Bayesian hierarchical modelling of nitrate concentration in a forest
stream affected by large-scale forest dieback
Abstract
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.