Transcript Climate Change Impacts on Water Arena of a River Basin in

CLIMATE CHANGE IMPACTS
ON
WATER ARENA OF A RIVER BASIN IN
INDIA
D. Roy ,S. Begam, S. Jana and S. Sinha
School of Water Resources Engineering
Jadavpur University
Kolkata,India
Presented by
School of Water Resources Engineering
Jadavpur University
BACKGROUND

Climate change is currently an issue of great concern .

Flood is expected to occur more frequently in certain regions.

Drought related and competing water issues is expected to intensify in other regions.

Rainfall distribution pattern is also expected to change.

These changes could imply some changes in water resources in different parts of the
world.

South Asia in general and India in particular, are considered particularly vulnerable to
climate change and its adverse socio-economic effects.

Reasons: low adaptive capacities to withstand the adverse impacts of climate change
due to the high dependence of the majority of the population on climate-sensitive
sectors like agriculture and forestry and lack of financial resources.

Vast regional variabilities exist in India that affect the adaptive capacity of the country
to climate change.

Therefore, there is a need to evaluate the impact of climate on water resources in India
at regional and local level.
o In this scenario, attempt has been made to assess the impacts of
climate induced changes on the water scenario in the upper portion
upto Ghatshila gauging site (area 14472 sq. km. , river length 175
km. )and lying between the latitudes 22018’ N and 22037’N and
longitudes 86038’E and 870E)of the interstate basin of the
Subarnarekha river (co-basin riparian states are Jharkhand , Orissa
and West Bengal ) of eastern part of India.
LOCATION OF THE STUDY AREA
SUBARNAREKHA RIVER BASIN
o
The smallest (0.6% of geographical area of the country) of the fourteen major river
basins of India(19,296 sq.km).
o
The river length is 450km.
o
It originates in Jharkhand highlands (23˚18’ N, 85˚11’E , elevation 740m).
o
It drains a sizable portions of the three States of Jharkhand, Orissa and West Bengal
and finally debouches into the Bay of Bengal.
o
Average annual rainfall 1350 mm.
o
Annual yield of water constitutes about 0.4% of the country’s total surface water
resources.
o
Annual utilisable water resources have been estimated to be 9.66 MCM
Land use
2009
1999
Parameter
January
October
January
October
Agriculture
(%)
20.83
21.32
26.74
30.49
Forest (%)
49.13
51.72
45.5
45.5
Grassland
(%)
10.77
11.41
7.59
9.39
Water body
(%)
10.12
8.66
Pervious (%)
90.86
93.13
7.11
87.26
8.66
94.2
Impervious (%)
9.14
6.87
12.74
5.8
Pervious
WORK
The work comprises:

Development of hydrologic model of the basin with the help of the catchment
simulation model viz. Hydrologic Modeling System (HEC-HMS 3.5) developed by
the Hydrologic Engineering Center, USA using historical data .

Running of the model for future period under Q0, Q1 and Q14 simulations of A1B
scenario—generated using regional climate model (RCM) PRECIS (Providing
Regional Climates for Impacts Studies) developed by the Hadley Centre, UK and
run at the Indian Institute of Tropical Meteorology (IITM), Pune, India at 50 km ×
50 km horizontal resolution over the South Asian domain for A1B scenario (Special
Report on Emissions Scenarios (SRES) prepared under the Intergovernmental Panel
on Climate Change (IPCC) coordination.
Analyzing precipitation, potential evapotranspiration, streamflow under changed
climate scenario and those under historical scenario to ascertain impact of climate
change on water resources in the basin.

Typical HEC-HMS
representation of watershed
runoff.
CLIMATE CHANGE SCENARIO



The IPCC SRES scenario set comprises four scenario
families: A1, A2, B1 and B2. The A1 family includes
three groups reflecting a consistent variation of the
scenario (A1T, A1FI and A1B). Hence, the SRES
emissions scenarios consist of six distinct scenario
groups, all of which are plausible and together
capture the range of uncertainties associated with
driving forces.
Scenario A1:
The A1 scenario family describes a future world of
very rapid economic growth, global population that
peaks in mid-century and declines thereafter, and the
rapid introduction of new and more efficient
technologies.




