Tracer-aided identification of hydrological and biogeochemical controls on in-stream water quality in a riparian wetland

Abstract

In-stream water quality reflects the integrated results of hydrological mixing of different water sources and associated biogeochemical transformations. However, quantifying the relative importance of these controls is often challenging, particularly in riparian wetlands due to complex process interactions and marked spatio-temporal heterogeneity in environmental gradients. Here, we established a two-step method to differentiate the dominance of hydrological and biogeochemical controls on water quality in a riparian peatland in northern Germany. First, an isotope-based mixing model was developed for distributed modelling of in-stream water balance over a two-year period. The simulation showed the predominance of groundwater inflows for most of the time period, while lateral inflows and channel leakage became more influential in mid-summer, as stream-groundwater connectivity weakened due to declining groundwater levels. A moderate downstream shift from groundwater to lateral inflow was also observed due to the changing channel network geometries and inflow from field drains. The mixing model was then further applied to predict the in-stream concentrations of nutrients, major ions and trace elements. The predicted concentrations were assumed to be those resulting from hydrological mixing only, while influence of biogeochemical controls were reflected by the prediction deviation from observation. Accordingly, 15 water quality parameters were grouped based on their simulation performances into hydrologically-controlled (Cl–, Mg, Na, K, and Si), biogeochemically-controlled (DOC, SO42−, Mn, and Zn), or controlled-by-both (SRP, NO3-N, Ca, Fe, Al, and Cu). The mixing modelling not only reproduced the spatiotemporal in-stream water balance with finer process conceptualisation, but also provided a generic method to quantitatively disentangle the relative strength of hydrological and biogeochemical controls. Such a method can be employed as a robust learning tool before extending a hydrological model for water quality simulation, as when, where and how strong biogeochemical controls are exerted provides a strong indicator on which dominant processes need to be conceptualised.