Computational models of flood inundation, precipitation-runoff, and; groundwater have traditionally been developed within their individual; scientific fields. Increasingly, there is a desire and need to couple; these models into an integrated system to solve complex problems and aid; studies in water resources; for example, the impact of land-use change; or climate variability on surface and subsurface flow in watersheds; could be simulated by linking a precipitation-runoff model to a; groundwater model. In this collaborative project, we factored the U.S.; Geological Survey (USGS) Precipitation-Runoff Modeling System (PRMS); into four independent process components: surface, soil, groundwater,; and streamflow. Each process component, written in Fortran, has a Basic; Model Interface (BMI), which gives the model a standardized set of; functions allowing it to be queried, modified, and updated in time. When; compiled through Cython, the BMI-equipped components become Python; packages, and can then be imported into Python with the Python Modeling; Toolkit (pymt), which provides a framework and tools for running and; coupling models. The addition of a Python interface for PRMS makes it; easier to use, especially for researchers lacking experience in; compiling and linking Fortran code, and pymt provides an easy; collaboration platform for developing and prototyping complex integrated; models. In the next phase of the project, we developed a Python package; for a data service to access gridMET climatological data distributed; over the web by the University of Idaho. The data service has a BMI, so; it can be used directly with pymt for model-data coupling. Finally,; using pymt, we coupled the PRMS process components and drove the coupled; system with climate data from the gridMET data component. As a simple; test, we were able to reproduce the results from running the standalone; PRMS model. (The figure shows that outflow for the last stream segment; in the coupled model system equals that from standalone PRMS.) This; project was a fruitful collaboration between USGS and University of; Colorado researchers, showing that research and operational models; written in different languages can be wrapped in Python and coupled in; an integrated modeling framework, making them more easily accessible for; a new generation of researchers.
