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

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

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?

CURRENT WEBSITE

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

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

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

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

validation of selected historic streamflow simulations

MAP LINKS TO FLOW FORECASTS monthly hydrographs

Background: UW west-wide system SWESoil MoistureRunoffPrecipTemp Mar-05 Apr-05 May-05

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

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

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

LDAS models NOAH MOSAIC SAC VIC Dag Lohmann, HEPEX 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

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

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

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

Individual Model Results

Monthly Avg Flow Monthly RMSE

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 –No base flow –peak flow is 1-2 months early and far too small (high evaporation)

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

Computing Model Weights Bayesian Model Averaging (BMA) (Raftery et al, 2005) Ensemble mean forecast = Σw k f k where f k = result of k th model w k = weight of k th 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,

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

Multi-model ensemble results June September

Multi-model ensemble results June Flow, September Flow,

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

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

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

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

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?

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