TY - JOUR
T1 - Tracer-aided identification of hydrological and biogeochemical controls on in-stream water quality in a riparian wetland
AU - Wu, Songjun
AU - Tetzlaff, Doerthe
AU - Goldhammer, Tobias
AU - Freymueller, Jonas
AU - Soulsby, Chris
N1 - Funding Information:
Songjun Wu is funded by t he Chinese Scholarship Council (CSC) . Doerthe Tetzlaff's contribution was partly funded through the Einstein Research Unit “Climate and Water under Change” from the Einstein Foundation Berlin and Berlin University Alliance. We acknowledge contributions from Chris Soulsby funded by the Leverhulme Trust's ISOLAND project . We thank Elisabeth Schütte, Claudia Schmalsch, and Thomas Rossoll for the chemical analyses, and David Dubbert for the support on isotope analyses. Hauke Dämpfling is acknowledged for conducting the UAV flights.
PY - 2022/8/15
Y1 - 2022/8/15
N2 - 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.
AB - 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.
KW - Biogeochemical control
KW - Hydrological mixing
KW - Mass balance modelling
KW - Stable water isotopes
KW - Water quality
KW - Wetlands
UR - http://www.scopus.com/inward/record.url?scp=85134521602&partnerID=8YFLogxK
U2 - 10.1016/j.watres.2022.118860
DO - 10.1016/j.watres.2022.118860
M3 - Article
C2 - 35853332
AN - SCOPUS:85134521602
VL - 222
JO - Water research
JF - Water research
SN - 0043-1354
M1 - 118860
ER -