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A partnership with theColorado Basin River Forecast Center:An experiment in Technology Transfer
Martyn P. Clark and Subhrendu Gangopadhyay
Center for Science and Technology Policy Research
David Brandon, Kevin Werner, and Steve Shumate
Colorado Basin River Forecast Center
Collaborators:
Lauren Hay
Andrea Ray
Jeff Whittaker
Tom Hamill
Balaji Rajagopalan
John Schaake
OUTLINE
Initial research on streamflow forecastingInitial research on streamflow forecasting
Roles and responsibilities of CSTPR and CBRFC scientistsRoles and responsibilities of CSTPR and CBRFC scientists
Evolution of the partnershipEvolution of the partnership
Technology TransferTechnology Transfer
OUTLINE
Evolution of the partnershipEvolution of the partnership
Identify societally-relevant
problem sensitive to
climate variability
The endangered species problem…
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Ma rch April Ma y J une J uly Augus t Se p tembe r
1917-49
1950-pre s e nt
c) Yampa River at Maybell
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Ma rch April Ma y J une J uly Augus t S e p tembe r
1900-49
1950-pre s e nt
c ) Colorado River at Glenwood Springs
augment the natural peak with releases from
reservoirs to benefit endangered fishJohn Pitlick
Identify societally-relevant
problem sensitive to
climate variability
Identify decision-makers
and their key stakeholders
Assess how potentially
predictable aspects of climate
interact with critical problems
Andrea Ray
Identify societally-relevant
problem sensitive to
climate variability
Identify decision-makers
and their key stakeholders
Assess how potentially
predictable aspects of climate
interact with critical problems
Prospecting for research
that meets user needs
Andrea Ray
Identify societally-relevant
problem sensitive to
climate variability
Begin developing
experimental methods
for forecasting runoff
Identify decision-makers
and their key stakeholders
Continue developing
experimental methods
and publish results
Assess how potentially
predictable aspects of climate
interact with critical problems
Identify societally-relevant
problem sensitive to
climate variability
Begin developing
experimental methods
for forecasting runoff
Identify decision-makers
and their key stakeholders
Continue developing
experimental methods
and publish results
Assess how potentially
predictable aspects of climate
interact with critical problems
Link with federal R&D labs
to improve potential transfer
to operational products
Pilot implementation of experimental streamflow
forecasting methodology in the Upper Colorado
River basin in spring 2003
Identify societally-relevant
problem sensitive to
climate variability
Begin developing
experimental methods
for forecasting runoff
Identify decision-makers
and their key stakeholders
Continue developing
experimental methods
and present results
Assess how potentially
predictable aspects of climate
interact with critical problems
Link with federal R&D labs
to improve potential transfer
to operational products
Pilot implementation of experimental streamflow
forecasting methodology in the Upper Colorado
River basin in spring 2003
Document and assess how knowledge is used
is used in reservoir operators’ decision process
as well as assess improvement of forecast
Identify societally-relevant
problem sensitive to
climate variability
Begin developing
experimental methods
for forecasting runoff
Identify decision-makers
and their key stakeholders
Continue developing
experimental methods
and present results
Assess how potentially
predictable aspects of climate
interact with critical problems
Link with federal R&D labs
to improve potential transfer
to operational products
Pilot implementation of experimental streamflow
forecasting methodology in the Upper Colorado
River basin in spring 2003
Document and assess how knowledge is used
is used in reservoir operators’ decision process
as well as assess improvement of forecast
OUTLINE
Initial research on streamflow forecastingInitial research on streamflow forecasting
Evolution of the partnershipEvolution of the partnership
PRECIPITATION BIASES
Precipitation biases are in excess
of 100% of the mean
TEMPERATURE BIASES
Temperature biases are in excess
of 3oC
Downscale global-scale atmospheric forecasts tolocal scales in river basins (e.g., individual stations).
Horizontal resolution
~ 200 km
Area of interest
~50 km
[scale mis-match]
Downscaling approach
For hydrologic applications we need to:– Obtain reliable local-scale forecasts of precipitation and
temperature– Preserve the spatial variability and temporal persistence in
the predicted temperature and precipitation fields– Preserve consistency between variables
Multiple linear Regression with forward selectionY = a0 + a1X1 + a2X2 + a3X3 . . . + anXn + e
A separate equation is developed for each station, each forecast lead time, and each month.
Use cross-validation procedures for variable selection –typically less than 8 variables are selected for a given equation
Stochastic modeling of the residuals in the regression equation to provide ensemble time series
Shuffling of the ensemble output to preserve the observed spatial variability, temporal persistence, and consistency between variables.
