts of changes in the last decades. Moreover, the survey of the effects of global dimming and brightening on ET0 changes in Iran is very important. In future studies, these main questions (dates and the amounts of changes beside the effects of global dimming and brightening on ET0) are to be answered. Three factors that influence the ET0 trend were determined naming global warming, global dimming and wind. Other studies are needed to clarify these factors and interactions.

6. Conclusion

Generally speaking, the downward trend of ET0 is desirable and upward trend is not beneficial. The upward trend of ET0 is a very important discussion in water resources management. Iran losses more than 70% of annual precipitation by ET0. It is obvious that in this country where there are many limitations to water resources management, increase in ET0 could lead to more problems. If potential ET0 is greater than actual precipitation, soil will dry out. In most parts of Iran the potential or reference ET0 is greater than actual precipitation.

Any changes in ET0 rates will have an impact both on terrestrial ecosystem in general and on crop production in particular. Negative ET0 rates will lower the need for irrigation in the semi-arid environment of Iran and will be beneficial for the natural vegetation as well, but positive trend impacts are not beneficial at all.

Acknowledgements

The authors gratefully appreciate the Cadastre group (Management Center for Strategic Projects) in Fars Organization of Agricultural Jahad for their support and providing research facilities.

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