Extremes of Precipitation, Temperature & Sea Level in a Changing Climate: Implications for Water Resources

Management

Jayantha Obeysekera (Obey)

Hydrologic & Environmental Systems Modeling

South Florida Water Management District

Jayantha Obeysekera (Obey)

Hydrologic & Environmental Systems Modeling

South Florida Water Management District

Statistical Assessment of Extreme Weather Phenomena under Climate Change US CLIBAR/NCAR ASP Research Colloquium, June 13-17, 2011

Outline

Why extremes are important (with emphasis on Florida) – Vulnerabilities in Water Resources Management

A systematic approach to analyze future extremes from models

Historical trends

Validation of models

Conclusions

Credit: R-Project, ExtRemes, Fields, RNetCDF packages

Natural Variability – Role of Teleconnections

(Kwon, Lall, and Obeysekera (2008))

Lake InflowENSO AMO

Lake OkeechobeeInflow

A high level conceptual model

Natural CyclesInterannual

(e.g. El Nino and La Nina) to

Multi-decadal(e.g. AMO*)

Human InducedLand use changesGreenhouse gases ->Global Warming

Climate Change Drivers

Quartet of change:Stressors

• Rising Seas

• Temperature

• Rainfall, floods, and droughts

• Tropical Storms & Hurricanes

Water Management Impacts

• Direct landscape impacts (e.g. storm surge)

• Water Supply(e.g. droughts, saltwater intrusion)• Flood Control(e.g. urban flooding, hurricanes)• Natural Systems(e.g. ecosystem impacts, both coastal and interior)

*Atlantic Multi-decadal Oscillation of temperature in the Atlantic Ocean

Adapatation

Two Important Questions:

• Which decisions are likely to be affected and could benefit from adaptation strategies (Type I) in the short term?

“No Regret Strategies”

• Which decisions are likely to be affected but for which adaptation strategies (Type II) could be deferred without serious consequences?

Courtesy: Chris Lansea.National Hurricane Center

Tropical Storms: Natural Variability versus Anthropogenic Effects?

Natural Variability?

Ass

ets

The Entire Region Floods in 1947

Managed System (~2003) Pre-Drainage System (1850’s)

Everglades National Park

Wat

er

Con

serv

atio

n

Are

as

Everglades Agricultural Area

Lo

wer

Eas

t C

oas

t U

rban

ized

A

rea

Lake Okeechobee

Aquifer Storage and Recovery

Surface Water Storage Reservoir

Removing Barriers to Sheetflow

Operational Changes

Wastewater Reuse

Seepage Management

Stormwater Treatment Areas

Everglades Restoration

Rising Sea Level – Historical Data

As means increase so will extremes

Credit:  Victoria Morrow (Broward County)

Coastal street flooding during high tide

Credit:Joseph Park (SFWMD)Ocean Avenue, A1A

Miami-Dade CountyCredit: Miami-Dade DERM

Current & Evolving Climate Conditions: Attribution?

Impacts of Rising Seas: Flood Control

Ocean Side(tailwater) Land Side(headwater)

Coastal Structure

Impacts of Rising Seas: Flood Control

Ocean Side(tailwater) Land Side(headwater)

Coastal Structure

Vulnerable Structures

Preliminary review based on original designs

28 gravity structures on the East Coast

Six gravity structures on the west coast, including a USACE structure, S-79.

Most vulnerable structures are in Miami Dade and Broward counties

New Pump StationSpillway

Adaptation to Rising SeasExample: Forward Pumping at S-26 Structure

Rising Seas - Water Supply:Saltwater Intrusion

Potential Impact of Rising Seas:Southern Everglades

Relocation and possible reduction of mangrove forests

Forced migration of wading birds northward

Potential peat collapse, coastal erosion, and redistribution of sediments

Salinity intrusion into freshwater marshes can: discharge toxic hydrogen sulfide, cause coastal fish kills, and increase habitat loss

50-year rainfall return level- How will these change?

1-hour 6 -hour 12-hour

24-hour 72-hour 120-hour

Resolution (lack of!) GCMs

Uncertainties in GCM predictions due to:

Poor resolution – South Florida not even modeled in some GCMs; greater errors at smaller scales

From IPCC AR4-WG1, Ch. 8 - Simulation of tropical precipitation, ENSO, clouds and their response to climate change, etc.

General Ciculation Models

(AOGCMs)

Observed Climate Data

Is there evidence that extremes are

changing?How well are climatic extremes represented by GCM/downscaled model results? Role of Teleconnections?

