HR: 09:30h
AN: IN31C-06    [Abstracts]
TI: Towards More Usable and Extendable Watershed Model: an Experience to Integrate RHESSys for HydroMet Forecasting System
AU: * Shin, D
EM: sdhyok@email.unc.edu
AF: University of North Carolina, Saunders Hall, CB 3220, Chapel Hill, NC 27599-3220, United States
AU: Hwang, T
EM: h7666@email.unc.edu
AF: University of North Carolina, Saunders Hall, CB 3220, Chapel Hill, NC 27599-3220, United States
AU: Band, L E
EM: lband@email.unc.edu
AF: University of North Carolina, Saunders Hall, CB 3220, Chapel Hill, NC 27599-3220, United States
AB: HydroMet is a project of RENCI (Renaissance Computing Institute) to develop a new hydrologic modeling and forecasting system. It aims to provide accurate predictions for geophysical hazards including flash floods, droughts, and fire hazard at a fine spatial resolution. For this purpose, the system needs to integrate multi- disciplinary models including weather forecasting models (WRF, Weather Research and Forecasting and LDAS, Land Data Assimilation Systems), and a distributed watershed model (RHESSys, Regional Hydro-Ecologic Simulation System). As a core component to simulate full cycles of water, carbon, and nutrients in watersheds, RHESSys is required to be re-engineered to have a highly usable and extendable architecture. To build the architecture, we restructure RHESSys as a dynamically loadable package for Python, a scripting language for rapid prototyping of new algorithms and seamless integration of external programs. The entire internal structure of RHESSys is exposed to external programming environment, which enables users and external programs to closely inspect the model's states and flexibly control its behavior. The output file format is also redesigned as platform-independent and fully-annotated binary files with intuitive data access interface. Furthermore, packages for the direct access to GIS database and uncertainty estimation on parallel computing resources are newly developed. These reconstruction efforts extend the functional capability of RHESSys as a versatile model able to assimilate and produce massive spatiotemporal data, which is a critical feature required for building the operational nowcasting and forecasting system.
DE: 0430 Computational methods and data processing
DE: 0500 COMPUTATIONAL GEOPHYSICS (3200, 3252, 7833)
DE: 1805 Computational hydrology
DE: 1821 Floods
DE: 6339 System design
SC: Earth and Space Science Informatics [IN]
MN: 2007 Fall Meeting