A1FI scenario : fossil intensive
A1T scenario : non-fossil energy sources
A1B scenario: balance across all sources where
balance is defined as not relying too heavily on one
particular energy source
Boundary conditions from three simulations from a
17-member Perturbed Physics Ensemble generated
using Hadley Center Coupled Model (HadCM3) for
the Quantifying Uncertainty in Model Predictions
(QUMP) project have been used to drive PRECIS at
IITM, Pune, India for the period 1961–2098 in order
to generate an ensemble of future climate change
scenarios (Q0, Q1 and Q14 ) for the Indian region at
50 km × 50 km horizontal resolution for A1B scenario.
The criteria for model evaluation adopted involves the following:
oSensitivity Analysis --- The sensitivity analysis of the model was performed to
determine the important parameters which needed to be precisely estimated to make
accurate prediction of basin yield.
oPercentage error in simulated volume (PEV)
o Percentage error in simulated peak (PEP), and
o Net difference of observed and simulated time to peak (NDTP)
o Nash–Sutcliffe model efficiency (EFF)
(Volo  Volc )
PEV 
 100
Volo
Volo = observed runoff volume (m3)
Volc = computed runoff volume (m3)
PEP 
(Q po  Q pc )
Q po
Qpo = observed peak discharge (m3/s)
 100
Qpc = computed peak discharge (m3/s)
Tpo = time to peak of observed discharged(h)
NDTP  (Tpo  Tpc)
Tpc = time to peak of computed discharge (h)
n
EFF 
 (Q
i 1
oi
n
 Q o )   (Qoi  Qci ) 2
2
i 1
n
 (Q
i 1
oi
 Qo )2
Qoi = ith ordinate of the observed discharge (m3/s)
Qo = mean of the ordinates of observed discharge (m3/s)
Qci = ith ordinate of the computed discharge (m3/s)
12000
10000
8000
simulated flow
6000
Observed flow
4000
2000
0
J J J J F F F F MMMM A A A A MMM J J J J
Stream flow hydrograph
Non-monsoon 1999
Stream flow hydrograph
Monsoon 1999
Performance measures table of the model
for calibration years
Season
Performance
Measures
PEV (%)
1999
2001
22.66
68.91
PEP (%)
22.45
4.59
NDTP
1 day
1 day
EFF
0.70
0.37
PEV (%)
32.85
10.18
PEP (%)
9.13
41.2
NDTP
0 day
0 day
EFF
0.50
0.66
Monsoon
Non-Monsoon
Stream flow hydrograph
Non-monsoon 2004
Stream flow hydrograph
Monsoon 2004
Performance measures table of the model for
validation years
Season
Performance
Measures
2004
2007
PEV (%)
-26.14
-2.46
PEP (%)
-5
-0.95
Monsoon
NDTP
Non-Monsoon
0 day
0 day
EFF
0.78
0.91
PEV (%)
- 11.5
- 14.9
PEP (%)
- 49
+ 11.15
NDTP
0 day
0 day
EFF
0.74
0.81



Annual rainfall in all the projected years are found to be normal or
above normal (1.5 – 35)% except for 5 years.
the highest value is 1860.2 mm
the lowest one 925.6 mm lower (by 32%)
Annual rainfall for historical and future
years under Q0, Q1 and Q14 simulations
The annual rainfall for Q0 simulation in the projected
years (except 2040 and 2050) is found to be higher than
the other two simulations----close to Q14 simulation.
Rainfall for Q14 in 2040 and 2050 is higher than
historical average(22% and 28%).
 annual rainfall lowest for Q1 simulation and also lower
than historical average(21 to 51%) .

Percentage variation of
monthly rainfall
1500
1300
1100
900
2014-2020
700
2020's
500
2030's
300
2050
100
-100
Q0 simulation

Monsoonal rainfall ( July, August and September) and March
rainfall in all the projected years do not show significant deviation
(compared to non-monsoonal rainfall)from historical values.

Non-monsoonal rainfall in future periods show marked deviation
(increase) from historical ones.

