medium-range hydrometeorological forecasts of the big wood basin in 2006 (plus a look forward at...

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Medium-range Hydrometeorological Medium-range Hydrometeorological Forecasts Forecasts of the Big Wood Basin in 2006 of the Big Wood Basin in 2006 (plus a look forward at 2007…) (plus a look forward at 2007…) A Project for the Pacific Northwest Regional Collaboratory

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Medium-range Hydrometeorological Forecasts Medium-range Hydrometeorological Forecasts of the Big Wood Basin in 2006of the Big Wood Basin in 2006

(plus a look forward at 2007…)(plus a look forward at 2007…)

A Project for the Pacific Northwest Regional Collaboratory

Contributors

University of Idaho, Dept. of Geography:

Troy R. Blandford, M.S. in 2006Brian J. Harshburger, PhD Candidate Karen S. Humes, Associate ProfessorBrandon C. Moore, M.S. in 2006Von P. Walden, Associate Professor

Idaho National Laboratory (INL):

Ryan Hruska, Senior Engineer/Scientist

Snowmelt Runoff Model

• SRM is a semi-distributed, temperature index (degree-day) model designed to simulate and forecast streamflow in snowmelt-dominated (mountainous) basins.

– Model inputs: • Current temperature and precipitation from SNOTEL sites

• Snow-covered Area– Derived from MODIS images

» INL (Ryan Hruska)

» NSIDC (Tom Painter)

• Forecasted Temperatures– Medium-range, 15-day from NCEP GFS (downscaled to SNOTEL sites)

– Short-range, 7-day from NDFD (4 day - precipitation)

– Temporal resolution: daily

Downscaling of Met Forecasts

• Example of downscaling of four NCEP Global Forecasting System (GFS) grid points (large yellow dots) to individual SNOTEL station locations (small black dots)

Clark and Hay, (2004)

Errors in NCEP and NDFD Temperatures

Moore et al., 2006

SRM Enhancements

• The use of both maximum and minimum critical temperatures (Tcritmax and Tcritmin) to partition precipitation into rain, snow, and rain/snow mixed.

– model currently uses a single critical temperature value

• The use of an antecedent temperature index (ATI) method to track snowpack cold-content and account for the delay in melt associated with diurnal refreezing.

– used to determine when the snowpack is ripe

– also to determine when the rain falling on the snowpack should contribute to the runoff

SRM Enhancements

15 DayDownscaled Temperature Forecasts

(Tmax, Tmin)Global Forecasting System

NCEP

Enhanced version of SRM

15 DayDownscaled Precipitation Forecasts

Global Forecasting System NCEP

Observed Temperature(Tmax, Tmin)

SNOTEL, NRCSObtained 1 day after measured

Observed Precipitation SNOTEL, NRCS

Obtained 1 day after measured

Observed Streamflow USGS

Obtained 1 day after measured

Forecasted Inputs

Model Updating(Day n-1)

Model Parameters(Retrospective Analysis)

Snow Depth and SWE observations SNOTEL, NRCS

(monitor degree-day factors)

Snow-covered Area (SCA)(Obtained from

snow depletion curves)

Ensemble StreamflowForecasts

Schaake Shuffle(reorder the ensembles)

Ensemble Streamflow Forecasts

Study Area for 2006

Big Wood River Basin

Stream Gauge: Hailey, ID

Contributing Area: 1,625 km2

Elevation Range: 250-3,630 m

location of SNOTEL sites

March 1 April 1 May 1 June 1 July 1 July 310

10

20

30

40

50

60

Date

Str

eam

flow

(cm

s)

Retrospective Model SimulationLeadtime 1

2002

Measured Discharge

Forecasted Discharge

Retrospective Forecast Results (2002)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Forecast lead time (days)

Co

effi

cie

nt o

f De

term

ina

tion

0.000.501.001.502.002.503.003.504.004.505.005.506.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Forecast lead time (days)

Me

an

Ab

solu

te E

rro

r (c

ms)

-6.00-5.00-4.00-3.00-2.00-1.000.001.002.003.004.005.006.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Forecast lead time (days)

Me

an

Bia

s E

rro

r (c

ms)

March 1 April 1 May 1 June 1 July 1 July 310

10

20

30

40

50

60

Date

Str

eam

flow

(cm

s)

Retrospective Model SimulationLeadtime 15

2002

Measured Discharge

Forecasted Discharge

March 1 April 1 May 1 June 1 July 1 July 310

10

20

30

40

50

60

Date

Str

eam

flow

(cm

s)

Retrospective Model SimulationLeadtime 7

2002

Measured Discharge

Forecasted Discharge

March 1 April 1 May 1 June 1 July 1 July 310

10

20

30

40

50

60

Date

Str

eam

flow

(cm

s)

