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
150 m resolution
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: , 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: , 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: , 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
These comparisons stressed the importance of accurate precipitation forecasts
NWRFC

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.
This applies at least to the hour 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
SURFACE EROSION
CHANNEL
EROSION
MASS WASTING
Watershed Sediment Module

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

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

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