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Journal Article

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


Mahecha,  M. D.
External Organizations;

Gans,  F.
External Organizations;

Sippel,  S.
External Organizations;


Donges,  Jonathan Friedemann
Potsdam Institute for Climate Impact Research;

Kaminski,  T.
External Organizations;

Metzger,  S.
External Organizations;

Migliavacca,  M.
External Organizations;

Papale,  D.
External Organizations;

Rammig,  A.
External Organizations;

Zscheischler,  J.
External Organizations;

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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.

Cite as: https://publications.pik-potsdam.de/pubman/item/item_21665
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.