Hydrologic Data Assimilation: State Estimation and Model Calibration

This thesis is a combination of two separate studies which examine hydrologic data assimilation techniques: 1) to determine the applicability of assimilation of remotely sensed data in operational models and 2) to compare the effectiveness of assimilation and other calibration techniques. The first study examines the ability of Data Assimilation of remotely sensed microwave radiance data to improve snow water equivalent prediction, and ultimately operational streamflow forecasts. Operational streamflow forecasts in the National Weather Service River Forecast Center are produced with a coupled SNOW17 (snow model) and SACramento Soil Moisture Accounting (SAC-SMA) model. A comparison of two assimilation techniques, the Ensemble Kalman Filter (EnKF) and the Particle Filter (PF), is made using a coupled SNOW17 and the Microwave Emission Model for Layered Snowpack model to assimilate microwave radiance data. Microwave radiance data, in the form of brightness temperature (TB), is gathered from the Advanced Microwave Scanning Radiometer-Earth Observing System at the 36.5GHz channel. SWE prediction is validated in a synthetic experiment. The distribution of snowmelt from an experiment with real data is then used to run the SAC-SMA model. Several scenarios on state or joint state-parameter updating with TB data assimilation to SNOW-17 and SACSMA models were analyzed, and the results show potential benefit for operational streamflow forecasting. The second study compares the effectiveness of different calibration techniques in hydrologic modeling. Currently, the most commonly used methods for hydrologic model calibration are global optimization techniques. While these techniques have become very ii efficient and effective in optimizing the complicated parameter space of hydrologic models, the uncertainty with respect to parameters is ignored. This has led to recent research looking into Bayesian Inference through Monte Carlo methods to analyze the ability to calibrate models and represent the uncertainty in relation to the parameters. Research has recently been performed in filtering and Markov Chain Monte Carlo (MCMC) techniques for optimization of hydrologic models. At this point, a comparison of the effectiveness of global optimization, filtering and MCMC techniques has yet to be reported in the hydrologic modeling community. This study compares global optimization, MCMC, the PF, the Particle Smoother, the EnKF and the Ensemble Kalman Smoother for the purpose of parameter estimation in both the HyMod and SACSMA hydrologic models. iii Acknowledgements In appreciation of the hard work of my thesis committee, research group and my advisor, I would like to extend my appreciation. For my advisor Dr. Moradkhani, you have spent countless hours teaching me the knowledge necessary to perform this research, provided me with necessary information to perform relevant research to the field, pushed me to achieve research objectives that seemed out of reach and helped to shape my research into the best it could be. Thank you for all of the time you have put into making this research possible. To my thesis committee, Dr. Jay and Dr. Daescu, I greatly appreciate your willingness to serve as members of my thesis committee in spite of the short notice and lack of involvement in my work. Your participation in editing my thesis and your suggestions for improving my research was quite valuable for my final thesis product and future work moving forward. To my research group, all of whom were always willing to help in improving my work as well as providing moral support to continue the hard work it has taken to complete my research, thank you all. Last but not least I would like to thank my parents or providing me with continued support throughout my studies. Your encouragement and guidance have been essential for my academic success. For the contributions from all of you, I am very grateful. iv Table of