seasonal outlooks for hydrology and streamflow in the western u.s. andy wood, alan hamlet and dennis...
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Seasonal outlooks for hydrology and streamflow in the western U.S.
Andy Wood, Alan Hamlet and Dennis P. Lettenmaier
Department of Civil and Environmental Engineering
for
UW Climate Impacts GroupWater Workshop
Portland, ORSeptember 22, 2004
Topics Background on forecasting system
Selected Results for winter 2003-04
Current forecasts
Final Comments
Background
Background: Forecast System Schematic
NCDC met. station obs.
up to 2-4 months from
current
local scale (1/8 degree) weather inputs
soil moisturesnowpack
Hydrologic model spin up
SNOTEL
Update
streamflow, soil moisture, snow water equivalent, runoff
25th Day, Month 01-2 years back
LDAS/other real-time
met. forcings for spin-up
gap
Hydrologic forecast simulation
Month 6 - 12
INITIAL STATE
SNOTEL/ MODIS*Update
ensemble forecasts ESP traces (40) CPC-based outlook (13) NCEP GSM ensemble (20) NSIPP-1 ensemble (9)
* experimental, not yet in real-time product
Seasonal Climate Forecast Data Sources
ESP
ENSO/PDO
ENSO
CPC Official Outlooks
Seasonal Forecast
Model (SFM)
CAS
OCN
SMLR
CCA
CA
NSIPP-1 dynamical
model
VIC Hydrology Model
NOAA
NASA
UW
CPC outlooks: probabilities => anomalies => ensembles
precipitation
CPC outlooks: probabilities => anomalies => ensembles
temperature
Background: Hydrology Model
Problem: met. data availability in 3 months prior to forecast has only a tenth of long term stations used to calibrate and run model in most of spin-up period
Background: Estimating Initial Conditions estimating spin-up period inputs
dense station network for model calibration
sparse station network in real-time
Solution: use interpolated monthly index station precip. percentiles and temperature anomalies to extract values from higher quality retrospective forcing data -- then disaggregate using daily index station signal.
Background: Estimating Initial Conditions SNOTEL assimilation
Problem sparse station spin-up period incurs some systematic errors, but snow state estimation is critical
Solution use SWE anomaly observations (from the 600+ station USDA/NRCS SNOTEL network and a dozen ASP stations in BC, Canada) to adjust snow state at the forecast start date
Background: Estimating Initial Conditions SNOTEL assimilation
Assimilation Method• weight station OBS’ influence over VIC cell based on distance and
elevation difference• number of stations influencing a given cell depends on specified
influence distances
spatial weighting function
elevationweightingfunction
SNOTEL/ASP
VIC cell
• distances “fit”: OBS weighting increased throughout season
• OBS anomalies applied to VIC long term means, combined with VIC-simulated SWE
• adjustment specific to each VIC snow band
Background: Estimating Initial Conditions SWE state adjustment (using SNOTEL/ASP obs)
April 25, 2004
Background: Streamflow Forecast Locations
in development: Colorado R., Upper Rio Grande
Columbia R. basin
California
Snake R. basin
Topics Background on forecasting system
Selected Results for winter 2003-04
Current forecasts
Final Comments
Initial Conditions for Winter 2003-04
Soil Moisture and Snow Water Equivalent (SWE)
Initial Conditions for Winter 2003-04
Soil Moisture and Snow Water Equivalent (SWE)
Initial Conditions for Winter 2003-04CPC estimates of seasonal precipitation and temperature
normal to wetnear normal
temperatures
Dec-Jan-Feb
Initial Conditions for Winter 2003-04
Soil Moisture and Snow Water Equivalent (SWE)
Initial Conditions for Winter 2003-04
Soil Moisture and Snow Water Equivalent (SWE)
Initial Conditions for Winter 2003-04
