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NYCEM 2nd Year Technical Report
1999-2000

Earthquake Loss Estimation Study for the New York City Area

by

Michael W. Tantala, Guy J. P. Nordenson, and George Deodatis

Department of Civil Engineering & Environmental Engineering
Princeton University

Summary

A forecast of the type of losses that the New York City area could suffer after an earthquake is the subject of this study funded by FEMA Region II and the New York State Emergency Management Office (NYSEMO) and coordinated by the Multidisciplinary Center for Earthquake Engineering Research (MCEER).

This study describes the scale and extent of damage and disruption that may result from potential earthquakes in Manhattan. In assessing the risks involved, this research has made a significant contribution toward improving our understanding of seismic hazards in Manhattan by forecasting potential losses so that strategies may be formed to reduce their impacts.

The primary objective of this study is to develop and implement a comprehensive risk and loss characterization for Manhattan in the event of an earthquake. To this end, a complete building inventory of every structure in Manhattan was assembled from a variety of sources. Combined with a detailed geotechnical soil characterization of Manhattan, this building inventory has been used to model scenario earthquakes in HAZUS (Hazards US), a standardized earthquake loss estimation methodology and modeling program. When viewed in context with additional information about regional demographics and seismic hazards, the model serves as a tool to identify the areas, structures and systems with highest risk and to quantify and ultimately reduce those risks.

Deterministic and probabilistic earthquake scenarios were modeled and simulated in Manhattan, which provided intensities of ground shaking, dollar losses associated with capital (the building inventory) and subsequent income losses. This study has also implemented a detailed critical (essential) facilities analysis, assessing damage probabilities and facility functionality after an earthquake.

This study is unique, because it is currently one of the most detailed and site-specific applications of HAZUS or any other earthquake loss estimation. This research has collected information about every building in Manhattan and a large amount of soil data. With this work, it is possible therefore to establish the building inventory information for the island of Manhattan at the individual level for all buildings—a unique accomplishment for HAZUS applications.

Eventually, the aim of this loss estimation project will provide a framework for businesses and agencies to take mitigative action to reduce potential damage and losses, which might be experienced after an earthquake.

 

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Table of Contents

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Page

Executive Summary

3
Table of Contents 4
List of Figures 5
List of Tables 12
1.0 Introduction 14
    1.1 Why is this Study Important? 16
    1.2 Key Findings 18
    1.3  How should these results be used? 20
    1.4 Seismicity of the New York Area 22
2.0 Methodology and Scenario Events 26
    2.1 HAZUS Methodology 26
    2.2 Lessons Learned from the Year One Study 27
    2.3 Sources of Information and Manipulations 28
    2.4 Assumptions and Simplifications 37
    2.5 Difficulties and Limitations with HAZUS Methodology and Code 39
    2.6 Scenario Events-Deterministic 43
    2.7 Scenario Events-Probabilistic 47
3.0 Risk and Impact Assessment for Manhattan 50
    3.1 Regional Description and Exposure Characteristics 50
    3.2 Earth Science Hazards-Soil Profile, PGA and PGV for Scenario Events 69
    3.3 Building Inventory Damage 79
    3.4 Economic Losses 95
    3.5 Social Impacts 120
    3.6 Critical Facilities Damage and Functionality 140
    3.7 Secondary Effects-Fire Following and Debris Generation 162
    3.8 Indirect Economic Impact  168
    3.A Addendum of Figures: Building Inventory Damage Statistics 171
4.0 Conclusions and Future Work (not available yet) 204
References 205

 

