LAUSR

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 D95 (the diameter for which 95% of the cumulative GSD is finer), we find that the median deposit size (D50) 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.