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Effects of variations in climatic parameters on evapotranspiration in the arid and semi-arid regions
Global and Planetary Change 78 (2011) 188–194
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Global and Planetary Change j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / g l o p l a c h a
Effects of variations in climatic parameters on evapotranspiration in the arid and semi-arid regions Saeid Eslamian ⁎, Mohammad Javad Khordadi, Jahangir Abedi-Koupai Department of Water Engineering, College of Agriculture, Isfahan University of Technology, Isfahan 8415683111, Iran
a r t i c l e
i n f o
Article history: Received 3 January 2010 Accepted 3 July 2011 Available online 19 July 2011 Keywords: Evapotranspiration Climatic parameter variability Arid zone
a b s t r a c t The main objective of this study is to investigate the effects of climatic parameters variability on evapotranspiration in five climatologically different regions of Iran. The regions include Tehran, Esfahan, Shiraz, Tabriz and Mashhad. Fifty four-year monthly records of temperature, relative humidity, sunshine duration, wind speed, and precipitation depth from 1951 to 2005 comprise the database. Trend and persistence analyses of the data are performed using the Mann–Kendall test, the Cumulative Deviation test, Linear Regression, and the Autocorrelation Coefficient. A sensitivity analysis of meteorological variables in these five regions is carried out using Penman-Monteith formula. In all of the studied regions, sensitivity analysis reveals that, temperature and relative humidity are the most sensitive parameters in PenmanMonteith formula respectively. The results of this study indicate that the effective climatic variables on evapotranspiration are changing, though in each region the variables have significant long-term trends and persistence. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Atmospheric carbon dioxide levels have been recorded continual increases since the 1950s, a phenomenon that may significantly alter the global and local climate characteristics, such as temperature and available water resources. Tickell (1993) predicted that the world mean temperature will be increased up to 1 °C by the year 2050 and up to 3 °C by the end of the next century. The effects of hydrological processes and water resources caused by climate change have also received much attention (Gleick, 1986). Rao and Al-Wagdany (1995) investigated the effects of changes in precipitation and temperature on runoff using a water-balance model. Mansell (1997) studied the effects of climate change on the rainfall trends and flood risks in the western part of Scotland. Herrington (1996) analyzed the impacts of climate change on the water demand in England and Wales and concluded that a 1.1 °C rise in temperature would increase the water demand for agriculture up to 12%. Tsuang et al. (1998) found that the temperatures of April, June, August, and October show a significantly increasing trend in Taiwan and predicted that average temperature will increase by about 0.13 °C per year. The above studies indicate that the global warming has clearly been increasing during the recent decades and global warming will be more important in the future.
⁎ Corresponding author. Tel.: + 98 311 3913432; fax: + 98 3113912254. E-mail addresses: [email protected] (S. Eslamian), [email protected] (M.J. Khordadi), [email protected] (J. Abedi-Koupai). 0921-8181/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.gloplacha.2011.07.001
Most studies regarding effects of climatic change on water balances have treated evapotranspiration as a function of temperature or as a function of the impact of elevated CO2 on stomatal conductance (Gleick, 1987). Evapotranspiration (ET) is a term used to describe the sum of evaporation and plant transpiration from the earth's land surface to atmosphere. Evaporation accounts for the movement of water to the air from sources such as the soil, canopy interception, and water bodies. Transpiration derives from the movement of water within a plant and the subsequent loss of water as vapor through stomata in its leaves. Evapotranspiration is an important part of the water cycle (Allen et al., 1989). The investigation by Yu et al. (2002) concluded that solar radiation and wind speed are the most sensitive and the least sensitive variables of the modified Penman formula, respectively and the relative humidity has the property that increasing their values will decrease the evapotranspiration estimates. A sensitivity analysis of Penman–Monteith potential evapotranspiration model that was performed by Bois et al. (2005) showed that wind speed and solar radiation temporal variability have a great impact on potential evapotranspiration computation. Wind speed omission in empirical formulae can thus be an important source of uncertainties for PET estimation (possible maximum evapotranspiration or potential ET), especially under Mediterranean conditions. Radiation based methods, using remotely sensed solar radiation from satellites images, are more accurate than temperature based methods in Oceanic and Mediterranean climates. Recently, considerable works have been devoted to the examination of the potential impact of climate change on the water resource
S. Eslamian et al. / Global and Planetary Change 78 (2011) 188–194
189
piration estimated by the Penman-Monteith formula multiply by crop coefficient (Kc) using CropWat software. Crop coefficients depend on the effects of crop height, crop-soil surface resistance, and the albedo of the crop-soil surface. The magnitude of the crop coefficient varies with the crop type and species and the characteristics of plant growth period. CropWat for Windows version 4.2 is software presented by FAO and computes the actual evapotranspiration using Penman– Monteith formula. According to results of Allen et al. (1989), the Penman–Monteith formula has the best results in comparing with the other methods for calculating evapotranspiration. In this research the Penman–Monteith results are compared with lysimeter data in all studied stations. Lysimeter is an important criterion to verify evapotranspiration estimated with Penman–Monteith formula. A high correlation is found between them in each station. The meteorological variables that are likely to be altered due to climate change (e.g., temperature, relative humidity, solar radiation, and wind speed) are the ones to be tested for their sensitivity in the evapotranspiration estimation using the Penman–Monteith formula.
