potential impacts of climate change on agroclimatic indicators in iran

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This article was downloaded by: [York University Libraries] On: 13 August 2014, At: 06:29 Publisher: Taylor & Francis Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK Arid Land Research and Management Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/uasr20 Potential Impacts of Climate Change on Agroclimatic Indicators in Iran A. Koocheki a , M. Nasiri a , G. A. Kamali a & H. Shahandeh b a Department of Agronomy , Ferdouwsi University of Mashhad , Mashhad, Iran b Department of Soil and Crop Sciences , Texas A&M University , College Station, Texas, USA Published online: 02 Sep 2006. To cite this article: A. Koocheki , M. Nasiri , G. A. Kamali & H. Shahandeh (2006) Potential Impacts of Climate Change on Agroclimatic Indicators in Iran, Arid Land Research and Management, 20:3, 245-259, DOI: 10.1080/15324980600705768 To link to this article: http://dx.doi.org/10.1080/15324980600705768 PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms- and-conditions

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Page 1: Potential Impacts of Climate Change on Agroclimatic Indicators in Iran

This article was downloaded by: [York University Libraries]On: 13 August 2014, At: 06:29Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Arid Land Research and ManagementPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/uasr20

Potential Impacts of Climate Change onAgroclimatic Indicators in IranA. Koocheki a , M. Nasiri a , G. A. Kamali a & H. Shahandeh ba Department of Agronomy , Ferdouwsi University of Mashhad ,Mashhad, Iranb Department of Soil and Crop Sciences , Texas A&M University ,College Station, Texas, USAPublished online: 02 Sep 2006.

To cite this article: A. Koocheki , M. Nasiri , G. A. Kamali & H. Shahandeh (2006) Potential Impactsof Climate Change on Agroclimatic Indicators in Iran, Arid Land Research and Management, 20:3,245-259, DOI: 10.1080/15324980600705768

To link to this article: http://dx.doi.org/10.1080/15324980600705768

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Page 2: Potential Impacts of Climate Change on Agroclimatic Indicators in Iran

Potential Impacts of Climate Change onAgroclimatic Indicators in Iran

A. KoochekiM. NasiriG. A. Kamali

Department of Agronomy, Ferdouwsi University of Mashhad,Mashhad, Iran

H. Shahandeh

Department of Soil and Crop Sciences, Texas A&M University,College Station, Texas, USA

The climate model, United Kingdom Meteorological Organization model (UKMO)and multivariate statistics, principal component analysis (PCA) and hierarchicalcluster analysis (HCA) were employed to determine the climate diversity and agro-climatic indicators in future climate change. Monthly weather data from 1968 to2000 at 36 weather stations in Iran were used to generate climate change scenariosfor years 2025 and 2050. The UKMO model predicted a temperature rise of 2.7�Cand a rainfall decrease of 12% by 2050. By 2050, length of the growth period is pre-dicted to increase by 16 days, length of the dry period will increase by 22 daysbecause of a delay in the first freezing day and an advance in the last freezingday, and the subsequent increase in temperature and decrease in rainfall. Clusteranalysis of weather station data shows that 10 currently defined agroenvironmentzones will be reduced to 8 by 2025 and to 7 by 2050. Climate change will decreasegeographic differences in temperature and precipitation in Iran, and precipitationwill be increasingly a determining indicator in the future.

Keywords growth period, hierarchical cluster analysis (HCA), principal compo-nent analysis (PCA), rainfall, temperature, United Kingdom MeteorologicalOrganization (UKMO)

Many scientists hypothesize that climate change will have both direct (elevated CO2

levels, increased temperature, dry conditions) and indirect effects (changes in compe-tition between C4 and C3 plants, reduction in transpiration, changes in mineraliza-tion dynamics, longer growing seasons, changes in soil erosion, changes in cropquality, stimulation of nitrogen fixation, poleward movement of weeds and pests,and change in rangeland species) on crop production systems (Field, 2001; Fuhre,

Received 11 August 2005; accepted 19 December 2005.Address correspondence to H. Shahandeh, Department of Soil and Crop Sciences, Texas

A&M University, College Station, TX 77843, USA. E-mail: [email protected]

