Management Workshop. Sponsored by: University of Florida Hinkley Center for Solid and Hazardous Waste

Transcription

1 Hurricane Debris Management Workshop Sponsored by: Alachua County Solid Waste and Emergency Management University of Florida Hinkley Center for Solid and Hazardous Waste April 9, 2008

2 Agenda 9:45 10:00 Refreshments 10:00 10:30 Introduction and Opening Remarks John Schert, Director Hinkley Center for Solid and Hazardous Waste, University of Florida Karen Deeter, Assistant tpublic Works Director Alachua County Waste Management David Donnelly, Director Alachua County Emergency Management Charles Kibert, Ph.D., P.E., Principal Investigator M.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.D. M.E. Rinker Sr., School of Building Construction, University of Florida 11:30 12:00 Lunch (provided) 2

3 Agenda 12:00 12:30 HAZUS MH: Part II Hurricane 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 1:15 1:30 Break Phil W. Worley, State Debris Coordinator Florida Division of Emergency Management (FDEM) 1:30 2:15 Special Discussion Topics Karen Deeter, Assistant Public Works Director Alachua County Waste Management David Donnelly, Director Alachua County Emergency Management 2:15 2:30 Closing Remarks 3

4 HAZUS 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 University it of Florida April 9, 2008

5 Introduction 2004 storms generated ~ 75M yd 3 of C&D and vegetative debris in six weeks, as much as would 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

6 Overview What is HAZUS MH? Hurricane i Simulation and DbiA Debris Assessment, PART I Charley, Frances & Ivan (2004) Hurricane Simulation and Debris Assessment, PART II Category 5 Katrina scenario 6

7 Overview 100% Building Damage (% T Total Units) 90% 80% 70% 60% 50% 2005 R2 = % % 20% 10% 0% Urban Conservation Peak Windspeed (m ph) 7

8 HAZUS MH: A Decision Support Tool for Disaster Preparedness and Emergency Management HAZUS Natural hazards loss estimation tool in GIS Characterized by: Geographically sensitive, risk based approach Validated scientific methodologyto to estimate damages, losses, and mitigation benefits Interactive visual display and identification of hazards and vulnerability Assistance with decision making and comprehensive approach to hazards 8

9 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 Updatedbuilding inventory, localcriticalfacilities critical 9

10 HAZUS scenario outputs Outputs include maps, reports, and tabular data 10

11 HAZUS wind model methodology Hazard load resistance damage loss methodology 11

12 HAZUS limitations HAZUS at present cannot model: Storm surges, wave aedamage, age, or wind damage age to transportation, ta spotato,utility 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 Hurricanes Frances and Jeanne in Alachua County HAZUS does not account for events thatprecede the scenario Soil saturation, sequential storm strikes, etc. Scale and accuracy Regional scale scenarios give most accurate damage and loss estimates 12

13 Examples of HAZUS scenarios 2007 Alachua and Nassau County Hurricane Simulation and DamageAssessment 5 scenarios, including: 2004 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

14 HAZUS scenario outputs Wind speeds Peak and max sustained winds 14

15 HAZUS scenario outputs Wind speeds Peak and max sustained winds 15

16 HAZUS scenario outputs Building damage and economic losses Severe damage: Failure of >50% roof cover >20% window impacts 16

17 HAZUS scenario outputs Building damage and economic losses Residential Loss 17

18 HAZUS scenario outputs Essential facility damage Schools 18

19 HAZUS scenario outputs Shelter requirements Displaced Households 19

20 HAZUS scenario outputs Debris Brick Wood Other 20

21 HAZUS scenario outputs Reports 21

22 HAZUS scenario outputs Reports 22

23 HAZUS scenario outputs Flood model Digital Elevation Model Inflow / outflow conditions affect scenarios 23

24 HAZUS scenario outputs Flood model 100 Year event Direct damage Induced damage Direct losses **Indirect losses 24

25 HAZUS scenario outputs Flood model Bridge Damage 25

26 HAZUS scenario outputs Flood model Total Economic Loss Nassau County Total losses: $440 Million 33% Business Interruption 50% Residential 26

27 HAZUS scenario outputs Flood model Residential Economic Loss 27

28 HAZUS scenario outputs Flood model Debris Total 152,000 tons 38% Finishes 29% Structures 33% Foundations 28

29 Hurricane Simulation and Debris Assessment, PART I Charley, Frances & Ivan (2004) 29

30 Hurricane Debris Modeling for 2004 Storms Scenarios to model debris for 2004 hurricanes: Charley Frances Ivan Impact on 5 urban coastal and ruralinland counties Charlotte and De Soto St. Lucie andokeechobee Escambia 30

