An experimental real-time seasonal hydrologic forecast system for the western U.S. Andrew W. Wood...

An experimental real-time seasonal An experimental real-time seasonal hydrologic forecast system for the hydrologic forecast system for the

western U.S. western U.S.

Andrew W. Wood and Andrew W. Wood and Dennis P. LettenmaierDennis P. Lettenmaier

Department of Civil and Environmental Department of Civil and Environmental EngineeringEngineering

University of WashingtonUniversity of Washington

Climate Diagnostics and Prediction WorkshopClimate Diagnostics and Prediction WorkshopPennsylvania State UniversityPennsylvania State University

October 27, 2005October 27, 2005

OutlineOutline

Background – UW West-wide hydrologic Background – UW West-wide hydrologic forecasting systemforecasting system

Preliminary multi-model ensemble workPreliminary multi-model ensemble work

Final CommentsFinal Comments

Background: UW west-wide systemBackground: UW west-wide systemwhere did it come from?where did it come from?1997 COE Ohio R. basin/NCEP ->1997 COE Ohio R. basin/NCEP ->-> UW East Coast 2000 (NCEP/ENSO) ->-> UW East Coast 2000 (NCEP/ENSO) ->-> UW PNW 2001 -> UW west-wide 2003-> UW PNW 2001 -> UW west-wide 2003

what are its objectives?what are its objectives?– evaluate climate forecasts in hydrologic applicationsevaluate climate forecasts in hydrologic applications

seasonal: CPC, climate model, index-based (e.g., SOI, PDO)seasonal: CPC, climate model, index-based (e.g., SOI, PDO)16-day: NCEP EMC Global Forecast System (GFS)16-day: NCEP EMC Global Forecast System (GFS)

– evaluate assimilation strategiesevaluate assimilation strategiesMODIS snow covered area; AMSR-E SWE MODIS snow covered area; AMSR-E SWE SNOTEL/ASP SWESNOTEL/ASP SWE

– evaluate basic questions about predictabilityevaluate basic questions about predictability– evaluate hydrologic modeling questionsevaluate hydrologic modeling questions

role of calibration, attribution of errors, role of calibration, attribution of errors, multiple-model usemultiple-model use– evaluate downscaling approachesevaluate downscaling approaches

what are its components?what are its components?

CURRENT CURRENT WEBSITEWEBSITE

Surface WaterSurface WaterMonitorMonitordaily updates

1-2 day lag

soil moisture& SWEpercentiles

½ degreeresolution

archive from1915-current

uses ~2130index stns

Background: UW west-wide systemBackground: UW west-wide system

NCDC met. station obs.

up to 2-4 months from

current

local scale (1/8 degree) weather inputs

soil moisturesnowpack

Hydrologic model spin up

SNOTEL

Update

streamflow, soil moisture, snow water equivalent, runoff

Now1-2 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 CFS ensemble (20) NSIPP ensemble (9)

* experimental, not yet in real-time product


Soil MoistureInitial

Condition

SnowpackInitial Condition

ESP

ENSO/PDO

ENSO

CPC Official Outlooks

Coupled Forecast

System CFS

CAS

OCN

SMLR

CCA

CA

NSIPP/GMAO dynamical

model

VIC Hydrology Model

NOAA

NASA

UW

Seasonal Climate Forecast Data Sources


Background: Background: UW west-wide UW west-wide systemsystem

validation of selected historic streamflowsimulations

MAP LINKS MAP LINKS TO FLOW TO FLOW FORECASTSFORECASTS

monthly hydrographs

Background: UW west-wide systemBackground: UW west-wide systemSWE Soil MoistureRunoffPrecip Temp

Mar-05

Apr-05

May-05

Background: UW west-wide systemBackground: UW west-wide systemwhat drives UW system activities?what drives UW system activities?

research goals: research goals: – exploration of CPC & NCEP productsexploration of CPC & NCEP products– data assimilation of NASA products data assimilation of NASA products

Klamath Basin, Sacramento River (particularly Feather)Klamath Basin, Sacramento River (particularly Feather)

collaborations:collaborations:– requests by WA State drought personnelrequests by WA State drought personnel

Yakima-basin forecasts, Puget SoundYakima-basin forecasts, Puget SoundSW Monitor type hydrologic assessmentSW Monitor type hydrologic assessment

– interests of Pagano, Pasteris & Co (NWCC): interests of Pagano, Pasteris & Co (NWCC): calibrated forecast points in Upper Colorado, upper Missouri R. basin, Snake R. calibrated forecast points in Upper Colorado, upper Missouri R. basin, Snake R. basinbasinspatial soil moisture, snow and runoff dataspatial soil moisture, snow and runoff dataone-off analysesone-off analyses

