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Andrew W. Wood and Dennis P. Lettenmaier

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1 An experimental real-time seasonal hydrologic forecast system for the western U.S.
Andrew W. Wood and Dennis P. Lettenmaier Department of Civil and Environmental Engineering University of Washington Climate Diagnostics and Prediction Workshop Pennsylvania State University October 27, 2005

2 Outline Background – UW West-wide hydrologic forecasting system
Preliminary multi-model ensemble work Final Comments

3 Background: UW west-wide system
where did it come from? 1997 COE Ohio R. basin/NCEP -> -> UW East Coast 2000 (NCEP/ENSO) -> -> UW PNW > UW west-wide 2003 what are its objectives? evaluate climate forecasts in hydrologic applications seasonal: CPC, climate model, index-based (e.g., SOI, PDO) 16-day: NCEP EMC Global Forecast System (GFS) evaluate assimilation strategies MODIS snow covered area; AMSR-E SWE SNOTEL/ASP SWE evaluate basic questions about predictability evaluate hydrologic modeling questions role of calibration, attribution of errors, multiple-model use evaluate downscaling approaches what are its components?

4 CURRENT WEBSITE

5 Surface Water Monitor daily updates 1-2 day lag soil moisture & SWE
percentiles ½ degree resolution archive from 1915-current uses ~2130 index stns

6 Background: UW west-wide system
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 Now 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 CFS ensemble (20) NSIPP ensemble (9) * experimental, not yet in real-time product

7 Background: UW west-wide system
Snowpack Initial Condition Soil Moisture Initial Condition

8 Background: UW west-wide system
Seasonal Climate Forecast Data Sources CCA NOAA CAS OCN CPC Official Outlooks SMLR CA Coupled Forecast System CFS VIC Hydrology Model NASA NSIPP/GMAO dynamical model ESP ENSO UW ENSO/PDO

9 Background: UW west-wide system
validation of selected historic streamflow simulations

10 MAP LINKS TO FLOW FORECASTS
monthly hydrographs

11 Background: UW west-wide system
Precip Temp SWE Runoff Soil Moisture Mar-05 Apr-05 May-05

12 Background: UW west-wide system
what drives UW system activities? research goals: exploration of CPC & NCEP products data assimilation of NASA products Klamath Basin, Sacramento River (particularly Feather) collaborations: requests by WA State drought personnel Yakima-basin forecasts, Puget Sound SW Monitor type hydrologic assessment interests of Pagano, Pasteris & Co (NWCC): calibrated forecast points in Upper Colorado, upper Missouri R. basin, Snake R. basin spatial soil moisture, snow and runoff data one-off analyses other, e.g., U. AZ project with USBR in lower Colorado basin

13 Background: UW west-wide system
research objectives include: climate forecasts data assimilation hydrologic predictability multi-model / calibration questions

14 Expansion to multiple-model framework
Seasonal Climate Forecast Data Sources CCA NOAA CAS OCN CPC Official Outlooks SMLR CA Coupled Forecast System CFS VIC Hydrology Model NASA NSIPP/GMAO dynamical model ESP ENSO UW ENSO/PDO

15 LDAS models An LDAS intercomparison conclusion: Model results, using default parameters, have a wide spread for some states and fluxes. Every model is doing something better than other models in some parts of the country NOAH VIC MOSAIC SAC Dag Lohmann, HEPEX

16 Multiple-model Framework
Multiple Hydrologic Models Schaake Shuffle (Clark et al) CCA NOAA CAS OCN CPC Official Outlooks NWS SAC SMLR CA Wood et al., 2002 Coupled Forecast System (CFS) VIC Hydrology Model NASA NSIPP-1 dynamical model NOAH LSM NWS: Day et al; Twedt et al ESP Hamlet et al., Werner et al. weightings calibrated via retrospective analysis ENSO UW ENSO/PDO

