Using CPC long lead climate outlooks for ensemble streamflow forecasting

Andy Wood and Dennis P. Lettenmaier

University of WashingtonDept. of Civil and Environmental Engineering

Session A24A2006 Joint Meeting of the AGU

Baltimore, MDMay 23, 2006

Climate forecast importance: temporal variability

Western US Water Cycle

Climate Forecasts

Imp

ort

ance Monthly Timestep

In Western US:

Jan – April forecasts of summer streamflow are critical for decision-making related to:

• agriculture• environmental flows• hydropower• navigation• water supply

Most basins east of the Sierras and Cascade Mtnsare heavilyinfluenced by spring precipitation.

Water supply forecaststhere have unavoidably high uncertainty because spring precipitationis relatively unknown.

Wet spring

Dry spring15%

65%

Apr-JunOct-Jun

Courtesy of Tom Pagano, NRCS

PCP

Climate forecast importance: spatial variability

Wet spring

Dry spring15%

65%Precip

Apr-JunOct-Jun

Example: Climate forecasts relatively unimportant by late Winter

Forecast Skill

Low High

Summer flow forecast skillAreas with dry spring ….

Climate forecast importance: spatial variability

Courtesy of Tom Pagano, NRCS

Wet spring

Dry spring15%

65%Precip

Apr-JunOct-Jun

Example: Climate Forecasts very important through Spring

Forecast Skill

Low High

Summer flow forecast skillAreas with wet spring ….

Climate forecast importance: spatial variability

Courtesy of Tom Pagano, NRCS

BackgroundCurrent Practice for Western US Streamflow Forecasting

combine: (1) estimate of current hydrologic state(2) forecast of historical climate…usually*

produce: streamflow forecast with uncertainty information

UPPER HUMBOLDT RIVER BASIN

Streamflow Forecasts - May 1, 2003

  <==== Drier === Future Conditions === Wetter ====>  

Forecast Pt ============ Chance of Exceeding * ===========  

   Forecast 90% 70% 50% (Most Prob) 30% 10% 30 Yr Avg

   Period (1000AF) (1000AF) (1000AF) (% AVG.) (1000AF) (1000AF) (1000AF)

MARY'S R nr Deeth, Nv

APR-JUL 12.3       18.7       23       59       27       34       39      

MAY-JUL 4.5       11.3       16.0       55       21       28       29      

Research ObjectiveCurrent Practice for Western US Streamflow Forecasting

combine: (1) estimate of current hydrologic state(2) forecast of historical climate CPC Outlook

produce: streamflow forecast with uncertainty information

ICsSpin-up Forecast

obs

recently observedmeteorological data

ensemble of met. datato generate forecast

ESP-type forecastmethod

hydrologicstate

We use a hydrologic model-based approach similar to the NWS River Forecast Center’s Ensemble Streamflow Prediction (ESP)

NWS Climate Prediction Center (CPC) Seasonal Outlooks

e.g., precipitation

CPC Seasonal Outlook UseChallenge: Seasonal (3-month) probabilities must be

converted to daily meteorological values at the scale of the hydrology model

-5

5

15

25

35

Mon1

Mon2

Mon3

Mon4

Mon5

Mon6

deg

C

CPC Seasonal Outlook Use spatial unit for raw forecasts is the Climate Division (102 for U.S.)

CDFs defined by 13 percentile values (0.025 - 0.975) for P and T, and μ and σ

Hydrologic Prediction using CPC Seasonal Outlooks

CPC climate outlooksvariables: mean temperature (Tavg) total precipitation (Ptot)scales: 102 climate division (CD) / US overlapping 3-month timestepinformation: forecast (μ, σ) at each timestep normal (μ, σ) at each timestep

disaggregate spatiallyclimate division unit

--- becomes ---1/8 degree (~12-13 km)

disaggregate to a daily timestep1/8 degree monthly Tavg and Ptot

--- becomes ---1/8 degree daily Ptot, Tmin and Tmax

Use CPC forecasts as inputs to a hydrologic model to produce

streamflow forecast ensembles

disaggregate temporallyoverlapping 3-month timestep

--- becomes ---non-overlapping 1-month timestep

link Tavg & Ptot ensemblesAssociate monthly variables

spatially & temporally

create Tavg & Ptot ensemble forecasts (μ, σ) at each timestep/CDgenerate seasonal ensemble data

CDscale

Several methods of doing this work well but not perfectly.

