Where have all the hazards gone? Studying complexity, uncertainty, and nonlinearity in coastal stratigraphy through Monte-Carlo simulations

Coastal areas represent complex, nonlinear depositional systems that form important stratigraphic records. These records are frequently used to reconstruct past natural hazards, including earthquakes, tsunamis, and storms, as well as to investigate processes associated with sea-level changes and the impacts of climate change. Of course, the underlying assumption is that understanding past events and processes can improve our ability to anticipate future environmental changes, hazards, and their consequences. While the geologic record provides tangible evidence of past phenomena, the inherent complexity and nonlinearity of coastal systems introduce significant uncertainties. These uncertainties affect what is preserved, how it is recorded, and ultimately how the record is interpreted. Often, we address these challenges through qualitative assumptions, which may inadvertently introduce biases into our interpretations.

In this study, we develop and apply a Monte Carlo-based stratigraphy generation model to explore and quantify uncertainties associated with coastal depositional environments and their responses to natural hazards. This approach provides a systematic framework to better understand how a stratigraphic record is formed due to changing environments, and how earthquakes, tsunamis, and storms influence the stratigraphic record. To analyze the impacts of these uncertainties, we employ Shannon’s entropy as our main quantitative tool.

Our findings shed light on the environmental conditions under which key events are most likely to be missed or misinterpreted within the geologic record. Additionally, we demonstrate how identical hazard sequences can produce differing stratigraphic signatures depending on varying and dynamic environmental contexts. These results underscore the remarkable complexity of the stratigraphic record and its susceptibility to potential  interpretation biases. By quantifying uncertainty and variability, our work offers critical insights into the processes governing the preservation and interpretation of coastal stratigraphy, with implications for advancing hazard assessment and stratigraphic analysis.