Forecasting Streamflow and Reservoir Storage Summer of 2003

Richard Palmer, Andre Ball, Ani Kameenui,

Kasey Kudamik, Michael Miller,

Nathan Van Rheenen, Matthew WileyCEE

University of Washington

October 2003

Talk OverviewA. Background on Forecast Approach

B. Evolving Summer Forecasts

C. Accuracy of Forecast

D. Conclusions

Study Goals

Create six-month forecasts for municipal water supplies in the Puget Sound area using NCEP forecasts:– Water Supply– Water Demand– Storage in Reservoir– Decision Support System

Forecasting

– The herd instinct among forecasters makes sheep look like independent thinkers.

Edgar R. Fiedler

– If you have to forecast, forecast often.Edgar R. Fiedler

– An unsophisticated forecaster uses statistics as a drunken man uses lamp-posts - for support rather than for illumination.

Andrew Lang

Other Quotes

The future will be better tomorrow. Dan Quayle (1947 - )

Where a calculator on the ENIAC is equpped with 18,000 vacuum tubes and weighs 30 tons, computers in the future may have only 1,000 vaccuum tubes and perhaps weigh 1.5 tons. Popular Mechanics, March 1949

- More quotations on: [Computers]

Other Quotes

The best way to predict the future is to invent it. Alan Kay

The future belongs to those who prepare for it today.

Malcolm X (1925 - 1965)

– The future is here. It's just not widely distributed yet.

• William Gibson (1948 - )

Other Quotes

Enjoy present pleasures in such a way as not to injure future ones.

Seneca (5 BC - 65 AD)

The future ain't what it used to be. Yogi Berra (1925 - )

Study Domain

Auburn

Renton

N

Auburn

Renton

N

Cedar River

Green River

Tolt River

Sultan River

Models Used to Generate Forecasts• NCEP Meteorological Forecasts

• Distributed Hydrology, Soil-Vegetation Model

•Dynamic Systems Model

•Water Demand Forecasts

June Demand Forecast (6.24-12.24.03)

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240

6/24/2003 7/24/2003 8/24/2003 9/24/2003 10/24/2003 11/24/2003 12/24/2003

de

ma

nd

, mg

d

Maximum Minimum25th percentile 75th percentileAverage Forecast using ave Tmax ('83-'02)Actual

Water Demand ForecastingPuget Sound Region

• Modelsshort (weekly-monthly)

and long (annual-decadal)-term

• Regions: Seattle, Tacoma, and EverettTacoma and Everett:

Municipal demandsSeattle: System-wide

demands

Purpose

A common characteristic of water resources planning is its failure to anticipate change. -D. Sewell, 1978

• Increase the accuracy of demand models for effective water resources planning and management.

• Provide information for monitoring and controlling demands during droughts, planning conservation programs, and supply and infrastructure changes.

• Create a framework for long-term forecasting while considering urban planning.

How well have we done?Water demand forecasts: 1968 an 1980 Seattle Water Plans

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350

1966-67 1980 1990 2000projected years

mg

d o

r p

er c

ap g

d

1968 forecast gpd per capita

1968 forecast mgd ann. ave.

1980 forecast mgd ann. ave.

Actual gpd per cap

Actual mgd ann ave

Increase the accuracy of demand models for effective water resources planning and management.

Provide information for monitoring and controlling demands during droughts, planning conservation programs, and supply and infrastructure changes.

