Critical to evaluating the economic viability of a wave energy project is: (1) a robust estimate of the electricity production throughout the project lifetime and (2) an understanding of the uncertainty associated with said estimate. Standardization efforts have established mean annual energy production (MAEP) as the metric for quantification of wave energy converter (WEC) electricity production and the performance matrix approach as the appropriate method for calculation. General acceptance of a method for calculating the MAEP uncertainty has not yet been achieved. Several authors have proposed methods based on the standard engineering approach to error propagation, however, a lack of available WEC deployment data has restricted testing of these methods. In this work the magnitude and sensitivity of MAEP uncertainty is investigated. The analysis is driven by data from simulated deployments of 2 WECs of different operating principle at 4 different locations. A Monte Carlo simulation approach is proposed for calculating the variability of MAEP estimates and is used to explore the sensitivity of the calculation. The uncertainty of MAEP ranged from 2%–20% of the mean value. Of the contributing uncertainties studied, the variability in the wave climate was found responsible for most of the uncertainty in MAEP. Uncertainty in MAEP differs considerably between WEC types and between deployment locations and is sensitive to the length of the input data-sets. This implies that if a certain maximum level of uncertainty in MAEP is targeted, the minimum required lengths of the input data-sets will be different for every WEC-location combination.

The natural boundary is intuitively understood to be the line dividing the bed of a water body from the adjacent land. It is used in many jurisdictions to define the boundary of ownership between a land parcel and a state-owned watercourse. It is typically defined on the ground through observations by a professional surveyor. Predicting changes to the natural boundary has obvious utility (e.g., with climate change); however, its’ observational nature does not lend itself to such predictions. This paper uses a case study of a large lake to explore the relationship between shoreline morphology and hydrology, and the elevation of the natural boundary. Significant correlation was found between the natural boundary elevation and average wave power, but not with variables beach slope, sediment type, or presence of vegetation. Below 0.4 W/m of average wave power, the correlation with natural boundary elevation is very weak, suggesting a lower limit of influence. Two process-based methods for estimation of natural boundary change are proposed, one based on the observed statistical relationship between the average wave power and the natural boundary elevation, and another method based on detailed analytical modelling of bottom stress from breaking waves using the Xbeach software (v1.23). Both methods were used to estimate natural boundary change under a hypothetical change in water level regime due to proposed weir upgrades. While observational data are not available to support evaluation, both approaches predict similar changes to the natural boundary despite their significantly different basis. While the modelling approach requires significant analyst and computational effort, the parametric approach is comparatively efficient, making it practical to apply at high resolution over very large shorelines.