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Andrew W. Wood Dennis P. Lettenmaier

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1 University of Washington experimental west-wide seasonal hydrologic forecast system
Andrew W. Wood Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington for presentation at Climate Prediction Applications Science Workshop International Research Institute Palisades, NY March 17, 2005

2 Forecast System Overview
Objective: To create a model-based testbed for evaluating potential sources of improvement in seasonal forecasts since inception of regression/ESP methods operational seasonal climate forecasts (model-based and otherwise) greater real-time availability of station data computing advances new satellite-based products (primarily snow cover) distributed, physical hydrologic modeling for macroscale regions

3 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 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

4 Modeling Framework Snowpack Initial Condition Soil Moisture

5 Forecast points and sample streamflow forecasts
monthly hydrographs targeted statistics e.g., runoff volumes

6 Background: W. US Forecast System
Seasonal Climate Forecast Data Sources ESP ENSO/PDO ENSO CPC Official Outlooks Seasonal Forecast Model (SFM) CAS OCN SMLR CCA CA NSIPP-1 dynamical model VIC Hydrology Model NOAA NASA UW

7 Approach: Bias Correction Scheme
from COOP observations from GSM climatological runs raw GSM forecast scenario bias-corrected forecast scenario month m

8 Approach: Bias Example
obs prcp GSM prcp obs temp GSM temp JULY Regional Bias: spatial example Sample GSM cell located over Ohio River basin important point(s): the first hurdle in making forecasts is to overcome the regional biases. this and the next slide show the bias -- first for one GSM cell, where you can see plotted the observed forcings vs. the GSM raw climatology forcings (for the climatology period ‘79-’99). biases are so large in temperature that a hydrologic model would be blown out of the water. this is from one set (May) of climatology & forecast ensembles back on the East Coast obs GSM

9 VIC initial condition estimation: 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

10 VIC model spinup methods: 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.

11 Results for Winter : volume runoff forecasts Comparison with RFC 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

12 Results for Winter : volume runoff forecasts Comparison with RFC forecast for Sacramento River near Redding, CA UW forecasts made on 25th of each month RFC forecasts made on 1st of month (UW’s ESP unconditional forecasts shown) RFC UW

13 Results for Winter 2003-04: volume forecasts for a sample of PNW locations

14 Results for Winter 2003-04: volume forecasts for a sample of PNW locations

15 Seasonal Hydrologic Forecast Uncertainty
Importance of uncertainty in ICs vs. climate vary with lead time … Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Uncertainty Forecast actual perfect data, model streamflow volume forecast period model + data uncertainty low high ICs low climate f’cast high ICs high climate f’cast low ESP addresses climate uncertainty, but the single model/calibration framework doesn’t address IC uncertainty -- ignoring calibration issues at moment – assuming reasonably well calibrated models can be further adjusted via bias-correction -- don’t forget issues to do w/ estimating inputs as an ensemble … hence importance of model & data errors also vary with lead time.

16 Relative important of initial condition and climate forecast error in streamflow forecasts
Columbia R. Basin fcst more impt ICs more impt Rio Grande R. Basin RMSE (perfect IC, uncertain fcst) RMSE (perfect fcst, uncertain IC) RE =

17 Expansion to multiple-model framework
It should be possible to balance effort given to climate vs IC part of forecasts Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep N ensembles climate ensembles IC streamflow volume forecast period low high climate forecasts more important ICs more important ESP addresses climate uncertainty, but the single model/calibration framework doesn’t address IC uncertainty -- ignoring calibration issues at moment – assuming reasonably well calibrated models can be further adjusted via bias-correction -- don’t forget issues to do w/ estimating inputs as an ensemble

18 Expansion to multiple-model framework
Multiple Hydrologic Models CCA NOAA CAS OCN CPC Official Outlooks NWS HL-RMS SMLR CA Seasonal Forecast Model (SFM) VIC Hydrology Model NASA NSIPP-1 dynamical model others ESP weightings calibrated via retrospective analysis ENSO UW ENSO/PDO

19 Winter 2004-5 – evolution of a drought and its prediction

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25 January 1 SWE forecasts (ensemble averages) using ESP for JAN-FEB-MAR

26 January 1 SWE forecasts (ensemble averages) using ESP for APR-MAY-JUN

27 January 1 SWE forecasts (ensemble averages) using CPC outlook for JAN-FEB-MAR

28 January 1 SWE forecasts (ensemble averages) using CPC outlook for APR-MAY-JUN

29 Next steps Improved data assimilation (snow cover extent, SNOTEL)
2-week forecasts Multi-model ensemble (hydrology and climate) Forecast domain expansion Augmented forecast products (e.g. nowcasts in real-time)

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