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Climate Change Impact on Water Resources of Phakal Lake using
SWAT model
Sri Lakshmi Sesha Vani JResearch Scholar
NIT Warangal
Introduction
• Climate change and its variability cause significant impacts on water
resources of the region.
• The uncertainties in precipitation and increasing temperature in the• The uncertainties in precipitation and increasing temperature in the
semi-arid regions are posing a serious stress on the water resources.
• Minor Irrigation tanks play a significant role in managing water
resources in semi-arid regions.
• Tank irrigation has a great significance in semi-arid regions, as the
small scale farmers depend mainly on these resources.
• Tank irrigation is significant in arid and semi-arid regions due to
the contribution to water resources development, agricultural
production, livelihood security and environmental sustainability.
• This study was carried out for Phakal watershed, which is situated in
the Krishna River Basin, India.
• This part of the basin is very important as the catchment provides• This part of the basin is very important as the catchment provides
water for Phakal lake – a medium irrigation project.
• So it is crucial to study and evaluate the potential impacts of climate
change on the hydrology and water resources availability.
• The assessment of future climate change impacts on Phakal lake has
been carried out using the Soil and Water Assessment Tool (SWAT).
Climate Data
Climate model data
Observed climate data
Geospatial data
DEM,LU/LC, SOIL MAP
MethodologyHydrological
Data
CWC Gauge Data
SWAT-CUP(Sufi-2)
Bias Correction Swat Model
Generation of Stream flow for future scenarios
Fig. 1 Methodology followed in the present research study
Calibration & Validation
Climate Change Impact Assessment
Study Area• Normal annual rainfall is about 1048mm
• Warangal District has an area of 12,846 km²
• Location: North Latitude 17019' and 180 36'
East Longitude 780 49' and 800 43‘
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Fig 2 Study Area – Warangal district (Undivided)
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Fig. 3 Phakal Watershed
Climate Data
• Observed climate data provided by Indian
Meteorological Department (IMD), Pune.
• Indian Meteorological Department has provided the
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• Indian Meteorological Department has provided the
rainfall data for the whole of India considering a grid
with a cell size of 0.5°×0.5°.
• Climate model data –RCM available with grid cell size
0.5°×0.5°.
• Source: CORDEX (RCP 4.5 )
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Fig.4 Climate model and IMD grid points of Warangal district
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ExperimentName
RCM Description Driving GCMContributingInstitute
CCAM(ACCESS) Commonwealth
Scientific andACCESS1.0
Table 1: List of RCMs used in the studySource: http://cccr.tropmet.res.in/home/ftp_data.jsp
Scientific andIndustrial ResearchOrganisation(CSIRO), Conformal-CubicAtmospheric Model(CCAM; McGregorand Dix, 2001)
CSIRO Marine andAtmosphericResearch,Melbourne,Australia
CCAM(CNRM) CNRM-CM5
CCAM(CCSM) CCSM4
CCAM(MPI) MPI-ESM-LR
Geospatial Data
• Digital Elevation Model – SRTM
• Landuse landcover map (LULC) -USGS
• Soil Map -FAO
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• Soil Map -FAO
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Fig 5 Digital Elevation Model
Fig 6 Landuse Landcover map
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Fig. 7 Soil map of Warangal district
Hydrological Modelling• Soil and Water Assessment Tool (SWAT) is selected for hydrological
modelling.
• SWAT model run by using Observed weather data obtained from
Indian Meteorological Department (IMD) from 1975-2005.
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Indian Meteorological Department (IMD) from 1975-2005.
• SWAT model is run for Konduru watershed which is downstream of
the study area due to the lack of gauge station at the Phakal lake.
• SWAT model calibration and validation is carried out for monthly
simulated stream flow using observed stream flow data from the
Purushothamagudem gauging station present in Konduru
watershed.