31 Hurricane Debris Study: Validation Actual debris data (2004 storms) Difficulty in obtainingaccurateaccurate 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

32 Hurricane Debris Study HAZUS Debris Estimates Census tract level Building inventory and vegetation Storm hazards (wind vs. flood) may affect results 32

33 Hurricane Charley scenario: Charlotte County Storm parameters NOAA H*wind post storm storm analyses Landfall 33

34 Hurricane Charley scenario: Charlotte County Winds Gusts >125 mph PORT CHARLOTTE PUNTA GORDA 34

35 Hurricane Charley scenario: Charlotte County Winds Max sustained >100 mph PORT CHARLOTTE PUNTA GORDA 35

36 Hurricane Charley scenario: Charlotte County Damage and loss estimates 48% 11% 5% 78% 36

37 Hurricane Charley scenario: Charlotte County Debris estimates Brick, wood, and other Concrete and steel Eligible tree db debris Total tree debris PORT CHARLOTTE PUNTA GORDA 37

38 Hurricane Charley scenario: Charlotte County Debris estimates Brick, wood, and other Concrete and steel Eligible tree db debris Total tree debris PORT CHARLOTTE PUNTA GORDA 38

39 Hurricane Charley scenario: Charlotte County Debris estimates Brick, wood, and other Concrete and steel Eligible tree db debris Total tree debris PORT CHARLOTTE PUNTA GORDA 39

40 Hurricane Charley scenario: Charlotte County Debris estimates Brick, wood, and other Concrete and steel Eligible tree db debris Total tree debris PORT CHARLOTTE PUNTA GORDA 40

41 Comparative debris summary, 2004 storms 41

42 Estimated Debris from 2004 Storms 42

43 Estimated Debris from 2004 Storms 43

44 Estimated Debris from 2004 Storms 44

45 Estimated Debris from 2004 Storms 45

46 Estimated Debris from 2004 Storms 46

47 Estimated Debris from 2004 Storms 47

48 Estimated Debris from 2004 Storms 48

49 Session Break 49

50 Hurricane Simulation and Debris Assessment, PART II Category 5 Katrina Scenario 50

51 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

52 Worst case category 5 scenario: Escambia Storm parameters Storm path remains same as Ivan Assistance from NWS meteorologists Landfall 52

53 Worst case category 5 scenario: Escambia Winds Gusts > 160 mph PENSACOLA 53

54 Worst case category 5 scenario: Escambia Winds Max Sustained >127 mph PENSACOLA 54

55 Worst case category 5 scenario: Escambia Damage and loss estimates 62% 71% 55

56 Worst case category 5 scenario: Escambia Debris estimates 15x Brick, wood, and other Concrete and steel Eligible tree db debris Total tree debris PENSACOLA 56

57 Worst case category 5 scenario: Escambia Debris estimates 100x Brick, wood, and other Concrete and steel Eligible tree db debris Total tree debris PENSACOLA 57

58 Worst case category 5 scenario: Escambia Debris estimates 3x Brick, wood, and other Concrete and steel Eligible tree db debris Total tree debris PENSACOLA 58

59 Worst case category 5 scenario: Escambia Debris estimates Brick, wood, and other Concrete and steel Eligible tree db debris Total tree debris PENSACOLA 59

60 Estimated Debris Worst Case Cat 5 Storms 60

61 Estimated Debris Worst Case Cat 5 Storms 61

62 Estimated Debris Worst Case Cat 5 Storms St. Lucie Worst case: 300x Frances 62

63 Estimated Debris Worst Case Cat 5 Storms St. Lucie Worst case: >30x Frances 63

64 Estimated Debris from Worst Case Cat 5 64

65 Estimated Debris Worst Case Cat 5 Storms 65

66 Estimated Debris Worst Case Cat 5 Storms 66

67 Estimated Debris Worst Case Cat 5 Storms 67

68 Trend Analyses and Future Activities 100% Building Damage (% T Total Units) 90% 80% 70% 60% 50% 2005 R2 = % % 20% 10% 0% Urban Conservation Peak Windspeed (m ph) 68

69 Trend Analyses Trend analyses were prepared to determine the extent of damageto 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. Beyond 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

70 Trend Analyses Building Damage 100% 90% Building Dam mage (% Total Un nits) 80% 70% 60% 50% 40% 30% 20% 10% R 2 = % Peak Windspeed (mph) 70

71 Trend Analyses Building Damage 100% 90% Building Dam mage (% Total Un nits) 80% 70% 60% 50% 40% 30% 20% 10% R 2 = % Peak Windspeed (mph) 71