management systems. These studies revealed that the demand for irrigation water is particularly sensitive to changes in precipitation, temperature, and the concentration of carbon dioxide (Frederick and Major, 1997). The investigation by McCabe and Wolock (1992), based on an irrigation model, concluded that the increase in annual mean of water requirement is strongly associated with the increase in temperature. Several studies have been performed to assess climate change on evapotranspiration (ET) and the land surface water balance. Unfortunately, there are no long-term ET observations. Methods that enable direct measurements (e.g., via eddy flux methods) have only been available for about 20 years, and are still used primarily in intensive research settings rather than for assessing long-term trends. Another source of evaporation data is records from evaporation pans, which are generally located in agricultural areas and have been used as an index to potential evaporation. These records are generally longer it is the case of USA where several hundred locations have record data for approximately 50 years. Several studies (e.g., Peterson et al., 1995; Golubev et al., 2001) have shown that pan evaporation records over the United States generally had downward trends over the second half of the 20th century. This is contrary to the expectation that a generally warming climate would increase evapotranspiration. These changes may be occurred in other regions of the world. Estimates of global warming are generally based on the application of general circulation models (GCMs), which attempt to predict the impact of increased atmospheric CO2 concentrations on the weather variables. Owing to the complex mechanism in the atmosphere motion and the uncertainty of the model structure, different GCMs produce different predictions. In Iran, as an arid and semi-arid country, about 93.5% of the total water resources have been used for agriculture purposes, therefore changes in climatic regimes may affect the agricultural water demands, because evapotranspiration may be affected by the changes in meteorological variables (e.g., temperature, solar radiation, wind speed, and relative humidity). In this research, a sensitivity analysis of these meteorological variables using the Penman–Monteith formula (Doorenbos and Pruitt, 1984) is first investigated to find which meteorological variables are significantly sensitive to the evapotranspiration estimation. The trend and persistence analyses of these sensitive meteorological variables are then studied further to discover whether their trends and persistence exist in the historical time series due to a climate change. Because changes in climatic regimes may affect agricultural water demands, and evapotranspiration may be affected by changes in meteorological variables (e.g., temperature, solar radiation, wind speed, and relative humidity), finally, the effects of climate change on evapotranspiration were observed based on a study of the sensitivity and trend analyses.
In order to evaluate effect of meteorological parameters on evapotranspiration, sensitivity analysis is performed to find more sensitive parameters. For a general definition of sensitivity, consider the variable V, which is a function of the input variables x1…xn :
2. Material and methods
V = f ðx1 ; …; xn Þ
Five stations under study in Iran display an appropriate geographical distribution for this study purpose as shown in Fig. 1 and Table 1. The stations include: Tehran, Esfahan, Shiraz, Tabriz and Mashhad. The 54 years monthly meteorological data from 1951 to 2005 have been used for this study. The meteorological data were temperature, relative humidity, sunshine duration, wind speed, and precipitation.
2.1.1. Penman–Monteith formula The Penman–Monteith formula (Karlsson and Pomade, 2003) is given by the following equation:
ET0 =
ð1Þ
where, ET0 Rn G T u2 es ea Δ γ
reference crop evapotranspiration, in mm/day, net radiation at the crop surface, in MJ/m 2/day, soil heat flux density, in MJ/m 2/day, mean daily air temperature at 2 meters height, in wind speed at 2 meters height, in m/s, saturation vapor pressure, in KPa, actual vapor pressure, in KPa, slope of vapor pressure curve, in KPa/ 0 C, and psychrometric constant, in KPa/ 0 C.
0
C,
2.2. Sensitivity analysis calculations
ð2Þ
If the variables x1…xn are independent of V, it may be written: V + ΔV = f ðx1 + Δx1 ; …; xn + Δxn Þ
ð3Þ
From a Taylor series expansion we have, neglecting higher-order terms:
2.1. Evapotranspiration calculation using Penman–Monteith formula ΔV = The Penman–Monteith formula has been recommended as a suitable method for estimating the reference evapotranspiration. Therefore, it has been chosen in this study to estimate the effect of climate change on the evapotranspiration for alfalfa crop. Alfalfa is planted in the studied stations and also in Iran widely. The actual crop evapotranspiration can be calculated from the reference evapotrans-
900 0:048ΔðRn −GÞ + γ T + 273 u2 ðes −ea Þ Δ + γ ð1 + 0:34u2 Þ
∂V ∂V Δ x1 + … + Δ xn ∂ x1 ∂ xn
ð4Þ
By definition, the partial differentials, δV/δxi , are the sensitivities, Sxi , of the dependent variable V to the independent input variable xi (McCuen, 1974, 2003; Saxton, 1975; Beven, 1979). They denote the change in V per unit change in xi.
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Fig. 1. Situation of the studied regions in Iran.
Now, the relative change in V can be expressed as (Saxton, 1975),
From Eq. (4) we have:
Sxi =
∂V ΔV = Δxi ∂xi
ð5Þ
Which show that Sxi may be obtained by calculating directly the value of the partial differential, or by applying a step change in xi, while leaving the variables other than xi constant. Here Sxi may be sensitive to the relative magnitude of V and xi. Therefore, Sxi may be divided by the ratio V/xi, which leads to the relative sensitivity or sensitivity coefficient RSxi:
RS xi =
∂ V xi ∂xi V
ð6Þ
ΔV Δx1 Δxn = RSx1 + … + RSxn V x1 xn
ð7Þ
which shows that the relative sensitivity coefficient denotes the part of the relative change in xi that is transferred to the relative change in V. If, for example, RSxi = 25%, a 10% change in xi will result in a 2.5% change in V. Sensitivity analysis was implemented with Matlab software and results are shown in Fig. 2.1.1 to 4.3. In these figures, RH (Relative Humidity) is in percent, Tmax (Temperature) is in centigrade degree, n (daily average of Sunshine Duration) is in hours, u (Wind Speed) is in km/day and Rs (Relative Sensitivity) is dimensionless. 2.3. Trends and persistence of meteorological variables
Table 1 Characteristics of the regions. Region
Latitude (n)
Longitude (e)
Elevation (m)
Average of temperature (°c)
Climate
Tehran Esfahan Shiraz Tabriz Mashhad
35° 32° 29° 38° 36°
51° 51° 52° 46° 59°
1190 1550 1484 1361 999
18 16.7 19 13 15
Cool semi-arid Cool semi-arid Warm semi-arid Cold semi-arid Cool semi-arid
41′ 37′ 32′ 5′ 16′
19′ 40′ 36′ 17′ 38′
Investigation of trends and persistence in historical meteorological data are helpful in understanding the effect of climate change on evapotranspiration in the stations under study. The trends and persistence of the time series can be tested by different methods. The approaches used in this study are the Mann–Kendall test (Kendall, 1975), the Cumulative Deviation test (Buishand, 1982), Regression Analysis (Yu et al., 2002), and the Autocorrelation Coefficient (Yu et al., 2002). Trend tests are only used for sensitive parameters in Penman–Monteith formula in each region. If at least three trend tests confirm existence of trend, the parameter displays the stable and significant trend.
