A Review of Modeling Approaches for Understanding and Monitoring the Environmental Effects of Marine Renewable Energy

Abstract

Understanding the environmental effects of marine energy (ME) devices is fundamental for their sustainable development and efficient regulation. However, measuring effects is difficult given the limited number of operational devices currently deployed. Numerical modeling is a powerful tool for estimating environmental effects and quantifying risks. It is most effective when informed by empirical data and coordinated with the development and implementation of monitoring protocols. We reviewed modeling techniques and information needs for six environmental stressor–receptor interactions related to ME: changes in oceanographic systems, underwater noise, electromagnetic fields (EMFs), changes in habitat, collision risk, and displacement of marine animals. This review considers the effects of tidal, wave, and ocean current energy converters. We summarized the availability and maturity of models for each stressor–receptor interaction and provide examples involving ME devices when available and analogous examples otherwise. Models for oceanographic systems and underwater noise were widely available and sometimes applied to ME, but need validation in real-world settings. Many methods are available for modeling habitat change and displacement of marine animals, but few examples related to ME exist. Models of collision risk and species response to EMFs are still in stages of theory development and need more observational data, particularly about species behavior near devices, to be effective. We conclude by synthesizing model status, commonalities between models, and overlapping monitoring needs that can be exploited to develop a coordinated and efficient set of protocols for predicting and monitoring the environmental effects of ME.

Observing fish interactions with marine energy turbines using acoustic cameras

“…At turbine sites, this may inform monitoring requirements or the determination of acceptable collision risk levels. Additionally, the risk of collision of a fish with a turbine blade is assumed to be higher for larger fish (Hammar et al, 2015), and fish length distributions can serve as inputs to probability‐ and physics‐based models that estimate the probability of encounter or collision (Buenau et al, 2022). Acoustic cameras offer one approach to collecting these data, but their limitations should be considered relative to both study design and data interpretation.…”

“…The interactions between fishes and their predators are important to understand when managing populations or communities, especially when anthropomorphic influences can lead to variations in predation levels (Murphy et al, 2021). Further, ecological modelling techniques used to study interactions between prey and predators have been applied to predict the risk of the collision of fish and turbines (Buenau et al, 2022). Avoidance and evasion behaviours associated with fish eluding predators can also be used to describe fish responses to the presence of moving turbine blades (Sparling et al, 2020), and acoustic camera observations of these behaviours are informative for collision risk assessments.…”

A Summary of Environmental Monitoring Recommendations for Marine Energy Development That Considers Life Cycle Sustainability

“…A comprehensive approach to stressor-receptor interaction research, monitoring, and modeling can advance the pace of ME development [16]. Predictive models can be used to evaluate the potential effects of ME projects, extend the utility of existing monitoring data, and prioritize future monitoring.…”

“…Deployment of hydrophones and underwater acoustic cameras near the WEC using similar technologies to [10,11] for short-term deployments may provide information necessary for understanding seabird diving behavior around WECs and the exposure time to underwater noise and collision risk stressors. Data may be used in ecological models to understand population effects over time [16]. Collectively, these data may inform mitigations to reduce localized impacts and promote ecological resilience [36].…”

“…During the course of performing these other tests, TFiT research identified a potential new stressor-anthropogenic light [15]. Furthermore, the TFiT recommendations provided empirical data that can inform models (as identified in [16]) and field-tested technology approaches for measuring various stressors (as identified in [17]).…”