solidwaste presentation 080408 final · hurricane simulation and debris assessment: “katrina”...

Hurricane DebrisHurricane DebrisManagement

Workshop

Sponsored by:

University of Florida Hinkley Center for Solid and Hazardous Waste

Alachua County Solid Waste and Emergency Management

April 9, 2008

g

Agenda

9:45‐10:00 Refreshments

10:00‐10:30 Introduction and Opening Remarks

John Schert, DirectorHinkley Center for Solid and Hazardous Waste, University of Florida

K D t A i t t P bli W k Di tKaren Deeter, Assistant Public Works DirectorAlachua County Waste Management

David Donnelly, DirectorAl h C E MAlachua County Emergency Management

Charles Kibert, Ph.D., P.E., Principal InvestigatorM.E. Rinker Sr., School of Building Construction, University of Florida

10:30‐11:30 HAZUS‐MH: Part I: Hurricane Simulation and Debris Assessment:“Charley, Frances and Ivan”

K R Grosskopf Ph D and Eric Kramer Ph DK.R. Grosskopf, Ph.D. and Eric Kramer, Ph.D.M.E. Rinker Sr., School of Building Construction, University of Florida

11:30‐12:00 Lunch (provided) 2

Agenda

12:00‐12:30 HAZUS‐MH: Part IIHurricane Simulation and Debris Assessment: “Katrina”

K.R. Grosskopf, Ph.D. and Eric Kramer, Ph.D.M.E. Rinker Sr., School of Building Construction, University of Florida

12:30‐1:15 Applying for FEMA Disaster Assistance: Eligible and Non‐Eligible Debris

Phil W. Worley, State Debris Coordinator Florida Division of Emergency Management (FDEM)

1:15‐1:30 Break

1:30‐2:15 Special Discussion Topics

Karen Deeter Assistant P blic Works DirectorKaren Deeter, Assistant Public Works DirectorAlachua County Waste Management

David Donnelly, DirectorAl h C t E M tAlachua County Emergency Management

2:15‐2:30 Closing Remarks3

HAZUS‐MH: Hurricane Simulation andHAZUS MH: Hurricane Simulation and Debris Assessment

K.R. Grosskopf, Ph.D and Eric W. Kramer, Ph.D

M.E. Rinker Sr., School of Building Construction

U i it f Fl idUniversity of Florida

April 9, 2008

Introduction

►2004 storms generated ~ 75M yd3 of C&D and vegetative debris in six weeks, as much as would g ,normally be seen statewide in three years

►~350 major staging areas; 4,000 acres

►Debris• Clean waste: recycled, energy generation

• Commingled (mixed) waste: burned, landfill

►“Storm chasers”• Out‐of‐state workforce

5

Overview

►What is HAZUS‐MH?

►H i Si l ti d D b i A t PART I►Hurricane Simulation and Debris Assessment, PART I• Charley, Frances & Ivan (2004)

►Hurricane Simulation and Debris Assessment, PART II• Category 5 “Katrina” scenario

6

Overview

60%

70%

80%

90%

100%

Tota

l Uni

ts)

Urban

R2 = 0.9765

0%

10%

20%

30%

40%

50%

60%

Bui

ldin

g D

amag

e (%

T

20602005

UrbanConservation

0%50 60 70 80 90 100 110 120 130 140 150

Peak Windspeed (mph)

7

HAZUS‐MH: A Decision Support Tool for Disaster Preparedness and Emergency Management

►HAZUS – Natural hazards loss estimation tool in GISCharacterized by:Characterized by:

• Geographically sensitive, risk‐based approach

• Validated scientific methodology to estimate damages• Validated scientific methodology to estimate damages, losses, and mitigation benefits

• Interactive visual display and identification of hazards and p yvulnerability

• Assistance with decision‐making and comprehensive h h dapproach to hazards

8

HAZUS‐MH Data

►Over 200 default layers• General Inventory

• Building profiles and occupancy

• Essential facilities

• Demographics

►Hazard Specific Inventory• Terrain, elevation, storm tracks

►User supplied data• Updated building inventory, local critical facilitiesUpdated building inventory, local critical facilities

9

HAZUS scenario outputs

►Outputs include maps, reports, and tabular data

10

HAZUS wind model methodology

►“Hazard‐load‐resistance‐damage‐loss” methodology

11

HAZUS limitations

►HAZUS at present cannot model:• Storm surges, wave damage, or wind damage to transportation, utility Sto su ges, a e da age, o d da age to t a spo tat o , ut ty

networks, tree damage to buildings

►Wind and Flood models are independent• No ability to model floods caused by hurricane rains or flooding by

urban storm water runoff; Potential for duplication of damage/losses

► Low intensity storms and winds are difficult to model► Low intensity storms and winds are difficult to model• Hurricanes Frances and Jeanne in Alachua County

►HAZUS does not account for events that precede the scenario►HAZUS does not account for events that precede the scenario• Soil saturation, sequential storm strikes, etc.