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Fig. 2. 1. 1. The ET relative sensitivity to temperature considered relative humidity. Fig. 2. 2. 1. The ET relative sensitivity to wind speed considered relative humidity.
Fig. 2. 1. 2. The ET relative sensitivity to temperature considered wind speed.
Fig. 2. 2. 2. The ET relative sensitivity to wind speed considered temperature.
Fig. 2. 1. 3. The ET relative sensitivity to temperature considered sunshine duration.
Fig. 2. 2. 3. The ET relative sensitivity to wind speed considered sunshine duration.
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Fig. 2. 3. 1. The ET relative sensitivity to sunshine duration considered temperature.
Fig. 2. 3. 2. The ET relative sensitivity to sunshine duration considered relative humidity.
Fig. 2. 3. 3. The ET relative sensitivity to sunshine duration considered wind speed.
Fig. 2. 4. 1. The ET relative sensitivity to relative humidity considered temperature.
Fig. 2. 4. 2. The ET relative sensitivity to relative humidity considered wind speed.
Fig. 2. 4. 3. The ET relative sensitivity to relative humidity considered sunshine duration.
S. Eslamian et al. / Global and Planetary Change 78 (2011) 188–194
2.3.1. Mann–Kendall test The Mann–Kendall test, suggested by the World Meteorological Organization (1988), is a common method to test the trend of the time series. This method defines the standard normal variate, T , as:
r T = qffiffiffiffiffiffiffi σ2r
σ r =
4p −1 nðn−1Þ
ð9Þ
2ð2n + 5Þ ½9nðn−1Þ
ð10Þ
where MSE is the mean square error and Sxx is: n 2 Sxx = ∑ Xi −X
ð17Þ
i=1
ˆ is not significantly If |T| N tα/2, n-2, the null hypothesis (H0: slope b ˆ is significantly different different from zero) is rejected and slope b from zero and shows trend existence.
r =
2
ð8Þ
193
where p is the number of pairs observations (xi, xj, j N i, xj N xi) and n is the number of years under consideration. The time series have a trend at the significant level of 5%, if |T| N Tα/2 = 1.96. α is the level of signification. A positive value of T indicates an increasing trend in the time series, while a negative value describes a decreasing trend. 2.3.2. Cumulative deviation test In order to confirm the presence of trends in meteorological historical records, a test for homogeneity in the data is performed. The test for homogeneity is based on the adjusted partial sums or cumulative deviations from the mean: k
Sk = ∑ Yi −Y ; i=1
k ¼ 1; 2…n
ð11Þ
where Y is the mean of Yi values and n is the number of values. For the homogeneous series of records, the values of Sk fluctuate around zero. The re-scaled adjusted partial sums S⁎k are obtained by dividing the Sk values by the sample standard deviation as follows:
Sk = Sk = DY ;
k = 1; 2…n
n 2 2 DY = ∑ Yi −Y = n i=1
rk =
ð18Þ
N 2 ∑ Xt −X
t=1
where, Xt is the meteorological variable at time t, N is the number of samples, k is the time lag, and X is the mean of Xt. The significance of r1 is tested by determining the (r1)t value with the following equation: ðr1 Þt =
pffiffiffiffiffiffiffiffiffiffiffi −1Ftg N−2 N−1
ð19Þ
where tg is the standard normal variate of a Gaussian distribution. In recent equation (Eq. (19)), if r1 is negative, the series has a significant oscillation with a high frequency and it's the associated period is short. In contrast, if r1 is positive, the series has a Markov linear type persistence.
ð13Þ
Fig. 2.1.1 to .4.3 show the effects of the change rate of input meteorological variables on the estimation of the Penman–Monteith formula. With an increase in relative humidity and wind speed, and a decrease in sunshine, evapotranspiration sensitivity to temperature increases directly. With an increase in temperature, and a decrease in relative humidity and sunshine, evapotranspiration sensitivity to wind speed directly increases too. With an increase in relative humidity and a decrease in temperature and wind speed evapotranspiration sensitivity to sunshine increases directly, and finally an increase in wind speed and a decrease in temperature and sunshine, evapotranspiration sensitivity to relative humidity increases inversely. Albeit all the meteorological variables are sensitive, the most sensitive parameters are temperature and relative humidity. Effect of humidity on evapotranspiration is more than wind speed, humidity with closing to 100% makes evapotranspiration decrease, therefore wind speed effect decrease. In lower humidity, evapotranspiration is more and wind speed changes affect on evapotranspiration with more severity. The results of sensitivity analysis are displayed in Table 2. The four above mentioned methods (Mann–Kendall test, Cumulative Deviation
ð14Þ
2.3.3. Regression analysis The meteorological variables in the regression analysis are first smoothed using the moving average method. Five years moving average is adopted in this analysis. A simple linear regression equation is then selected to detect the long-term trends of the meteorological variables. ð15Þ
ˆ are where Y is the meteorological variable, X is the time, and â and b the regression coefficients calculated by the least square method. Determining the T value distributed with n − 2 degrees of freedom by the following equations tests the significance of the regression slope: ˆb T = pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi MSE = Sxx
∑ Xt −X Xt + 1 −X
t=1
3. Results
pffiffiffi High values of Q indicate a change in mean. Critical values of Q/ n for the 95% confidence interval are found to be equal to 1.27.
