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Bayesian hierarchical modelling of nitrate concentration in a forest stream affected by large-scale forest dieback
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  • Hoseung Jung,
  • Cornelius Senf,
  • Burkhard Beudert,
  • Tobias Krüger
Hoseung Jung
Humboldt-Universität zu Berlin

Corresponding Author:[email protected]

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Cornelius Senf
Technische Universität München
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Burkhard Beudert
Bavarian Forest National Park
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Tobias Krüger
Humboldt-Universität zu Berlin
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The ecosystem function of vegetation to attenuate riverine export of nutrients is of substantial importance for securing water quality. This ecosystem function is at risk of deterioration due to an increasing risk of large-scale forest dieback under climate change. The present study explores the response of the nitrogen (N) cycle of a forest catchment in the Bavarian Forest National Park, Germany, in the face of a severe bark beetle () outbreak and resulting large-scale forest dieback. Outbreaks of bark beetle killed the dominant tree species Norway spruce () in up to 55 % of the area. A Bayesian hierarchical model that predicts stream NO concentration (C) with discharge (Q) and water temperature (T) as predictors (C-Q-T relationship) was found as the best fitting model. This informed top-down development of a catchment model to explain the C-Q-T relationship so that the annually-varying model parameter estimates provide mechanistic interpretations of the catchment processes. NO concentration increased after the dieback because N was released from the decaying fine litter of trees beyond the capacity of the terrestrial vegetation and riparian zone to regulate the nutrient export. Within a decade after the dieback, the released N was flushed out and nutrient retention capacity was restored with the regrowth of the vegetation. Greater understanding of canopy mortality due to climate change and other anthropogenic impacts are required to mitigate the deterioration of nutrient retention and prevent nutrient loss.