►Scale and accuracy►Scale and accuracy• Regional scale scenarios give most accurate damage and loss estimates

12

Examples of HAZUS scenarios

►2007 Alachua and Nassau County Hurricane Simulation and Damage AssessmentSimulation and Damage Assessment• 5 scenarios, including:

− 2004 storms2004 storms

− Historical 1896, 1898 storms

− Worst case event

− Probabilistic storms

− 100 year flood event

►Scenario development• Storm parameters

− NWS assistance to review and adjust deterministic scenarios

• NOAA H*Wind post‐storm analyses 13


►Wind speeds• Peak and max sustained windsPeak and max sustained winds

14


►Wind speeds• Peak and max sustained windsPeak and max sustained winds

15


►Building damage and economic losses

Severe damage:Failure of>50% roof cover>50% roof cover>20% window 10‐20 impacts

16


►Building damage and economic losses

Residential Loss

17


►Essential facility damage

Schools

18


►Shelter requirements

DisplacedHouseholds

19


►Debris

Brick WoodOther

20


►Reports

21


►Reports

22


►Flood model• Digital• Digital

ElevationModel

Inflow / outflow conditions affectscenarios

23


►Floodmodel• 100 Year• 100 Year

event

Direct damageInduced damageDirect losses**I di l**Indirect losses

24


►Flood model• Bridge• Bridge

Damage

25


►Flood model• Total• Total

EconomicLoss

Nassau CountyTotal losses: $440 Million33% Business Interruption50% Residential

26


►Flood model• Residential• Residential

EconomicLoss

27


►Flood model• Debris• Debris

Total

152,000 tons38% Finishes29% Structures33% Foundations

28

Hurricane Simulation and Debris Assessment, PART I

Charley, Frances & Ivan (2004)

29

Hurricane Debris Modeling for 2004 Storms

►Scenarios to model debris for 2004 hurricanes:• Charley• Charley

• Frances

• Ivan• Ivan

►Impact on 5 urban coastaland rural inland countiesand rural inland counties• Charlotte and De Soto

• St Lucie and Okeechobee• St. Lucie and Okeechobee

• Escambia

30

Hurricane Debris Study: Validation

►Actual debris data (2004 storms)• Difficulty in obtaining accurate data• Difficulty in obtaining accurate data

− Counties impacted by multiple storms; clean‐up not complete before next storm strike

− Cross‐county staging areas, regional transfer stations, etc.

− Private contractors

31

Hurricane Debris Study

►HAZUS Debris Estimates• Census tract level• Census tract level

• Building inventory and vegetation

• Storm hazards (wind vs flood) may affect results• Storm hazards (wind vs. flood) may affect results

32

Hurricane Charley scenario: Charlotte County

►Storm parameters• NOAA H*wind post storm analyses• NOAA H*wind post‐storm analyses

Landfall

33


►Winds

Gusts>125 mph

PORT CHARLOTTE PUNTA GORDA

34


►Winds

Max sustained>100 mph>100 mph


35


►Damage and loss estimates

48% 11%5%

78%

36


►Debris estimates• Brick wood• Brick, wood,

and other

• Concrete and steel

• Eligibled b


tree debris

• Total tree debristree debris

37



and other


• Eligibled b


tree debris


38



and other


• Eligibled b


tree debris


39



and other


• Eligibled b


tree debris


40

Comparative debris summary, 2004 storms

41

Estimated Debris from 2004 Storms

42


43


44


45


46


47


48

►Session Break

49

Hurricane Simulation and Debris Assessment, PART II

Category 5 “Katrina” Scenario

50

Part II: HAZUS‐MH Hurricane Simulation and Debris Assessment: Worst case “Katrina” storm

►Model the impacts that a hypothetical category 5 storm would have on the five counties

►Each scenario builds on the 2004 storm tracks, ramped up to strong category 5, a "worst case" scenario

51

Worst case category 5 scenario: Escambia

►Storm parameters• Storm path remains same as Ivan• Storm path remains same as Ivan

• Assistance from NWS meteorologists

Landfall

52


►Winds

Gusts > 160 mph

PENSACOLA

53


►Winds

MaxSustained>127 mph>127 mph

PENSACOLA

54


►Damage and loss estimates

62%

71%

55



and other

• Concrete

15x

and steel

• Eligibled btree debris


PENSACOLAPENSACOLA

56



and other


• Eligibled b

100x

tree debris


PENSACOLAPENSACOLA

57



and other



• Total tree debris

3x

tree debris

PENSACOLAPENSACOLA

58



and other




PENSACOLAPENSACOLA

59

Estimated Debris ‐Worst Case Cat 5 Storms

60


61


St. Lucie

62

Worst case: 300x Frances


St. Lucie

63

Worst case: >30x Frances

Estimated Debris from Worst Case Cat 5

64


65


66


67

Trend Analyses and Future Activities

60%

70%

80%

90%

100%

Tota

l Uni

ts)