Y = ˆa + ˆbX
N−k
ð12Þ
Based on the S⁎k values, a statistic Q, which is sensitive to departures from the homogeneity, can be defined as: Q = max0≤k≤n Sk
2.3.4. Autocorrelation coefficient Persistence analysis is used to detect the relationship of variables between two continuous time steps in the series. This study uses the autocorrelation coefficients of the meteorological variables to test their persistence in time. The autocorrelation coefficient with a lag time of k is defined as follows:
ð16Þ
Table 2 The results of sensitivity analysis using Penman–Montith formula. Region
Temperature
Humidity
Wind speed
Sunshine duration
Tehran
++
++
+
+
+ sensitive. + + very sensitive.
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Table 3 The statistical tests for various meteorological variables in Tehran, Esfahan, Shiraz, Tabriz, and Mashhad. Temperature
Humidity
Wind speed
Rainfall
Tehran M–K C-D L-R PER
+ (↑) – – +
+ (↑) – – +
+ (↓) + + +
– – – +
Esfahan M–K C-D L-R PER
– – – +
+ (↓) + + +
+ (↓) + + +
– – – +
Shiraz M–K C-D L-R PER
+ (↑) – – +
+ (↓) + + +
+ (↓) + + +
– – – +
Tabriz M–K C-D L-R PER
– – – +
+ (↓) + + +
– + – +
+ (↓) + + +
Mashhad M–K C-D L-R PER
+ (↑) – + +
+ (↓) + + +
– + – +
– – – +
↑: increasing trend; ↓: decreasing trend. +: significantly homogeneous data in trend; –: not significantly homogeneous data in trend. M–K: Mann–Kendall test; C-D: cumulative deviations test; L-R: simple linear regression; PER: persistence analysis by autocorrelation coefficient.
test, Regression Analysis, and Autocorrelation Coefficient) are utilized to detect trends of the meteorological variables for fifty-year records in five climatologically different stations of Iran. Trend tests did not perform for sunshine duration because of missing data in its time series and rainfall entered to trend test separately. The results are presented in Table 3. They reveals that humidity in Esfahan, Shiraz, Tabriz and Mashhad decreases and it has the significant and stable trends; wind speed in Esfahan, Tehran and Shiraz has the significant trends and such that it decreases. In Mashhad, temperature has a significant trend such that it increases, and in Tabriz, precipitation depth decreases with the significant trend. The rest of the studied parameters (including humidity in Tehran, wind speed in Mashhad and Tabriz, temperature in Tehran, Esfahan, Shiraz and Tabriz, precipitation depth in Tehran, Esfahan, Shiraz and Mashhad) have not the significant trends. Decreasing of humidity and rainfall and increasing temperature are the main effects of global warming. An important cause of decreasing in wind speed could be urbanization. 4. Discussion and conclusions Changes in the meteorological variables play an important role in the assessment of evapotranspiration with climate change. The simultaneous consideration of the effects of meteorological variables such as temperature, relative humidity, and sunshine duration on the evapotranspiration estimation with climate change is necessary. In each station, it is found that the different set of meteorological variables have an influence on the evapotranspiration estimation. These results can be affected by climate change phenomenon in arid and semi-arid regions such as Iran and it should be considered as a warning.
Effect of climate change on temperature and relative humidity is increasing and decreasing respectively, that makes evapotranspiration increase, because these parameters are sensitive in PenmanMonteith formula. When evapotranspiration increase, water requirement will be raised and much water is needed. Considering water limitation, crop will not function with high productivity and also lead to economic loss. It is necessary to use alternative methods to reduce damages for example tolerant variety, deficit irrigation techniques and so on. In addition to the meteorological variables, the physiological response of the alfalfa plant to the concentration of CO2 probably plays an important role in the evapotranspiration estimation. However, it has not been considered here and needs further research. The research was only used 54 years data; if more data (for example more than 100 years) were available, better results could be obtained.
Acknowledgment Special thanks are given to Dr. Safieh Mahmoodi, Assistant Professor of Mathematical Sciences Department in Isfahan University of Technology that helped us in this study and Iranian Meteorological Organization for providing the data.
