Abstract
Measurements recorded over monitoring networks often possess spatial and temporal correlation inducing redundancies in the information provided. For river water quality monitoring in particular, flow‐connected sites may likely provide similar information. This paper proposes a novel approach to principal components analysis to investigate reducing dimensionality for spatiotemporal flow‐connected network data in order to identify common spatiotemporal patterns. The method is illustrated using monthly observations of total oxidized nitrogen for the Trent catchment area in England. Common patterns are revealed that are hidden when the river network structure and temporal correlation are not accounted for. Such patterns provide valuable information for the design of future sampling strategies.
Highlights
Environmental monitoring networks are often designed with the aim of providing representative coverage of the spatial domain of interest and to provide a set of monitoring sites that can be used to identify variation and change in variables of interest over time
Geochemical variation between drainage catchment areas induces spatial correlation in the water quality measurements that may be related to Euclidean distance and river discharge, with measurements related over time
Using inverse weights based on autocorrelation means that variance contributions from upstream sites are removed, and in flow-weighted PCA, the reduced amount of total variance explained by the first principal components (PCs) can be thought of as the amount of variance explained once dependencies based on the river network structure are removed
Summary
Environmental monitoring networks are often designed with the aim of providing representative coverage of the spatial domain of interest and to provide a set of monitoring sites that can be used to identify variation and change in variables of interest over time. Identification of dominant spatial and temporal patterns in river network data can be used to identify areas where water quality has remained stable over time or to create groups of monitoring sites that exhibit similar temporal patterns. Such patterns can be hidden in the presence of multiple layers of spatial and temporal correlation. Identification of common patterns could be used to improve the focus and design of water quality monitoring programs and inform future monitoring strategies, for example, by providing guidance to the appropriate position for placing automatic monitoring stations
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