Parallelization of a distributed ecohydrological model. (March 2018)

• Record Type: Journal Article
• Authors: Liu, Ning; Shaikh, Mohsin Ahmed; Kala, Jatin; Harper, Richard J.; Dell, Bernard; Liu, Shirong; Sun, Ge
• Is Part Of: Environmental modelling & software. Volume 101(2018)
• Page Start: 51
• Page End: 63
• Publication Date: 2018-03
• Subjects: High performance computing -- Ecohydrological modeling -- Distributed memory parallelism -- Shared memory parallelism -- Water and carbon fluxes; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.11.033
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: WaSSI-C is an ecohydrological model which couples water and carbon cycles with water use efficiency (WUE) derived from global eddy flux observations. However, a significant limitation of the WaSSI-C model is that it only runs serially. High resolution simulations at a large scale are therefore computationally expensive and cause a run-time memory burden. Using distributed (MPI) and shared (OpenMP) memory parallelism techniques, we revised the original model as dWaSSI-C. We showed that using MPI was effective in reducing the computational run-time and memory use. Two experiments were carried out to simulate water and carbon fluxes over the Australian continent to test the sensitivity of the parallelized model to input data-sets of different spatial resolutions, as well as to WUE parameters for different vegetation types. These simulations were completed within minutes using dWaSSI-C, whereas they would not have been possible with the serial version. The dWaSSI-C model was able to simulate the seasonal dynamics of gross ecosystem productivity (GEP) reasonably well when compared to observations at four eddy flux sites. Sensitivity analysis showed that simulated GEP was more sensitive to WUE during the summer compared to winter in Australia, and woody savannas and grasslands showed higher sensitivity than evergreen broadleaf forests and shrublands. Although our results are model-specific, the parallelization approach can be adopted in other similar ecosystem models for large scale applications. Highlights: The WaSSI-C ecohydrological model serially calculates water and carbon fluxes and is therefore restricted to coarse resolution simulations. MPI and OpenMP techniques significantly reduced computing time allowing for simulations at much higher spatial resolutions. This approach was demonstrated at a 5 km resolution over the Australian continent, and could be performed in minutes. The same parallelization approach can be applied to other complex ecohydrological models.