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PI-Submitted Research Highlights for
Subsurface Biogeochemical Research Program

Managing Complexity in Simulations of Land Surface and Near-surface Processes

Ethan T. Coon
Los Alamos National Laboratory


August 2016

New multiphysics software framework facilitates more realistic process-based simulations of environmental systems.

The Science   
We developed and demonstrated a new approach for managing the rapidly increasing complexity of simulations of environmental systems in the critical zone near the land surface. The multiphysics Arcos framework combines modern software design principles in a novel way to create flexibly configured simulators, thus enabling significantly more complex and realistic simulations that combine many individual ecohydrological and biogeochemical processes.

The Impact
As simulations of environmental systems grow in complexity by incorporating more and more ecohydrological and biogeochemical process representations, it has become increasingly difficult to add new process understanding while ensuring that individual and coupled-process simulations are reliable.   Our new multiphysics framework helps tame this runaway complexity making process-rich simulations easier to develop, test, combine with data, and reconfigure for different numerical experiments. Our approach to developing models provides a more natural way for scientists to collaborate on these increasingly complex models and helps build confidence in the resulting simulations.

The Arcos system is based on two graph representations that interact to provide a flexible and extensible framework. The first graph is a process tree representation (left side in the figure) that defines the coupling among various environmental process representations denoted as Process Kernels (PKs). Two or more PKs are coupled together through Multiprocess Coordinators (MPCs). The second graph (right side of the figure) defines how the mass and energy balances depend on primary variables (unknowns to be solved for) through a series of intermediate variables. Formal representation of these dependencies in a graph structure makes it easier to substitute new constitutive models and ensures that intermediate variables are always current and consistent among different PKs. Taken together, these two graphs makes it possible to define which PKs are to be used and how they are to be coupled at run time. Such a flexibly configured and hierarchical structure is critical to systematically building up complexity supported by rigorous testing and evaluation against observations.

Contacts (BER PM)
David Lesmes and Paul Bayer
SC-23.1 (301-903-0289) and (301-903-1678)

(PI Contact)
Ethan T. Coon
Los Alamos National Laboratory; (505) 665-8289

This research was supported by the Interoperable Design of Extreme-scale Application Software (IDEAS) project funded by the US Department of Energy Office of Science. This work was also supported by Los Alamos National Laboratory’s Laboratory Directed Research and Development (LDRD) Predicting Climate Impacts and Feedbacks in the Terrestrial Arctic project (LDRD201200068DR).  

E. T. Coon, J. D. Moulton, and S. L. Painter, “Managing complexity in simulations of land surface and near-surface processes.” Environmental Modeling and Software 78 (2016) doi:10.1016/j.envsoft.2015.12.017.

Article (PDF):

Ethan Coon, J. David Moulton (LANL), Scott Painter (ORNL).  Funded by the IDEAS project.  This paper and highlite the Arcos multiphysics framework, a component of the Amanzi and ATS programs.

Generated by Ethan Coon for the IDEAS project.  Rights owned by Los Alamos National Laboratory.  Versions of these images have been used in the attached Environmental Modelling and Software journal article.  The PI gives permsission for it to be used in the highlights database and other BER uses.

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