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Evaporation and potential evapotranspiration in India under conditions of recent and future climate change
AGRICULTURAL AND FOREST METEOROLOGY
ELSEi’VlER
Agricultural and Forest Meteorology 87 (1997) 55-73
Evaporation and potential evapotranspiration in India under conditions of recent and future climate change N. Chattopadhyay Climatic Research Unit, School of Environmental
‘, M. Hulme
*
Sciences, University of East Anglia, Norwich NR4 7TJ, UK
Received 27 February 1996; accepted 1 November 1996
Abstract Long-term changes in evaporation and potential evapotranspiration can have profound implications for hydrologic processes as well as for agricultural crop performance. This paper analyses evaporation time series data for different stations in India, and for the country as a whole, for different seasons on both a short-term (15 years) and long-term (32 years) basis for pan evaporation and on a short-term basis alone for potential evapotranspiration. The analysis shows that both pan evaporation and potential evapotranspiration have decreased during recent years in India. The likely causative meteorological parameters for such changes are identified. Future scenarios of potential evapotranspiration, and its component energy and aerodynamic terms, for India based on results from six global climate model climate change experiments are also calculated and intercompared. Future warming seems likely to lead in general to increased potential evapotranspiration over India,
although this increase will be unequal between regions and seasons. Such changes could have marked implications for economic and environmental welfare in the country, especially if the increases in evaporation are not compensated by adequate increases in rainfall. 0 1997 Elsevier Science B.V. Keywords:
Climate change; Evaporation; Potential evapotranspirahon;
1. Introduction Many studies of climate change have shown that the Earth’s atmosphere is being modified by anthropogenic and biogenic emissions of carbon dioxide, other radiatively active gases and aerosol precursors such as sulphur dioxide. This modification of the atmosphere has coincided with the mean surface air temperature of the Earth increasing since the 1850s
* Corresponding author. ’Permanent address: Agricultural Meteorology Meteorological Department, Pune, India.
Division,
India
Water balance; India
by about 0.5% or about 0.04”C decade-’ (Nicholls et al., 1996). According to Raper et al. (1996), the most likely rate of future global warming over the period 1990-2100 due to continued human modification of the atmosphere is estimated to be between O.l”C and 0.3”C decade-‘, several times the mean rate of warming over the past 100 years. Although the likelihood of such an increase in future global mean temperature resulting from changes in atmospheric composition is already established with a fair degree of confidence (Houghton et al., 1996), changes in other climatic parameters such as rainfall, cloudiness, humidity and windiness, and derived quantities
0168-1923/97/$17.00 0 1997 Elsevier Science B.V. All rights reserved. PII SO168-1923(97)00006-3
56
PI. Chattopadhyay, M. Hulme/Agricultural
such as soil moisture and evaporation, are much harder to specify especially on a regional scale. In hydrology, and in studies relating to water availability for crops, both free surface evaporation (E,) and potential evapotranspiration (PE) play important roles. Free surface evaporation is a measured quantity and is the quantity of water evaporated from an idealised, extensive open water surface per unit area under existing atmospheric conditions (Penman, 1948). Potential evapotranspiration is a calculated quantity and is the maximum quantity of water capable of being lost as water vapour, under a given climate, by a continuous, extensive stretch of vegetation covering the ground when there is no shortage of water (Gangopadhyay et al., 1966). Thus, during evapotranspiration, two processes occur simultaneously: evaporation from the soil and transpiration from the leaf surface. Potential evapotranspiration can be characterised as a process of mass transport, wherein the rate of evaporation is treated as a diffusive process driven by the vapour pressure gradient (McKenney and Rosenberg, 1991). In humid climates, solar radiation supplies most of the energy required to change water from its liquid to vapour form. In arid regions, however, the advection of sensible heat from warm, dry regions provides an important additional energy source (Rosenberg, 1969, Rosenberg et al., 1983). Since PE is computed from radiation, humidity, windiness and temperature, any changes in these variables due to climate change are likely to change the value of PE. For further discussion and definitions about evaporation and potential evapotranspiration see Burman and Pochop (1994). India, located in south-central Asia, has great economic dependence on agriculture. Any major changes in the water budget will have major consequences for hydrologic processes and, in turn, the economy of the country and welfare of her population. In this context, studies relating to potential changes in E, and PE in India are very important. Climate change studies for India with respect to temperature and rainfall have already been completed by a number of Indian workers (Hingane et al., 1985, Thapliyal and Kulshrestha, 1991, Srivastava et al., 1992, Rupakumar et al., 1994, Govinda Rao et al., 1996). Climate change studies on Ep and PE in India, however, are scanty. The sensitivity of PE to climate change in some mid-latitude regions
and Forest Meteorology 87 (1997) 55-73
has been studied by, among others, Martin et al. (1989, Fennessey and Kirshen (1993), Lockwood (1993) and McKenney and Rosenberg (1993). In this paper we present results from a preliminary study of the historic variation of seasonal Ep and PE over the past few decades for a number of stations located in different regions of India. The most dominant meteorological variables determining such changes, if any, are identified and discussed. In the second part of the paper, we determine estimates of future changes in PE in India due to greenhouse gas induced climate change using results from six global climate model (GCM) climate change experiments. Changes in both the energy and aerodynamic components of PE are examined. The significance of these historic trends and future scenarios for India are discussed.
2. Data and methodology All the historic data used in the present study were collected from the National Data Centre, India Meteorological Department, Pune, India. Mean monthly minimum, maximum and mean temperature data for 27 well-distributed stations in India (Table 1 and Fig. 1) were used for observing the trends of temperatures in different seasons over India. Fifty years (1940-90) of data were used for this part of the study. The seasons are defined as winter (January and February), pre-monsoon (March to May), monsoon (June to September) and post-monsoon (October to December). For the evaporation study, data were recorded from US Class A evaporation pans located in different regions of the country. Pan evaporation anomalies were calculated on the basis of long-period (32 years) data and short-period (15 years) data for, respectively, ten and 19 stations in India (Table 2 and also see Fig. 4). PE was determined by the Penman (1948) equation using mean air temperature, mean relative humidity, average wind speed and global net radiation: PE=c[(A/A+y)R, +(7/A+
r)K”“i*sf(u)f(e)]
(1)
where A is the slope of the saturated vapour pressure versus temperature relationship in kPa “C-l, y is the psychrometric coefficient in kPa ‘C-l, R, is the net
N. Chuttopadhyay, M. Huhne/Agricultural Table 1 Location of the 27 stations used for trend analysis of minimum,
and Forest Meteorology 87 (1997) 55-73
maximum
and mean temperature
(1940-90)
Station
Latitude
Longitude
Station
Latitude
Longitude
Patna Jodhpur
25”36’N 26”18’N 27”lO’N 26’49’ N 26”32’N 3 l”O6’N 30’23’N 30”56’N 23W’N 23”47’N 23”53’N 22”32’N 18”54’N 08”29’N
85”lO’E 73”Ol’E 78”02’E 75”48’E 88=‘43’E 77”lO’E 76’46’E 75”52’E 72”38’E 86”26’E 91”15’E 88”2O’E 72’=49’E 76O57’E
Pune Begumpet Visakhapatnam Belgaum Anatapur Mangalore Bangalore Coimbatore Port Blair Minicoy Guahati Bhubaneswar Lucknow
18”32’N 17”27’N 17”43’E 15”51’N 14”41’N 12”55’N 12”57’N l1”OO’N 1 l”40’N 08”18’N 26”06’N 20’15’N 26’45’N
75’5 1’E 74”37’E 83”14’E 74”37’E 77”37’E 74’=53’E 77”38’E 76”58’E 93”43’E 73’WE 91”35’E 85”50’E 80”53’E
Agra Jaipur Jalpaiguri Simla Ambala Ludhiana Ahemedabad Dhanbad Agartala Calcutta Bombay Trivandrum
radiation in MJ day-’ m-‘, flu> is the wind function in m s-l, f(e) is the vapour pressure deficit term in lcPa and c .and Kunits are constants. f(e) is derived from mean temperature and relative humidity. The net radiation was calculated from the average sunshine hours per day and the latitude using the method outlined by Rietveld (1978). Because of the non-availability of long-period data sets for calculating PE, PE anomalies were computed over the short-period (15 years) for ten stations in India (see Fig. 7). Except for Nellore, all the short-period stations considered for Ep studies were used in the PE study (Table 2). On the basis of the measured data of Ep and the calculated values of PE, the anomalies and trends of Ep and PE over India are presented in the text. The reliability of a climate change study, especially climate for a particular region, will depend on the number and selection of stations used and also on the number years of data. Because of the limited number of station data that were initially available to us, our study uses a combination of both longer and short-term time series. The distribution of stations, however, is reasonably representative of different regions of the country. The findings of the present preliminary study will be validated using a larger number of stations having longer period data when these are made available. Results from the following six GCM climate change experiments were used for the determination of future changes in PE and in its energy and aerody-
namic terms. All of these experiments only considered future increases in greenhouse gas concentrations; changes in the aerosol composition of the atmosphere were not included. ccc GFDL GISS osu UKHI UKTR
Canadian Climate Centre: Boer et al. (1992) Geophysical Fluid Dynamics Laboratory: Wetherald and Manabe ( 1986) Goddard Institute for Space Studies: Hansen et al. (1984) Oregon State University: Schlesinger and Zhao (1989) UK Met. Office high resolution experiment: Mitchell et al. (1989) UK Met. Office transient experiment: Murphy and Mitchell (1995)
The resolutions of these models varied from 2.5” latitude by 3.75” longitude (UKHI and UKTR) to 7.8” latitude by 10” longitude (GISS). All of the GCM data were therefore interpolated onto a common 5” latitude/longitude grid over India and adjoining countries using a Gaussian space-filter and a total of 56 resulting grid boxes were considered. The above GCM experiments do not always directly provide the global net radiation or relative humidity values required for the estimation of PE. For the calculation of global net radiation, the total cloud amount data were first converted to sunshine hours
58
N. Chattopadhyay, M. Hulme /Agricultural and Forest Meteorology 87 (1997) 55-73 Trend in Tmean (“C/decade) Winter
(JF)
Pre-monsooon
(MAM) 40
35
T--F
SO
25
20
-0.3-0.2
<-0.3
-0.2:-0.1
<+0.1
,-a1
0000
+0.1:+0.2 +0.2:+0.5
6 C P P ‘: s
>+0.3
‘rn.0
WAS)
Monsoon
Post-monsoon
(OND)
r---l4o
,t .00. 25
0
-7
20
a0
0.
=. -
$” \\
O.
15 ‘,
-
“A
: 6
10 ,\
.
[,
-
;,,\ 0.
560
70
90
60o Longitude
100
(‘E)
Fig. 1. Linear trend (“C decade-’ ) in mean temperature stations (dots). Dot size is related to trend.
