projected impacts of climate change and …...projected impacts of climate change and implications...
TRANSCRIPT
![Page 1: Projected Impacts of Climate Change and …...Projected Impacts of Climate Change and Implications for River Flows in the Pacific Northwest Presented by: Austin Polebitski and Richard](https://reader034.vdocuments.net/reader034/viewer/2022042304/5ecfbaae0f1cd503cb1531b6/html5/thumbnails/1.jpg)
Projected Impacts of Climate Change and Implications for River
Flows in the Pacific Northwest
Presented by: Austin Polebitski and Richard Palmer
Department of Civil and Environmental EngineeringUniversity of Washington
November 7, 2006King County
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Precipitation
• Anyone notice it raining more lately?
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Changing Characteristics of Precipitation
• Outline– Investigate historic trends in precipitation– Explore data from global models to local
situations• Statistical Downscaling• Dynamic Downscaling
– Preliminary Results
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Projected Average Monthly Flows –Snoqualmie Falls
0
1000
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6000
SeptAugJulJunMayAprMarFebJanDecNovOctMonth
Ave
rage
Flo
w (c
fs)
0
5
10
15
20
25
30
Ave
rage
Pre
cipi
tatio
n (in
ches
)
PrecipitationHistoricECHAM5-2000ECHAM5-2025ECHAM5-2050ECHAM5-2075
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Changing Characteristics of Precipitation
• How will precipitation patterns change in a warmed climate?– More Extreme Events?– More Winter and less Summer Precipitation?– More precipitation as Rain rather than Snow?
• How can these questions be answered?– Historic trend evaluation– Use of GCM output
• Dynamical Downscaling• Statistical Downscaling
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Historic Trend Evaluation of Daily Precipitation
• Preliminary Results suggest:
– Most significant trend is in November
– Larger percentage of annual rainfall occuring in November, total annual precipitation has remained relatively constant
– Difficult to identify an increase in extreme events to date, models do forecast an increase.
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Decadal Trends in November Monthly Precipitation
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50
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350
400
450
500
Buck Cedar Everett Kent Landburg Palmer Snoq
Station
Ave
rage
Cum
ulat
ive
Prec
ipita
tion
(mm
)
1930194019501960197019801990
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Evaluation of Projected Daily Precipitation
• Two methods discussed
– Dynamical Downscaling*
– Statistical Downscaling*
*Downscaling – the process of transforming coarsely-gridded global data to a regionally relevant scale for use in models.
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Dynamic Downscaling
• Method under study by Cliff Mass and Eric Salathé of UW-Department of Atmospheric Sciences and the Climate Impact Group
• The method utilizes a mesoscaleforecasting model (MM5) coupled with GCM output to derive future weather and climate patterns
• Can produce output at high resolution and temporal frequency
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MM5 Climate Projections –Percent Change in Precipitation
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Statistical Downscaling
• Map cdf’s of future climate to historic cdf’sto produce climate impacted datasets (Wood)
• Historical records and site specific information used to downscale
• Precipitation and temperature are the two main outputs from this downscaling method
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Percent Change in 20-Year Design Storm from ECHAM5 2000
10%
15%
5%
20%
25%
15%
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Average Percent Change in 20 Year Storm
-5.00% 0.00% 5.00% 10.00% 15.00% 20.00% 25.00%
ABERDEEN20NNEBREMERTON
BUCKLEYCEDAR
CULMBACKCUSHMAN_POWER
DARRINGTONELMA
ELWHAEVERETT
FORKSKENT
LANDSBURGLONGMIREMCMILLINMONROE
MUDMTOLYMPIAPALMER
PARADISEPORTANGELES
PORTTOWNSENDQUILCENE
SEATACSFTOLT
SNOQUALMIESTAMPEDE
STARTUP
Percent Change relative to ECHAM5 2000
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Impacts Urban Flooding
• ECHAM5 GCM projects increases ‘extreme’precipitation
• More common events (5 year, 10 year, 20 year return events) change more significantly than 100 year event
• To determine impacts to urban systems, output needs to be run through urban models to determine whether code changes might be necessary
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Flooding in Natural Systems
• The Distributed-Hydrology-Soil-Vegetation-Model (DHSVM) uses physically based parameters to determine runoff and streamflow for a basin
• Downscaled ECHAM5 output can be used to drive DHSVM
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Projected Daily CDF’s of Flow at Snoqualmie Falls
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Projected Return Interval Flows for Natural Systems
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10000
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30000
40000
50000
60000
70000
1 2 5 10 20 50 100Return Interval
Flow
(cfs
) HistoricECHAM5-2000ECHAM5-2025ECHAM5-2050ECHAM5-2075
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Distribution of Events Exceeding 1-year Return Interval (7000 cfs)
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500
600
700
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Num
ber o
f Eve
nts
Ove
r 700
0 cf
s
Historic2000202520502075
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Next Steps
• Complete DHSVM runs for regional basins, i.e. Cedar, Green, Puyallup/White
• Identify key areas of flood inundation interest and possibly perform HEC-RAS models for those areas
• Run climate impacted datasets through Urban-basin models to examine effects of changing design-storms on conveyance