Introduction

Climate is one of the most important limiting factorsin agricultural production. In recent years, a change inclimate has been documented in many locationsthroughout the world. Global climate models coupling theatmosphere and the oceans replicate certain climatechanges well, in particular, changes in temperatureobserved over the last century, as well as the last fewdecades, if the following observed variants areincorporated (Santer et al., 1996): greenhouse gasconcentrations, stratospheric ozone, aerosols in thetroposphere, aerosols in the stratosphere, and solaractivity (sunspots). The first 3 of these are largely due tohuman activities; the last 2 are natural. The application ofstatistical tests to temperature measurements during thiscentury leads to the conclusion that the warming is man-made, with a confidence of 99% (Toll, 1994). The globalmean temperature trend between 1975 and 1997, andeven regional variations in this trend, can be explained

very well by observed changes in greenhouse-gasconcentrations (Hunt, 1998). In other words,greenhouse gas emissions are by far the leading cause ofclimate change during the last quarter of the 20thcentury. The last decade of the 20th century (especiallythe years 1990, 1993, and 1997) is almost certainly thewarmest since 1400 (Hunt, 1998). The minimumtemperature increased almost everywhere, and themaximum and mean temperature increased in northernand central Europe, across the Russian Federation andCanada (Bootsma, 1994), and in Australia and NewZealand (Plummer et al., 1999).

Atmospheric models have predicted that our planetwill be influenced by climate change (Toll, 1994). It iswidely accepted today that every change in the climaticsystem is important for water and natural resourcesmanagement. As climate changes and temperatureincreases, the hydrologic cycle changes accordingly. As aresult, there would be an increase in rainfall intensities

Turk J Agric For30 (2006) 439-448© TÜB‹TAK

439

Time Trend in the Mean Annual Temperature of Iran

Bijan GHAHRAMAN

Irrigation Department, College of Agriculture, Ferdowsi University of Mashhad, Mashhad 91775, IRAN

Received: 03.01.2006

Abstract: Many researchers around the world have reported a gradual increase in mean annual temperature. Yet, there are somereports of a reduction in this parameter. In this study, we investigated the long-term trend of mean annual temperature at 34synoptic stations in Iran (2 stations in cool humid climates, 14 stations in temperate humid climates, 11 stations in steppe climates,and 7 stations in desert climates, based on the Koppen climatic division) with a minimum record of 30 years by Student’s t-test.Results showed that there was a positive trend in 50% of the stations, while 41% of stations had a negative trend. Considering thesignificance level (α = 5%), there were 3 trend zones for mean annual temperature in Iran, i.e. positive trend, negative trend, andzero trend; however, it was difficult to define a specific spatial scheme for such a division. The results were supported by the Mann-Kendall method, while low harmony was found with the Wald-Wolfowitz test. As far as record length is concerned, during a commontime period (1968-1998), 65% of the stations showed a positive trend, while 32% of them followed a negative trend. There weresome shifts from one trend to another for some of the stations in the study, yet with no well-defined spatial structure. In this case,and at the 5% level of significance, 44%, 15%, and 41% of the stations had a positive trend, a negative trend, and zero trend forthe parameter of the study, respectively. In general, the behavior of trend direction was different for different climates and nospecific pattern was found. Based on the results, one may hypothesize that in the future more regions will experience highertemperatures. Some stations did not show any significant trend, yet their positive trends may be indicative of future warming.Including the years 1999-2002 in the data verified the results of our trend analysis. All of the stations showed higher average meanannual temperature compared to the average of the period 1968-1998.

Key Words: Temperature, climate, time trend, Iran

* Correspondence to: bijangh@ferdowsi.um.ac.ir

and runoff, in addition to increasing soil moisturedemands. Meanwhile, the universal hydrologic cyclewould be influenced by changes that are man-made(Lettenmaier et al., 1994; Karl et al., 1996; Vorosmartyet al., 2000). It was shown in Slovakia that during a 94-year period (1901-1994), air temperature rose by 0.8 °C(Lapin, 1995). Hess (1998) reported that monthly meandaily maximum, and in particular, minimum temperaturesof 3 stations in the northeast arid zone of Nigeriaexperienced an increase of up to 1.5 °C during the period1961-1991. It is also reported that from 1982 to 1998,Maryland experienced not only an increase of 0.43°C/decade for January and July temperature, but also itsmean annual maximum and minimum temperaturedecreased by 0.6 °C/decade (Menglin and Dickinson,2002).