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Fig. 8 Watersheds of Warangal district
Fig. 9 Hydrological Modelling of Konduru Watershed
Calibration and Validation of Konduru Watershed using SWAT- CUP• Observed data available at Purushothamgudem
• Data available for the period of 1988 to 2005
• SUFI-2 is used calibration and validation of the Model
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• SUFI-2 is used calibration and validation of the Model
Table 2 Calibration and validation results of Konduru watershed
Objective functions Calibration Validation
R2 0.7 0.39
NSE 0.67 0.35
Sensitivity Analysis
Parameter Name Description Best Parameter Value
MinimumValue
MaximumValue
V__ALPHA_BNK.rte Base flow alpha bank factor for bank storage (days)
0.045 0.000 1.000
V__CH_K2.rte Effective channel hydraulic conductivity, mm/hr
14.141 5.00 22.00
R__CN2.mgt Curve Number -0.000852 -0.007 0.007A__OV_N.hru Manning’s N 0.191 0.18 0.2
A__REVAPMN.gw Threshold depth for revaporation to occur, mm
265.37 0.00 500
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Table -3 The most sensitive parameters and their best parameter values and intervals
occur, mm
V__ESCO.bsn Soil evaporation compensation factor
0.812 0.8 1.0
V__CH_N2.rte Manning’s N 0.183 0.15 2.0R__SOL_K (...).sol Saturated hydraulic conductivity -0.594 -0.6 -0.57
V__GW_DELAY.gw Ground water delay, days 42.445 42 60A__GWQMN.gw Threshold depth for ground water
flow to occur, mm1385.84 1350 1390
V__GW_REVAP.gw Ground water revaporation coefficient
1.857 1.8 1.9
V__EPCO.bsn Plant uptake compensation factor 0.69 0.00 1.00
V__ALPHA_BF.gw Base flow recession factor, days 0.138 0.00 1.0
R__SOL_AWC (...).sol Available water capacity, m/m 0.338 0.33 0.4
R__SOL_BD (...).sol Moist bulk density(Mg/ m3)
-0.023 -0.04 -0.1
Fig. 10 Global Sensitivity Analysis
0
5
10
15
80
100
120
140
160
Av
era
ge
Ra
infa
ll(m
m)
Av
era
ge
Mo
nth
ly F
low
(m3/s
)
Rainfall Observed Simulated
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20
15
20
250
20
40
60
Av
era
ge
Ra
infa
ll(m
m)
Av
era
ge
Mo
nth
ly F
low
(m
Time(Years)
Fig. 11 Calibration of Konduru Watershed
0
2
4
6
8
1080
100
120
140
160
180
200
Av
era
ge
Ra
infa
ll(m
m)
Av
era
ge
Mo
nth
ly F
low
(m3/s
)
Rainfall Observed Simulated
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21
10
12
14
160
20
40
60
80
Av
era
ge
Ra
infa
ll(m
m)
Av
era
ge
Mo
nth
ly F
low
(m
Time(years)
Fig. 12 Validation of Konduru Watershed
Simulation of Konduru Watershed Water resources
200
250
300
350
Av
era
ge
Mo
nth
ly F
low
(m3/s
)
Observed IMD HISTORIC
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0
50
100
150
Av
era
ge
Mo
nth
ly F
low
(m
Time(Years)
Fig. 13 Average Monthly flow Variations of Konduru Watershed between observed, IMD and Historic
Simulation of Phakal Watershed Water resources
100
120
140
160
180
200
Str
eam
flo
w(c
ms)
Fig. 14 Average Monthly flow Variations of Phakal Watershed for different climate models for 2006-2040
0
20
40
60
80
100
200
620
06
200
720
08
200
820
09
2010
2010
2011
2012
2012
2013
2014
2014
2015
2016
2016
2017
2018
2018
2019
2020
2020
2021
2022
2022
2023
2024
2024
2025
2026
2026
2027
2028
2028
2029
2030
2030
2031
2032
2032
2033
2034
2034
2035
2036
2036
2037
2038
2038
2039
2040
2040
Str
eam
flo
w(c
ms)
Year
ACCESS CCSM CNRM MPI
30
40
50
60
70
80
Str
eam
flo
w(c
ms)
Fig. 15 Average Monthly flow Variations of Phakal Watershed for different climate models for 2041-2070
0
10
20
2041
2041
2042
2042
2043
2043
2044
2045
2045
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2060
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2062
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2063
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2064
2065
2066
2066
2067
2067
2068
2069
2069
2070
2070
Str
eam
flo
w(c
ms)
Year
ACCESS CCSM CNRM MPI
60
80
100
120
140
160
Str
eam
flo
w (
cms)
Fig. 16 Average Monthly flow Variations of Phakal Watershed for different climate models for 2071-2099
0
20