References Allen, R.G., Jensen, M.E., Wright, J.L., Burman, R.D., 1989. Operational estimates of evapotranspiration. Agronomy Journal 81, 650–662. Beven, K., 1979. A sensitivity analysis of the Penman–Monteith actual evapotranspiration estimates. Journal of Hydrology 44, 169–190. Bois, B., Pieri, P., Van Leeuwen, C., Gaudillere, J.P., 2005. Sensitivity analysis of the Penman–Montheith evapotranspiration formula and comparison of empirical methods used in viticulture soil water balance. Proceedings of the XIV International GESCO Viticulture Congress. Geisenheim, Germany, pp. 187–193. Buishand, T.A., 1982. Some methods for testing the homogeneity of rainfall records. Journal of Hydrology 58, 11–27. Doorenbos, J., Pruitt, W.O., 1984. Guidelines for Predicting Crop Water Requirements, 2nd edition. FAO Irrigation and Drainage, Paper 24. Rome. Frederick, K.D., Major, D.C., 1997. Climate change and water resource. Climate Change 37, 7–23. Gleick, P.H., 1986. Methods for evaluating the regional hydrologic impacts of global climatic change. Journal of Hydrology 88, 97–116. Gleick, P.H., 1987. Regional consequences of increases in atmospheric CO2 and other trace gases. Climatic Change 10, 137–161. Golubev, V.S., Lawrimore, J.H., Groisman, P.Y., Speranskaya, N.A., Zhuravin, S.A., Menne, M.J., Peterson, T.C., Malone, R.W., 2001. Evaporation changes over the contiguous United States and the former USSR: a reassessment. Geophysical Research Letters 28, 2665–2668. Herrington, P., 1996. Climate Change and the Demand for Water. Department of the Environment, HMSO, London, U.K. Karlsson, E., Pomade, L., 2003. Methods of estimating potential and actual evaporation. Department of Water Resources Engineering University of Kalmar, Sweden. Kendall, M.G., 1975. Rank Correlation Measures. Charles Griffin, London, U.K., p. 220. Mansell, M.G., 1997. The effect of climate change on rainfall trends and flood risk in the West f Scotland. Nordic Hydrology 28, 37–50. McCabe Jr., G.J., Wolock, D.M., 1992. Sensitivity of irrigation demand in a humidtemperature region to hypothetical climate change. Water Resource Bulletin 28 (3), 533–543. McCuen, R.H., 1974. A sensitivity analysis of procedures used for estimating evaporation. Water Resource Bulletin 10 (3), 486–497. McCuen, R.H., 2003. Modeling Hydrologic Change. Department of Civil and Environmental Engineering Lewis Publishers. University of Maryland. Peterson, T.C., Golubev, V.S., Groisman, P.V., 1995. Evaporation losing its strength. Nature 377, 687–688. Rao, A.R., Al-Wagdany, A., 1995. Effects of climatic change in Wabash river basin. Journal of Irrigation and Drainage Engineering 121 (2), 207–215. Saxton, K.E., 1975. Sensitivity analyses of the combination evapotranspiration equation. Agricultural Meteorology 15, 343–353. Tickell, C., 1993. Global Warming and Its effects. Engineering for Climate Change. Inst. Civil Engineers, U.K., pp. 9–16. Tsuang, B.J., Wu, M.C., Liu, C.C., Chen, H.H., 1998. Climatic change and prediction in Taiwan. Journal of Nature 58, 106–112 (in Chinese). World Meteorological Organization, 1988. Analyzing Long Time Series of Hydrological Data with Respect to Climate Variability, Wcap-3, WMO/TD 224. Yu, P.S., Yang, T.C., Chou, C.C., 2002. Effects of climate change on evapotranspiration from paddy fields in southern Taiwan. Climatic Change 54, 165–179.
Contents lists available at ScienceDirect
Global and Planetary Change j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / g l o p l a c h a
Effects of variations in climatic parameters on evapotranspiration in the arid and semi-arid regions Saeid Eslamian ⁎, Mohammad Javad Khordadi, Jahangir Abedi-Koupai Department of Water Engineering, College of Agriculture, Isfahan University of Technology, Isfahan 8415683111, Iran
a r t i c l e
i n f o
Article history: Received 3 January 2010 Accepted 3 July 2011 Available online 19 July 2011 Keywords: Evapotranspiration Climatic parameter variability Arid zone
a b s t r a c t The main objective of this study is to investigate the effects of climatic parameters variability on evapotranspiration in five climatologically different regions of Iran. The regions include Tehran, Esfahan, Shiraz, Tabriz and Mashhad. Fifty four-year monthly records of temperature, relative humidity, sunshine duration, wind speed, and precipitation depth from 1951 to 2005 comprise the database. Trend and persistence analyses of the data are performed using the Mann–Kendall test, the Cumulative Deviation test, Linear Regression, and the Autocorrelation Coefficient. A sensitivity analysis of meteorological variables in these five regions is carried out using Penman-Monteith formula. In all of the studied regions, sensitivity analysis reveals that, temperature and relative humidity are the most sensitive parameters in PenmanMonteith formula respectively. The results of this study indicate that the effective climatic variables on evapotranspiration are changing, though in each region the variables have significant long-term trends and persistence. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Atmospheric carbon dioxide levels have been recorded continual increases since the 1950s, a phenomenon that may significantly alter the global and local climate characteristics, such as temperature and available water resources. Tickell (1993) predicted that the world mean temperature will be increased up to 1 °C by the year 2050 and up to 3 °C by the end of the next century. The effects of hydrological processes and water resources caused by climate change have also received much attention (Gleick, 1986). Rao and Al-Wagdany (1995) investigated the effects of changes in precipitation and temperature on runoff using a water-balance model. Mansell (1997) studied the effects of climate change on the rainfall trends and flood risks in the western part of Scotland. Herrington (1996) analyzed the impacts of climate change on the water demand in England and Wales and concluded that a 1.1 °C rise in temperature would increase the water demand for agriculture up to 12%. Tsuang et al. (1998) found that the temperatures of April, June, August, and October show a significantly increasing trend in Taiwan and predicted that average temperature will increase by about 0.13 °C per year. The above studies indicate that the global warming has clearly been increasing during the recent decades and global warming will be more important in the future.
⁎ Corresponding author. Tel.: + 98 311 3913432; fax: + 98 3113912254. E-mail addresses: [email protected] (S. Eslamian), [email protected] (M.J. Khordadi), [email protected] (J. Abedi-Koupai). 0921-8181/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.gloplacha.2011.07.001
Most studies regarding effects of climatic change on water balances have treated evapotranspiration as a function of temperature or as a function of the impact of elevated CO2 on stomatal conductance (Gleick, 1987). Evapotranspiration (ET) is a term used to describe the sum of evaporation and plant transpiration from the earth's land surface to atmosphere. Evaporation accounts for the movement of water to the air from sources such as the soil, canopy interception, and water bodies. Transpiration derives from the movement of water within a plant and the subsequent loss of water as vapor through stomata in its leaves. Evapotranspiration is an important part of the water cycle (Allen et al., 1989). The investigation by Yu et al. (2002) concluded that solar radiation and wind speed are the most sensitive and the least sensitive variables of the modified Penman formula, respectively and the relative humidity has the property that increasing their values will decrease the evapotranspiration estimates. A sensitivity analysis of Penman–Monteith potential evapotranspiration model that was performed by Bois et al. (2005) showed that wind speed and solar radiation temporal variability have a great impact on potential evapotranspiration computation. Wind speed omission in empirical formulae can thus be an important source of uncertainties for PET estimation (possible maximum evapotranspiration or potential ET), especially under Mediterranean conditions. Radiation based methods, using remotely sensed solar radiation from satellites images, are more accurate than temperature based methods in Oceanic and Mediterranean climates. Recently, considerable works have been devoted to the examination of the potential impact of climate change on the water resource
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piration estimated by the Penman-Monteith formula multiply by crop coefficient (Kc) using CropWat software. Crop coefficients depend on the effects of crop height, crop-soil surface resistance, and the albedo of the crop-soil surface. The magnitude of the crop coefficient varies with the crop type and species and the characteristics of plant growth period. CropWat for Windows version 4.2 is software presented by FAO and computes the actual evapotranspiration using Penman– Monteith formula. According to results of Allen et al. (1989), the Penman–Monteith formula has the best results in comparing with the other methods for calculating evapotranspiration. In this research the Penman–Monteith results are compared with lysimeter data in all studied stations. Lysimeter is an important criterion to verify evapotranspiration estimated with Penman–Monteith formula. A high correlation is found between them in each station. The meteorological variables that are likely to be altered due to climate change (e.g., temperature, relative humidity, solar radiation, and wind speed) are the ones to be tested for their sensitivity in the evapotranspiration estimation using the Penman–Monteith formula.