How do projections of extremes affect water resources management?

Simulation of Late 20th Century

21st Century Climate

Projections

Downscale global information to regional information

A systematic approach for using climate model data

12 3

Extreme Value Data

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1 WEST PALM BEACH INTERNA 2 DAYTONA BEACH INTL AP 3 INGLIS 3 E 4 SAINT LEO 5 VENUS 6 DOWLING PARK 1 W 7 PENSACOLA REGIONAL AP 8 JACKSONVILLE INTL AP 9 MARINELAND 10 MIAMI INTERNATIONAL AP 11 ST PETERSBURG 12 MELBOURNE WFO 13 MOORE HAVEN LOCK 1 14 TAMIAMI TRAIL 40 MI BEN 15 LYNNE 16 ORTONA LOCK 2 17 PARRISH 18 PORT MAYACA S L CANAL 19 ST LUCIE NEW LOCK 1 20 LAKELAND 21 PENNSUCO 5 WNW 22 CLEWISTON 23 CANAL POINT GATE 5 24 FOLKSTON GA25 KEY WEST INTL AP 26 NICEVILLE 27 TALLAHASSEE WSO AP 28 BELLE GLADE HRCN GT 4 29 LISBON 30 NORTH NEW RVR CANAL 2 31 GRACEVILLE 1 SW 32 BRISTOL 33 FARGO GA34 APALACHICOLA AIRPORT

35 VENICE 36 BOCA RATON 37 FORT MYERS PAGE FIELD A 38 COOLIDGE GA39 WOODRUFF DAM 40 MIAMI WSO CITY 41 RAIFORD STATE PRISON 42 VERO BEACH 4 SE 43 BLACKMAN 44 BROOKSVILLE 7 SSW 45 ORLANDO INTL AP 46 ORLANDO WSO AIRPORT 47 LIGNUMVITAE KEY 48 LOXAHATCHEE 49 GRADY 50 OKEECHOBEE 51 LAMONT 6 WNW 52 ORANGE CITY 53 BRANFORD 54 GAINESVILLE 3 WSW 55 BROOKSVILLE CHIN HILL 56 CROSS CITY 2 WNW 57 KISSIMMEE 2 58 MONTICELLO 5 SE 59 PANAMA CITY 5 N 60 BAINBRIDGE GA61 TAMIAMI CANAL 62 BAINBRIDGE GA INTL PAPER63 VERO BEACH MUNI ARPT 64 PENSACOLA WB CITY 65 PANAMA CITY 2 66 VERNON 67 NORTH NEW RIVER CANAL 1 68 WAUSAU

Florida, Daily32 stationsPrecip, Temp.

University ofCentral Florida,Extreme Values1 hrs – 120 hrs

NARCCAP Data Set, 3-hourly, Precip,Temp

68 stations

Variables

Precip

Tmax

Tmin

Tave

DTR

Variable Extremes (by season)Precipitation(Rainfall)

Number of days of extreme values (> 1-in-2).Maximum seasonal valueNumber of heavy precipitation events (> 1-in-5) of duration 2, 3, 5, and 7 days

Daily temperature(Average, Maximum, Minimum, Temperature Range)

Number of days of extreme values (> 1-in-2).Maximum and minimum seasonal valuesNumber of extreme events (> 1-in-5) of duration 2, 3, 5, and 7 days

Table 1 Statistics used for trend detection

May Precipitation - POR

100

May Precipitation – post-1950

70

(a) (b)

Markers sized from +/- 0.2 to +/- 7.7 mm/decade. Markers sized from +/- 0.5 to +/- 21.1 mm/decade.

May Precipitation

Annual # of Dog Days - POR

310

Annual # of Dog Days – 1950-2008

48

(a) (b)

Markers sized from +/- 0.0 to +/- 6.9 days /decade. Markers sized from +/- 0.0 to +/- 11.5 days /decade.

Number of Dog Days

Historical decrease in the daily temperature range (DTR) for the period 1950-2008  is observed mainly due to increased daily minimum temperature (Tmin) and possibly attributable to heat island effect. 

Decadal population estimates for three USHCN stations in Florida. Population estimates were derived by Owen & Gallo (2000) for a 21 km by 21 km grid cell around each station. 

Ft. Myers (83186)

Ft. Lauderdale (83186)Arcadia (80228)

Trends in annual average daily temperature range (DTR) at (a) Arcadia, (b) Fort Myers, and (c) Fort Lauderdale for the period 1950-2008. The dotted line represents the linear trend from Sen-Theil regression with Zhang’s pre-whitening, while the solid line is the Lowess non-parametric regression line smoother which uses locally-weighted polynomial regression with a span of 0.25. 

