Effects of Incorporating a Climate Index into the Upper Klamath Operational Water Supply Forecast: Trans-Nino Index
Adam M. Kennedy (PSU)
Advisors: Roy W. Koch (PSU)*, David C. Garen (NRCS)**
Study Background
TNI Climate Signal Observed Oct-Dec
Forecast Model Results
90
Jackknife Standard Error (kaf)
Increased water demand and persistent drought cycles have prompted the
need to reduce early season water supply forecast uncertainty. Past studies
have identified relationships between equatorial Pacific Ocean sea surface
temperatures and the western water region’s hydrologic cycle. However,
none have proven adequate to significantly explain hydrologic variation
within the Upper Klamath basin, specifically during the current warm phase
of the Pacific Decadal Oscillation (PDO).
80
70
60
50
40
30
20
Ap-Se W/o TNI
Ap-Se With TNI
10
0
January
ER
C
Paulina Marsh
R
W
Silver Creek Pillow
il
a
li
Crater Lake
i
n R
mso
Klamath
Marsh
Crater Lake HQ
Long Creek
Summer
Lake
Sycan
Marsh
Summer Rim
S
r
r
ve
Seven Mile Marsh
v
y ca n R i
e
Taylor Butte
U p p e r
K l a m a t h
L a k e
S p r a g u e
Sprague Gage
Williamson Gage
Sp
r a g u e Ri v e r
Klamath Well
Upper Klamath
Lake
F
ish
ho
eC
l
e ek
r
#
Upper Klamath Lake Outlet
Gerber
Reservoir
er
Strawberry
R
iv
LO S T R
Quartz Mountain
Legend
K
la
m
a th
µ
O R E G O N
Williamson Forecast Variables
C A L I F O R N I A
Sprague Forecast Variables
Waterbodies
Surrounding Basins
"A" Canal
0 4 8
16
24
TNI Variable Reduces Uncertainty
Above: Region Niño 1+2 (circle) and region Niño 4 (square).
The TNI climate signal is
correlated with the major
hydrologic variables within the
Upper Klamath basin. Most
notable is the relationship
between Oc, No, and De TNI
and Ap–Se stream discharge.
0.90
0.80
0.70
0.60
0.50
0.40
0.30
Ap-Se Sprague Flow
Ap-Se Upper Williamson
Ap-Se U. Klamath Lake Inflow
Crater Lake Ap. 1 SWE
Signifigant at alpha = 0.05
0.20
0.10
Apr
May Jun
Jul
0.7
0.6
0.5
0.4
0.3
Oc-De TNI
0.2
Oc-Jan TNI
0.1
Se-No TNI
4. Subtract the standardized Niño 4 from the standardized Niño 1+2 to
obtain monthly TNI series.
0
15
10
Ap-Se W/o TNI
5
Ap-Se With TNI
0
Febuary
March
April
U. Williamson Forecast Points
Aug Sep
Oct
Nov Dec
Jan
Feb
May
Mar
6. Compute multi-month TNI by averaging months well correlated to
selected variables.
7. Divide multi-month TNI by its respective multi-month standard deviation.
Nov
Dec
Jan
Feb Mar Apr May
Monthly Stream Volume
Multi-month TNI values
are correlated with Upper
Williamson monthly
stream discharge. This
relationship could be
useful in hydrologic
modeling applications.
-130°
-120°
-110°
r-value (p < 0.05)
0.32 - 0.38
0.39 - 0.43
Jun
Jul
Aug Sep
0.50 - 0.57
Pacific
Ocean
0.58 - 0.73
Washington
HCDN Flow Gage
0.9
The Sprague river, while
hydrologically different than
the Williamson, still shows
a strong TNI correlation
during the months of peak
seasonal flow.