– other, e.g., U. AZ project with USBR in lower Colorado basinother, e.g., U. AZ project with USBR in lower Colorado basin


research objectives include:research objectives include:

climate forecastsclimate forecasts

data assimilationdata assimilation

hydrologic predictabilityhydrologic predictability

multi-model / calibration questionsmulti-model / calibration questions

ESP

ENSO/PDO

ENSO


Coupled Forecast

System CFS

CAS

OCN

SMLR

CCA

CA

NSIPP/GMAO dynamical

model

VIC Hydrology Model

NOAA

NASA

UW

Seasonal Climate Forecast Data Sources

Expansion to multiple-model framework

LDAS modelsLDAS models

NOAHNOAH

MOSAICMOSAICSACSAC

VICVIC

Dag Lohmann, HEPEXDag 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


Coupled Forecast

System (CFS)

CAS

OCN

SMLR

CCA

CA

NSIPP-1 dynamical

model

VIC Hydrology Model

NOAA

NASA

UW

Multiple Hydrologic Models

NWSSAC

NOAH LSM

weightings calibrated via retrospective analysis

Schaake ShuffleSchaake Shuffle(Clark et al)(Clark et al)

Wood et al., 2002Wood et al., 2002

NWS: Day et al; NWS: Day et al; Twedt et alTwedt et al

Hamlet et al., Hamlet et al., Werner et al.Werner et al.

Multiple-model Framework

Models:Models:VIC - Variable Infiltration Capacity (UW)VIC - Variable Infiltration Capacity (UW)SAC - Sacramento/SNOW17 model (National Weather Service)SAC - Sacramento/SNOW17 model (National Weather Service)NOAH – NCEP, OSU, Army, and NWS Hydrology LabNOAH – NCEP, OSU, Army, and NWS Hydrology Lab

ModelModel Energy BalanceEnergy Balance Snow BandsSnow BandsVICVIC YesYes YesYesSACSAC NoNo YesYesNOAHNOAH YesYes NoNo

Calibration parameters from NLDAS 1/8 degree grid (Mitchell et al Calibration parameters from NLDAS 1/8 degree grid (Mitchell et al 2004) – no further calibration performed2004) – no further calibration performedMeteorological Inputs: 1/8 degree COOP-based, 1915-2004Meteorological Inputs: 1/8 degree COOP-based, 1915-2004

Test Case Test Case

- - Salmon River Salmon River basin basin (upstream of (upstream of Whitebird, ID)Whitebird, ID)

- retrospective - retrospective (deterministic (deterministic evaluation):evaluation): 25 year training25 year training 20 year validation 20 year validation

Individual Model ResultsIndividual Model Results

Individual Model ResultsIndividual Model Results

Monthly Avg FlowMonthly Avg Flow Monthly RMSEMonthly RMSE

Individual Model ResultsIndividual Model ResultsVIC appears to be best “overall”VIC appears to be best “overall”– Captures base flow, timing of peak flowCaptures base flow, timing of peak flow– Lowest RMSE except for JuneLowest RMSE except for June– Magnitude of peak flow a little lowMagnitude of peak flow a little low

SAC is second “overall”SAC is second “overall”– No base flowNo base flow– peak flow is early but magnitude is close to observed*peak flow is early but magnitude is close to observed*

NOAH is lastNOAH is last– No base flowNo base flow– peak flow is 1-2 months early and far too small (high peak flow is 1-2 months early and far too small (high

evaporation)evaporation)

Combining models to reduce errorCombining models to reduce error

– Average the results of multiple modelsAverage the results of multiple models– Ensemble mean should be more stable than a Ensemble mean should be more stable than a

single modelsingle model– Combines the strengths of each modelCombines the strengths of each model– Provides estimates of forecast uncertaintyProvides estimates of forecast uncertainty

Computing Model WeightsComputing Model Weights

Bayesian Model Averaging (BMA) (Raftery Bayesian Model Averaging (BMA) (Raftery et al, 2005)et al, 2005)

Ensemble mean forecast = Ensemble mean forecast = ΣΣwwkkffkk

where where ffkk = result of k = result of kthth model model

wwkk = weight of k = weight of kthth model, related to model’s model, related to model’s

correlation with observations during trainingcorrelation with observations during training

Raftery, A.E., F. Balabdaoui, T. Gneiting, and M. Polakowski, 2005. Using Raftery, A.E., F. Balabdaoui, T. Gneiting, and M. Polakowski, 2005. Using Bayesian Model Averaging to Calibrate Forecast Ensembles. Bayesian Model Averaging to Calibrate Forecast Ensembles. Monthly Monthly Weather ReviewWeather Review, 133, 1155-1174. , 133, 1155-1174.

