Stability of spatial patterns in water chemistry across temperate ecoregions

Abstract

Human activity has polluted freshwater ecosystems across the planet, harming biodiversity, human health, and the economy. Improving water quality depends on identifying pollutant sources in river networks, but pollutant concentrations fluctuate in time. Continuous monitoring of many points in river networks is expensive, impeding progress in developing countries where water quality is degrading fastest. In this study, we analyzed 4523 water chemistry time series of ten parameters ( -NO ,2 Cl − , Na + , Ca 2+ , Mg 2+ , K + ) across four temperate ecoregions in France (ca. 560 000 km 2 ). We quantified the spatial stability of water chemistry across the monitoring stations using rank correlations between instantaneous concentrations and water quality metrics derived from 6-year time series (2010)(2011)(2012)(2013)(2014)(2015). The strength of this rank correlation represents how well a water quality evaluation metric can be characterized with a single sampling for a given water quality parameter. Results show that a single sampling captured a mean of 88% of the spatial variability of these parameters, across ecoregions with different climate and land-use conditions. The spatial stability resulted both from high spatial variability among sites and high temporal synchrony among time series. These findings demonstrate that infrequent but spatially dense water sampling can achieve two of the major goals of water quality monitoring: identify pollutant sources and inform ideal locations for conservation and restoration interventions.

Spatial variability in specific discharge and streamwater chemistry during low flows: Results from snapshot sampling campaigns in eleven Swiss catchments

“…Snapshot campaigns are considered a valuable tool to identify and interpret the contributions of different sources to streamflow (e.g., Dupas et al, 2019;Salvia et al, 1999;Soulsby et al, 2007;Zimmer & Lautz, 2015), especially during low-flow periods. Our study also showed that snapshot campaigns may reveal local landscape controls on low flows, like inflows from bedrock groundwater or flow through particular layers.…”

“…Results in existing studies highlight the great spatial heterogeneity of streamflow contributions and the limited knowledge of how catchments store, mix, and release water and solutes (e.g., Abbott et al, 2018;Bishop et al, 2008;McDonnell & Beven, 2014). Nevertheless, these and many other studies also suggest that targeted observations of the spatial variability in streamwater chemistry may provide useful information about the sources of streamflow (e.g., Dupas, Minaudo, & Abbott, 2019;Fischer et al, 2015;Inamdar et al, 2013;Tetzlaff & Soulsby, 2008;Zimmer et al, 2013). McGuire et al (2014) showed, based on a very high resolution streamwater chemistry dataset from Likens and Buso (2006), how streams interact with the landscape at different spatial scales.…”

Evaluating spatiotemporal variation in water chemistry of the upper Colorado River using longitudinal profiling

“…Precisely because of the convolution into a single signal, the Eulerian approach is generally poorly suited for capturing or understanding this heterogeneity. Moreover, recent work has indicated spatial patterns to be generally conserved through time (Dupas, Minaudo, & Abbott, ), suggesting sampling over a large number of spatially distributed sites, even at lower sampling frequencies, may be of great utility in identifying drivers of water quality variation and potential sources of degradation.…”

Co-located contemporaneous mapping of morphological, hydrological, chemical, and biological conditions in a 5th-order mountain stream network, Oregon, USA

“…Elevation in the basin ranges from about 410 to 1630 m a.m.s.l., and the landscape is heavily forested, including 400-year-old Douglas fir forests and areas of younger regrowth forest after wildfire or replanting after forest harvest. Additional detail about the climate, morphology, geology, and ecology of the site and region are well described by others (Dyrness, 1969;Swanson and James, 1975;Swanson and Jones, 2002;Jefferson et al, 2004;Deligne et al, 2017).…”

“…Spatially distributed studies along river corridors may provide increased information about biogeochemical processes in comparison to equal effort in characterization of local-scale processes at a size (Lee-Cullin et al, 2018). Similarly, these data sets are driving innovation in the frameworks used to interpret spatially distributed data sets, including foci on spatiotemporal variance (Abbott et al, 2018), the application of geostatistical approaches to characterize scaledependent relationships linking stream water chemistry and basin characteristics (Zimmer et al, 2013;McGuire et al, 2014;Dupas et al, 2019), and additional spatial statistics methods (Isaak et al, 2014;Lowe et al, 2006).…”