Recent HistorySPU Winter water use (1983-2003)

105

115

125

135

145

155

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003

mg

d

Data Resources

• WATER related– Sources: Seattle Public Utilities; Tacoma Water; City of Everett– Daily water demands– Rate History; Number of users

• CLIMATE– National Climate Data Center (NCDC): SeaTac daily Tmax

and precipitation– National Centers for Environmental Prediction (NCEP):

downscaled climate ensembles

• HOUSEHOLD– Puget Sound Regional Council (PSRC)– Urban simulation group (UrbanSim)

Short-term Model DesignSeattle Region

• Data must be on a weekly time-step• Log-linear regression: Water Demand = Intercept**∙Ax∙Bx2∙Cx3∙Dx4∙Ex5∙Fx6∙Gx7

Ln(Water Demand) = Intercept** + x∙Ln(A) + x2∙Ln(B) + x3∙Ln(C) + x4∙Ln(D) + x5∙Ln(E) + x6∙Ln(F) + x7∙Ln(G)

Dependent variable System (SPU)-wide weekly averages

Independent variables A. Temperature (average weekly max) (Tmax)

B. Precipitation (weekly average)

C. Winter water use

D.* System user population

E. Water rate/price

F. Temperature (max) (one-week lag)

G. System-wide weekly average (one-week lag)

Model CalibrationSeattle Region: Summer

Summer Calibration Model: 1989-1998 (R2 88%)

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175

225

275

1989 1989 1990 1991 1993 1993 1994 1995 1996 1996 1997 1998time

de

ma

nd

, mg

d

Actual

Predicted

Model ValidationSeattle Region: Summer

Summer validation: 1999-2003

R2 = 0.8483

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rec

as

ted

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ma

nd

, m

gd

Model CalibrationTacoma Region: Summer

Tacoma summer calibration: 1990-1999 (R2 83%)

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90

1990 1990 1991 1992 1993 1993 1994 1995 1996 1996 1997 1998 1999Time

Dem

and

, mg

d

ActualPredicted

Tacoma summer validation: 2000-2003

R2 = 0.8053

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80

30 40 50 60 70 80 90Actual demand, mgd

Fo

rec

as

ted

de

ma

nd

, m

gd

Model ValidationTacoma Region: Summer

Everett summer calibration: 1990-2003 (R2 84%)

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45

60

75

90

1990 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2002 2003

time

dem

and

, mg

d

ActualPredicted

Model ValidationEverett Region: Summer

Demand ForecastSeattle Region: April forecast

April Forecast: 4.29-10.22.03

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4/29/2003 5/29/2003 6/29/2003 7/29/2003 8/29/2003 9/29/2003

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ma

nd

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gd

MaxMin25th%75th%AverageActualForecast using ave Tmax ('83-'02)Forecast using real climate ('03)

Forecast Skill and ErrorAverage daily temperature maximum: past vs. present

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25

30

1/1 1/29 2/26 3/26 4/23 5/21 6/18 7/16 8/13 9/10 10/8 11/5 12/312/31week

Tm

ax,

C

2003 Tmax 1983-2002 Tmax ave

Summer 2003 was an climate outlier

•Model is calibrated during less dramatic conditions

•Validated during warming (hence the drop in correlation)

Comparison of Forecast Skill

-2.25

-1.75

-1.25

-0.75

-0.25

0.25

0.75

4/29 5/13 5/27 6/10 6/24 7/8 7/22 8/5 8/19 9/2 9/16 10/1week

skill

met

ric

AprilJuneAugust

Forecast skill metric (Hamlet):

•Skill = 1 - [∑(forecast - observed)2/N / ∑(historical - observed)2/M ]

•Rewards precision, punishes spread

•Valuable metric during outlier years

Create a framework for long-term forecasting while considering urban planning.

– Using PSRC and UrbanSim information from household survey or Parcel Index Number databases

– Highly disaggregated database for modeling household or class specific water demands.

– Incorporate household variables such as: size, income, house age, house value, yard size, etc.

– Investigate benefits and drawbacks of disaggregated model and consider water resources during urban planning and land development (UrbanSim component).

