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Volume 10, issue 3
Earth Syst. Sci. Data, 10, 1715-1727, 2018
https://doi.org/10.5194/essd-10-1715-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Earth Syst. Sci. Data, 10, 1715-1727, 2018
https://doi.org/10.5194/essd-10-1715-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Review article 20 Sep 2018

Review article | 20 Sep 2018

A weekly, continually updated dataset of the probability of large wildfires across western US forests and woodlands

Miranda E. Gray1, Luke J. Zachmann1,2, and Brett G. Dickson1,2 Miranda E. Gray et al.
  • 1Conservation Science Partners, Inc., Truckee, CA 96161, USA
  • 2Lab of Landscape Ecology and Conservation Biology, Northern Arizona University, Flagstaff, AZ 86011, USA

Abstract. There is broad consensus that wildfire activity is likely to increase in western US forests and woodlands over the next century. Therefore, spatial predictions of the potential for large wildfires have immediate and growing relevance to near- and long-term research, planning, and management objectives. Fuels, climate, weather, and the landscape all exert controls on wildfire occurrence and spread, but the dynamics of these controls vary from daily to decadal timescales. Accurate spatial predictions of large wildfires should therefore strive to integrate across these variables and timescales. Here, we describe a high spatial resolution dataset (250m pixel) of the probability of large wildfires ( > 405ha) across forests and woodlands in the contiguous western US, from 2005 to the present. The dataset is automatically updated on a weekly basis using Google Earth Engine and a continuous integration pipeline. Each image in the dataset is the output of a random forest machine-learning algorithm, trained on random samples of historic small and large wildfires and represents the predicted conditional probability of an individual pixel burning in a large fire, given an ignition or fire spread to that pixel. This novel workflow is able to integrate the near-term dynamics of fuels and weather into weekly predictions while also integrating longer-term dynamics of fuels, the climate, and the landscape. As a continually updated product, the dataset can provide operational fire managers with contemporary, on-the-ground information to closely monitor the changing potential for large wildfire occurrence and spread. It can also serve as a foundational dataset for longer-term planning and research, such as the strategic targeting of fuels management, fire-smart development at the wildland–urban interface, and the analysis of trends in wildfire potential over time. Weekly large fire probability GeoTiff products from 2005 to 2017 are archived on the Figshare online digital repository with the DOI https://doi.org/10.6084/m9.figshare.5765967 (available at https://doi.org/10.6084/m9.figshare.5765967.v1). Weekly GeoTiff products and the entire dataset from 2005 onwards are also continually uploaded to a Google Cloud Storage bucket at https://console.cloud.google.com/storage/wffr-preds/V1 (last access: 14 September 2018) and are available free of charge with a Google account. Continually updated products and the long-term archive are also available to registered Google Earth Engine (GEE) users as public GEE assets and can be accessed with the image collection ID users/mgray/wffr-preds within GEE.

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There is broad consensus that wildfire activity is likely to increase in western US forests and woodlands over the next century. Therefore, spatial predictions of the potential for large wildfires have immediate and growing relevance to near and long-term research, planning, and management objectives. The dataset described here is a weekly time series of images (250 m resolution) from 2005 to 2017 that depicts the probability of large fire across western US forests and woodlands.
There is broad consensus that wildfire activity is likely to increase in western US forests and...
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