Spatial Modeling for Resources Framework (SMRF) was developed at the USDA Agricultural Research Service (ARS) in Boise, ID, and was designed to increase the flexibility of taking measured weather data and distributing the point measurements across a watershed. SMRF was developed to be used as an operational or research framework, where ease of use, efficiency, and ability to run in near real time are high priorities. Highlights Robust meteorological spatial forcing data development for physically based models The Python framework can be used for research or operational applications Parallel processing and multi-threading allow for large modeling domains at high resolution Real time and historical applications for water supply resourses Features SMRF was developed as a modular framework to enable new modules to be easily intigrated and utilized. Load data into SMRF from MySQL database, CSV files, or gridded climate models (i.e. WRF) Variables currently implemented: Air temperature; Vapor pressure; Precipitation mass, phase, density, and percent snow; Wind speed and direction; Solar radiation; Thermal radiation Output variables to NetCDF files Data queue for multithreaded application Computation tasks implemented in C
- HTMLSMRF GitHub repository