Transcript IHA - University of Washington

ESTIMATING FUTURE FLOOD
EXTREMES IN THE SEATTLE AREA
Using Dynamically Downscaled
Precipitation Data
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Modeled Stream Basins
and GCM-RCM grid points
Basin
Juanita Ck
Total
Drainage
Area (ac)
Directly
Connected
Impervious
4352
34%
Steep
Terrain
7140
29%
High Flow
Bypass
(Jeff Burkey, KCDNRP)
Thornton Ck
(nhc, for SPU)
Comment
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Hydrologic Modeling Tool- HSPF
•Continuous precipitation-runoff simulation for
multi-decade periods
•Widely used and validated for urban to wildland
watersheds for several decades
•Regionally validated and accepted (USGS, WADOE, FEMA, Counties)
•Primary inputs (hourly or 15-min P, d or m PET)
•Robust flow prediction (Repeatable long term runs
with very low sensitivity to perturbations in initial
conditions- consequence of model formulation and
character of the physical system modeled- unlike
fully dynamic hydraulic models, GCMs, or RCMs).
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How Well Does it Do?
•Typically accurate given calibration with good
contemporaneous precipitation and flow data
•Less reliable without calibration, but still useful for
comparisons using USGS regional parameters
(Dinicola, 1990)
•Study used calibrated models for both Juanita
Creek (by Jeff Burkey,King County) and Thornton
Creek (nhc for Seattle Public Utilities)
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Juanita Creek Example
(includes match of extreme event of 12/3/07 )
Courtesy Jeff Burkey, King County DNRP
WY 2008
1
2
3
4
400
300
Obs
Sim
Field Obs
200
100
9/08
8/08
7/08
6/08
5/08
4/08
3/08
2/08
1/08
12/07
11/07
0
10/07
Daily Max Flow Rate (cfs)
Precip (in.)
0
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2nd Example
Illustrates good fit to observed base flows
WY 2001
0.5
1.0
1.5
2.0
300
250
Obs
Sim
200
Field Obs
150
100
50
9/01
8/01
7/01
6/01
5/01
4/01
3/01
2/01
1/01
12/00
11/00
0
10/00
Daily Max Flow Rate (cfs)
Precip (in.)
0.0
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Thornton Creek
Subbasins and
HSPF output
Sites Used in
Change Analysis
Bypass
Pipe
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Flow Regime Change Analysis Metrics
•
•
•
•
•
Peak Annual Flow*
Erosive Flow Energy
Seasonality of High Flows*
Low Flow Extremes
Flow Flashiness (TQmean)
*focus of today’s talk
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Example Hydrologic Validation of Precipitation BiasCorrection
Annual Peak Frequency Analysis. 1 hour moving average.
Fit Type:Log Pearson III distribution using the method of Bulletin 17B, Weibull Plotting Position
Ret Period-->
2
5
10
100
500
1
0.2
500.
SB_T R200 SB THORNTON CK, SEATAC 1970-2000
SB_T R200 SB THORNTON CK, CCSM3-A2 1970-2000
SB_T R200 SB THORNTON CK, ECHAM5-A1B 1970-2000
Discharge (cfs)
400.
300.
200.
100.
99.8
99
90
80
50
20
10
Percent Chance Exceedance
•CCSM3-WRF and ECHAM5-WRF generated peaks are
similar to peaks simulated with observed rainfall
•Some under-estimation of most extreme events in record
•Tightest fit for Kramer Ck (smallest subbasin)
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Historical to Future Change in Peak Flow
60%
Avg. Change 2-yr to 50-yr
50%
CCSM3-WRF
ECHAM5-WRF
40%
30%
20%
10%
0%
-10%
Kramer Ck
135 ac
•CCSM3 > ECHAM5
•ECHAM5 negative for
smallest basin
South Branch
2294 ac
North Branch
4143 ac
Thornton Ck
7140 ac
•ECHAM5 increasingly positive with
drainage area
•ECHAM5 projects decline in max
hourly P and increase in multiple
hour P
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Total Days Per Month Q>erosive Q, per Observed and
Simulated 31-year Precipitation Data Periods
Uniquely large increase in
high flow days projected by
CCSM3-WRF bias-corrected
data
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Uniquely large increase in
high flow days projected by
CCSM3-WRF bias-corrected
data
Key Points:
•Minor flaws in bias correction suggested by variation between
simulated and observed for 1970-2000 period. (Compare
brown with darker green and darker blue)
•Large change between CCSM3-A2 1970-2000 and 2020-2050
results for November. Distinct from other CCSM3-A2 months.
Distinct from ECHAM5-A1B results
•Clue to source of much larger CCSM3-A2 based peak annual
flow increases noted previously
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Tracing Large Projected Peak Flow Changes
based on CCSM3-WRF-A2 Precipitation
Large Δ Peak Q
Large, Numerous, November Peaks-Future Period
Uniquely, Large Δs Nov. max hour & total P in BiasCorr. CCSM3-WRF
Similar Δs in RAW Downscaled Data
Large Δ for Nov P for CCSM3-A2 over WA State
....BUT ONLY FOR RUN#5. RUN#5 NOV Δ IS 6
TIMES AVG. OF all CCSM3-A2 RUNS
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“Luck” of the Draw?
By chance, our Study Used IPCC Run
#5 (purple line)
This run has the lowest November P
for 1970-1999 and highest Nov. P for
2030-2059.
Not typical.
CCSM3-WRF-HSPF results for
increases in peak Q are pretty much
an accident or at least are not typical
of CCSM3-A2
CCSM3-A2 Prec. Ensemble for WA State,
Courtesy Eric Salathé, UW-CIG
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5-run Ensemble Mean,
CCSM3-A2
Daily P by Month
Average over WA State
Red = 2030-2060
Black = 1970-2000
Mean Annual Change = -.5 mm/day
Mean November Change= 0.3 mm/day
Run 5 November Change= 1.8 mm/day
CCSM3-A2 Prec. Ensemble for WA State,
Courtesy Eric Salathé, UW-CIG
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Hypotheses on Precipitation and Peak Flow
Changes Projected by CCSM3-WRF-A2
1. Results in this study are not typical of projections made by
CCSM3-A2 and are an accident derived from quirks in run
#5
2. Use of typical CCSM3 projections would result in peak Q
changes ≤ ECHAM5 changes
3. The November surprise in CCSM3 Run #5 data is striking
in magnitude and seasonal specificity. This needs
explaining.
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Conclusions with Respect to Indications from
Dynamically Downscaled Precipitation Data
1. Reliance on individual runs from GCM ensembles to predict urban
hydrologic change and inform stormwater management may lead us
astray.
2. We need to work with ensembles to assess of both “baseline” and
future hydrology that responds to GHG scenarios.
3. More analysis of GCM-RCM runs is needed to show that ensembles are
realistic expressions of the range of GHG-driven outcomes- not nonphysical, numerical artifacts.
4. Effort so far has been worthy, but (as far as I can tell) insufficient for
application in stormwater planning- however, I am always open to a
good argument
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Acknowledgments:
Thanks to:
Seattle Public Utilities (SPU) for supporting nhc’s
participation in this study
…and to UW-CIG staff and students for sharing data,
expertise, and opinions
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