72 Trend Analyses Building Damage 100.0% 90.0% ts) Building Damag ge (% Total Unit 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% Destroyed Severe Moderate Minor 0.0% CAT <1 CAT 1 CAT 2 CAT 3 CAT 4 72

73 Trend Analyses Debris 7,000, ,000, Total Debris (t ons) 5,000,000 4,000,000 3,000,000 2,000,000 1,000,000 R 2 = Total Debris per Cap pita (tons) R 2 = Max Sustained Windspeed (mph) Wind Speed Peak Windspeed (mph) Population 180 9, ,000 lding (tons) Total Debris per Bui R 2 = Total Debris per Square Mile (tons) 7,000 6,000 R 2 = ,000 4,000 3,000 2,000 1, ,000 Peak Windspeed (mph) Max Sustained Windspeed (mph) Buildings Land Area 73

74 Trend Analyses Debris 50,000 45,000 ity per Urban Dens apita/sq mile) Total Debris (tons/ca 40,000 35,000 30,000 25,000 20,000 15,000 10,000 5,000 R 2 = Peak Windspeed (mph) (1σ) 74

75 Trend Analyses Debris 50,000 45,000 sity per Urban Dens apita/sq mile) Total Debris (tons/ca 40,000 35,000 30,000 25,000 20,000 15,000 10, ,000 R 2 = Max Sustained Windspeed (mph) (1σ) 75

76 Trend Analyses Debris 4,000,000 3,500,000 3,000,000 Debris (tons) 2,500,000 2,000,000 1,500,000 Tree (ineligible) Tree (eligible) Building 1,000, ,000 0 CAT <1 CAT 1 CAT 2 CAT 3 CAT 4 76

77 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, FEMAPublication 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

78 Trend Analyses Debris Hurricane Debris Estimating Model Q = H * C * V * B * S * Z where: Q = the quantity of hurricane generated debris, yd 3 H = the estimated number of households impacted C = a storm category factor, yd 3 V = a vegetation characteristic multiplier B = a commercial/business/industrial il/b i /id tiluse multiplier li S = a storm precipitation characteristic multiplier Z = percentage of land area impacted 78

79 Trend Analyses Debris Hurricane Category Value of C Factor (yd3) Vegetative Cover Value of V Multiplier Light 1.1 Medium 1.3 Heavy

80 Trend Analyses Debris Commercial Density Value of B Multiplier Light Medium 1.2 Heavy Precipitation Characteristic Value of S Multiplier None to Light 1.0 Medium to Heavy

81 Trend Analyses Debris 1,600,000 1,400,000 Debris (tons) Total 1,200, ,000, , , ,000 HAZUS COE 200,000 0 Charlotte (Charley, 2004) Escambia (Ivan, 2004) St. Lucie (Frances, 2004) DeSoto (Charley, 2004) Okeechobee (Frances, 2004) Alachua (Frances, 2004) Alachua (Jeanne, 2004) HAZUS vs Corps of Engineers (FEMA Pub 325) 81

82 Trend Analyses Debris 1,600,000 1,400,000 1,393,961 1,519,747 Debris (tons) Total 1,200, ,000, , , , , ,515 HAZUS COE (FEMA) Actual Hurricane Ivan, 2004 Simulation vs Actual 82

83 Trend Analyses Variance in damage and debris output increases significantly with increase in storm intensity Ratio of building vs. tree debris increases significantly with increase in storm intensity Slightly higher correlation of building damage and debris to maximum sustained wind Cycle fatigue and progressive failure vs. catastrophic failure 83

84 Future Activities Service HAZUS simulations (state, regional and county) Building damage Debris generation Displaced populations and shelter requirements Emergency services impacts Flooding and storm surge (SLOSH) Economic loss Alachua, Charlotte, DeSoto, Escambia, Nassau, Okeechobee, St. Lucie 84

85 Future Activities Research Building mitigation Property appraiser data Manufactured housing 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 attachment Door and window impact resistance Infrastructure Transportation, utilities, etc. 85

86 Future Activities Research (con t) Landscape 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) Sea level rise Injury and fatality simulations Flood, storm surge, population density, socioeconomics, etc. USAR and first response Clean up and recovery workers 86

87 HAZUS Resources For more information about the Florida Hurricane Simulation and Damage amageassessment project, contact: Kevin R. Grosskopf: Eric W. Kramer: 87

88 Discussion Questions Q and comments? 88

89 References FEMA (2007). HAZUS MH Hurricane Wind Model Validation Study Florida. Washington, DC, Federal Emergency Management Agency (FEMA): 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): Vickery, P. J., P. F. Skerlj, et al. (2006). "HAZUS MH Hurricane Model Methodology. II: Damage and Loss Estimation." Natural Hazards Review 7: