Real-time experimental seasonal hydrologic forecasting for the western U.S. A. Wood, A.F. Hamlet, M. McGuire, S. Babu and Dennis P. Lettenmaier Department of Civil and Environmental Engineering for Session H22B Adv. Methods for Probabilistic Hydrometeorologic Forecasting II 2004 Joint Assembly: CGU, AGU, SEG and EEGS Montreal, Canada May 18

Outline of this talk Introduction – research rationale Framework and method of implementation Selected Results for current winter Conclusions and unsolved problems

Introduction: Research Rationale Are current seasonal hydrologic forecasts all that they can be? How can ongoing research on land-atmosphere interactions help to improve seasonal streamflow forecasts in the western U.S.? Potential sources of improvement since inception of regression/ESP methods: operational seasonal climate forecasts (model-based and otherwise) greater availability of station data computing new satellite-based products (primarily snow cover) distributed, physical hydrologic modeling for macroscale regions

Framework: Forecast System Schematic NCDC met. station obs. up to 2-4 months from current local scale (1/8 degree) weather inputs soil moisture snowpack Hydrologic model spin up SNOTEL Update streamflow, soil moisture, snow water equivalent, runoff 25th Day, Month 0 1-2 years back LDAS/other real-time met. forcings for spin-up gap Hydrologic forecast simulation Month 6 - 12 INITIAL STATE SNOTEL / MODIS* Update ensemble forecasts ESP traces (40) CPC-based outlook (13) NCEP GSM ensemble (20) NSIPP-1 ensemble (9) * experimental, not yet in real-time product

Framework: Hydrology Model

Framework: Estimating Initial Conditions estimating spin-up period inputs Problem: met. data availability in 3 months prior to forecast has only a tenth of long term stations used to calibrate and run model in most of spin-up period sparse station network in real-time dense station network for model calibration 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.

Framework: Estimating Initial Conditions SNOTEL assimilation Problem sparse station spin-up period 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

Framework: Estimating Initial Conditions SNOTEL assimilation Assimilation Method weight station OBS’ influence over VIC cell based on distance and elevation difference number of stations influencing a given cell depends on specified influence distances spatial weighting function elevation weighting function SNOTEL/ASP VIC cell distances “fit”: OBS weighting increased throughout season OBS anomalies applied to VIC long term means, combined with VIC-simulated SWE adjustment specific to each VIC snow band important point(s): the approach attempts to make use of forecast skill from 2 sources: better understanding of synoptic scale teleconnections and the effects of persistence in SSTs on regional climate, as reproduced in coupled ocean-atmosphere models; the macroscale hydrologic model yields an improved ability to model the persistence in hydrologic states at the regional scale (more compatible with climate model scales than prior hydrologic modeling). Climate forecasts with monthly and seasonal horizons are now operationally available, and if they can be translated to streamflow, then they may be useful for water management.

Framework: Estimating Initial Conditions SWE state adjustment (using SNOTEL/ASP obs) April 25, 2004

snow cover (MODIS) assimilation (Snake R. trial) Framework: Estimating Initial Conditions snow cover (MODIS) assimilation (Snake R. trial) Snowcover BEFORE update Snowcover AFTER update MODIS update for April 1, 2004 Forecast snow added removed

Framework: Downscaling Climate Model output NCEP GSM and NSIPP-1

Framework: Bias-correcting Climate Model output numerous methods of downscaling and/or 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

Framework: Downscaling CPC outlooks spatial unit for raw forecasts is the Climate Division (102 for U.S.) 13 percentile values (from 0.025 to 0.975) for P and T are given

Framework: Downscaling CPC outlooks downscaling uses Shaake Shuffle (Clark et al., J. of Hydrometeorology, Feb. 2004) to assemble monthly forecast timeseries from CPC percentile values

Framework: Streamflow Forecast Locations California Columbia R. basin in development: Colorado R., upper Rio Grande Snake R. basin

Framework: Streamflow Forecast Products monthly hydrographs targeted statistics e.g., volume runoff forecast anomalies raw ensemble data

Framework: Example of Spatial Forecasts

Results: Initial Conditions for Current Winter Soil Moisture and Snow Water Equivalent (SWE)

Results: current seasonal volume forecasts Comparison with RFC regression forecast for Columbia River at the Dalles, OR UW forecasts made on 25th of each month RFC forecasts made several times monthly: 1st, mid-month, late (UW’s ESP unconditional and CPC forecasts shown) UW RFC

Final Comments 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) For more information: www.hydro.washington.edu/Lettenmaier/Projects/fcst/