40
2071
2071
2072
2072
2073
2073
2074
2075
2075
2076
2076
2077
2078
2078
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2079
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020
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120
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520
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620
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820
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9120
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9620
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9720
98
2099
2099
Str
eam
flo
w (
cms)
Year
ACCESS CCSM CNRM MPI
10
15
20
25
Str
eam
flo
w (
cms)
Fig 16: Monthly flow variation of the stream flow and rainfall for all scenarios for 2006-2040
0
5
10
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Str
eam
flo
w (
cms)
Month
ACCESS CCSM CNRM MPI
6
8
10
12
14
16
Str
eam
flo
w (
cms)
Fig 17: Monthly flow variation of the stream flow and rainfall for all scenarios for 2041-2070
0
2
4
6
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Str
eam
flo
w (
cms)
Month
ACCESS CCSM CNRM MPI
6
8
10
12
14
16
18
Str
eam
flo
w (
cms)
Fig 18: Monthly flow variation of the stream flow and rainfall for all scenarios for 2071-2099
0
2
4
6
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC
Str
eam
flo
w (
cms)
Month
ACCESS CCSM CNRM MPI
Summary and Conclusions
• The study reveals that SWAT model works well in semi-arid regions
like Warangal district.
• The calibration and validation of the SWAT model indicate good
results
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results
• The climate model (CCSM4) predict very well on overall and even the
rainfall is increased by 4.5% overall still the stream flow generated by
model decreed by 31% percent this indicates model error.
• For the beginning of the century (2006-2040) rainfall is decreased by
58.3% but stream flow is decreases by 72.5%.
• For the mid-century (2041-2070) rainfall is decreased by 13.4%
but the stream flow is decreased by 37.4%.
• For the end century (2071-2099) rainfall is decreased by 14.4%,
the stream flow is decreased by 23%
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• As the results of this study reveal, surface runoff amounts are
going to be affected by the impact of climate change.
• The rainfall and stream flow follows decreeing trend in the
Warangal district.
• In light of this, it is necessary to revise the water budget of Tanks in
Warangal District to consider these changes in the budget.
• It is necessary for Telangana Government to think about policies and
strategies to help the District to adapt for impacts of climate change
and to plan the water resources of the district.
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and to plan the water resources of the district.
• Tanks revival is one of the major works needed by district to take
care about impact of climate change on water resources.
Future Work
• SWAT Model should be run using the Phakal Tank daily
water level data.
• SWAT Model run using RCP 8.5 scenario.• SWAT Model run using RCP 8.5 scenario.
• Further, various regionalization approaches should be
considered for calibration and validation.
• Development of Adaptation Tool with the water budgets
available at the study area for present and future
conditions.
References
• Ashok, K. . R., & Sasikala, C. (2012). Farmers’ Vulnerability to Rainfall Variability and Technology Adoption in Rain-fed Tank Irrigated Agriculture. Agricultural Economics Research Review, 25(2), 267–278.
• Barreteau O., Stefeno F., Perret S., (2015) “Joint management of water resources in response to climate change disruptions.” Climate Change and Agriculture Worldwide, Springer, pp: 155-165. doi: 10.1007/978-94-017-7462-8_12
• Bekiaris I. G., Panagopoulos I. N., Mimikou M. A., (2005) “Application of SWAT(Soil and Water Assessment Tool) Modelin Ronnea catchment of Sweden” GlobalNEST Journal. 7(3): 252-257.