Arid Land Research and Management, 20:245–259, 2006Copyright # Taylor & Francis Group, LLCISSN: 1532-4982 print=1532-4990 onlineDOI: 10.1080/15324980600705768

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2003; IPCC, 2001a,b; Smith & Almaraz, 2004). The Intergovermental Panel onClimate Change (IPCC) has projected a global temperature increase of 1.4–5.8�Cby 2100 (IPCC, 2001a). The increase will be smallest at the equator and greatestat the poles depending on latitude; for example, increases of 4 to 6�C in Canadaare predicted (Canadian Institute for Climate Studies, 2001).

Increased temperatures will lead to increased evaporation. Lockwood, (1999)has shown that evaporation will increase by 2 to 3% for each degree Celsius increasein mean annual temperature and will result in an increased water deficit. Underwater deficit, higher temperatures will usually shorten the growth cycle of a cropand, together with reduced water supply are likely to reduce crop production (Battset al., 1997; Turner, 2001). However, Lobell and Asner (2003) stated that as long asthe increased temperatures are not extreme, crop yield can increase.

Modeling indicates that increasing temperature up to 2.5�C may cause variableeffects on the agricultural sector, with improvements in many cases, particularly inmore developed nations; however, temperature increases above 2.5�C are generallyprojected to have overall negative effects (IPCC, 2001b). Increases in productivityare likely in some temperate areas, while tropical countries will suffer decreases of10–20% in crop production (IPCC, 2001b). For example, Attri and Rathore(2003) found that increases in temperature beyond 3�C in India would negate thebeneficial impacts of enhanced CO2, and wheat yield would decrease by 20%.

The vulnerability of crop production to climate change has been widely studiedusing crop simulation models, while climate change scenarios are generated fromGlobal Circulation Models (GCMs) (Asseng et al., 2004; Attri & Rathore, 2003;Belanger et al., 2002; Hoogenboom, 2000; Rai et al., 2004; Smith & Hulme, 1998;Yates & Strzepek, 1998). For example, the Canadian Community Climate Model(CCCM) projects less precipitation in southern regions. CCCM predicts large nega-tive impacts of about 4 to 30% on crop production in 2090 (Tubiello et al., 1999).However, the precise magnitude of climate change on regional scales is not veryaccurate (Asseng et al., 2004; Holden et al., 2003; Luo et al., 2003). To reduce gen-eralizations from global assessment of climate change, more regional assessment ofthe impacts of climate on agroclimatic parameters for crop production is needed.

Iran is located in the temperate zone (26� to 40�N latitude and 44� to 62� E long-itude) where climate change is predicted to result in migration of existing crop pro-duction systems to the north (Koocheki et al., 2003; Rosenzweig & Parry, 1994). Iranhas a variable climate, with extended period of subfreezing temperatures, tempera-tures exceeding 40�C, and annual precipitation of<100 mm to>1500 mm. In general,Iran has an arid to semiarid temperate climate with a subtropical climate along theCaspian Sea. Rosenzweig and Parry (1994) predicted that the plant growth period inIran would decrease significantly and crop production would decrease by 5 to 40%under drier conditions in 2080. The climate of Iran is Mediterranean with long drysummers and winter rainfall. Asseng et al. (2004) found that in the Mediterranean-type environment the impact of elevated CO2 and temperature on crop yield variedwith seasonal rainfall amount and distribution. For example, Kosmas and Danalatos(1994) projected wheat yields in Greece also with a Mediterranean-type climate todecrease by 90, 72, and 53% with rainfall reductions of 65, 50, and 30% in 2050,respectively.

Global and regional climate change studies on agroclimatic indicators aremostly temperature based whereas in arid, semiarid environments precipitation isthe most limiting parameter for crop production. The purpose of this study was to

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project in years 2025 and 2050 the impact of climate change on agroclimatic indica-tors with concurrent changes of temperature and precipitation given the diverseclimate of Iran.