T, - 116.91/T,
RH = lOO[ 1 - ( ea - e)/e”]
+ 237.3
70
80
60 Longitude
(2) (3)
100
(‘E)
for the period from 1940 to 1990 for different
using daylight hours and declination of sun, and then sunshine hours were converted to net radiation by the method already described earlier. Relative humidity was computed from actual vapour pressure and mean surface air temperature by the following two equations (Fennessey and Kirshen, 1993): 8 = exp[16.78
60
seasons over India based on 27
where e’, e, T, and RH are the saturated vapour pressure, actual vapour pressure, mean air temperature and relative humidity, respectively. The estimated changes in PE over India for each GCM experiment are presented as percent change per degree Celsius rise in global-mean temperature. This eliminates differences in the GCM results which are due merely to differences in the climate sensitivity of the models. Results from each GCM for three selected grid boxes (7S%J, 77S”E; 22.5”N, 77.5%;
59
N. Chattopadhyay, M. Hulme / Agricultural and Forest Meteorology 87 (1997) 55-73 Table 2 The stations used in the E, analysis Station
Latitude
Longitude
Station
Latitude
Longitude
Long-period (1961-1992) New Delhi a Karimganj Hebbal Bhubaneswar Rudrur
28”34’N 24W’ N 13”OO’N 20”15’N 18”30’N
77”lO’E 92”30’E 77”37’E 85”52’E 77”5O’E
Patambi a Pune Rajmundry Jodhpur Patna
lO”48’N 18”32’N 17”OO’N 26”18’N 25”36’N
76=‘12’E 73”51’E 81’46’E 73”Ol’E 85”lO’E
Short-period (1976-90) Jabalpur a Bikramganj a Varanasi a Hisar a Akola a
23”09’ N 25”lO’N 25”18’N 29”lO’N 20”42’N
79’58’E 84’15’E 83’03’E 75’46’E 77%2’E
Raipur a Annamalinagar Canning a Nellore
21”16’N 1 l”24’N 22”15’N 14”27’N
81”36’E 79’41’E 88WJ’E 79”59’E
’
a Also used in the PE analysis.
37.5”N, 77.5”E) representing the latitudinal extremes of the country (see Fig. 8 for location) were examined in greater detail.
3. Results and discussion
3.1. Historic analysis-
temperature
A general change: in surface air temperature might be expected to cau:se changes in both Er and PE, therefore we start o’ur analysis by discussing trends of temperature in India for the last few decades. Figs. 1 and 2 show the mean linear trends (“C decade-’ > in mean temperature and diumality, respectively, in different seasons over India. The maximum extent of warming in terms of mean temperature is observed in the post-monsoon season (OND). Except for Gujarat and some parts of the west coast, the whole of India has warmed in this season. In the winter (JF), pre-monsoon (MAM) and monsoon (JJAS) seasons the warming trends have been restricted mainly to the peninsular region of the country. North of about :!3”N, cooling at the rate of about O.l-0.2”C decade-’ has been observed. The diurnal temperature range (DTR) shows an increasing trend in all the seasons over most of peninsula India. This is in contrast to many other land regions of the Northern Hemisphere (Nicholls et al., 1996). Unlike mean temperature, the maximum extent of diumality increases in India is observed in
the monsoon season when an increasing trend in DTR is observed over almost the whole of the country. The maximum increase in this season of about 0.3”C decade-’ has occurred in north-eastern parts of the country where falling minima and rising maxima have been observed. In the post-monsoon season a decrease in the DTR is noticed from the peninsular to north-western parts of the country, although mostly away from the coast. In winter, pre-monsoon and post-monsoon seasons the increasing trend in diurnal temperature range is observed mainly along the west and east coasts and in the east and north-eastern regions of India. Analysis of minima and maxima temperature trends (not shown) indicates that the increased diumality of the monsoon and post-monsoon seasons has been due mostly to increased daytime temperatures, whereas in the winter and pre-monsoon seasons nighttime cooling was more than, or as important as daytime warming. Thus, in general, it can be concluded that an increasing trend in temperature has been observed in southern and central India in recent decades in all seasons and over all of India in the post-monsoon season. This warming has generally been accompanied by increases in diumality, except over northern India in the winter, pre-monsoon and post-monsoon seasons. These results can be compared with those from a number of other studies. Srivastava et al. (1992) observed increasing trends of annual mean, maximum and minimum temperature south of 23% and cooling trends north of 23”N. A number of other
60
N. Chuttopadhyay, M. Hulme/Agricultural Trend
and Forest Meteorology 87 (1997) 55-73
in diurnal temperature
range
Winter (JF)
‘Ol-----l
(“C/decade)
Pre-monsooon
(MAM)
Post-monsoon
(ONO)
r-----l-”
MONSOON
(JJAS)
‘Or-----l
r-----l’,
Longitude
Longitude
(‘E)
Fig. 2. Linear trend (“C decade-‘) in diurnal temperature range temperature India based on 27 stations (dots). Dot size is related to trend.
workers (Hingane et al., 1985, Rupakumar and Hingane, 1988, Govinda Rao, 1993, Rupakumar et al., 1994) have concluded from their studies that an increasing trend in mean temperature in most parts of the Indian subcontinent has been observed most strongly in the post-monsoon and winter seasons. The difference in seasonal emphasis in the warming between the observations of the present study and earlier findings may be due to the respective definitions of winter season months. In the present study only January and February are considered, while the
(‘E)
for the period from 1940 to 1990 for different
seasons over
earlier workers included December in winter season. The key point is that as a consequence of this general warming tendency over India it might be expected that Er and PE have also increased. We now examine the evaporation data to assess this proposition. 3.2.
Historic
Seasonal (1961-1992)
analysis-pan
evaporation
anomalies on the basis of long-period data for three stations (New Delhi,
Ep
N. Chattopadhyay,
M. Hulme/Agriculhual
and Forest Meteorology
I
I
61
trends for these and the other Ep time series were calculated and are plotted in Fig. 4. Ep has decreased at all of the stations in the monsoon and post-
77”lO’E; Hebbal, 13”OO’N 77”37’E; 28”34’N Patambi, lO“48’N 76”12’E) located in different regions of India are shown in Fig. 3. Mean linear
1
87 (1997) 55-73
I
I
I
I
---------IGZL New Delhi
winter
+1 -1
----- _ _
L.
- - __-- ------------ - ___________
+1 -1
-- -_,
-v-e_
--
- -_
--- n-
pre-monsoon
monsoon
post-monsoon
I
I
I 1970
1960
1990
1960
2000
Hebbal Winter
Pre-monsoon
Monsoon
Post-monsoon ,
I
1970
1960
I
L
I
1990
1980
2000
Patambi Winter
Pre-monsoon +1
--1 +1 _1
----_-____-
-++J
~---------&_________ ___ ----_ ___
,r,&_L!l_ II
1960
_______ _Q -XT
I
1970
4
1980
-
Monsoon
Post-monsoon I
1990
1
2000
Years Fig. 3. Annual Ep anomalies (mm day-’ ) between 1961 and 1992 with respect to the 1961-92 mean for three stations and for four seasons. Dashed lines show best-fit linear trend.
62
N. Chattopadhyay, M. Hulme/Agricultural Trend Winter 40
,
<-0.6
,
I
in E, (mm/day/decade)
(JF)
I
-0.6:-o.+
and Forest Meteorology 87 (1997) 55-73
I
-0.4:-o.*
Pre-monsooon
I
(MAM)
1
>-0.2
000~
I
<+0.2
+0.2:+0.4
+o.*:+o.*
40
.+o.e
‘0.0
MONSOON (JJAS)
Longitude
Fig. 4. Mean linear trend (mm day-’ decadestations (dots). Dot size is related to trend.
(‘E)
Post-monsoon
Longitude
(OND)
(‘E)
‘) in Ep for the period between 1961 and 1992 for different seasons over India based on 19
monsoon seasons. In the winter and pre-monsoon seasons the decrease has not been so uniform over the country and along the east coast of India slight increasing EP trends have occurred in these seasons. Ep anomalies for the country as a whole were calculated (Fig. 5), using all the 19 stations having either long- or short-period data. The regional anomalies were the unweighted average of the individual station anomalies calculated with respect to the average of the common 1976-90 period. The regional series emphasise the decrease in Ep in all seasons, amount-
ing to about 1 mm day- ’ over this 32-year period for the pre-monsoon and monsoon seasons. Out of 19 stations analysed, eight and ten stations showed significant (95%) decreasing Er trends in, respectively, the pre-monsoon and monsoon seasons, whereas in both the winter and post-monsoon seasons only five stations showed significant negative trends. In spite of the general increase in temperature over recent decades, there has therefore been a decreasing trend in Ep in almost all parts of India,
N. Chattopadhyay, M. Hulme/Agticultural
and Forest Meteorology
87 (1997) 55-73
63
Winter
Pre-monsoon
Monsoon -0.5 _ Post-monsoon
t 1960
I
1970
,
1960
I
1990
2000
Years Fig. 5. Regionally averaged annual Ep anomalies (mm day-’ ) for the period 1961-1992 with respect to the 1976-90 mean for different seasons over India. Number of stations is ten between 1961-75 and 1991-92 and 19 between 1976 and 1990. Dashed lines show best-fit linear trend.
particularly significant in the pre-monsoon and monsoon seasons. In order to identify the dominant variables associated with this change in EP, stepwise regression was applied between Ep and the various meteorological parameters which control evaporation: relative humidity (r.h.), wind speed (w.s.), net global radiation (r.n.> and maximum (t.x.) and mini-
mum temperature (t.n.> for eight stations (Bikramganj, Varanasi, Raipur, Jabalpur, Hisar, Canning, Akola, Annamalinagar) with 15 years of data (19761990). Relative humidity appears as the variable most strongly associated with the changes in EP, particularly in the pre-monsoon and monsoon seasons (Table 3). Radiation is the best predictor of Ep
Table 3 The number of times five different meteorological variables, in order of dominance and PE in steuwise reassion models established for stations over India (1976-90) Meteorological
variable
(i-iv),
were significantly
(at 95% level) related to Ep
Seasons Winter
Pre-monsoon
Monsoon
Post-monsoon
i
ii
iii
iv
i
ii
iii
iv
i
ii
iii
iv
i
ii
111 “’
iv
r.h. r.n.
3 4
1
-
-
5 1
-
-
-
5 -
1
1
-
4 -
1
1 1
-
LX. t.n.
1 -
1
1 1
-
2 -
1 1
2 -
-
2 -
2 -
1 -
1
3 -
-
-
-
W.S.
-
1
-
-
-
1
1
-
1
3
-
-
1
2
-
-
1
6
1
-
9
-
-
-
3
1
1
-
1
-
3 -
-
-
2 1
2
-
3 1
11-) -
-
_
For E, (eight stations)
For PE (ten stations) r.h. r.n. t.x. t.n. W.S.
.
62--712--4113211 1 1 1 3-2-121--41-1212 12 1 -
.
I, 11,111 and iv indicate the step at which the meteorological variable was selected in the stepwise regression model (i first, i.e. most dominant variable; iv fourth). Only variables significantly related to Ep or PE at the 95% level were included so the number of variables counted in each matrix is not constant. r.h., t.x., t.n., r.n. and W.S. denote, respectively, relative humidity, maximum temperature, minimum temperature, net global radiation and wind speed.
