nhc ESTIMATING FUTURE FLOOD EXTREMES IN THE SEATTLE AREA Using Dynamically Downscaled Precipitation Data

nhc Modeled Stream Basins and GCM-RCM grid points BasinTotal Drainage Area (ac) Directly Connected Impervious Comment Juanita Ck (Jeff Burkey, KC- DNRP) %Steep Terrain Thornton Ck (nhc, for SPU) %High Flow Bypass

nhc 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).

nhc 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)

nhc Juanita Creek Example ( includes match of extreme event of 12/3/07 ) Courtesy Jeff Burkey, King County DNRP

nhc 2 nd Example Illustrates good fit to observed base flows

nhc Thornton Creek Subbasins and HSPF output Sites Used in Change Analysis

nhc 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

nhc Example Hydrologic Validation of Precipitation Bias- Correction 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)

nhc 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

nhc 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

nhc Key Points: Minor flaws in bias correction suggested by variation between simulated and observed for period. (Compare brown with darker green and darker blue) Large change between CCSM3-A and 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

nhc 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

nhc “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 and highest Nov. P for 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

nhc 5-run Ensemble Mean, CCSM3-A2 Daily P by Month Average over WA State CCSM3-A2 Prec. Ensemble for WA State, Courtesy Eric Salathé, UW-CIG Red = Black = Mean Annual Change = -.5 mm/day Mean November Change= 0.3 mm/day Run 5 November Change= 1.8 mm/day

nhc 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

nhc 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

nhc 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

nhc