Temporal Changes to Fire Risk in Disparate WUI Communities in Southern California Nicola C

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Temporal Changes to Fire Risk in Disparate WUI Communities in Southern California Nicola C Temporal Changes to Fire Risk in Disparate WUI Communities in Southern California Nicola C. Leyshon M.S. PhD Student- Fire and Emergency Management Administration, Oklahoma State University Co authors: Dr. Christopher A. Dicus, California Polytechnic University San Luis Obispo David B. Sapsis, Cal Fire Fire Resource Assessment Program McIntire-Stennis Cooperative Forestry Research Program 20 Largest California Wildfires by Structure Loss Post-disaster Regulations YEAR 1961 1980 1989 1991 1993 2003 2007 S. CALIFORNIA S.CALIFORNIA PANORAMA OAKLAND HILLS LAGUNA BEL AIR FIRE 49ER FIRE FIRESTORM FIRESTORM Event FIRE FIRESTORM BEACH FIRE CEDAR FIRE WITCH FIRE 2 Killed Loss 4 Deaths 312 25 Killed 15 Killed 505 Structures 441 structures 1650 344 Structures Structures 2843 Structures 4847 Structures Details Structures WUI BUILDING Cal Fire California ‘BATES BILL’- State CALIFORNIA CODES adopted Awareness of established enacted Fire identified Very High Policy BUILDING CODE WOOD SHAKE VEGETATION Safe FIRE HAZARD SEVERITY requiring ignition PRC 4291- (2005) response ROOFING MANAGEMENT regulations ZONES in Local resistant Roofing 30m (100 feet) of PROGRAM PRC 4290 Response Areas (LRA) defensible space Southern CA Firestorm 2003 Cedar Fire 273,247 acres burned 15 Deaths 4847 Homes lost Southern CA Firestorm (2007) Witch Creek Fire 197,990 acres burned 2 Deaths 1650 Homes lost Fire Risk vs Fire Hazard? Risk = expected Hazard = physical damage conditions that can cause damage Fire Risk = Hazard - Mitigations Research Questions 1. Is Fire Risk increasing as people develop out into the Wildland Urban Interface (WUI)? 2. How are homeowners responding to catastrophic wildfire and mitigation policy? 3. Are mitigation levels different across disparate communities? Objectives of study 1. Conduct an analysis of historical data to assess impact of Home Ignition Zone characteristics on structure loss. 2. Develop a GIS model to assess changes in probability of structure loss over time 3. Analyze risk through time in 3 communities that vary in demographics, socioeconomic status, and local culture. Study Sites Study Site Population Average Income (USD) Average House Value (USD) RANCHO SANTA FE 3117 $180,612 $1,139,911 RAMONA 20,292 $60,033 $485,597 JULIAN 1,502 $65,781 $510,138 Witch Creek Fire (2007) Juilan Ramona Rancho Santa Fe Cedar Fire (2003) SELECTON CRITERIA • Demographically Disparate • Experienced major catastrophic wildfires in the last 20 years (Witch Fire, Cedar Fire) Rancho Santa Fe Ramona Julian Methodology Data Collection (2005, 2009, 2010, 2012) GIS data Analysis (Arc Map) Multispectral Imagery Analysis (Arc Map) Defensible Space Analysis Hyperspectral Imagery Analysis (ENVI) Data Validation Fit the Mitigation Model (SPSS) Fire Risk Model (Risk = Hazard- Mitigation) Demographic Comparison of Risk Methodology: GIS Data Analysis Mapping of urban expansion using land use data (SanGIS) Pre 1986- 1990- 1995- 2000- 2004- 2008- 1986 1990 1995 2000 2004 2008 2012 Results: GIS Data Analysis • WUI expanding in all three study sites • Most rapid development in Rancho Santa Fe • Largest Proportion of Non Urban Land in Julian Methodology: National Agriculture Imagery Program (NAIP) Imagery Supervised Image Classification (Arc Map 10.2) NAIP Imagery Available for years • 2005 • 2009 • 2010 • 2012 Image showing NDVI index- Brighter red: Healthy vegetation Composite RGB, IR and NDVI image used for classification Results : National Agriculture Imagery Program (NAIP) Imagery Result: Polygon of tree and grass cover across each study site to be used in defensible space analysis Producer User Tree 92% Tree 92% Grass 70% Grass 84% Urban 83% Urban 67% Producer Accuracy: The probability that the method will correctly identify the feature. User accuracy: The likelihood that the resulting polygon depicts the correct feature on the ground. Methodology: Defensible Space Analyzing defensible space in four divisions of the Home Ignition Zone (HIZ) HIZ Buffers used to (A) Structure intersect vegetation polygons and other (B) 0-1.5m structures (C) 1.5-9m (D) 9- 30m Outputs (For all 11,747 structures)… % Tree cover in each zone, % Grass cover in each zone, and presence of structure in B C & D Results: Defensible Space Average % Tree Cover in Average % Tree Cover in Zone A over time Zone B over time 50 50 • Average cover Zone A : 8% 45 45 40 40 35 35 • Average Cover Zone B : 17% 30 30 25 25 20 20 15 15 • Average Cover Zone C : 23% 10 10 5 5 0 0 2005 2009 2010 2012 2005 2009 2010 2012 • Average Cover Zone D : 26% RANCHO SANTA FE RAMONA JULIAN RANCHO SANTA FE RAMONA JULIAN Average % Tree cover in Average % Tree Cover in Zone C over time Zone D over time 7 YEAR AVERAGES 50 50 45 45 • Rancho Santa Fe: -4.21% 40 40 35 35 30 30 25 25 • Ramona: +2.69 20 20 15 15 10 10 • Julian: + 5.30 5 5 0 0 2005 2009 2010 2012 