N. Chanopadhyay,h4. Hulme/Agricultural and Foresrkteteorology 87 (1997) 55-73
64
five, two and three stations, respectively. The country as a whole showed significant negative trends only in the monsoon (0.3 mm day-’ decade-‘) and post-monsoon seasons (0.2 mm day-’ decade- ‘1. As with Ep, changes in PE were most strongly associated with changes in relative humidity, particularly in the winter and pre-monsoon seasons (Table 3). In the monsoon season, radiation was the dominant variable for regulating the PE variation at nearly all stations. Thus in the winter and pre-monsoon seasons, decreases in PE seem due largely to increases in relative humidity, while in the monsoon season the larger PE decrease seems due to a decrease of radiation resulting from more cloudiness. Changes in both radiation and relative humidity are associated with decreases in PE in the post-monsoon season. The above results relating to the most important controlling variables on PE changes are broadly similar to those from studies conducted in other regions. McKenney and Rosenberg (1993) found that on an annual basis humidity was the most important variable determining changes in PE, computed both by the Penman (1948) and Penman-Monteith formulae (Monteith, 19651, for five North American sites ranging from Alberta, Canada, to northern Texas, USA. According to their study the relative importance of solar radiation and wind speed for PE changes varied with location. Changes in plant physiology as a result of climate or CO, concentration changes were not considered in the present study,
in the winter season. It seems therefore that increasing relative humidity has been the most important variable in more than counterbalancing the effect of rising temperatures on Ep and retarding the evaporation process. 3.3. Historic
analysis-potential
euapotranspiration
Ten stations had sufficient data over a 15year period (1976-1990) to calculate PE using the Penman method. As for Ep, these time series were converted into anomalies from their respective means and a regional series for India calculated (Fig. 6). The mean linear trends for each station are shown in Rg. 7. The seasonal and spatial pattern of the changes are similar to those for Ep, but the magnitude of the changes is less. In the monsoon and post-monsoon seasons PE has decreased over the last 15 years over the whole country, whereas in the winter and premonsoon seasons the trends are less consistent. Some parts of southern and north-eastern of India show little change or a slight increase in PE in these seasons. The decreasing trend in PE is up to a maximum of about 0.3 mm day-’ decade-’ over west-central India in the monsoon and post-monsoon seasons. These trends are generally lower than for Ep and represent a reduction in PE of less than 3% decade-‘, In the monsoon season six stations showed significant decreasing trends at the 95% level, while in post-monsoon, winter and pre-monsoon seasons significant decreasing trends were observed in only
2 <
+o.s _
Winter
-0.5 _ +0.5 _
.z x B
Pre-monsoon -0.5
_
+0.5
_
_
Monsoon
:
6
-0.5 _
f
+o.s _ Post-monsoon
_
-0.5 _ 1880
1
1970
1
1910
6
1990
1
I 2000
Years Fig. 6. Regionally averaged annual PE anomalies (mm day-’ ) for the period from 1976 to 1990 with respect to the 1976-90 different seasons over India. Number of stations averaged is ten. Dashed lines show best-fit linear trend.
mean for
N. Chattopadhyay, h4. Huime/ Agricultural and Forest Meteorology 87 (1997) 55- 73 Trend Winter
65
in PE (mm/day/decade) Prs-monsooon
(JF)
(MAM)
30 -
<-a.0
-0.1:-m
-0.6:-o.*
<+a.2
>-0.2
0000 Monsoon 40
35
I
+cG!z+o.*
+Q.4:+0.6
>+O.S
‘0.0
I
I
(JJAS) I
I
Post-monsoon ,
(ONO)
I
-
30 B 25 ->‘::I;
ul
20 -
oO”
“-
15 ‘S
4
10 I
p SO
70
80 Longitude
Fig. 7. Mean linear trend (mm day-’ decade-‘) stations (dots). Dot size is related to trend.
90 (T)
’ 100 Longitude
(‘E)
in PE for the period from 1976 to 1990 for different
although potentially they represent an important additional control on future PE. 3.4. Future changes in potential evapotranspiration Future changes in PE over India and adjoining countries were computed from the results of six GCM climate change experiments in which atmospheric greenhouse gas concentrations were increased. The changes were calculated as percent change in PE per degree Celsius rise in global-mean
seasons over India based on ten
temperature and results from three of the models are presented in Figs. 8-10. The date by which these changes may be real&d depends on a variety of factors, including the emissions scenario chosen, the sensitivity of the climate system to greenhouse gas forcing, whether or not the cooling effect of sulphate aerosols are included and a variety of biogeophysical feedback mechanisms. A mid-range estimate suggests that an additional 1°C of global warming with respect to the 1961-90 period might be reached during the 2030s decade and 2°C of warming by the
66
N. Chattopadhyay, M. Hulme/Agricultural PE changes
and Forest Meteorology 87 (1997) 55-73 (%) far
CCC
Winter (JF)
Pre-monsooon
(MAM)
MO~~O~~ (JJAS)
Post-monsoon
(ONO)
Longitude
(“E)
Longitude
(‘E)
Fig. 8. Calculated change (%) in mean seasonal PE for 1°C of global warming for the CCC experiment. analysis of the energy and aerodynamic components of Penman PE.
2070s (Kattenberg et al., 1996). If the effects of sulphate aerosols on climate are included this global warming might be delayed by about a decade. In the winter season, all the models show increases in PE over southern and central India (up to around 25”N). For UKHI, UKTR and OSU, decreases in.PE occur north of 25”N, whereas for CCC, GFDL and GISS the increase in PE extends beyond 2YN, although of smaller magnitude to the changes further south. In most of the model experiments the maximum winter increase in PE is of the order of 3-4% “C-l of global warming and is seen in peninsular and most central parts of India.
Shaded boxes are those selected for
In the pre-monsoon season, all the models show increases in PE over the entire Indian region, although the increases of between 1% and 4% are generally smaller than in winter. For the CCC model, a 5% increase in PE is observed over northwestern India, the region covering Gujarat and the adjoining Rajasthan and Maharashtra. For GFDL, almost all the country shows a 2% increase in pre-monsoon PE. One salient feature of the changes in the monsoon season for the UKHI, UKTR, OSU and CCC experiments is a maximum increase in PE over north-westem India. For GISS, however, maximum increases in PE occur in southern India (up to 6%) these in-
N. Chattopadhyay, M. Hulme/Agricultural PE changes Winter
(JJAS)
Longitude
Fig. 9. Calculated
(X)
far
(JF)
100
h40t1~00fl
and Forest Meteorology 87 (1997) 55-73
(“E)
GFDL Pre-monsooon
(MAM)
Post-monsoon
(ONO)
60
Longituds
(=E)
change (%) in mean seasonal PE for 1°C of global warming for the GFDL experiment.
creases gradually decreasing towards the north. GFDL again shows a fairly uniform 2% increase in PE across the country in the monsoon season. For most models the maximum seasonal increase in PE over the Indian subcontinent is observed in the post-monsoon season. UKTR, OSU, CCC and GISS all show maximum increases in PE in this season and over central and north India these increases are in the range 3-10%. GFDL, in contrast, shows a maximum post-monsoon increase in PE to the northeast of the Indian region. Similar studies of PE changes derived from GCM
climate change experiments have been computed for mid-latitude regions. Using the GFDL experiment, McKenney and Rosenberg (1993), for example, showed that a small decrease (0 to -2%) in annual PE occurred over North America, while the GISS experiment produced larger annual decreases ( - 3% to -5%). Similar findings were observed by Fennessey and Kirshen (1993) for north-eastern USA. For the UK, Hulme (1996) used the UKTR experiment to calculate increases in summer PE over southern UK of up to 20% and decreases of up to 10% over northern UK. This latter study also showed
N. Chattopadhyay, M. Hulme/Agricultural
68
PE changes
E d 3 5 s
and Forest Meteorology 87 (1997) 55-73 (2)
for
UKTR Pre-monsooon
(MAM)
Post-monsoon
(OND)
25 20
MONSOON(JJAS)
,
40
i
‘“l--T-WI
60
Longituh (T)
70
60
90
100
Longitude(T)
Fig. 10. Calculated change (%) in mean seasonal PE for 1°C of global warming for the UKTR experiment.
that relative humidity, and to some extent radiation, was the strongest control on PE change. All of the values cited above are associated with 1°C of global warming. The calculated changes in the energy and aerodynamic terms of the Penman equation for three selected grid boxes (see Fig. 8 for location) are presented in Table 4. Since the GCMs differ in their prediction of climate change at the selected grid boxes, they also show different scenarios in respect of the energy and aerodynamic terms of Penman PE. Over central and south India the energy term shows
consistent, but relatively modest (2-5%), increases for all model experiments. Over the north of the region, however, the changes in the energy term are more variable and generally closer to zero. For this region, changes in PE are controlled much more by the aerodynamic term, with changes due to this process ranging from - 18% (CCC in post-monsoon) to +26% (GFDL in pm-monsoon). Over south India the contributions to the overall PE change of the energy and aerodynamic components am much mom similar, with two models (GFDL and CCC) showing decreases in the aerodynamic PE term. These results
N. Chattopadh~y, M. Hulme/Agricultural Table 4 Relative changes (%l in the energy (e) and aerodynamic (al terms in the Penman PE equation per degree Celsius rise in global mean temperature for three selected grid boxes computed from different GCM climate change experiments. The term with the bigger increase (or smaller decrease) in each case is in bold Models
Seasons Winter
Rre-monsoon
Monsoon
Post-monsoon
North India (grid box: latitude 37.YN. longitude 77.PE) UKTR e -1.1 1.1 0.3 0.0 a 5.9 1.6 1.5 1.7 UKHI e -0.7 0.7 0.9 0.3 a 9.4 23.7 15.5 13.3 OSU e 0.0 0.3 2.1 1.4 a 2.4 26.4 9.6 3.2 CCC e -0.3 2.5 -2.4 1.2 - 17.7 a 17.6 10.1 - - 12.5 GPDL e 0.9 1.6 7.5 2.2 a 11.4 21.2 3.5 26.2 GISS e 0.3 1.1 0.6 0.1 a 14.3 18.2 9.9 10.7 Central India (grid box: UKTR e 5.4 a 8.0 UKHI e 4.0 a 8.6 OSU e 2.3 a 6.9 CCC e 4.2 a - 17.3 GPDL e 2.9 a -3.2 GISS e 3.0 a 4.1
latitude 22.5”N. longitude 77.5oE) 2.3 3.1 11.7 13.5 14.9 1.7 3.3 3.2 2.2 7.1 9.4 9.2 1.1 1.7 3.6 10.1 3.9 9.2 3.9 -0.3 4.4 11.7 14.0 25.4 1.5 1.8 2.7 25.2 20.2 -2.1 1.6 1.7 3.1 2.7 4.1 3.8
South of the region (grid box: latitude 7.PN. longitude 77.PEJ UKTR e 3.4 2.4 4.5 1.9 a 3.3 3.2 6.8 3.1 UKHI e 2.5 2.8 2.9 2.0 a 1.9 7.3 11.4 6.5 OSU e 2.0 2.1 1.1 1.9 a 4.4 2.6 5.6 6.3 CCC e 2.3 3.0 3.7 2.8 a 0.4 .- 0.3 - 0.2 0.0 GFDL e 1.7 1.8 2.1 2.4 a - 10.2 2.4 -4.2 4.7 GISS e 2.9 3.6 5.1 2.4 a 9.2 2.9 2.9 6.9
are broadly consistent with the decreasing importance with latitude of the energy contributions to PE; in the tropics changes in the energy term will be much more influential on PE than in higher latitudes.
