Transcript The 2002 Version of SAMS - Architectural engineering

Forecasting of
Colorado Streamflows
Jose D. Salas, Chong Fu
Department of Civil & Environmental Engineering
Colorado State University
and
Balaji Rajagopalan & Satish Regonda
Department of Architectural, Civil & Environmental Engineering
University of Colorado
Acknowledgment: Colorado Water Resources Research institute
And the seven years of
plenteousness, that was in the
land of Egypt was ended. And
the seven years of dearth began
to come, according as Joseph
had said: and the dearth was in
all lands; but in the land of
Egypt there was bread.
Genesis 41:53
A Water Resources Management
Perspective
Inter-decadal
Decision Analysis: Risk + Values
T
• Facility Planning
i
m
– Reservoir, Treatment Plant Size
e
• Policy + Regulatory Framework
H
o
r
i
z
o
n
Climate
– Flood Frequency, Water Rights, 7Q10 flow
• Operational Analysis
– Reservoir Operation, Flood/Drought Preparation
• Emergency Management
– Flood Warning, Drought Response
Data: Historical, Paleo, Scale, Models
Hours
Weather
Climate Variability / Predictability





Daily
Annual

Inter-annual to Interdecadal

Centennial
Millenial



Diurnal cycle
Seasonal cycle
Ocean-atmosphere
coupled modes (ENSO,
NAO, PDO)
Thermohaline
circulation
Milankovich cycle
(earth’s orbital and
precision)
ENSO as a
“free” mode
of the
coupled
oceanatmosphere
dynamics in
the Tropical
Pacific Ocean
Global Impacts
of ENSO
Probabilistic
Perfect Ocean for Drought
The Perfect Ocean
Hoerling and Kumar (2003)

Pacific Ocean and Atmospheric
Conditions
Key to
Western US Hydrologic
Variability and Predictability
at interannual and interdecadal
Colorado (and Western US) Water Resources System
Characteristics


Flow
20
SNOW
60
50
15
40
10
30
20
5
10
0
Oct
J-00
Mean Monthly Flows (KAF)

Majority of annual
runoff is snowmelt
(70%)
Competing demand
management:
 Conservation and
delivery to meet
irrigation demands,
 hydropower
production
 environmental
releases
Limited Storage
capacity
Interannual hydrologic
variability
SWE (in)

0
Dec
J-00
Feb
J-00
Apr
J-00
Jun
J-00
Aug
J-00
For efficient and sustainable
water management  skilful
forecast of spring (Apr-Jul)
streamflows are needed
Modeling Framework
What Drives Year to Year
Variability in regional
Hydrology?
(Floods, Droughts etc.)
Hydroclimate Predictions – Scenario
Generation
(Nonlinear Time Series Tools,
Watershed Modeling)
Decision Support System
(Evaluate decision
strategies
Under uncertainty)
Diagnosis
Forecast
Application
Approaches used in the study
• Identify potential predictors from large scale land –
atmosphere – ocean system for each streamflow series
• Reduce the pool of potential predictors based on
statistical techniques
• Apply the PCA and Regression techniques and multimodel ensemble techniques for forecasting at multisites. (Regonda et al., 2006, WRR)
• Apply the PCA and Regression techniques for
forecasting at single sites
• Apply the CCA technique for forecasting at multiple
sites
• Test the forecasting models
- fitting
- validation (drop 10% and drop-1)
Some Examples
•
Gunnison
River Basin
•
•
Streamflow
at six
locations
Multimodel
ensemble
forecast
technique
Regonda et
al. (2006)
Examples
•Five other locations (Yampa,
Poudre, San Juan, Arkansas and
Rio-Grande)
PCA/regression and CCA
techniques
River sites used in the study
Brief description of the study sites
Coordinates
River and site names
Arkansas River at Canon
City, CO
Cache la Poudre River at
Mouth of Canyon, CO
Gunnison River above
Blue Mesa Dam, CO
Rio Grande at San
Marcial, NM
San Juan River near
Archuleta, NM
Yampa River near
Maybell, CO
USGS ID
Elevation
(ft)
Latitude Longitude
Drainage April-July
Area
flow
2
(mi )
(acre-ft)
07096000 38°26'02" 105°15'24"
5,342
3,117
198,262
06752000 40°39'52" 105°13'26"
5,220
1,056
230,998
09124700 38°27'08" 107°20'51"
7,149
3,453
747,519
08358500 33°40'50" 106°59'30"
4,455
27,700
391,969
09355500 36°48'05" 107°41'51"
5,653
3,260
747,519
09251000 40°30'10" 108°01'58"
5,900
3,410
995,245
PC1 (basin average) of Gunnison streamflows Correlated
with Winter (Nov-Mar)
Geopotential Heights
PC1 (basin average) of Gunnison streamflows correlated
with winter large scale climate variables
Meridional Wind
Zonal Wind
Surface Air Temperature
Sea Surface Temperature
Winter (Nov – Mar) Vector Wind Composites
Wet years
Dry years
April 1st SWE PC1 with Flow PC1