Computing Model WeightsComputing Model Weights

We transform flows to Gaussian domain and bias-correct them We transform flows to Gaussian domain and bias-correct them before computing weights using the BMA softwarebefore computing weights using the BMA software

Western U.S. – many streams have 3-parameter log-normal (LN-Western U.S. – many streams have 3-parameter log-normal (LN-3) distributions for monthly average flow3) distributions for monthly average flow

Each month, for each model, is given distinct distribution, Each month, for each model, is given distinct distribution, transformation, bias-correctiontransformation, bias-correction

Procedure Procedure

– monthly LN-3 transformationmonthly LN-3 transformation

– monthly bias correction based on regressionmonthly bias correction based on regression

– BMA process to calculate monthly weights, statisticsBMA process to calculate monthly weights, statistics

– weights used to recombine modelsweights used to recombine models

– transform outputs back to flow unitstransform outputs back to flow units

Multi-model ensemble resultsMulti-model ensemble results

June

September


June Flow, 1975-1995June Flow, 1975-1995

September Flow, 1975-1995September Flow, 1975-1995


June LN-3 & Bias-Corrected Flow, 1975-1995June LN-3 & Bias-Corrected Flow, 1975-1995

Sept LN-3 & Bias-Corrected Flow, 1975-1995Sept LN-3 & Bias-Corrected Flow, 1975-1995

Multi-model ensemble resultsMulti-model ensemble resultsdespite large biases, SAC had a stronger despite large biases, SAC had a stronger interannual correlation with observations interannual correlation with observations than VICthan VIC

post-processing fixes many of the biasespost-processing fixes many of the biases

BMA procedure only really uses the inter-BMA procedure only really uses the inter-annual signal supplied by the modelsannual signal supplied by the models

Follow-on questionsFollow-on questions

Can we infer anything about physical processes Can we infer anything about physical processes from the ensemble weights?from the ensemble weights?

How will this work in the ensemble forecast How will this work in the ensemble forecast context?context?

in gaining forecast accuracy, might we lose the in gaining forecast accuracy, might we lose the physical advantages of models?physical advantages of models?

other ways of applying BMA? e.g., not monthly other ways of applying BMA? e.g., not monthly timestep; with different bias-correction & timestep; with different bias-correction & transformationtransformation

ongoing workongoing workRESEARCH -- RESEARCH -- RESEARCHRESEARCH -- RESEARCH -- RESEARCH

assimilation of MODIS & other remote sensingassimilation of MODIS & other remote sensing

climate forecast (CPC outlooks, climate model, index-based)climate forecast (CPC outlooks, climate model, index-based)– downscalingdownscaling

shorter term forecasts (GFS-based) shorter term forecasts (GFS-based)

multiple-model explorationmultiple-model exploration

further development of SW Monitorfurther development of SW Monitor

generally, water / energy balance questions in face of climate generally, water / energy balance questions in face of climate change / variabilitychange / variability

HEPEX supportHEPEX support

HEPEX western US/BC testbedHEPEX western US/BC testbedTest Bed Leaders:Test Bed Leaders:

Frank Weber (BC Hydro, Burnaby, British Columbia, Canada)Frank Weber (BC Hydro, Burnaby, British Columbia, Canada)

Andrew Wood (University of Washington, Seattle, USA)Andrew Wood (University of Washington, Seattle, USA)

Tom Pagano (NRCS National Water and Climate Center, Portland, OR)Tom Pagano (NRCS National Water and Climate Center, Portland, OR)

Kevin Werner (NWS/WR)Kevin Werner (NWS/WR)

focus:focus:

hydrologic ensemble forecasting challenges that are particular to the hydrologic ensemble forecasting challenges that are particular to the orographically complex, snowmelt-driven basins of the Western US and orographically complex, snowmelt-driven basins of the Western US and British Columbia…prediction at monthly to seasonal lead times (i.e., 2 weeks British Columbia…prediction at monthly to seasonal lead times (i.e., 2 weeks t0 12 months). t0 12 months).

snow assimilation & model calibrationsnow assimilation & model calibration

basins:basins:

Mica (BC), Feather (CA), Klamath (OR/CA), Yakima (WA), Salmon (ID), Mica (BC), Feather (CA), Klamath (OR/CA), Yakima (WA), Salmon (ID), Gunnison (CO), others?Gunnison (CO), others?

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An experimental real-time seasonal hydrologic forecast system for the western U.S. Andrew W. Wood...

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