Forward and Inverse Modeling of Tsunami Sediment Transport

Abstract

Tsunami is one of the most dangerous natural hazards in the coastal zone worldwide. Large tsunamis are relatively infrequent. Deposits are the only concrete evidence in the geological record with which we can determine both tsunami frequency and magnitude. Numerical modeling of sediment transport during a tsunami is important interdisciplinary research to estimate the frequency and magnitude of past events and quantitative prediction of future events. The goal of this dissertation is to develop robust, accurate, and computationally efficient models for sediment transport during a tsunami. There are two different modeling approaches (forward and inverse) to investigate sediment transport. A forward model consists of tsunami source, hydrodynamics, and sediment transport model. In this dissertation, we present one state-of-the-art forward model for Sediment TRansport In Coastal Hazard Events (STRICHE), which couples with GeoClaw and is referred to as GeoClaw-STRICHE. In an inverse model, deposit characteristics, such as grain-size distribution and thickness, are inputs to the model, and flow characteristics are outputs. We also depict one trial-and-error inverse model (TSUFLIND) and one data assimilation inverse model (TSUFLIND-EnKF) in this dissertation. All three models were validated and verified against several theoretical, experimental, and field cases.