Download New Tools and Resources for Streamflow Forecasting Applications

New Tools and Resources for Streamflow
Forecasting Applications
JISAO Climate Impacts Group
and Department of Civil and Environmental Engineering
University of Washington
October, 2003
Alan F. Hamlet
Andy Wood
Seethu Babu
Marketa McGuire
Dennis P. Lettenmaier
Overview of Recent Advances in Streamflow
Forecasting Applications at the UW
Extension of driving data records and removal of temporal
inhomogeneities
Retrospective data now available from 1915-2000 – Adds 35 years.
Near real-time data assimilation
Updating of snow simulations using SNOTEL and remote sensing
Extension of locations where we can provide hydrologic forecast
products
Climate change scenarios
Seasonal to interannual streamflow forecasts
Bias correction software
Accurate linkages to specific reservoir models
Tools for creating water management applications
Importance of Long Records
Old
New
450000
Cool
Cool
Warm
Warm
350000
300000
250000
200000
2000
1990
1980
1970
1960
1950
1940
1930
1920
1910
150000
1900
Apr-Sept Flow (cfs)
400000
Importance of Increased Sample
Size for Forecast Interpretation
Blue = all years 1900-2002
Green = all enso neutral 1900-2002 (33)
Red = enso neutral 1961-2002 (12)
April-September Average
Streamflow (cfs)
450000
400000
350000
300000
250000
200000
Monte Carlo 90% confidence limits for 25th percentile
150000
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Probability of Exceedence
0.9
1.0
Removal of Temporal Inhomogeneities
800000
700000
600000
500000
400000
300000
Hist
200000
100000
0
19
25
19
25
19
25
19
26
19
26
19
27
19
27
19
27
19
28
19
28
19
29
19
29
19
30
19
30
19
30
19
31
19
31
19
32
19
32
19
32
19
33
19
33
19
34
19
34
Before
temporal
corrections
VIC
800000
700000
600000
500000
300000
200000
100000
0
19
25
19
25
19
25
19
26
19
26
19
27
19
27
19
27
19
28
19
28
19
29
19
29
19
30
19
30
19
30
19
31
19
31
19
32
19
32
19
32
19
33
19
33
19
34
19
34
After
temporal
corrections
400000
VIC
Hist
Basic Routing Network for the Columbia Basin
Extended PNW VIC Domain and Routing Locations
VIC Routing Network and Flow Simulation Nodes
Snake River Upstream of Ice Harbor
Calibration of Small Scale Basins
Owyhee Basin
Before Calibration
Owyhee Basin
After Calibration
Issues with Hydrologic Model Bias
900000
800000
700000
600000
500000
400000
300000
200000
100000
0
VIC
1999
1999
1998
1997
1997
1996
1995
1995
1994
1993
1993
1992
1991
1991
1990
1989
1989
1988
1987
1987
1986
1985
Observed
1985
Streamflow (cfs)
Columbia River at The Dalles
Quantile-Based Bias Correction (Wood et al. 2002)
VIC Input = 19000
35000
30000
Flow (cfs)
25000
20000
obs
15000
vic
10000
5000
0
0
0.2
0.4
0.6
Probability of Exceedence
Bias Corrected Output = 10000
0.8
1
Bias Correction Objectives:
Raw
Bias Corrected
Result: Bias corrected hydrologic simulations are quite consistent with
observed streamflows in absolute value and climate change signals are
translated without significant distortion.
Linkages to PNW Reservoir Models
ColSim (UW)
integrated model
of the Columbia River Basin
GENESYS (NWPPC)
hydropower analysis model
for the Columbia main stem
SnakeSim (UW and IDWR)
integrated planning model
for the Snake River basin
Applications:
Climate Change Assessments
Long-Range Planning
Seasonal to Interannual Forecasts
Water Management Applications
Tools for Incorporating Probabilistic Forecasts
in Water Management Plans:
An example for Libby Dam in the Columbia
River basin
CRB Current Operations use the Energy Content Curve
(Rule curves for Libby Dam in a wet year are shown)
7000000
Forecast
Information
Used
6000000
refill
4000000
3000000
2000000
1000000
crit
flood
No Forecast
Information
Used
ECC
Drought is
assumed
jul
jun
may
apr
mar
feb
jan
dec
nov
oct
sep
0
aug
Storage (acre-ft)
5000000
Constructing a New Reservoir Rule Curve called
the “Refill to Least Flood” Curve
Least Amount of Flood
Evacuation Expected Based on
Forecast
Refill Curve to Least Flood Target Based
on Lowest Streamflow Forecast
7000000
Flood 1
6000000
Flood 2
5000000
4000000
Flood 3
3000000
Flood 4
2000000
Flood 5
1000000
Ju
n
Ap
r
Fe
b
De
c
O
ct
Au
g
0
Refill to Least Flood
(wet)
Refill to Least Flood Curve for Libby Dam for the 2nd Lowest
Ensemble Member in the 2004 Forecast (“1994”)
(~85% likelihood of refill to April 1 Flood Curve)
7000000
Flood 1
6000000
5000000
Flood 3
4000000
3000000
Flood 4
2000000
Flood 5
1000000
Refill to Least Flood
(wet)
0
n
Ju
r
Ap
b
Fe
ec
D
O
ct
g
Status Quo
Au
Storage (acre-ft)
Flood 2
Refill to Least Flood Curve for Libby Dam for the Lowest
Ensemble Member in the 2004 Forecast (“1979”)
(~95 % likelihood of refill to April 1 Flood Curve)
7000000
Flood 1
6000000
5000000
Flood 3
4000000
Flood 4
3000000
2000000
Flood 5
1000000
Refill to Least Flood
(wet)
Status Quo
n
Ju
r
Ap
b
Fe
ec
D
O
ct
g
0
Au
Storage (acre-ft)
Flood 2
Interpretation:
Libby is an important project for maintaining fish flows (one of three projects
which supply supplementary water to help maintain flows at McNary Dam under
the BiOp). If Libby does not refill, fish flows may be strongly impacted.
The Refill to Least Flood rule curves (based on the 2004 forecast) show that
there is about a 15% risk of Libby Dam not refilling to the flood evacuation target
in April if the normal flood draft in December occurs. Hedging in the fall and
early winter to keep storage levels high may therefore be an appropriate
management action in 2003-2004 to help protect fish in the lower basin.
Conclusions
The opportunities for developing improved hydrologic forecasts for
water management applications is enhanced by:
•Extending the length of temperature and precipitation records and
removing temporal inhomogeneities
•Assimilating observed data to improve estimates of initial conditions
(especially snow)
•Extending the domain of hydrologic models and the number of
streamflow routing locations
•Calibrating the hydrologic model
•Bias Correcting streamflow simulations
•Creating custom linkages to specific water management models and
analytical tools for interpreting probabilistic forecasts for water
management applications.