Predicting paleohydraulics from storm surge and tsunami deposits: Using experiments to improve inverse model accuracy. Issue 4 (4th April 2017)

• Record Type: Journal Article
• Authors: Johnson, Joel P. L.; Delbecq, Katie; Kim, Wonsuck
• Is Part Of: Journal of geophysical research. Volume 122:Issue 4(2017)
• Page Start: 760
• Page End: 781
• Publication Date: 2017-04-04
• Subjects: tsunami deposits -- storm surge deposits -- paleohydraulics -- flume experiments -- inverse modeling; Geomorphology -- Periodicals; 551.3
• DOI: 10.1002/2015JF003816
• Languages: English
• ISSNs: 2169-9003

Abstract

Abstract: How accurately can flow depths and velocities of storm surges and tsunamis be predicted from sedimentary deposits? Inverse models have been proposed to quantify hydrodynamics from suspended sediment deposits, but assumptions about how deposit grain size distributions (GSDs) are influenced by flow characteristics remain largely untested. Using laboratory experiments, we evaluate an existing advection‐settling model in which suspended sediment transport is assumed to reflect horizontal advection (constraining flow velocity) and vertical settling from the water surface (constraining depth). While the original model assumed that depth and velocity would be best predicted by the deposit D 95 (the diameter for which 95% of the cumulative GSD is finer), we find that the median deposit size ( D 50 ) tends to better predict mean flow hydraulics. Two key factors influencing how flow characteristics control deposit GSDs are (a) dispersion caused by turbulence and (b) the transport distance required for suspension and settling to effectively sort grains. Deposits proximal to sediment sources primarily reflect the source GSD, while deposits farther from the source preferentially represent transport‐dependent sorting. In our experimental data, transport distances longer than 1–2 advection length scales are required for the deposit GSD to reasonably predict flow depths and velocities. These results suggest ways that event deposits can be used to more accurately assess coastal risks from tsunamis and storm waves. Key Points: Turbulent dispersion influences patterns of suspended sediment deposition from bores Mean bore flow depth and velocity are best predicted by intermediate deposit grain sizes Proximal bore deposits preferentially reflect source grain size distributions rather than suspended sediment sorting Plain Language Summary: Tsunamis and hurricanes can devastate coastal infrastructure and communities, but it is difficult to know the relative risk of a large event at any location because they occur so rarely. Historical records are not usually long enough to record many events, which is a problem because many events are required to statistically predict sizes and how often they typically occur. Fortunately, tsunamis and hurricanes can leave sediment deposits in coastal lagoons which may imperfectly record the sizes of these events. To better be able to "read" wave velocity and depth from sediment deposits, we conducted laboratory experiments using scaled experimental tsunamis. The data we collected let us relate sediment sizes to flow characteristics. We then use these data as inputs to a set of equations that represents a "model" for predicting flow depth and velocity. We test this model to determine how accurately it can predict depth and velocity and also to figure out how much uncertainty there is in predictions. Our results should improve our understanding of the risks associated with tsunamis and hurricanes at locations where sediment deposits from prehistoric events are preserved.