Soil Moisture and Snow Water Equivalent (SWE)
Initial Conditions for Winter 2003-04CPC estimates of seasonal precipitation and temperature
normal to dry generally warm
Mar-Apr-May
Initial Conditions for Winter 2003-04CPC estimates of seasonal precipitation and temperature
very dry hot
March Only
Initial Conditions for Winter 2003-04
Soil Moisture and Snow Water Equivalent (SWE)
Initial Conditions for Winter 2003-04
Soil Moisture and Snow Water Equivalent (SWE)
Initial Conditions for Winter 2003-04
Soil Moisture and Snow Water Equivalent (SWE)
Initial Conditions for Winter 2003-04CPC estimates of seasonal precipitation and temperature
normal to wet slightly warm
Jun-Jul-Aug
Initial Conditions for Winter 2003-04
Soil Moisture and Snow Water Equivalent (SWE)
Initial Conditions for Winter 2003-04
Soil Moisture and Snow Water Equivalent (SWE)
Initial Conditions for Winter 2003-04
Soil Moisture and Snow Water Equivalent (SWE)
Winter 2003-04: PNW streamflow
By Fall, slightly low flows were anticipated
By winter, moderate deficits were forecasted
Aside: volume forecast format
UPPER HUMBOLDT RIVER BASIN
Streamflow Forecasts - May 1, 2003
<==== Drier === Future Conditions === Wetter ====>
Forecast Pt ============ Chance of Exceeding * ===========
Forecast 90% 70% 50% (Most Prob) 30% 10% 30 Yr Avg
Period (1000AF) (1000AF) (1000AF) (% AVG.) (1000AF) (1000AF) (1000AF)
MARY'S R nr Deeth, Nv
APR-JUL 12.3 18.7 23 59 27 34 39
MAY-JUL 4.5 11.3 16.0 55 21 28 29
LAMOILLE CK nr Lamoille, Nv
APR-JUL 13.7 17.4 20 67 23 26 30
MAY-JUL 11.6 15.4 18.0 64 21 24 28
N F HUMBOLDT R at Devils Gate
APR-JUL 5.1 11.0 15.0 44 19.0 25 34
MAY-JUL 1.7 7.2 11.0 50 14.8 20 22
Aside: what is a “normal” year in “most-probable % of average” terms?
PNW CALI
median mean median mean
runoff histogram runoff histogram
Winter 2003-04: seasonal volume forecastsComparison with RFC forecast for Columbia River at the Dalles,
OR
UW forecasts made on 25th of each month
RFC forecasts madeseveral times monthly:1st, mid-month, late
(UW’sESP unconditional and CPC forecasts shown)
UWRFC
Winter 2003-04: seasonal volume forecastsComparison with RFC forecast for Sacramento River near
Redding, CA
UW forecasts made on 25th of each month
RFC forecasts madeon 1st of month
(UW’sESP unconditional forecasts shown)
UW
RFC
Last winter: volume forecastsfor a sample of PNW locations
OCT 1, 2003 Summer Runoff Volume Forecasts compared to OBS
50
60
70
80
90
100
110M
ICA
A
DU
NC
A
LIB
BY
HH
OR
S
JLA
KE
LGR
AN
DW
OR
S
DA
LLE
per
cen
t o
f av
erag
e
OBS %avgRFCUW ESP
Last winter: volume forecastsfor a sample of PNW locations
NOV 1, 2003 Summer Runoff Volume Forecasts compared to OBS
50
60
70
80
90
100
110M
ICA
A
DU
NC
A
LIB
BY
HH
OR
S
JLA
KE
LG
RA
N
DW
OR
S
DA
LL
E
pe
rce
nt
of
av
era
ge
OBS %avgRFCUW ESP
Last winter: volume forecastsfor a sample of PNW locations
DEC 1, 2003 Summer Runoff Volume Forecasts compared to OBS
50
60
70
80
90
100
110M
ICA
A
DU
NC
A
LIB
BY
HH
OR
S
JLA
KE
LGR
AN
DW
OR
S
DA
LLE
per
cen
t o
f av
erag
e
OBS %avgRFCUW ESP
Last winter: volume forecastsfor a sample of PNW locations
JAN 1, 2004 Summer Runoff Volume Forecasts compared to OBS
50
60
70
80
90
100
110M
ICA
A
DU
NC
A
LIB
BY
HH
OR
S
JLA
KE
LG
RA
N
DW
OR
S
DA
LL
E
pe
rce
nt
of
av
era
ge
OBS %avgRFCUW ESP
Last winter: volume forecastsfor a sample of PNW locations
FEB 1, 2004 Summer Runoff Volume Forecasts compared to OBS
50
60
70
80
90
100
110M
ICA
A
DU
NC
A
LIB
BY
HH
OR
S
JLA
KE
LGR
AN
DW
OR
S
DA
LLE
per
cen
t o
f av
erag
e
OBS %avgRFCUW ESP
Last winter: volume forecastsfor a sample of PNW locations
MAR 1, 2004 Summer Runoff Volume Forecasts compared to OBS
50
60
70
80