management systems. These studies revealed that the demand for irrigation water is particularly sensitive to changes in precipitation, temperature, and the concentration of carbon dioxide (Frederick and Major, 1997). The investigation by McCabe and Wolock (1992), based on an irrigation model, concluded that the increase in annual mean of water requirement is strongly associated with the increase in temperature. Several studies have been performed to assess climate change on evapotranspiration (ET) and the land surface water balance. Unfortunately, there are no long-term ET observations. Methods that enable direct measurements (e.g., via eddy flux methods) have only been available for about 20 years, and are still used primarily in intensive research settings rather than for assessing long-term trends. Another source of evaporation data is records from evaporation pans, which are generally located in agricultural areas and have been used as an index to potential evaporation. These records are generally longer it is the case of USA where several hundred locations have record data for approximately 50 years. Several studies (e.g., Peterson et al., 1995; Golubev et al., 2001) have shown that pan evaporation records over the United States generally had downward trends over the second half of the 20th century. This is contrary to the expectation that a generally warming climate would increase evapotranspiration. These changes may be occurred in other regions of the world. Estimates of global warming are generally based on the application of general circulation models (GCMs), which attempt to predict the impact of increased atmospheric CO2 concentrations on the weather variables. Owing to the complex mechanism in the atmosphere motion and the uncertainty of the model structure, different GCMs produce different predictions. In Iran, as an arid and semi-arid country, about 93.5% of the total water resources have been used for agriculture purposes, therefore changes in climatic regimes may affect the agricultural water demands, because evapotranspiration may be affected by the changes in meteorological variables (e.g., temperature, solar radiation, wind speed, and relative humidity). In this research, a sensitivity analysis of these meteorological variables using the Penman–Monteith formula (Doorenbos and Pruitt, 1984) is first investigated to find which meteorological variables are significantly sensitive to the evapotranspiration estimation. The trend and persistence analyses of these sensitive meteorological variables are then studied further to discover whether their trends and persistence exist in the historical time series due to a climate change. Because changes in climatic regimes may affect agricultural water demands, and evapotranspiration may be affected by changes in meteorological variables (e.g., temperature, solar radiation, wind speed, and relative humidity), finally, the effects of climate change on evapotranspiration were observed based on a study of the sensitivity and trend analyses.
In order to evaluate effect of meteorological parameters on evapotranspiration, sensitivity analysis is performed to find more sensitive parameters. For a general definition of sensitivity, consider the variable V, which is a function of the input variables x1…xn :
2. Material and methods
V = f ðx1 ; …; xn Þ
Five stations under study in Iran display an appropriate geographical distribution for this study purpose as shown in Fig. 1 and Table 1. The stations include: Tehran, Esfahan, Shiraz, Tabriz and Mashhad. The 54 years monthly meteorological data from 1951 to 2005 have been used for this study. The meteorological data were temperature, relative humidity, sunshine duration, wind speed, and precipitation.
2.1.1. Penman–Monteith formula The Penman–Monteith formula (Karlsson and Pomade, 2003) is given by the following equation:
ET0 =
ð1Þ
where, ET0 Rn G T u2 es ea Δ γ
reference crop evapotranspiration, in mm/day, net radiation at the crop surface, in MJ/m 2/day, soil heat flux density, in MJ/m 2/day, mean daily air temperature at 2 meters height, in wind speed at 2 meters height, in m/s, saturation vapor pressure, in KPa, actual vapor pressure, in KPa, slope of vapor pressure curve, in KPa/ 0 C, and psychrometric constant, in KPa/ 0 C.
0
C,
2.2. Sensitivity analysis calculations
ð2Þ
If the variables x1…xn are independent of V, it may be written: V + ΔV = f ðx1 + Δx1 ; …; xn + Δxn Þ
ð3Þ
From a Taylor series expansion we have, neglecting higher-order terms:
2.1. Evapotranspiration calculation using Penman–Monteith formula ΔV = The Penman–Monteith formula has been recommended as a suitable method for estimating the reference evapotranspiration. Therefore, it has been chosen in this study to estimate the effect of climate change on the evapotranspiration for alfalfa crop. Alfalfa is planted in the studied stations and also in Iran widely. The actual crop evapotranspiration can be calculated from the reference evapotrans-
900 0:048ΔðRn −GÞ + γ T + 273 u2 ðes −ea Þ Δ + γ ð1 + 0:34u2 Þ
∂V ∂V Δ x1 + … + Δ xn ∂ x1 ∂ xn
ð4Þ
By definition, the partial differentials, δV/δxi , are the sensitivities, Sxi , of the dependent variable V to the independent input variable xi (McCuen, 1974, 2003; Saxton, 1975; Beven, 1979). They denote the change in V per unit change in xi.
