WaComM: A Parallel Water Quality Community Model for Pollutant Transport and Dispersion Operational Predictions

Abstract

Accurate prediction of trends in marine pollution is strategic, given the negative effects of low water quality on human marine activities. We describe here the computational and functional performance evaluation of a decision making tool that we developed in the context of an operational workflow for food quality forecast and assessment. Our Water Community Model (WaComM) uses a particle-based Lagrangian approach relying on tridimensional marine dynamics field produced by coupled Eulerian atmosphere and ocean models. WaComM has been developed matching the hierarchical parallelization design requirements and tested in Intel X86_64 and IBM P8 multi core environments and integrated in FACE-IT Galaxy workflow. The predicted pollutant concentration and the amount of pollutants accumulated in the sampled mussels are compared in search of coherent trends to prove the correct model behaviour. In the case study shown in this paper, the predicted Lagrangian tracers, acting as pollutant concentration surrogates, tend to spread rapidly and undergo rapid dilution as expected depending on dominant water column integrated currents.