Understanding and Predicting Interannual Climate Variability : Applications to thailand Summer Monsoon and Truckee/Carson Streamflows Balaji Rajagopalan.

Understanding and Predicting Interannual Climate Variability : Applications to thailand Summer

Monsoon and Truckee/Carson Streamflows

Balaji RajagopalanNkrintra Singhrattna

Katrina GrantzCIVIL, ENVIRONMENTAL AND ARCHITECTURAL

ENGINEERING DEPARTMENTUNIVERSITY OF COLORADO AT BOULDER

Hydrology Seminar Spring 2004

Publications

Nkrintra Singhrattna’s MS thesis

http://civil.colorado.edu/~singhrat/nkrintra/papers/complete.pdf

Singhrattna et al. (2003): (under revision) Journal of Climate

Singhrattna et al.. (2004) (in review) International Journal of Climatology

(http://civil.colorado.edu/~balajir/)

Katrina Grantz’s MS thesis

http://cadswes.colorado.edu/~grant/papers/Thesis.pdf




http://civil.colorado.edu/~balajir/

http://cadswes.colorado.edu/~grant/papers/Thesis.pdf

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

The Approach

ClimateDiagnostics

ClimateDiagnostics

ForecastingModel

ForecastingModel

DecisionSupport System

DecisionSupport System

• Forecasting Modelstochastic models for ensemble forecasting - conditioned on climate information

• Climate DiagnosticsTo identify relevant predictors to streamflow / precipitation

• Decision Support System (DSS)Couple forecast with DSS to demonstrate utility of forecast

Applications

1. THAILAND SUMMER MONSOON

2. TRUCKEE/CARSON SPRING STREAMFLOWS

MOTIVATION

THAILAND BACKGROUND• Location between 5-20

N latitudes and 97-106 E longitudes

• Population ~ 61.2 million• Major occupation:

agriculture (50%-60% of national economy)

• Agriculture depends on precipitation and irrigation that is dependent on precipitation to store in reservoirs as well

• “Precipitation” is crucial

MOTIVATION

SEASON OF RAINFALL• 80%-90% of annual

precipitation occurs during monsoon season (May-Oct)

• Runoff is stored in reservoirs for use until the next year’s monsoon

• Variability over inter-annual and decadal time scales– Need to understand

this variability

Total Annual Rainfall

600.0

800.0

1000.0

1200.0

1400.0

1600.0

1800.0

1950 1960 1970 1980 1990 2000

Year

Rain

fall (

mm

)

DATA DETAILS

• http://hydro.iis.u-tokyo.ac.jp/GAME-T

• Thailand Meteorological Dept.

• Six rainfall stations (r ~ 0.51)

• Five temperature stations (r ~ 0.50)

• Atmospheric circulation variables such as SLPs, SSTs and vector winds: NCEP/NCAR Re-analysis (www.cdc.noaa.gov)

DATA DETAILS

• Correlation maps (CMAP and SATs) ensure their consistency

• Thus, average rainfall ~ “rainfall index”

average temperature ~ “temperature index”

CLIMATOLOGY

• Spring (MAM) temperatures set up land-ocean gradient driving the summer monsoon

• Summer monsoon (rainy season): Aug-Oct (ASO)

• Little peak in May: Due to Northward movement of ITCZ

• Enhanced MAM temperatures Enhanced ASO rainfall Decreasing monsoon seasonal (ASO) temperatures

CLIMATOLOGY

• ITCZ northward movement:- Cover Thailand in May- Move to China in June- Southward move to cover Thailand again in August

AM

SON

TRENDS• Decreasing MAM

temperature over decadal (-0.4 C)

• Decreasing ASO rainfall (-180 mm)

• Tend to cool land and atmosphere less Increasing ASO temperature

• Trends after 1980: Increasing MAM temperature Increasing ASO rainfall (IPCC 2001 report)

• Trends are part of global warming trends (IPCC 2001)

KEY QUESTION

“What drives the interannual and interdecadal variability of Thailand

summer monsoon?”

Schematic view of sea surface temperature and tropical rainfall in the the equatorial Pacific Ocean during normal, El Niño, and La Niña conditions

..

Global Impacts of ENSO

FIRST INVESTIGATION• 21-yr moving window correlation with SOI index: Strong

significant correlation only post-1980• Spectral Coherence with SOI index

CORRELATION MAPS

SS

TS

LP

Pre-1980 Post-1980

COMPOSITE MAPS

• To understand nonlinear relationship: Composite maps (pre- and post-1980) of high and low rainfall years (3 highest and lowest years)

Hig

hLo

w

Pre-1980 Post-1980

RELATIONSHIP WITH CONVECTION PARAMETERS

Pre-1980 Post-1980

corr

ela

tion

com

posi

te

El Nino-La Nina Pre-1980 El Nino-La Nina Post-1980

ENSO COMPOSITES

• Composite maps of SSTs:

• Strong and eastward anomalies during post-1980

Pre-1980

Post-1980

HYPOTHESIS

“East Pacific centered ENSO reduces convections in Western Pacific regions (Thailand) while dateline centered ENSO decreases convections in Indian subcontinent”