Long-Term Forecasting

Long-term Model DesignSeattle Region

Seasonal (3) Water Data:Aggregated to 12 years

Seasonal (3) Water Data:Disaggregated sample set

Seasonal (3) Water Data:Spatially disaggregated &

aggregated to 12 years each

Seasonal (3) Water Data:matching PSRC accounts

+

++++

Regional climate andhousehold data

Regional climate andhousehold data

Regional climate andhousehold data Regional climate and select

PSRC household data

Decent R^2, poor p-values Unreliable household data

due to aggregation Limited data points (12)

Poor stat performance Numerous data points;

variable water use withstagnant hh & climate data

Terrific R^2 values (75%+), mediocrep-values, questionable coefficients

Unreliable household data due toaggregation

Limited data points (12) for eachaccount

Must match water accountto PSRC wave surveyhousehold data via address

Climate remains regionalbased on 3-seasons andyears of survey data

Disaggregated householdsover 6-years with householdspecific water use, size, andincome

Approaches to Forecasting SPU Demand

Long-term Model DesignCurrent work

Seattle Region

SPUhousehold

billing (1990-2002)

database

UrbanSimPSRC PINdatabase

OUTSIDE:AREA,AGE &VALUE

PEOPLE

INSIDE:PEOPLE

&UNITS

Matched by PIN(bimonthly)

ClimateData:

Tmax andPrecip

Seasonal regressions with random sampleof accounts (5-10,000); R2 values of 50+%

Overview of Meteorological Forecast Process

• National Centers for Environmental Prediction

NCEP Forecast

A set of 20 equally likely ensembles of paired precipitation and temperatures generated by GSM with slight variations in initial conditions

Downloaded from NCEP ftp site

Forecasts bias-corrected and downscaled

DHSVMDistributed Hydrology, Soil-Vegetation Model

• System is initiated with one year of previous conditions – Twenty assembles of paired precipitation and

temperatures are run.– Initial conditions are extremely important

(same future conditions are different with different initiations)

– Typically model underestimate summer flows

Streamflow Forecast

Systems Simulation Model

• Model calculates movement of water throughout system

• Integrates water supply, demands, fish flows and other operational considerations

• Lacks subtleties of actual operation

April Forecast Streamflows on Cedar River at Chester Morse

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May-03 Jun-03 Jul-03 Aug-03 Sep-03 Oct-03

Av

era

ge

Mo

nth

ly F

low

(c

fs)

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Median Ensemble Forecast Ave. Actual Hist. Ave

April 2003 Forecast: Total Seattle Reservoir Storage

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4/29

/03

5/13

/03

5/27

/03

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/03

6/24

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7/08

/03

7/22

/03

8/05

/03

8/19

/03

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/03

9/16

/03

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1/03

10/1

5/03

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ns

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median Ensembles Forecast Average Observed Storage

Low Reservoir Conditions

April 2003 Forecast: Resolving Discrepancies from Observed Storage

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4/29

/03

5/13

/03

5/27

/03

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6/24

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7/08

/03

7/22

/03

8/05

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8/19

/03

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9/16

/03

10/0

1/03

10/1

5/03

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ObservedStorage

frcst inputs/frcst demands

act inputs/frcst demands

act inputs/ actdemands

FISH actinputs/ actdemands

.75 morainereturn

Low Reservoir Conditions

Adjusted April 2003 Forecast: Total Seattle Reservoir Storage

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4/29

/03

5/13

/03

5/27

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/03

6/24

/03

7/08

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7/22

/03

8/05

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8/19

/03

9/02

/03

9/16

/03

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1/03

10/1

5/03

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median Ensembles Forecast Average Observed Storage

Low Reservoir Conditions

April Retrospective Forecast Three Month Total Flow Observed vs. Forecast

y = 1.0773x - 38.822

R2 = 0.8468

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Forecasted Flow (cfs)

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se

rve

d F

low

(c

fs)

April Retrospective Forecast Six Month Total Flow Observed vs. Forecast

y = 1.1435x - 111.51

R2 = 0.6654

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Forecasted Flow (cfs)

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se

rve

d F

low

(c

fs)

Conclusions

NCEP ensemble forecasts, combined with hydrologic model, produced good summer forecasts for 2003.

Typically, NCEP ensemble forecasts, combined with hydrologic model, provides does useful information (exceptions noted).

Forecasts ranked by ENSO provides some insight into forecast quality