Monitor the Energy and Carbon Emissions of Process-Based Models: Processc

Abstract

Sustainable modeling to reduce carbon emissions from heavy computations is being adopted in machine learning communities. However, this concept has not been considered in process-based model communities, with carbon emissions rarely monitored and reported. This study developed ProcessC, a multi-platform program designed to monitor energy usage and carbon emissions in process-based models’ simulations. ProcessC was tested through a case study involving a process-based model, RZ-SHAW for winter artificial drainage simulation. Results indicated that the RZ-SHAW model consumed 115 times more energy and released 29397 times more carbon emissions compared to machine learning (ML) models for the winter artificial drainage simulation. The study suggests deploying computing systems in regions with low grid carbon intensity, choosing energy-efficient systems, and reducing simulation time as potential solutions for a more carbon-sustainable modelling. The findings from the current study urge the process-based community to commence considering and reporting carbon emissions in future modeling studies.

Keywords: Sustainable modeling, carbon emissions, RZWQM, climate change, Process-based models, Computation carbon footprint