The highest increase (1331.6 %) is found for the month of May in
decade of 2020 and the second highest increase (774.4 %) in the
month of Nov. in decade of 2030 and the third highest increase in
the month of Dec. and Jan. in 2050.
Percentage deviation of monthly
rainfall
700
600
500
2020
2030
2040
2050
400
300
200
100
0
-100
Q1 simulation
Q1 simulation
oThe highest increase (586%) in rainfall is found for the month of Nov in 2050
following the second highest increase (470%) in the month of December 2050,October
2050 and February 2050.
oA noticeable increase has also been found for February 2030 March 2020,September
2030 only.
oA decrease in monthly rainfall values (-36% to-95%) from corresponding historical
values has been observed for almost all the months with a maximum decrease (95%) in
month of June, 2040.
Percentage deviation of
monthly rainfall
2000
1500
2020
1000
2030
2040
500
2050
0
-500
Q14 simulation
Q14 simulation
o A noticeable increase in monthly rainfall has been found in April2020, November and
December of 2030 and 2050 with maximum increase (1766%) in December 2050
o Monthly rainfall deviation for Q0 ,Q14 almost similar for 2030 and 2050.
Annual 24 hr Maximum Rainfall
Rainfall in mm
350.00
300.00
250.00
200.00
150.00
100.00
50.00
0.00
Q0 Simulation
Rainfall (mm)
Annual 24 hr Maximum Rainfall
350
300
250
200
150
100
50
0
Highest
lowest
Q0
Q1
Q14
Annual 24-h maximum rainfall
o Projected to be lower than the historical
highest for the future years excepting for
five years.
o The quantum of decrease in the value ranges
from 20 % to 80%
o Projected to be lower than the historical
highest for Q1 and Q14 simulations.
o is highest for Q14 (excepting 2030)
o Rainfall is higher for Q1 than for Q0
excepting 2030 and 2040
POTENTIAL EVAPOTRANSPIRATION
Percentage deviation of
monthly PET
15
2014-2020
10
2020s
5
2030s
2050
0
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
-5
-10
-15
Monthly variation of Potential Evapotranspiration
Q0 simulation
Q1 simulation
Q14 simulation