Pre-1980

Post-1980

COMPARISON WITH INDIAN MONSOON

• To show changes in regional impacts of ENSO• 21-yr moving window correlation: Indian monsoon lose

its correlation with ENSO around post-1980• Thailand monsoon picks up correlation at the same time

CASE STUDIES

1997 2002

SS

TC

MA

P

SUMMARY

• Strong relationship between Thailand monsoon and ENSO during post-1980 – when the Indian monsoon shows weakening relationship

• Descending branch of Walker Cell associated with Eastern Pacific ENSO (post-1980) tend to be over Western pacific (including thailand) decreased Thailand monsoon rainfall

• Dateline-centered ENSOs (Pre-1980) tend to suppress convection over the Indian subcontinent

Predictor identification

• Good relation with monsoon rainfall (post-1980) at reasonable lead-time

• Correlate summer rainfall with large-scale climate variables from prior seasons identify regions with strong correlations and develop predictor indices

CORRELATED WITH STANDARD INDICES

• Significant correlations at1-2 seasons lead-time

CORRELATION MAPS WITH LARGE-SCALE VARIABLES

MAM AMJ

MJJ

SATs


MAM AMJ

MJJ

SLPs


MJJ

AMJMAM

SSTs

TEMPORAL VARIABILITY OF PREDICTORS

• Predictors are related to Thailand Monsoon only in the post-1980 period

• SST and SLP Predictors are selected for Rainfall Forecasting

MAM

AMJ

MJJ

TRADITIONAL MODEL: LINEAR REGRESSION

• Y = a * SLP + b * SST + e• e = residual: normal (Gaussian) distribution

with mean = 0, variance = 2

• Y assumed normally (Gaussian) distributed• Drawbacks:

– unable to capture non-Gaussian/nonlinear features– High order fits require large amounts of data– Not portable across data sets

Modified K-nn

0

100

200

300

400

500

600

700

800

900

1000

0 2 4 6 8 10 12 14

x

y

NONPARAMETRIC MODEL: local polynomials

• Y = (SLPs, SSTs) + e = local regression (residual: e are

saved)• Capture any arbitrary: Linear or

nonlinear• To forecast at any given “x*”, the

mean forecast “y*” obtained by local regression (first step)

• To generate ensemble forecasts: Resample residuals (e) in the neighborhood of “X*”

• Add residual to mean forecast “y*”• Assume a normal distribution

“locally” in the neighborhood of “x*”

• Be able to generate unseen values in historical data

y*

x*

Resample “e” of neighbors

E1E2

E3

E4

Local Regression

-100 -50 0 50

02

00

40

06

00

Spring Flow vs. Winter Geopotential Height

Winter Geopotential Height Anomaly

Tru

cke

e S

pri

ng

Vo

lum

e (

kaf)

-100 -50 0 50

020

040

060

0

Spring Flow vs. Winter Geopotential Height


Tru

ckee

Spr

ing

Vol

ume

(kaf

)

yt* et*

xt*

yt* = f(xt

*) + et*

Residual Resampling

200

220

240

260

280

Truckee Spring Flow 1989

Vol

ume

(kaf

)

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 Scoreology)RPS(climat

st)RPS(foreca1RPSS

k

j

i

nn

i

nn dP

kdpRPS

1 111

1),(

N

N

tc

N

tij

ijP

PL

1

1

1,

,

MODEL SKILL

ALL YEARS WET YEARS DRY YEARS

R = 0.65

llh = 2.09

RPSS = 0.79

llh = 2.85 llh = 1.90

RPSS = 0.98 RPSS = 0.22

PDFs

• PDF obtain exceedence probability for extreme events (wet: >700 mm and dry: <400 mm) show good skill (especially for wet scenarios)

Year Cl imatol ogy K-nn1983 10.0% 89.0%1988 10.0% 82.9%1995 10.0% 25.1%

WET YEARSYear Cl imatol ogy K-nn1984 90.0% 84.1%1987 90.0% 100.0%1994 90.0% 39.5%

DRY YEARS

Applications

TRUCKEE/CARSON SPRING STREAMFLOWS

INDEPENDENCE

DONNERMARTIS

STAMPEDE

BOCA

PROSSER

TRUCKEERIVER

CARSONRIVER

CARSONLAKE

Truckee

CarsonCity

Tahoe City

Nixon

Fernley

DerbyDam

Fallon

WINNEMUCCALAKE (dry)

LAHONTAN

PYRAMID LAKE

NewlandsProject

Stillwater NWR

Reno/Sparks

NE

VA

DA

CA

LIF

OR

NIA

LAKE TAHOE

Study Area

TRUCKEE CANAL

Farad

Ft Churchill

NEVADA

CALIFORNIA

Carson

Truckee

Study Area

Prosser Creek Dam Lahontan Reservoir

Basin Precipitation

NEVADA

CALIFORNIA

Carson

Truckee

Average Annual Precipitation

Basin Climatology

• Streamflow in Spring (April, May, June)

• Precipitation in Winter (November – March)

