Experimental Real-time Seasonal Hydrologic Forecasting

1. Experimental Real-time Seasonal Hydrologic Forecasting Andrew Wood Dennis P. Lettenmaier University of Washington presented: AMS Conference on Applied Climatology, 2002 Portland, OR May 2002

2. Project Overview • Research Objective: • To produce monthly to seasonal snowpack, streamflow, runoff & soil moisture forecasts for continental scale river basins • Underlying rationale/motivation: • Global numerical weather prediction / climate models (e.g. GSM) take advantage of SST – atmosphere teleconnections • Hydrologic models add soil-moisture – streamflow influence (persistence)

3. Topics • Approach • Columbia River basin (summer 2001) results • Ongoing Work • Comments

4. General Approach • climate model forecast • meteorological outputs • ~1.9 degree resolution (T62) • monthly total P, avg T • Use 3 steps: 1) statistical bias correction • 2) downscaling and disaggregation • 3) hydrologic simulation hydrologic model inputs • streamflow, soil moisture, snowpack, • runoff • 1/8-1/4 degree resolution • daily P, Tmin, Tmax

5. Models: 1. Global Spectral Model (GSM) ensemble forecasts from NCEP/EMC • forecast ensembles available near beginning of each month, extend 6 months beginning in following month • each month: • 210 ensemble members define GSM climatology for monthly Ptot & Tavg • 20 ensemble members define GSM forecast

6. Models: 2. VIC Hydrologic Model

7. Flow Routing Network domain slide

8. TOBS a. b. c. TGSM One Way Coupling of GSM and VIC models a) bias correction: climate model climatology  observed climatology b) spatial interpolation: GSM (1.8-1.9 deg.)  VIC (1/8 deg) c) temporal disaggregation (via resampling of observed patterns): monthly  daily

9. Bias Example:JFM precipitation from Parallel Climate Model (DOE)climate model vs. “observed” distributions at climate model scale (T42)

10. Dealing with bias using a climatology-based correction Note: we apply correction to both forecast ensemble and climatology ensemble itself (to use as a baseline)

11. monthly GSM anomaly (T62) interpolated to VIC scale VIC-scale monthly forecast observed mean fields (1/8-1/4 degree) note: month m, m = 1-6 ens e, e = 1-20 Downscaling: add spatial VIC-scale variability

12. Lastly, temporal disaggregation… for each VIC-scale monthly forecast value, e.g.:

13. 1-2 years back VIC forecast ensemble VIC model spin-up VIC climatology ensemble NCDC met. station obs. up to 2-4 months from current LDAS/other met. forcings for remaining spin-up climate forecast information (from GSM) data sources Forecast Products streamflow soil moisture runoff snowpack Simulations start of month 0 end of month 6

14. Columbia River Basin Application

15. Initial Conditionslate-May SWE &water balance

16. Initial Conditionslate-May SWE &water balance(percentiles)

17. forecast observed May climate forecast forecast medians

18. May snowpack forecast hindcast “observed” forecast forecast medians

19. forecast May runoff & soil moisture forecast hindcast “observed” forecast medians

20. May streamflow forecast

21. Ongoing Work: Assessment and Expansion

22. Tercile Prediction “Hit Rate”e.g.,GSM Ensemble “Forecast” Average, January(based on retrospectiveperfect-SST ensemble forecasts)Masked for local significance

23. U.S. West-wide Hydrologic Forecasting

24. Summary Comments • climate-hydrology model forecasting method has potential • hydrologic persistence was most important in the CRB example • bias-correction of climate model outputs (using a climate model hindcast climatology) is critical • access to quality met data for hydrologic model initialization is also essential