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A comparison of statistical downscaling methods suited for wildfire applications

Author(s): John T. Abatzoglou, Timothy J. Brown
Year Published: 2011

Place-based data is required in wildfire analyses, particularly in regions of diverse terrain that foster not only strong gradients in meteorological variables, but also complex fire behaviour. However, a majority of downscaling methods are inappropriate for wildfire application due to the lack of daily timescales and variables such as humidity and winds that are important for fuel flammability and fire spread. Two statistical downscaling methods, the daily Bias corrected Spatial Downscaling (BCSD) and the Multivariate Adapted Constructed Analogs (MACA) that directly incorporate daily data from global climate models, were validated over the western US using global reanalysis data. While both methods outperformed results obtained from direct interpolation from reanalysis, MACA exhibited additional skill in temperature, humidity, wind, and precipitation due to its ability to jointly downscale temperature and dew point temperature, and its use of analog patterns rather than interpolation. Both downscaling methods exhibited value added information in tracking fire danger indices and periods of extreme fire danger; however, MACA outperformed the daily BCSD due to its ability to more accurately capture relative humidity and winds.

Citation: Abatzoglou, John T.; Brown, Timothy J. 2011. A comparison of statistical downscaling methods suited for wildfire applications. International Journal of Climatology. 32(5): 772-780.
Topic(s): Fire Behavior, Data Evaluation or Data Analysis for Fire Modeling, Fire & Climate
Ecosystem(s): None
Document Type: Book or Chapter or Journal Article
Hot Topic(s): Fire Behavior Prediction
NRFSN number: 11973
FRAMES RCS number: 14497
Record updated: Jun 12, 2018