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Experimental seasonal hydrologic forecasting for the Western U.S.

Experimental seasonal hydrologic forecasting for the Western U.S. Dennis P. Lettenmaier Andrew W. Wood, Alan F. Hamlet Climate Impacts Group University of Washington Climate Prediction Applications Workshop Tallahassee, FL March 10, 2004. Project Domain / Website.

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Experimental seasonal hydrologic forecasting for the Western U.S.

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  1. Experimental seasonal hydrologic forecasting for the Western U.S. Dennis P. Lettenmaier Andrew W. Wood, Alan F. Hamlet Climate Impacts Group University of Washington Climate Prediction Applications Workshop Tallahassee, FL March 10, 2004

  2. Project Domain / Website

  3. Forecasting System Evolution 1998-9 Ohio R. basin w/ COE: First tried climate model-based seasonal forecasts on experimental (retrospective) basis Eastern US: First attempted real-time seasonal forecasts during drought condition in southeastern states -- results published in: Wood et al. (2001), JGR 2000 Columbia R. basin: Implemented approach during the PNW drought, again using climate model based approach 2001 Western US: Retrospective analysis of forecasts over larger domain (for one climate model and for ESP) 2002 Columbia R. basin: New funding for “pseudo-operational” implementation for western US; began with pilot project in CRB 2003 • (Funding from: NASA NSIPP; OGP/ARCS; OGP GAPP), NASA Applications) Western US: expanded to western U.S domain for real-time forecasts; working to improve and evaluate methods each forecast cycle 2004

  4. UW Experimental West-wide hydrologic prediction system climate model output (NCEP, NSIPP) ESP as baseline fcst Real-time Ensemble Forecasts CPC official forecasts (in progress) Downscaling VIC hydrologic simulations Ensemble Hindcasts (for bias-correction and preliminary skill assessment) West-wide forecast products streamflow soil moisture, snowpack tailored to application sectors fire, power, recreation * ESP extended streamflow prediction (unconditional climate forecasts run from current hydrologic state)

  5. Challenge:Climate Model Forecast Use bias-correcting… then downscaling… CRB domain,June precip

  6. Overview: Bias Correction • numerous methods of downscaling and bias correction exist • the relatively simple one we’ve settled on requires a sufficient retrospective climate model climatology, e.g., • NCEP: hindcast ensemble climatology, 21 years X 10 member • NSIPP-1: AMIP run climatology, > 50 years, 9 member specific to calendar month and climate model grid cell

  7. Overview: VIC Hydrologic Model

  8. Overview: Hydrologic Simulations start of month 0 end of mon 6-12 1-2 years back forecast ensemble(s) model spin-up initial conditions climatology ensemble NCDC met. station obs. up to 2-4 months from current 2000-3000 stations in western US LDAS/other real-time met. forcings for remaining spin-up ~300-400 stations in western US climate forecast information data sources Forecast Products streamflow soil moisture runoff snowpack derived products e.g., reservoir system forecasts obs snow state information

  9. Challenge: Hydrologic State Initialization Problem: met. data availability in 3 months prior to forecast has only a tenth of long term stations used to calibrate model Solution: use interpolated monthly index station precip percentiles and temperature anomalies to extract values from higher quality retrospective forcing data, then disaggregate using daily index station signal. dense station network for model calibration sparse station network in real-time

  10. Overview: Initial snow state assimilation Problem sparse station spinup incurs some systematic errors, but snow state estimation is critical Solution use SWE anomaly observations (from the 600+ station USDA/NRCS SNOTEL network and a dozen ASP stations in BC, Canada) to adjust snow state at the forecast start date

  11. Overview: Initial snow state assimilation SWE state differences due to assimilation of SNOTEL/ASP observations, Feb. 25, 2004

  12. Overview: Initial conditions Snow Water Equivalent (SWE) and Soil Moisture

  13. Overview: Streamflow Forecast Locations

  14. Overview: Spatial Forecasts monthly values, anomalies and percentiles of: precip, temp, SWE, soil moisture, runoff give streamflow forecasts greater context SWE soil moisture

  15. Overview: Streamflow Forecasts targeted statistics hydrographs raw ensemble data

  16. Challenge:Balancing IC effort with forecast effort Columbia R. Basin (CRB) Rio Grande R. Basin (RGB) RMSE (perfect IC, uncertain fcst) RMSE (perfect fcst, uncertain IC) RE =

  17. Some obstacles and opportunities in hydrological application of climate information • The “one model” problem • Calibration and basin scale (post-processing as an alternative to calibration) • The value of visualization • Opportunities to utilize non-traditional data (e.g. remote sensing) www.hydro.washington.edu/Lettenmaier/Projects/fcst/

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