1. Alan F. Hamlet (hamleaf@u.washington.edu) Andy Wood Dennis P. Lettenmaier JISAO Center for Science in the Earth System Climate Impacts Group and the Department of Civil and Environmental Engineering University of Washington February, 2003 Streamflow Forecasting Using Distributed Hydrologic Models and Long-Range Climate Forecasts

2. Introduction to the Columbia River Basin

3. Hydrological Characteristics of the Columbia Basin Elevation (m) Avg Naturalized Flow The Dalles, OR Snowmelt Dominant Winter Climate Determines Summer Peak Flows

4. State of the Practice for Operational Streamflow Forecasts

5. Current operational forecasts in the Columbia basin are based primarily upon statistical relationships between observed mountain snowpack and seasonally averaged streamflow volumes. Snow Observations Seasonal Volume Forecasts Linear Regression http://www.nwrfc.noaa.gov/water_supply/ws_runoff.cgi Forecasts typically become available on ~ January 1 Maximum skill achieved ~ April 1 In late spring these forecasts are quite accurate

7. Long-Range Climate Forecasts

8. 1980’s: Climate is something that happens. Climate is the long-term statistics of short-term weather events that have a maximum predictability of perhaps two weeks. The argument was: short term weather itself is predictable only a few days out, so how can climate be known ahead of time? 1990’s: Climate is something that can be understood and forecast in its own right. Some atmospheric scientists realized that if the ocean’s behavior could be observed and forecast with skill and the subsequent telleconnections affecting the long-term statistics of regional weather were also understood, then skillful regional climate forecasts would become possible.

9. A history of the PDO warm cool warm A history of ENSO 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 Pacific Decadal OscillationEl Niño Southern Oscillation

12. 1998 ✔ 1999 ✔ 2000 ✔ 2001 X 2002 ✔ 2003 ✔ June 1 ENSO Predictive Skill In 5 out of 6 test years, accurate categorical ENSO forecasts (warm, neutral, cool) have been available in June preceding the water year.

13. Hydroclimatology of the Columbia River

14. Effects of the PDO and ENSO on Columbia River Summer Streamflows Cool Warm

15. Naturalized Summer Streamflow at The Dalles

16. Distributed Macro-Scale Hydrologic Model

17. Variable Infiltration Capacity (VIC) Model http://www.hydro.washington.edu/Lettenmaier/Models.html

18. 1/4 - 1/8 degree latitude/longitude spatial resolution Daily time step water balance Hourly time step snow model Forcings are interpolated and topographically corrected data based on daily time step precipitation and temperature records. Some applications use GCM simulated wind speed. Other hydrologic driving data are derived from these basic quantities. Typical VIC Implementation

20. Streamflow Forecasts http://www.ce.washington.edu/~hamleaf/dalles_forecast/iri_enso_theme.html

21. Global Climate Models Regional Climate Models Hydrology Models Water Resources Models Overview of Modeling Linkages Used in UW Forecasting Systems Streamflow Bias correction Downscaling Observed Meteor. Data Optimization or Simulation Resampling Initial Conditions Soil moisture Snow Reservoir levels

22. ENSO PDO Run Initialized Hydrologic Model Ensemble Streamflow Forecast Select Temperature and Precipitation Data from Historic Record Associated with Forecast Climate Category Climate Forecast Schematic for Forecasting Experiments Using Resampled Observed Data

23. Time Line for 12-month Lead Time Retrospective Forecasts Climate Forecast Estimated Initial Conditions Forecast Ensemble Lead time = 12 months Statistical Methods Based on Snowpack

24. Warm ENSO High Flow Unlikely Cool ENSO Low Flow Unlikely

25. 1999 cool PDO/cool ENSO

26. 2001 cool PDO/ENSO neutral

27. 2002 ENSO Neutral

28. 2002 Cool PDO/ENSO Neutral

29. Red line is long-term simulated climatological mean The Dalles

30. Sources of Forecast Skill and Error

31. 50 different initial soil conditions Same water year (1992) simulated 50 times, once from each initial condition Range =16% of ensemble summer mean Range =16% of ensemble summer mean Range =16% of ensemble summer mean Range =16% of ensemble summer mean Effect of October 1 Soil Moisture on Summer Streamflows

32. Comparison of Forecast Skill Based Upon: 1)October 1 Soil Moisture and ENSO/PDO Climate Forecasts 2) January 1 Hydrologic Persistence.

33. Definitions: Forecast Skill: Forecast performance as measured by a quantitative skill metric relative to some standard (in this case climatology). Skill = 1.0 Perfect forecast Skill > 0 Superior to climatology Skill = 0 Equivalent to climatology Skill < 0 Worse than climatology

34. ENSOENSO/PDOJan 1 ESP ENSO/PDO forecast superior to Jan 1 ESP Blue = ensemble mean Red = simulated observation

35. ENSOENSO/PDOJan 1 ESP ENSO/PDO forecast comparable to Jan 1 ESP

36. ENSOENSO/PDOJan 1 ESP ENSO/PDO forecast inferior to Jan 1 ESP

37. Same Metric, More Test Years

39. Conclusions Advances in climate forecasts of ENSO and PDO have facilitated streamflow forecasts with a useful lead time of roughly 12 months in the Columbia River basin. June 1 PDO/ENSO streamflow forecasts typically eliminate about 1/3 of the observations as a probable outcome, which has important implications for water management. PDO/ENSO forecasts available from June 1- October 1 are more skillful than climatology about half the time, but are generally less skillful and less robust than Jan 1 ESP forecasts based solely on hydrologic persistence. The methods discussed here are portable and can be applied to different climate phenomena affecting other areas of the globe, different hydrologic models than those used here, etc.