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Monte Carlo Simulation to Characterize Stormwater Runoff Uncertainty in a Changing Climate

G.S. Karlovits, J.C. Adam, Washington State University 2010 AGU Fall Meeting, San Francisco, CA. Monte Carlo Simulation to Characterize Stormwater Runoff Uncertainty in a Changing Climate. Outline. Climate change and uncertainty in the Pacific Northwest Data, model and methods Climate data

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Monte Carlo Simulation to Characterize Stormwater Runoff Uncertainty in a Changing Climate

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  1. G.S. Karlovits, J.C. Adam, Washington State University 2010 AGU Fall Meeting, San Francisco, CA Monte Carlo Simulation to Characterize Stormwater Runoff Uncertainty in a Changing Climate

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

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

  4. Uncertainty • 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. Objectives/Motivation • 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. General Methodology • 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. Design Storms • 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 Elsneret al. (2010)

  8. VIC Hydrology Model • 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. Monte Carlo Simulation • 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. Monte Carlo Simulation • 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. Monte Carlo Results 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. Monte Carlo Results Percent change, historical to future runoff due to 50-year storm Coefficient of variation for runoff for 5000 simulations of 50-year storm

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

  14. CDFs

  15. Conclusions • 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 21st century • Probabilistic methods can improve forecasts and isolate uncertainties

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

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