Quantitative Trend, Sensitivity and Contribution …profdoc.um.ac.ir/articles/a/1062366.pdf · Quantitative Trend, Sensitivity and Contribution Analyses of Reference Evapotranspiration

Quantitative Trend, Sensitivity and ContributionAnalyses of Reference Evapotranspiration in some AridEnvironments under Climate Change

Milad Nouri1 & Mehdi Homaee2 &

Mohammad Bannayan3

Received: 7 September 2016 /Accepted: 19 March 2017 /Published online: 4 April 2017# Springer Science+Business Media Dordrecht 2017

Abstract The temporal trend of reference crop evapotranspiration (ET0) and contribution ofassociated meteorological factors to the ET0 trend were assessed for 17 arid areas. Sensitivity ofET0 to changes in key meteorological variables was also analyzed. To study temporal trend ofET0, Mann-Kendall trend test was employed. Quantitative contribution and sensitivity analyseswere carried out, respectively, using a dimensionless relative sensitivity coefficient and detrendingmethod. Results indicated that ET0 has an increasing trend in 70.6, 64.7, 70.6, 76.5 and 70.0%, ofsites respectively, in winter, spring, summer, autumn and entire year. This positive trend wassignificant (p ≤ 0.05) in 47.0, 35.3, 35.3, 29.4 and 35.3% of sites, respectively, for the sameseasons. There was a significant change-point in winter, spring, summer, autumn andannual ET0 series at 64.7, 52.9, 64.7, 64.7 and 82.3% of stations, respectively. In 35.3and 35.3% of sites, solar radiation and wind speed were the most sensitive climaticfactors on ET0, respectively. ET0 exhibited the highest sensitivity to the relativehumidity changes in coastal sites. Changes of wind speed contributed much morethan other factors to the annual ET0 trend in 58.8% of investigated sites. The negative trend inwind speed nearly nullified the positive effects of increased air temperature on ET0 over 1966–2012 in 23.5% of stations. Changes in ET0 were attributed to wind speed changes in mostlocations. Given the upward trend of ET0 in themajority of locations, proper water managementis required to avoid negative impacts of climate change in arid regions.

Water Resour Manage (2017) 31:2207–2224DOI 10.1007/s11269-017-1638-1

* Mehdi [email protected]

1 Department of Soil Science, Faculty of Agriculture, Tarbiat Modares University, P.O. Box 14115-336,Tehran, Iran

2 Department of Irrigation and Drainage, Faculty of Agriculture, Tarbiat Modares University,P.O. Box 14115-336, Tehran, Iran

3 Department of Agronomy, Faculty of Agriculture, Ferdowsi University of Mashhad,P.O. Box 91775-1163, Mashhad, Iran

http://crossmark.crossref.org/dialog/?doi=10.1007/s11269-017-1638-1&domain=pdf

Keywords Detrending . Global warming . Sensitivity coefficient .Watermanagement

1 Introduction

Evapotranspiration as a key component of the water and energy cycles returns approximately 60%of land precipitation back to the atmosphere and uses up more than 50% of the total absorbed solarenergy by land surface (Homaee et al. 2002; Kaniewski et al. 2012; Seneviratne et al. 2010). Due toconsiderable complexities ofmeasuringwater flux through crops, crop evapotranspiration ismostlyestimated based on reference crop evapotranspiration (Kite and Droogers 2000; Xie and Zhu2013). Reference crop evapotranspiration, denoted as ET0, is of great importance in estimating cropwater requirement and scheduling, planning andmanaging irrigation systems (Godfray et al. 2010;Homaee et al. 2002; Irmak et al. 2006; Maestro et al. 2014). In addition, ET0 plays a key role indetermining regional actual evapotranspiration and preparing input data to hydrological water-balance models (Gong et al. 2006; Homaee and Schmidhalter 2008). ET0 is defined by Allen et al.(1998) as the evapotranspiration rate of a hypothetical reference crop which has an assumed heightof 12 cm, a fixed surface resistance of 70 s m−1 and an albedo of 0.23. Among all methodsproposed for the ET0 estimation, the Penman-Monteith FAO 56 (PMF-56) equation has beenworldwide used to calculate ET0 in different climatic conditions (Allen et al. 1998).

Iran is located in Middle East and its climate is predominantly semi-arid and arid (the water-limited ecosystems) (Nouri et al. 2016). Agricultural activities and meeting food security are,therefore, largely depended on implementing irrigation. Agriculture sector consumes by far thelargest portion of available water resources in Iran (Al-Faraj et al. 2016; Alizadeh andKeshavarz 2005). As a result, considering major role of ET0 in irrigation scheduling andplanning, assessing temporal trend of ET0 can be very helpful to decision makers in Iran.

