Stoch Environ Res Risk Assess (2013) 27:1407–1421 DOI 10.1007/s00477-012-0677-7
ORIGINAL PAPER
Analysing trends in reference evapotranspiration and weather variables in the Tons River Basin in Central India Darshana • Ashish Pandey • R. P. Pandey
Published online: 26 December 2012 Springer-Verlag Berlin Heidelberg 2012
Abstract In this study, reference evapotranspiration (ETo) has been estimated using Penman–Monteith (PM) method on monthly time step. The monthly values have been subsequently used to estimate annual and seasonal ETo values. The trend analysis has been carried out for monthly, annual and seasonal ETo values for three meteorological stations namely Allahabad, Rewa and Satna located in the Tons River Basin in Central India. Further, the trend of weather variables that affect ETo have been examined using the Mann–Kendall test after removing the effect of significant lag-1 serial correlation from the time series using trend free-pre-whitening (TFPW) method. The magnitude of trends has been calculated using Sen’s slope estimator. Almost all the months show the significant decreasing trend in ETo values at a significance level of 1, 5 and 10 %. The significant decreasing trends were also found in annual and seasonal ETo values during the period of analysis. The magnitude of decrease in annual ETo varied from -1.75 to -8.98 mm/year. On the seasonal scale, stronger decreasing trends were identified in ETo in pre monsoon and monsoon season as compare to that of winter and post monsoon season. The significant decreasing trends were found in monthly, annual and seasonal wind speed. However, significant increase was found in annual air temperature Darshana (&) Department of Water Resources Development and Management, IIT Roorkee, Roorkee 247 667, India e-mail:
[email protected] A. Pandey Department of Water Resources Development and Management, IIT Roorkee, Roorkee, Uttrakhand, India e-mail:
[email protected] R. P. Pandey National Institute of Hydrology, Roorkee 247667, India e-mail:
[email protected]
(maximum, minimum, mean and dew point temperature) and relative humidity. Using the sensitivity analysis, maximum temperature and net solar radiation was found to be the most dominant variables which influence the rate of annual ETo over all the stations. Keywords Reference evapotranspiration Mann–Kendall test Trends Weather variable Sensitivity analysis Tons River Basin Madhya Pradesh India
1 Introduction Evapotranspiration is one of the important parameter for irrigation scheduling and regional water allocation. Any change in meteorological variables due to climate change will affect evapotranspiration or crop water requirement. In recent years, numerous studies have been conducted to examine the trend in Pan Evaporation (Epan) and in reference evapotranspiration (ETo) for many regions resulting in different conclusions. Golubev et al. (2001) reported 1–6 % decreases in Epan over most of the United States and the former Soviet Union national during the period of 1951–1990. In Australia, the decrease in Epan was 6.8 % (Roderick and Farquhar 2004) over the last 33 years (1970–2002). Zhang et al. (2007) found 1 and 4 % decreases in Epan and ETo respectively across the Tibetan Plateau during the period 1966–2001. Hobbins el al. (2004) found decreasing trends in Epan at 64 % of pans in the conterminous U.S. over the period of 1950–2002. Liu et al. (2004) observed decrease in Epan ranged between 3 and 24 % in China from 1955 to 2000. Decreasing trends in annual potential and actual evapotranspiration were found in most parts of the Haihe River Basin during 1960–2002 (Gao et al. 2012). The decrement of 5.04 % in annual ETo compared to 48 years
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(1961–2008) average value (Yin et al. 2010) over entire China. The ETo decrease in North China Plain was 5 % (Song et al. 2010) during 1961–2006. Xu et al. (2006) reported decrease in both Epan (11 %) and ETo (5.6 %) for the whole Changjiang (Yangtze River) catchment, China during 1960–2000. Tebakari et al. (2005) observed decreasing trend in Epan in the Chao Phraya River basin, Thailand over a period of 19 years (1982–2000). On the other hand, several researchers also reported increases in ETo trend across the world. Yu et al. (2002) observed increasing trend in ETo at Kao-Hsiung, south Taiwan, using 48 years of data (1950–1997). Burn and Hesch (2007) established both increasing and decreasing trends in lake evaporation calculated at 48 stations of the Canadian Prairies for 1971–2000. Further, this study concluded that increasing trends were more in northern regions and decreasing trends were more in the southern regions. Dinpashoh et al. (2011) reported maximum increase was 28 % and maximum decreasing was 18 % in annual ETo over Iran during the period 1965–2005. Analysis of Epan (from 1964 to 1998) in the central coastal plains of Israel showed a significant increase of 7 % (Cohen et al. 2002). Tabari et al. (2011a) found ETo significant increasing trends in 75 % of the stations in the western half of Iran in past 50 years (1955–2001). The significant increase was 8 % in annual ETo during the 45 years (1960–2005) in Southern Spain (Espadafor et al. 2011). da Silva (2004) reported maximum increase was 42 and 25.2 % in Epan and ETo respectively in Northeast Brazil using the 30 years data (1961–1990) for different stations. There is very few studies in literature related to ETo in India which shows decrease in recent decades. Bandyopadhayay et al. (2009) reported maximum decrease in ETo were 0.03 mm/day/year all over India during 1971–2002. Jhajharia et al. (2011) indicated annual significant decrease in ETo at the rate of 7.7 % in north east India during 1979–2000. The meteorological variables which can have influence on ETo are: air temperature, sunshine duration, relative humidity, wind speed and solar radiation. However, as reported by various researchers important meteorological parameters controlling ETo are sunshine duration or solar radiation in Russia and United States (Peterson et al. 1995), in China (Gao et al. 2006; Liu et al. 2004; Thomas 2000), in southwest England (Ishak et al. 2010) and in Israel (Cohen et al. 2002) while others have mainly attributed it to wind speed in Australia (Rayner 2007; Roderick et al. 2007), Tibetan Plateau (Chen et al. 2006; Zhang et al. 2007), Canadian Prairies (Burn and Hesch 2007), Iran (Dinpashoh et al. 2011) and North East India (Jhajharia et al. 2011), to relative humidity in India (Chattopadhyay and Hulme 1997), as well as to maximum temperature in China (Cong and Yang 2009) and in western half of Iran (Tabari et al. 2011a).
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Evapotranspiration (ET) affects crop water requirement and future planning and management of water resources. Therefore, for future crop planning and management of water resources, expected change in Evapotranspiration will be a prerequisite. Quantitative estimation of the ETo trend using long term data may provide insight into the possible impacts of climate change on the future water balance and water resource planning in the Tons River Basin. The present study has been undertaken with the following specific objectives: (1) to estimate the ETo using the Food and Agriculture Organization (FAO-56) Penman– Monteith (PM) method; (2) to detect the monotonic linear trends in monthly, annual and seasonal ETo series and weather variables which effect ETo using the non-parametric Mann–Kendall test; (3) to estimate the magnitude of trend in ETo times series and weather variables using the Theil–Sen’s estimator method; and (4) to identify the most dominating meteorological variables which affect the ETo time series using sensitivity analysis.
