Forecasting Streamflow and Reservoir Storage Summer of 2003 - PowerPoint PPT Presentation

About This Presentation
Title:

Forecasting Streamflow and Reservoir Storage Summer of 2003

Description:

Forecasting Streamflow and Reservoir Storage Summer of 2003 – PowerPoint PPT presentation

Number of Views:30
Avg rating:3.0/5.0
Slides: 41
Provided by: Nept3
Category:

less

Transcript and Presenter's Notes

Title: Forecasting Streamflow and Reservoir Storage Summer of 2003


1
Forecasting Streamflow and Reservoir Storage
Summer of 2003
  • Richard Palmer, Andre Ball, Ani Kameenui,
  • Kasey Kudamik, Michael Miller,
  • Nathan Van Rheenen, Matthew Wiley
  • CEE
  • University of Washington
  • October 2003

2
Talk Overview
  1. Background on Forecast Approach
  2. Evolving Summer Forecasts
  3. Accuracy of Forecast
  4. Conclusions

3
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

4
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

5
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

6
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 - )

7
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 - )

8
Study Domain
9
(No Transcript)
10
(No Transcript)
11
Value of Forecasting
  • Provide timely information for determining
  • Timing of Spring Refill
  • Instream Flow Requirements
  • Necessity of water-use alerts
  • Timing of Fall Drawdown

12
Models Used to Generate Forecasts
  • Water Demand Forecasts
  • Dynamic Systems Model

13
Water Demand ForecastingPuget Sound Region
  • Models
  • short (weekly-monthly) and long
    (annual-decadal)-term
  • Regions Seattle, Tacoma, and Everett
  • Tacoma and Everett Municipal demands
  • Seattle System-wide demands

14
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.

15
How well have we done?
  • Increase the accuracy of demand models for
    effective water resources planning and management.

16
Recent History
  • Provide information for monitoring and
    controlling demands during droughts, planning
    conservation programs, and supply and
    infrastructure changes.

17
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)

18
Short-term Model DesignSeattle Region
  • Data must be on a weekly time-step
  • Log-linear regression
  • Water Demand InterceptAxBx2Cx3Dx4Ex5Fx6
    Gx7
  • Ln(Water Demand) Intercept xLn(A)
    x2Ln(B) x3Ln(C) x4Ln(D) x5Ln(E)
    x6Ln(F) x7Ln(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)
19
Model CalibrationSeattle Region Summer
20
Model ValidationSeattle Region Summer
21
Model CalibrationTacoma Region Summer
22
Model ValidationTacoma Region Summer
23
Model ValidationEverett Region Summer
24
Demand ForecastSeattle Region April forecast
25
Forecast Skill and Error
  • Forecast skill metric (Hamlet)
  • Skill 1 - ?(forecast - observed)2/N /
    ?(historical - observed)2/M
  • Rewards precision, punishes spread
  • Valuable metric during outlier years
  • Summer 2003 was an climate outlier
  • Model is calibrated during less dramatic
    conditions
  • Validated during warming (hence the drop in
    correlation)

26
Long-Term Forecasting
  • 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).

27
Long-term Model DesignSeattle Region
28
Long-term Model DesignCurrent workSeattle Region
29
Overview of Meteorological Forecast Process
  • National Centers for Environmental Prediction

30
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

31
DHSVMDistributed Hydrology, Soil-Vegetation Model
  • Characterizes basin hydrology with
  • Elevation, aspect and slope data
  • Soil type and vegetation data
  • Stream network
  • Meteorological data
  • Energy balance for snow
  • Mass balance for precipitation and run-off

32
DHSVMDistributed Hydrology, Soil-Vegetation Model
33
Streamflow Forecast
  • 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

34
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

35
(No Transcript)
36
(No Transcript)
37
(No Transcript)
38
(No Transcript)
39
(No Transcript)
40
(No Transcript)
41
(No Transcript)
42
(No Transcript)
43
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
Write a Comment
User Comments (0)
About PowerShow.com