A New Approach to Hydrologic Data Assimilation

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0003361 McLaughlin Hydrology is experiencing rapid changes as a result of improved scientific understanding and measurement technology. Advances in global modeling and remote sensing are likely to provide large amounts of new information in the coming decades. There will be an increased need for efficient methods to process and interpret all of this information. Many of the most promising data processing options combine observations with model predictions, a process commonly known as data assimilation. The data assimilation methods which have been most successful in practical applications are based on either variational or recursive estimation concepts. Each of these approaches has distinctive advantages and limitations but neither provides a satisfactory solution for very large applications (e.g., applications that work with large amounts of remote sensing data over continental-scale regions).In this project, we propose to develop a computationally efficient and robust approach to hydrologic data assimilation which combines the best aspects of variational and recursive estimation. This work will be methodical in nature but its overall goal is to advance scientific understanding of large-scale hydrologic processes. The data assimilation methods we develop in this project will be tested on a case study, which will provide insight about scientific questions of hydrologic interest. The testing and application phase of our project will rely on our previous experiences with data assimilation techniques and on methods which have been successfully applied in meteorology and oceanography.The case study will be concerned with the estimation of near-surface soil moisture. Such estimates are especially useful for weather prediction and analyses of climate change. The case study will rely on an existing hydrologic/measurement model and will be based on data obtained from SGP97 and SGP99 field experiments in central Oklahoma. The tests carried out in this study will help to demonstrate the benefits of data assimilation in large-scale case studies of particular interest to hydrologists.