2 Materials and methods 2.1 Study area The Tons River Basin (also known as the Tamsa River) is a tributary of the Ganges flowing through the states of Madhya Pradesh (MP) and Uttar Pradesh (UP) in Central India. It originates from Kamore hills in Satna district of MP which lies between 23570 and 25200 N latitudes and 80200 E to 83250 E longitudes (Fig. 1). The Tamsa rises in a tank at Tamakund in the Kaimur Range at an elevation of 610 m. It flows through the fertile districts of Satna and Rewa in MP. The river receives the Belan in UP and joins the Ganga at Sirsa, about 311 km downstream of the confluence of Ganga and Yamuna. The total length of the river is 264 kilometres (km). The total catchment area of the Tons River Basin is 18,158 km2, out of which 11,974 km2 lies in MP and the remaining lies in UP. The river meets Ganga after flowing 246 km in MP, 7 km making boundary between MP and UP and finally 67 km in UP. The total annual rainfall in Tons River Basin varies from 930–1,116 mm/year (Table 1). About 90 % of the total rainfall occurs during monsoon (June–September) season. The numbers of rainy days are maximum in the months of July and August. May–June is the hottest month with daily maximum temperature of up to 46 C while January is coolest with minimum temperature of 5 C. The wind speed varies from 0.43 m/s (November) to 1.29 m/s (June) in the region (monthly average from 1969 to 2003 between stations). The predominant wind direction is West to North West. The weather remains dry for all the seasons except monsoon when the humidity is around
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80 %. The mean relative humidity varies from 29 to 80 %. The main crops grown in Tons catchment are wheat, rice, soyabean, millets and pluses. The major crop is wheat and the minor crop is millets. The popular cropping pattern is wheat–pulses and rice–wheat–pulses. The soil type in study area is mainly the deep black soil, shallow black soil and the mixed red and black soil. The Tons River Basin is characterized by mean annual ETo of 1,486–1,578 mm/ year.
bulb temperature (C), relative humidity (%) and wind speed (km/h) which were averaged over each calendar month in order to get the monthly values of each meteorological variable. The 24-h wind speed was recorded in km/h at 10 m height. Quality of data was checked prior to the analysis. By visual inspection some noticeable error has been found. There were few missing observations in the time series of variables. These missing data were substituted with the corresponding long-term mean.
2.2 Details of data
2.3 Methods
There are only three meteorological stations (i.e. Satna, Rewa, and Allahabad) in the Tons River Basin which are maintained by Indian Meteorological Department (IMD) Pune, from where daily data were obtained. The details of these stations are given in Table 2. The data were comprised of the daily values of the following seven variables maximum air temperature (C), minimum air temperature (C), dew point temperature (C), dry bulb temperature (C), wet
2.3.1 Penman–Monteith (PM) method The modified Penman–Monteith FAO-56 (Allen et al. 1998) was used to calculate ETo. It has two important advantages. First, it can be used in a great variety of environments and climate scenarios without any local calibrations due to its physical basis. Second, it is a well documented method that has been validated using lysimeters under a wide range of
Fig. 1 Location map of the Tons River basin. Source India and Madhya Pradesh map was taken from Wikipedia (http://en.wikipedia.org/wiki/ River_basins_in_Madhya_Pradesh) and TonsRiver Basin map was prepared using topo-sheet of river basin
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Table 1 Mean monthly total rainfall (mm) over Tons River Basin Months/ stations
January
February
March
April
May
June
July
August
September
October
November
December
Annual
Satna
20.5
23.9
11.4
6.9
22.4
134.2
328.4
317.6
206.3
29.3
10.8
5.3
1116.9
Rewa
21.3
22.9
7.3
6.9
10.1
128.7
319.7
344.3
207.9
38.4
11.1
5.1
1123.6
Allahabad
17.0
14.9
9.1
8.1
14.6
105.8
250.9
265.1
198.8
31.1
7.9
7.2
930.4
Table 2 Locations of weather stations of Tons River Basin (IMD, Pune) S. N.
Station code
Station name
Lat. (N)
Long. (E)
Alt. (m amsl)
Duration of data
1
42475
Allahabad
25.27
81.44
98
1969–2008
2
42471
Satna
24.34
80.5
317
1969–2008
3
42474
Rewa
24.32
81.18
299
1969–2003
Lat., long., alt., and m amsl and denote latitude, longitude, altitude and meter above mean sea level respectively
climate conditions. The drawback of this method is that a large number of meteorological parameter are required for its application, i.e. air temperatures, relative humidity, wind speed and solar radiation (Landeras et al. 2008). The Penman–Monteith equation recommended by FAO (Allen et al. 1998) has been successfully used to calculate ETo under different climatic conditions (Abdelhadi et al. 2000; Kite and Droogers 2000; Kang et al. 2003; Goyal 2004; Gavilan and Castillo-Llanque 2009; Trajkovic and Kolakovic 2009; Jhajharia et al. 2011; Tabari et al. 2011b). The modified Penman–Monteith (Allen et al. 1998) equation used to calculate the ETo is given in Eq. 1: ET0 ¼
900 0:408DðRn GÞ þ c Tþ273 u2 ðes ea Þ D þ cð1 þ 0:34U2 Þ
ð1Þ
where ETo is the reference crop evapotranspiration (mm/day); D is the slope of vapor pressure versus temperature curve at temperature T (kPaC-1); c is the psychrometric constant (kPaC-1); u2 is the wind speed at a 2 m height (m/s); Rn is the net radiation at crop surface (MJ m-2 day-1); G is the soil heat flux density (MJ m-2 day-1); T is the average air temperature at 2 m height (C); es is the saturation vapor pressure at the temperature of air. ea is the actual vapor pressure (kPa); (es–ea) is the saturation vapor pressure deficit (kPa). In this study, the wind speed at 2 m height, used in the P–M method, was transformed from wind speed at 10 m height which is observed at all the stations in the study area. The weather variables used to calculate the parameters of ETo such as maximum temperature; minimum temperature; dew point temperature; mean temperature and wind speed. Moreover, various parameters used in the calculation of ETo (Eq. 1) are (actual vapour pressure, vapour pressure deficit, extraterrestrial radiation, net radiation and soil heat flux) calculated using
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the procedure suggested by Allen et al. (1998) and shown in Table 3. Because of the absence of sunshine hour measurements in the study area, the method suggested by Allen et al. (1998) was used for calculation of solar radiation. The method estimates solar radiation using the diurnal temperature range (Tmax - Tmin), based on the fact that differences between maximum and minimum temperatures were closely related to the existing daily solar radiation at a given location. Equation 1 is implemented using monthly averages, with radiation modelling performed for the 15th day of each month, to calculate a mean daily monthly value of ETo (Allen et al. 1998). These results are then multiplied by the number of days in each month to provide a monthly estimate of ETo. Seasonal ETo was obtained by adding monthly values during particular season and similarly, annual ETo is derived by summing monthly values in a year. 2.3.2 Mann–Kendall (MK) test The MK test (Kendall 1975; Mann 1945) is the rank based nonparametric test for assessing the significance of a trend. This test has the several advantages over parametric methods. Some of these advantages include: (1) does not require the assumption of normality or the assumption of homogeneity of variance (2) compare medians rather than means and, as a result, if the data have one or two outliers, their influence is negated (3) prior transformations are not required, even when approximate normality could be achieved; (4) greater power is achieved for the skewed distributions (5) data below the detection limit can be incorporated without fabrication of values or bias (Helsel 1987). This method has been used widely across the world to detect trend in ETo and other hydrological variables (Burn and Hesch 2007; Dinpashoh et al. 2011; Golubev et al. 2001; Hobbins el al. 2004; Jhajharia et al. 2011; Roderick and Farquhar 2004; Wang et al. 2011; Yang et al. 2009; Yes¸ ilırmak 2012; Zhang et al. 2007, 2011, 2012). It is based on the test statics S defined as S¼
n1 X n X
sgn Xj Xi
ð2Þ
i¼1 j¼iþ1
where, x1, x2, … xn represent n data points where xj represents the data point at time j.