List of Figures

Figure Title

Page
1.1 Seismicity of the United States: 1899-1990 14
1.2 Earthquakes of New England and Adjacent Regions(1638-1995) 24
2.1 Earthquake Loss Estimation using HAZUS 26
2.2 Schematic Representation of Physical Survey with Lower Manhattan Building and Building Elevation Model 32
2.3 “Representative” Census Tracts Surveyed (Highlighted) and sample Kips Bay Residential Building 33
2.4 Comparison of Default and Modified Soil Map for Manhattan 36
2.5 Comparison of Default and Modified Census Tract Map for Manhattan 42
2.6 Deterministic Earthquake Scenarios (Star Symbols) 42
2.7 USGS Eastern United State Contour Map for PGA with 10% Probability of Exceedance in 50 years (500 year return interval) 48
2.8 Probabilistic Loss Curve and the Area under the Curve 49
3.1 Building Exposure by Occupancy Type with Total Replacement Value 50
3.2 Aerial Photograph of Manhattan and Designations of Neighborhoods 51
3.3 Distribution of Population in Manhattan 52
3.4 Distribution of Total Square Footage in Manhattan 54
3.5 Comparison of the default and modified square footage distributions for the 28 occupancy categories by value 56
3.6 Comparison of the default and modified square footage distributions for the 28 occupancy categories by percent of total 56
3.7 Comparison of the Building Square Footage Distributions by Occupancy: RES3 (Multi Family Dwelling) and COM10 (Professional/Technical Services) Respectively 57
3.8 Comparison of the default and modified square footage distributions for the 36 building type categories by value 59
3.9 Comparison of the default and modified square footage distributions for the 36 building type categories by percent of total 59
3.10 Distribution of Number of Structures in Manhattan 62
3.11 Distribution of Average Age of Structures in Manhattan 63
3.12 Distribution of Average Number of Stories in Manhattan 64
3.13 Building Count by Structural Type and Neighborhood 65
3.14 Distributions of Structure Type for All of Manhattan by Count and Percent 66
3.15 Manhattan and its considerable concentration of tall buildings 67
3.16 Distribution of Number of Stories in Manhattan with the Tail of Distribution Enlarged 68
3.17 Distribution of Year of Construction in Manhattan with Digital Model of Lower Manhattan for Visual Inspection (Pre-1945 Buildings are shaded red) 68
3.18 Comparison of Default and Modified Soil Map21 for Manhattan 70
3.19 Fixed Location Scenarios (Magnitudes 5, 6 and 7), Peak Ground Acceleration 73
3.20 Constant Probability Scenarios (Magnitudes 5, 6 and 7), Peak Ground Acceleration 74
3.21 Probabilistic Scenarios (100 year, 500 year, 2500 year), Peak Ground Acceleration 75
3.22 Fixed Location Scenarios (Magnitudes 5, 6 and 7), Peak Ground Velocity 76
3.23 Constant Probability Scenarios (Magnitudes 5, 6 and 7), Peak Ground Velocity 77
3.24 Probabilistic Scenarios (100 year, 500 year, 2500 year), Peak Ground Velocity 78
3.25 Distributions of Damage State by Building Count for Fixed Location M5.0 Event to All Buildings 87
3.26 Distributions of Damage State by Building Count for Fixed Location M6.0 Event to All Buildings 88
3.27 Distributions of Damage State by Building Count for Fixed Location M7.0 Event to All Buildings 89
3.28 Distributions of Damage State by Building Count for Constant Probability M6.0 Event to All Buildings 90
3.29 Distributions of Damage State by Building Count for Constant Probability M7.0 Event to All Buildings 91
3.30 Distributions of Damage State by Building Count for a Probabilistic Event with 100 Year Return Period (10% in 50 years exceedance probability) to All Buildings 92
3.31 Distributions of Damage State by Building Count for a Probabilistic Event with 500 Year Return Period (5% in 50 years exceedance probability) 93
3.32 Distributions of Damage State by Building Count for a Probabilistic Event with 2500 Year Return Period (2% in 50 years) 94
3.33 Capital Stock Loss Estimates by Value and by Percent for All Scenarios 98