In contrast to the following reports on the positivetrend of temperature, the literature reports thepossibility of temperature decrease. Perez et al. (2000)utilized temperature data for 41 years (1958-1998)from the National Centers for EnvironmentalPrediction–National Center for Atmospheric Research(NCEP–NCAR) for reanalysis. They showed that in thenorthern hemisphere a positive trend in temperatureencompassed most of Europe, North America, and theAtlantic Ocean in the 30-50°N band, while there was anegative trend over Iceland, Greenland, and theNortheastern coast of Canada, including Hudson Bay. Box(2002) analyzed temporal and spatial variability fromGreenland instrumental temperature records for 24coastal and 3 ice sheet locations. Trends over the longestperiod available, 1873-2001, at Ilulissat/Jakobshavnindicate statistically significant spring and summercooling. Such a cooling is also reported by Nicholls et al.(1996) for the northern and northwestern AtlanticOcean, and middle latitudes of the north Pacific Oceanbetween 1951 and 1999. With the Illinois State WaterSurvey, Angel (2004) showed that several Illinois stationspresented no warming during the last few decades. Yet,some Illinois stations, particularly in the southern part ofthe state, followed a cooling trend. Srivastava et al.(1992) observed the increasing trends of annual mean,maximum, and minimum temperatures south of 23°N,and cooling trends north of 23°N. There was no sign ofmean annual temperature increase for 122 years (1878-1999) as recorded by the Pisa meteorological station inItaly (Moonen et al., 2002).

As the mean annual temperature trend varies bylocation and time period worldwide, and since there is noreport on temperature trend analysis in Iran, this studyaimed to fill the gap.

Materials and Methods

Trend definition and its calculation

Any time series of a stochastic variable consists of 2stochastic and deterministic components. Deterministiccomponents are in the form of trend, cyclicity, or jump(Salas, 1992). Generally, there isn’t any cyclicity of theannual scale. In addition, there is no report for annualtemperature series. Therefore, this paper is focused ontrend analysis of the deterministic components of meanannual temperature of synoptic stations in Iran.

There are different statistical methods consideringtrend analysis (Haan, 1977; Bobee and Ashkar, 1991;Salas, 1992). Student’s t-test is a common method fortrend analysis of climatic parameters (e.g.,Chattopadhyay and Hulme, 1997). In this method, aregression line establishes differences betweenindependent (time; x) and dependent (temperature; y)variables. Intercept and line slope can be calculatedthrough error minimization. Afterwards t statistics(=b/sb, where b is the intercept of the regression line andsb is the standard deviation of the data, Eq. (1)) iscomputed.

S b2 = S2 / Σ (Xi – X

–)2 (1)

where S2 can be determined through Eq. (2).

S2 = Σ (Yi – Y^

i )2 / (n – 2) (2)

The null and alternate hypothesis are defined as:

H0: b = 0, H1: b ≠ 0 (3)

The Ho hypothesis at α level of significance is rejectedwhenever | t | > t1 –

α2

, n–2 from the t-student table (n =sample size). As a result, the slope of the trend line issignificantly different from zero; there is a trend in timeseries.

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Station used

Record length is important in statistical investigations.It is well known that as record length increases thevalidity of the results increases accordingly. There are160 synoptic stations in Iran. Record lengths from thesestations vary between 10 and 48 years (up to 1998, forwhich we could obtain the data). It is not wise to proceedwith low record lengths. Therefore, a minimum recordlength of 30 years was considered. This length isappreciated in climatological studies. With this limitingcriterion, 34 stations were available. These stations werelocated in different climatic conditions. Based on Koppenclimatic divisions, there were 2 stations (6%) in coolhumid (D), 14 stations (41%) in temperate humid (C), 11stations (32%) in steppe (Bs), and 7 stations (21%) indesert (Bw) climates. See Table 1 for geographicalspecifications and Figure 1 for spatial distribution of theselected stations.

