1. G.S. Karlovits, J.C. Adam, Washington State University 2010 AGU Fall Meeting, San Francisco, CA

2. 1. Climate change and uncertainty in the Pacific Northwest 2. Data, model and methods 1. Climate data 2. Design storms 3. VIC 4. Monte Carlo simulation 3. Results and uncertainty analysis

3. 95th percentile (10- year moving average) 5th percentile (10-year moving average) LOWESS-smoothed 21-model ensemble averages Modeled historical (with bounds) 2045 From Mote and Salathé (2010)

4.  Projections for future climate based on many assumptions  Greenhouse gas emissions scenario  Global climate model (GCM)  Downscaling of climate data  Effects of changing temperature and precipitation on hydrology uncertain as well  Effects on moisture storage (moderation or enhancement) ▪ Snowpack ▪ Soil moisture  Other sources of uncertainty in forecasting hydrology ▪ Hydrologic model structure ▪ Model calibration parameters

5.  How much uncertainty is there in forecasting future runoff in the Pacific Northwest due to climate change?  What causes this uncertainty?  Can we improve our forecast for runoff in the future so planners and engineers have a tool to help prepare for climate change?

6.  Find change in 2, 25, 50, 100-year 24-hour storm intensities for different emissions scenarios/GCMs  Use a hydrology model to compare future projected storm runoff to historical  Use a probabilistic method to isolate uncertainty and improve forecast

7.  Storms with an average return interval of 2, 25, 50 and 100 years from extreme value distribution  Annual probability of exceedance = 0.50, 0.96, 0.98, 0.99  Historical: 92 years of data (1915-2006)  Future: 92 realizations of 2045 climate  Hybrid delta downscaling method ▪ Delta method with bias correction Historical and future data aggregated from data in Elsner et al. (2010)

8.  Need to take changes in precipitation and temperature and turn them into changes in runoff Variable Infiltration Capacity Model Process-based, distributed model run at 1/2-degree resolution Sub-grid variability (soil, vegetation, elevation) handled with statistical distribution Gridded results for fluxes and states No interaction between grid cells Gao et al. (2010), Liang et al. (1994)

9.  Modeling random combination for met data and hydrologic model parameters  Emissions scenario (equal probability)  GCM (weighted by hindcasting ability) ▪ GCMs with higher bias in recreating 1970-1999 PNW climate given lower selection probability  Snowpack  Soil moisture Modeled in VIC, fit to discrete normal distribution

10.  For each return interval, 5000 combinations of emissions scenario, GCM, soil moisture and snowpack quantile were made  (Pseudo-)random numbers generated using the Mersenne Twister algorithm (Matsumoto and Nishimura 1998)

11. Historical 50-year storm Random selection of soil moisture and SWE Future 50-year storm Random selection of emissions scenario, GCM, soil moisture and SWE

12. Percent change, historical to future runoff due to 50-year storm Coefficient of variation for runoff for 5000 simulations of 50-year storm

13. Absolute difference in runoff due to emissions scenario (A1B – B1) (mm) Coefficient of variation due to selection of GCM (50-year storm)

15.  Runoff is projected to increase for many places in the Pacific Northwest  Largest increases related to most uncertainty  Uncertainty in emissions scenario is a factor in all future projections  Even A1FI scenario low for 21 st century  Probabilistic methods can improve forecasts and isolate uncertainties

16. Chehalis, WA Photo: Bruce Ely (AP) via http://www.darkroastedblend.com/2008/06/floods.html Contact me: Gregory Karlovits WSU gskarlov@wsu.edu Jennifer Adam WSU jcadam@wsu.edu