Management of coastal eutrophication: Integration of field data, ecosystem-scale simulations and screening models

Abstract

A hybrid approach for eutrophication assessment in estuarine and coastal ecosystems is presented. The ASSETS screening model ( http://www.eutro.org) classifies eutrophication status into five classes: High (better), Good, Moderate, Poor and Bad (worse). This model was applied to a dataset from a shallow coastal barrier island system in southwest Europe (Ria Formosa), with a resulting score of Good. A detailed dynamic model was developed for this ecosystem, and the outputs were used to drive the screening model. Four scenarios were run on the research model: pristine, standard (simulates present loading), half and double the current nutrient loading. The Ria Formosa has a short water residence time and eutrophication symptoms are not apparent in the water column. However, benthic symptoms are expressed as excessive macroalgal growth and strong dissolved oxygen fluctuations in the tide pools. The standard simulation results showed an ASSETS grade identical to the field data application. The application of the screening model to the other scenario outputs showed the responsiveness of ASSETS to changes in pressure, state and response, scoring a grade of High under pristine conditions, Good for half the standard scenario and Moderate for double the present loadings. The use of this hybrid approach allows managers to test the outcome of measures against a set of well-defined metrics for the evaluation of state. It additionally provides a way of testing and improving the pressure component of ASSETS. Sensitivity analysis revealed that sub-sampling the output of the research model at a monthly scale, typical for the acquisition of field data, may significantly affect the outcome of the screening model, by overlooking extreme events such as occasional night-time anoxia in tide pools.