资源环境科技发展态势分析平台

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 (NO3-, PO43-, TP, DOC, SO42-, Cl-, Na+, Ca2+, Mg2+, 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 (2010-2015). 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.