Trend computations

Trend analysis, or significance test for line slope, wasperformed in 3 stages: (a) for the entire time period forall stations (up to 1998), (b) for a common period for allstations (1968-1998), and (c) consecutive time periods,in which in every step, the oldest data was dropped andtrend analysis was performed accordingly, the analysiswas stopped for a 5-year record length.

Results and Discussion

1. Entire time period

Table 2 shows the slopes of the long-term trend linesfor the 34 synoptic stations studied. Of the 34 stations,17 (50%) showed a positive trend. On the other hand, anegative trend was demonstrated by 14 stations (41%).The remaining 3 stations (9%) showed a zero trend. Yet,this ratio was not the same for the 4 different climates.

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Table 1. Geographical specifications (degrees for longitude and latitude, and meters for altitude) for the selected stations in Iran.

(1)* (2) (3) (4) (5) (1) (2) (3) (4) (5)

Desert climate (Bw) Temperate humid (C)

Bw01 Abadan 4825 3036 11 C01 Arak 4970 3410 1720

Bw02 Ahwaz 4866 3133 22 C02 Babulsar 5265 3671 21

Bw03 Bandar Abbas 5636 2721 10 C03 Gorgan 5446 3681 155

Bw04 Bushehr 5085 2895 8 C04 Hamedan 4853 3485 1749

Bw05 Kerman 5696 3025 1754 C05 Kermanshah 4711 3426 1322

Bw06 Yazd 5440 3190 1230 C06 Khoram Abad 4830 3350 1125

Bw07 Zabol 6148 3133 489 C07 Mashhad 5963 3626 980

Steppe (Bs) C08 Orumieh 4508 3753 1312

Bs01 Bam 5840 2910 1067 C09 Ghazvin 5000 3625 1278

Bs02 Birjand 5920 3286 1491 C10 Ramsar 5066 3690 20

Bs03 Fasa 5368 2896 1383 C11 Rasht 4960 3725 7

Bs04 Isfahan 5166 3261 1590 C12 Shiraz 5258 2953 1491

Bs05 Kashan 5145 3398 982 C13 Tabriz 4628 3808 1361

Bs06 Sabzevar 5766 3621 941 C14 Zanjan 4848 3668 1663

Bs07 Semnan 5338 3555 1171 Cool humid (D)

Bs08 Shahrud 5503 3641 1345 D01 Sanandaj 4700 3533 1373

Bs09 Tehran 5135 3568 1191 D02 Shahre-Kord 5085 3231 2078

Bs10 TorbatHeydarieh 5921 3526 1333

Bs11 Zahedan 6088 2946 1370

* (1) Code, (2) Name, (3) Longitude, (4) Latitude, (5) Altitude

Time Trend in the Mean Annual Temperature of Iran

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Steppe

Desert

Cool Humid

TemperateHumid

N

S

30 0 30 60 90 120 150 Kilometers

Figure 1. Location of selected stations in different climatic regions of Iran.

Table 2. Slope of mean annual temperature line for synoptic stations of Iran. x denotes the levelof significance at α = 5%.

Code Whole Common Code Whole Commonperiod period period period

Desert climate (Bw) Temperate humid (C)

Bw01 0.01 0.02x C01 –0.03x –0.02

Bw02 0.03x 0.06x C02 0.02x 0.03x

Bw03 –0.03x –0.02 C03 –0.01 –0.01

Bw04 0.02x 0.04x C04 –0.02 0.02

Bw05 –0.02 0.03x C05 0.02x 0.05x

Bw06 0.02x 0.04x C06 –0.05x –0.07x

Bw07 0.00 0.00 C07 0.03x 0.08x

Steppe (Bs) C08 –0.04x 0.01

Bs01 0.3x 0.3x C09 –0.03x –0.01

Bs02 –0.03x –0.03x C10 0.01 0.02

Bs03 –0.04x –0.05x C11 0.02 0.05x

Bs04 0.02x 0.02 C12 0.03x 0.06x

Bs05 –0.03 –0.03x C13 0.02x 0.04x

Bs06 0.06x 0.07x C14 –0.03x –0.02

Bs07 0.01 0.02 Cool humid (D)