Materials and Methods

Data Collection

Long-term (1968–2000) meteorological data were obtained from the MeteorologicalOrganization and National Climate Center in Iran (Koocheki et al., 2003). Theperiod 1961–1990 is designated by the IPCC (2001a) as the baseline against whichclimate change is to be measured. The weather data for the base line period(1961–1990) for all locations were not available and data for 1968 to 2000 were used.Data included daily minimum and maximum mean monthly temperature, monthlyrain fall, and monthly and annual potential and actual evapotranspiration. Datawere collected from 36 meteorological stations around the country (Figure 1).

Climate Change Scenarios

Climate change scenarios were based on a GCM approach and included the UnitedKingdom Meteorological Office (UKMO) model. In this experiment CO2 concentra-tions of 425 and 500 ppm and temperature increases of 2.0 to 3.8�C for spring and

Figure 1. Location of 36 weather stations throughout Iran in 2000.

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summer of 2025 and 2.4 to 4.7�C for spring and summer of 2050 were used in themodel (IPCC, 2001b). The sensitivity of the climate system to increases in air tem-perature due to greenhouse gas concentrations has been defined as medium(2.5�C) for 2025 and high (4.5�C) for 2050 according to the IPCC (2001a).

Data Analysis

Multivariate statistics, such as hierarchical cluster analysis (HCA) and principalcomponent analysis (PCA), that simultaneously take into account correlationsamong several variables were used in this study. Hierechical cluster analysis(HCA) was used to verify similarity among the geographic locations of weatherstations for present or future climate change in the country. A multivariate approachof PCA was used to distinguish the agroenvironment zones in the country and todetermine the most important agroclimatic indicators to characterize them.

For multivariate analysis of agroclimatic indicators, 14 variables were usedincluding minimum temperature, maximum temperature, mean of maximum andminimum temperatures, rainfall, total growing degree days, total freeze free days,and mean dates of the first freezing day in the fall and the last freezing day in thespring. Since agroclimatic indices during the months vary and means are not accu-rate (Antle, 1996) for evaluation of the effect of climatic change on these indices,rainfall, temperature and degree day parameters were broken down and new vari-ables for all seasons were determined. Principal component analysis (PCA) andHCA were carried out on 14 variables from 36 weather stations and 14 variablesusing SAS (SAS, 1985).

Ward hierarchical techniques were used for HCA (Johnson, 1998). HCAobserves the similarity in data among stations, where the station data are aggregatedin a stepwise fashion according to similarity of features. In fact, a cluster describes agroup of stations that are more similar to each other than to stations outside thegroup. The results are presented in the form of dendrograms, allowing visualizationof the distances between weather station locations. The K-nearest neighbor (KNN)technique was applied (SAS, 1985).

Results and Discussion

Climate Change and Length of the Growing Season

Changing length of the growing season is one of the main indicators of climatechange (Feng & Hu, 2004). The length of the growing season is set by the first fallfrost and the last spring frost (Table 1). First frost for different locations aroundthe country in the 1968 to 2000 period occurred in the period from last week ofOctober to the last week of December (Table 1). Occurrence of the last freeze dateranged from the last week of March to the last week in April. However, the UKMOmodel projected that the mean first fall frost for the whole country could be delayedby six days in 2025 and 11 days in 2050 and the last frost date could be advanced byseven days in 2025 and 11 days in 2050 (Table 1). In general, delays in first frost datesincrease from north to south and west to east (Table 1, Figure 1). A similar trend ofdecreasing annual number of frost days at a rate of three days per decade (1951–2000) has been observed in past climate (Feng & Hu, 2004) or in future climatechange predictions (Frohlich & Lean, 1998; Lin et al., 1997; Menzel & Fabian,

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1999; Reyenga et al., 1999), especially at higher latitude (Dinar et al., 1998; Myeniet al., 1997).

The mean length of the growing season in 1968 to 2000 was 231 days (Table 1)and climate change scenarios by the UKMO model predicted the potential growingseason to increase by about 15 days by 2025 and by 29 days in 2050. The increase inpotential growth days like delays in first frost date was greater at higher latitude. Forexample, in Tabriz in northern Iran (Figure 1), the potential growth period was pro-jected to increase by 32 days, while in Ahvaz in southern Iran the growth period wasprojected to increase by 14 days (Table 1).

