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University of Washington experimental west-wide seasonal hydrologic forecast system Dennis P. Lettenmaier Department of Civil and Environmental Engineering.

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Presentation on theme: "University of Washington experimental west-wide seasonal hydrologic forecast system Dennis P. Lettenmaier Department of Civil and Environmental Engineering."— Presentation transcript:

1 University of Washington experimental west-wide 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

2 Introduction: Hydrologic prediction
PNW Snow water content on April 1 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

3 Technical Advances related to Hydrologic Forecasting
physical hydrologic models Internet / real-time data snow cats snow survey / graphical forecasts / index methods / i.e., regression satellite imagery computing in water resources SNOTEL network ENSO / seasonal climate forecasts ESP method conceptual hydrologic models aerial snow surveys desktop computing 1920s 1930s 1940s 1950s 1960s 1970s 1980s 1990s 2000s

4 Modeling Framework

5 Model Testing VIC model runoff is routed to streamflow gages, and verified against observations

6 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. ICs Spin-up Forecast obs RMSE recently observed meteorological data ensemble of met. data to generate forecast ESP forecast hydrologic state

7 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

8 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

9 Forecast Initialization
Snowpack Initial Condition Soil Moisture Initial Condition

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

11 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

12 Approach: Bias correction scheme for climate model forcings
from COOP observations from CFS climatological runs raw CFS forecast scenario bias-corrected forecast scenario month m

13 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

14 Introduction: Seasonal Climate Prediction
e.g., precipitation

15 Background: CPC Seasonal Outlook Use
spatial unit for raw forecasts is the Climate Division (102 for U.S.) CDFs defined by 13 percentile values ( ) for P and T are given

16 Background: CPC Seasonal Outlook Use probabilities => anomalies
precipitation

17 Approach: CPC Seasonal Outlook Use climate division anomalies => model forcing ensembles
(1) “Shaake Shuffle” (2) CPC monthly climate division anomaly CDFs spatial / temporal disaggregation ensemble formation monthly climate division T & P ensembles daily 1/8 degree Prcp, Tmax and Tmin ensemble timeseries “downscaling” 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

18 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

19 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.

20 VIC model spinup methods: SNOTEL assimilation
April 25, 2004

21 Results for Winter 2003-04: streamflow hydrographs
By Fall, slightly low flows were anticipated By winter, moderate deficits were forecasted

22 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

23 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

24 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.

25 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 =

26 VIC model spinup methods: originally, used N-LDAS P&T
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.

27 VIC model spinup methods: N-LDAS had problems in the West
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.

28 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.

29 VIC model spinup methods: index stations
Example for daily precipitation monthly gridded to 1/8 degree Index stn pcp pcp percentile 1/8 degree pcp disagg. to daily using interpolated daily fractions from index stations 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. 1/8 degree dense station monthly pcp distribution (N years for each 1/8 degree grid cell)

30 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

31 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

32 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

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

34

35 Results: WY2005, Dec. 1 hydrologic conditions

36 Results: WY2005, Jan. 1 hydrologic conditions

37 Results: WY2005, Feb. 1 hydrologic conditions

38 Results: WY2005, Mar. 1 hydrologic conditions

39

40

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

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

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

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

45 Results: WY2005 vs. WY1977 Precip, Temp
How does the WY2005 current year compare to WY1977? Puget Sound Drainage Basin

46 Results: WY2005 vs. WY1977 SM, SWE WY1977 WY2005

47 3/15 ESP fcst: WY2005 vs. WY1977 Runoff
Apr-Sep % of avg max 80 min 45 WY2005 Puget Sound Drainage Basin WY1977

48 Results: WY2005 vs. WY1977 Precip, Temp
How does the WY2005 current year compare to WY1977? BC portion of Columbia R. Basin

49 Results: WY2005 vs. WY1977 SM, SWE WY1977 WY2005

50 3/15 ESP fcst: WY2005 vs. WY1977 Runoff
Apr-Sep % of avg max 95 min 64 BC portion of Columbia R. Basin WY2005 WY1977

51 Results: WY2005 vs. WY1977 Precip, Temp
How does the WY2005 current year compare to WY1977? Columbia R. basin upstream of The Dalles, OR

52 Results: WY2005 vs. WY1977 SM, SWE WY1977 WY2005

53 3/15 ESP fcst: WY2005 vs. WY1977 Runoff
Apr-Sep % of avg max 88 min 55 Columbia R. basin upstream of The Dalles, OR WY2005 WY1977

54 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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