17 Multiple-model Framework
Models: VIC - Variable Infiltration Capacity (UW) SAC - Sacramento/SNOW17 model (National Weather Service) NOAH – NCEP, OSU, Army, and NWS Hydrology Lab Model Energy Balance Snow Bands VIC Yes Yes SAC No Yes NOAH Yes No Calibration parameters from NLDAS 1/8 degree grid (Mitchell et al 2004) – no further calibration performed Meteorological Inputs: 1/8 degree COOP-based,

18 Test Case - Salmon River basin (upstream of Whitebird, ID) - retrospective (deterministic evaluation): 25 year training 20 year validation

19 Individual Model Results

20 Individual Model Results
Monthly Avg Flow Monthly RMSE

21 Individual Model Results
VIC appears to be best “overall” Captures base flow, timing of peak flow Lowest RMSE except for June Magnitude of peak flow a little low SAC is second “overall” No base flow peak flow is early but magnitude is close to observed* NOAH is last peak flow is 1-2 months early and far too small (high evaporation)

22 Combining models to reduce error
Average the results of multiple models Ensemble mean should be more stable than a single model Combines the strengths of each model Provides estimates of forecast uncertainty

23 Computing Model Weights
Bayesian Model Averaging (BMA) (Raftery et al, 2005) Ensemble mean forecast = Σwkfk where fk = result of kth model wk = weight of kth model, related to model’s correlation with observations during training Raftery, A.E., F. Balabdaoui, T. Gneiting, and M. Polakowski, Using Bayesian Model Averaging to Calibrate Forecast Ensembles. Monthly Weather Review, 133,

24 Computing Model Weights
We transform flows to Gaussian domain and bias-correct them before computing weights using the BMA software Western U.S. – many streams have 3-parameter log-normal (LN-3) distributions for monthly average flow Each month, for each model, is given distinct distribution, transformation, bias-correction Procedure monthly LN-3 transformation monthly bias correction based on regression BMA process to calculate monthly weights, statistics weights used to recombine models transform outputs back to flow units

25 Multi-model ensemble results
June September

26 Multi-model ensemble results
June Flow, September Flow,

27 Multi-model ensemble results
June LN-3 & Bias-Corrected Flow, Sept LN-3 & Bias-Corrected Flow,

28 Multi-model ensemble results

29 Multi-model ensemble results

30 Multi-model ensemble results

31 Multi-model ensemble results

32 Multi-model ensemble results
despite large biases, SAC had a stronger interannual correlation with observations than VIC post-processing fixes many of the biases BMA procedure only really uses the inter-annual signal supplied by the models

33 Follow-on questions Can we infer anything about physical processes from the ensemble weights? How will this work in the ensemble forecast context? in gaining forecast accuracy, might we lose the physical advantages of models? other ways of applying BMA? e.g., not monthly timestep; with different bias-correction & transformation

34 RESEARCH -- RESEARCH -- RESEARCH
ongoing work RESEARCH -- RESEARCH -- RESEARCH assimilation of MODIS & other remote sensing climate forecast (CPC outlooks, climate model, index-based) downscaling shorter term forecasts (GFS-based) multiple-model exploration further development of SW Monitor generally, water / energy balance questions in face of climate change / variability HEPEX support

35 HEPEX western US/BC testbed
Test Bed Leaders: Frank Weber (BC Hydro, Burnaby, British Columbia, Canada) Andrew Wood (University of Washington, Seattle, USA) Tom Pagano (NRCS National Water and Climate Center, Portland, OR) Kevin Werner (NWS/WR) focus: hydrologic ensemble forecasting challenges that are particular to the orographically complex, snowmelt-driven basins of the Western US and British Columbia…prediction at monthly to seasonal lead times (i.e., 2 weeks t0 12 months). snow assimilation & model calibration basins: Mica (BC), Feather (CA), Klamath (OR/CA), Yakima (WA), Salmon (ID), Gunnison (CO), others?

36 END

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