Bs08 0.01 0.05x D01 0.00 0.01

Bs09 0.03x 0.05x D02 –0.03 –0.04x

Bs10 –0.01 –0.02

Bs11 0.00 0.01

We summarized the results in Table 3. Based on Table 3,desert climate had the highest positive trend (57%),while the highest negative trend was in humid climates(temperate and cool).

One may obtain different results if the significance ofthe slopes is considered. Based on all stations, 14 stations(41.2%) showed no significant slope (α = 5%), whilesignificant slopes were found for the other 20 stations(58.8%), of which 12 (35.3%) and 8 (23.5%) stationsshowed positive and negative trends, respectively (Table 4).

Previous studies have principally confirmed the positivetrend of mean annual temperature in most parts of theworld (e.g., Hess, 1998, for Nigeria). In addition, thereare some forecasts for temperature increase in the future(Zhang et al., 2000). Yet some reports indicate localtemperature decrease. Microclimatic conditions may be thecause. The results of this study on the possibility that theremay exist a region with a non-uni-trend in mean annualtemperature are supported by other studies. Angel (2004)not only showed that some parts of Illinois did notdemonstrate any signs of warming, but also that

temperature cooling was recorded in some parts of thestate. The writer did not report the reason, but stated,“possible candidates include, but are not limited to,changes in the distribution and amount of sulfates (smallairborne particles that result from the burning of fossilfuels, especially coal), the shift in land use from prairie andforest to agriculture, increases in the amount of cloudcover, and changes in the sea surface temperatures of thePacific and Atlantic”. Chattopadhyay and Hulme (1997)indicated some signs of cooling in India. The writersanalyzed temperature trends of 27 stations for 4 seasons,winter, monsoon, pre-monsoon, and post-monsoon, from1940-1990. Three zones of cooling may be distinguishedfrom their Figure 1 in all seasons. The writers did notexplain this cooling, either on a seasonal scale or an annualscale. Elagib and Mansell (2000) reported that 2 out of 13stations under study in Sudan (1941-1996) had a negativetemperature trend. Greenland and Kittel (2002) showed anegative trend for 4 stations (out of 18) around the USAfrom 1957 to 1990. However, as statistical methods arenot crucial, the results may change as a consequence ofchanges in the methods used.

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Table 3. Numbers and percentages of Iranian synoptic stations withdefinite trends of mean annual temperature in differentclimactic regions.

Trend Entire period Common periodClimate direction

No. % No. %

Desert (7*) positive 4 57 5 72negative 2 29 1 14zero 1 14 1 14

Steppe (11) positive 6 55 7 64negative 4 36 4 36zero 1 9

Temperate positive 7 50 9 64humid (14) negative 7 50 5 36

zero 0 0

Cool positive 0 0 1 50humid (2) negative 1 50 1 50

zero 1 50

Total number of positive 17 50 22 65stations (34) negative 14 41 11 32

zero 3 9 1 3

* number of stations

Table 4. Numbers and percentages of Iranian synoptic stations withdefinite trends of mean annual temperature under differentclimates (α = 5%).

