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  Detecting impacts of extreme events with ecological in-situ monitoring networks

Mahecha, M. D., Gans, F., Sippel, S., Donges, J. F., Kaminski, T., Metzger, S., Migliavacca, M., Papale, D., Rammig, A., Zscheischler, J. (2017): Detecting impacts of extreme events with ecological in-situ monitoring networks. - Biogeosciences, 14, 18, 4255-4277.
https://doi.org/10.5194/bg-14-4255-2017

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 Urheber:
Mahecha, M. D.1, Autor
Gans, F.1, Autor
Sippel, S.1, Autor
Donges, Jonathan Friedemann2, Autor              
Kaminski, T.1, Autor
Metzger, S.1, Autor
Migliavacca, M.1, Autor
Papale, D.1, Autor
Rammig, A.1, Autor
Zscheischler, J.1, Autor
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Zusammenfassung: Extreme hydrometeorological conditions typically impact ecophysiological processes on land. Satellite-based observations of the terrestrial biosphere provide an important reference for detecting and describing the spatiotemporal development of such events. However, in-depth investigations of ecological processes during extreme events require additional in situ observations. The question is whether the density of existing ecological in situ networks is sufficient for analysing the impact of extreme events, and what are expected event detection rates of ecological in situ networks of a given size. To assess these issues, we build a baseline of extreme reductions in the fraction of absorbed photosynthetically active radiation (FAPAR), identified by a new event detection method tailored to identify extremes of regional relevance. We then investigate the event detection success rates of hypothetical networks of varying sizes. Our results show that large extremes can be reliably detected with relatively small networks, but also reveal a linear decay of detection probabilities towards smaller extreme events in log–log space. For instance, networks with  ≈  100 randomly placed sites in Europe yield a  ≥  90 % chance of detecting the eight largest (typically very large) extreme events; but only a  ≥  50 % chance of capturing the 39 largest events. These findings are consistent with probability-theoretic considerations, but the slopes of the decay rates deviate due to temporal autocorrelation and the exact implementation of the extreme event detection algorithm. Using the examples of AmeriFlux and NEON, we then investigate to what degree ecological in situ networks can capture extreme events of a given size. Consistent with our theoretical considerations, we find that today's systematically designed networks (i.e. NEON) reliably detect the largest extremes, but that the extreme event detection rates are not higher than would be achieved by randomly designed networks. Spatio-temporal expansions of ecological in situ monitoring networks should carefully consider the size distribution characteristics of extreme events if the aim is also to monitor the impacts of such events in the terrestrial biosphere.

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 Datum: 2017
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
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 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.5194/bg-14-4255-2017
PIKDOMAIN: Earth System Analysis - Research Domain I
eDoc: 7617
Research topic keyword: Extremes
Research topic keyword: Climate impacts
Research topic keyword: Ecosystems
Model / method: Machine Learning
Model / method: Nonlinear Data Analysis
Regional keyword: Europe
Regional keyword: North America
Organisational keyword: FutureLab - Earth Resilience in the Anthropocene
Organisational keyword: RD1 - Earth System Analysis
Working Group: Ecosystems in Transition
Working Group: Whole Earth System Analysis
 Art des Abschluß: -

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Titel: Biogeosciences
Genre der Quelle: Zeitschrift, SCI, Scopus, p3, oa
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Ort, Verlag, Ausgabe: -
Seiten: - Band / Heft: 14 (18) Artikelnummer: - Start- / Endseite: 4255 - 4277 Identifikator: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/journals47