3.34 Income Loss Estimates by Value and by Percent for All Scenarios 98
3.35 Total Loss Estimates (Direct Building and Business Interruption) for Fixed Location Scenarios (Magnitudes 5, 6 and 7) 99
3.36 Total Loss Estimates (Direct Building and Business Interruption) for Constant Probability Scenarios (Magnitudes 5, 6 and 7) 100
3.37 Total Loss Estimates (Direct Building and Business Interruption) for Probabilistic Scenarios (100 year, 500 year, 2500 year) 101
3.38 Total Building Loss Estimates (Structural and Non-Structural) for Fixed Location Scenarios (Magnitudes 5, 6 and 7) 103
3.39 Total Building Loss Estimates (Structural and Non-Structural) for Constant Probability Scenarios (Magnitudes 5, 6 and 7) 104
3.40 Total Building Loss Estimates (Structural and Non-Structural) for Probabilistic Scenarios (100 year, 500 year, 2500 year) 105
3.41 Wood Buildings: Total Loss Estimates for Fixed Location Scenarios (Magnitudes 5, 6 and 7) 108
3.42 Wood Buildings: Total Loss Estimates for Constant Probability Scenarios (Magnitudes 5, 6 and 7) 109
3.43 Wood Buildings: Total Loss Estimates for Probabilistic Scenarios 110
3.44 Unreinforced Masonry Buildings: Total Loss Estimates for Fixed Location Scenarios (Magnitudes 5, 6 and 7) 111
3.45 Unreinforced Masonry Buildings: Total Loss Estimates for Constant Probability Scenarios (Magnitudes 5, 6 and 7) 112
3.46 Unreinforced Masonry Buildings: Total Loss Estimates for Probabilistic Scenarios (100 year, 500 year, 2500 year) 113
3.47 Steel Buildings: Total Loss Estimates for Fixed Location Scenarios (Magnitudes 5, 6 and 7) 114
3.48 Steel Buildings: Total Loss Estimates for Constant Probability Scenarios (Magnitudes 5, 6 and 7) 115
3.49 Steel Buildings: Total Loss Estimates for Probabilistic Scenarios (100 year, 500 year, 2500 year) 116
3.50 Reinforced Concrete Buildings: Total Loss Estimates for Fixed Location Scenarios (Magnitudes 5, 6 and 7) 117
3.51 Reinforced Concrete Buildings: Total Loss Estimates for Constant Probability Scenarios (Magnitudes 5, 6 and 7) 118
3.52 Reinforced Concrete Buildings: Total Loss Estimates for Probabilistic Scenarios (100 year, 500 year, 2500 year) 119
3.53 2am Event: Severity 3 (death) and Severity 4 (life-threatening) Predictions for Fixed Location Scenarios (Magnitudes 5, 6 and 7) 158
3.54 2am Event: Severity 3 (death) and Severity 4 (life-threatening) Predictions for Constant Probability Scenarios (Magnitudes 5, 6 and 7) 159
3.55 2am Event: Severity 3 (death) and Severity 4 (life-threatening) Predictions for Probabilistic Scenarios (100 year, 500 year, 2500 year recurrence intervals) 160
3.56 2pm Event: Severity 3 (death) and Severity 4 (life-threatening) Predictions for Fixed Location Scenarios (Magnitudes 5, 6 and 7) 161
3.57 2pm Event: Severity 3 (death) and Severity 4 (life-threatening) Predictions for Constant Probability Scenarios (Magnitudes 5, 6 and 7) 162
3.58 2pm Event: Severity 3 (death) and Severity 4 (life-threatening) Predictions for Probabilistic Scenarios (100 year, 500 year, 2500 year recurrence intervals 163
3.59 5pm Event: Severity 3 (death) and Severity 4 (life-threatening) Predictions for Fixed Location Scenarios (Magnitudes 5, 6 and 7) 164
3.60 5pm Event: Severity 3 (death) and Severity 4 (life-threatening) Predictions for Constant Probability Scenarios (Magnitudes 5, 6 and 7) 165
3.61 5pm Event: Severity 3 (death) and Severity 4 (life-threatening) Predictions for Probabilistic Scenarios (100 year, 500 year, 2500 year recurrence intervals 166
3.62 Short term Shelter Requirement (Displaced People) for Fixed Location Scenarios (Magnitudes 5, 6 and 7) 168
3.63 Short term Shelter Requirement (Displaced People) for Constant Probability Scenarios (Magnitudes 5, 6 and 7) 169
3.64 Short term Shelter Requirement (Displaced People) for Probabilistic Scenarios (100 year, 500 year, 2500 year recurrence intervals) 170
