Interannual and Interdecadal Variability of Thailand Summer ...

Interannual and Interdecadal Variability of Thailand Summer ...

Understanding and Predicting Interannual Climate Variability : Applications to thailand Summer Monsoon and Truckee/Carson Streamflows Balaji Rajagopalan Nkrintra Singhrattna Katrina Grantz CIVIL, ENVIRONMENTAL AND ARCHITECTURAL ENGINEERING DEPARTMENT UNIVERSITY OF COLORADO AT BOULDER Hydrology Seminar Spring 2004 Publications Nkrintra Singhrattnas MS thesis http://civil.colorado.edu/~singhrat/nkrintra/pa pers/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 Grantzs MS thesis http://cadswes.colorado.edu/~grant/papers/Thesis. pdf

A Water Resources Management Perspective Inter-decadal Decision Analysis: Risk + Values T Facility Planning i Reservoir, Treatment Plant Size m e Policy + Regulatory Framework H o r i z o n Climate Flood Frequency, Water Rights, 7Q10 flow Operational Analysis Reservoir Operation, Flood/Drought Preparation Emergency Management Flood Warning, Drought Response

Data: Historical, Paleo, Scale, Models Hours Weather The Approach Climate Climate Diagnostics Diagnostics Climate Diagnostics Forecasting Forecasting Model Model Forecasting Model Decision Decision Support SupportSystem System To identify relevant predictors to

streamflow / precipitation stochastic models for ensemble forecasting conditioned on climate information 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 years monsoon Variability over interannual and decadal time scales 1800.0 1600.0 Rainfall (mm) Need to understand this variability Total Annual Rainfall

1400.0 1200.0 1000.0 800.0 600.0 1950 1960 1970 1980 Year 1990 2000 DATA DETAILS http://hydro.iis.utokyo.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 Nio, and La Nia 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 SLP SST 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) Low High Pre-1980

Post-1980 RELATIONSHIP WITH CONVECTION PARAMETERS Post-1980 composite correlation Pre-1980 El Nino-La Nina Pre-1980 El Nino-La Nina Post-1980 ENSO COMPOSITES Pre-1980 Composite maps of SSTs: Strong and eastward anomalies during post-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

2002 CMAP SST 1997 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 leadtime 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 MAM

AMJ MJJ 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 NONPARAMETRIC MODEL: local polynomials Modified K-nn 1000 900 Resample e of neighbors 800 700 E1 600 y* y 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 E2 E3 500 E4 400 300 200 100 0

0 2 4 6 8 x x* 10 12 14 Local Regression 600 400 200 0 Truckee Spring Volume (kaf)

Spring Flow vs. Winter Geopotential Height -100 -50 0 Winter Geopotential Height Anomaly 50 Residual Resampling Truckee Spring Flow 1989 e t* 260 200 200 220 yt *

240 Volume (kaf) 400 280 600 yt* = f(xt*) + et* x t* 0 Truckee Spring Volume (kaf) Spring Flow vs. Winter Geopotential Height -100 -50 0 Winter Geopotential Height Anomaly

50 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 RPS ( p, d ) 1 k i Pn k 1 j 1 n 1 i dn n 1 RPSS 1 RPS(forecast) RPS(climatology)

Likelihood Skill Score L N P j ,i t 1 N P c j ,i t 1

1 N MODEL SKILL R = 0.65 llh = 2.85 llh = 1.90 llh = 2.09 RPSS = 0.98 RPSS = 0.22 RPSS = 0.79 ALL YEARS WET YEARS DRY YEARS PDFs Yea r 1983

1988 1995 WE T YE A R S C l ima t o l ogy 10.0% 10.0% 10.0% K -n n 89.0% 82.9% 25.1% Yea r 1984 1987 1994 DR Y YE A R S C l ima t ol ogy K -n n 90.0% 84.1% 90.0% 100.0% 90.0% 39.5%

PDF obtain exceedence probability for extreme events (wet: >700 mm and dry: <400 mm) show good skill (especially for wet scenarios) Applications TRUCKEE/CARSON SPRING STREAMFLOWS Study Area PYRAMID LAKE WINNEMUCCA LAKE (dry) Carson RN IA STAMPEDE NEVADA CA

LI FO CALIFORNIA NEVADA Truckee Nixon Reno/Sparks BOCA DONNER Tahoe City LAKE TAHOE TRUCKEE CANAL Fernley Fallon

TRUCKEE RIVER INDEPENDENCE PROSSER Truckee Stillwater NWR Derby Dam Newlands Project Farad MARTIS Carson City Ft Churchill CARSON RIVER

LAHONTAN CARSON LAKE Study Area Prosser Creek Dam Lahontan Reservoir Basin Precipitation NEVADA Truckee CA LI FO Carson RN IA Average Annual Precipitation Basin Climatology

Average Monthly Flow Volumes 120 Truckee 100 Volume (kaf) Carson 80 60 Streamflow in Spring (April, May, June) 40 20 0 Oct Nov Dec Jan Feb