Trend Entire period Common periodClimate direction

No. % No. %

Desert (7*) positive 3 42.9 5 14.3negative 1 14.3 0 0.0zero 3 42.9 2 28.6

Steppe (11) positive 4 36.4 4 36.4negative 2 18.2 3 27.3zero 5 45.4 4 36.4

Temperate positive 5 35.7 6 42.5humid (14) negative 5 35.7 1 7.1

zero 4 28.6 7 50.0

Cool positive 0 0.0 0 0.0humid (2) negative 0 0.0 1 50.0

zero 2 100 1 50.0

Total number of positive 12 35.3 15 44.1stations (34) negative 8 23.5 5 14.7

zero 14 41.2 14 41.2

* number of stations

The detailed results of significant temperature trendfor different Iranian climates are presented in Table 4.Desert climate showed the maximum significant positivetrend (42.9%), while the minimum trend (0%) was incool humid climate. On the other hand, the maximum(35.7%) significant negative trend was observed intemperate humid climate and 0% of the stations had aminimum significant negative trend in cool humid climate.Both humid climates (cool and temperate) possessed themaximum (100%) and minimum (28.6%) of stationswith zero trends. Overall, 35%, 24%, and 41% of thestations showed positive, negative, and zero trends,respectively. Karim-Zadeh and Ghahraman (2002)support this study with the Mashhad temperature trend(1970-2000).

To check whether the trend was influenced by one ormore outliers, we counted the number of events higherand lower than the mean annual temperature. Thedifference between these 2 numbers may be consideredas criteria denoting the outlier situation. This differencewas divided by the record length of its station, as recordlength differed for each station. Our results showed thatthis criterion was only 6.5%; the maximum deviation(9.5%) was due to cool humid climate. Therefore, onemay conclude that the trend was not affected by someoutlier data; however, the results had more fluctuationsduring different decades (data not shown). Low record

length (10 years) may have been a cause for suchfluctuations

Figure 2 shows the spatial significant trends for meanannual temperature of Iran for the entire record length.It seems that there exists no geographical pattern ofdifferent trend directions throughout the country.

2. Common period

We chose a common period of 31 years (1968-1998)for all stations studied. Comparing the results withprevious cases showed that there are some changes intrend direction (Table 2). In this case, 22 stations (65%)had a positive trend, 11 stations (32%) had a negativetrend, while a zero trend was recorded from only 1station (Bw07) (3%). Trend directions for 3 stations(Bw05 in the southeast, and C04 and C08 in thenorthwest) shifted from negative to positive, ascompared with the previous case. One possible reason forthe shift of these 3 stations is that all of them experienceda cool period followed by a warm one (Table 5).Therefore, excluding the warm period, there would be agreater chance for a positive trend direction.

The literature supports such a change in trenddirection. Box (2002), in Greenland, showed that generalperiods of warming occurred from 1885 to 1947 and1984 to 2001, and cooling occurred from 1955 to1984. Greenland and Kittel (2002) showed that the

Time Trend in the Mean Annual Temperature of Iran

444

Positive Trend

Negative Trend

Zero Trend

N

S

30 0 30 60 90 120 150 Kilometers

Figure 2. Spatial pattern of mean annual temperature in Iran for total record length.

mean annual temperature trend for the Jornada station inNew Mexico (subtropical desert climate) and for theLuquillo Exp. Forest station located in Puerto Rico(tropical rainforest climate) from 1957-1990 waspositive and negative, respectively. However, increasingthe record length of these stations to 76 and 62 years,respectively, reversed their trend directions.

Changing the record length to the common period of1968-1998 provided some significant changes in trend inthe present study. A comparison of this common periodand the entire time period is presented in Table 4. Basedon the results, a significant trend was present in 20stations (58.8%), of which 15 (44.1%) and 5 (14.7%)had positive and negative significant trends, respectively.

There were no trends in the other stations. Moreover, thenumber of stations with significant trends diminished.There were also some shifts in trend directions, e.g., 5stations without a trend changed to a significant trend (4positive, and 1 negative).

In this case, humid climates (temperate and cool) hadthe maximum (42.5%) and minimum (0%) percentagesof positive trend, respectively. The results for significantnegative trend were 50% for maximum and 0% forminimum, respectively, in cool humid and desert climates.On the other hand, the 2 humid climates (cool andtemperate) had the maximum (50%) and desert climatehad the minimum (28.6%) of zero trends (Table 4).

The geographical pattern for trend direction in Iranduring the common period is presented in Figure 3. Onemay compare Figure 1 with Figure 2 to verify the effectof record length on trend analysis. Despite different viewsof these Figures, the results shows that trend directionfor only 5 stations (15%) changed. These stations arestaggered throughout the country. Yet, a positive trend iscommon among these stations.