The annual distribution of projected monthly PET values is found to
follow the pattern of historical average PET values.
For Q0 simulation monthly deviation is small(-13to +13%).
Monthly PET values lie close to the historical one for the year of
2014 – 2020.
The monthly PET values for FEB to APR for the decade of 2020 is
found to be higher than the historical one.
The monthly PET values for APR to JUN and SEP, OCT for the
decade of 2030 is found to be higher than the historical one.
As per Q1 and Q14 simulations, monthly PET values in projected
years are higher than corresponding historical values (excepting for
the year 2020)---larger increase has been found in quantum of
monthly PET during the months of March, April, May(for
Q1~19%) and June .
Streamflow Hydrographs
Q1 simulation
Q0 simulation
Q14 simulation
Flow pattern
o No change in pattern of stream flow over that of historical flow is observed in the
projected years for Q1 simulation
o As per Q0 simulation,(4) of the years showed annual peak in May and (8)
in October and (1) of the years in November
o As per Q14 simulation , annual peak flow is observed in May and in October (rather
than in monsoon)in 2040 and 2050 respectively.
Deviation in annual stream-flow volume (MCM) from historical stream-flow
volume for projected years under Q0, Q1 and Q14 simulations
The stream-flow volumes for projected years (excluding year 2014 and 2020)
are higher than the corresponding historical one. The highest increase(166%)
during 2031-40,followed by 2021-2030 (147%) and 2014-2020
For Q1 simulation annual stream-flow volumes for all the projected years
have been found to be lower (range 32% to 70%) than the average historical
value and for 2020 and 2030 for Q14.
The annual stream-flow volumes have been found to lie very close to the
historically observed flow volume for 2040 under Q0 & Q14 simulations and
also for 2050 under Q14 simulation only.
Percentage variation of flow in volume
2500
2000
2014-2020
1500
2021-2030
1000
2031-2040
500
0
-500
Deviation of monthly flow volume (future decadal average) from historical flow for Q0 simulation
oStream-flow volumes during monsoon in the projected years show
smaller deviation (10 to 50%) from historical values compared to
those in non-monsoon.(35% to 270%----even higher in month of
May)
oStream-flow volumes for projected months from January to April
(excluding year 2014- 2020) are lower than the corresponding
historical one.
oFrom October to December, stream-flow volumes are higher than
the corresponding historical one showing maximum variation in the
month May for two future periods (2021-2030 & 2031-2040).
Annual Peak Flow(2014-40)
12000
Discharge(cumec)
10000
8000
6000
4000
2000
0
Time Period
Peak flows for Q0 simulation have been found to be lower than the historically
observed annual highest peak --- the peak flow approaches historical value for
three years only – and on one occasion peak flow is higher than historically
observed 2nd and 3rd highest peak flow.
Annual peak flows (1st, 2nd and 3rd highest) in the years 2020,2030, 2040, 2050
have been found to be much lower than hist. av. in Q0, Q1 and Q14 simulation.
Peak flow is the lowest for Q1 simulation among the three.
Flow-duration Curve for historical observed data
Discharge (cumec)
6000
2014-2020
5000
4000
3000
2000
1000
0
0
10
20
30
40
50
60
70
Pp=Percentage time indicated discharge is equalled or exceeded
80
90
100
Discharge (cumec)
7000
2021-2030
6000
5000
4000
3000
2000
1000
0
Discharge (cumec)
0
10000
9000
8000
7000
6000
5000
4000
3000
2000
1000
0
20
40
60
80
Pp=Percentage time indicated discharge is equalled or exceeded
2031-2040
0
10
20
30
40
50
60
Pp=Percentage time indicated discharge is equalled or exceeded
70
80
90
100
100
Flow characteristic of the stream during historical and future years was found
to be similar. Non-perennial flow condition was found to exist in both
historical and projected years
80% of time the discharge of the stream was found to equal or exceed 80
cumec ,117 cumec and 107 cumec in 2014-20,2021-30 and in 2031-40,
(against historical flow of 20 cumec) and 90% dependable flow for those
period was found to exceed 22.3,57.8 and 36.2 cumec (against historical flow
of 8.3 cumec).
o Annual rainfall in all the projected years are found to be normal or above
normal except for five years (1.5 – 35.13 % )---the highest value is 1860.2 mm in
2015 --the lowest one 925.6 mm (by 32%) for 2035
o Monsoonal rainfall ( July, August and September) and March rainfall in all the projected
years do not show significant deviation from historical values.
o Non-monsoonal monthly rainfall in future periods is expected to increase
o Annual 24-hr maximum rainfall projected to be lower than the historical highest for the
future years excepting for five years.
o The quantum of decrease in the value ranges from 20 % to 80%
o The annual distribution of projected monthly PET values is found to follow the pattern of
historical average PET values.
o Monthly deviation in PET values from the historical average is small for the
future years(- 13% to +13% --- the non monsoonal deviation is higher).
o Change in pattern of stream flow over that of historical flow is observed in the
projected years ----18%(4) of the years showed annual peak in May and 30%
(8) in October(8) and 3%(1) of the years in November
o The stream-flow volumes for projected years (excluding two years) are
higher than the corresponding historical one(by 6 % to 166% ).
o Stream-flow volumes during monsoon in the projected years show smaller deviation
(10 to 50%) from historical values compared to those in non-monsoon.(35% to
270% ---even higher for May )
o Peak flows for Q0 simulation have been found to be lower than the historically
