1. Modeling Hydrological Processes Ed Maurer PRISM Science Retreat Friday, September 27, 2002

2. Acknowledgments

3. Hydrological Modeling Hydromet System – provides a valuable regional research tool. Maintenance Improvement Expansion

4. MM5-DHSVM Streamflow Forecast System UW Real-time MM5 Distributed-Hydrology- Soil- Vegetation Model Completely automated In use since WY 1998 Streamflow and other forecasts DHSVM

5. Summary of Hydromet System Real-time Streamflow Forecast System 26 basins ~60 USGS Gauge Locations 48,896 km 2 2,173,155 pixels DHSVM @ 150 m resolution MM5 @ 4 & 12 km

6. Some Recent Publications Westrick, K.J., P. Storck, and C.F. Mass, Description and Evaluation of a Hydrometeorological Forecast System for Mountainous Watersheds, Weather and Forecasting 17: 250- 262, 2002. Mass, C.F., D. Ovens, K. Westrick, and B.A. Colle, Does Increasing Horizontal Resolution Produce More Skillful Forecasts?. Bull. Amer. Meteorol. Soc. 83: 407-430, 2002. Westrick, K.J. and C.F. Mass, An Evaluation of a High- Resolution Hydrometeorological Modeling System for Prediction of a Cool-Season Flood Event in a Coastal Mountainous Watershed, J. Hydrometeorology 2: 161-180, 2001.

7. Maintenance of System As models evolve and data formats change, the system must adapt Data format for streamflow observations Extending forecasts to 48 hours as with 4 km MM5

8. Performance of Hydromet System MM5-DHSVM Observed NWRFC SaukSnoqualmie

9. Hydromet Performance 2 Observed MM5-DHSVM NWRFC Deschutes Nisqually

10. Summary of Performance Sauk Skykomish N.Fork Snoq M.Fork Snoq. Snoqualmie Cedar Average Relative Error in Peak Flow Forecast Obs-based Control No Bias NWRFC Results from 6 events – Westrick et al., 2002 Best forecasts w/obs., avg. error 31% Not significantly better than control or RFC

11. Opportunity for Improving Hydromet Forecasts One key finding from Westrick et al., 2002: Precipitation uncertainties in observed data due to: Instrument error Areal representativeness of point obs. Interpolation method These errors can be nearly as large as uncertainty in meteorological forecast.

12. Lack of Observations To improve forecasts, we must identify the relative magnitudes of the errors. Precipitation observations at a spatial resolution sufficient to determine “reality” do not exist in domain IMPROVE – 2 study provides a valuable context for examining the orographic precipitation for several events, and provides a basis for intercomparing the errors

13. IMPROVE-2 Orographic Precipitation Study Nov-Dec 2001 Raingauges Snotel Co-op Observer Radar Disdrometer

14. Expansion of Forecasting Tools DHSVM produces more than just streamflow Soil moistures, slopes in model provide additional forecasting capabilities Investigate landslide hazard forecasting

15. DHSVM Sediment Production and Transport MASS WASTING SURFACE EROSION CHANNEL EROSION Watershed Sediment Module

16. DHSVM Structure Modifications Soil moistureOverland flow Channel flow DEM Met. data Vegetation (type, LAI, height) Soil texture Soil depth f(Soil Cohesion) f(Veg. Cohesion)

17. Mass Wasting Module MASS WASTING Factor of Safety Multiple realizations of total failure locations Multiple time series of sediment supply

18. Summary Many Opportunities to Build on the Past Successes Coordination with Others in the PRISM Community is an Essential Component