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
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
CORRELATION MAPS WITH LARGE-SCALE VARIABLES
MAM AMJ
MJJ
SLPs
CORRELATION MAPS WITH LARGE-SCALE VARIABLES
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
Winter Geopotential Height Anomaly
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
Winter Geopotential Height Anomaly
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
nov dec jan feb mar apr
Month
Co
rre
lati
on
Co
eff
GpH & SWE
SWE
Truckee Likelihood Results
0
0.5
1
1.5
2
2.5
nov dec jan feb mar apr
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
Truckee Spring Flow (kaf)
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
Lahontan Storage for Irrigation (kaf)
PD
F
0 100 200 300 400 500 600
0.00
0.04
Truckee Canal Diversion (kaf)
PD
F
0 100 200 300 400 500 600
0.00
00.
010
Water Remaining in Truckee (kaf)
PD
F
Ensemble forecast results
Climatology forecast results
Observed value results
NRCS official forecast results
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
Truckee Spring Flow (kaf)
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
Lahontan Storage for Irrigation (kaf)
PD
F
0 100 200 300 400 500 600
0.00
00.
015
0.03
0
Truckee Canal Diversion (kaf)
PD
F
0 100 200 300 400 500 600
0.00
0.02
Water Remaining in Truckee (kaf)
PD
F
Ensemble forecast results
Climatology forecast results
Observed value results
NRCS official forecast results
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