• Primarily snowmelt dominated basins

Average Monthly Flow Volumes

0

20

40

60

80

100

120

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Month

Vo

lum

e (k

af)

Truckee

Carson

Average Monthly Preciptation

0

0.5

1

1.5

2

2.5

3

3.5

4

Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep

Month

Pre

cip

itat

ion

(in

)

Winter Climate Correlations

500mb Geopotential Height Sea Surface Temperature

Truckee Spring Flow

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

Persistence of Climate Patterns

• Strongest correlation in Winter (Dec-Feb)

• Correlation statistically significant back to August

Persistence of Correlations between Climate Variables and Spring Flow

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Jul-Sep Aug-Oct Sep-Nov Oct-Dec Nov-Jan Dec-Feb Jan-Mar

Months

Co

rrel

atio

n V

alu

e (a

bs.

)

SST

Geopotential Height

High Streamflow Years Low Streamflow Years

Vector Winds

Climate Composites

High Streamflow Years Low Streamflow Years

Sea Surface Temperature

Climate Composites

Physical Mechanism

L

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

Forecasting Model Predictors

•SWE •Geopotential Height •Sea Surface Temperature

-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5

020

040

060

0

SST Correlation

Winter SST Anomaly

Tru

ckee

Spr

ing

Vol

ume

(kaf

)

r=0.41

-100 -50 0 50

020

040

060

0

Geopotential Height Correlation


Tru

ckee

Spr

ing

Vol

ume

(kaf

)

r=-0.59

0 50 100 150 200 250

020

040

060

0

SWE Correlation

April 1st SWE (% of Normal)

Tru

ckee

Spr

ing

Vol

ume

(kaf

)

r=0.93

Forecasting Results

PredictorsPredictors• April 1April 1stst SWESWE• Dec-Feb Dec-Feb geopotential geopotential heightheight

95th

50th

5th

April 1st forecast

95th

50th

5th

0 1- 0 1 3

Forecast Skill Scores

April 1April 1stst forecastforecast

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

• Truckee slightly better than Carson

Truckee RPSS results

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

nov dec jan feb mar apr

Month

Me

dia

n R

PS

S (

all y

ear

s)

GpH & SWE

SWE

Truckee Forecasted vs. Observed Correlation Coeff.

0

0.2

0.4

0.6

0.8

1


Month

Co

rre

lati

on

Co

eff

GpH & SWE

SWE

Truckee Likelihood Results

0

0.5

1

1.5

2

2.5


Month

Me

dia

n L

ike

liho

od

(al

l ye

ars

)

GpH & SWE

SWE

Model Skills in Water Resources Decision Support System

Ensemble Forecasts are passed through a Decision Support System of the Truckee/Carson Basin

Ensembles of the decision variables are compared against the “actual” values

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

0 100 200 300 400 500 600

0.00

00.

006

Truckee Spring Flow (kaf)

PD

F

0 100 200 300 400 500 600

0.00

00.

006

0.01

2Carson Spring Flow (kaf)

PD

F

0 100 200 300 400 500 600

0.00

00.

006

0.01

2

Lahontan Storage for Irrigation (kaf)

PD

F

0 100 200 300 400 500 600

0.00

00.

010

Truckee Canal Diversion (kaf)

PD

F

0 100 200 300 400 500 600

0.00

00.

010

0.02

0

Water Remaining in Truckee (kaf)

PD

F

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

0 100 200 300 400 500 600

0.00

00.

010


PD

F

0 100 200 300 400 500 600

0.00

00.

006

0.01

2

Carson Spring Flow (kaf)

PD

F

0 100 200 300 400 500 600

0.00

00.

006

0.01

2


PD

F

0 100 200 300 400 500 600

0.00

0.04


PD

F

0 100 200 300 400 500 600

0.00

00.

010


PD

F





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

0 100 200 300 400 500 600

0.00

00.

015


PD

F

0 100 200 300 400 500 600

0.00

00.

008

Carson Spring Flow (kaf)P

DF

0 100 200 300 400 500 600

0.00

00.

010


PD

F

0 100 200 300 400 500 600

0.00

00.

015

0.03

0


PD

F

0 100 200 300 400 500 600

0.00

0.02


PD

F





CONCLUSIONS

• Interannual/Interdecadal variability of regional hydrology (precipitation, streamflows) is modulated by large-scale ocean-atmospheric features

• Incorporating Large scale Climate information in regional hydrologic forecasting models (Seasonal streamflows and precipitation) provides significant skill at long lead times

• Nonparametric methods offer an attractive and flexible alternative to traditional methods.

• capability to capture any arbitrary relationship• data-drive• easily portable across sites

• Significant implications to water (resource) management and planning

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

• Edie Zagona, Martyn Clark, K. Krishna Kumar, Tom Chase

• Paul Sperry of CIRES and the Innovative Reseach Project• Tom Scott of USBR Lahontan Basin Area Office • CADSWES• IUGG Travel support for Nkrintra Singhrattna

Understanding and Predicting Interannual Climate Variability : Applications to thailand Summer Monsoon and Truckee/Carson Streamflows Balaji Rajagopalan.

Documents