shown that
age annual
thy, 1984;
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; period of
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icant long-
igannathan
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1 Kumar et
peninsula,
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es (1871 -
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alysis, Lal
s based on
.tmosphere
l of the
the inland
n surface
temperature may rise between 3.5°C and 5.5°C by 2080. On
seasonal basis, the projected surface warming is higher in winter
then during summer monsoon. The spatial pattern of
temperature change has a large seasonal dependency. The
spatial distribution of the surface warming suggests that north
India may experience an annual mean surface warming of 3°C
rise or more by 2050s. GCM models have simulated peak
warming of 3°C over north and central India in winter. Over
much of the southern peninsula, the warming is likely to be
under 2°C during winter season. The surface temperature rise
would be more pronounced over northern and eastern region
(-2°C) during the monsoon season.
The increase in annual mean precipitation over the India sub-
continent is projected to be 7 to 10% by 2080s. Winter
precipitation may decrease by 5 to 25% in the Indian sub-
continent. An increase of 10 to 15% is projected in area average
summer monsoon rainfall over the Indian sub-continent. Over
north-west India, during monsoon season an increase of about
30% or more is suggested by 2050s. The western semi-arid
margins of India could receive higher than normal rainfall in the
warmer atmosphere. It is also likely that date of onset of
summer monsoon over India could become more variable in
future. IPCC (2001a, b) has indicated that variability in Asian
summer monsoon is expected to increase along with changes in
the frequency and intensity of extreme climate events in this
region. All climate models simulated an enhanced hydrological
cycle and increase in annual mean rainfall over South Asia
(under non-aerosol forcing). Future projection of increase in
temperature and changes in precipitation over Indian
subcontinent are shown in Table 1.
Table 1. Climate change projections for the Indian sub-
continent [Source Lal (2001)]
Increase in Change in
Scenarios Temperature rainfall
CC) (%)
Annual 1.00 - 1.41 2.16 - 5.97
2020s | Winter 1.08 - 1.54 (-)1.95 - 4.36
Monsoon 0.87 - 1.17 1.81 - 5.10
Annual 2.23 - 2.27 5.36 - 9.34
2050s | Winter 2.54 - 3.18 (-)9.22 - 3.82
Monsoon 1.81 - 2.37 7.18 - 10.52
Annual 3.53 - 5.55 7.48 - 9.90
2080s | Winter 4.14 - 6.31 (-)24.83 - 4.50
Monsoon 2.91 - 4.62 10.10 - 15.18
Based on these studies and IPCC A2/B2 recommendations, 16
hypothetical combination of scenarios were developed for the
present analysis by increasing; temperature by 1, 2 and 3°C;
rainfall by 5, 10 and 15%; and their combination.
4. HYDROLOGICAL MODEL SET-UP AND DATA
ASSIMILATION
An effort has been made to investigate advantageous SVAT
‘variable infiltration capacity (VIC)’ model to assess runoff
potential and other hydrological components for entire India.
VIC is a semi-distributed macroscale hydrological model
designed to represent surface energy, hydrological fluxes and
states at scales from large river basins to the entire globe (Liang,
1994; Liang et al., 1994; Liang et al, 1996). It is grid based
model which quantifies the dominant hydro-meteorological
process taking place at the land surface atmospheric interface.
In the present study, the model was forced with daily
precipitation, maximum and minimum air temperature procured
from Indian Meteorological Department (IMD) at daily time
step on 25 x 25 km grid. The 25 x 25 km grid map laid over
land mass of India is shown in Figure 3. It was identified that
4707 number of grids lie on land mass and are to be run for
analysis. The base map in the figure is GTopo30 digital
elevation model (DEM) with resolution 3 arc seconds, which
has been used for elevation and slope parameters.
pr FOE ol m
SN
END
x 25 Gus
2 GTOPO 36 DEM
Figure 3. GTopo 30 DEM map of India showing its location
In order to implement VIC model, five main input files are
required namely forcing, soil parameter, vegetation parameter,
vegetation library and global parameter file in ASCII format.
4.1 Meteorological Forcing File
In the present study, as the model was employed in water
balance mode, the meteorological parameters considered to
force the model were daily precipitation, daily minimum and
maximum temperature. The 0.5? x 0.5? precipitation; and jo xi"
minimum and maximum temperature gridded data of IMD have
been procured for the period of 1991 - 2005 (Rajeevan and
Bhate, 2008). For each grid, forcing files containing daily
precipitation, minimum and maximum temperature from 1991
to 2005 have been generated using the programming in IDL.
4.2 Soil Parameter File
The soil parameter file describes the unique soil properties (in
addition to several other variables) for each grid cell in the
model domain. As mentioned above VIC2L has been adopted in
the present analysis, hence, 02 layers of soil with 300 mm and
700 mm depth have been considered. The soil information,
namely soil texture, bulk density and saturated hydraulic
conductivity correspond to each layer depth has been extracted
from FAO's digitized soil map of the world at scale of
1:5,000,000 (FAO, 2003). The related other soil variables such
as maximum velocity of baseflow; fraction of maximum soil
moisture where non-linear baseflow occurs; average soil
temperature; particle density; fractional soil moisture content at
the critical and wilting point; surface roughness; residual
moisture of each layer have been picked as the standard values,
those are provided at VIC website. All these parameters have
been designated for each grid along with its longitude, latitude,
median elevation, mean annual rainfall and initial soil moisture
to initiate the model.
