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| Titel |
A geostatistical synthesis study of factors affecting gross primary productivity in various ecosystems of North America |
| VerfasserIn |
V. Yadav, K. L. Mueller, D. Dragoni, A. M. Michalak |
| 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. 9 ; Nr. 7, no. 9 (2010-09-09), S.2655-2671 |
| Datensatznummer |
250004964
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| Publikation (Nr.) |
 copernicus.org/bg-7-2655-2010.pdf |
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| Zusammenfassung |
| A coupled Bayesian model selection and geostatistical regression modeling
approach is adopted for empirical analysis of gross primary productivity
(GPP) at six AmeriFlux sites, including the Kennedy Space Center Scrub Oak,
Vaira Ranch, Tonzi Ranch, Blodgett Forest, Morgan Monroe State Forest, and
Harvard Forest sites. The analysis is performed at a continuum of temporal
scales ranging from daily to monthly, for a period of seven years. A total
of 10 covariates representing environmental stimuli and indices of plant
physiology are considered in explaining variations in GPP. Similarly to other
statistical methods, the presented approach estimates regression
coefficients and uncertainties associated with the covariates in a selected
regression model. Unlike traditional regression methods, however, the
approach also estimates the uncertainty associated with the selection of a
single "best" model of GPP. In addition, the approach provides an enhanced
understanding of how the importance of specific covariates changes with the
examined timescale (i.e. temporal resolution). An examination of changes in
the importance of specific covariates across timescales reveals thresholds
above or below which covariates become important in explaining GPP. Results
indicate that most sites (especially those with a stronger seasonal cycle)
exhibit at least one prominent scaling threshold between the daily and
20-day temporal scales. This demonstrates that environmental variables that
explain GPP at synoptic scales are different from those that capture its
seasonality. At shorter time scales, radiation, temperature, and vapor
pressure deficit exert the most significant influence on GPP at most
examined sites. At coarser time scales, however, the importance of these
covariates in explaining GPP declines. Overall, unique best models are
identified at most sites at the daily scale, whereas multiple competing
models are identified at longer time scales. |
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