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Groundwater flowing from hillslopes through riparian (near-stream) soils often undergoes chemical transformations that can substantially influence stream water chemistry. We used landscape analysis to predict total organic carbon (TOC) concentration profiles and groundwater levels measured in the riparian zone (RZ) of a 67 km2 catchment in Sweden. TOC exported laterally from 13 riparian soil profiles was then estimated based on the riparian flow–concentration integration model (RIM). Much of the observed spatial variability of riparian TOC concentrations in this system could be predicted from groundwater levels and the topographic wetness index (TWI). Organic riparian peat soils in forested areas emerged as hotspots exporting large amounts of TOC. These TOC fluxes were subject to considerable temporal variations caused by a combination of variable flow conditions and changing soil water TOC concentrations. Mineral riparian gley soils, on the other hand, were related to rather small TOC export rates and were characterized by relatively time-invariant TOC concentration profiles. Organic and mineral soils in RZs constitute a heterogeneous landscape mosaic that potentially controls much of the spatial variability of stream water TOC. We developed an empirical regression model based on the TWI to move beyond the plot scale and to predict spatially variable riparian TOC concentration profiles for RZs underlain by glacial till.

Direct monitoring of stream water chemistry is an increasingly important tool for securing stream water quality and assessing stream ecological functioning as it relates to overall ecosystem health. Such monitoring is often discontinuous in spatial extent and, thus, needs to be interpolated at unsampled locations if the desired end product is a continuous map of stream water chemistry. Recently there have been major advances in the use and development of geostatistical methods (such as kriging) for interpolating between observations of stream water chemistry within stream networks. This study investigated the influence of distance definition on interpolation of synoptically collected stream water chemistry samples. In particular, we developed a new methodology for adjusting instream distances between stream water chemistry observations such that instream lakes (which are ubiquitous in northern, boreal landscapes) are explicitly accounted for in geostatistical interpolations. The methodology developed was tested using stream chemistry data for five different constituents coming from synoptic sampling campaigns conducted across four boreal Swedish catchments during two distinct seasons. The ability of this new, lake adjusted instream distance (LAID) to produce interpolated maps of stream water chemistry was compared to that of traditional Euclidean distance (ED) and instream distance (ID). The results indicated that using LAIDs in this boreal landscape tended to improve interpolation compared to the other distance definitions considered. The grade of improvement, however, tended to vary between the constituent, watershed and season considered suggesting that the influence of instream lakes on water chemistry is quite variable in this landscape throughout the year.