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Fig. 1. Situation of the studied regions in Iran.
Now, the relative change in V can be expressed as (Saxton, 1975),
From Eq. (4) we have:
Sxi =
∂V ΔV = Δxi ∂xi
ð5Þ
Which show that Sxi may be obtained by calculating directly the value of the partial differential, or by applying a step change in xi, while leaving the variables other than xi constant. Here Sxi may be sensitive to the relative magnitude of V and xi. Therefore, Sxi may be divided by the ratio V/xi, which leads to the relative sensitivity or sensitivity coefficient RSxi:
RS xi =
∂ V xi ∂xi V
ð6Þ
ΔV Δx1 Δxn = RSx1 + … + RSxn V x1 xn
ð7Þ
which shows that the relative sensitivity coefficient denotes the part of the relative change in xi that is transferred to the relative change in V. If, for example, RSxi = 25%, a 10% change in xi will result in a 2.5% change in V. Sensitivity analysis was implemented with Matlab software and results are shown in Fig. 2.1.1 to 4.3. In these figures, RH (Relative Humidity) is in percent, Tmax (Temperature) is in centigrade degree, n (daily average of Sunshine Duration) is in hours, u (Wind Speed) is in km/day and Rs (Relative Sensitivity) is dimensionless. 2.3. Trends and persistence of meteorological variables
Table 1 Characteristics of the regions. Region
Latitude (n)
Longitude (e)
Elevation (m)
Average of temperature (°c)
Climate
Tehran Esfahan Shiraz Tabriz Mashhad
35° 32° 29° 38° 36°
51° 51° 52° 46° 59°
1190 1550 1484 1361 999
18 16.7 19 13 15
Cool semi-arid Cool semi-arid Warm semi-arid Cold semi-arid Cool semi-arid
41′ 37′ 32′ 5′ 16′
19′ 40′ 36′ 17′ 38′
Investigation of trends and persistence in historical meteorological data are helpful in understanding the effect of climate change on evapotranspiration in the stations under study. The trends and persistence of the time series can be tested by different methods. The approaches used in this study are the Mann–Kendall test (Kendall, 1975), the Cumulative Deviation test (Buishand, 1982), Regression Analysis (Yu et al., 2002), and the Autocorrelation Coefficient (Yu et al., 2002). Trend tests are only used for sensitive parameters in Penman–Monteith formula in each region. If at least three trend tests confirm existence of trend, the parameter displays the stable and significant trend.
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Fig. 2. 1. 1. The ET relative sensitivity to temperature considered relative humidity. Fig. 2. 2. 1. The ET relative sensitivity to wind speed considered relative humidity.
Fig. 2. 1. 2. The ET relative sensitivity to temperature considered wind speed.
Fig. 2. 2. 2. The ET relative sensitivity to wind speed considered temperature.
Fig. 2. 1. 3. The ET relative sensitivity to temperature considered sunshine duration.
Fig. 2. 2. 3. The ET relative sensitivity to wind speed considered sunshine duration.
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Fig. 2. 3. 1. The ET relative sensitivity to sunshine duration considered temperature.
Fig. 2. 3. 2. The ET relative sensitivity to sunshine duration considered relative humidity.
Fig. 2. 3. 3. The ET relative sensitivity to sunshine duration considered wind speed.
Fig. 2. 4. 1. The ET relative sensitivity to relative humidity considered temperature.
Fig. 2. 4. 2. The ET relative sensitivity to relative humidity considered wind speed.
Fig. 2. 4. 3. The ET relative sensitivity to relative humidity considered sunshine duration.
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2.3.1. Mann–Kendall test The Mann–Kendall test, suggested by the World Meteorological Organization (1988), is a common method to test the trend of the time series. This method defines the standard normal variate, T , as:
r T = qffiffiffiffiffiffiffi σ2r
σ r =
4p −1 nðn−1Þ
ð9Þ
2ð2n + 5Þ ½9nðn−1Þ
ð10Þ
where MSE is the mean square error and Sxx is: n 2 Sxx = ∑ Xi −X
ð17Þ
i=1
ˆ is not significantly If |T| N tα/2, n-2, the null hypothesis (H0: slope b ˆ is significantly different different from zero) is rejected and slope b from zero and shows trend existence.
r =
2
ð8Þ
193
where p is the number of pairs observations (xi, xj, j N i, xj N xi) and n is the number of years under consideration. The time series have a trend at the significant level of 5%, if |T| N Tα/2 = 1.96. α is the level of signification. A positive value of T indicates an increasing trend in the time series, while a negative value describes a decreasing trend. 2.3.2. Cumulative deviation test In order to confirm the presence of trends in meteorological historical records, a test for homogeneity in the data is performed. The test for homogeneity is based on the adjusted partial sums or cumulative deviations from the mean: k
Sk = ∑ Yi −Y ; i=1
k ¼ 1; 2…n
ð11Þ
where Y is the mean of Yi values and n is the number of values. For the homogeneous series of records, the values of Sk fluctuate around zero. The re-scaled adjusted partial sums S⁎k are obtained by dividing the Sk values by the sample standard deviation as follows:
Sk = Sk = DY ;
k = 1; 2…n
n 2 2 DY = ∑ Yi −Y = n i=1
rk =
ð18Þ
N 2 ∑ Xt −X
t=1
where, Xt is the meteorological variable at time t, N is the number of samples, k is the time lag, and X is the mean of Xt. The significance of r1 is tested by determining the (r1)t value with the following equation: ðr1 Þt =
pffiffiffiffiffiffiffiffiffiffiffi −1Ftg N−2 N−1
ð19Þ
where tg is the standard normal variate of a Gaussian distribution. In recent equation (Eq. (19)), if r1 is negative, the series has a significant oscillation with a high frequency and it's the associated period is short. In contrast, if r1 is positive, the series has a Markov linear type persistence.
