multi-variable bias correction: application of forest fire risk in present and future climate in sweden
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2015
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Abstract
As the risk of a forest fire is largely influenced by weather, evaluating
its tendency under a changing climate becomes important for management and
decision making. Currently, biases in climate models make it difficult to
realistically estimate the future climate and consequent impact on fire
risk. A distribution-based scaling (DBS) approach was developed as a
post-processing tool that intends to correct systematic biases in climate
modelling outputs. In this study, we used two projections, one driven by
historical reanalysis (ERA40) and one from a global climate model (ECHAM5)
for future projection, both having been dynamically downscaled by a regional
climate model (RCA3). The effects of the post-processing tool on relative
humidity and wind speed were studied in addition to the primary variables
precipitation and temperature. Finally, the Canadian Fire Weather Index
system was used to evaluate the influence of changing meteorological
conditions on the moisture content in fuel layers and the fire-spread risk.
The forest fire risk results using DBS are proven to better reflect risk
using observations than that using raw climate outputs. For future periods,
southern Sweden is likely to have a higher fire risk than today, whereas
northern Sweden will have a lower risk of forest fire.
| Reference Key |
yang2015naturalmulti-variable
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|---|---|
| Authors | ;W. Yang;M. Gardelin;J. Olsson;T. Bosshard |
| Journal | anziam journal |
| Year | 2015 |
| DOI |
10.5194/nhess-15-2037-2015
|
| URL | |
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