2005 2009 2010 2012 RANCHO SANTA FE RAMONA JULIAN RANCHO SANTA FE RAMONA JULIAN Methodology: Hyperspectral Imagery Analysis (ENVI) Detecting Wood shingle roofing with Mixed Tuned Matched Filtering Target Detection using NASA Aerial Visible Infrared Imaging Spectrometer (AVARIS) 1. Set Region of Interest (ROI) using known location of wood shake roof 2. Plot spectral result and search for matching cells Methodology based on research by Herold et. al (2003) Results: Hyperspectral Imagery Analysis (ENVI) • 85 structures identified as wood shake roof out of 2089 Homes in RSF study site. • 70 homes verified to have woodshake using on site collection and Google Earth • Method 82% accurate • 89% of detected wood shake homes pre 1990. • Data limited to Rancho Santa Fe 2014 • Period of urban development used in model to represent roof type and structural materials Methodology: Fitting the Model Proposed Home Ignition Zone Mitigation Model Poor Moderate Good (1) (2) (3) I ran a logistic regression using the 2005 data and known locations of homes destroyed in the Witch fire to test this model. Methodology: Fitting the Model = + + + + + + + + + + + + − Y= Home burned in the witch fire (0,1) X1- Within the Fire perimeter (0,1) X2- Period of urban development (1-5) (Values 6 & 7 were out of range of 2005 data) X3- House Size (m2) X4-Percent Tree coverage Zone A (%) X5-Percent Tree coverage Zone B (%) X6-Percent Tree coverage Zone C (%) X7-Percent Tree coverage Zone D (%) X8-Percent Grass coverage Zone B (%) X9-Percent Grass coverage Zone C (%) X10-Percent Grass coverage Zone D (%) X12- Distance to Wildland vegetation (0-2) Sample of all homes within Witch Fire Boundary and .5km zone (n = 3,669) Results: Fitting the Model Variable Significance Interpretation Period of Urban development <.000 Older homes have a higher probability of loss. Increases in percentage of tree in B increase odds of Percentage of Trees in Zone B (0-1.5m) <.000 losing home Increases in percentage of grass in B increase odds of Percentage of Grass in Zone B (0-1.5m) <.024 losing home Increases in distance from wildland vegetation Distance from wildland vegetation <.005 decrease odds of losing home. Mean % Tree Mean % Grass Burned Cover in Zone B Cover in Zone B No (n= 1846) 16% 10% Yes (n= 1823) 18% 11% Methodology: Fitting the model Home Ignition Zone Mitigation Model Input Structure Period of Urban Development Pre 1990 Other Within Distance to Wildland Vegetation Other Wildland Vegetation Over 18% Tree Over 11% Grass Under 18% Presence of cover and no cover and no Tree and Zone 2 - 0-1.5m structure structure structure Grass cover Presence of No Zone 3 - 1.5-9m structure structures Presence of Zone 4 - 9-30m structure Poor Moderate Good (1) (2) (3) Model Limitations • Ground cover next to house • Vents and other openings • Vertical continuity of landscaping • Siding materials • Leaf litter and Property Hygiene • Tree Species Results: Home Ignition Zone Mitigation Model Home Ignition Zone Mitigations across study sites over time 9000 • Rancho Santa Fe had the greatest improvement in 8000 mitigation over the 7 year study period. 7000 • Small changes in Mitigation 6000 over time in each 5000 community Structures 4000 • All three communities improved over the 7 year 3000 period 2000 • Greatest improvements 2005-2009 1000 0 2005 2009 2010 2012 2005 2009 2010 2012 2005 2009 2010 2012 Rancho Santa Fe Ramona Julian Poor Moderate Good Methodology: Fire Risk Model RISK = HAZARD –MITIGATION Hazard Cal Fire Fire Hazard Severity Zone (FHSZ) model Implemented Mitigation Proximity to wildland vegetation Period of Urban development ‘Defensible Space’ Proximity of other structures Methodology: Fire Risk Model RISK = HAZARD –MITIGATION HOME IGNITION ZONE MITIGATION RISK = (Hazard – HIZ FIRE RISK HAZARD SCORE Mitigation) SCORE 3 -2 MFR Moderate (1) 2 -1 MFR 1 0 HFR 3 -1 MFR High (2) 2 0 HFR 1 1 VHFR 3 0 HFR Very High (3) 2 1 VHFR 1 2 VHFR MFR ≤ -1 Moderate Fire Risk -1 < HFR <1 High Fire Risk VHFR ≥ 1 Very High Fire Risk Results: Fire Risk Model Fire Risk across study sites over time 9000 • Julian had the greatest proportion of Very High 8000 Risk structures 7000 • Rancho Santa Fe had greatest reductions in Risk 6000 over 7 years 5000 • Proportion of Very High risk Structures 4000 structures in Ramona increased over the 7 year 3000 study period. 2000 1000 0 2005 2009 2010 2012 2005 2009 2010 2012 2005 2009 2010 2012 Rancho Santa Fe Ramona Julian Very High High Moderate Results: Fire Risk Model Rancho Santa Fe • Very High Risk in Higher Income Areas • Secluded hilltop mansions in wildland vegetation (chaparral). • Poorer Mitigation in lower Income Areas • High prevalence of ornamental vegetation and secondary structures Results: Fire Risk Model Ramona • Very High risk in lower income areas. • Poor mitigation in lower income areas • Larger family size • Lower disposable income. Results: Fire Risk Model Julian • Very High Risk overall • High fire hazard (coniferous trees, steep slopes) • Wooden construction.
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