Abstract:
The Land Information System (LIS; http://lis.gsfc.nasa.gov) is a hydrologic modeling system that integrates various community land surface models, ground and satellite-based observations, and high performance computing and data management tools to enable assessment and prediction of hydrologic conditions at various spatial and temporal scales. The LIS architecture is designed using advanced software engineering principles, allowing the interoperability of land surface models, meteorological inputs, land surface parameters and observational data. In this work, we describe the extension of the LIS framework to incorporate data assimilation capabilities, through a collaborative effort. The extensible LIS data assimilation framework allows the incorporation and interplay of multiple observational sources, multiple data assimilation algorithms, and multiple land surface models. These capabilities are demonstrated using a suite of experiments that assimilate various sources observational data into different land surface models to propagate observational information in space and time. The available data assimilation algorithms include direct insertion, rule-based approaches, and ensemble Kalman Filtering (EnKF). The assimilation of soil moisture and snow water equivalent data is demonstrated using the Noah, Community Land Model (CLM), and Catchment Land Surface Model using a number of different assimilation algorithms. We will also demonstrate the ability of the system to simultaneously assimilate multiple observations. These experiments are used to demonstrate the use of the flexible, extensible LIS data assimilation framework to effectively apply hydrological observations and modeling tools to understand and improve the prediction land surface water and energy cycling.