Table 1. First frost date in the fall for 26 meteorological stations in Iran during1968, to 2000 and probability (at 50%) of frost days delayed in 2025 and 2050 andprojected potential growth days according to UKMO model in 2000, 2025, and 2050

First frost Last frostFrost days delayed Potential growth daysy

Station 2000 2001 2025 2050 1968–2000 2025 2050

mm=dd dayAhvaz 12=24 02=04 9 14 326z 331 342Arak 11=15 04=03 9 13 219 240 254Bandar Abas 12=16 02=14 9 14 311 327 343Birjand 10=26 03=28 8 14 215 234 248Boshehr 12=25 02=07 8 15 321 331 336Ghazvin 11=09 04=04 5 9 215 229 245Gorgan 12=12 03=02 6 9 286 299 311Hamadan 10=15 04=19 5 10 181 195 214Isfahan 11=19 03=18 7 13 266 283 295Kerman 10=31 03=29 7 11 217 240 256Kermanshah 11=04 04=11 5 9 208 224 243Khoramabad 11=26 03=15 5 9 258 274 289Khoy 11=01 04=11 6 10 215 232 240Mashhad 10=26 04=01 8 13 205 221 238Oromeyeh 11=11 04=11 6 9 217 234 246Rasht 12=08 03=13 6 10 270 282 292Sabzevar 11=19 03=26 7 13 239 255 268Sanandaj 11=02 04=13 7 13 204 225 240Semnan 12=04 03=14 8 12 264 279 295Shar e Kord 10=21 04=21 7 10 183 203 225Shiraz 11=27 03=09 6 13 265 277 293Tabriz 11=12 04=03 6 11 221 235 253Tehran 11=30 03=17 5 10 258 270 285Yazd 11=23 03=05 7 12 259 275 288Zahedan 11=11 03=10 8 15 240 257 274Zanjan 10=22 03=22 5 9 184 200 217

Mean – – 6 11 231 246 260

yDays above 0�C.zMean values for period 1968–2000.

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In general, the data in Table 1 suggest that the potential growing season in allweather station locations is favorable for plant growth regardless of present or futureclimate change in Iran.

Climate Change and Length of Dry Period

The UKMO model predicted that average temperature in Iran by 2025 and 2050 willrise by 1.7 to 2.6�C, respectively, compared to 1968 to 2000 (Table 2). The tempera-ture increase for the potential growing season (spring and summer) was predicted to

Table 2. Projected change in temperature, rainfall and length of the dry period for26 weather station locations in Iran by UKMO model in 2025 and 2050 compared to1968–2000

Change in temperature Change in rainfall Length of dry periody

Station 2025 2050 2025 2050 1968–2000 2025 2050

�C % dayAhvaz 2.4 3.1 �5.6 �8.3 242z 253 264Arak 2.1 2.6 �6.3 �10.9 176 184 192Bandar Abas 2.3 2.9 �7.5 �11.1 289 301 313Birjand 1.9 2.7 �8.9 �13.3 216 227 236Boshehr 2.4 3.0 �7.5 �11.1 265 275 284Ghazvin 1.5 2.3 �7.2 �12.0 167 174 184Gorgan 1.4 2.2 �7.5 �10.9 141 148 157Hamadan 1.5 2.2 �7.4 �12.0 165 174 184Isfahan 1.3 2.4 �7.1 �12.6 191 203 215Kerman 1.6 2.5 �8.4 �13.6 224 235 248Kermanshah 1.4 2.2 �8.2 �12.0 177 185 194Khoramabad 1.8 2.4 �6.4 �11.0 203 213 223Khoy 1.5 2.1 �5.0 �10.1 176 188 197Mashhad 1.8 2.4 �8.4 �14.6 197 208 213Oromeyeh 1.5 2.5 �4.1 �8.6 170 180 189Rasht 1.4 2.1 �8.8 �11.7 181 191 198Sabzevar 1.7 2.6 �8.8 �14.1 204 216 226Sanandaj 1.5 2.2 �7.0 �11.3 164 173 182Semnan 1.7 2.6 �8.4 �12.5 182 194 207Shar e Kord 1.4 2.0 �7.0 �11.4 207 215 223Shiraz 1.6 2.6 �7.6 �12.8 229 242 252Tabriz 1.5 2.4 �7.7 �12.0 156 163 173Tehran 1.8 2.5 �8.9 �13.1 193 202 212Yazd 1.6 2.4 �8.2 �13.2 220 233 247Zahedan 1.9 2.7 �8.8 �14.0 254 265 279Zanjan 1.5 2.2 �7.7 �11.8 169 181 190

