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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 Total Drainage Area (ac)

DirectlyConnectedImpervious

Comment

Juanita Ck(Jeff Burkey, KC-DNRP)

4352 34% Steep Terrain

Thornton Ck(nhc, for SPU)

7140 29% High Flow Bypass

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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, WA-DOE, 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

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/07

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ily M

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s)

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(in

.)

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2nd Example Illustrates good fit to observed base flows

WY 2001

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Thornton Creek Subbasins and HSPF output Sites Used in

Change AnalysisBypass PipeBypass 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 Bias-Correction

Percent Chance Exceedance

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

Fit Type:Log Pearson III distribution using the method of Bulletin 17B, Weibull Plotting PositionAnnual Peak Frequency Analysis. 1 hour moving average.

Dis

char

ge (

cfs)

100.

200.

300.

400.

500.Ret Period--> 5001001052

99.8 0.299 190 1080 2050

•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

-10%

0%

10%

20%

30%

40%

50%

60%

Av

g. C

ha

ng

e 2

-yr

to 5

0-y

r

CCSM3-WRF

ECHAM5-WRF

Kramer Ck135 ac

South Branch 2294 ac

North Branch4143 ac

Thornton Ck7140 ac

•CCSM3 > ECHAM5

•ECHAM5 negative for smallest basin

•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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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

Uniquely large increase in high flow days projected by CCSM3-WRF bias-corrected data

Uniquely large increase in high flow days projected by CCSM3-WRF bias-corrected data

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Large Δ Peak Q

Large, Numerous, November Peaks-Future Period

Uniquely, Large Δs Nov. max hour & total P in Bias-Corr. 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

Tracing Large Projected Peak Flow Changes based on CCSM3-WRF-A2 Precipitation

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“Luck” of the Draw?

CCSM3-A2 Prec. Ensemble for WA State, Courtesy Eric Salathé, UW-CIG

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

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5-run Ensemble Mean, CCSM3-A2

Daily P by MonthAverage over WA State

CCSM3-A2 Prec. Ensemble for WA State, Courtesy Eric Salathé, UW-CIG

Red = 2030-2060Black = 1970-2000

Mean Annual Change = -.5 mm/dayMean November Change= 0.3 mm/dayRun 5 November Change= 1.8 mm/day

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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.

Hypotheses on Precipitation and Peak Flow Changes Projected by CCSM3-WRF-A2

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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 non-physical, 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

Conclusions with Respect to Indications from Dynamically Downscaled Precipitation Data

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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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