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

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

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

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)

ENSO as a “free” mode of the coupled ocean-atmosphere dynamics in the Tropical Pacific Ocean

Global Impacts of ENSO

Probabilistic

The Perfect Ocean

Perfect Ocean for Drought

Hoerling and Kumar (2003)

Pacific Ocean and Atmospheric Conditions

Key to

Western US Hydrologic Variability and Predictability

at interannual and interdecadal

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

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5

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J-00 J-00 J-00 J-00 J-00 J-00

SW

E (

in)

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Mea

n M

on

thly

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Oct Dec Feb Apr Jun Aug

SNOWFlow

For efficient and sustainable water management skilful forecast of spring (Apr-Jul) streamflows are needed

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

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)

Some Examples• Gunnison

River Basin

• Streamflow at six locations

• Multi-model ensemble forecast techniqueRegonda 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 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

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

variablesSurface Air Temperature

Zonal Wind Sea Surface Temperature

Meridional Wind

Wet years Dry years

Winter (Nov – Mar) Vector Wind Composites

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

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

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

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)

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

Jan 1st

Apr 1st

RPSS: 0.51

RPSS: 0.77

Model Validation for Tomichi River (1949-2002)

Apr 1st

Jan 1st RPSS: 0.32

RPSS: 0.95

Model Validation for Tomichi River (Dry Years)

Jan 1st

Apr 1st

RPSS: 0.75

RPSS: 1.00

Model Validation for Tomichi River (Wet Years)

Influence of PDSI

Model 1: PC1 SWE Model 2: PC1 SWE + PDSI

PDSI shifts ensembles in the right direction

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0.25

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1 2 3 4 5Month

RP

SS

Climate indices + Soil moisture Climate indices + Soil moisture + SWE

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0.50

0.75

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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

Time series of flows, SST, geopotential heights, SWE and PDSI for the San Juan

River

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2000

1945 1955 1965 1975 1985 1995 2005Time (year)

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mean

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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

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SST (C)

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SST4 vs f low

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Geopotential Height (m)

Flo

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GH1 vs f low

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PDSI

Flo

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KA

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PDSI1 vs f low

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SWE (in)

Flo

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KA

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SWE3 vs f low

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1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Years

Flo

w, T

AF

Observed

Forecast

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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

Comparison of flow forecasts for fitting and validation (drop 10%) for the SanJuan River

fitting

validation

Single site

Multisite

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Observed streamflow

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ecas

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Observed streamflow

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Observed streamflow

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Observed streamflow

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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

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

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, 2006Grantz, 2006

• balajir@colorado.edu