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-1yr-1); and (3) the high-denitrification zone (mean of ln-transformed rate = 3.86 ± 1.96 mg N L-1yr-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.
- multiple-point geostatistics
- process-based understandings
- subsurface resistivity
- training image
- Programme Area 2: Water Resources