Continuous nutrient observations capture fine-scale estuarine variability simulated by a 3D biogeochemical model

The fine-scale (<100 m; <1 day) skill of a 3D biogeochemical estuarine model cannot be assessed by traditional discrete monitoring programmes. Whilst continuous temperature, salinity, chlorophyll, turbidity and oxygen sensors based on electrical and optical properties have been deployed at individual sites, in underway and in profiling configurations, continuous estuarine nutrient sensors are in their infancy. In this paper we present data from 3 continuous nutrient observing systems deployed-in a temperate estuary: a Systea Wet Chemistry analyser deployed at the head of the estuary to characterise river water entering the estuary; an in situ ultra-violet optical nitrate analyser deployed on a benthic lander at the mouth of the estuary to ascertain the marine nitrate concentrations entering the estuary; and a rapid reverse flow injection analysis system to achieve a high resolution spatial survey of conditions throughout the estuary. Our observations capture seasonal and synoptic timescale variability in marine and river nutrient concentrations and characterise plumes of nutrient enrichment associated with point source discharge against a background estuarine gradient. At the head of the estuary nutrient concentrations were greater and varied in ratio compared to previous observations. Results from a fine-scale 3D biogeochemical model (Wild-Allen et al., 2013) captured 66% of the observed synoptic timescale variability in nitrate concentration at the mouth of the estuary, but did not propagate the observed synoptic timescale fluctuations in river nutrients into the domain when forced with an upstream boundary condition of interpolated monthly observations. The absence of proxies for observing river nutrients dictates further investment in automated nutrient analysis to better inform the model and characterise evolving river conditions. Modelled surface nutrient fields matched observed spatial snapshots of estuarine conditions in contrasting years with surprising accuracy and suggest persistent spatial patterns that might usefully be characterised with neural network self organising maps. Crown Copyright (C) 2014 Published by Elsevier B.V. All rights reserved.