Using Large-Scale Climate Information to Forecast Seasonal Streamflows
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Using Large-Scale Climate Information to Forecast Seasonal Streamflows in the Truckee and Carson Rivers. Katrina A. Grantz Dept. of Civil, Architectural, and Environmental Engineering Masters Defense Fall 2003. The Research Project. Study Area & Motivation Outline the Approach Results

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Using Large-Scale Climate Information to Forecast Seasonal Streamflows in the Truckee and Carson Rivers

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The research project

Using Large-Scale Climate Information to Forecast Seasonal Streamflows in the Truckee and Carson Rivers

Katrina A. Grantz

Dept. of Civil, Architectural, and Environmental Engineering

Masters Defense

Fall 2003


The research project

The Research Project

  • Study Area & Motivation

  • Outline the Approach

  • Results

  • Summary


Study area

Study Area

PYRAMID

LAKE

WINNEMUCCA

LAKE (dry)

CALIFORNIA

NEVADA

Nixon

Stillwater NWR

Derby

Dam

STAMPEDE

Reno/Sparks

Fernley

Fallon

TRUCKEE

RIVER

INDEPENDENCE

BOCA

Newlands

Project

PROSSER

Truckee

CARSON

LAKE

MARTIS

LAHONTAN

Carson

City

DONNER

Tahoe City

CARSON

RIVER

LAKE TAHOE

NEVADA

Truckee

Carson

CALIFORNIA

TRUCKEE

CANAL

Farad

Ft Churchill


Study area1

Study Area

Lahontan Reservoir

Prosser Creek Dam


Basin precipitation

Basin Precipitation

NEVADA

Truckee

Carson

CALIFORNIA

Average Annual Precipitation


Motivation

Motivation

  • US Bureau of Reclamation (USBR) searching for an improved forecasting model for the Truckee and Carson Rivers (accurate and with long-lead time)

  • Forecasts determine reservoir releases and diversions

  • Protection of

    listed species

Cui-ui

Truckee Canal

Lahontan Cutthroat Trout


Current forecasting methods

Current Forecasting Methods

  • Use Snowpack Information

  • Linear regression

  • Sometimes subjectively alter the forecast

    • El Nino years

    • NRCS official forecast


Hydroclimate in western us

Hydroclimate in Western US

  • Past studies have shown a link between large-scale ocean-atmosphere features (e.g. ENSO and PDO) and western US hydroclimate (precipitation and streamflow)

  • E.g., Pizarro and Lall (2001)

    Correlations between peak

    streamflow and Nino3


Outline of approach

Outline of Approach

Climate

Diagnostics

  • Climate Diagnostics

    To identify relevant predictors to spring runoff in the basins

  • Forecasting Model

    Nonparametric stochastic model conditioned on climate indices and snow water equivalent

Forecasting

Model

Decision

Support System

  • Decision Support System

    Couple forecast with DSS to demonstrate utility of forecast


Data used

Data Used

  • 1949-2003 monthly data sets:

    • Natural Streamflow (Farad & Ft. Churchill gaging stations)

    • Snow Water Equivalent (SWE)- basin average

    • Large-Scale Climate Variables


Outline of approach1

Outline of Approach

Climate

Diagnostics

  • Climate Diagnostics

    To identify relevant predictors to spring runoff in the basins

Forecasting

Model

  • Forecasting Model

    Nonparametric stochastic model conditioned on climate indices and snow water equivalent

Decision

Support System

  • Decision Support System

    Couple forecast with DSS to demonstrate utility of forecast


Climate diagnostics

Climate Diagnostics

  • Climatology Analysis

    • To determine the timing of precipitation and runoff in the basin

  • Correlation Analysis

    • Correlate spring streamflows with winter/ fall ocean-atmosphere variables to identify predictors for the forecasting model

  • Composite Analysis

    • Look at atmospheric circulation patterns in extreme streamflow years– provides explanation of physical mechanism that drives the atmosphere-ocean-land relationship


Basin climatology

Basin Climatology

  • Streamflow in Spring (April, May, June)

  • Precipitation in Winter (November – March)