3.65 Long term Shelter Requirement (Public Shelter) for Fixed Location Scenarios (Magnitudes 5, 6 and 7) 171
3.66 Long term Shelter Requirement (Public Shelter) for Constant Probability Scenarios (Magnitudes 5, 6 and 7) 172
3.67 Long term Shelter Requirement (Public Shelter) for Probabilistic Scenarios (100 year, 500 year, 2500 year recurrence intervals) 173
3.68 Essential Facilities (Major Hospitals) Functionality, number of people requiring hospitalization and shortest distances between them for Fixed Location Scenarios (Magnitudes 5, 6 and 7) 179
3.69 Essential Facilities (Major Hospitals) Functionality, number of people requiring hospitalization and shortest distances between them for Constant Probability Scenarios (Magnitudes 5, 6 and 7) 180
3.70 Essential Facilities (Major Hospitals) Functionality, number of people requiring hospitalization and shortest distances between them for Probabilistic Scenarios (100 year, 500 year, 2500 year recurrence intervals) 181
3.71 Essential Facilities (Schools) Day 1 Functionality, number of people requiring hospitalization and shortest distances between them for Fixed Location Scenarios (Magnitudes 5, 6 and 7) 183
3.72 Essential Facilities (Schools) Day 1 Functionality, number of people requiring hospitalization and shortest distances between them for Constant Probability Scenarios (Magnitudes 5, 6 and 7) 184
3.73 Essential Facilities (Schools) Day 1 Functionality, number of people requiring hospitalization and shortest distances between them for Probabilistic Scenarios (100 year, 500 year, 2500 year recurrence intervals) 185
3.74 Essential Facilities (Emergency Response) Day 1 Functionality, number of people requiring hospitalization and shortest distances between them for Fixed Location Scenarios (Magnitudes 5, 6 and 7) 187
3.75 Essential Facilities (Emergency Response) Day 1 Functionality, number of people requiring hospitalization and shortest distances between them for Constant Probability Scenarios (Magnitudes 5, 6 and 7) 188
3.76 Essential Facilities (Emergency Response) Day 1 Functionality, number of people requiring hospitalization and shortest distances between them for Probabilistic Scenarios (100 year, 500 year, 2500 year recurrence intervals) 189
3.77 Essential Facilities (Fire Stations) Functionality, Number of Fire Ignitions, Population Exposed, GPM Demand and Dollar Value Exposed for Fixed Location Scenarios (Magnitudes 5, 6 and 7) 192
3.78 Essential Facilities (Fire Stations) Functionality, Number of Fire Ignitions, Population Exposed, GPM Demand and Dollar Value Exposed for Constant Probability Scenarios (Magnitudes 5, 6 and 7) 193
3.79 Essential Facilities (Fire Stations) Functionality, Number of Fire Ignitions, Population Exposed, GPM Demand and Dollar Value Exposed for Probabilistic Scenarios (100 year, 500 year, 2500 year recurrence intervals) 194
3.80 Contours of the Distances to the Nearest Major Fire Station 195
3.81 Secondary Effects (Debris Generation), Percentage by District, Number of 25-Ton Truck Loads Required to Haul Debris (Brick, Wood, Steel and Concrete) for Constant Probability Scenarios (Magnitudes 5, 6 and 7) 199
3.82 Secondary Effects (Debris Generation), Percentage by District, Number of 25-Ton Truck Loads Required to Haul Debris (Brick, Wood, Steel and Concrete) for Fixed Location Scenarios (Magnitudes 5, 6 and 7) 200
3.83 Secondary Effects (Debris Generation), Percentage by District, Number of 25-Ton Truck Loads Required to Haul Debris (Brick, Wood, Steel and Concrete) for Probabilistic Scenarios (100 year, 500 year, 2500 year recurrence intervals) 201
3.84 Percent Change of Income and Employment for the First 1000 Days After an Event for all Scenarios 203
3.85 Average Percent Change of Income and Employment for 6 to 15 Years After an Event for all Scenarios 203
3.A1 Distributions of Damage State of Wood Buildings by Count for Fixed Location M5.0 Event 122
3.A2 Distributions of Damage State of Steel Buildings by Count for Fixed Location M5.0 Event 123
