Bayesian Hierarchical Modeling of Nitrate Concentration in a Forest Stream Affected by Large-Scale Forest Dieback

The ecosystem function of vegetation to attenuate 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 (Ips typographus Linnaeus) outbreak and resulting large-scale forest dieback using top-down statistical-mechanistic modeling. Outbreaks of bark beetle killed the dominant tree species Norway spruce (Picea abies (L.) H.Karst.) in stands accounting for 55% of the catchment area. A Bayesian hierarchical model that predicts daily stream NO₃ concentration (C) over three decades with discharge (Q) and temperature (T) (C-Q-T relationship) outperformed alternative statistical models. A catchment model was subsequently developed to explain the C-Q-T relationship in top-down fashion. Annually varying parameter estimates provide mechanistic interpretations of the catchment processes. Release of NO₃ from decaying litter after the dieback was tracked by an increase of the nutrient input parameter c_s0. The slope of C-T relation was near zero during this period, suggesting that the nutrient release was beyond the regulating capacity of the vegetation and soils. Within a decade after the dieback, the released N was flushed out and nutrient retention capacity was restored with the regrowth of the vegetation.