  • Primarily snowmelt dominated basins


Basin climatology1

Basin Climatology

  • Spring runoff and winter precipitation are highly variable

Spring Streamflow (1949-2003)

Winter SWE (1949-2003)


Swe correlation

SWE Correlation

  • Winter SWE and spring runoff in Truckee and Carson Rivers are highly correlated

  • April 1st SWE better than March 1st SWE


Correlation analysis

Correlation Analysis

  • Correlate spring streamflows in Truckee and Carson Rivers with ocean-atmosphere variables from the preceding Winter/Fall


Winter climate correlations

Winter Climate Correlations

Carson Spring Flow

500mb Geopotential Height

Sea Surface Temperature


Winter climate correlations1

Winter Climate Correlations

Truckee Spring Flow

500mb Geopotential Height

Sea Surface Temperature


Fall climate correlations

Fall Climate Correlations

Carson Spring Flow

500mb Geopotential Height

Sea Surface Temperature


Persistence of climate patterns

Persistence of Climate Patterns

Strongest correlation in Winter (Dec-Feb)

Correlation statistically significant back to August


Composite analysis

Composite Analysis

  • Look at atmospheric circulation patterns in extreme streamflow years– provides explanation of physical mechanism that drives the atmosphere-ocean-land relationship

  • High years: years above 90th percentile

  • Low years: years below 10th percentile


Climate composites

Climate Composites

Vector Winds

Low Streamflow Years

High Streamflow Years


Climate composites1

Climate Composites

Sea Surface Temperature

Low Streamflow Years

High Streamflow Years


Physical mechanism

Physical Mechanism

  • Winds rotate counter-clockwise around area of low pressure bringing warm, moist air to mountains in Western US

L


Climate indices

Climate Indices

  • Use areas of highest correlation to develop indices to be used as predictors in the forecasting model

  • Area averages of geopotential height and SST

500 mb Geopotential Height

Sea Surface Temperature


Forecasting model predictors

Forecasting Model Predictors

SWE

Geopotential Height

Sea Surface Temperature


Nonlinear relationship

Nonlinear Relationship

  • Relationships among variables highly nonlinear

  • Correlations strongest when geopotential height index is low


Climate diagnostics summary

Climate Diagnostics Summary

  • Winter/ Fall geopotential heights and SSTs over Pacific Ocean related to Spring streamflow in Truckee and Carson Rivers

  • Physical explanation for this correlation

  • Relationships are nonlinear


Outline of approach2

Outline of Approach

Climate

Diagnostics

  • Climate Diagnostics

    To identify relevant predictors to spring runoff in the basins

  • Forecasting Model

    Nonparametric stochastic model conditioned on climate indices and SWE

Forecasting

Model

Decision

Support System

  • Decision Support System

    Couple forecast with DSS to demonstrate utility of forecast


Forecasting model requirements

Forecasting Model Requirements

  • Forecast spring streamflow (total volume)

  • Capture linear and nonlinear relationships in the data

  • Produce ensemble forecasts (to calculate exceedence probabilities)


Modified k nearest neighbor k nn

Modified K- Nearest Neighbor(K-NN)

  • Uses local polynomial for the mean forecast (nonparametric)

  • Bootstraps the residuals for the ensemble (stochastic)

Benefits

  • Produces flows not seen in the historical record

  • Captures any non-linearities in the data, as well as linear relationships

  • Quantifies the uncertainty in the forecast (Gaussian and non-Gaussian)


Local regression

Local Regression


Local regression1

Local Regression


Residual resampling

Residual Resampling

e

*

t

y

*

t

xt*

yt* = f(xt*) + et*


Model validation skill measure

Model Validation & Skill Measure

  • Cross-validation: drop one year from the model and forecast the “unknown” value


Model validation skill measure1

Model Validation & Skill Measure

  • Cross-validation: drop one year from the model and forecast the “unknown” value

  • Compare median of forecasted vs. observed (obtain “r” value)


Model validation skill measure2

Model Validation & Skill Measure

  • Cross-validation: drop one year from the model and forecast the “unknown” value

  • Compare median of forecasted vs. observed (obtain “r” value)

  • Rank Probability Skill Score


Model validation skill measure3

Model Validation & Skill Measure

  • Cross-validation: drop one year from the model and forecast the “unknown” value

  • Compare median of forecasted vs. observed (obtain “r” value)

  • Rank Probability Skill Score

  • Likelihood Skill Score


Model predictors

Model Predictors

  • SWE

  • Geopotential

    Height

  • SWE

  • Geopotential

    Height

  • SST

?