3. Trend for different record lengths

We analyzed trend after increasingly shortening therecord length of the data (variable starting year, and fixedend year, i.e. 1998). There was a fluctuation in trend

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Table 5. Mean temperature for 3 stations corresponding to 2 differenttime series.

Mean temperature for:

Station up to 1968 1968-1998

Bw05 16.04 15.42

C04 11.56 10.61

C08 12.41 10.86

Positive Trend

Negative Trend

Zero Trend

N

S

30 0 30 60 90 120 150 Kilometers

Figure 3. Spatial pattern of mean annual temperature in Iran for the common period of 1968-1998.

direction for consecutively shortened record length (Table6). Not all stations offered unique responses to recordlength shortening. Some stations maintained a uni-direction trend. In such stations (22) there was only apositive significant trend, yet their start year was not thesame; Bandar Abbas (Bw03), in the south, was anexception. Hamedan (C04), in the west, did not show anysignificant trend throughout the analysis, yet it had apositive and negative trend for the entire and commonrecord lengths, respectively (Table 2). The other 11stations fluctuated, of which 10 stations with an initialnegative trend changed to positive; Isfahan (Bs04), in thecenter of the country, was an exception. This leads to amajor conclusion that 31 out of 34 stations experienceda positive temperature trend, either initially or someyears later.

The reason for trend direction fluctuation, though notsignificance, for different record lengths, may have beenannual cyclic variations of temperature. One may comparethe sinusoidal cycle through a 5-year moving run average.

This moving run average makes cycles, which are aboveor below the long-term mean; based on this, the trendline is augmented by cycle and a negative or positive trendwill be the result.

4. Sensitivity to the method used

In addition to Student’s t-test, which is the main trustof this paper, we used 2 other methods; Wald-Wolfowitz(Bobee and Ashkar, 1991) and Mann-Kendall (Salas,1992). There was virtually complete harmony betweenStudent’s t-test and Mann-Kendall; for the entire recordlength, only 3 stations were different. However,Student’s t-test showed a significant negative trend, whilethe Mann-Kendall method showed only a negative trend.On the other hand, during the common period, 2 stationswere different. As neither station showed a non-significant trend under either method, the difference maynot be regarded. Skewness coefficients of all stations forboth overall and common time periods were computed.Based on the skewness test of normality for sample sizesless than 150 (Salas et al., 1980), all the stations, except

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Table 6. First year for significant (α = 5%) mean annual temperature trend slope for synoptic stationsof Iran.

first year with first year withstart of significant trend: start of significant trend:record record

code length positive negative code length positive negative

Desert climate (Bw) Temperate humid (C)

Bw01 1951 1967 ---- C01 1958 1989 1958

Bw02 1957 1957 ---- C02 1951 1951 ----

Bw03 1957 ---- 1957 C03 1956 1991 ----

Bw04 1951 1951 ---- C04 1955 ---- ----

Bw05 1951 1978 1955 C05 1951 1951 ----

Bw06 1953 1953 ---- C06 1951 1981 1951

Bw07 1963 1991 ---- C07 1951 1951 ----

Steppe (Bs) C08 1953 1971 1953

Bs01 1957 1957 ---- C09 1959 1973 1959

Bs02 1956 1981 1956 C10 1965 1972 ----

Bs03 1967 1992 1967 C11 1956 1956 ----

Bs04 1951 1951 1987 C12 1951 1951 ----

Bs05 1967 1990 ---- C13 1951 1951 ----

Bs06 1955 1955 ---- C14 1955 1989 1955

Bs07 1966 1972 ---- Cool humid (D)

Bs08 1954 1961 ---- D01 1960 1971 ----

Bs09 1951 1951 ---- D02 1960 1992 1960

Bs10 1959 1991 1973

Bs11 1951 1955 ----

for Bw05, passed the test. Therefore, for normal series,there is some support in the literature (Yue et al., 2002;Önöz and Bayazit, 2003) that both parametric t-test andnon-parametric Mann-Kendall tests perform identically.