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Table 3 Parameters used in the reference evapotranspiration equations (Allen et al. 1998) S. N.
Parameter
Equation
Definition
1
Wind speed (u2)
4:87 u2 ¼ uz lnð67:8z5:42 Þ
Uz = Wind speed measurement at height z (m/ s) U2 = Wind speed measurement at 2 m height (m/s)
2
z = Height of wind speed measurements (m)
D = Slope vapour pressure curve (kPa/C)
Slope vapour pressure curve (D)
D¼
3
Psychrometric constant (c)
c ¼ 0:665 103 P
4
Atmospheric pressure (P)
P ¼ 101:3
5
Mean saturation vapour pressure (es)
eo ðT Þ ¼ 0:6108eðTþ237:3Þ es ¼ e
6
Actual vapour pressure
4098
17:27T 0:6108eðTþ237:3Þ
ðTþ237:3Þ
T = Mean air temperature (oC)
2
c = Psychrometric constant (kPa/C) P = Atmospheric pressure (kPa)
2930:0065z5:26
Z = Elevation above mean sea level (m)
293 17:27T
ea ¼ 0:6108e
o
ðTmax Þþeo ðTmin Þ 2
es = Saturation vapour pressure (kPa)
ea = Average vapour pressure (kPa)
17:27Tdew Tdew þ237:3
Tdew = Dew point temperature (oC)
7
Extraterrestrial radiation (Ra)
Ra ¼ 37:6dr ½ws sinðuÞ sinðdÞ þ cosðuÞ cosðdÞ sinðws Þ
8
Inverse relative distance earth– sun (dr)
dr ¼ 1 þ cos
9
Solar declination (d)
10
Sunset hour angle (xs)
11
Soil heat flux (G)
12
Solar radiation (Rs)
eo ðT Þ = Saturation vapor pressure at temperature T (kPa)
Ra = Extraterrestrial radiation (MJ/m2/day) u = Latitude (rad)
2p 365 J
J = Number of day in the month
2p d ¼ 1 þ sin 365 J 1:39 xs ¼ cos1 ð tanðuÞ tanðdÞÞ G ¼ 0:14 Tmonth;i Tmonth;i1
Rs ¼ 0:16 Ra
pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Tmax Tmin
xs = Sunset hour angle (rad) G = Soil heat flux (MJ/m2/day) Tmonth,i-1 = Mean temperature of previous month (C) Rs = Solar radiation (MJ/m2/day)
A very high positive value of S is an indicator of an increasing trend, and a very low negative value indicates a decreasing trend. 8 9 if ðXj Xi Þ [ 0 = <1 if ðXj Xi Þ ¼ 0 sgnðXj Xi Þ ¼ 0 ð3Þ : ; 1 if ðXj Xi Þ ¼ 0
The standardized test statistic (Z) is computed as follows: 8 S1 pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi if S [ 0 > < VAR(S) if S = 0 ð6Þ Z= 0 > Sþ1 : pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi if S \0 VAR (S)
It has been documented that when n C 10, the statistic S is approximately normally distributed with the mean
The null hypothesis, H0, meaning that no significant trend is present, is accepted if the test statistic (Z) is not statistically significant, i.e. -Za/2 \ Z \ Za/2, where Za/2 is the standard normal deviate.
EðSÞ ¼ 0
ð4Þ
and its variance is VARðSÞ ¼
nðn 1Þð2n þ 5Þ
Pm
i¼1 ti ðti
2.3.3 Theil–Sen’s estimator 1Þð2ti þ 5Þ
18 ð5Þ
where n is the number of data points, m is the number of tied groups (a tied group is a set of sample data having the same value), and ti is the number of data points in the ith group.
The slope of n pairs of data points were estimated using the Eq. 7 which is given by the following relation: Xj Xi b ¼ Median for all i\j ð7Þ ji In which 1 \ j\i \ n and b is the robust estimate of the trend magnitude. A positive value of b indicates an upward
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trend, while a negative value of b indicates a downward trend.
