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Improved Forest Fire Danger Rating Using Regression Kriging with the Canadian Precipitation Analysis (CaPA) System in Alberta Open Access


Other title
Regression kriging
Canadian Precipitation Analysis (CaPA)
Fire danger rating
Type of item
Degree grantor
University of Alberta
Author or creator
Supervisor and department
Mike Flannigan (Renewable Resources)
Examining committee member and department
Xianli Wang (Canadian Forest Service)
Jen Beverly (Renewable Resources)
Nadir Erbilgin (Renewable Resources)
Department of Renewable Resources
Forest Biology and Management
Date accepted
Graduation date
2017-11:Fall 2017
Master of Science
Degree level
Estimating precipitation is currently one of the key challenges of accurate Fire Danger Rating. New gridded precipitation analysis systems such as the Canadian Precipitation Analysis System (CaPA) may be superior to the current analytical interpolation strategies, thin plate spline (TPS), and inverse distance weighting (IDW). To compare the performance of CaPA and interpolation methods in the forested area of Alberta, I evaluated precipitation estimates from CaPA and 17 algorithms of five interpolation methods, including IDW, smoothed TPS, non-smoothed TPS, ordinary kriging, and regression kriging. Precipitation estimates were generated using station observations through leave-one-out cross-validation and were evaluated using a range of skill scores (MAE, Bias, ETS, and FBI). I also assessed impacts of these precipitation estimates on the Canadian Forest Fire Weather Index (FWI) System and examined the impacts of weather station density on model performance. Results show that CaPA was only a mid-tiered method (13th of 18), except in Doppler radar covered areas, where CaPA performed second best. Regression kriging (with CaPA as a covariate) was the best method and produced precipitation estimates with 19.6% lower MAE compared with IDW. I found that the best method shifted with station density; CaPA was the best method when fire weather station density fell below 0.6 stations per 10 000km2 while regression kriging was the best method above this threshold. Additionally, this study showed that the FWI System responded to precipitation estimates differently due to their varying drying time lag of the indexes. Quick drying indexes (FFMC, ISI, FWI) preferred methods with lower MAE (e.g., regression kriging with smoothing), while slow drying indexes (DMC, DC, BUI) preferred methods with lower Bias (e.g., regression kriging without smoothing). Overall, I recommend the use of regression kriging with CaPA as a covariate to estimate fire danger across landscapes.
This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for the purpose of private, scholarly or scientific research. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
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