Abstract:
The Land Information System (LIS) is a high-resolution, high-performance, land surface modeling and data assimilation system to support a wide range of land surface research activities and applications. LIS integrates various community land surface models, ground and satellite-based observations, and high performance and data management tools to enable assessment and prediction of hydrologic conditions at various spatial and temporal scales of interest. The system has been demonstrated at high spatial resolutions (such as 1km) globally, with the use of scalable computing technologies. The ability of the system to operate at the same fine spatial scales of the atmospheric boundary layer and cloud models enables improved characterization of water and energy cycle processes. LIS has been coupled to the Weather Research and Forecasting (WRF) model, enabling a high-resolution land atmosphere system. Recently, the LIS framework has been enhanced by developing an interoperable extension for sequential data assimilation, thereby providing a comprehensive framework that can integrate data assimilation techniques, hydrologic models, observations and the required computing infrastructure. The capabilities are demonstrated using a suite of experiments that assimilate different sources of observational data into different land surface models to propagate observational information in space and time using assimilation algorithms of varying complexity. These experiments demonstrate the assimilation of various sources of hydrologic observations of soil moisture, snow and skin temperature using different sequential data assimilation algorithms into the land surface models operating in LIS. Several functional extensions to LIS, including an on-line, dynamic bias correction component, and generic support for parameter estimation are also being developed. The integrated use of these key modeling capabilities demonstrates the use of LIS framework as a valuable tool in the development, evaluation, and application of techniques for hydrological modeling.