S¼
2.3.4 Trend-free pre-whitening The TFPW-MK procedure of Yue et al. (2002) is applied in the following manner to detect a significant trend in a serially correlated time series. 1. The slope (b) of a trend in sample data is estimated using the approach proposed by Theil (1950) and Sen (1968). The original sample data Xt were unitized by dividing each of their values with the sample mean E (Xt) prior to conducting the trend analysis (Yue et al. 2002). By this treatment, the mean of each data set is equal to one and the properties of the original sample data remain unchanged. If the slope is almost equal to zero, then it is not necessary to continue to conduct trend analysis. If it differs from zero, then it is assumed to be linear, and the sample data are de-trended by: X0t ¼ Xt Tt ¼ XT b t
ð8Þ
2. The lag-1 serial correlation coefficient (r1) of the detrended series Xt. If r1 is not significantly different from zero, the sample data are considered to be serially independent and the MK test is directly applied to the original sample data. Otherwise, it is considered to be serially correlated and AR (1) is removed from the X0t by Y0t ¼ X0t r1 X0t1
ð9Þ
This pre-whitening procedure after detrending the series is referred to as the trend-free pre whitening (TFPW) procedure. The residual series after applying the TFPW procedure should be an independent series. 3. The identified trend (Tt) and the residual Y0t are combined as: Yt = Y0t + Tt
oETo jXj ETo oX
ð11Þ
Neglecting the higher order, first-order Taylor series approximation was applied to calculate S (Lenhart et al. 2002): S¼
DETo jXj ETo DX
ð12Þ
where DX is the relative change of model input value X and DETo is the relative change in ETo induced by DX. The coefficient of S represents changes in ETo induced by changing meteorological variable X. If S is 1.5, then a 10 % increase of X would cause a 15 % increase in ETo, while other meteorological variables are fixed. Positive or negative values of S indicate that the behaviour of ETo is consistent with or contrary to the behaviour of input factors. The higher the value of S, the greater the impact of the meteorological variable on ETo. The primary advantage of S is its being dimensionless, which is useful to sort by order of influence for variables with different units. In this study, sensitivity analyses for daily average ETo in each month were carried out at the study area from -20 to ?20 % at an interval of ±5 % (eight scenarios) to each of the six variables (i.e. maximum air temperature, minimum air temperature, vapor pressure deficit, wind speed at 2 m above the ground, net solar radiation and ground heat flux) while keeping all the other parameters constant. Annual values of S were calculated by averaging monthly values.
3 Results and discussion 3.1 Mean monthly and annual total ETo distribution
ð10Þ
The blended series (Yt) just includes a trend and a noise, and is no longer influenced by serial correlation. Then the MK test is applied to the blended series to assess the significance of the trend. 2.3.5 Sensitivity of ETo to meteorological variables In order to evaluate effect of meteorological parameters on ETo, sensitivity analysis was carried out to find the most sensitive parameters. The sensitivity of ETo to a meteorological variable X is expressed by its derivative, i.e. qETo/ qX (Beven 1979). For the modified PM method with multiple independent variables of different dimensions and ranges, the sensitivity coefficient itself is sensitive to the relative value of ETo and X. To be dimensionless, the
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relative sensitivity coefficient (S) was calculated as (McCuen 1974):
Table 4 presents the average monthly total values of ETo obtained through the PM method for three stations (i.e. Satna, Rewa and Allahabad) in the Tons River Basin. The value of ETo increased from January to May with the most rapid increase from March through May and deceased from May to December. The monthly total ETo in January and February stayed low around 105–115 mm. The monthly ETo reached a peak value in May, for all the stations, in the range of 165–186 mm. The lowest ETo was observed in August (94–100 mm). On a seasonal time scale, the pre monsoon and monsoon seasons ETo values accounted for 33–34 and 37–38 % of the annual total ETo, respectively. Comparatively low temperature (17.4 C), light wind speed (0.3–1.2 m/s) and relatively moderate relative humidity (57–76 %) in the winter season were responsible for low values of ETo. On the other hand, comparatively high
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values of temperature (of about 30 C) and relative humidity (about 33–42 %) in the summer season have led to the occurrence of higher ETo values in the region in hot and sunny months of the summer season. 3.2 Monthly trends in ETo Table 5 shows the ETo trend results in terms of Z statistic and b values for all the months. Almost all the months show the significant decreasing trends in all the stations at 1, 5 and 10 % significance level. Estimation of the magnitude of trends in ETo by Theil– Sen’s estimator for the Satna station revealed that the steep downward ETo trend slopes varies from 0.16 mm per year (in December) to 1.53 mm per year (in July). At the Rewa station, maximum downward slope was observed in June (0.83 mm per year) and upward slope in December (0.17 mm per year). Similarly, Allahabad station shows the maximum downward slope in the June with a value of 2.05 mm per year. Therefore, it can be concluded that maximum decrease in ETo values have prevailed during June at the Allahabad station. 3.3 Annual and seasonal trends in ETo Table 6 enlists significant decreasing trend in annual and seasonal scale obtained from Mann–Kendall test. Significant decreasing trends in annual ETo were observed at all the stations. The magnitude of decreasing trends in annual ETo values varied from -8.987 mm per year for Allahabad to -1.75 mm per year for Rewa. Significant decreasing ETo trend was found mainly in pre monsoon and monsoon seasons at all the stations. In winter and post monsoon seasons only Allahabad and Satna stations show significant decreasing trends over the period of 1969–2008. The magnitude of decreasing trend varied between -3.82 mm per year in pre monsoon season and -0.35 mm per year in post monsoon season for the Allahabad station. 3.4 Trend analysis of monthly weather variables The weather variables affecting ETo are air temperature, relative humidity, sunshine duration, and wind speed. The
Table 5 Monthly reference evopotranspiration trends by Mann– Kendall test and Sen Slope Station name
Satna
Rewa
Allahabad
Z
b
b
January
-2.57
–0.25
February
-2.09
–0.33
0.13
–2.31*
–0.25
March
-4.11
–0.76
–0.79
–0.13
-3.55
–0.72
April
-4.72
–1.03
-2.72
–0.49
-3.97
–1.16
May
-5.23
–1.48
-3.62
–0.55
-4.45
–1.83
June
-4.21
–1.53
-3.16
–0.83
-4.85
–2.05
July
-1.69**
–0.33
–0.61
–0.18
-2.9
–0.6
August
-1.41
–0.11
–0.09
–0.02
–2.13*
–0.21