Mean 1.7 2.6 �7.5 �12.1 206 212 222

y(PET-AET). Based on integration of temperature above zero and moisture availability(Fischer et al., 2002; Pauwa et al., 2000).zMean values for period 1968–2000.

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be 2.0 to 3.8�C in 2025 and 2.4 to 4.7�C in 2050. This increase is in the range of tem-perature rise, 1.5 to 4.5�C predicted by Vaughn et al. (2001) for the next 100 years.

The UKMO model also predicted a drier future climate where rainfall willdecrease by 7.5 to 12.1% in 2025 and 2050, respectively (Table 2). The UKMO modelresults agree with reported changes in rainfall due to climate change in arid-semiaridregions (Saunders, 1999; Kerr, 2003). The length of the dry period is projected toincrease by six days in 2025 and 16 days in 2050 compared to 1968–2000 (Table 2).In arid and semiarid environments of Iran, water is the most limiting factor for cropproduction and crop growth is proportionally associated with the amount of watertranspired. Data in Figure 2 show the potential evapotranspiration (PET), andactual evapotranspiration (AET) and growth reduction factor in Kermanshah andMashhad in western and eastern Iran in year 2050. The growth rate reduction rate

Figure 2. Projected potential evapotranspiration (PET), actual evapotranspiration (AET), andgrowth reduction factor for crops growing near weather station locations in western(a, Kermanshah) and eastern (b, Mashhad) parts of Iran in 2050.

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is generally evaluated by the ratio of actual evapotranspiration to potential evapo-transpiration (AET=PET) (Figure 2) (Monteith, 1981). This ratio is equal to unitywhen there is no water deficiency during the growth period. However, at the heightof the growth period for most crops in Iran the growth rate reduction factor isnormally less than 1 (Figure 2) indicating a long dry period.

The average length of the dry season for the whole country under present climateconditions (1968 to 2000) was 204 days (Table 2). Length of dry period was calcu-lated on the basis of integration of temperatures above zero and moisture availability(Figure 2) (Fischer et al., 2002; Pauwa et al., 2000). Average length of the dry periodsunder future climate change are projected to increase by six days by 2025 and by16 days by 2050. In some locations, like Khoy (Figure 1), this increase may be morethan three weeks by 2050 (Table 2). Longer dry periods associated with climatechange have been reported for many parts of the world (Holden et al., 2003; Linet al., 1997; NDMC, 1998; Nicholls, 2000; Reilly, 1995; Tubiello et al., 1999; Yates& Strzepek, 1998). A longer dry period usually translates to a shorter growth period(Monteith, 1981). This means that with a sufficient water supply, air temperature risemay provide a longer potential growing season (Table 1), but under deficient watersupply (Figure 2) high air temperature may translate into shorter growth periods andlonger dry periods (Table 2) for future climate change in Iran.

Multivariate Statistical Analysis of Agroclimatic Indicators

PCA and HCA Methods

Principal component analysis (PCA) was performed on climatic parameters to iden-tify those variables most significant in distinguishing different weather stationsaround the country at present or projected future climate change (Table 3). Principal

Table 3. Total variance of climatic data explained by different principal componentsin the years 1968–2000, 2025, and 2050

Principal component Eigen value Variance (%) Cumulative variance (%)

Years 1968–2000PC1y 39.99 72.71 72.71PC2 8.93 16.23 88.94PC3 2.03 3.68 92.62PC4 1.25 2.28 94.98

Year 2025PC1y 33.04 61.11 61.11PC2 14.04 30.52 91.63PC3 2.67 4.01 95.64

Year 2050PC1y 29.79 55.62 55.62PC2 17.91 36.52 92.14PC3 2.02 3.72 95.86

yPC1¼ temperature, PC2¼ rainfall and minimum winter temperature, PC3¼maximumwinter temperature, PC4¼winter precipitation and maximum temperature in spring summerand fall.