OR

  • Compare model results between using SST and not using SST in the predictor set


Model predictors1

Model Predictors

  • SWE

  • Geopotential

    Height

  • SWE

  • Geopotential

    Height

  • SST

?

OR

  • Little or no improvement in results with SST index

    • Possibly due to redundancy in physical mechanism driving correlation

    • Possibly due to relatively decreased correlation coefficient

  • In the interest of parsimony, use SWE and geopotential height only


Forecasting results

Forecasting Results

Predictors

  • April 1st SWE

  • Dec-Feb geopotential height

95th

50th

5th

95th

50th

5th

April 1st forecast


Forecasting results1

Forecasting Results

April 1st forecast


Forecasting results2

Forecasting Results

  • Ensemble forecasts provide range of possible streamflow values

  • Water manager can calculate exceedence probabilites

Wet Year

Dry Year


The research project

RPSS

All Years

Wet Years

Dry Years

  • Beats climatology in most years

  • Performs best in wet years

0 1


Likelihood skill score

Likelihood Skill Score

All Years

Wet Years

Dry Years

  • Beats climatology in most years

  • Performs best in wet years

0 1 3


Forecast skill scores

Forecast Skill Scores

April 1st forecast

  • Median skill scores significantly beat climatology in all year subsets, both Truckee and Carson

  • Truckee slightly better than Carson


Use of climate index in forecast

Use of Climate Index in Forecast

Extremely Wet Years

  • In general, the boxes are much tighter with geopotential height (greater confidence)

  • Underprediction w/o climate index– might not be fully prepared with flood control measures

95th

95th

50th

50th

5th

5th

95th

95th

50th

50th

5th

5th

SWEand Geopotential Height

SWE


The research project

Use of Climate Index in Forecast

Extremely Dry Years

  • Tighter prediction interval with geopotential height

  • Overprediction w/o climate index (esp. in 1992)

    • Might not implement necessary drought precautions in sufficient time

95th

95th

50th

50th

5th

5th

95th

95th

50th

50th

5th

5th

SWEand Geopotential Height

SWE


March 1 st forecast

March 1st Forecast

Uses March 1st SWE and Dec-Jan geopotential height index

Skill decreases with March 1st forecast (less SWE information)

Still pretty good results


March 1 st forecast skill

March 1st Forecast Skill

  • Median skill scores significantly beat climatology in all year subsets, both Truckee and Carson

  • RPSS and Likelihood show mixed results on which river is better (Truckee or Carson)


Fall forecast results

Fall Forecast Results

Uses only fall (Sept-Nov) geopotential height index

Fall climate forecast captures whether season will be above or below average


Fall forecast skill scores

Fall Forecast Skill Scores

  • Though not highly accurate, fall forecast improves on climatology

  • Typically no SWE in fall, no forecast available


Monthly disaggregation

Monthly Disaggregation

  • Important for setting monthly storage targets on Lahontan Reservoir

  • Done using K-NN scheme: resampling neighbor’s monthly proportions of total volume


Outline of approach3

Outline of Approach

Climate

Diagnostics

  • Climate Diagnostics

    To identify relevant predictors to spring runoff in the basins

  • Forecasting Model

    Nonparametric stochastic model conditioned on climate indices and SWE

Forecasting

Model

  • Decision Support System

    Couple forecast with DSS to demonstrate utility of forecast

Decision

Support System


Truckee carson riverware model

Truckee-Carson RiverWare Model


Truckee riverware model

Truckee RiverWare Model

  • Physical Mechanisms

    • Reservoir releases, diversions, evap, reach routing


Truckee riverware model1

Truckee RiverWare Model

  • Physical Mechanisms

    • Reservoir releases, diversions, evap, reach routing

  • Policies

    • Implemented with “rules” (user defined prioritized logic)