Deviations from linear
relationship (solid
circles)
Suggests role of
antecedent land
conditions?
PC1 Flows
Vs. PC1
SWE
Feb.
Mar.
Apr.
Correlation
Coefficient
0.67
0.72
0.82
April 1st SWE PC1
Role of antecedent Land Conditions
Years with high snow and
proportional low flows
Years with low snow and
proportional high flows
Palmer Drought Severity Index (dry ------wet)
Correlation between Apr-Jul flows for the Poudre
River and Jan-Mar geopotential heights (700 mb)
Correlation between Apr-Jul flows at S. Juan River
and previous Oct-Dec geopotential heights (700 mb)
Multi-models


December 1st forecast selected 15 models
April 1st forecast selected 6 models


PC1 SWE is present in all models
PDSI is also selected in half of the models
Multi-model ensemble forecast
(for any year)
Use best models
(weights are
function of
goodness of fit)
Generate an ensemble of estimated
flows (traces) from each model as a
function of explained and
unexplained model variance
Model 1
(0.6)
Esti. flow 1,1
------------Esti. flow 1,100
Model 2
(0.3)
Esti. flow 2,1
…………..
Esti. flow 2,100
Model 3
(0.1)
Esti. flow 3,1
……………
Esti. flow 3,100
Final ensemble =
weighted
combination of
traces
Esti. flow 1,a
Esti. flow 1,b
Esti. flow 1,c
Esti. flow 1,d
Esti. flow 1,e
Esti. flow 1,f
Esti. flow 2,a
Esti. flow 2,b
Esti. flow 2,c
Esti. flow 3,a
Experimental Forecasts
BoxPlots Show Probability Distribution of
Ensemble Forecast
Forecasted spring streamflows = {896,795.65, 936, 1056, 891.76,…… }
Actual spring streamflows
95th percentile
75th percentile
50th percentile
25th percentile
5th percentile
Model Validation for Tomichi River (1949-2002)
Jan 1st
RPSS: 0.51
Apr 1st
RPSS: 0.77
Model Validation for Tomichi River (Dry Years)
Jan 1st
RPSS: 0.32
Apr 1st
RPSS: 0.95
Model Validation for Tomichi River (Wet Years)
Jan 1st
RPSS: 0.75
Apr 1st
RPSS: 1.00
Influence of PDSI