3.A3 Distributions of Damage State of Concrete Buildings by Count for Fixed Location M5.0 Event 124
3.A4 Distributions of Damage State of Unreinforced Masonry Buildings by Count for Fixed Location M5.0 Event 125
3.A5 Distributions of Damage State of Wood Buildings by Count for Fixed Location M6.0 Event 126
3.A6 Distributions of Damage State of Steel Buildings by Count for Fixed Location M6.0 Event 127
3.A7 Distributions of Damage State of Concrete Buildings by Count for Fixed Location M6.0 Event 128
3.A8 Distributions of Damage State of Unreinforced Masonry Buildings by Count for Fixed Location M6.0 Event 129
3.A9 Distributions of Damage State of Wood Buildings by Count for Fixed Location M7.0 Event 130
3.A10 Distributions of Damage State of Steel Buildings by Count for Fixed Location M7.0 Event 131
3.A11 Distributions of Damage State of Concrete Buildings by Count for Fixed Location M7.0 Event 132
3.A12 Distributions of Damage State of Unreinforced Masonry Buildings by Count for Fixed Location M7.0 Event 133
3.A13 Distributions of Damage State of Wood Buildings by Count for Constant Probability M6.0 Event 134
3.A14 Distributions of Damage State of Steel Buildings by Count for Constant Probability M6.0 Event 135
3.A15 Distributions of Damage State of Concrete Buildings by Count for Constant Probability M6.0 Event 136
3.A16 Distributions of Damage State of Unreinforced Masonry Buildings by Count for Constant Probability M6.0 Event 137
3.A17 Distributions of Damage State of Wood Buildings by Count for Constant Probability M7.0 Event 138
3.A18 Distributions of Damage State of Steel Buildings by Count for Constant Probability M7.0 Event 139
3.A19 Distributions of Damage State of Concrete Buildings by Count for Constant Probability M7.0 Event 140
3.A20 Distributions of Damage State of Unreinforced Masonry Buildings by Count for Constant Probability M7.0 Event 141
3.A21 Distributions of Damage State of Wood Buildings by Count for a Probabilistic Event with 100 Year Return Period (10% in 50 years exceedance probability) 142
3.A22 Distributions of Damage State of Steel Buildings by Count for a Probabilistic Event with 100 Year Return Period (10% in 50 years exceedance probability) 143
3.A23 Distributions of Damage State of Reinforced Concrete Buildings by Count for a Probabilistic Event with 100 Year Return Period (10% in 50 years exceedance probability) 144
3.A24 Distributions of Damage State of Unreinforced Masonry Buildings by Count for a Probabilistic Event with 100 Year Return Period (10% in 50 years exceedance probability) 145
3.A25 Distributions of Damage State of Wood Buildings by Count for a Probabilistic Event with 500 Year Return Period (5% in 50 years exceedance probability) 146
3.A26 Distributions of Damage State of Steel Buildings by Count for a Probabilistic Event with 500 Year Return Period (5% in 50 years exceedance probability) 147
3.A27 Distributions of Damage State of Reinforced Concrete Buildings by Count for a Probabilistic Event with 500 Year Return Period (5% in 50 years exceedance probability) 148
3.A28 Distributions of Damage State of Unreinforced Masonry Buildings by Count for a Probabilistic Event with 500 Year Return Period (5% in 50 years exceedance probability) 149
3.A29 Distributions of Damage State of Wood Buildings by Count for a Probabilistic Event with 2500 Year Return Period (2% in 50 years exceedance probability) 150
3.A30 Distributions of Damage State of Steel Buildings by Count for a Probabilistic Event with 2500 Year Return Period (2% in 50 years exceedance probability) 151
3.A31 Distributions of Damage State of Reinforced Concrete Buildings by Count for a Probabilistic Event with 2500 Year Return Period (2% in 50 years exceedance probability) 152
3.A32 Distributions of Damage State of Unreinforced Masonry Buildings by Count for a Probabilistic Event with 2500 Year Return Period (2% in 50 years exceedance probability) 153
 

