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Hydrologic Forecasting Alan F. Hamlet Dennis P. Lettenmaier JISAO/CSES Climate Impacts Group Dept. of Civil and Environmental Engineering University of Washington Winter Climate of the Western U.S. DJF Temp (°C) NDJFM Precip (mm) Runoff Timing in the PNW is Determined Primarily by Winter Temperature Regimes Normalized Precip or Runoff 3.0 2.5 Snow Dominated 2.0 Transient Snow 1.5 Rain Dominated 1.0 PNW Precip 0.5 0.0 10 11 12 1 2 3 4 Month 5 6 7 8 9 Typical Effects of Urbanization on a Small Watershed Des Moines Creek (developed) Source: Booth D.B., 2000, Forest Cover, Impervious-Surface Area, and the Mitigation of Urbanization Impacts in King County, WA http://depts.washington.edu/cwws/Research/Reports/forest.pdf Effects of the Pacific Decadal Oscillation (PDO) and El Niño Southern Oscillation (ENSO) on Columbia River Summer Streamflows Red = Warm ENSO, Blue = Cool ENSO, Green = ENSO neutral 450000 Cool PDO Warm PDO 400000 Warm PDO 350000 300000 250000 200000 2000 1990 1980 1970 1960 1950 1940 1930 1920 1910 150000 1900 Apr-Sept Flow (cfs) Cool PDO Global Surface Temperatures are Increasing Rapidly Weather Forecasts Flood Control and Hydropower Management Flood Forecasts 1hr - 1 week Seasonal to Interannual Climate Forecasts Seasonal Streamflow Volumes Water Resources Management 1– 24 months Forecast Lead Time Climate Change Scenarios Long-Range Streamflow Forecasts Water Resources Planning 10-100 years Schematic Diagram of a Hydrologic Forecasting System Initial Hydrologic State •Soil Moisture •Snowpack Hydrologic Forecast: Hydrologic Model Future Temperature and Precipitation Forecast •Streamflow •Soil Moisture •Snowpack •Evaporation Simulated Water Balance for the Pacific Northwest 250 precipitation swe 200 active soil storage evapotranspiration 150 100 50 sep aug jul jun may apr mar feb jan dec nov 0 oct Area Average Water (depth in mm) runoff+baseflow Relative Roles of Future Precipitation and Initial Hydrologic State at Different Forecast Dates In October future precipitation dominates the inputs to the water balance. 900 800 700 600 500 400 In April inputs to the water balance from future precipitation and storage are comparable. 99% precipitation snowmelt soil drainage streamflow evaporation 300 200 46% Water Balance (depth in mm) 1000 100 0 oct-sept apr-sept Simulated Long-Term Water Balance for the Pacific Northwest Examples of Hydrologic Forecasting Systems Example of a Short Time Scale Flood Forecasting System MM5 mesoscale atmospheric model Estimated Hydrologic State DHSVM distributed hydrologic model Streamflow Forecast River Stage Forecast Example of a Seasonal Forecasting System Based on Regression Models NRCS SNOTEL Network NRCS/NWRFC Water Supply Forecasts Hydrologic Index Regression Equation Streamflow Volume Example of a Seasonal Forecasting System Using a Physically-Based Hydrologic Model Temperature and Precipitation Forecast Estimated Hydrologic State VIC Hydrologic Model UW West-Wide Seasonal Hydrologic Forecast System http://www.hydro.washington.edu/forecast/westwide/ Hydrologic Forecast Background: Forecast System Schematic local scale (1/8 degree) weather inputs Hydrologic model spin up NCDC met. station obs. up to 2-4 months from current 1-2 years back LDAS/other real-time met. forcings for spin-up gap soil moisture snowpack INITIAL STATE streamflow, soil moisture, snow water equivalent, runoff Hydrologic forecast simulation ensemble forecasts SNOTEL SNOTEL Update Update 25th Day, Month 0 ESP traces (40) CPC-based outlook (13) NCEP GSM ensemble (20) NSIPP-1 ensemble (9) Month 6 - 12 Climate forecasts based on ENSO predictions can provide useful information about future streamflows with lead times up to 12 months. Retrospective tests in the Columbia River basin have shown that during cool or warm events, ENSO-based streamflow forecasts are superior to assumptions of “normal” conditions about 65 % of the time on Oct 1 Natural Streamflow (cfs) Natural Streamflow Columbia River at The Dalles, OR Red = Unconditional mean Blue = Ensemble mean Black = 2005 Observed Conclusions Useful hydrologic forecasts based on weather or climate forecasts are available with lead times ranging from a few hours (flood forecasts) to 50 years or more (climate change scenarios). Many operational hydrologic forecasting systems are currently based on statistical models, however dynamic, physically-based tools are increasingly being used in both academic and operational forecasting systems. Dynamic forecasting systems based on weather or climate models directly linked to physically-based hydrologic models have important advantages in a rapidly evolving climate system. Short-term forecasts based on weather models have already reached a useful state of development, but many challenges remain at seasonal or longer time scales.