and Forest Meteorology 87 (1997) 55-73
69
4. Discussion and conclusions This paper has explored the. impact of recent and future climate change on pan evaporation and potential evapotranspiration over India. Since temperature has a direct bearing on both Ep and PE, recent (1940-90) trends in mean temperature and the diurnal temperature range over India were first analysed. Increasing trends in temperature over most parts of south and central India have occurred in all seasons during this period and over northern India in the post-monsoon season. Smallest temperature increases, or even slight cooling, were recorded over the northern parts of the country. The diurnal temperature range increased most substantially in the monsoon season primarily due to rising daytime temperatures. Nighttime temperatures remained fairly constant or, over northern India, fell slightly. Most of the stations considered in the present study showed decreasing trends in Er and PE between 1961 and 1992 in all seasons. Maximum decreases in Ep were observed in the pre-monsoon and monsoon seasons, whereas PE decreased by the greatest amount in the monsoon and post-monsoon seasons. Thus, in spite of the general increase in temperature in recent decades over the Indian region, both Ep and PE have decreased. Regression analysis showed that increased relative humidity was most strongly related to the overall decrease in Ep and that increases in relative humidity and decreases in radiation were both important correlates with the decreasing trend in PE. On a larger-scale (for the United States and former Soviet Union) and for a longer time period (195 1 to 19901, Peterson et al. (1995) also reported decreases in Er, and they suggest increases in cloud cover, and by implication decreases in radiation, are most likely to be responsible. They did not examine changes in relative humidity. These conclusions of declining Ep and PE for India should be regarded as provisional since we have only used a subset of the existing Ep and PE data. Nevertheless, they do raise a number of important points. Temperature changes alone do not provide a satisfactory indication of changes in Ep or PE, as is commonly presumed. Changes in relative humidity appear to be far more closely related to the direction and magnitude of evaporation changes, both
70
N. Chattopadhyay, M. Hulme/Agricultural
actual and potential. The apparent recent increase in relative humidity over India, which seems to have suppressed both measured and calculated evaporation, may be related to the more general tendency in recent years for increased humidities in the lower troposphere over tropical oceans as reported by FlBhn and Kappala (1989; see also Nicholls et al., 1996). Whether or not this is the case, or whether such increases in atmospheric humidity are related to global warming, cannot be answered here. We merely point out that relative humidity changes over a tropical land mass can have greater importance for the surface water balance than temperature alone. These implications of climate change will affect both the water resource and agricultural sectors, which in a country such as India make key contributions to the national economy. We have also examined changes in PE which may result from future climate change related to enhanced greenhouse gas forcing. Results from six global climate model climate change experiments were used to calculate changes in PE over the Indian region and in its two component terms, the energy and aerodynamic. For all six GCM experiments, PE increased over most parts of India, but this increase was found to be unequal in magnitude between different regions of the country and between different seasons. The maximum increase in PE (up to 8% for 1°C global warming) was calculated from the UKTR experiment over central India in the post-monsoon season. In general, all the model experiments showed smaller increases in PE, or even small decreases, over the northern part of the region. An analysis of the energy and aerodynamic components of PE for different regions of India showed the bigger influence of the energy term on PE change over the Tropics than in higher latitudes where the aerodynamic term tends to control PE to a greater extent. The change in PE due to the energy term is at a maximum (up to 12%) in central India during the post-monsoon and winter seasons, whereas the maximum change due to the aerodynamic contribution to PE (up to 25%) occurs during the pre-monsoon and monsoon seasons over the central and northern parts of the region. If the historic sensitivity of PE to changes in relative humidity and radiation persists, these future increases in PE over the Indian region would result from reduced relative humidity and
and Forest Meteorology 87 (1997) 55-73
increased radiation, the latter resulting from reduced cloudiness. Whilst the range of these calculated increases in PE for the Indian region associated with modelsimulated global warming are in general agreement with findings in other mid-latitude regions, the PE changes for India differ in that virtually no decrease in PE results from any GCM in any season. PE in India seems uniformly set to increase in warmer world. This apparent contradiction between historic trends in PE (generally a decrease) and future trends as modelled by GCM climate experiments (generally an increase) is important to note, although not easy to reconcile. It may be that although a future warmer world would likely lead to higher specific humidities, the rate of temperature rise over the Indian region would exceed that which has historically been observed and perhaps lead to a reduction in relative humidity. A reduction in relative humidity has been shown here to be associated with increases in PE. Additionally, reduced cloudiness (hence increased radiation) over the Indian region in the future may result from larger-scale changes in the tropical circulation which may weaken the Indian monsoon. This seems unlikely, however, since most of the GCM experiments used here (and others reported elsewhere) suggest that the South Asian monsoon will be enhanced due to elevated greenhouse gas concentrations (Kattenberg et al., 1996). The exception is for GCM experiments which have considered the simultaneous cooling effect of increased sulphate aerosols in the atmosphere; here there is evidence that the monsoon may be weakened and cloudiness decrease (Mitchell et al., 1995). Whatever the reason for these future increases in PE, given these model results and the conclusions of our historic analysis, it would appear that pan evaporation is also likely to increase under conditions of elevated greenhouse gas concentrations. As with the historic decreasing trends in Ep and PE, these future changes would have implications for water management in India. Whether or not these implications will be detrimental to the economy will depend to a large measure on the magnitude of the associated change in rainfall under these GCM scenarios. If rainfall increases sufficiently to offset the estimated evaporation losses, then there may be no major threat to the hydrological and agricultural economy. If rainfall
N. Chattopadhyay, hf. Hulme/Agricultural Number
and Forest Meteorology 87 (1997) 55-73
of GCMs with increase
in P/PE
71
ratio
Winter (JF)
Pre-monsooon
(MAM)
Monsoon (JJAS)
Post-monsoon
(ONO)
25
Longitude ("E)
Fig. 11. Number of GCM experiments which yield an increase in P/PE in the sign of the change between all six GCMs are shaded.
increases are more modest, or if rainfall decreases, then climate change will present a major challenge to India’s water resource and agricultural sectors. Fig. 11 presents one way of assessing such interactions by mapping the number of GCM experiments which yield an increase in the P/PE (rainfall/potential evapotranspiration) ratio. For the monsoon season, all six GCMs agree that the P/PE ratio becomes more favourable over northeastern India and five out of the six agree that this ratio increases, apart froml the extreme south, over the rest of the country. Changes in this ratio are less
Longitude ('E)
ratio for each season. Maximum
number is six. Areas of agreement
favourable in the post-monsoon season and in the extreme south of the country. The state of climate change modelling means, however, that results from such GCM experiments should continue to be regarded as exploratory and provisional. To highlight this provisionality, when the effects of sulphate aerosols are included in such model experiments the general tendency for models to report increases in Indian monsoon rainfall due to greenhouse gas forcing (and hence P/PE ratios, see above), is replaced by a tendency for rainfall reductions in the monsoon season (Kattenberg et al., 1996).
12
N. Chuttopadhyay, M. Hulme/Agricultural
Acknowledgements
Chattopadhyay is thankful to the India Meteorological Department for deputing him to the UK to study climate change. Grateful thanks are also due to the British Council for awarding the fellowship for 6 months. The authors are indebted to Dr P.M. Kelly of the Climatic Research Unit, University of East Anglia, for fruitful discussions and making available some software used in the present study. Dr V.N. Sanjeevan and Dr B. Mukopadhyay and the members of Climatic Research Unit are thanked for their encouragement and help.
References Boer, G.J., McFarlene, N.A., Lazare, M., 1992. Greenhouse-gasinduced climate change simulated with the CCC second-generation General Circulation Model. J. Clim. 5, 1045-1077. Burman, R., Pcchop, L.O., 1994. Evaporation, Evapotranspiration and Climatic Data. Elsevier, Amsterdam, 278 pp. Fennessey, N.M., Kirshen, P.H., 1993. Evaporation and evapotranspiration under climate change in New England. J. Water Res. Plann. Manage., ASCE 17, 32-41. FRihn, H., Kappala, A., 1989. Changes of tropical sea-air interaction process over a 30-year period. Nature 338, 244-246. Gangopadhyay, M., Uryvaev, V.A., Oman, M.H., Nordenson, T.J., Harbeck, GE., 1966. Measurement and estimation of evaporation and evapotranspiration. WMO Technical No. 83, Geneva, Switzerland. Govinda Rao, P., 1993. Climate changes and trends over a major river basin in India. Clim. Res. 2, 215-223. Govinda Rao, P., Kelly, P.M., Hulme, M., 1996. Climate change, greenhouse gas emissions, future climate and response strategies: the implications for India. Theor. Appl. Climatol. 55, 41-64. Hansen, I., Lacis, A., Rind, D., Russell, L., Stone, P., Fung, I., Lemer, J., 1984. Climate sensitivity analysis of feedback mechanism. In: Hansen, J., Takahashi, T. (Eds.) Climate Process and Climate Sensitivity. Geophysical Monograph No. 29, American Geophysical Union, Washington, DC, pp. 130-163. Hingane, L.S., Rupakmnar, K., Ramamurthy, Bh.V., 1985. Longterm trends of surface air temperature in India. J. Climatol. 5, 521-528. Houghton, J.T., Meiro Filho, L.G., Callendar, B.A., Harris, N., Kattenburg, A., Maskell, K. (Eds.), 1996. Climate Change 1995: The Science of Climate Change. Cambridge University Press, Cambridge, UK, 572 pp. Hulme, M., 1996. Changing climate and sea-level. In: Parry, M.L. (Ed.), Review of Potential Effects of Climate Change in the United Kingdom. HMSO, London, UK, 247 pp.