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component analysis is one of the most interesting and applicable statistical techni-ques used in meteorology and climatology (Horel, 1984). The 14 climatic variableswere entered in an initial PCA analysis (temperature, rainfall and minimum wintertemperature).

All of the 14 variables showed high loading on different principal componentsunder present climate conditions or year 2000. The PC1 was positively loaded withtemperature parameter information, PC2 accounted for rainfall information andminimum temperature in winter. PC3 accounted for maximum temperature in winter.PC4 accounted for precipitation in winter and maximum temperature in spring,summer, and fall. The first PC (temperature) accounted for 72.71% of variability,and the fourth accounted for 2.28% of the variability. The first two principal com-ponents were found to account for 88.94% of the variance in the data from the 36weather stations in year 2000 (Table 3). The first four principal components havingeigenvalues> 1 accounted for almost 95% of variability (Table 3).

Principal component output consists of eigenvalues score images (plots) andloading vectors. Only eigenvalues are reported here (Table 3). Eigenvalues expressparts of single components in total variation and provide the PC composition relatedto weather stations. Loading vectors provide this same composition related to vari-ables. The first component indicates the mean value of the variable while the second,the third and the rest of the components represent changing elements of successivelydecreasing magnitude.

Data in Table 3 also show the total variance explained by different principalcomponents under future climate change. Similarly, the 14 climatic variables wereentered in PCA analysis and the first two principal components, temperature andrainfall, were found to account for 91.62% of the variance among weather stationdata in 2025 and 92.14% of variance in 2050 (Table 3). Three main componentsPC1 to PC3 determined more than 95% of total variation under future climatechange.

The first two principal components for present climate conditions and future cli-mate change are temperature and rainfall (Table 3). However, contribution of tem-perature and rainfall components, as measured by eigenvalues, was different underfuture climate change compared to present climate. Eigenvalues for the temperaturecomponent under future climate change decreased, whereas the contribution ofeigenvalues of the rainfall component increased. This suggests that the rainfall com-ponent probably will become a more important agroclimatic indicator with climatechange in Iran.

Temperature and rainfall were shown to be the two most important parametersaffecting length of plant growth development or other agroclimatic indicators infuture climate change (Tables 1, 2 and 3; Figure 2). However, to assist in planningfor future climate change, the differentiation and contribution of temperature andrainfall to agro-climatic indicators for each location is important.

The projected decrease in precipitation with increased temperature and theireffects on length of the dry period for the 36 weather station locations in 2050 areshown in Figure 3. The surface response curve (Figure 3a) shows the increase inthe length of the dry period is influenced by temperature and rainfall regardless ofweather station location. Figure 3b shows the contour plot map of length of thedry period (isolines) as influenced by temperature and precipitation across thecountry for weather stations in 2050. For example, the length of the dry periodmay increase by 17 days with a 5�C increase in temperature and a 10% decrease in

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precipitation for one location, and a 1�C increase in temperature and a 25% decreasein precipitation for another location (Figure 3b). For most of the weather stationlocations, the increased length of the dry period in future climate change seemedto be the result of the combined effect of temperature increase and precipitation

Figure 3. Projected increase in the length of the dry period influenced by temperature and pre-cipitation for 36 weather station locations around Iran shown as surface response curve (a) orcontour map (b, isolines of increase in length of dry period) in 2050.

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Figure 4. Dendrograms showing the similarity among weather station locations obtained byHCA for the years 1968–2000, 2025, and 2050.

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decrease. However, there are stations where either temperature increase or precipita-tion decrease can be the most important factor for increasing the length of the dryperiod in future climate change.

