A partnership with the Colorado Basin River Forecast Center: An experiment in Technology Transfer

A partnership with the Colorado 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 Evolution of of the the partnership Initial Initial research on on streamflow forecasting Roles Roles and and responsibilities of of CSTPR and and CBRFC scientists Technology Transfer

Evolution of of the the partnership OUTLINE

Identify societally-relevant problem sensitive to climate variability

The endangered species problem c) Yampa River at Maybell 250 200 1917-49 1950-pre se nt 150 100 50 0 March April May June July August September c) Colorado River at Glenwood Springs 400 350 300 250 200 150 100 50 0 1900-49 1950-pre se nt Ma rch Ap ril Ma y J une J uly Aug us t S e p te mbe r John Pitlick augment the natural peak with releases from reservoirs to benefit endangered fish

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 Identify decision-makers and their key stakeholders Begin developing experimental methods for forecasting runoff 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 Identify decision-makers and their key stakeholders Assess how potentially predictable aspects of climate interact with critical problems Begin developing experimental methods for forecasting runoff Continue developing experimental methods and publish results 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 Identify decision-makers and their key stakeholders Assess how potentially predictable aspects of climate interact with critical problems Begin developing experimental methods for forecasting runoff Continue developing experimental methods and present results 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 Identify decision-makers and their key stakeholders Assess how potentially predictable aspects of climate interact with critical problems Begin developing experimental methods for forecasting runoff Continue developing experimental methods and present results 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 Evolution of of the the partnership Initial Initial research on on streamflow forecasting

PRECIPITATION BIASES Precipitation biases are in excess of 100% of the mean

TEMPERATURE BIASES Temperature biases are in excess of 3 o C

Downscale global-scale atmospheric forecasts to local scales in river basins (e.g., individual stations). Horizontal resolution ~ 200 km [scale mis-match] Area of interest ~50 km

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 selection Y = a 0 + a 1 X1 + a 2 X2 + a 3 X3... + a n Xn + 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.

The Schaake Shuffle method ( Observed Ensemble) (Downscaled Ensemble) Maximum Temperature Maximum Temperature 18 16 14 12 10 8 6 4 2 0 16 14 12 10 8 6 4 2 0 5 4 4 5 4 3 6 7109 98 56 1 2 3 4 5 6 7 8 9 10 11 12 13 14 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Forecast Lead Time 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 Ensemble 1 Ensemble 2 Ensemble 3 Ensemble 4 Ensemble 5 Ensemble 6 Ensemble 7 Ensemble 8 Ensemble 9 Ensemble 10 8th - 22nd Jan 1996

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 realtime forecasts of streamflow

EXPERIMENTAL PATHWAY NCEP NCEPprovides initial initial conditions for for experimental forecasts CDC CDCexperimental forecasts are are run run at at about about midnight data becomes available at at about about 6am 6am CSTPR run run the the downscaling code code at at 7am, 7am, and and transfer the the downscaled output output to to CBRFC CBRFC use use the the downscaled output output in in their their operational models

Cle Elum Snowmelt Dominated BASINS Compare ESP and SDS 9-day forecasts of runoff every 5 days East Fork of the Carson 526km 2 Animas Snowmelt Dominated Snowmelt Dominated Alapaha Rainfall Dominated 3626km 2 922km 2 1792km 2

Alapaha River Basin (Southern Georgia)

Animas River Basin (Southwest Colorado)

Cle Elum River Basin (Central Washington)

Carson River Basin (CA/NV Border)

OUTLINE Evolution of of the the partnership Initial Initial research on on streamflow forecasting Roles Roles and and responsibilities of of CSTPR and and CBRFC scientists

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 responsibilities of different institutions 0 th 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 1 st 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

July Weather Generator Stats

January Lag-1 and spatial stats

July Lag-1 and spatial stats

Conditioning on Nino 3.4 index La Nina El Nino Desert SW La Nina El Nino Pacific NW

OUTLINE Evolution of of the the partnership Initial Initial research on on streamflow forecasting Roles Roles and and responsibilities of of CSTPR and and CBRFC scientists Technology Transfer

Implementation in the Upper Colorado

Today s forecast at 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 researchoperational partnership

QUESTIONS?