and Forest Meteorology 87 (1997) 55-73 Kattenberg, A., Giorgi, F., Grassl, H., Meehl, G.A., Mitchell, J.F.B., Stouffer, R.J., Tokioka, T., Weaver, A.J., Wigley, T.M.L., 1996. Climate models - projections of future climate. In: Houghton, J.T., Meiro Filho, L.G., Callendar, B.A., Harris, N., Kattenburg, A., Maskell, K. (Eds.), Climate Change 1995: The Science of Climate Change. Cambridge University Press, Cambridge, UK, 572 pp. Lockwood, J.G., 1993. Impact of global warming on evapotranspiration. Weather 48, 291-299. Martin, P., Rosenberg, N.J., McKenney, M.S., 1989. Sensitivity of evapotranspiration in a wheat field, a forest and a grassland to changes in climate and direct effect of carbon dioxide. Clim. Change 14, 117-151. McKenney, M.S., Rosenberg, N.J., 1991. Simulating the impacts of climate change on potential evapotranspimtion: a comparison of the climate sensitivity to selected methods. Discussion Paper ENR 91- 17, Report to the US Department of the Interior Task 1. McKenney, M.S., Rosenberg, N.J., 1993. Sensitivity of some potential evapotranspiration estimation methods to climate change. Agric. For. Meteorol. 64, 81-110. Mitchell, J.F.B., Senior, C.A., Ingram, W.J., 1989. CO, and climate: a missing feedback. Nature 341, 132-134. Mitchell, J.F.B., Davis, R.A., Ingram, W.J., Senior, C.A., 1995. On surface temperature, greenhouse gases and aerosols: models and observations. J. Clim. 8, 2364-2386. Monteith, J.L., 1965. Evaporation and environment. Symp. Sot. Exp. Biol. 19, 205-234. Murphy, J.M., Mitchell, J.F.B., 1995. Transient response of the Hadley Centre coupled ocean-atmosphere model to increasing carbon dioxide Part 1. Control climate and flux correction. J. Clim. 8, 57-80. Nicholls, N., Gruza, G.V., Jouzel, J., Karl, T.R., Ogallo, L.A., Parker, D.E., 1996. Observed climate variability and change. In: Houghton, J.T., Meiro Filho, L.G., Callendar, B.A., Kattenburg, A., Maskell, K. (Eds.1, Climate Change 1995: The Science of Climate Change. Cambridge University Press, Cambridge, UK, 572 pp. Penman, H.L., 1948. Natural evaporation from open water, bare soil and grass. Proc. R. Sot. Ser. A 193, 120-145. Peterson, T.C., Golubev, V.S., Groisman, P.Ya., 1995. Evaporation losing its strength. Nature 377, 687-688. Raper, S.C.B., Warrick, R.A., Wigley, T.M.L., 1996. Global sea level rise: past and future. In: Milliman, J.D., Haq, B.U. (Eds.), Sea-level Rise and Coastal Subsidence: Causes, Consequences and Strategies. Kluwer Academic Publishers, Dordrecht, Germany, 369 pp. Rietveld, M.R., 1978. A new method for estimating the regression coefficient in the formula relating solar radiation to sunshine. Agric. For. Meteorol. 19, 243-252. Rosenberg, N.J., 1969. Advective contribution of energy utilised in evapotranspiration by alfalfa in the east central Great Plains. Agric. Meteorol. 6, 179-184. Rosenberg, N.J., Blad, B.L., Verma, S.H., 1983. Microclimate: The Biological Environment, 2nd ed. John Wiley, New York. Rupakumar, K., Hingane, L.S., 1988. Long-term variations of surface air temperature at major industrial cities of India. Clim. Change 13, 287-307.
N. Chattopadhyay, M. Hulme/Agricultural Rupakumar, K., Krishnakumar, K., Pant, G.B., 1994. Diurnal asymetry of surface air temperature trends over India. Geophys. Res. Lett. 15, 677-680. Schlesinger, M.E., Zhao, Z.C., 1989. Seasonal climate changes induced by doubled CO, as simulated by the OSU atmospheric GCM mixed layer ocean model. J. Clim. 2, 459-495. Srivastava, H.N., Dewan, B.N., Dishit, SK., Prakash Rao, G.S.,
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Singh, S.S., Rao, K.R., 1992. Decadal trends in climate over India. Mausam. 43, 7-20. Thapliyal, V., Kulshrestha, SM., 1991. Climate change and trends over India. Mausam. 42, 333-338. Wetherald, R.T., Manabe, S., 1986. An investigation of cloud cover in response to thermal forcing. Clim. Change 8, 5-23.
ELSEi’VlER
Agricultural and Forest Meteorology 87 (1997) 55-73
Evaporation and potential evapotranspiration in India under conditions of recent and future climate change N. Chattopadhyay Climatic Research Unit, School of Environmental
‘, M. Hulme
*
Sciences, University of East Anglia, Norwich NR4 7TJ, UK
Received 27 February 1996; accepted 1 November 1996
Abstract Long-term changes in evaporation and potential evapotranspiration can have profound implications for hydrologic processes as well as for agricultural crop performance. This paper analyses evaporation time series data for different stations in India, and for the country as a whole, for different seasons on both a short-term (15 years) and long-term (32 years) basis for pan evaporation and on a short-term basis alone for potential evapotranspiration. The analysis shows that both pan evaporation and potential evapotranspiration have decreased during recent years in India. The likely causative meteorological parameters for such changes are identified. Future scenarios of potential evapotranspiration, and its component energy and aerodynamic terms, for India based on results from six global climate model climate change experiments are also calculated and intercompared. Future warming seems likely to lead in general to increased potential evapotranspiration over India,
although this increase will be unequal between regions and seasons. Such changes could have marked implications for economic and environmental welfare in the country, especially if the increases in evaporation are not compensated by adequate increases in rainfall. 0 1997 Elsevier Science B.V. Keywords:
Climate change; Evaporation; Potential evapotranspirahon;
1. Introduction Many studies of climate change have shown that the Earth’s atmosphere is being modified by anthropogenic and biogenic emissions of carbon dioxide, other radiatively active gases and aerosol precursors such as sulphur dioxide. This modification of the atmosphere has coincided with the mean surface air temperature of the Earth increasing since the 1850s
* Corresponding author. ’Permanent address: Agricultural Meteorology Meteorological Department, Pune, India.
Division,
India
Water balance; India
by about 0.5% or about 0.04”C decade-’ (Nicholls et al., 1996). According to Raper et al. (1996), the most likely rate of future global warming over the period 1990-2100 due to continued human modification of the atmosphere is estimated to be between O.l”C and 0.3”C decade-‘, several times the mean rate of warming over the past 100 years. Although the likelihood of such an increase in future global mean temperature resulting from changes in atmospheric composition is already established with a fair degree of confidence (Houghton et al., 1996), changes in other climatic parameters such as rainfall, cloudiness, humidity and windiness, and derived quantities
0168-1923/97/$17.00 0 1997 Elsevier Science B.V. All rights reserved. PII SO168-1923(97)00006-3
56
PI. Chattopadhyay, M. Hulme/Agricultural
such as soil moisture and evaporation, are much harder to specify especially on a regional scale. In hydrology, and in studies relating to water availability for crops, both free surface evaporation (E,) and potential evapotranspiration (PE) play important roles. Free surface evaporation is a measured quantity and is the quantity of water evaporated from an idealised, extensive open water surface per unit area under existing atmospheric conditions (Penman, 1948). Potential evapotranspiration is a calculated quantity and is the maximum quantity of water capable of being lost as water vapour, under a given climate, by a continuous, extensive stretch of vegetation covering the ground when there is no shortage of water (Gangopadhyay et al., 1966). Thus, during evapotranspiration, two processes occur simultaneously: evaporation from the soil and transpiration from the leaf surface. Potential evapotranspiration can be characterised as a process of mass transport, wherein the rate of evaporation is treated as a diffusive process driven by the vapour pressure gradient (McKenney and Rosenberg, 1991). In humid climates, solar radiation supplies most of the energy required to change water from its liquid to vapour form. In arid regions, however, the advection of sensible heat from warm, dry regions provides an important additional energy source (Rosenberg, 1969, Rosenberg et al., 1983). Since PE is computed from radiation, humidity, windiness and temperature, any changes in these variables due to climate change are likely to change the value of PE. For further discussion and definitions about evaporation and potential evapotranspiration see Burman and Pochop (1994). India, located in south-central Asia, has great economic dependence on agriculture. Any major changes in the water budget will have major consequences for hydrologic processes and, in turn, the economy of the country and welfare of her population. In this context, studies relating to potential changes in E, and PE in India are very important. Climate change studies for India with respect to temperature and rainfall have already been completed by a number of Indian workers (Hingane et al., 1985, Thapliyal and Kulshrestha, 1991, Srivastava et al., 1992, Rupakumar et al., 1994, Govinda Rao et al., 1996). Climate change studies on Ep and PE in India, however, are scanty. The sensitivity of PE to climate change in some mid-latitude regions
and Forest Meteorology 87 (1997) 55-73
has been studied by, among others, Martin et al. (1989, Fennessey and Kirshen (1993), Lockwood (1993) and McKenney and Rosenberg (1993). In this paper we present results from a preliminary study of the historic variation of seasonal Ep and PE over the past few decades for a number of stations located in different regions of India. The most dominant meteorological variables determining such changes, if any, are identified and discussed. In the second part of the paper, we determine estimates of future changes in PE in India due to greenhouse gas induced climate change using results from six global climate model (GCM) climate change experiments. Changes in both the energy and aerodynamic components of PE are examined. The significance of these historic trends and future scenarios for India are discussed.
