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Improving seasonal range hydro-meteorological predictions -- Hydrologic perspective Dennis P. Lettenmaier Department of Civil and Environmental Engineering.

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Presentation on theme: "Improving seasonal range hydro-meteorological predictions -- Hydrologic perspective Dennis P. Lettenmaier Department of Civil and Environmental Engineering."— Presentation transcript:

1 Improving seasonal range hydro-meteorological predictions -- Hydrologic perspective Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington Hydromet Workshop National Center for Atmospheric Research November 17, 2006

2 Seasonal Hydrologic prediction – long history should add my personal pics of - snow sampling snotel sites (and scan in curve method figure) SNOTEL network Snow water content on April 1 April to August runoff McLean, D.A., 1948 Western Snow Conf. NRCS SNOTEL Network

3 Variable Infiltration Capacity (VIC) Model

4 UW West-wide Forecast System Overview 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 25 th 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

5 Experimental Western US Hydrologic Forecast System ESP ENSO/PDO ENSO CPC Official Outlooks NCEP CFS CAS OCN SMLR CCA CA NSIPP/GMAO dynamical model VIC Hydrolog y Model NOAA NASA UW Multiple Seasonal Climate Forecast Data Sources

6 Introduction UW Forecast System www.hydro.washington.edu/ forecast/westwide/ Developing “focus regions” -- Klamath R. basin -- Yakima R. basin -- Feather R. basin -- WA State 1/16

7 targeted statistics e.g., runoff volumes monthly hydrographs spatial forecast maps

8 ICs Spin-upForecast obs RMSE recently observed meteorological data ensemble of met. data to generate forecast ESP forecast hydrologic state “obs” = perfect spinup + perfect fcst simulation Retrospective ESP-type simulations can shed light on the relative value of initial conditions to a given forecast application. Estimating relative impact of initial conditions and forecast accuracy ICsSpin-upForecast obs RMSE ensemble of met data to generate ensemble of ICs perfect retrospective met forecast “Reverse-ESP” forecast hydrologic state Analysis performed over 21-year period (1979-99), from which spinup and fcst traces were taken.

9 Initial Conditions: Balancing IC and forecast accuracy Columbia R. Basin Rio Grande R. Basin RMSE (perfect IC, uncertain fcst) RMSE (perfect fcst, uncertain IC) RE = ICs more impt fcst more impt

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

11 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. Initial Conditions: estimating run-up conditions dense station network for model calibration sparse station network in real-time

12 Initial Conditions: snow state 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

13 Initial Conditions: Initial snow state 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

14 Flow location maps give access to monthly hydrograph plots, and also to raw forecast data. Clicking the stream flow forecast map also accesses current basin-averaged conditions Applications: streamflow

15 Winter 2003-04: 11/25/03 Soil Moisture and Snow Water Equivalent (SWE)

16 Winter 2003-04: 12/25/03 Soil Moisture and Snow Water Equivalent (SWE)

17 Winter 2003-04: 2/25/04 Soil Moisture and Snow Water Equivalent (SWE)

18 Winter 2003-04: 3/25/04 Soil Moisture and Snow Water Equivalent (SWE)

19 Winter 2003-04: 4/25/04 Soil Moisture and Snow Water Equivalent (SWE)

20 Westwide forecast system: Comparison with NWS River Forecast Center (Portland) “official” forecasts for Columbia River at the Dalles Solid blue: UW ensemble mean; solid brown: RFC forecast mean. UW forecasts started below average and persisted through the fall, winter, and spring; RFC started close to 100% and declined to final observed value of about 80% of average. Main reason for difference was better UW representation of abnormally low Canadian snowpack UW RFC

21 This year’s forecast as of September  water balance Note that there is variability in soil moisture now… current

22 Sep 1 ESP / ESP-El Nino fcst: Summer Volumes Forecasts of April-September Flow Dalles: 100 / 88 ESPESP - El Nino

23 Sep 1 ESP / ESP-El Nino fcst: Summer Volumes Forecasts of April-September Flow Snake: 96 / 83 ESPESP - El Nino

24 Sep 1 ESP / ESP-El Nino fcst: Summer Volumes Snake: 96 / 83 El Nino flow deficits come in April through July

25 Sep 1 ESP / ESP-El Nino fcst: Summer Volumes Forecasts of April-September Flow Arrow: 101 / 92 ESPESP - El Nino

26 Sep 1 ESP / ESP-El Nino fcst: Summer Volumes Arrow: 101 / 92 El Nino flow deficits come in June and July

27 West-wide and CONUS nowcasts www.hydro.washington.edu/ forecast/monitor/

28 All Years from 1950-2003 for which J. Nino3.4 Anom. >= 0.2 AND <=1.2 Obs. System Storage Oct 1, 2005

29 October 1 Spin Up System Storage Forecast from SnakeSim: Jackson Lake Palisades Island Park Ririe American Falls Lake Walcott Nino3.4 anomaly between 0.2 and 1.2 C Demand aligned with water cond. Active Reservoir Storage (kaf) Obs. System Storage Oct 1, 2005

30 Annual hydropower production in the West has become more variable and more regionally synchronous in the period 1977-2002 in comparison with the rest of the 20 th century. Correlation: CRB-SSJ = 0.07 CRB-PNW = 0.08 SSJ-PNW = 0.36 Correlation: CRB-SSJ = 0.14 CRB-PNW = -0.14 SSJ-PNW = 0.06 Correlation: CRB-SSJ = 0.73 CRB-PNW = 0.51 SSJ-PNW = 0.65

31 Open questions Improving ICs via data assimilation (especially snow) The calibration problem (and the role of spatial scale) Data QC The “one model” problem and potential for multimodel ensembles

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