In this work we directly evaluate two PGAS programming models, CAF and OpenSHMEM, as candidate technologies for improving the performance and scalability of scientific applications on future exascale HPC platforms. PGAS approaches are considered by many to represent a promising research direction with the potential to solve some of the existing problems preventing codebases from scaling to exascale levels of performance. The aim of this work is to better inform the exacsale planning at large HPC centres such as AWE. Such organisations invest significant resources maintaining and updating existing scientific codebases, many of which were not designed to run at the scales required to reach exascale levels of computational performance on future system architectures. We document our approach for implementing a recently developed Lagrangian-Eulerian explicit hydrodynamics mini-application in each of these PGAS languages. Furthermore, we also present our results and experiences from scaling these different approaches to high node counts on two state-of-the-art, large scale system architectures from Cray (XC30) and SGI (ICE-X),and compare their utility against an equivalent existing MPI implementation. Copyright is held by the owner/author(s). Publication rights licensed to ACM.