Modelling dissolved oxygen and benthic algae dynamics in a coastal ecosystem by exploiting real-time monitoring data

In this work we propose a methodological approach for the detection and simulation of relevant changes in coastal ecosystems, i.e. oxygen depletion and proliferation of benthic algae. This approach is based on the integration of the data provided by real-time monitoring systems with the output of complex ecosystem models. We tested the method in a case study, where real-time Dissolved Oxygen (DO) data and a 2D Reaction-Transport model were used to simulate the growth of macroalgae and the daily dynamics of DO in the Lagoon of Venice (Italy). The spatiotemporal relationships among the macroalgae distribution and the DO observations were quantified by analysing and comparing the Dissolved Oxygen time series and model outputs. The outcomes were used for the inverse estimation of the initial, i.e. late winter, biomass of macroalgae. The model was then applied to simulate the growth of macroalgae and the daily dynamic of DO during the productive (i.e., spring and summer) seasons. The comparison between the model output and the real-time data indicates that the model had skill in simulating the short term (daily) DO dynamic at several lagoon sites impacted by macroalgae proliferation. The simulated intra-daily variability of Dissolved Oxygen is significantly correlated with the observations in half of the monitoring sites, as well the simulated algal growth, which resulted comparable with the field measurements. The estimated average value of macroalgae biomass was ∼1 kgfw m−2 in 2007, which is consistent with a good to moderate quality status of the Lagoon of Venice. The proposed methodology can be useful in the assessment of the environmental status of coastal ecosystems as required by recent national and international legislation.