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Discussion papers | Copyright
https://doi.org/10.5194/essd-2017-136
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Review article 09 Jan 2018

Review article | 09 Jan 2018

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This discussion paper is a preprint. It is a manuscript under review for the journal Earth System Science Data (ESSD).

A weekly, near real-time dataset of the probability of large wildfire 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 (250-m pixel) of the probability of large wildfire (>405ha) across all western US forests and woodlands, from 2005 to the present. The dataset is automatically updated on a weekly basis and in near real-time (i.e., up to the present week) using Google Earth Engine and a "Continuous Integration" pipeline. Each image in the dataset is the output of a machine-learning algorithm, trained on 10 independent, random samples of historic small and large wildfires, and represents the predicted probability of an individual pixel burning in a large fire. This novel workflow is able to integrate the short-term dynamics of fuels and weather into weekly predictions, while also integrating longer-term dynamics of fuels, climate, and the landscape. As a near real-time product, the dataset can provide operational fire managers with immediate, on-the-ground information to closely monitor changing potential for large wildfire occurrence and spread. It can also serve as a foundational dataset for longer-term planning and research, such as strategic targeting of fuels management, fire-smart development at the wildland urban interface, and analysis of trends in wildfire potential over time. Weekly large fire probability GeoTiff products from 2005 through 2017 are archived on Figshare online digital repository with the DOI 10.6084/m9.figshare.5765967 (available at https://doi.org/10.6084/m9.figshare.5765967.v1). Near real-time weekly GeoTiff products and the entire dataset from 2005 on are also continuously uploaded to a Google Cloud Storage bucket at https://console.cloud.google.com/storage/wffr-preds/V1, and also available free of charge with a Google account. Near real-time 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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Miranda E. Gray et al.
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Miranda E. Gray et al.
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Weekly Large Wildfire Probability in Western US Forests and Woodlands, 2005-2017 M. Gray, L. Zachmann, and B. Dickson https://doi.org/10.6084/m9.figshare.5765967

Miranda E. Gray et al.
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Short summary
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 2005–2017 that depict 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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