Spatial and temporal distributions of rainfall significantly impact surface water processes and the dynamic exchange between groundwater and surface water. This seminar presents a Bayesian approach that integrates gauged and radar rainfall data to improve the accuracy and resolution of historical rainfall estimation in the Northern Tampa Bay region. The method accentuates the strengths of the gauged and radar data while de-emphasizing their weaknesses. The resulting rainfall improved the accuracy of an integrated hydrologic model that was developed to support water management decision making. They also improve the future rainfall Monte Carlo simulation that is based on a hidden Markov model.
Dr. Chin Man W. Mok is a Vice President at GSI International, Inc. He is a Professional Engineer and Professional Geologist with 36 years of consulting experience in water resources, environmental, and infrastructure projects. He has been an adjunct faculty member at several universities and a Principal Investigator of many applied research projects funded by federal agencies. He frequently teaches at the University of California at Berkeley and Rice University on groundwater, engineering risk, and data sciences applications.