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Sökning: onr:"swepub:oai:DiVA.org:su-148078" > Detecting impacts o...

LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00004167naa a2200445 4500
001oai:DiVA.org:su-148078
003SwePub
008171024s2017 | |||||||||||000 ||eng|
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-1480782 URI
0247 a https://doi.org/10.5194/bg-14-4255-20172 DOI
040 a (SwePub)su
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Mahecha, Miguel D.4 aut
2451 0a Detecting impacts of extreme events with ecological in situ monitoring networks
264 c 2017-09-25
264 1b Copernicus GmbH,c 2017
338 a print2 rdacarrier
520 a 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 approximate to 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.
650 7a NATURVETENSKAPx Biologi0 (SwePub)1062 hsv//swe
650 7a NATURAL SCIENCESx Biological Sciences0 (SwePub)1062 hsv//eng
650 7a NATURVETENSKAPx Geovetenskap och miljövetenskap0 (SwePub)1052 hsv//swe
650 7a NATURAL SCIENCESx Earth and Related Environmental Sciences0 (SwePub)1052 hsv//eng
7001 a Gans, Fabian4 aut
7001 a Sippel, Sebastian4 aut
7001 a Donges, Jonathan F.u Stockholms universitet,Stockholm Resilience Centre,Potsdam Institute for Climate Impact Research, Germany4 aut0 (Swepub:su)jdong
7001 a Kaminski, Thomas4 aut
7001 a Metzger, Stefan4 aut
7001 a Migliavacca, Mirco4 aut
7001 a Papale, Dario4 aut
7001 a Rammig, Anja4 aut
7001 a Zscheischler, Jakob4 aut
7102 a Stockholms universitetb Stockholm Resilience Centre4 org
7730 t Biogeosciencesd : Copernicus GmbHg 14:18, s. 4255-4277q 14:18<4255-4277x 1726-4170x 1726-4189
8564 u https://doi.org/10.5194/bg-14-4255-2017y Fulltext
8564 u https://www.biogeosciences.net/14/4255/2017/bg-14-4255-2017.pdf
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-148078
8564 8u https://doi.org/10.5194/bg-14-4255-2017

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