Rising greenhouse gases chiefly as a consequence of landuse change, agricultural activities andfossil fuel overuse has caused climate change and global warming over the globe (IPCC 2013).Results of studies conducted to analyze the long-term trend of some climatic variables such asprecipitation (Modarres and Sarhadi 2009; Some'e et al. 2012) and temperature (Tabari and Talaee2011) over Iran revealed that the climate of Iran is changing. Quantitatively assessing the contribu-tion of each key meteorological variable to ET0 changes is, hence, highly needed in Iran’s changingclimate. Using multiple stepwise regression (a qualitative method), Dinpashoh et al. (2011) foundrelative humidity inwintertime andwind speed in other seasons as themost important climatic factorcontributing to ET0 changes during 1965–2005 in 16 stations located over Iran. Recently, detrendingmethod has been employed to quantitatively investigate the contribution of the controlling variablesto the ET0 trend (Huo et al. 2013; Liu et al. 2010; Xie et al. 2015; Xu et al. 2006). Using detrendingmethod, Xu et al. (2006) concluded that the decreasing trends in the net total radiation and windspeed are the main causes of the negative trend detected in ET0 for the Changjiang catchment,China. Liu et al. (2010) also reported that much of the ET0 increase and decrease is, respectively, dueto temperature rise and declined wind speed in the Yellow River Basin, china by means ofdetrending. Huo et al. (2013) concluded that the trend in wind speed made a much largercontribution to the ET0 changes in arid northwestern regions of China by applying detrending.

Sensitivity analysis is necessary to understand relative role of each key meteorologicalvariable in modeling ET0 (Saxton 1975). Analyzing the sensitivity of an ET0 equation is of akey importance in determining the required accuracy for the measurement of variables used forET0 estimation (Irmak et al. 2006). Moreover, quantifying the sensitivity of ET0 can determineinappropriate sampling of which factor would cause a greater error in estimating ET0 (Hupet

2208 M. Nouri et al.

and Vanclooster 2001). The results of studies addressed the ET0 sensitivity showed that itvaries with location and climate (Debnath et al. 2015; Gao et al. 2016; Huo et al. 2013; Songand Su 2015). The ET0 sensitivity analysis seems, however, not to be well-addressed in Iran(Mosaedi et al. 2016; Sharifi and Dinpashoh 2014). Using sensitivity curves, Sharifi andDinpashoh (2014) concluded that average temperature and actual vapor pressure are the mostand least sensitive variables on the annual ET0, respectively, in eight sites located in Iran.Based on sensitivity curves, Mosaedi et al. (2016) found that relative humidity and maximumtemperature are the most influential climatic factors on ET0 in 5 stations in Iran over 1963–2007.

Since there is merely one study (i.e. Dinpashoh et al. 2011) qualitatively addressed the ET0contribution, further assessments to quantitatively investigate the contribution of climatic changes tothe ET0 dynamics over arid areas of Iran are required. In addition, there is no study to quantitativelyanalyze the ET0 sensitivity using sensitivity coefficient approach and the ET0 sensitivity analysesconducted in Iran (i.e.Mosaedi et al. 2016; Sharifi andDinpashoh 2014) have used a same graphicalmethod. Furthermore, the assessments investigated the ET0 contribution and sensitivity in Iran haveconsidered relatively few numbers of sites for different climates (from hyper-arid to humid).Undertaking a comprehensive quantitative analysis of the ET0 sensitivity and contribution usingmore sites and with a particular focus on arid regions seems, thus, to be very helpful to decisionmakers for controlling irrigationwater demand in Iran. Furthermore, there is a need for analyzing theET0 change-point in Iran as no investigation has yet addressed this issue. Therefore, this study aimedto assess i) the trend and change-point occurrence in seasonal and annual reference evapotranspi-ration (ET0, mm), average temperature (Tmean,

°C), wind speed (U, m s−1), solar radiation (SR, MJm−2 d−1) and relative humidity (RH, %) series, ii) the sensitivity of ET0 to changes in averagetemperature (Tmean), wind speed (U), solar radiation (SR) and relative humidity (RH) and iii) thecontribution of average temperature (Tmean), wind speed (U), solar radiation (SR) and relativehumidity (RH) to the ET0 trend in 17 arid stations of Iran during 1966–2012.

2 Materials and Methods

2.1 Data

Monthly meteorological data of 17 arid stations (Fig. 1) including minimum temperature(Tmin), maximum temperature (Tmax), wind speed (U), sunshine hours (SH, h) and relativehumidity (RH) were obtained from the Iran Meteorological Organization (IRIMO) in1966–2012. The climate of all stations is arid based on UNEP (1992) aridity index.

