Stability of spatial patterns in water chemistry across temperate ecoregions

Rémi Dupas,Camille Minaudo,Benjamin W Abbott

doi:10.1088/1748-9326/ab24f4

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 ( TP, DOC, Cl−, Na+, Ca2+, Mg2+, K+) across four temperate ecoregions in France (ca. 560 000 km2). 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 (2010–2015). 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.

Highlights

Water pollution kills approximately 1.8 million people every year (Landrigan et al 2018) and degrades ecosystem functioning at a global scale (Foley et al 2011, Steffen et al 2015)
Improving water quality depends on identifying pollutant sources in river networks, but pollutant concentrations fluctuate in time
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 (2010–2015)

Summary

Introduction

Water pollution kills approximately 1.8 million people every year (Landrigan et al 2018) and degrades ecosystem functioning at a global scale (Foley et al 2011, Steffen et al 2015). Because water chemistry varies widely on event, seasonal, and interannual timescales (Kirchner and Neal 2013, Isaak et al 2014, Dupas et al 2018, Abbott et al 2018b), most monitoring frameworks sample locations repeatedly, in some cases nearly continuously (Jordan et al 2007, Skeffington et al 2015, Rode et al 2016, Bieroza et al 2018, Fovet et al 2018) While these high-frequency datasets can reveal important ecological dynamics (e.g. catchment and in-stream biogeochemical processing), they are expensive, precluding widespread deployment especially in developing countries, where water quality is degrading fastest (Crocker and Bartram 2014) and where poor water quality has the most direct consequences for public health (Landrigan et al 2018). The objective of our study was to test whether a single sampling at multiple locations could capture the spatial variability of water chemistry at large scales, evaluating how well temporally sparse but spatially extensive water sampling could identify pollutant sources in a cost-effective manner

Data extraction

Data analysis

Spatially stable water chemistry across ecoregions

What causes spatial stability in water quality?

Graduating from monitoring to managing global water quality crises