September
-0.83
–0.09
0.4
0.05
–1.83**
–0.23
October
-1.48
–0.22
–0.42
–0.09
–1.84**
–0.2
November
-3.1
–0.24
–0.06
–0.01
–2.57*
–0.2
December
-2.41*
–0.16
0.17
–2.46*
–0.22
Z 0.27
1.65**
0.017 0.016
Z
b
–3.18
–0.39
Significant trends at 1 % indicated by bold numbers, significant trends at 5 % indicated by *, significant trends at 10 % indicated by **
high vapor pressure deficit and net radiation causes the ETo rate to be higher, while the high actual vapor pressure and soil heat flux causes the ETo to be lower (Allen et al. 1998). The results of Mann–Kendall test and Sen’s Slope estimator on monthly weather variables are shown in Table 7 at 1, 5 and 10 % significance levels. 3.4.1 Trend of air temperature The solar radiation absorbed by the atmosphere and the heat emitted by the earth increases the air temperature. The sensible heat of the surrounding air transfers energy to the crop and exerts as such a controlling influence on the rate of evapotranspiration (Allen et al. 1998). In sunny, warm weather, the loss of water by evapotranspiration is greater as compared to cloudy and cool weather. Results of trend analysis for daily air temperature for each month are shown in Table 7. Only 1 or 2 months show the significant increasing trends in maximum temperature. The increase in magnitude of monthly maximum temperature ranges from 0.22 C per decade in August at Satna to 0.53 C per decade in December at Rewa station. For minimum temperature, the
Table 4 Mean monthly total reference evopotranspiration (mm) obtained through Penman–Monteith method Months/ stations
January
February
March
April
May
June
July
August
September
October
November
December
Annual
Satna
110.3
119.4
165.1
177.0
185.9
153.5
105.5
94.1
104.7
129.1
121.2
112.2
1578
Rewa
105.4
113.5
154.5
163.2
165.9
137.0
Allahabad
101.3
113.5
164.2
179.9
184.8
150.2
102.8
95.6
102.5
124.4
115.1
106.2
1486
110.1
100.4
106.5
126.8
115.9
103.5
1557
The italic value represent the lowest value of ETo
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Table 6 Annual and seasonal trend in reference evapotranspiration Seasons
Annual
Winter
Station
Z
b (mm/year)
Z
Allahabad
-4.86
–8.987
-2.49
Rewa
–1.87**
–1.75
Satna
-4.81
–6.418
Pre-monsoon
Monsoon
Post-monsoon
b (mm/year)
Z
b (mm/year)
Z
b (mm/year)
Z
b (mm/year)
–0.75
-4.18
–3.82
-4.59
–3.079
-2.53
–0.35
0.76
0.254
-3.07
–1.35
–2.14*
–0.968
–0.06
–0.02
-2.69
–0.668
-5.56
–3.25
-3.41
–2.035
–1.92**
–0.41
Significant trends at 1 % indicated by bold numbers, significant trends at 5 % indicated by *, significant trends at 10 % indicated by **
significant increasing trends occurred in February, March, August and September at Allahabad and Satna stations. While Rewa station showed significant increasing trends only in February and July. The magnitude of minimum temperature varied from 0.17 C per decade in September at Satna station to 0.77 C per decade in February at Allahabad station. Further, in mean temperature significant increase was found in February, August, September, November and December at all the stations. The minimum increase in mean temperature magnitude was found in August (0.21 C per decade) at Satna and maximum in February (0.7 C per decade) at Allahabad. Almost all the months show the significant increasing trends in dew point temperature at all the stations. The increase in magnitude varied from 0.15 C per decade in August at Satna station to 1.94 C per decade in March at Rewa station. 3.4.2 Trend of wind speed The process of air movement and vapour removal depends to a large extent on wind which is a major driving factor in transporting the water vapour transpired from the plant into the atmosphere. Wind can help to maintain a vapour pressure deficit around the plant surface. The results of trend analysis for daily wind speed for each month are shown in Table 7. The decreasing trends were found in every month at all the stations, except at the Rewa station where 3 months showed no significant trends. The magnitude of decreasing trends varied from -0.93 metre per second per decade in June to -0.1 metre per second per decades in November. 3.4.3 Trend of relative humidity Relative humidity is the ratio between the amount of water the ambient air actually holds and the amount it can hold at the same temperature. The higher the relative humidity, lowers the ETo rate and vice versa. The results of trend analysis for daily relative humidity for each month are shown in Table 7. The result showed the significant rising trend in 2–8 months depending on the stations. The increase in magnitude varied from 1.8 % per decade in November at Allahabad to 3.1 % per decade in June at Satna station.
123
3.5 Trend analysis of annual and seasonal weather variables Table 8 lists the results of weather variable using MK test and Sen Slope estimate on annual and seasonal scale. The significant increasing trend was observed in annual maximum temperature at Rewa and Satna stations. The magnitude of annual maximum temperature were 0.14 C per decade at Satna and 0.17 C per decade at Rewa station. Seasonally, significant increasing trends were observed in winter and post monsoon seasons at Rewa and Satna stations and in monsoon season at Rewa station. The increasing trends varied between 0.24 C per decade in monsoon and 0.33 C per decade in post monsoon at Rewa. For minimum temperature, significant increasing trends were detected at Allahabad and Satna stations and increase in magnitude were 0.46 and 0.19 C per decade respectively. Seasonally, the magnitude ranges from 0.14 C per decade in monsoon at Satna to 0.55 C per decade in winter at Allahabad. For annual mean temperature, all the stations observed statistically significant increasing trends and increase in magnitude in the range of 0.18–0.31 C per decade. Almost all the stations showed statistically significant increasing trends in annual and seasonal dew point temperature. The increase in dew point temperature magnitude varied from 0.27 C per decade in monsoon at Allahabad to 1.32 C per decade in winter at Rewa station. The significant increasing trend in annual relative humidity was observed at Allahabad and Satna stations. Seasonally, pre monsoon season showed the significant increasing trend at Allahabad and Satna. Only winter season shows the significant increase at Allahabad station. Such a rising trend of relative humidity indicates that specific humidity increase was large enough to produce positive relative humidity trend despite the warming conditions. All the stations showed significant decreasing trend in wind speed on annual and seasonal basis. The annual decrease in wind speed magnitude varied from 0.10 to 0.23 m/s per decade. The decrease in magnitude varied from -0.06 m/s per decade in post monsoon to -0.36 m/s per decade in monsoon season. The potential combination of decrease in wind speed, rise in relative humidity and increase in air temperature (max, min, mean and dew point temperature) has led to decreasing trends in ETo.
