Forecasting Streamflow and Reservoir Storage Summer of 2003

1. Forecasting Streamflow and Reservoir Storage Summer of 2003 Richard Palmer, Andre Ball, Ani Kameenui, Kasey Kudamik, Michael Miller, Nathan Van Rheenen, Matthew Wiley CEE University of Washington October 2003

2. Talk Overview Background on Forecast Approach Evolving Summer Forecasts Accuracy of Forecast Conclusions

3. Study Goals Create six-month forecasts for municipal water supplies in the Puget Sound area using NCEP forecasts: • Water Supply • Water Demand • Storage in Reservoir • Decision Support System

4. Forecasting • The herd instinct among forecasters makes sheep look like independent thinkers. Edgar R. Fiedler • If you have to forecast, forecast often. Edgar R. Fiedler • An unsophisticated forecaster uses statistics as a drunken man uses lamp-posts - for support rather than for illumination. Andrew Lang

5. Other Quotes The future will be better tomorrow. Dan Quayle (1947 - ) Where a calculator on the ENIAC is equpped with 18,000 vacuum tubes and weighs 30 tons, computers in the future may have only 1,000 vaccuum tubes and perhaps weigh 1.5 tons. Popular Mechanics, March 1949- More quotations on: [Computers]

6. Other Quotes The best way to predict the future is to invent it. Alan Kay The future belongs to those who prepare for it today. Malcolm X (1925 - 1965) • The future is here. It's just not widely distributed yet. • William Gibson (1948 - )

7. Other Quotes Enjoy present pleasures in such a way as not to injure future ones. Seneca (5 BC - 65 AD) The future ain't what it used to be. Yogi Berra (1925 - )

8. Sultan River N N Tolt River Cedar River Renton Renton Auburn Auburn Green River Study Domain

10. NCEP Meteorological Forecasts • Distributed Hydrology, Soil-Vegetation Model Models Used to Generate Forecasts • Water Demand Forecasts • Dynamic Systems Model

11. Water Demand ForecastingPuget Sound Region • Models • short (weekly-monthly) and long (annual-decadal)-term • Regions: Seattle, Tacoma, and Everett • Tacoma and Everett: Municipal demands • Seattle: System-wide demands

12. Purpose A common characteristic of water resources planning is its failure to anticipate change. -D. Sewell, 1978 • Increase the accuracy of demand models for effective water resources planning and management. • Provide information for monitoring and controlling demands during droughts, planning conservation programs, and supply and infrastructure changes. • Create a framework for long-term forecasting while considering urban planning.

13. How well have we done? • Increase the accuracy of demand models for effective water resources planning and management.

14. Recent History • Provide information for monitoring and controlling demands during droughts, planning conservation programs, and supply and infrastructure changes.

15. Data Resources • WATER related • Sources: Seattle Public Utilities; Tacoma Water; City of Everett • Daily water demands • Rate History; Number of users • CLIMATE • National Climate Data Center (NCDC): SeaTac daily Tmax and precipitation • National Centers for Environmental Prediction (NCEP): downscaled climate ensembles • HOUSEHOLD • Puget Sound Regional Council (PSRC) • Urban simulation group (UrbanSim)

16. Short-term Model DesignSeattle Region • Data must be on a weekly time-step • Log-linear regression: Water Demand = Intercept**∙Ax∙Bx2∙Cx3∙Dx4∙Ex5∙Fx6∙Gx7 Ln(Water Demand) = Intercept** + x∙Ln(A) + x2∙Ln(B) + x3∙Ln(C) + x4∙Ln(D) + x5∙Ln(E) + x6∙Ln(F) + x7∙Ln(G)

17. Model CalibrationSeattle Region: Summer

18. Model ValidationSeattle Region: Summer

19. Model CalibrationTacoma Region: Summer

20. Model ValidationTacoma Region: Summer

21. Model ValidationEverett Region: Summer

22. Demand ForecastSeattle Region: April forecast

23. Forecast Skill and Error • Forecast skill metric (Hamlet): • Skill = 1 - [∑(forecast - observed)2/N / ∑(historical - observed)2/M ] • Rewards precision, punishes spread • Valuable metric during outlier years • Summer 2003 was an climate outlier • Model is calibrated during less dramatic conditions • Validated during warming (hence the drop in correlation)

24. Long-Term Forecasting • Create a framework for long-term forecasting while considering urban planning. • Using PSRC and UrbanSim information from household survey or Parcel Index Number databases • Highly disaggregated database for modeling household or class specific water demands. • Incorporate household variables such as: size, income, house age, house value, yard size, etc. • Investigate benefits and drawbacks of disaggregated model and consider water resources during urban planning and land development (UrbanSim component).

25. Long-term Model DesignSeattle Region

26. Long-term Model DesignCurrent workSeattle Region

27. Overview of Meteorological Forecast Process • National Centers for Environmental Prediction

28. NCEP Forecast A set of 20 equally likely ensembles of paired precipitation and temperatures generated by GSM with slight variations in initial conditions Downloaded from NCEP ftp site Forecasts bias-corrected and downscaled

29. DHSVMDistributed Hydrology, Soil-Vegetation Model

30. Streamflow Forecast • System is initiated with one year of previous conditions • Twenty assembles of paired precipitation and temperatures are run. • Initial conditions are extremely important (same future conditions are different with different initiations) • Typically model underestimate summer flows

31. Systems Simulation Model • Model calculates movement of water throughout system • Integrates water supply, demands, fish flows and other operational considerations • Lacks subtleties of actual operation

40. Conclusions NCEP ensemble forecasts, combined with hydrologic model, produced good summer forecasts for 2003. Typically, NCEP ensemble forecasts, combined with hydrologic model, provides does useful information (exceptions noted). Forecasts ranked by ENSO provides some insight into forecast quality