2.2 The Penman–Monteith FAO-56 (PMF-56) Model

In this study, the Penman–Monteith FAO-56 (PMF-56) method was used to estimate ET0(Allen et al. 1998):

ET0 ¼0:408Δ Rn−Gð Þ þ γ

900

Tmean þ 273U es−eað Þ

Δþ γ 1þ 0:34Uð Þ ð1Þ

where ET0 is the reference crop evapotranspiration (mm d−1), Δ is the slope of saturationvapor pressure curve (kPa °C−1), Rn is the net radiation at the reference crop surface

Quantitative Trend, Sensitivity and Contribution Analyses of Reference... 2209

(MJ m−2 d−1), G is the soil heat flux density (MJ m−2 d−1), Tmean is the daily mean airtemperature at 2 m height (°C), U is the average wind speed at 2 m height (m s−1), es is thesaturation vapor pressure (kPa), ea. is the actual vapor pressure (kPa), es-ea. is the saturationvapor pressure deficit (kPa) and γ represents the psychrometric constant (kPa °C−1).

2.3 Trend Analysis

Nonparametric rank-based Mann-Kendall test was used to temporally detect significant ET0and climatic factors changes. The Mann-Kendall test statistics is calculated as follows(Yue et al. 2002):

S ¼ ∑n−1

k¼1∑n

j¼kþ1sign x j−xk

� � ð2Þ

sign x j−xk� � ¼ 1

0−1

x j−xk� �

> 0x j−xk� � ¼ 0x j−xk� �

< 0

8<: ð3Þ

Fig. 1 The location of surveyed stations in the studied area


Var Sð Þ ¼ n n−1ð Þ 2nþ 5ð Þ½ �− ∑m

i¼1ti ti−1ð Þ 2ti þ 5ð Þ

� �=18 ð4Þ

Z ¼S−1=

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiVar Sð Þ

p0S þ 1=

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiVar Sð Þ

pS > 0S ¼ 0S < 0

8<: ð5Þ

where S represents the Mann-Kendall statistics, n denotes data set length, xj and xk are thesequential data values, ti is the number of ties of extent i, m is number of the tied groups and Zis the standardized Mann-Kendall statistics.

In this study, the trend-free prewhitening approach (TFPW) (Yue et al. 2002) was applied toeliminate the effect of serial correlation on the trend test.

2.4 Time Series Homogeneity Analysis

The non-parametric method introduced by Pettitt (1979) was employed to detect any signif-icant change-point in the studied time series. The test considers a sequence of random variablesX1, X2, ..., XT, which has a change-point at τ. It is noted that Xt for t = 1, 2, ..., τ and t = τ +1,..., T has a common distribution function of F1(x) and F2(x), respectively, whichF1(x) ≠ F2(x). The null and alternative hypothesis of the test are, respectively, H0: τ = T andH1: 1 ≤ τ < T (Busuioc and von Storch 1996; Pettitt 1979). The following non-parametricstatistic is used to test H0 against H1:

KT ¼ max1≤ t<T

Ut;T�� ð6Þ

The numbers of Ut,T can be calculated iteratively as follows:

Ut;T ¼ Ut−1;T þ ∑T

j¼1sign xt−x j

� �for t ¼ 1; :::; T ð7Þ

The significant probability of a change-point (p) associated with KT is approximated by(Pettitt 1979):

p≈2exp −6 KTð Þ2= T3 þ T2� �h i

ð8Þ

When p is lower than the considered significance level (0.05 for this study), H0 is rejected.

2.5 The Sensitivity Analysis

The partial derivatives can be used to analyze the response of ET0 estimated by PMF-56 (amathematically tractable equation) to the perturbations of the controlling independent variables(Saxton 1975). Using the partial derivatives, comparing the results of sensitivity analysis ofET0 to different variables may, however, be very difficult and even misleading as differentvariables have different ranges and dimensions in a multivariable mathematical equation (e.g.


PMF-56). A dimensionless relative sensitivity coefficient firstly proposed by McCuen (1974)and Saxton (1975) has been thus widely employed in the hydrology literature for quantitativesensitivity analysis (Estévez et al. 2009; Gao et al. 2016; Gong et al. 2006; Huo et al. 2013).The relative sensitivity coefficient (SC) is given by:

SCxi ¼ limΔxi→0

ΔET0=ET0

Δxi=xi

� ¼ ∂ET0

∂xi:xiET0

ð9Þ

where SCxi is the sensitivity coefficient of ET0 to xi (the independent factor). It should be notedthat Eq. 9 is the first term of Taylor’s expansion (Saxton 1975).

In this investigation, Eq. 9 was used to quantitatively assess the sensitivity of ET0 to thechanges in the controlling independent variables i.e. Tmean, RH, Rs and U for the studiedstations. The positive (negative) magnitude of SCxi logically implies an increment (a decline) inET0 as xi increases (decreases). Assuming all other variables are constant, a ±10% perturbationof xi would yield a ±1% change in ET0 if the SCxi equals 0.1. Note that the greater the SCxi, themore the sensitivity of ET0 to xi is expected (Gong et al. 2006; Huo et al. 2013).