Results
January
b
b
Tons Basin
Allahabad
Rewa
Satna
–0.011
b
-3.37
-2.71
–0.011
Z
–1.36
b
–0.015
b
Z
-4.91
Z
0.029
b
Wind speed trends
1.36
Z
Allahabad
2.53* 0.101
0.044
b
Z b
1.39
Z
-3.72
–0.015
-3.09
–0.018
–2.28*
–0.014
-5.35
0.118
3.82
3.79 0.184
0.061
1.51
0.07
0.008
Rewa
Satna
3.56
0.046
2.18*
0.032
1.97*
0.60
0.014
b
Z
1.14
0.008
Z
0.80
b
0.077
0.034
Z
Dew point temperature trends
Allahabad
Rewa
Satna
Mean air temperature trends
3.74
0.033
0.037 1.67**
1.81**
0.01
b
Z
0.001 0.45
b Z
Rewa
Allahabad
0.03
Z
Satna
1.85**
0.047
–0.020
b
0.036 1.77**
0.018
–1.10
b
1.33
0.02
0.61
February
Z
0.71
0.006
b
Z
0.28
Z
Minimum air temperature trends
Allahabad
Rewa
Satna
Maximum air temperature trends
Station
Table 7 Monthly trends in weather variables
-3.29
–0.017
-2.85
–0.018
–1.46
-0.016
-4.32
0.126
4.32
4.37 0.194
0.095
2.66
0.042
1.88**
0.011
0.82
0.016
1.03
0.065
3.11
0.014
0.030 0.89
2.01*
0.017
0.66
0.026
0.54
0.004
0.15
March
-4.17
–0.025
-3.61
–0.056
-3.49
–0.02
-4.82
0.118
2.68
0.82 0.051
0.079
1.65**
0.015
0.83
0.012
0.89
0.008
0.69
0.017
0.97
0.006
0.011 0.45
0.69
0.008
0.52
0.0012
0.03
0.01
0.40
April
-4.63
–0.036
-4.27
–0.05
-4.06
–0.021
-5.57
0.122
2.76
0.89 0.061
0.138
3.41
–0.002
–0.19
0.00
0.20
0.009
0.58
0.03
1.45
0.012
0.042 0.75
2.65
–0.024
–1.14
–0.001
–0.01
–0.016
–0.61
May
-5.53
–0.043
-4.65
–0.093
-5.47
-0.031
-5.55
0.087
2.91
0.11 0.003
0.064
2.17*
–0.039
–1.45
–0.005
–0.64
–0.002
–0.19
–0.003
–0.23
0.005
0.009 –0.09
0.61
–0.058
–1.64
–0.019
–0.65
–0.017
–0.42
June
-4.97
–0.039
-4.28
–0.075
-2.68
–0.026
-5.37
0.019
1.67**
0.43 0.008
0.017
1.77**
0.018
0.95
0.04
2.08*
0.013
0.92
0.028
1.47
0.038
0.018 1.81**
2.14*
0.006
0.16
0.036
1.01
0.016
0.59
July
-4.36
–0.033
-3.66
–0.071
-2.82
-0.026
-4.68
0.01
1.39
2.20* 0.019
0.015
1.93**
0.029
3.43
0.029
1.87**
0.021
2.784
0.033
3.57
0.028
0.018 1.50
3.74
0.027
2.38*
0.029
1.33
0.022
1.96**
August
-5.27
–0.029
-4.72
–0.063
-3.23
–0.023
-5.01
0.012
1.34
1.43 0.026
0.016
1.04
0.038
3.58
0.033
2.23*
0.022
2.12*
0.042
3.53
0.025
0.017 1.45
2.73
0.037
2.51*
0.041
2.17*
0.0314
1.88**
September
-3.85
–0.014
-2.99
–0.036
-3.41
–0.014
-4.88
0.020
1.15
2.37* 0.066
0.00
–0.01
0.037
2.26*
0.027
1.35
0.013
1.11
0.046
2.15*
0.029
0.009 0.69
0.62
0.022
1.28
0.029
1.32
0.0209
1.20
October
-3.99
–0.01
-3.22
–0.024
-2.89
–0.014
-5.27
0.059
3.15
2.95 0.108
0.022
0.55
0.05
3.29
0.06
2.44*
0.038
2.20*
0.055
2.24*
0.042
0.037 1.25
1.30
0.023
1.68**
0.042
1.79**
0.033
2.24*
November
-3.11
–0.011
-2.89
–0.012
–1.40
–0.012
-5.07
0.06
3.25
3.45 0.128
0.06
2.52*
0.034
2.83
0.035
1.99**
0.033
2.58*
0.057
2.64
0.027
0.034 1.59
1.59
0.008
0.62
0.053
2.47*
0.0397
3.02
December
Stoch Environ Res Risk Assess (2013) 27:1407–1421 1415
123
0.005
0.003
0.005
3.6 Sensitivity of ETo to meteorological variables
0.004 b
Satna
Significant trends at 1 % indicated by bold numbers, significant trends at 5 % indicated by *, significant trends at 10 % indicated by **
0.009 0.005 –0.001 0.003 0.005 0.009 –0.003
0.002 –0.11 –0.02 0.62 –0.011 1.11 0.004 1.46 –0.005 –0.39 0.009 0.52 0.01 0.43 b Z
0.004
1.19
0.015 0.94 0.008 0.57 0.009 0.66 0.016 1.01
–0.0122
0.16
0.005 1.04
0.44
–0.0064 –0.0071
0.46 0.92
–0.006 –0.007
–0.92
–1.38
0.0063
0.39 –0.29
–0.004 –0.004
0.58 0.50
–0.02
Z
b
Rewa
–0.59 1.46 –1.04 -2.27 Allahabad
Z
–0.45
–0.0025
–0.0063
0.22
–0.69 –0.90 –0.62 –0.58 –0.62 –1.27
–0.0024
-1.92
0.27 0.179 0.00 –0.033 –0.056 0 0.28 0.231 0.188 0.292 0.216 0.22 b
Net solar radiation (Rn)
0.113
2.66 3.11
0.213 0.20
0.48 -0.61
0.034 –0.061
–1.28 0
–0.119
2.04*
0.063 0.014
2.43* 1.91**
–0.019 0.258
3.56 2.4*
0.291
2.00*
0.078 b
Z
–1.07 0.15 -0.12 2.19* 1.93** 0.60 Z Rewa
Allahabad
1.11 1.42 1.47 0.33
1.54
0.16
-0.91
0.051
0.59 –0.47
–0.061 0.00
–0.05 –0.73
0.00 0.307
-0.031
0.02 2.15*
0.25
2.78 1.46
0.111 0.25
2.28*
0.123 b
1.03 1.2 Z Satna
Relative humidity trends
0.156
November October September August July June May April March February January Results Station
Table 7 continued
123
0.135
Stoch Environ Res Risk Assess (2013) 27:1407–1421 December
1416
ETo is one of the main elements in the hydrological cycle, which is governed by air temperature, sunshine duration, relative humidity and wind speed. ETo is a nonlinear complex function of many parameters and change in any one parameter influences the other parameter and therefore, the effect of such changes on ETo is very difficult to understand (Dinpashoh et al. 2011). Table 9 presents the results of sensitivity of ETo to meteorological variables. Except minimum temperature, ETo reacted co-directionally to maximum temperature, wind speed, vapour pressure deficit and net solar radiation. By comparing sensitivity coefficient (S), ETo was found to be most sensitive to maximum temperature, followed by net solar radiation, minimum temperature, vapor pressure deficit and wind speed respectively. The sensitivity of ETo to maximum temperature was highest with S of 1.49 at Satna station which indicate that ETo would increase by 14.9 % in response to the 10 % increment of maximum temperature if other meteorological variables remain constant. Similarly, the sensitivity of ETo to maximum temperature was 1.31 and 1.24 for Allahabad and Rewa stations respectively. On monthly time scale, maximum temperature has the highest S in July–September ranged between 1.83 and 2.74 between the stations.
