Future Change in Landscape Fire Risk for Hawai‘i Under Various Climate Change Scenarios for 2050 and 2100
Dates
Publication Date
2024-04-12
Start Date
1999
End Date
2100
Citation
Clay Trauernicht, Nathan DeMaagd, and Matt Lucas, 20240412, Future Change in Landscape Fire Risk for Hawai‘i Under Various Climate Change Scenarios for 2050 and 2100: U.S. Geological Survey, https://doi.org/10.21429/jwcd-k009.
Summary
This product provides a metric of percentage increase in the number of fires expected per year under various climate change scenarios in both the dry and wet season compared to a 1996-2016 baseline at a per-pixel basis for the main Hawaiian Islands (excluding Niʻihau) at 30 m x 30 m resolution. Future climate scenarios include statistically and dynamically downscaled RCP 8.5 in the year 2100 in addition to statistically downscaled RCP 8.5 in the year 2050. This is a modeled data product trained using historical fire perimeters, ignition density, mean annual temperature, mean annual soil moisture, historical rainfall data , and remotely sensed vegetation cover.
Summary
This product provides a metric of percentage increase in the number of fires expected per year under various climate change scenarios in both the dry and wet season compared to a 1996-2016 baseline at a per-pixel basis for the main Hawaiian Islands (excluding Niʻihau) at 30 m x 30 m resolution. Future climate scenarios include statistically and dynamically downscaled RCP 8.5 in the year 2100 in addition to statistically downscaled RCP 8.5 in the year 2050. This is a modeled data product trained using historical fire perimeters, ignition density, mean annual temperature, mean annual soil moisture, historical rainfall data , and remotely sensed vegetation cover.
The purpose of this data is to show the change in annual fire risk under various downscaling approached and various time periods across the main Hawaiian islands for uses such, but not limited to, natural resource management, community wildfire preparedness, and emergency preparedness. Note that the University of Hawai‘i at Mānoa or Dr. Trauernicht will not bear any responsibility for the consequences of using this dataset, which are entirely the responsibility of the user. The data do not offer actual quantification of the potential exposure of homes to the ignition, spread, and intensity of wildfires or embers produced by wildfires. Although the dataset and subsequent analyses may indicate general wildfire patterns for a given area, the actual risk to homes and property can deviate based on the characteristics of the site around an individual home, community, or natural resource area, as well as real-time weather conditions, which the model does not take into account.