2.6 Climatic Changes Contribution to the ET0 Trend

In the current study, the detrending method was used to quantitatively determine the contri-bution of Tmean, RH, Rs and U to the ET0 changes. In order to implement the detrendingprocedure in first step, the trend in Tmean, RH, Rs and U was removed and the series becamestationary (Xu et al. 2006). Afterwards, for assessing the contribution of a specific variable (forinstance Tmean), ET0 was recalculated with the detrended data series (the detrended Tmean)while the original data set was used for other variables. Finally, the average difference betweenET0 calculated with original data and ET0 recalculated with detrended data was computed.This difference quantifies the contribution of a specific variable to ET0 trend. The largerabsolute difference indicates the higher contribution of a given variable to the ET0 changes(Huo et al. 2013; Xu et al. 2006).

3 Results and Discussions

3.1 ET0 Trend Analysis

In 12 out of 17 surveyed stations, positive trends were detected in annual ET0 during 1966–2012 (Fig. 2). The significant trend in ET0 series was, however, identified only in 6 sites (i.e.Zabol, Zahedan, Iranshahr, Semnan, Kashan and Bam) at the 95% confidence level. It is worthmentioning that 4 out of these 6 sites with significant trend in annual ET0 i.e. Zabol, Zahedan,Iranshahr and Bam were located in the southeast of the country indicating considerableimpacts of climate change on ET0 over this region of the country. Annual ET0 at Birjand,Boshehr, Esfahan, Shahroud and Kerman exhibited an insignificant decreasing trend.According the results presented in Fig. 2, the trend in ET0 was positive in winter, spring,summer and autumn time for 70.6, 64.7, 70.6 and 76.5% of the investigated locations,respectively. However, 47.0, 35.3, 35.3 and 29.4% of all studied stations showed significantpositive trend in ET0 during winter, spring, summer and autumn, respectively.


Based on the Pettitt homogeneity test, except at Birjand, Boshehr and Bandar-e-lengehsites, a significant change-point was detected in annual ET0 series (Table 1). Further, there is achange-point in winter, spring, summer and autumn ET0 series at 64.7, 52.9, 64.7 and 64.7%of sites, respectively. At both annual and seasonal scales, the change-point mainly occurred inthe1990s for the ET0 time series.

3.2 Trend Analysis of the Controlling Atmospheric Variables

On annual scale, Tmean and Rs series exhibited significant upward trends in 82.3% and 29.4%of the surveyed stations, respectively, in 1966–2012 (Fig. 2). The increase in Tmean is attributedto global warming induced by elevated CO2. The trend test detected a significant decreasingtrend in annual RH at 41.2% of the sites. The declined RH and increased Rs can be consideredas the sequences of the decrease of cloudiness in arid regions of Iran leading to a negative trendin annual precipitation in these areas (Some'e et al. 2012). There was a significant upward

-6

-4

-2

0

2

4

6

8

wi su an sp au wi su an sp au wi su an sp au wi su an sp au wi su an sp au wi su an sp au wi su an sp au wi su an sp au wi su an

Abadan Ahwaz Bandar-e-

lengeh

Esfahan Shahroud Kerman Yazd Boshehr Zahedan Bandar-e-

abbas

Semnan Bam Tehran Zabol Birjand Iranshahr Kashan

(a) ET0

-6

-4

-2

0

2

4

6



lengeh


abbas


(c) U

-4

-2

0

2

4

6

8



lengeh


abbas


(b) T mean

Fig. 2 The Mann-Kendall Z values for ET0 (a) Tmean (b), U (c), RH (d) and RS (e) and ET0 during winter(black), spring (red), summer (yellow), autumn (green) and entire year (blue). The dotted lines indicate thesignificant level at 5%


trend in Bam, Zabol, Kashan and Iranshahr stations and a significant downward trend inBoshehr, Esfahan and Shahroud sites in annual U. Further, 52.9, 82.3, 76.5 and 76.5% of allsites revealed a significant positive trend in Tmean during winter, spring, summer and autumn,respectively. Moreover, about 17.6, 35.3, 35.3 and 5.9% of stations had a significant upwardtrend in Rs, respectively, during winter, spring, summer and autumn. A significant negativetrend was also detected in approximately 11.8, 47.0, 41.2 and 17.6% of sites for RH and 23.5,29.4, 17.6 and 17.6% of locations for U series during winter, spring, summer and autumn,respectively. On the other, a positive trend in winter, spring, summer and autumn U wassignificant in about 17.6, 23.5, 29.4 and 29.4% of sites, respectively.