90
100
110M
ICA
A
DU
NC
A
LIB
BY
HH
OR
S
JLA
KE
LG
RA
N
DW
OR
S
DA
LL
E
pe
rce
nt
of
av
era
ge
OBS %avgRFCUW ESP
Last winter: volume forecastsfor a sample of PNW locations
APR 1, 2004 Summer Runoff Volume Forecasts compared to OBS
50
60
70
80
90
100
110M
ICA
A
DU
NC
A
LIB
BY
HH
OR
S
JLA
KE
LGR
AN
DW
OR
S
DA
LLE
per
cen
t o
f av
erag
e
OBS %avgRFCUW ESP
Last winter: volume forecastsfor a sample of PNW locations
MAY 1, 2004 Summer Runoff Volume Forecasts compared to OBS
50
60
70
80
90
100
110M
ICA
A
DU
NC
A
LIB
BY
HH
OR
S
JLA
KE
LG
RA
N
DW
OR
S
DA
LL
E
pe
rce
nt
of
av
era
ge
OBS %avgRFCUW ESP
Last winter: volume forecastsfor a sample of PNW locations
JUN 1, 2004 Summer Runoff Volume Forecasts compared to OBS
50
60
70
80
90
100
110M
ICA
A
DU
NC
A
LIB
BY
HH
OR
S
JLA
KE
LGR
AN
DW
OR
S
DA
LLE
per
cen
t o
f av
erag
e
OBS %avgRFCUW ESP
Last winter: volume forecastsfor a sample of PNW locations
JUL 1, 2004 Summer Runoff Volume Forecasts compared to OBS
50
60
70
80
90
100
110M
ICA
A
DU
NC
A
LIB
BY
HH
OR
S
JLA
KE
LG
RA
N
DW
OR
S
DA
LL
E
pe
rce
nt
of
av
era
ge
OBS %avgRFCUW ESP
Topics Background on forecasting system
Selected Results for winter 2003-04
Current forecasts
Final Comments
December
Winter ClimateForecasts Dominate
Hydrologic State Variables Dominate
June March
April 1 SWE (mm)
CPC-based climate forecasts
CPC-based SWE (% average) forecasts
JJASON DJF MAM
CPC-based soil moisture (anomaly) forecasts
JJASON DJF MAM
CPC-based runoff (anomaly) forecasts
JJASON DJF MAM
PNW monthly flow forecasts
PNW monthly flow forecasts
PNW monthly flow forecasts
Current volume forecast summary
September 1 Forecasts compared to Climatology
60
70
80
90
100
110M
ICA
AR
EV
EL
DU
NC
AC
OR
RA
AR
RO
WW
AN
ET
LIB
BY
CO
LF
AH
HO
RS
KE
RR
RC
HIE
FP
RIE
SD
WO
RS
ICE
HA
DA
LL
ET
RIN
IS
HA
ST
CL
EA
RS
AC
_B
CO
TT
OO
RO
VI
SM
AR
TB
EA
RC
FO
L_
IC
ON
SU
PR
D-C
N_
HO
GN
_M
EL
DP
R_
IL
K_
MC
MIL
LE
pe
rce
nt
of
av
era
ge
ESPfcst-med ENSO-med
CPCfcst-med OBS med%avg
Canada WA-OR-ID California
Topics Background on forecasting system
Selected Results for winter 2003-04
Current forecasts
Final Comments
For more information:
www.hydro.washington.edu/Lettenmaier/Projects/fcst/
Final Comments 2005 will resemble 2004, but flow deficits will be smaller
forecast system is for research, not operations
strengths of western U.S. forecast system may lie at boundaries of current forecasting approach strengths longer lead times use of non-traditional inputs visualization disaggregation diagnosis
apparent skill cannot be determined from one season alone
misc slides
Framework: Estimating Initial Conditions snow cover (MODIS) assimilation (Snake R. trial)
Snowcover BEFORE update
Snowcover AFTER update
MODIS update for April 1, 2004 Forecast
snowadded
removed
Framework: Downscaling Climate Model output
NCEP GSM and NSIPP-1
Framework: Bias-correcting Climate Model output
numerous methods of downscaling and/or bias correction exist
the relatively simple one we’ve settled on requires a sufficient retrospective climate model climatology, e.g., NCEP: hindcast ensemble climatology, 21 years X 10 member NSIPP-1: AMIP run climatology, > 50 years, 9 member
specific to calendar monthand climate model grid cell
Framework: Downscaling CPC outlooks spatial unit for raw forecasts is the Climate Division (102 for U.S.)
13 percentile values (from 0.025 to 0.975) for P and T are given
Framework: Downscaling CPC outlooks
downscaling uses Shaake Shuffle (Clark et al., J. of Hydrometeorology, Feb. 2004) to assemble monthly forecast timeseries from CPC percentile values
Results: Initial Conditions for Current Winter
Soil Moisture and Snow Water Equivalent (SWE)