Model 1: PC1 SWE
Model 2: PC1 SWE + PDSI
 PDSI shifts ensembles in the right direction
Forecast Skill of Spring Flows at Different Lead
Times
Climate indices + Soil moisture
Climate indices + Soil moisture + SWE
All Years
1.00
RPSS
0.75
0.50
0.25
Dec 1st
0.00
1
Jan 1st
Feb 1st
2
3
Month
1.00
1.00
Wet Years
0.75
Apr 1st
4
5
Dry Years
0.75
RPSS
RPSS
Mar 1st
0.50
0.50
0.25
0.25
0.00
0.00
1
Dec
1st
Jan2 1st
Feb 13st
Month
Mar 14 st
Apr 1st5
1 Dec 1st
2 1st
Jan
Feb 31st
Month
Mar 41st
Apr 1st5
Time series of flows, SST, geopotential heights,
SWE and PDSI for the San Juan River
Flow (KAF)
2000
flow
mean
1000
0
1945
1955
1965
1975
Time (year)
1985
1995
2005
25
SST
24
23
SST4
mean
Geo. Height (m)
22
21
1945
3160
3120
GH1
mean
1965
1975
Time (year)
1985
1995
2005
3080
3040
1945
1955
1965
1975
Time (year)
1985
1995
2005
10
60
SWE3
mean
40
PDSI
SWE (in)
1955
20
0
1945
PD
me
5
0
-5
1955
1965
1975
Time (year)
1985
1995
2005
-10
1945
1955
1965
1975
Time (year)
1985
1995
Relationships between Apr-Jul flows of the San
Juan River and potential predictors
2000
2000
PDSI1 vs flow
SST4 vs flow
1500
Flow (KAF)
Flow (KAF)
1500
1000
1000
500
500
0
0
22
22.5
23
23.5
24
24.5
-8
25
-4
0
4
8
PDSI
SST (C)
Flow vs SST
Flow vs PDSI
2000
2000
SWE3 vs flow
GH1 vs flow
Flow (KAF)
1500
Flow (KAF)
1500
1000
1000
500
500
0
0
3020
0
3040
3060
3080
3100
Geopotential Height (m )
3120
3140
Flow vs geopotential height
10
20
30
SWE (in)
Flow vs SWE
40
50
Validation San Juan River forecast
2000
Observed
Forecast
Flow, TAF
1500
Fitting
1000
500
0
1945
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
Years
2000
Observed
Forecast
Flow, TAF
1500
Validation – 10%
dropping
1000
500
0
1945
1950
1955
1960
1965
1970
1975
Years
1980
1985
1990
1995
2000
2005
Comparison of flow forecasts for fitting and
validation (drop 10%) for the SanJuan River
2000
2000
Forecasted streamflow
1000
fitting
500
Forecasted streamflow
1500
1500
1000
500
0
0
0
500
1000
Observed streamflow
1500
2000
0
validation
2000
1500
1500
1000
500
1000
Observed streamflow
1500
2000
1500
2000
Multisite
2000
Forecasted streamflow
Forecasted streamflow
Single site
500
1000
500
0
0
0
500
1000
Observed streamflow
1500
2000
0
500
1000
Observed streamflow
Comparison of forecast model performances
R-squares
Single site models
Method
Fitting
Drop
10%
Item
R2
adj. R2
R2
adj. R2
Poudre
0.69
0.65
0.55
0.49
Arkansas
0.77
0.73
0.68
0.64
Gunnison
0.87
0.84
0.78
0.74
Rio Grande
0.88
0.86
0.83
0.80
San Juan
0.88
0.84
0.82
0.77
Yampa
0.86
0.84
0.81
0.79
Multisite models
Method
Fitting
Drop 10%
Item
R2
adj. R2
R2
adj. R2
Poudre
0.41
0.33
0.24
0.15
Arkansas Gunnison Rio Grande
0.61
0.70
0.75
0.56
0.66
0.71
0.48
0.56
0.67
0.41
0.50
0.62
San Juan
0.61
0.56
0.48
0.41
Yampa
0.76
0.72
0.63
0.58
Comparison of forecast model performances
Forecast skill scores
Single site models
Method
Fitting
Drop 10%
Item
Accuracy
HSS
Accuracy
HSS
Poudre
0.57
0.42
0.49
0.32
Arkansas
0.64
0.52
0.60
0.47
Gunnison
0.58
0.45
0.49
0.32
Rio Grande
0.62
0.50
0.60
0.47
San Juan
0.72
0.62
0.72
0.62
Yampa
0.72
0.62
0.72
0.62
San Juan
0.53
0.37
0.51
0.35
Yampa
0.66
0.55
0.58
0.45
Multisite models
Method
Fitting
Drop 10%
Item
Accuracy
HSS
Accuracy
HSS
Poudre
0.43
0.24
0.38
0.17
Arkansas Gunnison
0.43
0.66
0.25
0.55
0.45
0.53
0.27
0.37
Rio Grande
0.55
0.40
0.53
0.37
Summary
• Use of large-scale climate information lends long-lead
predictability of spring season streamflows in the
Colorado River system
• Simple statistical methods incorporating climate
information provides skilful ensemble streamflow
forecast
• Skills in the forecast can lead to efficient management
and operations of reservoir systems
• Aspinall Unit (Regonda, 2006)
• Pecos river basin, NM (Grantz, 2006)
• Truckee/Carson basins (truckee canal operations),
Grantz et al., 2007
• ABCD water utilities (Ben & Subhrendu, AMEC)
•Potential use in Climate Change studies and simulation
Summary
• Partial funding from Colorado Water Research
Institute is thankfully acknowledged
• http://cadswes.colorado.edu/publications
(PhD thesis)
Regonda, 2006
Prairie, 2006
Grantz, 2006
• [email protected]