Date of Award

Winter 2018

Project Type

Thesis

Program or Major

Earth Sciences

Degree Name

Master of Science

First Advisor

Thomas C Lippmann

Second Advisor

Brian Calder

Third Advisor

Andrew Armstrong

Abstract

As the area of U.S. coastal waters vastly exceeds the capacity of annual hydrographic surveying, prioritization is necessary to optimize survey benefits. Obtaining new survey coverage over the most vital locations allows for an efficient use of funds; however, identifying these locations is a complex task. The current model to address survey prioritization, called the Hydrographic Health Model (or HHM), was created by personnel at the National Oceanographic and Atmospheric Administration (NOAA), the authoritative agency tasked with chart maintenance and hydrographic survey collection. While the HHM incorporates potential sources of bathymetric change, it does not include nor lend itself to the inclusion of actual measured changes associated with these sources. In order to integrate quantified estimates of change, the HHM fundamental equation must be adapted. Here we introduce the Hydrographic Uncertainty Gap (HUG) model as an adapted version of the HHM. Fundamental to HUG is the quantification of hydrographic survey uncertainties and changes to bathymetry, the calculations of which are outlined and performed for Chesapeake Bay and surrounding areas. Ultimately, we argue that the HUG model survey priorities are more realistic and more constrained than those from the HHM.

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