Urban

R2 = 0.9765

0%

10%

20%

30%

40%

50%

60%

Bui

ldin

g D

amag

e (%

T

20602005

UrbanConservation

0%50 60 70 80 90 100 110 120 130 140 150


68

Trend Analyses

►Trend analyses were prepared to determine the extent of damage to buildings and debris generationextent of damage to buildings and debris generation associated with intensity of peak and sustained wind speeds.

►Results indicate that little building damage occurs until peak wind speeds exceed approximately 90 mph. p p pp y pBeyond this point, the increase in damage and building debris generation increases exponentially with respect to increases in peak wind speed.

►However, significant damage to buildings from fallen trees and, significant vegetative debris generation is likely at sub‐hurricane wind speed 69

Trend Analyses – Building Damage

80%

90%

100%

nits

)

R2 = 0.946450%

60%

70%

mag

e (%

Tot

al U

n

10%

20%

30%

40%

Bui

ldin

g D

am

0%

10%

60 80 100 120 140 160 180 200


70


80%

90%

100%

nits

)

R2 = 0.976550%

60%

70%

mag

e (%

Tot

al U

n

10%

20%

30%

40%

Bui

ldin

g D

am

0%

10%

50 60 70 80 90 100 110 120 130 140 150


71


80.0%

90.0%

100.0%

ts)

50.0%

60.0%

70.0%

ge (%

Tot

al U

nit

Destroyed

Severe

Moderate

20.0%

30.0%

40.0%

Bui

ldin

g D

amag Moderate

Minor

0.0%

10.0%

CAT <1 CAT 1 CAT 2 CAT 3 CAT 4

72

Trend Analyses – Debris

R2 = 0.78755,000,000

6,000,000

7,000,000

ons) R2 = 0.6672

50

60

70

pita

(ton

s)

1,000,000

2,000,000

3,000,000

4,000,000

Tota

l Deb

ris (t

10

20

30

40

Tota

l Deb

ris p

er C

ap

060 70 80 90 100 110 120 130 140 150 160

Max Sustained Windspeed (mph)

060 80 100 120 140 160 180 200


Wind Speed Population

R2 = 0.6545120

140

160

180

ldin

g (t

ons)

R2 = 0.80495 000

6,000

7,000

8,000

9,000

e M

ile (t

ons)

20

40

60

80

100

Tota

l Deb

ris p

er B

ui

1,000

2,000

3,000

4,000

5,000To

tal D

ebris

per

Squ

are

73

060 80 100 120 140 160 180 200


Land AreaBuildings

-1,000

060 70 80 90 100 110 120 130 140 150 160



40,000

45,000

50,000

ity

R2 = 0.9265

25,000

30,000

35,000

per U

rban

Den

sap

ita/s

q m

ile)

5 000

10,000

15,000

20,000

Tota

l Deb

ris

(ton

s/ca

0

5,000

60 80 100 120 140 160 180 200


74

(1σ)


40,000

45,000

50,000

sity

R2 = 0.9264

25,000

30,000

35,000

per U

rban

Den

sap

ita/s

q m

ile)

5 000

10,000

15,000

20,000

Tota

l Deb

ris

(ton

s/c a

0

5,000

60 70 80 90 100 110 120 130 140 150 160


75

(1σ)


3,500,000

4,000,000

2,000,000

2,500,000

3,000,000

s (t

ons)

Tree (ineligible)

Tree (eligible)

1,000,000

1,500,000Deb

ris

Building

0

500,000

CAT <1 CAT 1 CAT 2 CAT 3 CAT 4

76

Trend Analyses ‐ Debris

►Hurricane Debris Estimating Model

• Developed by the U.S. Army Corps of Engineers

• Contained in the Federal Emergency Management Agency (FEMA) 2003 Debris Management Guide, FEMA Publication 325Debris Management Guide, FEMA Publication 325

• Based on actual data from Hurricanes Frederic, Hugo and Andrew and has a reported accuracy of +/‐ 30%

• Assumes approximately 30% of the debris will be clean woody wastes; remaining 70% assumed to be mixed (C&D) debris

• Of the 70% mixed C&D, 42% is assumed to be burnable after sorting, 5% is soil, 15% are metals and 38% landfill

