Predictability and Long Range Forecasting of Colorado Streamflows
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Transcript of Predictability and Long Range Forecasting of Colorado Streamflows
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Predictability and Long Range Forecasting of
Colorado Streamflows
Jose D. Salas, Chong FuDepartment of Civil & Environmental Engineering
Colorado State Universityand
Balaji Rajagopalan & Satish RegondaDepartment of Architectural, Civil & Environmental
EngineeringUniversity of Colorado
Acknowledgment: Colorado Water Resources Research institute
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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
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A Water Resources Management Perspective
Time
Horizon
Inter-decadal
Hours Weather
ClimateDecision Analysis: Risk + Values
Data: Historical, Paleo, Scale, Models
• Facility Planning
– Reservoir, Treatment Plant Size
• Policy + Regulatory Framework
– Flood Frequency, Water Rights, 7Q10 flow
• Operational Analysis
– Reservoir Operation, Flood/Drought Preparation
• Emergency Management
– Flood Warning, Drought Response
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Climate Variability / Predictability
Daily Annual
Inter-annual to Inter-decadal
Centennial Millenial
Diurnal cycle Seasonal cycle
Ocean-atmosphere coupled modes (ENSO, NAO, PDO)
Thermohaline circulation
Milankovich cycle (earth’s orbital and precision)
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ENSO as a “free” mode of the coupled ocean-atmosphere dynamics in the Tropical Pacific Ocean
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Global Impacts of ENSO
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Probabilistic
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The Perfect Ocean
Perfect Ocean for Drought
Hoerling and Kumar (2003)
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Pacific Ocean and Atmospheric Conditions
Key to
Western US Hydrologic Variability and Predictability
at interannual and interdecadal
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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
Colorado (and Western US) Water Resources System Characteristics
0
5
10
15
20
J-00 J-00 J-00 J-00 J-00 J-00
SW
E (
in)
0
10
20
30
40
50
60
Mea
n M
on
thly
Flo
ws
(KA
F)
Oct Dec Feb Apr Jun Aug
SNOWFlow
For efficient and sustainable water management skilful forecast of spring (Apr-Jul) streamflows are needed
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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
strategiesUnder uncertainty)
Modeling Framework
Forecast
Diagnosis
Application
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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 multi- model ensemble techniques for forecasting at multi-sites. (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)
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Some Examples• Gunnison
River Basin
• Streamflow at six locations
• Multi-model ensemble forecast techniqueRegonda et al. (2006)
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Examples
•Five other locations (Yampa, Poudre, San Juan, Arkansas and Rio-Grande)
PCA/regression and CCA techniques
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River sites used in the study
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Brief description of the study sites
Coordinates River and site names USGS ID
Latitude Longitude Elevation
(ft)
Drainage Area (mi2)
April-July flow
(acre-ft) Arkansas River at Canon City, CO
07096000 38°26'02" 105°15'24" 5,342 3,117 198,262
Cache la Poudre River at Mouth of Canyon, CO
06752000 40°39'52" 105°13'26" 5,220 1,056 230,998
Gunnison River above Blue Mesa Dam, CO
09124700 38°27'08" 107°20'51" 7,149 3,453 747,519
Rio Grande at San Marcial, NM
08358500 33°40'50" 106°59'30" 4,455 27,700 391,969
San Juan River near Archuleta, NM
09355500 36°48'05" 107°41'51" 5,653 3,260 747,519
Yampa River near Maybell, CO
09251000 40°30'10" 108°01'58" 5,900 3,410 995,245
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PC1 (basin average) of Gunnison streamflows Correlated with Winter (Nov-Mar)Geopotential Heights
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PC1 (basin average) of Gunnison streamflows correlated with winter large scale climate
variablesSurface Air Temperature
Zonal Wind Sea Surface Temperature
Meridional Wind
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Wet years Dry years
Winter (Nov – Mar) Vector Wind Composites
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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 with Flow PC1
April 1st SWE PC1
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Role of antecedent Land Conditions
Palmer Drought Severity Index (dry ------wet)
Years with low snow and proportional high flows
Years with high snow and proportional low flows
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Correlation between Apr-Jul flows for the Poudre River and Jan-Mar geopotential
heights (700 mb)
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Correlation between Apr-Jul flows at S. Juan River and previous Oct-Dec geopotential
heights (700 mb)
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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
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Model 1(0.6)
Model 2(0.3)
Model 3(0.1)
Esti. flow 1,1
-------------Esti. flow 1,100
Esti. flow 2,1
…………..Esti. flow 2,100
Esti. flow 3,1
……………Esti. flow 3,100
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