There was not much similarity between Wald-Wolfowitzand either Student’s t-test or Mann-Kendall. There were59% and 68% similar results for the entire record lengthand common period, respectively. There were a fewpublished documents dealing with the comparison ofdifferent trend-detection tests. Rakhecha (Rakhecha, P.R.Secular changes in the extreme rainfalls in India. 6 p.balwois.mpl.ird.fr/balwois/administration/ full_paper/ffp-527.pdf) investigated secular changes in the annual extremerainfall series of 1 to 3 days duration at 316 stations inIndia during the period of 1901 to 1980. He used 5 tests,including Mann-Kendall and Wald-Wolfowitz. The numberof stations showing a significant trend at the 95%confidence level was completely different (42 for Mann-Kendall and only 15 for Wald-Wolfowitz, for 1-day rainfalltime series). Though Rakhecha did not explain the probablereason, this finding is in agreement with our trend analysisof temperature in Iran. More research is needed to delineatethe cause of such differences.

5. Validity of the results with more recent data

For the period of 1999-2002, temperature data arenow available from the Iranian Meteorological Society.Averages of annual temperature for these years weregreater than the average of the 31-year common period forall the stations we studied. While this result confirms ourprevious positive trend, some stations did possess negativetrends. This is not a controversy, however. Slope of the linetrend only determines the deterministic component of thevariable under study. Yet the stochastic component maycause such undulations over the trend line, which is notstudied here. Rate change of the 31-year average to therecent 4-year period was a minimum of 0.4% for Bushehr(Bw04), in desert climate, to a maximum of 18.57% forHamedan (C04), in temperate humid climate, with anoverall average of around 7.16% for all stations.

Discussion and Conclusion

Mean annual temperatures in recent years weregreater than those in previous years for many stations.

This finding is consistent with the literature (e.g., Karim-Zadeh and Ghahraman, 2002, for Mashhad in Iran; Lapin,1995, for the Danube in Slovakia). The majority of thestations we studied revealed increasing temperature;however, since the majority of the synoptic stations inIran are located in airports and suburbs, it is hard torelate these findings to climate change and globalwarming. Land use change in suburbs, from agricultureand pasture to residential areas, is another cause forgradual increase in temperature. Yet, it is not possible toseparate these 2 factors, while the synoptic stations inIran are used extensively for irrigation water requirementcomputations. Land use change occurs slowly and,therefore, cannot lead to a jump in temperature timeseries. Meanwhile, constructing any instrument in aregion may not necessarily lead to a jump in temperature(e.g., hydroelectric construction on the Danube River inSlovakia, as reported by Lapin, 1995).

Two of the 3 methods used (Student’s t-test and Mann-Kendall) verified each other. Based on the results of long-run mean annual temperature, it can be hypothesized thatthe majority of the region will experience an increase intemperature in the future. Although the mean annualtemperature trend lines in some stations were notsignificant, their positive signs may be a clue totemperature increase. This is especially true for the finalyears of the record. The results of the Wald-Wolfowitzmethod are relatively inconsistent with those of the other 2methods. However, we could not find any preference ofone over the other in the literature. As 2 of the 3 methodscompletely verified each other, it is possible to rely on theresults. Excluding the Wald-Wolfowitz method, for non-skewed series, Student’s t-test may be preferred over theMann-Kendal method. The latter method requires acontinuous data set, while the former performs equally wellfor series with missing data. Such series are common inundeveloped countries.

This study clearly confirmed that the results may besensitive to the initial and final years of the recorded data;therefore, in any comparison, one must pay attention tothis point.

The announcement of temperature increase in thefuture is merely important as a pre-information. Othermeteorological parameters, e.g., rainfall, must also beanalyzed for a comprehensive understanding of the global

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situation. Furthermore, as far as agriculture is concerned,study of mean seasonal temperature may bring about moreinsights for future planning. The literature clearly supportsthat trend slopes differ for different seasons; however,

temperature increase may act negatively and uni-directionally in the sense of increasing requirements for soilmoisture, decreasing water content in the upper layers ofsoil, decreasing ground water levels, and discharge in rivers.

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