Abstract:
The Land Information System (LIS) is a hydrologic modeling framework 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 interoperability of land surface models, meteorologic inputs, land surface parameters and observational data. In this work, we describe a data assimilation extension of the LIS framework that allows the incorporation and interplay of multiple sequential data assimilation algorithms, multiple observational sources and multiple land surface models. The implemented data assimilation algorithms vary in complexity, ranging from direct insertion to Ensemble Kalman Filtering (EnKF). The LIS data assimilation extension is uniquely suited to compare the assimilation of various data types in different land surface models within a single framework, which is demonstrated here with a suite of synthetic soil moisture and snow assimilation experiments. The high performance infrastructure in LIS provides adequate support to efficiently conduct the data assimilation simulations of high computational granularity.