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
Final ensemble = weighted combination of traces
Experimental Forecasts
Multi-model ensemble forecast (for any year)
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5th percentile
25th percentile
50th percentile
75th percentile
95th percentile
Forecasted spring streamflows = {896,795.65, 936, 1056, 891.76,…… }
Actual spring streamflows
BoxPlots Show Probability Distribution of Ensemble Forecast
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Jan 1st
Apr 1st
RPSS: 0.51
RPSS: 0.77
Model Validation for Tomichi River (1949-2002)
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Apr 1st
Jan 1st RPSS: 0.32
RPSS: 0.95
Model Validation for Tomichi River (Dry Years)
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Jan 1st
Apr 1st
RPSS: 0.75
RPSS: 1.00
Model Validation for Tomichi River (Wet Years)
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Influence of PDSI
Model 1: PC1 SWE Model 2: PC1 SWE + PDSI
PDSI shifts ensembles in the right direction
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0.00
0.25
0.50
0.75
1.00
1 2 3 4 5Month
RP
SS
Climate indices + Soil moisture Climate indices + Soil moisture + SWE
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5Month
RP
SS
All Years
Wet Years
0.00
0.25
0.50
0.75
1.00
1 2 3 4 5
Month
RP
SS
Dry Years
Dec 1st Jan 1st Feb 1st Mar 1st Apr 1stDec 1st Jan 1st Feb 1st Mar 1st Apr 1st
Dec 1st Jan 1st Feb 1st Mar 1st Apr 1st
Forecast Skill of Spring Flows at Different Lead Times
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Time series of flows, SST, geopotential heights, SWE and PDSI for the San Juan
River
0
1000
2000
1945 1955 1965 1975 1985 1995 2005Time (year)
Flo
w (
KA
F) flow
mean
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1945 1955 1965 1975 1985 1995 2005Time (year)
SS
T
SST4mean
3040
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1945 1955 1965 1975 1985 1995 2005Time (year)
Geo
. H
eig
ht
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GH1mean
-10
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1945 1955 1965 1975 1985 1995 2005Time (year)
PD
SI
PDSI1mean
0
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1945 1955 1965 1975 1985 1995 2005Time (year)
SW
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in)
SWE3mean
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Relationships between Apr-Jul flows of the San Juan River and potential predictors
Flow vs SST
Flow vs geopotential height
Flow vs PDSI
Flow vs SWE
0
500
1000
1500
2000
22 22.5 23 23.5 24 24.5 25
SST (C)
Flo
w (
KA
F)
SST4 vs f low
0
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1000
1500
2000
3020 3040 3060 3080 3100 3120 3140
Geopotential Height (m)
Flo
w (
KA
F)
GH1 vs f low
0
500
1000
1500
2000
-8 -4 0 4 8
PDSI
Flo
w (
KA
F)
PDSI1 vs f low
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500
1000
1500
2000
0 10 20 30 40 50
SWE (in)
Flo
w (
KA
F)
SWE3 vs f low
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0
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1000
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2000
1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Years
Flo
w, T
AF
Observed
Forecast
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2000
1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005
Years
Flo
w, T
AF
Observed
Forecast
Fitting
Validation – 10%dropping
Validation San Juan River forecast
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Comparison of flow forecasts for fitting and validation (drop 10%) for the SanJuan River
fitting
validation
Single site
Multisite
0
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Observed streamflow
For
ecas
ted
stre
amflo
w
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0 500 1000 1500 2000
Observed streamflow
For
ecas
ted
stre
amflo
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0 500 1000 1500 2000
Observed streamflow
For
ecas
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stre
amflo
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0 500 1000 1500 2000
Observed streamflow
For
ecas
ted
stre
amflo
w
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Comparison of forecast model performancesR-squares
Method Item Poudre Arkansas Gunnison Rio Grande San Juan Yampa R2 0.69 0.77 0.87 0.88 0.88 0.86
Fitting adj. R2 0.65 0.73 0.84 0.86 0.84 0.84
R2 0.55 0.68 0.78 0.83 0.82 0.81 Drop 10% adj. R2 0.49 0.64 0.74 0.80 0.77 0.79
Method Item Poudre Arkansas Gunnison Rio Grande San Juan Yampa R2 0.41 0.61 0.70 0.75 0.61 0.76
Fitting adj. R2 0.33 0.56 0.66 0.71 0.56 0.72
R2 0.24 0.48 0.56 0.67 0.48 0.63 Drop 10%
adj. R2 0.15 0.41 0.50 0.62 0.41 0.58
Single site models
Multisite models
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Method Item Poudre Arkansas Gunnison Rio Grande San Juan Yampa
Accuracy 0.57 0.64 0.58 0.62 0.72 0.72 Fitting
HSS 0.42 0.52 0.45 0.50 0.62 0.62 Accuracy 0.49 0.60 0.49 0.60 0.72 0.72
Drop 10% HSS 0.32 0.47 0.32 0.47 0.62 0.62
Comparison of forecast model performances
Forecast skill scores
Method Item Poudre Arkansas Gunnison Rio Grande San Juan Yampa Accuracy 0.43 0.43 0.66 0.55 0.53 0.66
Fitting HSS 0.24 0.25 0.55 0.40 0.37 0.55
Accuracy 0.38 0.45 0.53 0.53 0.51 0.58 Drop 10%
HSS 0.17 0.27 0.37 0.37 0.35 0.45
Single site models
Multisite models
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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
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Summary
• Partial funding from Colorado Water Research Institute is thankfully acknowledged
• http://cadswes.colorado.edu/publications (PhD thesis) Regonda, 2006
Prairie, 2006Grantz, 2006