Biological and Environmental Research - Earth and Environmental System Sciences
Earth and Environmental System Modeling

Infrastructure Upgrades to Support Model Longevity and New Applications: The Variable Infiltration Capacity Model Version 5.0 (VIC 5.0)

Friday, December 18, 2015 - 08:00 to 12:20
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The Variable Infiltration Capacity (VIC) model is a macro-scale semi-distributed hydrologic model. VIC development began in the early 1990s and it has been used extensively, applied from basin to global scales. VIC has been applied in a many use cases, including the construction of hydrologic data sets, trend analysis, data evaluation and assimilation, forecasting, coupled climate modeling, and climate change impact analysis. Ongoing applications of the VIC model include the University of Washington's drought monitor and forecast systems, and NASA's land data assimilation systems. The development of VIC version 5.0 focused on reconfiguring the legacy VIC source code to support a wider range of modern modeling applications. The VIC source code has been moved to a public Github repository to encourage participation by the model development community-at-large. The reconfiguration has separated the physical core of the model from the driver, which is responsible for memory allocation, pre- and post-processing and I/O. VIC 5.0 includes four drivers that use the same physical model core: classic, image, CESM, and Python. The classic driver supports legacy VIC configurations and runs in the traditional time-before-space configuration. The image driver includes a space-before-time configuration, netCDF I/O, and uses MPI for parallel processing. This configuration facilitates the direct coupling of streamflow routing, reservoir, and irrigation processes within VIC. The image driver is the foundation of the CESM driver; which couples VIC to CESM's CPL7 and a prognostic atmosphere. Finally, we have added a Python driver that provides access to the functions and datatypes of VIC's physical core from a Python interface. This presentation demonstrates how reconfiguring legacy source code extends the life and applicability of a research model.

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