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Geographic area covered: 
The Wellington Region, including rural areas across Wellington, Hutt Valley, Upper Hutt, Porirua, and Wairarapa districts. 

Summary: 
This report outlines a model for assessing rural fire hazard across the Wellington Region. It builds on earlier work focused on the urban/rural interface but shifts the emphasis to a rural vegetation-based fire hazard model. The model uses vegetation classifications from the Land Cover Database (LCDB) and integrates environmental and human factors to assess wildfire risk. 
 
The model identifies four wildfire hazard classes based on vegetation type, which is fewer than the urban/rural interface model due to the broader classification of shrubland. Vegetation types are ranked by flammability, with shrubland, grassland, and wetlands generally posing higher fire risks than indigenous forest or pastoral land. Planted forests vary in hazard depending on age, weed presence, and recent logging activity. 
 
Key factors driving wildfire hazard classes in the rural model include: 
- Vegetation Type: Different LCDB classes are assigned flammability rankings based on fuel load and burn potential. 
- Slope: Fires spread more rapidly uphill; slope is modeled as a continuous variable to reflect this. 
- Aspect and Solar Radiation: North-facing slopes receive more sunlight, increasing drying and fire risk. 
- Weather Variables: Two modeling options are presented—Option One uses rainfall and temperature overlays; Option Two uses soil moisture deficit as a proxy for drought stress and curing. 
- Light-up Hazard: Areas near roads and human activity are considered higher risk due to ignition sources like arson, controlled burns, and recreation. 
 
The model combines these factors using weighted overlays to produce a wildfire hazard map. Each location is scored based on vegetation, slope, weather, and proximity to ignition sources. The final hazard scores are categorized into five classes: low, medium, high, very high, and extreme. 
 
The report recommends using Option Two (soil moisture deficit) for better accuracy, despite slightly higher costs. It also notes limitations in mapping resolution and weather data availability. The model is intended to support regional fire management planning and can be adapted as better data becomes available.