2. Data and methodology All the historic data used in the present study were collected from the National Data Centre, India Meteorological Department, Pune, India. Mean monthly minimum, maximum and mean temperature data for 27 well-distributed stations in India (Table 1 and Fig. 1) were used for observing the trends of temperatures in different seasons over India. Fifty years (1940-90) of data were used for this part of the study. The seasons are defined as winter (January and February), pre-monsoon (March to May), monsoon (June to September) and post-monsoon (October to December). For the evaporation study, data were recorded from US Class A evaporation pans located in different regions of the country. Pan evaporation anomalies were calculated on the basis of long-period (32 years) data and short-period (15 years) data for, respectively, ten and 19 stations in India (Table 2 and also see Fig. 4). PE was determined by the Penman (1948) equation using mean air temperature, mean relative humidity, average wind speed and global net radiation: PE=c[(A/A+y)R, +(7/A+
r)K”“i*sf(u)f(e)]
(1)
where A is the slope of the saturated vapour pressure versus temperature relationship in kPa “C-l, y is the psychrometric coefficient in kPa ‘C-l, R, is the net
N. Chuttopadhyay, M. Huhne/Agricultural Table 1 Location of the 27 stations used for trend analysis of minimum,
and Forest Meteorology 87 (1997) 55-73
maximum
and mean temperature
(1940-90)
Station
Latitude
Longitude
Station
Latitude
Longitude
Patna Jodhpur
25”36’N 26”18’N 27”lO’N 26’49’ N 26”32’N 3 l”O6’N 30’23’N 30”56’N 23W’N 23”47’N 23”53’N 22”32’N 18”54’N 08”29’N
85”lO’E 73”Ol’E 78”02’E 75”48’E 88=‘43’E 77”lO’E 76’46’E 75”52’E 72”38’E 86”26’E 91”15’E 88”2O’E 72’=49’E 76O57’E
Pune Begumpet Visakhapatnam Belgaum Anatapur Mangalore Bangalore Coimbatore Port Blair Minicoy Guahati Bhubaneswar Lucknow
18”32’N 17”27’N 17”43’E 15”51’N 14”41’N 12”55’N 12”57’N l1”OO’N 1 l”40’N 08”18’N 26”06’N 20’15’N 26’45’N
75’5 1’E 74”37’E 83”14’E 74”37’E 77”37’E 74’=53’E 77”38’E 76”58’E 93”43’E 73’WE 91”35’E 85”50’E 80”53’E
Agra Jaipur Jalpaiguri Simla Ambala Ludhiana Ahemedabad Dhanbad Agartala Calcutta Bombay Trivandrum
radiation in MJ day-’ m-‘, flu> is the wind function in m s-l, f(e) is the vapour pressure deficit term in lcPa and c .and Kunits are constants. f(e) is derived from mean temperature and relative humidity. The net radiation was calculated from the average sunshine hours per day and the latitude using the method outlined by Rietveld (1978). Because of the non-availability of long-period data sets for calculating PE, PE anomalies were computed over the short-period (15 years) for ten stations in India (see Fig. 7). Except for Nellore, all the short-period stations considered for Ep studies were used in the PE study (Table 2). On the basis of the measured data of Ep and the calculated values of PE, the anomalies and trends of Ep and PE over India are presented in the text. The reliability of a climate change study, especially climate for a particular region, will depend on the number and selection of stations used and also on the number years of data. Because of the limited number of station data that were initially available to us, our study uses a combination of both longer and short-term time series. The distribution of stations, however, is reasonably representative of different regions of the country. The findings of the present preliminary study will be validated using a larger number of stations having longer period data when these are made available. Results from the following six GCM climate change experiments were used for the determination of future changes in PE and in its energy and aerody-
namic terms. All of these experiments only considered future increases in greenhouse gas concentrations; changes in the aerosol composition of the atmosphere were not included. ccc GFDL GISS osu UKHI UKTR
Canadian Climate Centre: Boer et al. (1992) Geophysical Fluid Dynamics Laboratory: Wetherald and Manabe ( 1986) Goddard Institute for Space Studies: Hansen et al. (1984) Oregon State University: Schlesinger and Zhao (1989) UK Met. Office high resolution experiment: Mitchell et al. (1989) UK Met. Office transient experiment: Murphy and Mitchell (1995)
The resolutions of these models varied from 2.5” latitude by 3.75” longitude (UKHI and UKTR) to 7.8” latitude by 10” longitude (GISS). All of the GCM data were therefore interpolated onto a common 5” latitude/longitude grid over India and adjoining countries using a Gaussian space-filter and a total of 56 resulting grid boxes were considered. The above GCM experiments do not always directly provide the global net radiation or relative humidity values required for the estimation of PE. For the calculation of global net radiation, the total cloud amount data were first converted to sunshine hours
58
N. Chattopadhyay, M. Hulme /Agricultural and Forest Meteorology 87 (1997) 55-73 Trend in Tmean (“C/decade) Winter
(JF)
Pre-monsooon
(MAM) 40
35
T--F
SO
25
20
-0.3-0.2
<-0.3
-0.2:-0.1
<+0.1
,-a1
0000
+0.1:+0.2 +0.2:+0.5
6 C P P ‘: s
>+0.3
‘rn.0
WAS)
Monsoon
Post-monsoon
(OND)
r---l4o
,t .00. 25
0
-7
20
a0
0.
=. -
$” \\
O.
15 ‘,
-
“A
: 6
10 ,\
.
[,
-
;,,\ 0.
560
70
90
60o Longitude
100
(‘E)
Fig. 1. Linear trend (“C decade-’ ) in mean temperature stations (dots). Dot size is related to trend.
T, - 116.91/T,
RH = lOO[ 1 - ( ea - e)/e”]
+ 237.3
70
80
60 Longitude
(2) (3)
100
(‘E)
for the period from 1940 to 1990 for different
using daylight hours and declination of sun, and then sunshine hours were converted to net radiation by the method already described earlier. Relative humidity was computed from actual vapour pressure and mean surface air temperature by the following two equations (Fennessey and Kirshen, 1993): 8 = exp[16.78
60
seasons over India based on 27
where e’, e, T, and RH are the saturated vapour pressure, actual vapour pressure, mean air temperature and relative humidity, respectively. The estimated changes in PE over India for each GCM experiment are presented as percent change per degree Celsius rise in global-mean temperature. This eliminates differences in the GCM results which are due merely to differences in the climate sensitivity of the models. Results from each GCM for three selected grid boxes (7S%J, 77S”E; 22.5”N, 77.5%;
59
N. Chattopadhyay, M. Hulme / Agricultural and Forest Meteorology 87 (1997) 55-73 Table 2 The stations used in the E, analysis Station
Latitude
Longitude
Station
Latitude
Longitude
Long-period (1961-1992) New Delhi a Karimganj Hebbal Bhubaneswar Rudrur
28”34’N 24W’ N 13”OO’N 20”15’N 18”30’N
77”lO’E 92”30’E 77”37’E 85”52’E 77”5O’E
Patambi a Pune Rajmundry Jodhpur Patna
lO”48’N 18”32’N 17”OO’N 26”18’N 25”36’N
76=‘12’E 73”51’E 81’46’E 73”Ol’E 85”lO’E
Short-period (1976-90) Jabalpur a Bikramganj a Varanasi a Hisar a Akola a
23”09’ N 25”lO’N 25”18’N 29”lO’N 20”42’N
79’58’E 84’15’E 83’03’E 75’46’E 77%2’E
Raipur a Annamalinagar Canning a Nellore
21”16’N 1 l”24’N 22”15’N 14”27’N
81”36’E 79’41’E 88WJ’E 79”59’E
’
a Also used in the PE analysis.
37.5”N, 77.5”E) representing the latitudinal extremes of the country (see Fig. 8 for location) were examined in greater detail.
3. Results and discussion
3.1. Historic analysis-
temperature
A general change: in surface air temperature might be expected to cau:se changes in both Er and PE, therefore we start o’ur analysis by discussing trends of temperature in India for the last few decades. Figs. 1 and 2 show the mean linear trends (“C decade-’ > in mean temperature and diumality, respectively, in different seasons over India. The maximum extent of warming in terms of mean temperature is observed in the post-monsoon season (OND). Except for Gujarat and some parts of the west coast, the whole of India has warmed in this season. In the winter (JF), pre-monsoon (MAM) and monsoon (JJAS) seasons the warming trends have been restricted mainly to the peninsular region of the country. North of about :!3”N, cooling at the rate of about O.l-0.2”C decade-’ has been observed. The diurnal temperature range (DTR) shows an increasing trend in all the seasons over most of peninsula India. This is in contrast to many other land regions of the Northern Hemisphere (Nicholls et al., 1996). Unlike mean temperature, the maximum extent of diumality increases in India is observed in
the monsoon season when an increasing trend in DTR is observed over almost the whole of the country. The maximum increase in this season of about 0.3”C decade-’ has occurred in north-eastern parts of the country where falling minima and rising maxima have been observed. In the post-monsoon season a decrease in the DTR is noticed from the peninsular to north-western parts of the country, although mostly away from the coast. In winter, pre-monsoon and post-monsoon seasons the increasing trend in diurnal temperature range is observed mainly along the west and east coasts and in the east and north-eastern regions of India. Analysis of minima and maxima temperature trends (not shown) indicates that the increased diumality of the monsoon and post-monsoon seasons has been due mostly to increased daytime temperatures, whereas in the winter and pre-monsoon seasons nighttime cooling was more than, or as important as daytime warming. Thus, in general, it can be concluded that an increasing trend in temperature has been observed in southern and central India in recent decades in all seasons and over all of India in the post-monsoon season. This warming has generally been accompanied by increases in diumality, except over northern India in the winter, pre-monsoon and post-monsoon seasons. These results can be compared with those from a number of other studies. Srivastava et al. (1992) observed increasing trends of annual mean, maximum and minimum temperature south of 23% and cooling trends north of 23”N. A number of other
60
N. Chuttopadhyay, M. Hulme/Agricultural Trend
and Forest Meteorology 87 (1997) 55-73
in diurnal temperature
range
Winter (JF)
‘Ol-----l
(“C/decade)
Pre-monsooon
(MAM)
Post-monsoon
(ONO)
r-----l-”
MONSOON
(JJAS)
‘Or-----l
r-----l’,
Longitude
Longitude
(‘E)
Fig. 2. Linear trend (“C decade-‘) in diurnal temperature range temperature India based on 27 stations (dots). Dot size is related to trend.
workers (Hingane et al., 1985, Rupakumar and Hingane, 1988, Govinda Rao, 1993, Rupakumar et al., 1994) have concluded from their studies that an increasing trend in mean temperature in most parts of the Indian subcontinent has been observed most strongly in the post-monsoon and winter seasons. The difference in seasonal emphasis in the warming between the observations of the present study and earlier findings may be due to the respective definitions of winter season months. In the present study only January and February are considered, while the
(‘E)
for the period from 1940 to 1990 for different
seasons over
earlier workers included December in winter season. The key point is that as a consequence of this general warming tendency over India it might be expected that Er and PE have also increased. We now examine the evaporation data to assess this proposition. 3.2.
Historic
Seasonal (1961-1992)
analysis-pan
evaporation
anomalies on the basis of long-period data for three stations (New Delhi,
Ep
N. Chattopadhyay,
M. Hulme/Agriculhual
and Forest Meteorology
I
I
61
trends for these and the other Ep time series were calculated and are plotted in Fig. 4. Ep has decreased at all of the stations in the monsoon and post-
77”lO’E; Hebbal, 13”OO’N 77”37’E; 28”34’N Patambi, lO“48’N 76”12’E) located in different regions of India are shown in Fig. 3. Mean linear
1
87 (1997) 55-73
I
I
I
I
---------IGZL New Delhi
winter
+1 -1
----- _ _
L.
- - __-- ------------ - ___________
+1 -1
-- -_,
-v-e_
--
- -_
--- n-
pre-monsoon
monsoon
post-monsoon
I
I
I 1970
1960
1990
1960
2000
Hebbal Winter
Pre-monsoon
Monsoon
Post-monsoon ,
I
1970
1960
I
L
I
1990
1980
2000
Patambi Winter
Pre-monsoon +1
--1 +1 _1
----_-____-
-++J
~---------&_________ ___ ----_ ___
,r,&_L!l_ II
1960
_______ _Q -XT
I
1970
4
1980
-
Monsoon
Post-monsoon I
1990
1
2000
Years Fig. 3. Annual Ep anomalies (mm day-’ ) between 1961 and 1992 with respect to the 1961-92 mean for three stations and for four seasons. Dashed lines show best-fit linear trend.
62
N. Chattopadhyay, M. Hulme/Agricultural Trend Winter 40
,
<-0.6
,
I
in E, (mm/day/decade)
(JF)
I
-0.6:-o.+
and Forest Meteorology 87 (1997) 55-73
I
-0.4:-o.*
Pre-monsooon
I
(MAM)
1
>-0.2
000~
I
<+0.2
+0.2:+0.4
+o.*:+o.*
40
.+o.e
‘0.0
MONSOON (JJAS)
Longitude
Fig. 4. Mean linear trend (mm day-’ decadestations (dots). Dot size is related to trend.
