GIS‐based prediction of stream chemistry using landscape composition, wet areas, and hydrological flow pathways

Abstract

Landscape morphology exerts strong, scale‐dependent controls on stream hydrology and biogeochemistry in heterogeneous catchments. We applied three descriptors of landscape structure at different spatial scales based on new geographic information system tools to predict variability in stream concentrations for a wide range of solutes (Al, Ba, Be, Ca, Fe, K, Mg, Na, S, Si, Sr, Sc, Co, Cr, Ni, Cu, As, Se, Rb, Y, Cd, Sb, Cs, La, Pb, Th, U, DOC, and Cl) using a linear regression analysis. Results showed that less reactive elements, which can be expected to behave more conservatively in the landscape (e.g., Na, K, Ca, Mg, Cl, and Si), generally were best predicted from the broader‐scale description of landscape composition (areal coverage of peat, tills, and sorted sediments). These results highlight the importance of mineral weathering as a source of some elements, which was best captured by landscape‐scale descriptors of catchment structure. By contrast, more nonconservative elements (e.g., DOC, Al, Cd, Cs, Co, Th, Y, and U), were best predicted by defining wet areas and/or flow path lengths of different patches in the landscape. This change in the predictive models reflect the importance of peat deposits, such as organic‐rich riparian zones and mire ecosystems, which are favorable environments for biogeochemical reactions of more nonconservative elements. As such, using this understanding of landscape influences on stream chemistry can provide improved mitigation strategies and management plans that specifically target source areas, so as to minimize mobilization of undesired elements into streams.

Carbon and nutrient export regimes from headwater catchments to downstream reaches

“…Furthermore, biogeochemical processes take place in the riparian and upslope compartments, which may lead to additional seasonal variability linked to mobilization and/or retention of C, N and P sources. Biogeochemical processes are temperature dependent, and they are also influenced by residence time (Hrachowitz et al, 2016) and by the presence/absence of reactants in biogeochemical hotspots such as the riparian zone (Pinay et al, 2015;Tiwari et al, 2017;Dick et al, 2015). In this respect, high temperature and low flow velocity during the summer season (leading to high residence time in the riparian zone) provide favorable conditions for riparian denitrification and DOC and SRP mobilization.…”

“…Seasonal concentration variability is traditionally explained by the varying contribution of several conceptual compartments (end-members) with distinct chemical signatures , the contribution of these compartments to stream discharge being controlled by changing hydroclimatic conditions on a seasonal basis. In addition, biogeochemical processes controlled by temperature and by the convergence of reactants in reactive hotspots such as the riparian zone (Pinay et al, 2015;Tiwari et al, 2017;Dick et al, 2015) could lead to temporal variability in the concentrations within different conceptual compartments. Thus the interplay of hydrological and biogeochemical processes controls stream NO − 3 , DOC and SRP concentrations (Thomas et al, 2016).…”

“…A comparison of export regimes in contrasting catchments representing different landscape types can be performed to investigate the effect of, for example, contrasting dominant land use, dominant flow paths or climate (Outram et al, 2014;Dupas et al, 2017;Minaudo et al, 2017), sometimes aided by the use of models (e.g., Dupas et al, 2016a;Hartmann et al, 2016). In headwater catchments, several studies have highlighted the important role of landscape heterogeneity within hillslopes (Herndon et al, 2015;Musolff et al, 2017), notably the crucial role of reactive zones such as riparian wetlands (Dick et al, 2015;Pinay et al, 2015;Tiwari et al, 2017) in controlling solute export regimes.…”

“…To upscale headwater signals to downstream reaches, previous landscape mixing models, i.e., "models mixing headwater signals in proportion to their patch coverage" (Tiwari et al, 2017), often lack consideration of temporal variability in headwater signals and explicit consideration of in-stream transformations (e.g., Laudon et al, 2011;Agren et al, 2014). Few opportunities exist to study the export regimes of several headwater catchments representing "archetypes" of the main landscape units in a larger catchment, for multiple solutes and on different time scales, and to compare headwater export regimes to the integral signal measured in downstream reaches.…”

Multiple linear regression models to predict the formation efficiency of triplet excited states of dissolved organic matter in temperate wetlands

“…The chromophores of DOM are categorized into two overlapping groups that correspond to their excited state structure: (1) discreet chromophores which form singlet and triplet excited state species upon light absorption and (2) chargetransfer (CT) complexes which form between closely associated donor and acceptor groups. Discreet chromophores include olefins, carbonyls, and aromatic moieties (Kujawinski et al 2004;Gonsior et al 2009;Turro et al 2010). Singlet excited states of CDOM form when ground state electrons absorb light energy and are promoted to high-energy atomic orbitals, maintaining their original electronic spin state.…”