observed annual highest peak --- the peak flow approaches historical value for three
years only---– and on one occasion peak flow is higher than historically observed 2nd
and 3rd highest peak flow.
o Flow characteristic of the stream during historical and future years was found to be
similar. Non-perennial flow condition was found to exist in both historical and
projected years
o 80% of time the discharge of the stream was found to equal or exceed 80 cumec ,117
cumec and 107 cumec in 2014-20,2021-30 and in 2031-40 (against historical flow of
20 cumec) and 90% dependable flow for those period was found to exceed 22.3,57.8
and 36.2 cumec (against historical flow of 8.3 cumec).
Inter comparison of simulations
o Annual rainfall for Q0 simulation in the projected years (except 2040 and 2050)
is found to be higher than the other two simulations----close to Q14 simulation.
o Annual rainfall is the lowest for Q1 simulation
o Monthly rainfall deviation is almost similar Q0 ,Q14 for 2030 and 2050
o A decrease in monthly rainfall values from corresponding historical values has
been observed for almost all the months for Q1 simulation.
o Annual 24-h maximum rainfall lie close to each other (within 30%) for three
simulations---excepting for 2030.
o As per Q1 and Q14 simulations, monthly PET values in projected years are higher
than corresponding historical values (excepting for the year 2020)---larger
increase has been found in quantum of monthly PET during the months of March,
April, May(for Q1 simulation ~19%) and June.
o No change in pattern of stream flow over that of historical flow is
observed in the projected years for Q1 simulation
o Pattern of flow Q0 and Q14 similar –non monsoonal flow higher than
monsoonal
o Annual stream-flow volumes for all the projected years have been
found to be lower (range 32% to 70%) than the average historical value
for Q1 simulation and for 2020 and 2030 for Q0 and Q14 simulations.
o The annual stream-flow volumes have been found to lie very close to
the historically observed flow volume for 2040 under Q0 & Q14
simulations and also for 2050 under Q14 simulation only.
o Annual peak flows (1st, 2nd and 3rd highest) in the years 2030, 2030,
2040, 2050 have been found to be much lower than hist. av. in Q0, Q1
and Q14 simulation .
o Peak flow is the lowest for Q1 simulation among the three.
Impact of Climate change on Water Arena
o
Water availability in the basin is expected to be normal or above normal.
o
Seasonal shift in stream flow pattern is expected and it may have some effects on
aquatic ecosystem.
o
Low flow characteristic of the river is expected to be better than historical and it
may be good for aquatic ecosystem.
o
Increased peak flow (~9200cumec) is expected on one occassion and this may lead
to disastrous situation.
o
Decreased peak flow (~1000 cumec)is expected on one occassion and this may
hinder natural flushing of the channel—leading to loss of its carrying capacity.
o
Higher PET values during non monsoon (March to June and October to Dec.))is
projected and non-monsoonal monthly rainfall in future periods is expected to
increase (by large amount) ----this may affect crop production (Rabi and Boro crops)
o
Ensemble of scenario should be considered.
o
Q0 and Q14----similar outcome.
o
Q1 simulation outcome is different:
o
No change in streamflow pattern ;
o
Reduced water availability (upto 70 % less flow);Peak flow less, higher PET;
ACKNOWLEDGEMENT
Sincere thanks are being acknowledged for kind assistance rendered in the
form of data and related matter by officials and personnel of the India
Meteorological Department GoI, Central Water Commission GoI, National
Remote Sensing Center, GoI, National Bureau of Soil Survey and Land
Use Planning (NBSS and LUP), GoI and Irrigation and Waterways,
GOWB. Thanks are also acknowledged to the Ministry of Water Resources,
Government of India for providing financial assistance for the work.
THANK
YOU
Deviation of Projected Flow for Q0 Simulation
Historical
14-20
dv
21-30
dv
31-40
dv
JAN
737.528
463.8571429 -37.1065 106.9961 -85.4926 246.2793 -66.6075
FEB
544.4403 353.4714286 -35.0762 16.52649 -96.9645 153.9528 -71.7227
MAR
405.1881
APR
345.0158 1276.942857 270.1115 138.2952 -59.9163 194.4142 -43.6506
MAY
441.514
JUN
6305.441 3198.342857 -49.2765 7301.846 15.80231 10399.69 64.93203
JUL
14914.27 10794.81429 -27.6209 19008.95 27.45471 21730.83 45.70491
AUG
21659.49 19252.87143 -11.1112 27193.92 25.55198 25544.1 17.93489
SEP
18112.98 27643.51429 52.61712 27207.49 50.20989 28167.52 55.51013
OCT
7595.29
NOV
2585.268 6865.471429 165.5614 5737.941 121.9477 7367.176 184.9676
DEC
1167.928 3800.114286 225.3722 2959.877 153.4296 3267.858 179.7995
275.4
-32.0316 108.0219 -73.3403 58.20821 -85.6343
701.4714286 58.87864 9347.455 2017.137 7050.926 1496.988
20422.1
168.8785 13244.55 74.37853 21315.68 180.6434
May
May
May
May
May
May
2021
2029
2033
2035
2022
2023
2032
2033
2034
2036
Peak Rain
216.49
216.49
231.27
228.02
4675
62187
17451
29361
9222
6143
Month
Sep
Sep
Oct
July
PET
decrease
decrease
increase(2%)
decrease
Table for Input SMA parameters (calibrated) used in the
model
SMA parameter
Season
Monsoon
Non- Monsoon
Canopy Storage (mm)
Surface Storage (mm)
Max Rate of Infiltration (mm/hr)
Impervious (%)
Soil Storage (mm)
Tension Storage (mm)
Soil Percolation (mm/hr)
Groundwater 1 Storage (mm)
4.67
50.8
3
11.07
316.16
106.4
0.29
11
4.67
50.8
3
16.88
320.75
117.35
0.29
12
Groundwater 1 Percolation (mm/hr)
0.29
78
18
0.29
42
19
0.21
670
0.21
610
Groundwater 1 Coefficient (hr)
Groundwater 2 Storage (mm)
Groundwater 2 Percolation (mm/hr)
Groundwater 2 Coefficient (hr)
Landuse/ Landcover Map of Subarnarekha River Basin October 2009
Landuse/ Landcover Map of Subarnarekha River Basin January 2009
HEC-HMS MODEL

