Mark New Ana Lopez, Fai Fung, Milena Cuellar Funded by Tyndall and Environment Agency
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From Climate Data to Adaptation Large-ensemble GCM Information and an
Operational Policy-Support Model
Mark NewAna Lopez, Fai Fung, Milena CuellarFunded by Tyndall and Environment Agency
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Adaptation Challenges
1. Uncertainty in climate information
2. Interactions with other uncertain changes
3. Integrated assessment
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Wimbleball Water Resource Zone
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Route Map
• Large ensemble climate data
• River flow ensemble
• Water resource system modelling
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Large GCM Ensemble: CPdN• Explore model uncertainty by varying settings of poorly
constrained model parameters• HADCM3L model: standard atmosphere & low resolution
ocean.• 26 perturbed parameters (radiation, large scale cloud
formation, ocean circulation, sulphate cycle, sea ice formation and energy convection)
• Initial condition ensembles.• Transient runs:
– 1920-2000 forced with historical CO2, solar and volcanic forcing.– 2000-2080 forced with different possible scenarios
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First 246 Completed Simulations
IPCC 4AR models
CPDN model runs
Global Mean Temperature: SRES A2
An
om
aly
fro
m 1
96
1-1
99
0
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Data Available• 10-year seasonal mean fields• Monthly mean (time series):
– Large regions (Giorgi)– Selected grid-boxes (including UK)
• Variables include– Total precipitation rate– Convective cloud amount– Surface air temperature (1.5m)– Relative humidity (1.5m)
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Modelling Set-up
• Downscale climate in space and time– SW England -> River Exe– Monthly -> Daily
• Generate ensemble of daily river flows– CATCHMOD rainfall-runoff model
• Run flow-ensemble through water resource model
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Downscaling: Precipitation
• Gamma transform method– Remove GCM monthly biases– Select daily values from observations
August 1930-1985
Fre
quen
cy
Monthly Precip (mm/d)
ModelObserved
August 2020-2060
Fre
quen
cy
Monthly Precip (mm/d)
ModelObserved
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Downscaling: Precipitation
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Downscaling: PET
• Calculate GCM PET from– Temperature, RH & cloud-cover (radiation)– Adjust for climatological bias– No daily downscaling
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Downscaling: PET
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River Flows
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River Flows
Month
% C
hang
e
Mean Flow Change: 2020-2039 from 1961-1990
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Wimbleball Water Resource Model
• Supplies:– Somerset & Devon (Exeter, Tiverton)
• River & reservoir dominated• 50 ML/d Groundwater• Lancmod WR model
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Wimbleball Reservoir: Historic
Monthly Storage, 1930-2005
Month
Sto
rage
(M
l x 1
04 )
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Wimbleball Reservoir: 2040 Ensemble
Monthly Storage, 2040
Month
Sto
rage
(M
l x 1
04 )
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Wimbleball Reservoir: Changing Risk
September Storage
Year
Sto
rage
(M
l x 1
04 )
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Failure to Meet Demand
Devon Demand Failure
Year
No.
Sim
ulat
ions
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Failure to Meet Demand
Devon Demand Failure
No.
Sim
ulat
ions
Year
Ave
. Sho
rtfa
ll
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Outstanding Issues / Future Work
• Biases in runoff simulations• Simplistic downscaling• Higher multiple year failures in simulations• Scenarios / ensembles of changing demand• Incorporating adaptation options• Staged methodology• Relative likelihoods• Comparison with UKCIP08 / ENSEMBLES