The Pettitt test revealed the presence of a significant change-point (p ≤ 0.05) in around 94.1,76.5, 52.9 and 47.0% of stations in annual Tmean, U, RH and Rs series, respectively (Table 1).A significant change-point was also detected at approximately 41.2, 88.2, 88.2 and 82.3% ofsites for Tmean, 64.7, 82.3, 58.8 and 70.6% of all locations for U, 17.6, 52.9, 52.9 and 29.4% ofstations for RH and 17.6, 47.0, 52.9 and 11.7% of all sites for Rs during winter, spring, summerand autumn, respectively. Except in wintertime, a significant change-point in annual andseasonal Tmean series was, hence, found in more stations relative to the other variables.Furthermore, the change-point was chiefly observed during the 1990s in annual Tmean, RSand RH series and the 1970s for annual U (Table 1).

3.3 Quantitative ET0 Sensitivity Analysis

The positive SCs for Tmean, Rs and U imply the positive effects of these variables on ET0,whereas negative SC for RH indicates the inverse correlation of ET0 with RH changes (Fig. 3).On a full year basis, Tmean was the most influential variable on annual ET0 in two stationslocated in the southwest of Iran i.e. Ahwaz and Abadan. In addition, in Boshehr, Bandar-e-

-6

-4

-2

0

2

4



lengeh


abbas


(d) RH

-3

-2

-1

0

1

2

3

4

5

6



lengeh


abbas


(e) Rs

Fig. 2 (continued)


Tab

le1

The

significantchange-point

(year)detected

byPettitttest

Station

variable

Winter

Spring

Summer

Autum

nAnnual

Station

variable

Winter

Spring

Summer

Autum

nAnnual

Abadan

ET0

2001

--

-1988

Bandar-e-abbas

ET0

1998

1998

1997

-1997

Tmean

1993

1993

1995

1995

1993

Tmean

-1997

1995

1997

1997

U1978

--

-1977

U-

1984

--

-Rs

1976

-1987

--

Rs

--

--

-RH

-1998

1980

-1980

RH

-1998

--

1998

Ahw

azET0

1993

-1989

-1993

Semnan

ET0

1993

1999

1999

2000

1999

Tmean

1993

1993

1994

1985

1993

Tmean

--

1988

-1994

U1973

1973

--

1973

U2001

2001

1999

2000

1999

Rs

1993

--

--

Rs

-1996

--

1993

RH

-1998

--

1997

RH

--

1988

--

Bandar-e-lengeh

ET0

--

--

-Bam

ET0

1993

1986

1986

1984

1986

Tmean

1993

1997

1995

1996

1997

Tmean

-1997

1987

1986

1997

U-

--

1989

-U

2002

1986

1986

1984

1986

Rs

--

1989

-1992

Rs

-1997

--

-RH

--

--

-RH

1993

1996

1988

1984

1986

Esfahan

ET0

--

1977

1977

1978

Tehran

ET0

--

--

2000

Tmean

-1999

1982

1982

1986

Tmean

1993

1997

1994

1987

1994

U1978

1981

1988

1978

1978

U-

--

--

Rs

-1994

1992

-1993

Rs

--

--

-RH

-1998

--

-RH

--

1980

--

Shahroud

ET0

1982

1982

-1983

1995

Zabol

ET0

1993

1997

1999

1997

1997

Tmean

1993

1997

1982

1984

1993

Tmean

-1999

1996

1996

1997

U1983

1983

1985

1983

1983

U1983

1981

1977

1977

1977

Rs

--

--

-Rs

--

--

-RH

--

--

1984

RH

1999

1999

-2000

1999

Kerman

ET0

--

1974

1977

1996

Birjand

ET0

--

-1994

-Tmean

1989

1997

1993

1986

1996

Tmean

-1997

--

1997

U1975

1976

1975

1977

1975

U-

1994

-1994

1994

Rs

-1986

1982

1987

1985

Rs

-1998

1992

1981

-RH

--

-1981

-RH

--

1997

--

Yazd

ET0

1996

1996

1996

1995

1996

Iranshahr

ET0

1995

1995

1995

1994

1995

Tmean

1993

1997

1993

1994

1993

Tmean

-1997

2000

1996

1997


Tab

le1

(contin

ued)

