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The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program has the objective to develop technologies and other solutions that can improve the reliability, sustain the safety, and extend the life of the current reactors. To accomplish this program objective, there are multiple LWRS "pathways," or research and development (R&D) focus areas. One LWRS focus area is called the Risk-Informed Safety Margin Characterization (RISMC) Pathway. RISMC R&D primarily focuses on qualitatively and quantitatively characterizing risk specifically in terms of safety margin. The RISMC approach probabilistically combines riskassessment with multi-physics models of plant physical processes (e.g., thermal-hydraulic models) that govern aging and degradation of systems, structures, and components (SSCs) in order to better optimize plant safety and performance. Initial efforts to combine probabilistic and plant multi-physics models to quantify safety margins included simplified human reliability analysis (HRA). HRA researchers at Idaho National Laboratory have been collaborating with other risk analysts to develop a computational HRA approach, called the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), for inclusion into the RISMC framework. The HUNTER computational HRA method is a hybrid approach that leverages past work from cognitive psychology, human performance modeling, and HRA, but it is also a departure from existing static and even dynamic HRA methods. The basic premise of this research is to leverage applicable computational techniques, namely simulation and modeling, to develop and then, using the Risk Analysis in a Virtual Environment (RAVEN) as a controller, seamlessly integrate virtual operator models created in HUNTER with 1) the Multiphysics Object Oriented Simulation Environment (MOOSE) as a runtime environment that includes a full-scope plant model, and 2) the RISMC risk models already developed. This report is divided into five chapters that cover the development of an external flooding event example and associated statistical modeling considerations. The first chapter is an overview of RISMC and the HUNTER computational HRA approach. Chapter 2 is a flooding event case study that significantly affected main control room and auxiliary operator performance. Chapter 3 addresses statistical modeling considerations for the development of HUNTER. And finally, Chapter 4 discusses the path forward for the next phase of RISMC research on computation-based HRA.