Transcript Slide 1
University of Washington experimental westwide seasonal hydrologic forecast system Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington for presentation at Scripps Institution of Oceanography Climate Research Division March 23, 2005 Introduction: Hydrologic prediction Snow water content on April 1 PNW should add my personal pics of snow sampling snotel sites (and scan in curve method figure) SNOTEL network SNOTEL Network McLean, D.A., 1948 Western Snow Conf. April to August runoff Technical Advances related to Hydrologic Forecasting physical Internet / hydrologic snow cats real-time models data snow survey / satellite graphical imagery computing forecasts / SNOTEL in water index methods / network resources i.e., regression ENSO / ESP seasonal method climate conceptual aerial forecasts hydrologic snow models surveys desktop computing 1920s 1930s 1940s 1950s 1960s 1970s 1980s 1990s 2000s Modeling Framework Model Testing VIC model runoff is routed to streamflow gages, and verified against observations Introduction: Hydrologic prediction and NWS NWS River Forecast Center (RFC) approach: rainfall-runoff modeling (i.e., NWS River Forecast System, Anderson, 1973 offspring of Stanford Watershed Model, Crawford & Linsley, 1966) Ensemble Streamflow Prediction (ESP) • used for shorter lead predictions; • ~ used for longer lead predictions Currently, some western RFCs and NRCS coordinate their seasonal forecasts, using mostly statistical methods. recently observed meteorological data ESP forecast Spin-up hydrologic state ensemble of met. data to generate forecast ICs Forecast obs RMSE Forecast System Overview 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 / MODIS* Update Update 25th Day, Month 0 ESP traces (40) CPC-based outlook (13) NCEP GSM ensemble (20) NSIPP-1 ensemble (9) Month 6 - 12 * experimental, not yet in real-time product Forecast Initialization Snowpack Initial Condition Soil Moisture Initial Condition Forecast points and sample streamflow forecasts monthly hydrographs targeted statistics e.g., runoff volumes Background: W. US Forecast System CCA NOAA CAS OCN SMLR Seasonal Climate Forecast Data Sources CPC Official Outlooks CA Seasonal Forecast Model (SFM) NASA VIC Hydrolog y Model NSIPP-1 dynamical model ESP ENSO UW ENSO/PDO Approach: Bias correction scheme for climate model forcings bias-corrected forecast scenario month m raw CFS forecast scenario from COOP observations month m from CFS climatological runs Approach: Bias Example Regional Bias: spatial example Sample GSM cell located over Ohio River basin JULY obs prcp GSM prcp obs temp GSM temp obs GSM Introduction: Seasonal Climate Prediction e.g., precipitation Background: CPC Seasonal Outlook Use spatial unit for raw forecasts is the Climate Division (102 for U.S.) CDFs defined by 13 percentile values (0.025 - 0.975) for P and T are given Background: CPC Seasonal Outlook Use probabilities => anomalies precipitation Approach: CPC Seasonal Outlook Use climate division anomalies => model forcing ensembles CPC monthly climate division anomaly CDFs (1) (2) ensemble formation monthly climate division T & P ensembles “Shaake Shuffle” we want to test (1) and (2): testing (2) is easy, using CPC retrospective climate division dataset testing (1) is more labor-intensive, less straightforward spatial / temporal disaggregation daily 1/8 degree Prcp, Tmax and Tmin ensemble timeseries “downscaling” 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 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 • 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 spatial weighting function elevation weighting function SNOTEL/ASP VIC cell VIC model spinup methods: SNOTEL assimilation April 25, 2004 Results for Winter 2003-04: streamflow hydrographs By Fall, slightly low flows were anticipated By winter, moderate deficits were forecasted Results for Winter 2003-04: 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 Results for Winter 2003-04: 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 Seasonal Hydrologic Forecast Uncertainty Importance of uncertainty in ICs vs. climate vary with lead time … ICs low climate f’cast high ICs high climate f’cast low Forecast Uncertainty high low streamflow volume forecast period actual perfect data, model model + data uncertainty Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep … hence importance of model & data errors also vary with lead time. 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) RE = RMSE (perfect fcst, uncertain IC) VIC model spinup methods: originally, used N-LDAS P&T VIC model spinup methods: N-LDAS had problems in the West VIC model spinup methods: index stations 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. VIC model spinup methods: index stations Example for daily precipitation Index stn pcp pcp percentile gridded to 1/8 degree monthly 1/8 degree pcp 1/8 degree dense station monthly pcp distribution (N years for each 1/8 degree grid cell) disagg. to daily using interpolated daily fractions from index stations VIC model spinup methods: snow cover (MODIS) assimilation (Snake R. trial) Snowcover BEFORE update Snowcover AFTER update MODIS update for April 1, 2004 Forecast snow added removed Expansion to multiple-model framework It should be possible to balance effort given to climate vs IC part of forecasts climate forecasts more important N ensembles high climate ensembles ICs more important streamflow volume forecast period IC ensembles low Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Expansion to multiple-model framework CCA NOAA CAS OCN SMLR CPC Official Outlooks NWS HL-RMS CA Seasonal Forecast Model (SFM) NASA Multiple Hydrologic Models VIC Hydrolog y Model NSIPP-1 dynamical model others ESP ENSO UW ENSO/PDO weightings calibrated via retrospective analysis Winter 2004-5 – evolution of a drought and its prediction Results: WY2005, Dec. 1 hydrologic conditions Results: WY2005, Jan. 1 hydrologic conditions Results: WY2005, Feb. 1 hydrologic conditions Results: WY2005, Mar. 1 hydrologic conditions January 1 SWE forecasts (ensemble averages) using ESP for JAN-FEB-MAR January 1 SWE forecasts (ensemble averages) using ESP for APR-MAY-JUN January 1 SWE forecasts (ensemble averages) using CPC outlook for JANFEB-MAR January 1 SWE forecasts (ensemble averages) using CPC outlook for APRMAY-JUN Results: WY2005 vs. WY1977 Precip, Temp WY1977 How does the WY2005 current year compare to WY1977? Puget Sound Drainage Basin WY2005 Results: WY2005 vs. WY1977 SM, SWE WY1977 WY2005 3/15 ESP fcst: WY2005 vs. WY1977 Runoff WY2005 Puget Sound Drainage Basin WY1977 Apr-Sep % of avg max 80 0.75 60 0.50 54 0.25 49 min 45 Results: WY2005 vs. WY1977 Precip, Temp WY1977 How does the WY2005 current year compare to WY1977? BC portion of Columbia R. Basin WY2005 Results: WY2005 vs. WY1977 SM, SWE WY1977 WY2005 3/15 ESP fcst: WY2005 vs. WY1977 Runoff BC portion of Columbia R. Basin WY2005 WY1977 Apr-Sep % of avg max 95 0.75 83 0.50 78 0.25 74 min 64 Results: WY2005 vs. WY1977 Precip, Temp WY1977 How does the WY2005 current year compare to WY1977? Columbia R. basin upstream of The Dalles, OR WY2005 Results: WY2005 vs. WY1977 SM, SWE WY1977 WY2005 3/15 ESP fcst: WY2005 vs. WY1977 Runoff Columbia R. basin upstream of The Dalles, OR WY2005 WY1977 Apr-Sep % of avg max 88 0.75 73 0.50 69 0.25 65 min 55 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)