Using Groundwater Data Sondes to Produce High-Quality In Situ Tide and Wave Hydrographs along Wallops Island, Virginia

摘要

The challenges to coastal regions posed by rising sea levels will differ from community to community. In situ wave and tide time-series data are vital for calibrating numerical coastal models used to predict and mitigate the effects of coastal flooding and high-energy erosive waves and to inform coastal engineering projects. However, for many communities, local, high-quality data sets do not exist. Here, we present a proof-of-concept study for a new method to collect local, low-cost, and high-quality, nearshore hydrographs that can be used to calibrate and test coastal models. In 2011, we deployed self-contained pressure sensors at ocean-and bay-side locations along Wallops Island, Virginia, home of the National Aeronautics and Space Administration Wallops Flight Facility. We used a linear array of data loggers to characterize nearshore wave morphology at two sites along the barrier island beach. During the period of observation, wave height and frequency were both higher along the southern, eroding portion of Wallops Island. We also installed stilling wells in a tidal creek and at the Curtis Merritt Harbor (CMH) on Chincoteague Island to compare tide levels, range, and lag between the back bay and the inlet. Our data show an approximately 1-hour lag between sites, which contrasts with existing forecasted tidal lags in the region that range from 9 minutes to nearly 2 hours. These data loggers are a less-expensive alternative to traditional hydrographic equipment, such as acoustic Doppler current profilers, and can be used in shallow, nearshore environments where buoy-deployed level sensors are impractical. These instruments can be used for specific studies that range in time from a few minutes to a few years; are capable of high-resolution time series (1 Hz); can be installed rapidly as single units or deployed as low-cost, multinode arrays; and can even be used to investigate individual storm events.