Effect of Land Use Changes?

Florida - Main Observations

Hydrologic & Environmental Systems Modeling

number of wet days during the dry season – POR

May precipitation throughout the state – POR and especially post-1950. May be linked to changes in start of the wet season.

Urban heat island effect – urban (and drained) areas Tave and number of dog days for wet

(warm) season especially post-1950 Decrease in DTR ( Tmin > Tmax) Annual maximum of Tave and Tmin for all

seasons in POR and especially post-1950

General Ciculation Models

(AOGCMs)

Observed Climate Data

Is there evidence that extremes are

changing?How well are climatic extremes represented by GCM/downscaled model results? Role of Teleconnections?

How do projections of extremes affect water resources management?

Simulation of Late 20th Century

21st Century Climate

Projections

Downscale global information to regional information

A systematic approach for extremes

12 3

Dynamical DownscalingNorth American Regional Climate Change Assessment Program

Acknowledgement:NARCCAP is funded by the National Science Foundation (NSF), the U.S. Department of Energy (DoE), the National Oceanic and Atmospheric Administration (NOAA), and the U.S. Environmental Protection Agency Office of Research and Development (EPA)."

A2 Emissions Scenario

GFDL CCSMHADCM3link to European

Prudence

CGCM3

1971-2000 current 2040-2070 futureProvide boundary conditions

MM5Iowa State/PNNL

RegCM3UC Santa CruzICTP

CRCMQuebec,Ouranos

HADRM3Hadley Centre

RSMScripps

WRFNCAR/PNNL

CAM3Time slice

50km

GFDLTime slice

50 km

NARCCAP Scenario & Models

UCF Data set – Rainfall Extremes

GEV parameters of annual maxima

Location Scale Shape

Duration = 6-hours

Duration = 24 hours

Model Skill

All locations – Location Parameter

6-Hour 24-Hour

Model Comparison – Shape Parameter

Model Comparison – 25 year Return Level

Non-stationarity: Likelihood Ratio Test with AMO as the co-variate on Location

Probability Probability

6-hour duration 24-hour duration

Return Level: Current versus Future

Rainfall(25 year-24 hour)

Temperature(50 year-30 day)

Current Future Current Future

Climate Change : Sea Level Rise

Future Projections: Considerable Spread

79

31.5

UN

EP

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09)

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Bro

wa

rd

• Resilience• Adaptive Capcity• “no regret strategies”• Adaptive Management

• Alternative Futures• Contingency Plans

Future Projections of Sea Level Rise: Polar Ice Uncertainty

Greenland(~ 2 million sq.km.)

Antarctica(~5.4 million sq. km.)

1950 2000 2050 2100

05

00

10

00

15

00

20

00

Year

Global

Key West

What is the future rate of acceleration?

Rapid acceleration due to ice sheet loss

Medium acceleration

Continuing current trend

Sea

Lev

el R

ise

rela

tive

to 2

010

(mm

)

Probability Distribution of Extremes

Total Sea Level Rise ,

T(t) = G(t) + L(t)

Global Local

G(t) = b t + 0.5a t2

T(t) = [L(t) + b t] + 0.5a t2

= c t + 0.5a t2

zT(t)

d)d()f(fz]P[T(t) acac ac

Probability Distribution of Extremes

Assume Annual Maxima of SLR

~ Generalize Extreme Value (GEV) Distribution

TE,t ~ GEV(μt , , )

and location, μ t = T(t) + e

p-year return level:1920 1960 2000

-0.4

0.2

0.8

Year

Mean,

Maxi

mum

(m

)

Key West,Florida

e

Probabilistic Projections of Mean Sea Level & Extremes

Summary

Water Resource Management in South Florida is highly vulnerable to potential changes in both climate and sea level rise

Skills of regional climate models in predicting extremes may not be adequate for decision making.

Water managers need methods to deal with uncertainties in decision making

Papers Submitted, Accepted, and Published

Questions!

Recent cabinet meeting of the island nation, Maldives

Storm Surge – AMO Dependence

1. Park, J., Obeysekera, J., Barnes, J., Irizarry, M., Park-Said, W.,Climate Links and Variability of Extreme Sea Level Events at Key West, Pensacola, and Mayport Florida,ASCE Journal of Port, Coastal, Waterway and Ocean Engineering, 136 (6), 350-356, 2010

a) b)