Truckee riverware model2

Truckee RiverWare Model

  • Physical Mechanisms

    • Reservoir releases, diversions, evap, reach routing

  • Policies

    • Implemented with “rules” (user defined prioritized logic)

  • Water Rights

    • Implemented in accounting network


Simplified seasonal model

Method to test the utility of the forecasts and the role they play in decision making

Model implements major policies in lower basin (Newlands Project OCAP)

Seasonal timestep

Simplified Seasonal Model


Seasonal model policies

Seasonal Model Policies

  • Use Carson water first

  • Max canal diversions: 164 kaf

  • Storage targets on Lahontan Reservoir: 2/3 of projected April-July runoff volume

  • Minimum Lahontan storage target: 1/3 of average historical spring runoff

  • No minimum fish flows


Decision variables

Lahontan Storage Available for Irrigation

Truckee River Water Available for Fish

Diversion through the Truckee Canal

Decision Variables


Seasonal model results 1992

Seasonal Model Results: 1992

  • Irrigation Water less than typical– decrease crop size or use drought-resistant crops

  • Truckee Canal smaller diversion-start the season with small diversions (one way canal)

  • Very little Fish Water- releases from Stampede coordinated with Canal diversions

Ensemble forecast results

Climatology forecast results

Observed value results

NRCS official forecast results


Seasonal model results 1993

Irrigation Water more than typical– plenty for irrigation and carryover

Truckee Canal larger diversion-start the season at full diversions (limited capacity canal)

Plenty Fish Water- FWS may schedule a fish spawning run

Seasonal Model Results:1993

Ensemble forecast results

Climatology forecast results

Observed value results

NRCS official forecast results


Seasonal model results 2003

Seasonal Model Results: 2003

  • Irrigation Water pretty average: business as usual

  • Truckee Canal diversions normal: not full capacity, but don’t hold back too much

  • Plenty Fish Water- no releases necessary to augment low flows, may choose a fish spawning run

Ensemble forecast results

Climatology forecast results

Observed value results

NRCS official forecast results


Seasonal model results

Seasonal Model Results

All Years


Seasonal model fall results

Seasonal Model Fall Results

All Years


Outline of approach4

Outline of Approach

Climate

Diagnostics

  • Climate Diagnostics

    To identify relevant predictors to spring runoff in the basins

  • Forecasting Model

    Nonparametric stochastic model conditioned on climate indices and snow water equivalent

Forecasting

Model

Decision

Support System

  • Decision Support System

    Couple forecast with DSS to demonstrate utility of forecast


The research project

Summary & Conclusions

  • Climate indicators improve forecasts and offer longer lead time

  • Water managers can utilize the improved forecasts in operations and seasonal planning


Future work

Future Work

  • Couple ensemble forecasts with RiverWare model

  • Temporal disaggregation

  • Forecast improvements

    • Joint Truckee/Carson forecast

    • Objective predictor selection

  • Compare results with physically-based runoff model (e.g. MMS)


Acknowledgements

Acknowledgements

Advising and support

  • Balaji Rajagopalan, Edie Zagona, and Martyn Clark

Funding

  • Paul Sperry of CIRES and the Innovative Reseach Project

  • Tom Scott of USBR Lahontan Basin Area Office

Technical Support

  • CADSWES personnel


Thank you

Thank You

Questions or Comments?


Extra slides follow

Extra Slides Follow


Geopotential height correlation

Geopotential Height Correlation


Sst correlation

SST Correlation


Climate composites2

Climate Composites

High-Low Years

Vector Winds

Sea Surface Temperature


Prms upper truckee vicinity

PRMSUpper Truckee Vicinity

PRMS Basin delineation for Upper Truckee Basin


The research project

ENSO


The research project

PDO


Pna pattern

PNA Pattern

Positive PNA has a warm western ridge and cold eastern trough.

Negative PNA has a cold western trough and a warm eastern ridge.


Pna pattern1

PNA Pattern

Pattern plotted as a regression map. Red, black, and blue contours on the maps indicate positive, zero, and negative isopleths, respectively. Contour interval of 0.35 for correlations and 2 dam for regressions. Variability of the PNA pattern explains over 50% of the December, January, February interannual variability at its four centers of action.


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