Montana
0.8
0.7
0.6
Oregon
0.4
0.3
Se-No TNI
0.2
Oc-De TNI
0.1
Oc-Ja TNI
0.73
Wyoming
California
Nevada
0
Utah
Oct
Nov
Dec
Jan
Feb Mar Apr May
Monthly Stream Volume
Jun
Jul
Aug
Sep
Colorado
Ap-Se U. Will. Discharge (KAF)
Ap-Se Sprague Discharge (KAF)
-120°
600
r = 0.65
500
400
300
200
100
0
-100
-2
-1
0
1
Oc-De Trans-Niño Index
2
Idaho
0.68
0.5
TNI=(Niño 1+2std - Niño 4std)avg
The TNI is the
standardized sea surface
temperature (SST)
gradient between Niño
1+2 and Niño 4 regions.
TNI Signal Regionally Distributed
Regional-scale correlations may extend the usefulness of the TNI outside of
the Upper Klamath basin. Thus, the TNI may prove useful for long lead
streamflow forecast operations, ecosystem scale modeling, and a variety of
other environmental science applications.
0.44 - 0.49
Oct
5. Perform correlations with the variables of interest to identify months
which contain the predictive signal.
Averaged Oc-De TNI
20
40°
3. Standardize both series by subtracting the monthly means and dividing by
the standard deviation of the complete period of record.
0.8
TNI Correlation Coefficient (r)
2. Compute monthly means over the complete period of record (1951-2004).
1950 1956 1962 1968 1974 1980 1986 1992 1998 2004
25
January
0.9
TNI Corelation Coefficient (r)
(Revised from Trenberth (2001))
1. Download Niño 1+2 and Niño 4 area averaged monthly SST time series.
Homogenous high variability
30
0.00
Kilometers
32
Trans-Niño Index (TNI)
Homogenous low variability
35
Analysis results indicate
that incorporating the
TNI into the list of
physically meaningful
variables decreases early
season forecast
uncertainty.
Monthly TNI Value
2.5
2
1.5
1
0.5
0
-0.5
-1
-1.5
-2
-2.5
May
50°
I LL
Febuary
March
April
Sprague River Forecast Points
Jackknife Standard Error (kaf)
M
W i l l i a m s o n
C orrelation C oefficient (r)
Chemault Alternative
Diamond Lake
This research focuses large
scale climate variables
affecting major inputs to
Upper Klamath Lake in an
effort to reduce early
season water supply
forecast uncertainty. The
specific basins analyzed
are the Williamson River
and Sprague River near
Chiloquin, Oregon, which
are the main inputs to
Upper Klamath Lake.
A regression analysis
model (Garen, 1992) is
used to develop twelve
optimized early season
water supply forecast
equations. Six trials
included the TNI and six
did not.
A simple linear
regression shows a
useful relationship
between the warm
PDO Oc-De TNI
and Klamath basin
stream discharge.
300
r = 0.73
250
200
150
100
50
0
-2
-1
0
1
Oc-De Trans Niño Index
2
-110°
Preliminary results
of a spatial correlation
matrix between the TNI and
HCDN stream flow data
reveal a broad regional
pattern which may prove
useful in other modeling
applications where an early
water year indication is
desired. Because of
limitations in the HCDN
dataset, the period of record
is limited to 1951 – 1988 for
all stations other than the U.
Williamson and Sprague.
Corresponding author address: Adam M. Kennedy, Environmental Sciences and Resources Program, Portland State
University, PO Box 751, Portland OR. 97207. Email: kenna@pdx.edu. Home page: http://web.pdx.edu/~kenna
* Dr. Roy W. Koch, Professor, Environmental Sciences and Resources Program, Portland State University, Portland,
Oregon.
**Dr. David C. Garen, Hydrologist, USDA, Natural Resources Conservation Service, National Water and Climate
Center, Portland, Oregon.
Literature Cited:
Garen, D.C. 1992. Improved techniques in regression-based stream flow volume forecasting. Journal of Water
Resources Planning and Management (American Society of Civil Engineers), 118(6), 654-670.
Trenberth, K. T, and D.P. Stepaniak. 2001. Indices of El Niño evolution. Journal of Climate Letters, 14, 1697-1701.