Automated model integration at source code level: An approach for implementing models into the NASA Land Information System

Abstract:

Model integration bridges the data flow between modeling frameworks and models. However, models usually do not fit directly into a particular modeling environment, if not designed for it. An example includes implementing different types of models into the NASA Land Information System (LIS), a software framework for land-surface modeling and data assimilation. Model implementation requires scientific knowledge and software expertise and may take a developer months to learn LIS and model software structure. Debugging and testing of the model implementation is also time-consuming due to not fully understanding LIS or the model. This time spent is costly for research and operational projects.

To address this issue, an approach has been developed to automate model integration into LIS. With this in mind, a general model interface was designed to retrieve forcing inputs, parameters, and state variables needed by the model and to provide as state variables and outputs to LIS. Every model can be wrapped to comply with the interface, usually with a FORTRAN 90 subroutine. Development efforts need only knowledge of the model and basic programming skills. With such wrappers, the logic is the same for implementing all models. Code templates defined for this general model interface could be re-used with any specific model. Therefore, the model implementation can be done automatically.

An automated model implementation toolkit was developed with Microsoft Excel and its built-in VBA language. It allows model specifications in three worksheets and contains FORTRAN 90 code templates in VBA programs. According to the model specification, the toolkit generates data structures and procedures within FORTRAN modules and subroutines, which transfer data between LIS and the model wrapper. Model implementation is standardized, and about 80 – 90% of the development load is reduced.

In this presentation, the automated model implementation approach is described along with LIS programming interfaces, the general model interface and five case studies, including a regression model, Noah-MP, FASST, SAC-HTET/SNOW-17, and FLake. These different models vary in complexity with software structure. Also, we will describe how these complexities were overcome through using this approach and results of model benchmarks within LIS.