(‘E)
Post-monsoon
Longitude
(OND)
(‘E)
‘) in Ep for the period between 1961 and 1992 for different seasons over India based on 19
monsoon seasons. In the winter and pre-monsoon seasons the decrease has not been so uniform over the country and along the east coast of India slight increasing EP trends have occurred in these seasons. Ep anomalies for the country as a whole were calculated (Fig. 5), using all the 19 stations having either long- or short-period data. The regional anomalies were the unweighted average of the individual station anomalies calculated with respect to the average of the common 1976-90 period. The regional series emphasise the decrease in Ep in all seasons, amount-
ing to about 1 mm day- ’ over this 32-year period for the pre-monsoon and monsoon seasons. Out of 19 stations analysed, eight and ten stations showed significant (95%) decreasing Er trends in, respectively, the pre-monsoon and monsoon seasons, whereas in both the winter and post-monsoon seasons only five stations showed significant negative trends. In spite of the general increase in temperature over recent decades, there has therefore been a decreasing trend in Ep in almost all parts of India,
N. Chattopadhyay, M. Hulme/Agticultural
and Forest Meteorology
87 (1997) 55-73
63
Winter
Pre-monsoon
Monsoon -0.5 _ Post-monsoon
t 1960
I
1970
,
1960
I
1990
2000
Years Fig. 5. Regionally averaged annual Ep anomalies (mm day-’ ) for the period 1961-1992 with respect to the 1976-90 mean for different seasons over India. Number of stations is ten between 1961-75 and 1991-92 and 19 between 1976 and 1990. Dashed lines show best-fit linear trend.
particularly significant in the pre-monsoon and monsoon seasons. In order to identify the dominant variables associated with this change in EP, stepwise regression was applied between Ep and the various meteorological parameters which control evaporation: relative humidity (r.h.), wind speed (w.s.), net global radiation (r.n.> and maximum (t.x.) and mini-
mum temperature (t.n.> for eight stations (Bikramganj, Varanasi, Raipur, Jabalpur, Hisar, Canning, Akola, Annamalinagar) with 15 years of data (19761990). Relative humidity appears as the variable most strongly associated with the changes in EP, particularly in the pre-monsoon and monsoon seasons (Table 3). Radiation is the best predictor of Ep
Table 3 The number of times five different meteorological variables, in order of dominance and PE in steuwise reassion models established for stations over India (1976-90) Meteorological
variable
(i-iv),
were significantly
(at 95% level) related to Ep
Seasons Winter
Pre-monsoon
Monsoon
Post-monsoon
i
ii
iii
iv
i
ii
iii
iv
i
ii
iii
iv
i
ii
111 “’
iv
r.h. r.n.
3 4
1
-
-
5 1
-
-
-
5 -
1
1
-
4 -
1
1 1
-
LX. t.n.
1 -
1
1 1
-
2 -
1 1
2 -
-
2 -
2 -
1 -
1
3 -
-
-
-
W.S.
-
1
-
-
-
1
1
-
1
3
-
-
1
2
-
-
1
6
1
-
9
-
-
-
3
1
1
-
1
-
3 -
-
-
2 1
2
-
3 1
11-) -
-
_
For E, (eight stations)
For PE (ten stations) r.h. r.n. t.x. t.n. W.S.
.
62--712--4113211 1 1 1 3-2-121--41-1212 12 1 -
.
I, 11,111 and iv indicate the step at which the meteorological variable was selected in the stepwise regression model (i first, i.e. most dominant variable; iv fourth). Only variables significantly related to Ep or PE at the 95% level were included so the number of variables counted in each matrix is not constant. r.h., t.x., t.n., r.n. and W.S. denote, respectively, relative humidity, maximum temperature, minimum temperature, net global radiation and wind speed.
N. Chanopadhyay,h4. Hulme/Agricultural and Foresrkteteorology 87 (1997) 55-73
64
five, two and three stations, respectively. The country as a whole showed significant negative trends only in the monsoon (0.3 mm day-’ decade-‘) and post-monsoon seasons (0.2 mm day-’ decade- ‘1. As with Ep, changes in PE were most strongly associated with changes in relative humidity, particularly in the winter and pre-monsoon seasons (Table 3). In the monsoon season, radiation was the dominant variable for regulating the PE variation at nearly all stations. Thus in the winter and pre-monsoon seasons, decreases in PE seem due largely to increases in relative humidity, while in the monsoon season the larger PE decrease seems due to a decrease of radiation resulting from more cloudiness. Changes in both radiation and relative humidity are associated with decreases in PE in the post-monsoon season. The above results relating to the most important controlling variables on PE changes are broadly similar to those from studies conducted in other regions. McKenney and Rosenberg (1993) found that on an annual basis humidity was the most important variable determining changes in PE, computed both by the Penman (1948) and Penman-Monteith formulae (Monteith, 19651, for five North American sites ranging from Alberta, Canada, to northern Texas, USA. According to their study the relative importance of solar radiation and wind speed for PE changes varied with location. Changes in plant physiology as a result of climate or CO, concentration changes were not considered in the present study,
in the winter season. It seems therefore that increasing relative humidity has been the most important variable in more than counterbalancing the effect of rising temperatures on Ep and retarding the evaporation process. 3.3. Historic
analysis-potential
euapotranspiration
Ten stations had sufficient data over a 15year period (1976-1990) to calculate PE using the Penman method. As for Ep, these time series were converted into anomalies from their respective means and a regional series for India calculated (Fig. 6). The mean linear trends for each station are shown in Rg. 7. The seasonal and spatial pattern of the changes are similar to those for Ep, but the magnitude of the changes is less. In the monsoon and post-monsoon seasons PE has decreased over the last 15 years over the whole country, whereas in the winter and premonsoon seasons the trends are less consistent. Some parts of southern and north-eastern of India show little change or a slight increase in PE in these seasons. The decreasing trend in PE is up to a maximum of about 0.3 mm day-’ decade-’ over west-central India in the monsoon and post-monsoon seasons. These trends are generally lower than for Ep and represent a reduction in PE of less than 3% decade-‘, In the monsoon season six stations showed significant decreasing trends at the 95% level, while in post-monsoon, winter and pre-monsoon seasons significant decreasing trends were observed in only
2 <
+o.s _
Winter
-0.5 _ +0.5 _
.z x B
Pre-monsoon -0.5
_
+0.5
_
_
Monsoon
:
6
-0.5 _
f
+o.s _ Post-monsoon
_
-0.5 _ 1880
1
1970
1
1910
6
1990
1
I 2000
Years Fig. 6. Regionally averaged annual PE anomalies (mm day-’ ) for the period from 1976 to 1990 with respect to the 1976-90 different seasons over India. Number of stations averaged is ten. Dashed lines show best-fit linear trend.
mean for
N. Chattopadhyay, h4. Huime/ Agricultural and Forest Meteorology 87 (1997) 55- 73 Trend Winter
65
in PE (mm/day/decade) Prs-monsooon
(JF)
(MAM)
30 -
<-a.0
-0.1:-m
-0.6:-o.*
<+a.2
>-0.2
0000 Monsoon 40
35
I
+cG!z+o.*
+Q.4:+0.6
>+O.S
‘0.0
I
I
(JJAS) I
I
Post-monsoon ,
(ONO)
I
-
30 B 25 ->‘::I;
ul
20 -
oO”
“-
15 ‘S
4
10 I
p SO
70
80 Longitude
Fig. 7. Mean linear trend (mm day-’ decade-‘) stations (dots). Dot size is related to trend.
90 (T)
’ 100 Longitude
(‘E)
in PE for the period from 1976 to 1990 for different
although potentially they represent an important additional control on future PE. 3.4. Future changes in potential evapotranspiration Future changes in PE over India and adjoining countries were computed from the results of six GCM climate change experiments in which atmospheric greenhouse gas concentrations were increased. The changes were calculated as percent change in PE per degree Celsius rise in global-mean
seasons over India based on ten
temperature and results from three of the models are presented in Figs. 8-10. The date by which these changes may be real&d depends on a variety of factors, including the emissions scenario chosen, the sensitivity of the climate system to greenhouse gas forcing, whether or not the cooling effect of sulphate aerosols are included and a variety of biogeophysical feedback mechanisms. A mid-range estimate suggests that an additional 1°C of global warming with respect to the 1961-90 period might be reached during the 2030s decade and 2°C of warming by the
66
N. Chattopadhyay, M. Hulme/Agricultural PE changes
and Forest Meteorology 87 (1997) 55-73 (%) far
CCC
Winter (JF)
Pre-monsooon
(MAM)
MO~~O~~ (JJAS)
Post-monsoon
(ONO)
Longitude
(“E)
Longitude
(‘E)
Fig. 8. Calculated change (%) in mean seasonal PE for 1°C of global warming for the CCC experiment. analysis of the energy and aerodynamic components of Penman PE.
2070s (Kattenberg et al., 1996). If the effects of sulphate aerosols on climate are included this global warming might be delayed by about a decade. In the winter season, all the models show increases in PE over southern and central India (up to around 25”N). For UKHI, UKTR and OSU, decreases in.PE occur north of 25”N, whereas for CCC, GFDL and GISS the increase in PE extends beyond 2YN, although of smaller magnitude to the changes further south. In most of the model experiments the maximum winter increase in PE is of the order of 3-4% “C-l of global warming and is seen in peninsular and most central parts of India.
Shaded boxes are those selected for
In the pre-monsoon season, all the models show increases in PE over the entire Indian region, although the increases of between 1% and 4% are generally smaller than in winter. For the CCC model, a 5% increase in PE is observed over northwestern India, the region covering Gujarat and the adjoining Rajasthan and Maharashtra. For GFDL, almost all the country shows a 2% increase in pre-monsoon PE. One salient feature of the changes in the monsoon season for the UKHI, UKTR, OSU and CCC experiments is a maximum increase in PE over north-westem India. For GISS, however, maximum increases in PE occur in southern India (up to 6%) these in-
N. Chattopadhyay, M. Hulme/Agricultural PE changes Winter
(JJAS)
Longitude
Fig. 9. Calculated
(X)
far
(JF)
100
h40t1~00fl
and Forest Meteorology 87 (1997) 55-73
(“E)
GFDL Pre-monsooon
(MAM)
Post-monsoon
(ONO)
60
Longituds
(=E)
change (%) in mean seasonal PE for 1°C of global warming for the GFDL experiment.
creases gradually decreasing towards the north. GFDL again shows a fairly uniform 2% increase in PE across the country in the monsoon season. For most models the maximum seasonal increase in PE over the Indian subcontinent is observed in the post-monsoon season. UKTR, OSU, CCC and GISS all show maximum increases in PE in this season and over central and north India these increases are in the range 3-10%. GFDL, in contrast, shows a maximum post-monsoon increase in PE to the northeast of the Indian region. Similar studies of PE changes derived from GCM
climate change experiments have been computed for mid-latitude regions. Using the GFDL experiment, McKenney and Rosenberg (1993), for example, showed that a small decrease (0 to -2%) in annual PE occurred over North America, while the GISS experiment produced larger annual decreases ( - 3% to -5%). Similar findings were observed by Fennessey and Kirshen (1993) for north-eastern USA. For the UK, Hulme (1996) used the UKTR experiment to calculate increases in summer PE over southern UK of up to 20% and decreases of up to 10% over northern UK. This latter study also showed
N. Chattopadhyay, M. Hulme/Agricultural
68
PE changes
E d 3 5 s
and Forest Meteorology 87 (1997) 55-73 (2)
for
UKTR Pre-monsooon
(MAM)
Post-monsoon
(OND)
25 20
MONSOON(JJAS)
,
40
i
‘“l--T-WI
60
Longituh (T)
70
60
90
100
Longitude(T)
Fig. 10. Calculated change (%) in mean seasonal PE for 1°C of global warming for the UKTR experiment.