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0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Forecast Lead Time
Ma
xim
um
Te
mp
era
ture
Ensemble 1
Ensemble 2
Ensemble 3
Ensemble 4
Ensemble 5
Ensemble 6
Ensemble 7
Ensemble 8
Ensemble 9
Ensemble 10
8th - 22nd Jan 1996
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1 2 3 4 5 6 7 8 9 10 11 12 13 14
Ma
xim
um
Te
mp
era
ture
8th - 22nd Jan 1996
17th - 31 Jan 1982
13th - 27th Jan 2000
22nd Jan - 5 Feb 1998
12th - 26th Jan 1968
9th - 23rd Jan 1976
10th - 24th Jan 1998
19th Jan - 2nd Feb 1980
16th - 30th Jan 1973
9th - 23rd Jan 1999
(“O
bs
erv
ed
” E
ns
em
ble
)(D
ow
ns
ca
led
En
se
mb
le)
The “Schaake Shuffle” method
5 4 54 4 3 6 7 10 9 9 8 5 6
The CDC reforecast experiment
Jeff Whittaker and Tom Hamill at the NOAA-CIRES Climate Diagnostics Center have used the 1998 NCEP MRF to generate medium-range forecasts for the period 1979 to the present
CDC are continuing to run the 1998 NCEP MRF in real time.
We use the period of the NWP hindcast (1979-2001) to develop regression models between MRF output and precipitation and temperature at individual stations, and apply the regression coefficients to the CDC experimental forecasts in real-time
The resultant local-scale precipitation and temperature forecasts are used as input to the CBRFC hydrologic modeling system to provide real-time forecasts of streamflow
EXPERIMENTAL PATHWAY
CDC experimental forecasts are run at about midnight—data
becomes available at about 6am
CDC experimental forecasts are run at about midnight—data
becomes available at about 6am
CSTPR run the downscaling code at 7am, and transfer the
downscaled output to CBRFC
CSTPR run the downscaling code at 7am, and transfer the
downscaled output to CBRFC
NCEP provides initial conditions for experimental forecastsNCEP provides initial conditions for experimental forecasts
CBRFC use the downscaled output in their operational modelsCBRFC use the downscaled output in their operational models
East Fork
of
the Carson
Cle Elum
Animas
Alapaha
Snowmelt
Dominated
Snowmelt
Dominated
Snowmelt
Dominated
Rainfall
Dominated
BASINS
1792km2
526km2
922km2
3626km2
Compare ESP and SDS
9-day forecasts of
runoff every 5 days
Alapaha River Basin (Southern Georgia)
Animas River Basin (Southwest Colorado)
Cle Elum River Basin (Central Washington)
Carson River Basin (CA/NV Border)
OUTLINE
Initial research on streamflow forecastingInitial research on streamflow forecasting
Roles and responsibilities of CSTPR and CBRFC scientistsRoles and responsibilities of CSTPR and CBRFC scientists
Evolution of the partnershipEvolution of the partnership
Photo: Brad Udall
Meetings
Initial planning meeting October 2002 at CDC
Follow-up meeting with John Schaake at the NWS-OHD (the beginnings of the Schaake Shuffle!)
David Brandon and Kevin Werner visited CSTPR and CDC in February 2003 for a “whiteboard session”
Andrea, Martyn, and Subhrendu gave a briefing to Colorado basin reservoir operators in March 2003–CBRFC scientists were also present
Martyn and Subhrendu visited CBRFC in May 2003 to learn about their operational systems and to discuss research progress
Regular e-mail and telephone conversations
Roles and responsibilitiesof different institutions
0th level—week+2 streamflow forecasts– CDC run the experimental medium-range forecast model in
real-time
– CSTPR scientists use output from the CDC MRF, and provide CBRFC with real-time forecasts of precipitation and temperature, tailored to their basins
– CBRFC use these experimental forecasts in their operational systems
What actually happens– CSTPR and CBRFC scientists share code, and work
collaboratively on developing improved streamflow forecasts
– New projects are constantly identified and developed
Defining projects of mutual interest
0-14 day forecasts of streamflow– Based on shuffled downscaling
– Forecasts provided to CBRFC each day since Jan 1st 2003
– Forecasts implemented in CBRFC operational systems—new forecasts now part of the CBRFC operational suite of products
Seasonal forecasts of streamflow– Weather generator conditioned on climate
indices and probabilistic climate forecasts
– Research currently in progress—will (hopefully) be implemented by CBRFC in the next few months.
Weather Generator Results
Weather Generator Stats
January
Weather Generator Stats
July
Lag-1 and spatial stats
January
Lag-1 and spatial stats
July
Conditioning on Nino 3.4 index
Pacific NW
Desert SW
La Nina
La Nina
El Nino
El Nino
OUTLINE
Initial research on streamflow forecastingInitial research on streamflow forecasting
Roles and responsibilities of CSTPR and CBRFC scientistsRoles and responsibilities of CSTPR and CBRFC scientists
Evolution of the partnershipEvolution of the partnership
Technology TransferTechnology Transfer
Implementation in the Upper Colorado
Today’s forecastat Cameo
Why is technology transfer effective?
We have fun down at the pub!
Dave Brandon (HIC) has given one of his employees (Kevin Werner) responsibility to take the CDC-CSTPR experimental forecasts and implement them in the CBRFC operational systems
We work within the existing operational framework. We are not inventing a completely new approach to forecasting streamflow—we break off small parts of the problem and work collaboratively on improving those components
CBRFC operational hydrologists have a great deal of professional pride (and are very capable people), who are very interested in developing the best possible forecasting system
All parties get “brownie points” for a successful research-operational partnership
QUESTIONS?
