Volume 14, issue 18 | Copyright
Biogeosciences, 14, 4255-4277, 2017
https://doi.org/10.5194/bg-14-4255-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Ideas and perspectives 25 Sep 2017

Ideas and perspectives | 25 Sep 2017

Detecting impacts of extreme events with ecological in situ monitoring networks

Miguel D. Mahecha1,2,3, Fabian Gans1, Sebastian Sippel1,4, Jonathan F. Donges5,6, Thomas Kaminski7, Stefan Metzger8,9, Mirco Migliavacca1, Dario Papale10,11, Anja Rammig12, and Jakob Zscheischler4 Miguel D. Mahecha et al.
  • 1Max Planck Institute for Biogeochemistry, 07745 Jena, Germany
  • 2German Centre for Integrative Biodiversity Research (iDiv), Deutscher Platz 5e, 04103 Leipzig, Germany
  • 3Michael Stifel Centre Jena for Data-Driven and Simulation Science, 07743 Jena, Germany
  • 4Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
  • 5Earth System Analysis, Potsdam Institute for Climate Impact Research, Telegrafphenberg A62, 14473 Potsdam, Germany
  • 6Stockholm Resilience Centre, Stockholm University, Kräftriket 2B, 114 19 Stockholm, Sweden
  • 7The Inversion Lab, Tewessteg 4, 20249 Hamburg, Germany
  • 8National Ecological Observatory Network, Fundamental Instrument Unit, Boulder, CO, USA
  • 9University of Colorado, Institute for Arctic and Alpine Research, Boulder, CO, USA
  • 10Department for Innovation in Biological, Agro-Food and Forest Systems, University of Tuscia, Viterbo, Italy
  • 11Euro-Mediterranean Centre on Climate Change (CMCC), 01100 Viterbo, Italy
  • 12Technische Universität München, Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany

Abstract. 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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We investigate the likelihood of ecological in situ networks to detect and monitor the impact of extreme events in the terrestrial biosphere.
We investigate the likelihood of ecological in situ networks to detect and monitor the impact of...
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