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Modeling Hydrological Processes

Modeling Hydrological Processes. Ed Maurer PRISM Science Retreat Friday, September 27, 2002. Acknowledgments. Hydrological Modeling. Hydromet System – provides a valuable regional research tool. Maintenance Improvement Expansion. MM5-DHSVM Streamflow Forecast System. UW Real-time MM5

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Modeling Hydrological Processes

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  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 DHSVM Streamflow and other forecasts

  5. Summary of Hydromet System Real-time Streamflow Forecast System 26 basins ~60 USGS Gauge Locations 48,896 km2 2,173,155 pixels DHSVM @ 150 m resolution MM5 @ 4 & 12 km http://hydromet.atmos.washington.edu

  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 Sauk Snoqualmie Observed MM5-DHSVM NWRFC

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

  10. Average Relative Error in Peak Flow Forecast Obs-based Control No Bias NWRFC Sauk Skykomish N.Fork Snoq M.Fork Snoq. Snoqualmie Cedar Summary of Performance • 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. MASS WASTING SURFACE EROSION CHANNEL EROSION DHSVM Sediment Production and Transport Watershed Sediment Module

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

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

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

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