Designed to simulate the precipitation–runoff processes of dendritic watershed systems ,with
soil moisture accounting (SMA)algorithm , it accounts for a watershed’s soil moisture balance
over a long-term period and is suitable for simulating daily, monthly, and seasonal streamflow.
The SMA algorithm takes explicit account of all runoff components including direct runoff
surface flow) and indirect runoff (interflow and groundwater flow)(Ponce (1989) .The model
requires inputs of daily rainfall, soil condition and other hydro meteorological data.

The HMS SMA algorithm represents the watershed with five storage layers viz., canopy –
interception, surface-depression ,soil profile ,groundwater storages (1 and 2) as shown in the
Fig.2 involving twelve parameters viz., canopy interception storage, surface depression storage,
maximum infiltration rate, soil storage, tension zone storage and soil zone percolation rate and
groundwater 1 and 2 storage depths ,storage coefficients and percolation rates.

Rates of inflow to, outflow from and capacities of the layers control the volume of water lost
from or gained by each of these storage layers. Current storage contents are calculated during
the simulation and vary continuously both during and between storms. Besides precipitation the
only other input to the SMA algorithm is a potential evapotranspiration rate (HEC 2000).
For the present study:-

Runoff depth was computed using SMA method.

Clark unit hydrograph technique with the peak and time to peak computed by Snyder’s unit
hydrograph technique method was adopted to compute streamflow hydrograph.

Linear reservoir method was used to model base flow .

