@phdthesis{Siegmund2018,
  author    = {Siegmund, Jonatan Frederik},
  title     = {Quantifying impacts of climate extreme events on vegetation},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-407095},
  school      = {Universit{\"a}t Potsdam},
  pages     = {129},
  year      = {2018},
  abstract  = {Together with the gradual change of mean values, ongoing climate change is projected to increase frequency and amplitude of temperature and precipitation extremes in many regions of Europe. The impacts of such in most cases short term extraordinary climate situations on terrestrial ecosystems are a matter of central interest of recent climate change research, because it can not per se be assumed that known dependencies between climate variables and ecosystems are linearly scalable. So far, yet, there is a high demand for a method to quantify such impacts in terms of simultaneities of event time series. In the course of this manuscript the new statistical approach of Event Coincidence Analysis (ECA) as well as it's R implementation is introduced, a methodology that allows assessing whether or not two types of event time series exhibit similar sequences of occurrences. Applications of the method are presented, analyzing climate impacts on different temporal and spacial scales: the impact of extraordinary expressions of various climatic variables on tree stem variations (subdaily and local scale), the impact of extreme temperature and precipitation events on the owering time of European shrub species (weekly and country scale), the impact of extreme temperature events on ecosystem health in terms of NDVI (weekly and continental scale) and the impact of El Ni{\~n}o and La Ni{\~n}a events on precipitation anomalies (seasonal and global scale). The applications presented in this thesis refine already known relationships based on classical methods and also deliver substantial new findings to the scientific community: the widely known positive correlation between flowering time and temperature for example is confirmed to be valid for the tails of the distributions while the widely assumed positive dependency between stem diameter variation and temperature is shown to be not valid for very warm and very cold days. The larger scale investigations underline the sensitivity of anthrogenically shaped landscapes towards temperature extremes in Europe and provide a comprehensive global ENSO impact map for strong precipitation events. Finally, by publishing the R implementation of the method, this thesis shall enable other researcher to further investigate on similar research questions by using Event Coincidence Analysis.},
  language  = {en}
}