0

10

20

30

40

50

19

75

19

76

19

77

19

78

19

79

year

inc

he

s

observed PCP1-month (disag) PCP3-month PCP

disaggregate temporallyoverlapping 3-month timestep

--- becomes ---non-overlapping 1-month timestep

• Schneider et al., Weather & Forecasting (2005) – applied monthly/seasonal mean correction factors – approach being adopted by CPC

• We are trying multiple linear regression: monthly values = f(seasonal values)

Sample Results

ML regression approach appears to yield better variance, but is not markedly superior

CPC approach

std dev-10

-5

0

5

10

15

19

75

19

76

19

77

19

78

19

79

year

inc

he

s

observed PCP anomaly

predicted PCP anomaly

disaggregate temporallyoverlapping 3-month timestep

--- becomes ---non-overlapping 1-month timestep

ML regression approach

Schneider et al. (2005)

disaggregate temporallyoverlapping 3-month timestep

--- becomes ---non-overlapping 1-month timestep

Sample Results

ML Regression based disaggregation

y = 0.6335x + 0.1099-15

-10

-5

0

5

10

15

-15 -10 -5 0 5 10 15

observed pcp anomalies

pre

dic

ted

pre

cip

a

no

ma

lies

R = 0.80

CPC approachML regression approach

Schneider et al. (2005)

0

510

1520

25

3035

40

0 1 2 3 4 5month

pre

cip

(m

m)

Challenge:Given monthly distributions for a climate variable, how do you associate the values in time to yield a single sequence of one variable? Of two variables?

05

1015202530354045

0 1 2 3 4 5month

Tem

p (

C)

link Tavg & Ptot ensemblesAssociate monthly variables

spatially & temporally

0

510

1520

25

3035

40

0 1 2 3 4 5month

pre

cip

(m

m)

Challenge:Given monthly distributions in adjacent cells, how might sequences in one climate division be associated with those in another?

05

1015202530354045

0 1 2 3 4 5month

Tem

p (

C)

link Tavg & Ptot ensemblesAssociate monthly variables

spatially & temporally

Schaake ShuffleClark et al., J. of Hydromet (2004)

link Tavg & Ptot ensemblesAssociate monthly variables

spatially & temporally

Spatial and Temporal Downscaling

disaggregate spatiallyclimate division unit

--- becomes ---1/8 degree (~12-13 km)

disaggregate to a daily timestep1/8 degree monthly Tavg and Ptot

--- becomes ---1/8 degree daily Ptot, Tmin and Tmax

• Spatial sampling of anomalies within climate divisions

• Re-sampling of daily patterns

• Scaling/shifting to reproduce CPC forecast anomalies

Average Flow, Columbia R. at The Dalles, OR

0

100000

200000

300000

400000

500000

600000

700000

jan feb mar apr may jun jul aug sep oct nov dec

cfs

coop avg

coop stdev

raw cpc avg

raw cpc stdev‘OBS’

downscaled

University of Washington Forecast System Website

project led by Dennis Lettenmaier

funded byNOAA, NASA

Streamflow Forecast Results: Westwide at a Glance

Flow location maps give access to monthly hydrograph plots, and also to raw forecast data.

Streamflow Forecast Details

Clicking the stream flow forecast map also accesses current basin-averaged conditions

Streamflow Forecast Results: SpatialSWE Soil MoistureRunoffPrecip Temp

Apr-06

May-06

Jun-06

½ degree VIC implementation

Free running since last June

Uses data feed from NOAA ACIS server

“Browsable” Archive, 1915-present

UW Real-time Daily NowcastSM, SWE

(RO)

We are currently migrating the CPC forecast approach to a national US implementation

For more information:

http://www.hydro.washington.edu / forecast / westwide /

Conclusions

Our current approach for downscaling CPC seasonal outlooks is adequate from hydrologic perspective.

Simple temporal disaggregation approaches are sufficent, although it’s possible that slightly higher performance can be achieved via more elaborate disaggregation methods

Ensemble formation step bears further analysis at the monthly to seasonal time scale.

Translation of CPC outlooks to ensembles for hydrologic forecasting should not be an obstacle for their use.

Thank You