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| Titel |
Power law distributions of wildfires across Europe: benchmarking a land surface model with observed data |
| VerfasserIn |
B. Mauro, F. Fava, P. Frattini, A. Camia, R. Colombo, M. Migliavacca |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
2198-5634
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics Discussions ; 2, no. 6 ; Nr. 2, no. 6 (2015-11-24), S.1553-1586 |
| Datensatznummer |
250115201
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| Publikation (Nr.) |
 copernicus.org/npgd-2-1553-2015.pdf |
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| Zusammenfassung |
Monthly wildfire burned area frequency is here modeled with a power law
distribution and scaling exponent across different European biomes are
estimated. Data sets, spanning from 2000 to 2009, comprehend the inventory
of monthly burned areas from the European Forest Fire Information System
(EFFIS) and simulated monthly burned areas from a recent parameterization of
a Land Surface Model (LSM), that is the Community Land Model (CLM). Power
law exponents are estimated with a Maximum Likelihood Estimation (MLE) for
different European biomes. The characteristic fire size (CFS), i.e. the area
that most contributes to the total burned area, was also calculated both
from EFFIS and CLM data set. We used the power law fitting and the CFS
analysis to benchmark CLM model against the EFFIS observational wildfires
data set available for Europe.
Results for the EFFIS data showed that power law fittings holds for 2–3
orders of magnitude in the Boreal and Continental ecoregions, whereas the
distribution of the Alpine, Atlantic are fitted only in the upper tail.
Power law instead is not a suitable model for fitting CLM simulations.
CLM benchmarking analysis showed that the model strongly overestimates
burned areas and fails in reproducing size-frequency distribution of
observed EFFIS wildfires. This benchmarking analysis showed that some
refinements in CLM structure (in particular regarding the anthropogenic
influence) are needed for predicting future wildfires scenarios, since the
low spatial resolution of the model and differences in relative frequency of
small and large fires can affect the reliability of the predictions. |
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