that relative humidity, and to some extent radiation, was the strongest control on PE change. All of the values cited above are associated with 1°C of global warming. The calculated changes in the energy and aerodynamic terms of the Penman equation for three selected grid boxes (see Fig. 8 for location) are presented in Table 4. Since the GCMs differ in their prediction of climate change at the selected grid boxes, they also show different scenarios in respect of the energy and aerodynamic terms of Penman PE. Over central and south India the energy term shows
consistent, but relatively modest (2-5%), increases for all model experiments. Over the north of the region, however, the changes in the energy term are more variable and generally closer to zero. For this region, changes in PE are controlled much more by the aerodynamic term, with changes due to this process ranging from - 18% (CCC in post-monsoon) to +26% (GFDL in pm-monsoon). Over south India the contributions to the overall PE change of the energy and aerodynamic components am much mom similar, with two models (GFDL and CCC) showing decreases in the aerodynamic PE term. These results
N. Chattopadh~y, M. Hulme/Agricultural Table 4 Relative changes (%l in the energy (e) and aerodynamic (al terms in the Penman PE equation per degree Celsius rise in global mean temperature for three selected grid boxes computed from different GCM climate change experiments. The term with the bigger increase (or smaller decrease) in each case is in bold Models
Seasons Winter
Rre-monsoon
Monsoon
Post-monsoon
North India (grid box: latitude 37.YN. longitude 77.PE) UKTR e -1.1 1.1 0.3 0.0 a 5.9 1.6 1.5 1.7 UKHI e -0.7 0.7 0.9 0.3 a 9.4 23.7 15.5 13.3 OSU e 0.0 0.3 2.1 1.4 a 2.4 26.4 9.6 3.2 CCC e -0.3 2.5 -2.4 1.2 - 17.7 a 17.6 10.1 - - 12.5 GPDL e 0.9 1.6 7.5 2.2 a 11.4 21.2 3.5 26.2 GISS e 0.3 1.1 0.6 0.1 a 14.3 18.2 9.9 10.7 Central India (grid box: UKTR e 5.4 a 8.0 UKHI e 4.0 a 8.6 OSU e 2.3 a 6.9 CCC e 4.2 a - 17.3 GPDL e 2.9 a -3.2 GISS e 3.0 a 4.1
latitude 22.5”N. longitude 77.5oE) 2.3 3.1 11.7 13.5 14.9 1.7 3.3 3.2 2.2 7.1 9.4 9.2 1.1 1.7 3.6 10.1 3.9 9.2 3.9 -0.3 4.4 11.7 14.0 25.4 1.5 1.8 2.7 25.2 20.2 -2.1 1.6 1.7 3.1 2.7 4.1 3.8
South of the region (grid box: latitude 7.PN. longitude 77.PEJ UKTR e 3.4 2.4 4.5 1.9 a 3.3 3.2 6.8 3.1 UKHI e 2.5 2.8 2.9 2.0 a 1.9 7.3 11.4 6.5 OSU e 2.0 2.1 1.1 1.9 a 4.4 2.6 5.6 6.3 CCC e 2.3 3.0 3.7 2.8 a 0.4 .- 0.3 - 0.2 0.0 GFDL e 1.7 1.8 2.1 2.4 a - 10.2 2.4 -4.2 4.7 GISS e 2.9 3.6 5.1 2.4 a 9.2 2.9 2.9 6.9
are broadly consistent with the decreasing importance with latitude of the energy contributions to PE; in the tropics changes in the energy term will be much more influential on PE than in higher latitudes.
and Forest Meteorology 87 (1997) 55-73
69
4. Discussion and conclusions This paper has explored the. impact of recent and future climate change on pan evaporation and potential evapotranspiration over India. Since temperature has a direct bearing on both Ep and PE, recent (1940-90) trends in mean temperature and the diurnal temperature range over India were first analysed. Increasing trends in temperature over most parts of south and central India have occurred in all seasons during this period and over northern India in the post-monsoon season. Smallest temperature increases, or even slight cooling, were recorded over the northern parts of the country. The diurnal temperature range increased most substantially in the monsoon season primarily due to rising daytime temperatures. Nighttime temperatures remained fairly constant or, over northern India, fell slightly. Most of the stations considered in the present study showed decreasing trends in Er and PE between 1961 and 1992 in all seasons. Maximum decreases in Ep were observed in the pre-monsoon and monsoon seasons, whereas PE decreased by the greatest amount in the monsoon and post-monsoon seasons. Thus, in spite of the general increase in temperature in recent decades over the Indian region, both Ep and PE have decreased. Regression analysis showed that increased relative humidity was most strongly related to the overall decrease in Ep and that increases in relative humidity and decreases in radiation were both important correlates with the decreasing trend in PE. On a larger-scale (for the United States and former Soviet Union) and for a longer time period (195 1 to 19901, Peterson et al. (1995) also reported decreases in Er, and they suggest increases in cloud cover, and by implication decreases in radiation, are most likely to be responsible. They did not examine changes in relative humidity. These conclusions of declining Ep and PE for India should be regarded as provisional since we have only used a subset of the existing Ep and PE data. Nevertheless, they do raise a number of important points. Temperature changes alone do not provide a satisfactory indication of changes in Ep or PE, as is commonly presumed. Changes in relative humidity appear to be far more closely related to the direction and magnitude of evaporation changes, both
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actual and potential. The apparent recent increase in relative humidity over India, which seems to have suppressed both measured and calculated evaporation, may be related to the more general tendency in recent years for increased humidities in the lower troposphere over tropical oceans as reported by FlBhn and Kappala (1989; see also Nicholls et al., 1996). Whether or not this is the case, or whether such increases in atmospheric humidity are related to global warming, cannot be answered here. We merely point out that relative humidity changes over a tropical land mass can have greater importance for the surface water balance than temperature alone. These implications of climate change will affect both the water resource and agricultural sectors, which in a country such as India make key contributions to the national economy. We have also examined changes in PE which may result from future climate change related to enhanced greenhouse gas forcing. Results from six global climate model climate change experiments were used to calculate changes in PE over the Indian region and in its two component terms, the energy and aerodynamic. For all six GCM experiments, PE increased over most parts of India, but this increase was found to be unequal in magnitude between different regions of the country and between different seasons. The maximum increase in PE (up to 8% for 1°C global warming) was calculated from the UKTR experiment over central India in the post-monsoon season. In general, all the model experiments showed smaller increases in PE, or even small decreases, over the northern part of the region. An analysis of the energy and aerodynamic components of PE for different regions of India showed the bigger influence of the energy term on PE change over the Tropics than in higher latitudes where the aerodynamic term tends to control PE to a greater extent. The change in PE due to the energy term is at a maximum (up to 12%) in central India during the post-monsoon and winter seasons, whereas the maximum change due to the aerodynamic contribution to PE (up to 25%) occurs during the pre-monsoon and monsoon seasons over the central and northern parts of the region. If the historic sensitivity of PE to changes in relative humidity and radiation persists, these future increases in PE over the Indian region would result from reduced relative humidity and
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increased radiation, the latter resulting from reduced cloudiness. Whilst the range of these calculated increases in PE for the Indian region associated with modelsimulated global warming are in general agreement with findings in other mid-latitude regions, the PE changes for India differ in that virtually no decrease in PE results from any GCM in any season. PE in India seems uniformly set to increase in warmer world. This apparent contradiction between historic trends in PE (generally a decrease) and future trends as modelled by GCM climate experiments (generally an increase) is important to note, although not easy to reconcile. It may be that although a future warmer world would likely lead to higher specific humidities, the rate of temperature rise over the Indian region would exceed that which has historically been observed and perhaps lead to a reduction in relative humidity. A reduction in relative humidity has been shown here to be associated with increases in PE. Additionally, reduced cloudiness (hence increased radiation) over the Indian region in the future may result from larger-scale changes in the tropical circulation which may weaken the Indian monsoon. This seems unlikely, however, since most of the GCM experiments used here (and others reported elsewhere) suggest that the South Asian monsoon will be enhanced due to elevated greenhouse gas concentrations (Kattenberg et al., 1996). The exception is for GCM experiments which have considered the simultaneous cooling effect of increased sulphate aerosols in the atmosphere; here there is evidence that the monsoon may be weakened and cloudiness decrease (Mitchell et al., 1995). Whatever the reason for these future increases in PE, given these model results and the conclusions of our historic analysis, it would appear that pan evaporation is also likely to increase under conditions of elevated greenhouse gas concentrations. As with the historic decreasing trends in Ep and PE, these future changes would have implications for water management in India. Whether or not these implications will be detrimental to the economy will depend to a large measure on the magnitude of the associated change in rainfall under these GCM scenarios. If rainfall increases sufficiently to offset the estimated evaporation losses, then there may be no major threat to the hydrological and agricultural economy. If rainfall
N. Chattopadhyay, hf. Hulme/Agricultural Number
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of GCMs with increase
in P/PE
71
ratio
Winter (JF)
Pre-monsooon
(MAM)
Monsoon (JJAS)
Post-monsoon
(ONO)
25
Longitude ("E)
Fig. 11. Number of GCM experiments which yield an increase in P/PE in the sign of the change between all six GCMs are shaded.
increases are more modest, or if rainfall decreases, then climate change will present a major challenge to India’s water resource and agricultural sectors. Fig. 11 presents one way of assessing such interactions by mapping the number of GCM experiments which yield an increase in the P/PE (rainfall/potential evapotranspiration) ratio. For the monsoon season, all six GCMs agree that the P/PE ratio becomes more favourable over northeastern India and five out of the six agree that this ratio increases, apart froml the extreme south, over the rest of the country. Changes in this ratio are less
Longitude ('E)
ratio for each season. Maximum
number is six. Areas of agreement
favourable in the post-monsoon season and in the extreme south of the country. The state of climate change modelling means, however, that results from such GCM experiments should continue to be regarded as exploratory and provisional. To highlight this provisionality, when the effects of sulphate aerosols are included in such model experiments the general tendency for models to report increases in Indian monsoon rainfall due to greenhouse gas forcing (and hence P/PE ratios, see above), is replaced by a tendency for rainfall reductions in the monsoon season (Kattenberg et al., 1996).
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Acknowledgements
Chattopadhyay is thankful to the India Meteorological Department for deputing him to the UK to study climate change. Grateful thanks are also due to the British Council for awarding the fellowship for 6 months. The authors are indebted to Dr P.M. Kelly of the Climatic Research Unit, University of East Anglia, for fruitful discussions and making available some software used in the present study. Dr V.N. Sanjeevan and Dr B. Mukopadhyay and the members of Climatic Research Unit are thanked for their encouragement and help.
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