Station

variable

Winter

Spring

Summer

Autum

nAnnual

Station

variable

Winter

Spring

Summer

Autum

nAnnual

U-

1980

1972

1995

1973

U1995

1983

1984

1994

1995

Rs

-1993

1985

-1993

Rs

1996

1997

1995

-1995

RH

-1996

1998

-1993

RH

-1998

1990

2000

1997

Boshehr

ET0

--

--

-Kashan

ET0

1977

1976

2004

1996

1976

Tmean

-1993

1978

1985

1995

Tmean

--

--

-U

1977

1977

1977

1977

1977

U1976

1976

1981

1983

1976

Rs

--

--

-Rs

--

1974

-1974

RH

--

--

-RH

--

1984

--

Zahedan

ET0

1984

1996

1997

1997

1997

Tmean

-1997

1993

1996

1997

U-

1981

--

-Rs

-1994

1989

-1984

RH

1985

1997

1984

1984

1984


lengeh and Bandar-e-abbas which are all coastal sites, RH had the greatest influence uponannual ET0. Further, U and Rs were found to be the most sensitive variables on ET0 in 35.3%(Tehran, Bam, Birjand, Yazd, Zahedan and Zabol) and 35.3% (Esfahan, Iranshahr, Kashan,Kerman, Shahroud and Semnan) of investigated stations, respectively (Fig. 3). As a result,annual ET0 exhibited a greater sensitivity to Rs and U relative to the other key meteorologicalvariables in the most sites. However, ET0 showed a higher sensitivity to RH in arid southerncoastal locations.

Averaged across all sites, the greatest and lowest SC of ET0 to Tmean (SCTmean) wascalculated in summer (0.495) and winter (0.178), respectively. Thus, the warmer the airtemperature is, the more the sensitivity of ET0 to Tmean is. The averaged seasonal SC of ET0to U (SCU) was found to be 0.269, 0.299, 0.334 and 0.309 during winter, spring, summer andautumn periods, respectively. Therefore, one may conclude that the averaged SCU does notvary as much as SCTmean over different seasons (Fig. 3). It is noteworthy that a small

0

0.2

0.4

0.6

0.8

1



lengeh


abbas


(a) Tmean

0

0.1

0.2

0.3

0.4

0.5

0.6



lengeh


abbas


(b) U

-0.9

-0.8

-0.7

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0



lengeh


abbas


(c) RH

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7



lengeh


abbas


(d) Rs

Fig. 3 The sensitivity coefficients (SCs) of ET0 to Tmean (a), U (b), RH (c) and Rs (d) in winter (black column),spring (red column), summer (yellow column) and autumn (green column)


perturbation in U results in a large ET0 change in arid regions (Allen et al. 1998; Irmak et al.2006). Hence, the findings of the current study and some others e.g. Huo et al. (2013) suggestthat the sensitivity of ET0 to U is considerable in water-limited arid ecosystems. The averagedSCRs was 0.257, 0.442, 0.446 and 0.328 in winter, spring, summer and autumn, respectively.Like Tmean, Rs is, hence, more influential on ET0 in warmer months of year (Fig. 3). Thegreatest and lowest absolute SCRH, averaged across all stations, were obtained in winter(−0.465) and summer (−0.135), respectively. Thus, as the air temperature rises, the sensitivityof ET0 to Rs and T increases and to RH decreases. Irmak et al. (2006) also concluded that theET0 sensitivity to the saturation vapor pressure deficit (VPD) is smaller as the temperatureincreases. This can be explained by the fact that temperature increment increases the slope ofsaturation vapor pressure versus air temperature (Δ) and consequently decreases the magni-tude of the term of [1/Δ + γ] (in Eq. 1) (Allen et al. 1998). As a result, the slighter decline inET0 is yielded as RH rises under warmer climatic condition (Irmak et al. 2006; Monteith andUnsworth 2013). Increasing temperature under climate change would therefore likely enhancethe ET0 sensitivity to Rs and Tmean and reduce the RH influences on ET0 in the twenty-firstcentury.

Averaged over all sites, the highest absolute SC was calculated for RH (−0.465), Rs (0.442),Tmean (0.495), Rs (0.328) and Rs (0.368) in winter, spring, summer, autumn and the entire year,respectively. Consequently, the most sensitive variable on annual, spring and autumn ET0 isRs. Sharifi and Dinpashoh (2014) also concluded that the greatest sensitivity of ET0 was forvapor pressure, Rs, Tmean, Tmean and Tmean during winter, spring, summer, autumn and theentire year, respectively, over 8 sites in Iran. The differences between the results of Sharifi andDinpashoh (2014) and ours may be attributed to different sensitivity analysis procedures, sitesand the period of times considered in the studies.

