the application of Bayesian networks to evaluate risks from multiple stressors to water quality of freshwater ecosystems

Abstract

It is difficult to predict and manage the ecological consequences of multiple water quality stressors on our freshwater systems. this is due to the dynamism of the source-stressor-response relationships and multiple factors including lack of data, complex impact pathways and risks, and uncertainties that are difficult to parameterise. we present a risk-based probability modelling approach using a Bayesian network (Bn), to manage multiple water quality stressors at multiple spatial scales. we illustrate the use of this approach, by evaluating the probable ecological effects of altered water quality associated with multiple sources in three case study rivers in South Africa. water quality and land use activity were used to describe conceptual risk pathways, parameterise the Bns and model the probable consequences of multiple water quality stressors. the Bn model demonstrated that the endpoints that were selected for the study reflected the risks associated with the levels of the input water quality variables. the model further demonstrated that the electrical conductivity Bn was just as sensitive as the more complex salt model. the Bn model was further able to accurately represent risks to all systems irrespective the water quality data base size. this approach can contribute towards more sustainable water resource management.