Hierarchical cluster analysis (HCA) was developed to assess the variabilityamong weather stations in future climate change for the years 2025 and 2050 andcompared to present climate conditions in year 2000 (Figure 4 and Table 4). HCA

Table 4. Cluster (zone) membership profile of weather stations in years 1968–2000,and projected stations in years 2025 and 2050

Zone Stations (code)Total

number

Years 1968–2000

1 Abadan(1)y, Ahvaz(5), Zabol(19), Zahedan(20) 42 Ramsar(17), Rasht(18), Anzali(8), Babolsar(6) 43 Yazd(36), Shahrood(26), Bam(7), Kerman(31),

Birjand(11), Semnan(24)6

4 Oromeyeh(3), Saghez(23), Sanandaj(25),Khoy(16), Tabriz(12)

5

5 Bosher(10), Bandar Abas(9) 26 Isfehan(4), Tehran(14), Kashan(30), Sabzevar(22) 47 Mashhad(34), Shiraz(28), Torbat Heydaryeh(13) 48 Gorgan(33) 19 Arak(2), Khoramabad(15), Ghazvin(29), Zanjan(21) 4

10 Sharekord(27), Kermanshah(32), Hamedan(35) 3Year 2025

1 Abadan, Ahvaz, Zabol, Zahedan, Bosher,Bandar Abas

6

2 Ramsar, Rasht, Anzali, Babolsar, Gorgan 53 Yazd, Shahrood, Bam, Kerman, Birjand, Semnan,

Tehran, Sabzevar8

4 Oromeyeh, Saghez, Sanandaj, Khoy, Tabriz 55 Isfehan, Kashan 26 Mashhad, Shiraz, Torbat Heydaryeh 37 Arak, Khoramabad, Ghazvin, Zanjan 48 Sharekord, Kermanshah, Hamedan 3Year 2050

1 Abadan, Ahvaz, Zabol, Zahedan, Bosher,Bandar Abas

6

2 Ramsar, Rasht, Anzali, Babolsar, Gorgan 53 Yazd, Shahrood, Bam, Kerman, Birjand, Semnan,

Tehran, Sabzevar, Isfehan8

4 Oromeyeh, Saghez, Sanandaj, Khoy, Tabriz 55 Mashhad, Shiraz, Torbat Heydaryeh, Kashan 46 Arak, Khoramabad, Ghazvin, Zanjan 47 Sharekord, Kermanshah, Hamedan 3

yWeather station locations shown in Figure 1.

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has been successfully used in many studies (Giorgi & Mearns, 1991; Briggs & Lemin,1992). The association among the weather stations can be presented in the form of adendogram using rescaled distances (Figure 4) (Romesburg, 1984). Figure 4 showsthe position of the clusters based on the first component (PC1), which is temperaturecomponent related information, and the second main component (PC2) which israinfall. Both components describe about 90% of total variation. The hierarchicalcluster analysis showed the formation of 10 clusters with 1 to 6 stations in each zonein the year 2000 (Figures 1 and 4 and Table 4).

The association among different weather stations in dendograms by 2025 and2050 showed the formation of eight clusters (zones) with each having two to eightstations, and seven clusters with each having three to eight stations, respectively.The clustering pattern of stations revealed that, with time, the weather station databecame less diverse and stations from different regions could be grouped into thesame cluster. In other words, in future climate change geographical diversity maynot necessarily represent weather diversity. For example, the weather station inGorgan which is in zone 8 by itself in 2000 could be clustered in zone 2 in futureclimate change. This indicated that the distribution of station variability was notdetermined by temperature (Table 3) and precipitation can become a determiningfactor in divergence of weather stations in the future (Figure 3 and Table 4).

Conclusions

This study showed that the effects of climate change on agricultural indicators aremostly temperature based. Frost-free days, the potential growing season and thelength of the dry period will increase. Statistical analysis of data from weather sta-tions from 1968 to 2000 showed that temperature and rainfall are the two mostimportant parameters in distinguishing different weather stations for either presentor future climate change. Under projected climate change, the climate of Iran willbe less diverse due to increased temperature and reduced rainfall. Variability in cropproduction in Iran due to future climate change will be governed by temperature andprecipitation, but precipitation may become more important with time. Under theclimate change scenarios used in this study, crop production areas in Iran will likelyexperience substantial modification in agrometeorological indicators that will affecttheir productivity.

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