3.4 Quantitative ET0 Contribution Analysis

The average difference values between annual and seasonal ET0 calculated with originalmeteorological data (ET0-org) and recalculated with detrended U (ET0-det.U), Tmean

(ET0-det.Tmean), RH (ET0-det.RH), Rs (ET0-det.Rs) and all climatic data series (ET0-det.all) in1966–2012 are given in Fig. 4. The positive (negative) values suggest the positive (negative)effects of the trend of meteorological variables on the ET0 trend. There was an over 100 mm y−1

difference (averaged over 1966–2012) between ET0-org and ET0-det.U in Bam, Iranshahr, Kashan,Semnan and Zabol stations (Fig. 5b). The differences between annual ET0-org and ET0-det.Tmean,ET0-det.RH and ET0-det.Rs were lower than 50 mm y−1 at these sites (Fig. 4a, c and d). As a result,one attributes the significant upward trend in annual ET0 (Fig. 2a) at Zabol, Semnan, Bam,Kashan and Iranshahr greatly to the increased U over 1966–2012. Around 51.78, 35.92, 37.01,33.44 and 50.18% of difference between ET0-org and ET0-det.all occurred during summertime inSemnan, Bam, Zabol, Iranshahr and Kashan, respectively. It seems that summer ET0 rise made amore important contribution to increment in annual ET0 owing to greater increase ofsummer wind speed in Semnan, Bam, Zabol, Iranshahr and Kashan. Given an over0.90 correlation coefficient between annual ET0 and U in Semnan, Bam, Zabol,Iranshahr and Kashan (Fig. 5), the ET0 changes are highly correlated with the changesof U in these sites.

An over 40 mm y−1 difference calculated between annual ET0-org and ET0-det.Tmean

(ET0-org-ET0-det.Tmean) as well as ET0-det.RH (ET0-org-ET0-det.RH) indicated that the significantincrease in Tmean and decline in RH contributed much more than other variables changes to the


significant positive trend of annual ET0 at Zahedan site (Fig. 4). Despite a positive difference,averaged over 1966–2012, between annual ET0-org and ET0-det.Tmean, a less than −50 mm y−1

average difference between annual ET0-org and ET0-det.U led to an insignificant down-ward trend in annual ET0 at Esfahan, Birjand, Boshehr, Shahroud and Kerman locations(Figs. 2a, 4a and b). In these sites, the negative trend of U appears to be the prime cause of aninsignificant decreasing trend in annual ET0. The correlation coefficient between annual ET0and U was larger than that for other variables (Fig. 5) in Esfahan, Birjand, Boshehr, Shahroudand Kerman. Since, approximately 55.87, 43.20, 42.67 and 43.08% of average decrease inannual U at Esfahan, Birjand, Boshehr and Kerman, respectively, occurred in summertime,declined summer U seems to play more significant role in decreasing trend of annual ET0 inthese sites.

Changes in Tmean was found to be the most contributing factor affecting the ET0 trend inBandar-e-lengeh, Abadan, Ahwaz and Yazd (Fig. 4a). The positive influence of increasedTmean (global warming) on annual trend of ET0 was, however, partially nullified by decreased

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Fig. 4 The average differences between ET0 calculated with original data and ET0 recalculated with detrendedTmean (a), U (b), RH (c), Rs (d) and all (e) data over winter (black column), spring (red column), summer (yellowcolumn), autumn (green column) and entire year (blue column)


U in these sites. Meanwhile, increase in Rs and decline in RH caused an insignificant upwardtrend in annual ET0 (Figs. 2a, 4c and d) at Bandar-e-lengeh, Abadan, Ahwaz and Yazd. Inaddition, increased Tmean and decreased RH was found to be the most contributing factor to theinsignificant increasing ET0 trend in Tehran and Bandar-e-abbas sites, respectively (Fig. 4aand c). The difference determined between ET0-org and ET0-det.all was larger in summertimefor Ahwaz, Bandar-e-abbas and Bandar-e-lengeh, in autumn for Abadan and Yazdstations and during spring in Tehran site (Fig. 4e). The highest correlation coefficientwas obtained between annual U and ET0 series in Yazd, Ahwaz, Abadan, Tehran andBandar-e-abbas (Fig. 5). Averaged over all sites, the correlation coefficient betweenannual ET0 and Tmean, U, RH, and Rs was, respectively, 0.31, 0.84, −0.44 and 0.32,indicating that ET0 changes are highly correlated with U trend in the studied sites(Fig. 5).

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Fig. 5 The correlation coefficient between annual ET0 and Tmean (black column), U (red column), Rs

(yellow column) and RH (green column) series

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Fig. 4 (continued)


Dinpashoh et al. (2011) concluded that RHmax (maximum RH) in Zahedan and U inAhwaz, Abadan, Tehran, Zabol, Birjand, Esfahan and Kerman had the main contribution tothe ET0 changes in 1965–2005. Unlike the results of Dinpashoh et al. (2011), Tmean made thegreater contribution to the ET0 dynamics for Tehran, Ahwaz and Abadan sites which may bedue to different contribution analysis methods and the time periods considered in the studies. Itshould be noted that U was not the main contributing factor to the ET0 changes in someregions i.e. Tehran, Abadan, Ahwaz and Bandar-e-abbas based on the detrending method,although there was a higher correlation coefficient between ET0 and U in these stations(Fig. 5). Huo et al. (2013) in arid northwestern areas of China, Zhang et al. (2007) andShenbin et al. (2006) across the Tibetan Plateau, China (a water-limited semi-arid ecosystem)and Shan et al. (2015) in some water-limited desertification-prone regions of China reported Uas the most important meteorological variable contributing to the ET0 changes. In addition toRs, U has been also found by Xu et al. (2006) in Changjiang basin, China (an energy-limitedhumid region) as the leading cause of the ET0 changes. Further, the decreasing trend in panevaporation (ETpan) has been attributed to the reduced U by Zhang et al. (2007) in the TibetanPlateau, China, Roderick et al. (2007) in Australia and You et al. (2013) in the southwesternChina.

