posted on 2021-11-22, 20:46authored byHyojin Kim, Rasmus Jakobsen, Jens Aamand, Niels Claes, Mogens Erlandsen, Birgitte Hansen
The
spatial and temporal variability of denitrification makes it
challenging to integrate conceptual, process-based understandings
of nitrate transport and retention into numerical modeling at the
catchment scale, although it is critical for the realism and predictive
power of the model. In this study, we propose a novel approach where
the conceptual understandings of the spatial structure of denitrification
zones and the corresponding representative denitrification rates are
transformed into a form that can be integrated into a multi-point
statistical simulation framework. This is done by constructing a denitrification
training image (TI) coupled to a geophysically based TI of the hydrogeological
structure. The field observations and laboratory analyses of denitrification
rates and the chemistry of water and sediment revealed that the study
catchment’s subsurface can be characterized by three zones:
(1) the oxic zone with no nitrate reduction; (2) the slow-denitrification
zone (mean of ln-transformed rate = −1.19 ± 0.52 mg N
L–1 yr–1); and (3) the high-denitrification
zone (mean of ln-transformed rate = 3.86 ± 1.96 mg N L–1 yr–1). The underlying controls on the spatial
distribution of these zones and the representativeness of denitrification
rates were investigated. Then, a TI illustrating the subsurface structure
of the denitrification zone was constructed by synthesizing the results
of these geochemical interpretations and the hydrogeology TI.