Using CPC long lead climate outlooks for ensemble streamflow forecasting

1. Using CPC long lead climate outlooks for ensemble streamflow forecasting Andy Wood and Dennis P. Lettenmaier University of Washington Dept. of Civil and Environmental Engineering Session A24A 2006 Joint Meeting of the AGU Baltimore, MD May 23, 2006

2. Western US Water Cycle Climate Forecasts Monthly Timestep Importance Climate forecast importance: temporal variability • In Western US: • Jan – April forecasts of summer streamflow are critical for decision-making related to: • agriculture • environmental flows • hydropower • navigation • water supply

3. Climate forecast importance: spatial variability Most basins east of the Sierras and Cascade Mtns are heavily influenced by spring precipitation. Water supply forecasts there have unavoidably high uncertainty because spring precipitation is relatively unknown. 65% Wet spring Apr-Jun Oct-Jun PCP Dry spring 15% Courtesy of Tom Pagano, NRCS

4. Climate forecast importance: spatial variability Example: Climate forecasts relatively unimportant by late Winter Areas with dry spring …. Summer flow forecast skill Courtesy of Tom Pagano, NRCS 65% Wet spring Forecast Skill Precip Apr-Jun Oct-Jun Low High Dry spring 15%

5. Climate forecast importance: spatial variability Example: Climate Forecasts very important through Spring Areas with wet spring …. Summer flow forecast skill Courtesy of Tom Pagano, NRCS 65% Wet spring Forecast Skill Precip Apr-Jun Oct-Jun Low High Dry spring 15%

6. Background Current Practice for Western US Streamflow Forecasting combine: (1) estimate of current hydrologic state (2) forecast of historical climate…usually* produce: streamflow forecast with uncertainty information

7. recently observed meteorological data ensemble of met. data to generate forecast ESP-type forecast method Spin-up ICs Forecast obs hydrologic state Research Objective Current Practice for Western US Streamflow Forecasting combine: (1) estimate of current hydrologic state (2) forecast of historical climate CPC Outlook produce: streamflow forecast with uncertainty information We use a hydrologic model-based approach similar to the NWS River Forecast Center’s Ensemble Streamflow Prediction (ESP)

8. e.g., precipitation NWS Climate Prediction Center (CPC) Seasonal Outlooks

9. CPC Seasonal Outlook Use Challenge: Seasonal (3-month) probabilities must be converted to daily meteorological values at the scale of the hydrology model

10. CPC Seasonal Outlook Use • spatial unit for raw forecasts is the Climate Division (102 for U.S.) • CDFs defined by 13 percentile values (0.025 - 0.975) for P and T, and μ and σ

11. create Tavg & Ptot ensemble forecasts (μ, σ) at each timestep/CD generate seasonal ensemble data disaggregate temporally overlapping 3-month timestep --- becomes --- non-overlapping 1-month timestep Hydrologic Prediction using CPC Seasonal Outlooks CD scale CPC climate outlooks variables: mean temperature (Tavg) total precipitation (Ptot) scales: 102 climate division (CD) / US overlapping 3-month timestep information: forecast (μ, σ) at each timestep normal (μ, σ) at each timestep disaggregate spatially climate division unit --- becomes --- 1/8 degree (~12-13 km) disaggregate to a daily timestep 1/8 degree monthly Tavg and Ptot --- becomes --- 1/8 degree daily Ptot, Tmin and Tmax Use CPC forecasts as inputs to a hydrologic model to produce streamflow forecast ensembles link Tavg & Ptot ensembles Associate monthly variables spatially & temporally

12. Several methods of doing this work well but not perfectly. disaggregate temporally overlapping 3-month timestep --- becomes --- non-overlapping 1-month timestep • Schneider et al., Weather & Forecasting (2005) – applied monthly/seasonal mean correction factors – approach being adopted by CPC • We are trying multiple linear regression: monthly values = f(seasonal values)

13. disaggregate temporally overlapping 3-month timestep --- becomes --- non-overlapping 1-month timestep Sample Results • ML regression approach appears to yield better variance, but is not markedly superior ML regression approach CPC approach std dev Schneider et al. (2005)

14. disaggregate temporally overlapping 3-month timestep --- becomes --- non-overlapping 1-month timestep Sample Results ML regression approach CPC approach R = 0.80 Schneider et al. (2005)

15. link Tavg & Ptot ensembles Associate monthly variables spatially & temporally Challenge: Given monthly distributions for a climate variable, how do you associate the values in time to yield a single sequence of one variable? Of two variables?

16. link Tavg & Ptot ensembles Associate monthly variables spatially & temporally Challenge: Given monthly distributions in adjacent cells, how might sequences in one climate division be associated with those in another?

17. Schaake Shuffle link Tavg & Ptot ensembles Associate monthly variables spatially & temporally Clark et al., J. of Hydromet (2004)

18. Spatial and Temporal Downscaling disaggregate spatially climate division unit --- becomes --- 1/8 degree (~12-13 km) • Spatial sampling of anomalies within climate divisions disaggregate to a daily timestep 1/8 degree monthly Tavg and Ptot --- becomes --- 1/8 degree daily Ptot, Tmin and Tmax • Re-sampling of daily patterns • Scaling/shifting to reproduce CPC forecast anomalies ‘OBS’ downscaled

19. University of Washington Forecast System Websiteproject led by Dennis Lettenmaierfunded byNOAA, NASA

20. Streamflow Forecast Results: Westwide at a Glance

21. Clicking the stream flow forecast map also accesses current basin-averaged conditions Streamflow Forecast Details Flow location maps give access to monthly hydrograph plots, and also to raw forecast data.

22. Streamflow Forecast Results: Spatial Precip Temp SWE Runoff Soil Moisture Apr-06 May-06 Jun-06

23. UW Real-time Daily Nowcast SM, SWE (RO) ½ degree VIC implementation Free running since last June Uses data feed from NOAA ACIS server “Browsable” Archive, 1915-present We are currently migrating the CPC forecast approach to a national US implementation

24. Conclusions • Our current approach for downscaling CPC seasonal outlooks is adequate from hydrologic perspective. • Simple temporal disaggregation approaches are sufficent, although it’s possible that slightly higher performance can be achieved via more elaborate disaggregation methods • Ensemble formation step bears further analysis at the monthly to seasonal time scale. • Translation of CPC outlooks to ensembles for hydrologic forecasting should not be an obstacle for their use. For more information: http://www.hydro.washington.edu / forecast / westwide /

25. Thank You