Overall, although temperature rise due to high CO2 emission in the last half of century isexpected to increase ET0, decreasing trend in U caused a reduction in ET0 in some sites i.e.Esfahan, Kerman, Birjand, Shahroud and Boshehr. Further, despite positive impacts of globalwarming upon annual ET0 in Zabol, Bam, Iranshahr, Kashan and Semnan, the U incrementseems to be the primary cause of the significant increasing trend of annual ET0 in theselocations. The positive impacts of temperature rise on ET0 were nearly counterbalanced bydecreased U in Abadan, Ahwaz, Bandar-e-lengeh and Yazd. Change in wind speed was,therefore, made the major contribution to annual ET0 trend over the period of 1966–2012 inmost arid areas of Iran.

In the surveyed sites particularly those located in the southeastern Iran where the Uvariation remarkably contributes to the ET0 changes, using the ET0 temperature-based (e.g.Hargreaves–Samani model) and radiation-based (e.g. Priestley–Taylor equation) models inwhich U is lumped into a constant coefficient or not taken into account would highly likelylead to an erroneous estimate of ET0. Consequently, error in estimating ET0 would adverselyinfluence irrigation projects. Chen et al. (2005) recommended not using Thornthwaite method(a temperature-based model) for estimating ET0 over China as ET0 is highly conditioned by Rs

and U there.Increased water demand due to the ET0 rise at most studied sites wherein surface and

groundwater resources are already seriously depleted can severely jeopardize water suppliesand food security. Hence, proper water resource management particularly in agriculture sectoris urgently required to mitigate the negative impacts of increased ET0 and climate change inIran’s drought-prone arid regions. In addition, decreasing U (for instance by windbreaker)seems to substantially reduce ET0 and consequently irrigation water requirement in our studyarea. Due to lack of climatic data, spatial analyses of the ET0 sensitivity to the meteorologicalvariables and the contribution of the climatic factors to the ET0 trend could not beundertaken in our studied area. Therefore, some studied needed to be carried out tospatially analyze the ET0 sensitivity and contribution using statistical interpolation orremote-sensing techniques. Further, it is worth pointing out that the results of currentstudy must be verified for other climates and also other arid regions where climaticchanges might differently affect ET0.


4 Conclusions

The trend of reference evapotranspiration (ET0) and ET0 sensitivity to mean temperature(Tmean), solar radiation (Rs), relative humidity (RH) and wind speed (U) were quantitativelystudied in 17 arid sites in Iran over 1966–2012. The contribution of changes in Tmean, Rs, RHand U to the ET0 trend was also quantitatively investigated. The results indicated that there wasan increasing trend in annual ET0 for about 70.0% of station. However, the increasing trend ofannual ET0 was significant (p ≤ 0.05) in only 35.3% of all surveyed sites. Approximately 47.0,35.3, 35.3 and 29.4% of stations exhibited significant positive trend in ET0 during winter,spring, summer and autumn, respectively. In addition, a significant change-point was detectedin winter, spring, summer, autumn and annual ET0 series at 64.7, 52.9, 64.7, 64.7 and 82.3% ofstations, respectively. Rs and U are the most sensitive variables on ET0 in the majority ofstudied sites. In addition, RH was the most sensitive variable on ET0 in the coastal southernsites. Decreasing trend of U caused a downward trend in annual ET0 for 29.4% of the sites andpartly offset the positive effects of temperature increment on annual ET0 in around 23.5% ofstudied sites. Moreover, increase of U contributed much more than other variables to theupward ET0 trend in about 29.4% of locations. Therefore, U was the most important climaticfactor contributing to the ET0 trend over 1966–2012 in most arid sites.

Overall, the results showed that the climatic changes had positive impacts on the ET0 trendin the most studied sites causing an increase in irrigation water requirement in arid regions ofIran. Increase in water demand as a result of ET0 rise puts a tremendous pressure on watersupplies in Iran. Therefore, adopting appropriate agricultural water management is highlyneeded to cope with climate change in the arid environments of Iran. Furthermore, U as themost important climatic factor contributing to the ET0 trend must be taken into account forassessing ET0, irrigation water demand and hydrological cycle components under climatechange.

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