Abstract
Marine hydrokinetic (MHK) energy is a renewable resource that helps meet growing energy demands, but potential environmental impacts due to site development and device operation have not been fully investigated [1]. Environmental monitoring is used to detect impacts caused by anthropogenic disturbances and is a mandatory requirement of operating licenses in the United States [2]. Because the number of operating sites is limited in the United States, studies describing environmental change due to the presence and operation of tidal and surface wave energy converters are scarce [3], restricting information that can be used to quantify regulatory thresholds.
A successful biological monitoring program provides data that will help developers and regulators make informed operational decisions and modifications to devices [4]. To achieve this goal, it is essential that monitoring programs detect changes that are biologically relevant, which we term impacts. To detect an impact, baseline data (data previous to change [5]) must be collected to facilitate comparison to any data collected during installation and operation [6]. Determining the maximum amount of change that constitutes an impact is a high priority when forming a monitoring plan [7]. Detection of change above a defined threshold may determine if a MHK project is allowed to continue operating [8]. Thus it is imperative to characterize relevant variables and potential impacts before operation and environmental monitoring begin at a MHK site [9].
Because little information exists that can inform impact characterization for MHK monitoring program development [10], regulators must model or estimate thresholds of biological change. Extreme value theory (EVT) is an approach used to model values that are infrequent but are potentially associated with impacts [11]. A distinct advantage of EVT is the ability to model outcomes of unobserved, rare values since the full range of outcomes may not be observed during baseline sampling. Results from EVT can be used by developers and regulators to characterize extreme but rare values associated with environmental impacts, and construct monitoring programs to include operational protocols for conditions under which these events occur. The goal of this study was to evaluate whether EVT is an appropriate method to characterize infrequent events that may result in biological impacts at a tidal MHK site. We tested the utility of EVT using a baseline, active acoustic dataset collected at a proposed turbine site in Admiralty Inlet, WA.