4 Discussion ETo was estimated using the widely used Penman–Monteith method over the three sites in the Tons River Basin. During the entire study period, the average yearly value of the reference evapotranspiration was between 1,486 and 1,578 mm. The highest values of ETo are registered where the air temperature has higher values (Satna station), located at lower longitude. It is also noticed from analysis that the values of the reference evapotranspiration decrease from the west towards the east, however, air temperature decreases from the south to the north. The average seasonal total ETo varied from 240 mm (in post monsoon) to 594 mm (in pre-monsoon) for the entire study area. The monthly ETo reached a peak value in May, for all the sites, in the range of 165–186 mm. The lowest ETo has been observed in August (94–100 mm). The largest amount of water (891–959 mm) evaporates during April–October as a result of an increase in wind speed and temperature. Over a half of this amount of water evaporates during the period between March and May (about 484–529 mm), when plants require a high amount of water. The annual average reference evapotranspiration overtakes the amount of average yearly rainfall by 387–520 mm shows the deficient water balance. The maximum deficit was observed at the Allahabad station and minimum at the Rewa station. During October to June, the reference evapotranspiration is very high compared to the
Stoch Environ Res Risk Assess (2013) 27:1407–1421
1417
fallen rainfall (24–178 mm difference) and rainfall exceeds ETo only during July–September (73–306 mm difference). The trends in monthly, annual and seasonal ETo were investigated using the MK test and the magnitudes of trends were estimated using Theil-Sen’s nonparametric test. The effect of significant lag-1 serial correlation was removed from the data series by Trend Free Pre-Whitening approach prior to trend analysis. Statistically significant decreasing trends in monthly, annual and seasonal ETo were obtained during the study periods in almost all the stations. The decreases in annual ETo were 4 % (1.75 mm per year) for Rewa to 23 % (8.987 mm per year) for Allahabad station. Seasonally, the decrement of winter ETo ranged between 8 and 9 %. Pre-monsoon ETo showed the decrease of 9, 25 and 29 % at Rewa, Satna and Allahabad stations respectively. Similarly, monsoon ETo also showed the decrease and varied from 8 to 26 % in the study area. Again, the decrement of
6–7 % was found in post monsoon ETo. These findings are consistent with the finding of Bandyopadhayay et al. (2009) and Jhajharia et al. (2011) in India and North East India. Similar attributions have been reported for China (Chen et al. 2005; Xu et al. 2006), Tibetan Plateau (Chen et al. 2006; Zhang et al. 2007), the south of Canada (Burn and Hesch 2007) and in Australia (Rayner 2007; Roderick et al. 2007). Around 75 % of months show the significant increasing trends in mean temperature, minimum temperature and dew point temperature and only 32 % in maximum temperature. For annual time step significant warming ranged between 1.4 and 1.7 C per 100 year in maximum temperature. The minimum temperature showed an increase of about 1.9–4.0 C per 100 year between the stations. From the above, it reveals that the night temperature is increasing at faster rate than day temperature. The annual mean temperature has been increased by 1.8–3.1 C per 100 year over the
Table 8 Annual and seasonal trends in weather variables Annual
Winter b
Z
Pre-monsoon b
Z
Monsoon b
Z
Post-monsoon b
Z
b
Z
Maximum temperature trends Allahabad
0.28
0.004
1.33
0.015
–0.13
–0.002
0.36
0.006
1.56
0.022
Rewa
2.32*
0.017
2.27*
0.04
0.07
0.004
1.78**
0.024
1.73**
0.033
Satna
2.11*
0.014
2.26*
0.027
0.14
0.001
1.08
0.013
1.69**
0.026
0.046
3.05
0.055
3.93
0.039
1.55
0.028
2.55*
0.045
Minimum temperature trends Allahabad
3.93
Rewa
1.14
0.009
1.85**
0.033
0.56
0.008
0.59
0.019
1.02
0.025
Satna
3.18
0.019
1.28
0.019
2.47*
0.026
2.38*
0.014
1.27
0.02
Mean temperature trends Allahabad
3.05
0.031
3.54
0.036
1.29
0.019
1.71**
0.016
3.03
0.038
Rewa
2.18*
0.025
3.39
0.039
0.35
0.004
1.47
0.025
2.07*
0.044
Satna
2.84
0.018
2.52*
0.024
1.18
0.012
1.47
0.013
2.09*
0.026
Dew point temperature trends Allahabad
4.67
0.066
3.44
0.058
4.15
0.120
3.07
0.029
3.26
0.043
Rewa Satna
3.76 2.55*
0.076 0.048
3.93 1.90**
0.132 0.046
2.22* 3.04
0.103 0.108
0.81 2.30*
0.007 0.027
3.12 0.20
0.089 0.004 0.107
Relative humidity trends Allahabad
3.37
0.164
2.31
0.199
3.35
0.267
0.87
0.046
1.59
Rewa
0.97
0.049
1.48
0.191
0.67
0.067
–0.83
–0.075
1.52
0.142
Satna
1.89**
0.116
1.26
0.129
3.15*
0.213
1.20
0.071
–0.19
–0.020
–0.023
-3.75
–0.013
–0.026
-5.70
–0.036
-3.72
–0.012
Wind speed trends Allahabad
-4.95
-4.80
Rewa
-4.52
–0.010
–1.92**
–0.003
-3.20
–0.008
-4.21
–0.016
-3.62
–0.006
Satna
-5.53
–0.019
-5.24
–0.015
-5.50
–0.019
-5.71
–0.025
-5.24
–0.014
–0.78
–0.004
Net solar radiation (Rn) Allahabad
–1.47
–0.006
–1.67**
–0.012
Rewa
0.50
0.003
1.32
0.014
–0.59
0.023
–0.005
0
Satna
1.27
0.004
1.43
0.008
0.79
0.005
–1.12
–0.006
0
0
0.79
0.011
0.48
0.002
0.98
0.005
Significant trends at 1 % indicated by bold numbers, significant trends at 5 % indicated by *, significant trends at 10 % indicated by **
123
123 0.05 0.47 0.82 0.01
Wind
VPD
Rn
G –0.15
–0.08 0.07 0.14 0.87 0.01
Tmin
Wind
VPD
Rn
G
–0.02
0.87
0.17
0.10
0.93
–0.02
0.82
0.5
0.07
–0.17
0.87
0.74 –0.02
0.19
0.12
0.87
Tmax
0.78 –0.11
Tmin
0.75 0.01
Rn G
Tmax
0.16
VPD
–0.04
0.81
0.24
0.17
–0.23
0.96
–0.04
0.78
0.54
0.12
–0.25
0.93
0.70 –0.04
0.25
0.18
–0.24
1.09
March
Bold values indicate the most sensitive variable which effect ETo
Allahabad
Rewa
0.09
Wind
–0.17
1.05
0.98 –0.11
Tmin
Satna
February
January
Tmax
Parameters
Stations name
–0.04
0.73
0.31
0.24
–0.31
0.98
–0.04
0.74
0.58
0.17
–0.35
1.01
0.63 –0.04
0.32
0.26
–0.33
1.12
April
