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| Titel |
Modelling burned area in Africa |
| VerfasserIn |
V. Lehsten, P. Harmand, I. Palumbo, A. Arneth |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1726-4170
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| Digitales Dokument |
URL |
| Erschienen |
In: Biogeosciences ; 7, no. 10 ; Nr. 7, no. 10 (2010-10-20), S.3199-3214 |
| Datensatznummer |
250005019
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| Publikation (Nr.) |
 copernicus.org/bg-7-3199-2010.pdf |
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| Zusammenfassung |
The simulation of current and projected wildfires is essential for
predicting crucial aspects of vegetation patterns, biogeochemical cycling as
well as pyrogenic emissions across the African continent. This study uses a
data-driven approach to parameterize two burned area models applicable to
dynamic vegetation models (DVMs) and Earth system models (ESMs). We
restricted our analysis to variables for which either projections based on
climate scenarios are available, or that are calculated by DVMs, and we
consider a spatial scale of one degree as the scale typical for DVMs and
ESMs. By using the African continent here as an example, an analogue
approach could in principle be adopted for other regions, for global scale
dynamic burned area modelling.
We used 9 years of data (2000–2008) for the variables: precipitation over
the last dry season, the last wet season and averaged over the last 2 years,
a fire-danger index (the Nesterov index), population density, and annual
proportion of area burned derived from the MODIS MCD45A1 product. Two
further variables, tree and herb cover were only available for 2001 as a
remote sensing product. Since the effect of fires on vegetation depends
strongly on burning conditions, the timing of wildfires is of high interest
too, and we were able to relate the seasonal occurrence of wildfires to the
daily Nesterov index.
We parameterized two generalized linear models (GLMs), one with the full
variable set (model VC) and one considering only climate variables (model
C). All introduced variables resulted in an increase in model performance.
Model VC correctly predicts the spatial distribution and extent of fire
prone areas though the total variability is underrepresented. Model VC has a
much lower performance in both aspects (correlation coefficient of predicted
and observed ratio of burned area: 0.71 for model VC and 0.58 for model C).
We expect the remaining variability to be attributed to additional variables
which are not available at a global scale and thus not incorporated in this
study as well as its coarse resolution. An application of the models using
climate hindcasts and projections ranging from 1980 to 2060 resulted in a
strong decrease of burned area of ca. 20–25%. Since wildfires are an
integral part of land use practices in Africa, their occurrence is an
indicator of areas favourable for food production. In absence of other
compensating land use changes, their projected decrease can hence be
interpreted as a indicator for future loss of such areas. |
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