77


►Hurricane Debris Estimating Model

Q = H * C * V * B * S * Z

where: Q = the quantity of hurricane‐generated debris, yd3

H = the estimated number of households impacted

C = a storm category factor, yd3

V = a vegetation characteristic multiplier

B i l/b i /i d t i l lti liB = a commercial/business/industrial use multiplier

S = a storm precipitation characteristic multiplier

Z = percentage of land area impacted

78

p g p


Hurricane Category Value of “C” Factor (yd3)

1 21 2

2 8

3 263 26

4 50

5 805 80

Vegetative Cover Value of “V” Multiplierg p

Light 1.1

Medium 1.3

79

Heavy 1.5


Commercial Density Value of “B” Multiplier

Li h 1 0Light 1.0

Medium 1.2

Heavy 1 3Heavy 1.3

Precipitation Characteristic Value of “S” MultiplierPrecipitation Characteristic Value of S Multiplier

None to Light 1.0

Medium to Heavy 1.3 y

80


1 200 000

1,400,000

1,600,000

800,000

1,000,000

1,200,000

Deb

ris (t

ons) HAZUS

COE

200,000

400,000

600,000

Tota

l

0Charlotte(Charley,

2004)

Escambia(Ivan, 2004)

St. Lucie(Frances,

2004)

DeSoto(Charley,

2004)

Okeechobee(Frances,

2004)

Alachua(Frances,

2004)

Alachua(Jeanne,

2004)

81

HAZUS vs Corps of Engineers (FEMA Pub 325)


1,393,9611,519,747

1 200 000

1,400,000

1,600,000

928,515

800,000

1,000,000

1,200,000

Debr

is (t

ons)

200,000

400,000

600,000

Tota

l

0HAZUS COE (FEMA) Actual

Hurricane Ivan, 2004

82

Simulation vs Actual

Trend Analyses

►Variance in damage and debris output increases significantly with increase in storm intensitysignificantly with increase in storm intensity

►Ratio of building vs. tree debris increases significantly with increase in storm intensitywith increase in storm intensity

►Slightly higher correlation of building damage and debris to maximum sustained winddebris to maximum sustained wind• Cycle fatigue and progressive failure vs. catastrophic failure

83

Future Activities

►Service• HAZUS simulations (state regional and county)• HAZUS simulations (state, regional and county)

− Building damage

− Debris generationg

− Displaced populations and shelter requirements

− Emergency services impacts

− Flooding and storm surge (SLOSH)

− Economic loss

• Alachua, Charlotte, DeSoto, Escambia, Nassau, Okeechobee, St. Lucie

84

Future Activities

►Research• Building mitigation• Building mitigation

− Property appraiser data

− Manufactured housingg

• Improvements to 1994 HUD Code and 1999 FAC 15‐C

• 2002 FBC add‐on structures

− Site‐built housing and commercial buildings

• Improvements to 2002 FBC and (Miami‐Dade) PAS

• Roof coverings and sheathing attachmentRoof coverings and sheathing attachment

• Door and window impact resistance

• Infrastructure− Transportation, utilities, etc.

85

Future Activities

►Research (con’t)• Landscape designLandscape design

− Satellite tree survey, type and distribution of vulnerable species

• Land use planning− 2020 and 2050 state comp plans and future growth

− Climate change

• Storm intensity and frequency (ASCE‐07 wind zones)• Storm intensity and frequency (ASCE‐07 wind zones)

• Sea level rise

• Injury and fatality simulations− Flood, storm surge, population density, socioeconomics, etc.

− USAR and first response

− Clean‐up and recovery workers− Clean‐up and recovery workers

86

HAZUS Resources

►www.HAZUS.org

► HAZUS /SEHUG►www.HAZUS.org/SEHUG

►www.flhug.HAZUS.org

►For more information about the Florida Hurricane Simulation and Damage Assessment project, contact:Simulation and amage Assessment project, contact:• Kevin R. Grosskopf: [email protected]

• Eric W. Kramer: [email protected]

87

@

Discussion

►Questions and comments?Q

88

References

► FEMA (2007). HAZUS‐MH Hurricane Wind Model Validation Study ‐ Florida. Washington, DC, Federal Emergency Management Agency (FEMA): 1‐131.

► Vickery, P. J., J. Lin, et al. (2006). "HAZUS‐MH Hurricane Model Methodology. I: Hurricane Hazard, Terrain, and Wind Load Modeling." Natural Hazards Review 7(2): 82‐93.

► Vickery, P. J., P. F. Skerlj, et al. (2006). "HAZUS‐MH Hurricane Model Methodology. II: Damage and Loss Estimation." Natural Hazards Review 7: 94.9 .

89

solidwaste presentation 080408 final · hurricane simulation and debris assessment: “katrina”...

Documents