1. Water Supply Forecast using the Ensemble Streamflow Prediction Model Kevin Berghoff, Senior Hydrologist Northwest River Forecast Center Portland, OR

2. Overview  Community Hydrologic Prediction System (CHPS)  3 Components to model CalibrationCalibration Operational Forecast SystemOperational Forecast System Ensemble Streamflow Prediction - ESPEnsemble Streamflow Prediction - ESP

3. 3 Analysis Historical Data Calibration System (CS) Real-Time Observed and Forecast Data Operational Forecast System (OFS) Ensemble Streamflow Prediction (ESP) System Hydrologic and Hydraulic Models Analysis and Data Assimilation Hydrologic and Hydraulic Models short term forecasts current states Statistical Analyses Probabilistic Short term to Extended time Analysis window Interactive Adjustments flow Community Hydrologic Prediction System - CHPS Hydrologic and Hydraulic Models HEFS

4. CHPS Model Calibration  Historical precipitation and temperature used to generate Mean Areal Precipitation (MAP) and Mean Areal Temperature (MAT) for each basin  SAC-SMA and SNOW-17 model parameters are adjusted to match the simulated river flow to the observed flow data over the entire calibration period of record  Timing and attenuation of routed flows from upstream points

5. Precipitation and andTemperature Rain or Snow Accumulated Snow Cover (SWE ) Energy Exchange At Snow-Air Interface Areal Extent of Snow Cover Heat deficit Liquid Water Storage Rain plus Melt Deficit = 0 (to Soil Moisture Model) Snow Outflow Snow-17 Model Snow-17 Model Transmission of Excess Water Ground Melt

6. Snow Model Soil Moisture/Runoff Consumptive Use River Routing Reservoir Regulation Flow and Stage Forecasts Sacramento Soil Moisture Accounting (SAC-SMA) Model

7. CHPS Model Calibration

8. Operational Forecast System  Observed and 10 Day Forecast Inputs  Precip, Temperature

9. Operational Forecast System Observed Deterministic Forecast

10. Ensemble Streamflow Prediction

11. ESP Trace Ensemble Plot 1966 1992

12. ESP Accumulated Flow Volume April – Sept Forecast Period

13. ESP Example: NF John Day at Monument NF John Day at Monument Median Forecast 622 KAF 101% Initial model states: 01/18/2011 Analysis Period: 4/1/2011 – 9/30/2011 Each point represents possible outcome based on initial model states, 10 Day fx, historical precip and temperature scenarios

14. Example: Dillon Reservoir 2011 Forecast Spaghetti Forecast April – July forecast Median forecast: 195 kaf

15. Calibration (parameter uncertainty) © The COMET Program Output of streamflow ensembles (cumulative uncertainty) Meteorological Input (precip and temp variability) Current model states User/forecaster (level of experience, personal bias) Computer model (model structure uncertainty) Real-time data (variability and uncertainty) Sources of Uncertainty

16. Data Issues

17. ESP Uncertainty

18. Observed Streamflow Simulated Streamflow

19. Summary  ESP Assumption: Past meteorological data is a reasonable representation of future scenarios  Ensemble of forecasts generated using past precip and temperature data, current model states (soil moisture, snowpack)  Flexibility – allows user to specify desired forecast period and statistical analysis  Allows users to incorporate probabilistic information into operational decisions  ESP forecasts useful when strong climatological signal present

20. ESP Cautions  Less than 30 day lead time (NWRFC specific)  Unaccounted for sources of uncertainty  Tend to under forecast high years, over forecast low years

21. Questions?

23. Data Issues

24. ESP Uncertainty

25. ESP Sensitivity Study: Summer/Fall Soil Moisture

26. hours days/weeks months seasonal annual Forecast time Uncertainty Advances in Time Scales Forecast Services provided for all time domains

27. NWRFC Forecast Products ESP Concept Time Flow Deterministic Probabilistic

28. 1959 1989 1954

29. ESP Trace Ensemble Plot 1966

30. ESP Verification Dworschak Dam

31. ESP Verification Hungry Horse Dam

32. Recent Historical Perspective 96.6 – 90% 63.6 - 101% 25.5 – 85%

33. Calibration (parameter uncertainty) © The COMET Program Output of “Raw” streamflow ensembles (cumulative uncertainty) Meteorological Input (precip and temp variability) Current model states (spatial and temporal scale dependent bias) User/forecaster (level of experience, personal bias) Computer model (model structure uncertainty) Real-time data (variability and uncertainty) ESP uncertainty This slide from Kevin Werner

34. Northwest River Forecast Center Total Area: 315,795 Grand Coulee Dam The Willamette at Salem The Dalles Dam Lower Granite Dam Columbia and Snake River Basins Coastal Drainages of Oregon and Washington 6 States & CANADA Support for 9 NWS Field Offices (WFOs)

35. 35 Exceedance Probability Plot: Ranked flow volumes (each point represents area under individual ensemble traces) for Jan-Jul period. 50% Value is comparable to WS forecasts Median Forecast (most expected) Initial model states: 12/21/2010 Analysis Period: 4/1/2011 – 8/1/2011 Each trace represents possible outcome based on initial model states, QPF, historical MAP, MAT

36. ESP Water Supply Forecast

37. http://www.nwrfc.noaa.gov/espadp/espadp.cgi ESP Interactive Ensemble Analyzer

38. ESP Interactive Web Site