• Bhave A,G.,Ashok,M.,Narendra,S,-R.,(2014) “ Evaluation of hydrological
1/19/2018
33
• Bhave A,G.,Ashok,M.,Narendra,S,-R.,(2014) “ Evaluation of hydrological effect of stakeholder prioritized climate change adaptation options based on multi-model regional climate projections .” Climatic Change (2014) 123:225–239
• Chaturvedi, R. K., Joshi, J., Jayaraman, M., Bala, G., & Ravindranath, N. H. (2012). Multi-model climate change projections for India under representative concentration pathways. Current Science, 103(7), 791–802.
• Cheng,H.,Yuanan,Hu., (2012) “Improving China’s water resources management for better adaptation to climate change .” Climatic Change 112:253–282.
• Erwan M., Gao X., Scott J.R., Sokolov A, P., Schlosser C, A., (2015) “A framework for modeling uncertainty in regional climate change.” Climatic Change (2015) 131(1) : 51-66. doi:10.1007/s10584-014-1112-5
• Filippo G., Linda O. M., (2002) “Calculation of Average, Uncertainty Range, and Reliability of Regional Climate Changes from AOGCM Simulations via the ‘‘Reliability Ensemble Averaging’’ (REA) Method” Journal of Climate. 15:1141-1158.
• Flower H. J., Blenkinsop S., Tebaldi C., (2007) “Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling” International Journal of Climatology. 27(12): 1547-1578.
• Flower H. J., Kilsby C.G., (2007) “Using regional climate model data to simulate historical and future river flows in northwest England.” Climatic Change. 80(3): 337-367.
1/19/2018
34
Change. 80(3): 337-367.• Gassman, P. W., Reyes, M. R., Green, C. H., and Arnold, J. G. (2007) “The
Soil and Water Assessment Tool: Historical Development, Applications, And Future Research Directions” Transactions of the American Society of Agricultural and Biological Engineer, Invited Review Series, 50(4): 1211-1250 (http://www.card. iastate.edu/environment/items/asabe_swat.pdf) .
• George , B., B. Davidson , B. Nawarathna , D. Ryu , H. Malano , A. Kulkarni , S. Patwardhan, N. Deshpande , P. Pavelic , J. Anshuman and K. Kumar. (2011) “A modeling framework to evaluate climate change and watershed development impacts on water security”, 19th International Congress on Modelling and Simulation, Perth, Australia, 12–16 December 2011, 2599-2605. http://mssanz.org.au/ modsim2011.
• Gosain A.K., Sandhya R., Debajit B., (2006) “Climate change impact assessment on hydrology of Indian river basins” Current Science. Special Edition on ‘Climate change and India.’ 90(3).
• Hay L.E., Clark M. P., Wilby R. L., Gutowski W. J., Leavesley G. H., Pan Z., Arritt R. W., and Takle E. S., (2002) “ Use of regional climate model output for hydrologic simulations” Journal of Hydrometeorology. 3:571-590.
• Joh, H.-K., J.-W. Lee, M.-J. Park, H.-J. Shin, J.-E. Yi, G.-S. Kim, R. Srinivasan, S.-J. Kim. (2011) “Technical Note: Assessing Climate Change Impact on Hydrological Components of a Small Forest Watershed through SWAT Calibration of Evapotranspiration and Soil Moisture.” Transactions of the ASABE. 54(5): 1773-1781.
1/19/2018
35
of the ASABE. 54(5): 1773-1781. • Juan,M.-B.,Walter,C.,Adriano,R.,Daniel,A.,Fedrico,D., (2014) “Impact of
projected climate change on hydrologic regime of the Upper Paraguay River basin.” Climatic Change 127:27–41
• Kitoh A.,,Nohara D., Hosaka M., Oki T.,(2006) “Impact of Climate Change on River Discharge Projected by Multimodel Ensemble” Journal of Hydrometeorology. 7:1076-1089.
• Krishna Kumar, K., Patwardhan, S. K., Kulkarni, A., Kamala, K., KoteswaraRao, K., & Jones, R. (2011). Simulated projections for summer monsoon climate over India by a high-resolution regional climate model (PRECIS). Current Science, 101(3), 312–326.