Table 9 Sensitivity of reference evapotranspiration to meteorological variables
–0.02
0.65
0.36
0.28
–0.35
1.11
–0.03
0.65
0.65
0.23
–0.39
1.15
0.56 –0.03
0.38
0.33
–0.38
1.22
May
0.00
0.63
0.35
0.26
–0.38
1.44
0.01
0.62
0.65
0.22
–0.43
1.45
0.54 0.01
0.37
0.29
–0.38
1.61
June
0.04
0.74
0.22
0.14
–0.56
2.11
0.05
0.72
0.53
0.09
–0.64
1.95
0.64 0.05
0.22
0.14
–0.60
2.43
July
0.01
0.84
0.17
0.09
–0.76
2.34
0.01
0.80
0.48
0.05
–0.88
2.08
0.73 0.01
0.17
0.09
–0.85
2.74
August
0.01
0.87
0.14
0.08
–0.69
2.00
0.00
0.80
0.49
0.07
–0.80
1.83
0.75 0.00
0.16
0.09
–0.74
2.26
September
0.02
0.88
0.12
0.07
–0.38
1.25
0.02
0.80
0.48
0.06
–0.42
1.16
0.75 0.01
0.14
0.10
–0.39
1.38
October
0.05
0.85
0.12
0.07
–0.15
0.93
0.04
0.79
0.46
0.05
–0.18
0.88
0.73 0.04
0.15
0.10
–0.17
1.05
November
0.04
0.85
0.12
0.06
–0.07
0.86
0.04
0.80
0.49
0.04
–0.09
0.76
0.74 0.04
0.14
0.08
–0.09
0.94
December
0.00
0.80
0.21
0.13
–0.34
1.31
0.00
0.76
0.53
0.10
–0.39
1.24
0.69 0.00
0.22
0.15
–0.37
1.49
Annual
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Stoch Environ Res Risk Assess (2013) 27:1407–1421
study area. Seasonally, the significant increasing trends were found in winter and post-monsoon seasons in maximum and mean temperature. However, minimum and dew point temperature showed increasing trend in all the seasons. This warming trend in study area is supported by previous studies (Pant and Kumar 1997; Arora et al. 2005; Dash et al. 2007) in India using different duration data. Pant and Kumar (1997) reported a significant warming trend of 0.57 C per 100 year using the data for 1881–1997. Further, Darshana et al. (2012) observed an increase of 0.60 C per 102 year in the annual mean temperature, 0.60 C per 102 year in the mean maximum temperature and 0.60 C per 102 year in the mean minimum temperature during the period of 1901–2002 over Madhya Pradesh, India. The annual mean temperature over India has increased about 0.7 C and maximum mean temperature has increased about 0.8 C (Dash et al. 2007) during the period of 1901–2003. From the above it can be inferred that climate is getting warmer during the time and this warming is more pronounced in regional scale than large scale area. The increasing trends in air temperature have been related to several factors such as global warming, increased concentrations of anthropogenic green house gases (Soltani and Soltani 2008), increased emissions of anthropogenic aerosols (Cohen and Stahill 1996), increased cloud cover and urbanization (Darshana et al. 2012; Ji and Zhou 2011; Tabari and Talaee 2011). The decreasing trends in wind speed are observed in monthly, annual and seasonal scale at all the stations. Declining trend of wind speed in Tons River Basin indicates the weakening of the East Asian monsoon. The reduction of the East Asian monsoon wind speed can be caused by many factors such as global warming (Hori and Ueda 2006; Chen et al. 2005), regional and global atmospheric circulation change (Chen et al. 2006; Rayner 2007), human factors such as land use change (Bandyopadhayay et al. 2009; Lopes et al. 2011, Vautard et al. 2010; McVicar and Roderick 2010) and air pollution (Xu et al. 2006; Zhang et al. 2009). As for human influences in present study area, stations characterized by significantly decreasing wind speed seem to near the cities, which might imply influences of urbanization on wind speed. Because it is a complicated issue, the reason for the decreasing wind speeds in the Tons River Basin needs to be further studied. Again, increasing trends in relative humidity was observed only in few months. The annual and pre monsoon relative humidity shows the significant increase at almost all the stations. Further, sensitivity analysis shows that maximum temperature, net solar radiation, wind speed and vapour pressure deficit had a positive effect and minimum temperature had negative effects on ETo change on a monthly and annual basis. By comparing sensitivity coefficient (S), ETo was found to be most sensitivity to maximum temperature, followed by net solar radiation,
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minimum temperature, vapor pressure deficit and wind speed respectively. Therefore, changes in maximum temperature and net solar radiation were found to produce a large effect in change of ETo, changes in other key variables each reduced rates, resulting in an overall negative trend in ETo. Present study findings are also consistent with Goyal (2004) who also reported that temperature followed by radiation; wind speed and vapour pressure have an effect on ETo over an arid zone of Rajasthan in India.
5 Conclusions In the present study, an attempt has been made to study the trends in calculated ETo using PM methods and its meteorological parameters on monthly, annual and seasonal basis using MK test and Sen Slope estimates. The significant decreasing trends in the monthly ETo series were found in all the months except August, September and October. Significant decreasing trends in annual and seasonal ETo values were observed at all the station in the study basin. The significant increase was observed in annual air temperature (maximum, minimum, mean and dew point temperature) and relative humidity. However, the wind speed indicated significant decreasing trends in monthly, annual and seasonal scale. Further, the results of sensitivity analysis shows that ETo was found to be most sensitivity to maximum temperature, followed by net solar radiation, minimum temperature, vapor pressure deficit and wind speed respectively. Acknowledgments The authors are thankful to the Department of Science and Technology (DST), New Delhi for providing financial support during the study period. We are also thankful to anonymous reviewers for their thoughtful suggestions to improve this manuscript significantly.
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