ð13Þ
Fig. 2.1.1 to .4.3 show the effects of the change rate of input meteorological variables on the estimation of the Penman–Monteith formula. With an increase in relative humidity and wind speed, and a decrease in sunshine, evapotranspiration sensitivity to temperature increases directly. With an increase in temperature, and a decrease in relative humidity and sunshine, evapotranspiration sensitivity to wind speed directly increases too. With an increase in relative humidity and a decrease in temperature and wind speed evapotranspiration sensitivity to sunshine increases directly, and finally an increase in wind speed and a decrease in temperature and sunshine, evapotranspiration sensitivity to relative humidity increases inversely. Albeit all the meteorological variables are sensitive, the most sensitive parameters are temperature and relative humidity. Effect of humidity on evapotranspiration is more than wind speed, humidity with closing to 100% makes evapotranspiration decrease, therefore wind speed effect decrease. In lower humidity, evapotranspiration is more and wind speed changes affect on evapotranspiration with more severity. The results of sensitivity analysis are displayed in Table 2. The four above mentioned methods (Mann–Kendall test, Cumulative Deviation
ð14Þ
2.3.3. Regression analysis The meteorological variables in the regression analysis are first smoothed using the moving average method. Five years moving average is adopted in this analysis. A simple linear regression equation is then selected to detect the long-term trends of the meteorological variables. ð15Þ
ˆ are where Y is the meteorological variable, X is the time, and â and b the regression coefficients calculated by the least square method. Determining the T value distributed with n − 2 degrees of freedom by the following equations tests the significance of the regression slope: ˆb T = pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi MSE = Sxx
∑ Xt −X Xt + 1 −X
t=1
3. Results
pffiffiffi High values of Q indicate a change in mean. Critical values of Q/ n for the 95% confidence interval are found to be equal to 1.27.
Y = ˆa + ˆbX
N−k
ð12Þ
Based on the S⁎k values, a statistic Q, which is sensitive to departures from the homogeneity, can be defined as: Q = max0≤k≤n Sk
2.3.4. Autocorrelation coefficient Persistence analysis is used to detect the relationship of variables between two continuous time steps in the series. This study uses the autocorrelation coefficients of the meteorological variables to test their persistence in time. The autocorrelation coefficient with a lag time of k is defined as follows:
ð16Þ
Table 2 The results of sensitivity analysis using Penman–Montith formula. Region
Temperature
Humidity
Wind speed
Sunshine duration
Tehran
++
++
+
+
+ sensitive. + + very sensitive.
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Table 3 The statistical tests for various meteorological variables in Tehran, Esfahan, Shiraz, Tabriz, and Mashhad. Temperature
Humidity
Wind speed
Rainfall
Tehran M–K C-D L-R PER
+ (↑) – – +
+ (↑) – – +
+ (↓) + + +
– – – +
Esfahan M–K C-D L-R PER
– – – +
+ (↓) + + +
+ (↓) + + +
– – – +
Shiraz M–K C-D L-R PER
+ (↑) – – +
+ (↓) + + +
+ (↓) + + +
– – – +
Tabriz M–K C-D L-R PER
– – – +
+ (↓) + + +
– + – +
+ (↓) + + +
Mashhad M–K C-D L-R PER
+ (↑) – + +
+ (↓) + + +
– + – +
– – – +
↑: increasing trend; ↓: decreasing trend. +: significantly homogeneous data in trend; –: not significantly homogeneous data in trend. M–K: Mann–Kendall test; C-D: cumulative deviations test; L-R: simple linear regression; PER: persistence analysis by autocorrelation coefficient.
test, Regression Analysis, and Autocorrelation Coefficient) are utilized to detect trends of the meteorological variables for fifty-year records in five climatologically different stations of Iran. Trend tests did not perform for sunshine duration because of missing data in its time series and rainfall entered to trend test separately. The results are presented in Table 3. They reveals that humidity in Esfahan, Shiraz, Tabriz and Mashhad decreases and it has the significant and stable trends; wind speed in Esfahan, Tehran and Shiraz has the significant trends and such that it decreases. In Mashhad, temperature has a significant trend such that it increases, and in Tabriz, precipitation depth decreases with the significant trend. The rest of the studied parameters (including humidity in Tehran, wind speed in Mashhad and Tabriz, temperature in Tehran, Esfahan, Shiraz and Tabriz, precipitation depth in Tehran, Esfahan, Shiraz and Mashhad) have not the significant trends. Decreasing of humidity and rainfall and increasing temperature are the main effects of global warming. An important cause of decreasing in wind speed could be urbanization. 4. Discussion and conclusions Changes in the meteorological variables play an important role in the assessment of evapotranspiration with climate change. The simultaneous consideration of the effects of meteorological variables such as temperature, relative humidity, and sunshine duration on the evapotranspiration estimation with climate change is necessary. In each station, it is found that the different set of meteorological variables have an influence on the evapotranspiration estimation. These results can be affected by climate change phenomenon in arid and semi-arid regions such as Iran and it should be considered as a warning.
Effect of climate change on temperature and relative humidity is increasing and decreasing respectively, that makes evapotranspiration increase, because these parameters are sensitive in PenmanMonteith formula. When evapotranspiration increase, water requirement will be raised and much water is needed. Considering water limitation, crop will not function with high productivity and also lead to economic loss. It is necessary to use alternative methods to reduce damages for example tolerant variety, deficit irrigation techniques and so on. In addition to the meteorological variables, the physiological response of the alfalfa plant to the concentration of CO2 probably plays an important role in the evapotranspiration estimation. However, it has not been considered here and needs further research. The research was only used 54 years data; if more data (for example more than 100 years) were available, better results could be obtained.
Acknowledgment Special thanks are given to Dr. Safieh Mahmoodi, Assistant Professor of Mathematical Sciences Department in Isfahan University of Technology that helped us in this study and Iranian Meteorological Organization for providing the data.
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