OS22A-06:
Flow Field Thresholds for Bottom Roughness Transformation in Full Scale Laboratory Generated Waves and Solitary Waves

Tuesday, 16 December 2014: 11:35 AM
Meagan E. Wengrove and Diane L Foster, University of New Hampshire Main Campus, Durham, NH, United States
Abstract:
In field environments, bottom roughness transformation have been observed in response to extreme storm events, flooding, and tsunamis. Bottom roughness transformation is considered to be instances when an observed stable bed state (e.g. ripples) rapidly transforms into an alternate stable state (e.g. flat bed). This type of extreme change is observed when forcing mechanisms due to shear stress and pressure gradients reach significant magnitude and duration. This research utilizes a full scale wave laboratory environment (O.H. Hinsdale Large Wave Flume at Oregon State University) over a sandy substrate to closely investigate bottom boundary layer dynamics coupled with observations of extreme morphologic change from a rippled to a flat bed. The observational array includes two millimeter scale resolution profiling ADVs (Acoustic Doppler Velocimeter), a PIV (Particle Image Velocimetry) used to estimate velocity fields as well as morphologic evolution, porewater pressure sensors, and multiple single point ADVs and wave gages. An emphasis is made towards investigating the effects of solitary waves (i.e. tsunamis) upon events of extreme morphologic change, both isolated as well as introduced into bimodal wave groups. Additionally, observations demonstrate that instances of roughness flattening and then rebuilding occurring within sets of irregular waves (i.e. storm events). During instances of rapid bed flattening boundary layer streaming is observed in coincidence with estimates of excess applied bed stress and exceedance of critical Shields parameter for sediment motion. Additionally, during extreme flattening, measured pressure gradients indicate conditions for pressure gradient induced sediment transport, supported by the porewater pressure sensor data and the estimated Sleath parameter.