Retrospective Model SimulationLeadtime 4

2002

Measured Discharge

Forecasted Discharge

Coefficient of Determination (R2)

Mean Bias Error (cms)Mean Absolute Error (cms)

Retrospective Forecast Results (2003)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Forecast lead time (days)

Co

effi

cie

nt o

f De

term

ina

tion

0.000.501.001.502.002.503.003.504.004.505.005.506.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Forecast lead time (days)

Me

an

Ab

solu

te E

rro

r (c

ms)

-6.00-5.00-4.00-3.00-2.00-1.000.001.002.003.004.005.006.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Forecast lead time (days)

Me

an

Bia

s E

rro

r (c

ms)

March 1 April 1 May 1 June 1 July 1 July 310

20

40

60

80

100

120

Date

Str

eam

flow

(cm

s)

Retrospective Model SimulationLeadtime 1

2003

Measured Discharge

Forecasted Discharge

March 1 April 1 May 1 June 1 July 1 July 310

20

40

60

80

100

120

Date

Str

eam

flow

(cm

s)

Retrospective Model SimulationLeadtime 15

2003

Measured Discharge

Forecasted Discharge

March 1 April 1 May 1 June 1 July 1 July 310

20

40

60

80

100

120

Date

Str

eam

flow

(cm

s)

Retrospective Model SimulationLeadtime 7

2003

Measured Discharge

Forecasted Discharge

March 1 April 1 May 1 June 1 July 1 July 310

20

40

60

80

100

120

Date

Str

eam

flow

(cm

s)

Retrospective Model SimulationLeadtime 4

2003

Measured Discharge

Forecasted Discharge

Coefficient of Determination (R2)

Mean Bias Error (cm)Mean Absolute Error (cm)

Retrospective Forecast Results (2004)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Forecast lead time (days)

Co

effi

cie

nt o

f De

term

ina

tion

-6.00-5.00-4.00-3.00-2.00-1.000.001.002.003.004.005.006.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Forecast lead time (days)

Me

an

Bia

s E

rro

r (c

ms)

0.000.501.001.502.002.503.003.504.004.505.005.506.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Forecast lead time (days)

Me

an

Ab

solu

te E

rro

r (c

ms)

March 1 April 1 May 1 June 1 July 1 July 310

5

10

15

20

25

30

Date

Str

eam

flow

(cm

s)

Retrospective Model SimulationLeadtime 1

2004

Measured Discharge

Forecasted Discharge

March 1 April 1 May 1 June 1 July 1 July 310

5

10

15

20

25

30

Date

Str

eam

flow

(cm

s)

Retrospective Model SimulationLeadtime 15

2004

Measured Discharge

Forecasted Discharge

March 1 April 1 May 1 June 1 July 1 July 310

5

10

15

20

25

30

Date

Str

eam

flow

(cm

s)

Retrospective Model SimulationLeadtime 7

2004

Measured Discharge

Forecasted Discharge

March 1 April 1 May 1 June 1 July 1 July 310

5

10

15

20

25

30

Date

Retrospective Model SimulationLeadtime 4

2004

Measured Discharge

Forecasted Discharge

Coefficient of Determination (R2)

Mean Bias Error (cm)Mean Absolute Error (cm)

Real-time Forecasting in 2006

Real-time Forecasting in 2006

• SRM correctly forecasted the timing of the peak discharge (May 22, 2006) out 6 days in advance.

– Early by 1 day at a lead time of 7 days and 2 days at 10 days

• The magnitude of the peak was slightly under-predicted for all of the lead-times illustrated here.

• The forecasts of the secondary peak, which occurred in early June, require further investigation and may be due to errors in the input data (i.e. snow covered area).

• The timing of the forecasts are off (timing of smaller peaks) during the early and late portions of the snowmelt season.– May be due to time lag between snowmelt and precipitation events and

the resulting stream discharge

• Create a decision support system for interested parties that is– Easy to use– Accurate

• Intention is to create tools for many basins, not just a single basin, for end-users (NRCS, COE)– UI training session on basin disaggregation and

how to process snow-cover images (snow-covered area)

Preparation for 2007

Preparation for 2007

• Automation - Improved tools for ArcGIS

ArcGIS Tools

ArcGIS Tools

ArcGIS Tools

ArcGIS Tools

Preparation for 2007

• Automation - Improved tools for ArcGIS

• Additional basins

• North Fork of the Clearwater

• St. Joe River

• Big Lost River

• Little Wood River

• Fisher River (Montana)

• South Fork of the Flathead (Montana)

• And perhaps basins in Washington and Oregon as well

Potential New Basins for FY07

Preparation for 2007

• Automation - Improved tools for ArcGIS

• Additional basins

• Ensemble and Single-value (like 2006) forecasting

• Downscaling of global climate model output for simulation of future stream flow conditions in the PNW