Muskingum method of channel routing was used to generate discharge hydrograph at
downstream point in channel.
The soil moisture accounting
loss method uses five layers to
represent the dynamics of water
movement above and in the soil.
Layers
include
canopy
interception, surface depression
storage,
soil,
upper
groundwater,
and
lower
groundwater. The soil layer is
subdivided into tension storage
and
gravity
storage.
Groundwater layers are not
designed to represent aquifer
processes; they are intended to
be used for representing shallow
interflow processes.
METHODOLOGY
 Delineation of catchment boundary and stream network of the sub-basin in
Google Earth 6.1.0.5001 with the help of topo-sheets; and find the basin
characteristics (sub-basin area, main stream length and slope etc.)
Processing of all input data for use in HEC-HMS (version 3.4) model
which include the following steps: Computation of Average rainfall of sub-basin by Theissen polygon
method for historical and projected years.
 Two of the 12 parameters needed for the SMA algorithm (canopy
interception storage and imperviousness) were estimated by the processing
of land use land cover (LULC) Satellite Imagery. The land use data is
created with the help of Geomatica Freeview 10.3.
 Four of the 12 parameters needed for the SMA algorithm (maximum
infiltration rate, maximum soil storage, tension zone storage and soil
percolation rate) were estimated from the information on soil of the study
area.
 Other parameters (GW1 and GW2 storage and coefficient) needed for the
SMA algorithm and parameters needed for routing method (value of
Muskingum K and X) were estimated from the daily discharge data at gauging
station Jamshedpur and Ghatsila. The parameter GW1 and GW2 percolation
rate were estimated through calibration.
 Computation of monthly Evapotranspiration rate by Penman’s method.
 Creating the basin network in HEC-HMS model and setting of all input
parameters properly for the model.
 Calibration of the model for all the input parameter related to the basin.
 Validation of the model for the sub-basin.
 Running the model for projected years under changed climate.
 Software Packages:
HEC-HMS 3.4
HEC-DSSVue 2.0.1
Google Earth (Version 6.1.0.5001)
Geomatica Freeview 10.3
GrADS (Version 2.0.a9.oga.1)
Fast Stone Capture 7.4
 Toposheets: The Survey of India at Kolkata, W.B.
 Rainfall and Temperature data (daily): India Meteorological Department, GoI Pune and
Indian Institute of Tropical Meteorology, GoI, Pune. Other daily meteorological data such as
relative humidity, wind speed and actual sunshine hours were collected from India
Meteorological Department, GoI, Kolkata.
 Hydrological Discharge data (daily):
Central Water Commission, GoI, Bhubaneswar, Odisha .
 Satellite imagery data: National Remote Sensing Center, GoI, Hyderabad.
 Soil data: National Bureau of Soil Survey & Land Use Planning (NBSS & LUP), GoI,
Salt lake, Kolkata.
Unit Hydrograph Transform Method
Snyder Unit Hydrograph
Parameter
Standard Lag (Tp, hr)
Peaking Co-efficient (Cp)
Upper Sub-basin
50
0.6
Muskingum Routing Method
Muskingum Routing
Parameter
K (hr)
X
Upper Sub-basin
49
0.3
o Acharya, A., K. Lamb, and T.C. Piechota, 2013. Impacts of Climate Change on Extreme
Precipitation Events Over Flamingo Tropicana Watershed. Journal of the American Water
Resources Association.
o
Ayka, A., 2008. Hydrological Models Comparison for Estimation of Floods in the AbayaChamo Sub-Basin. A thesis presented to the school of Graduate studies CIVIL
Engineering Department of the Addis Ababa University.
o
Bae, Beg-Hyo, Il-Won Jung, and D.P. Lettenmaier, 2011. Hydrologic Uncertainties in
Climate Change from IPCC AR4 GCM simulations of the Chungju Basin, Korea. Journal
of Hydrology, Vol. 401(1): 90-105.
o
Bingner, R.L., C.E. Murphee, and C.K. Mutchler, 1989. Comparison of sediment yield
models on various watershed in Mississippi. Trans. ASAE. 32(2): 529-534.
o
Das, S. and S.P. Simonovic, 2012. Assessment of Uncertainty in Flood Flows under
Climate Change Impacts in the Upper Thames River Basin, Canada. British Journal of
Environment & Climate Change, 2(4): 318-338.
o
Divya & S. K Jain, 1993. Sensitivity of catchment response to climatic change scenarios.
IAMAP/IAHS Workshop, 11-23 July, Yokohama, Japan.
Continued
o Fleming, M. and V. Neary, 2004. Continuous Hydrologic Modeling Study with the
Hydrologic Modeling System. Journal of Hydrologic Engineering, 9(3): 175-183.
o Hydrologic Modeling System HEC-HMS. Technical Refrence Manual, 2000. US
Army Corps of Engineers, Hydrologic Engineering Center, 609 Second Street, Davis,
CA 95616-4687 USA.
o IPCC (2007) Climate Change, 2007. Synthesis Report. Contribution of Working
Group I, II and III to the Fourth Assessment Report (AR4) of the Intergovernmental
Panel on Climate Change (IPCC), Cambridge University Press, Cambridge. United
Kingdom and New York.
o Kumar, K.K., S.K. Patwardhan, A. Kulkarni, K. Kamala, K.K. Rao and R. Jones,
2011. Simulated Projections for Summer Monsoon Climate over India by a highresolution regional Climate Model (PRECIS). Current Science, Vol. 101, No. 3.
o Subramanya, K., 2002. Engineering Hydrology. Second Edition, Tata McGraw-Hill
Publishers, New Delhi.
Q1 simulation
Q14 simulation