Mar Apr May Jun Jul Aug Sep Month Average Monthly Preciptation Precipitation in Winter (November March) 4 Precipitation (in) 3.5 3

Primarily snowmelt dominated basins 2.5 2 1.5 1 0.5 0 Oct Nov Dec Jan Feb Mar Apr Month May

Jun Jul Aug Sep Winter Climate Correlations Truckee Spring Flow 500mb Geopotential Height Sea Surface Temperature 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

Persistence of Correlations between Climate Variables and Spring Flow Correlation Value (abs.) 0.7 0.6 0.5 Strongest correlation in Winter (Dec-Feb) 0.4 0.3 0.2 SST 0.1 Geopotential Height 0 Jul-Sep Aug-Oct Sep-Nov Oct-Dec Nov-Jan Dec-Feb Jan-Mar Months Correlation

statistically significant back to August Climate Composites Vector Winds High Streamflow Years Low Streamflow Years Climate Composites Sea Surface Temperature High Streamflow Years Low Streamflow Years Physical Mechanism L Winds rotate counterclockwise around area of low pressure bringing warm,

moist air to mountains in Western US Forecasting Model Predictors SWE Geopotential Height 0 50 100 150 200 April 1st SWE (% of Normal) 250 r=-0.59 -100 -50

0 50 Winter Geopotential Height Anomaly 600 400 200 200 400 600 Truckee Spring Volume (kaf) SST Correlation r=0.41 0 r=0.93

0 200 400 600 Truckee Spring Volume (kaf) Geopotential Height Correlation 0 Truckee Spring Volume (kaf) SWE Correlation Sea Surface Temperature -1.5 -1.0 -0.5 0.0

0.5 1.0 Winter SST Anomaly 1.5 Forecasting Results 95th 50th 5th 95th 50th 5th April 1st Predictors April 1st SWE Dec-Feb geopotential height

Forecast Skill Scores April 1st forecast - 0 1 0 1 3 Median skill scores significantly beat climatology in all year subsets, both Truckee and Carson Truckee slightly better than Carson Median RPSS (all years) Truckee RPSS results 1.0 0.8 0.6 0.4 0.2

GpH & SWE 0.0 -0.2 SWE nov dec jan feb mar apr Truckee Likelihood Results Median Likelihood (all years) Month 2.5 2

1.5 1 0.5 GpH & SWE 0 SWE nov C o r r e latio n C o e ff Truckee Forecasted vs. Observed Correlation Coeff. 0.8 0.6 0.4 0.2 GpH & S WE nov dec

jan f eb Month mar S WE jan feb Month 1 0 dec apr mar apr

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 0.006 0.000 PDF 0.006 0.000 PDF 0.012 Seasonal Model Results: 1992 0

100 200 300 400 500 600 0 100 300 400 500 600 500 600

Carson Spring Flow (kaf) 0 100 200 300 400 500 600 0 100 200 300 400

0.020 Truckee Canal Diversion (kaf) Ensemble forecast results 0.010 Lahontan Storage for Irrigation (kaf) Climatology forecast results Observed value results 0.000 PDF 0.010 0.000 PDF 0.006 0.000 PDF

0.012 Truckee Spring Flow (kaf) 200 0 100 200 300 400 500 Water Remaining in Truckee (kaf) 600 NRCS official forecast results 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 0.006 0.000 PDF 0.010 0.000 PDF 0.012 Seasonal Model Results:1993 0 100 200

300 400 500 600 0 100 300 400 500 600 500 600 Carson Spring Flow (kaf)

0.00 0.04 PDF 0.006 0.000 PDF 0.012 Truckee Spring Flow (kaf) 200 0 100 200 300 400

500 600 Lahontan Storage for Irrigation (kaf) 0 100 200 300 400 Truckee Canal Diversion (kaf) 0.010 Climatology forecast results Observed value results 0.000 PDF

Ensemble forecast results 0 100 200 300 400 500 Water Remaining in Truckee (kaf) 600 NRCS official forecast results 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 0.008 0.000 PDF 0.015 0.000 PDF Seasonal Model Results: 2003 0 100 200 300 400 500

600 0 100 300 400 500 600 500 600 Carson Spring Flow (kaf) PDF 0.000 0.015

0.010 0.000 PDF 0.030 Truckee Spring Flow (kaf) 200 0 100 200 300 400 500 600 Lahontan Storage for Irrigation (kaf)

0 100 200 300 400 Truckee Canal Diversion (kaf) 0.02 Climatology forecast results Observed value results 0.00 PDF Ensemble forecast results NRCS official forecast results 0 100

200 300 400 500 Water Remaining in Truckee (kaf) 600 Irrigation Water pretty average: business as usual Truckee Canal diversions normal: not full capacity, but dont hold back too much Plenty Fish Water- no releases necessary to augment low flows, may choose a fish spawning run CONCLUSIONS Interannual/Interdecadal variability of regional hydrology (precipitation, streamflows) is modulated by large-scale oceanatmospheric 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

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