List of Tables

Table

Title

 Page
1.1 Total Number of Buildings in each Damage State for Manhattan Scenario Events 19
1.2 Total Loss Estimates (Direct Building and Business Interruption) for All Scenarios 19
2.1 NEHRP Soil Type Classifications 35
2.2 Assumption Regarding Steel Frame Structures 37
2.3 Assumption Regarding Concrete Frame Structures 38
2.4 Deterministic Earthquake Scenarios 44
2.5 Magnitude, Average Return Period (T) and Annual Probability (%) of Several Earthquakes (the chosen ones are highlighted) 46
2.6 Probabilistic Earthquake Scenarios 49
3.1 Comparison of the Default and Modified Distributions of Square Footage by Occupancy Type 55
3.2 Comparison of the Default and Modified Distributions of Building Type 58
3.3 Number of Buildings by Count and Neighborhood District 61
3.4 Deterministic and Probabilistic Earthquake Scenarios 71
3.5 Average PGA values for Manhattan Scenarios and Percent Differences from the Base Case for a Fixed Location Magnitude 5.0 Earthquake 72
3.6 Average PGV values for Manhattan Scenarios and Percent Differences from the Base Case for a Fixed Location Magnitude 5.0 Earthquake 72
3.7 Total Number of Buildings in each Damage State for Manhattan Scenario Events 84
3.8 Number of buildings in each damage state for the 5 damages states (N, S, M, E, C) for each building type (W, S, C, URM) and for each scenario earthquake event 85
3.9 Number of buildings in each damage state for the 5 damages states (N, S, M, E, C) for each building type (W, S, C, URM) and for each scenario earthquake event 86
3.10 Total Loss Estimates (Direct Building and Business Interruption) for All Scenarios 97
3.11 Severity 3 and Severity 4 predictions were made using this HAZUS Injury Scale 155
3.12 Injury and Casualty Summary for Scenario Earthquake for Multiple Times (2am, 2pm, 5pm) with Severity 2 (requires hospital), Severity 3 (life-threatening) and Severity 4 (instant death) Predictions 157
3.13 Short and Long Term Shelter Needs for Scenario Earthquakes and Average School Functionality for Scenarios 167
3.14 Essential Facility Database Summary 177
3.15 Summary of Medical Facility Day 1 Functionality and Beds Available and Required 178
3.16 Summary of School Facility Day 1 Functionality and those Requiring Shelter 182
3.17 Summary of Police Station Day 1 Functionality and People Requiring Rescue 186
3.18 Summary of Fire Stations Day 1 Functionality Number of Ignitions, Dollars Exposed, People Exposed and GPM Supply and Demand Comparison 191
3.19 Fire Following Earthquake Effects for Scenario Events 197
3.20 Debris Generation Estimates and Comparison with Average Daily Debris Hauling 198

 
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