A Hybrid Lagrangian–Eulerian Particle Model for Ecosystem Simulation

• Main Authors: Pengfei Xue, David J Schwab, Xing Zhou, Chenfu Huang, Ryan Kibler, Xinyu Ye
• Format: Article
• Language: English
• Published: MDPI AG 2018-09-01
• Series: Journal of Marine Science and Engineering
• Subjects: property-carrying particle model; coupled models; ecosystem simulation; biophysical modeling; Sandusky Bay; Great Lakes
• Online Access: http://www.mdpi.com/2077-1312/6/4/109

Current numerical methods for simulating biophysical processes in aquatic environments are typically constructed in a grid-based Eulerian framework or as an individual-based model in a particle-based Lagrangian framework. Often, the biogeochemical processes and physical (hydrodynamic) processes occur at different time and space scales, and changes in biological processes do not affect the hydrodynamic conditions. Therefore, it is possible to develop an alternative strategy to grid-based approaches for linking hydrodynamic and biogeochemical models that can significantly improve computational efficiency for this type of linked biophysical model. In this work, we utilize a new technique that links hydrodynamic effects and biological processes through a property-carrying particle model (PCPM) in a Lagrangian/Eulerian framework. The model is tested in idealized cases and its utility is demonstrated in a practical application to Sandusky Bay. Results show the integration of Lagrangian and Eulerian approaches allows for a natural coupling of mass transport (represented by particle movements and random walk) and biological processes in water columns which is described by a nutrient-phytoplankton-zooplankton-detritus (NPZD) biological model. This method is far more efficient than traditional tracer-based Eulerian biophysical models for 3-D simulation, particularly for a large domain and/or ensemble simulations.