1. Estimation of Future Earthquake Annualized Losses in California B. Rowshandel, M. Reichle, C. Wills, T. Cao, M. Petersen, and J. Davis California Geological Survey Sacramento, CA Disaster Resistant California Conference, May 3-5, 2004, Sacramento, CA

2. Purpose:  Estimate the expected long-term, average annual economic losses in California due to future earthquakes in and around the State  Prepare results useful for making policy decisions regarding earthquake regulations and codes, mitigation, preparation, and response planning

3. Approach:  Analysis of Earth Science Data and Estimation of Earthquake Hazards  Estimation of Losses Using HAZUS-SR2  Analysis of Loss Estimation Results

4. Input Data: - Fault Data - Seismicity Data

5. Input Data: - Liquefaction Data San Francisco Bay Area Los Angeles Area

6. Input Data: - Surface Geologic Materials - Other Information and Assumptions Other Information and Assumptions:  Model for Earthquake Occurrence in Time  Model for Distribution of Earthquake Magnitude  Model for Attenuation of Traveling Seismic Waves

7. Seismic Shaking Hazard Maps: 10% probability of being exceeded in 50 years.

8. Hazard Data for Annual Loss Computation Using HAZUS:  Use USGS/CGS PSHA Methodology to Calculate Ground Motions for Base Rock, with Return Periods of: 100 Years (~40% chance of being exceeded in 50 years), 100 Years (~40% chance of being exceeded in 50 years), 250 Years (~20%), 500 Years (~10%), 250 Years (~20%), 500 Years (~10%), 750 Years (~6.5%), 1000 Years (~5%), 750 Years (~6.5%), 1000 Years (~5%), 1500 Years (~3.3%), 2000 Years (~2.5%), 1500 Years (~3.3%), 2000 Years (~2.5%), 2500 Years (~2.0% chance of being exceeded in 50 years) 2500 Years (~2.0% chance of being exceeded in 50 years)  Prepare and Modify HAZUS Input Data for Ground Motion, Soil, Liquefaction and Other Data at the Census Tract Level

9. Loss Estimation Methodology:  Used HAZUS-SR2 with Users’-Supplied Hazard Data and Default Data on the Built Environment and Demographics  Ground Motion and Liquefaction Hazards were Considered  Hazards Due to Ground Rupture, Landslide, and Tsunami were not Considered due to Lack of Data  Only the Building Inventory was Considered. Other Elements of the Built Environment in HAZUS were not Considered Due to Lack of Adequate Data

10. Building Inventory:  Building Count: 7,971,000 Residential: 76% Commercial: 17% Residential: 76% Commercial: 17% Industrial: 4.5% Others: 2.5% Industrial: 4.5% Others: 2.5%  Building Types: 36  General Occupancy Classes: 7 (Residential, Commercial, Industrial, Agricultural, Religious, Government, Education) (Residential, Commercial, Industrial, Agricultural, Religious, Government, Education)  Specific Occupancy Classes: 28  Building Replacement Value: $1,594,626 Billion (1994 Dollars) of Which 76% is Residential

11. Economic Losses:  Direct Losses (Considered) : - Capital Stock Losses (Due to Structural Damage, - Capital Stock Losses (Due to Structural Damage, Non-Structural Damage, Contents, and Inventory) Non-Structural Damage, Contents, and Inventory) - Income Losses (Relocation, Capital Related, Wages, - Income Losses (Relocation, Capital Related, Wages, and Rental) and Rental)  Indirect Losses (Not Included) : - Losses due to Long-term economic effects : Capital - Losses due to Long-term economic effects : Capital Related to Income, Employment, and Economic Output Related to Income, Employment, and Economic Output

12. Annual Building Damage Economic Loss: - By County - By Census Tract

13. Per-Capita Annual Loss: - By County - By Census Tract

14. Annual Loss As Percentage of Building Replacement Value: - By County - By Census Tract

15. Summary Results:

16. Distribution of Annual Losses By Building Type:

17. Distribution of Annual Losses By Occupancy Type:

18. Uncertainty in the Estimates: The Level of Uncertainty in the Estimates Obtained using HAZUS is Relatively High. This is Due to Inadequacies in the Methodology and Data.  Hazard Characterization: Magnitude and Distribution of Ground Motions Have Major Effect on the Estimated Loss (~30-40% change in estimated annualized losses resulted from switching from one method of soil correction to another)  Hazard to Damage: Equations relating ground motion to damage (fragility functions) play a major role (~15% decrease in estimated annualized loss resulted from using HAZUS-SR2 compared to HAZUS-SR1; this change is most likely due to fragility curves)  Damage to Loss  Inventory Data

19. Summary and Conclusions:  Estimate of the Direct Economic Loss Due to Building Damage Exceeds $2 Billion Annually, of which 61% is due to residential buildings. This corresponds to an average per-capita loss of $100 and a loss in building replacement value of 0.2% each year.  The uncertainty associated with this estimate is relatively high, implying that this estimate could be an order of magnitude larger.  Indirect losses due to building damage, and losses due to secondary effects (landslide, tsunami, fire), which were not included in this study, would add to the above estimate.  Losses due to damage to other facilities, such as transportation, utility lifelines, essential facilities and high potential loss facilities were not included due to the incomplete data in HAZUS.  Estimates of losses were found to be particularly sensitive to soil effects. Better characterization of soils and geologic units help reduce the uncertainty in the estimates.  HAZUS data inventory needs much improvement. Improvements in data inventory would have major effects on estimated losses.

20. Other CGS Loss Estimation Studies: http://www.consrv.ca.gov/CGS/rghm/loss/_______________________________ Thank You!