Envisioning a future with marine energy: how predictive modeling informs decision making
With Kate Buenau
Marine renewable energy (MRE) research doesn’t always happen from a boat or in a laboratory; much of this impactful work is conducted in front of a computer screen. Predictive modeling has become an integral part of understanding the environmental effects of MRE deployments and Pacific Northwest National Laboratory (PNNL)'s ecological modeling expert, Kate Buenau, is bringing her modeling prowess to the Triton Initiative.
Buenau is a senior research scientist who specializes in quantitative ecology and modeling for the Coastal Sciences Division at PNNL’s Marine Sciences Laboratory (MSL). She joined MSL in 2009 after earning a PhD in ecology and marine biology at the University of California, Santa Barbara. For the past 11 years, Buenau has primarily worked on large-scale ecosystem restoration projects, including recovery programs for the Missouri River and the Columbia River Estuary, and habitat restoration in Puget Sound.
In addition to ecological modeling and data analysis for these programs, she works on the design and implementation of large-scale adaptive management programs. She has modeled everything from coral-algae competition, to eelgrass physiology, and shorebird populations, with a focus on how individuals and populations interact with their habitats. The spatial models she creates show the actions needed to improve habitats of plants and animal species in distress. Buenau’s passion for ecosystem health and longevity has guided her career in coastal sciences.
“A common challenge is helping people from a wide variety of backgrounds understand what uncertainty in model predictions means, and how to use it as a tool for making better decisions,” states Buenau. “For example, an engineer might have a very different approach to risk than a research scientist, and a manager responsible for budget decisions may have yet another view. Interested members of the public may also need to understand what uncertainty means and how it fits into decisions.”
According to Buenau, building a model used to make decisions may be one-third technical work and two-thirds communication and engagement. Buenau’s experience with stakeholders, her technical expertise, and her passion for ecology make her uniquely positioned to serve the MRE industry. There are many actors striving to understand the environmental effects of MRE, and modeling helps make sense of the available information so progress can be made.
Supporting the development of MRE so that it benefits both humans and the environment is an important part of Buenau’s broader motivation to build a more sustainable world. Her work with the U.S. Department of Energy Water Power Technologies Office’s Triton Initiative on the Triton Field Trials (TFiT) team has involved reviewing models that can be used in conjunction with monitoring to improve environmental assessment of MRE projects.
Recently, she conducted an extensive review of the types of predictive models that exist or could be developed for evaluating MRE environmental stressors. A stressor is a known effect caused by an MRE device to the environment, a population of animals, or an individual species. The review examines how models and monitoring are intertwined, and how they can be used symbiotically to improve one-another. Buenau developed a report based on the findings from this review with MSL ecologists Lenaig Hemery and Lysel Garavelli, outlining how predictive models can help the industry determine monitoring needs and estimate the magnitude of potential environmental impacts. The report highlighted existing MRE models and described the data used to inform them, how they have been applied and adapted, and how monitoring can support future models. The team reviewed models in six categories of stressors related to tidal, wave, and ocean current energy devices, including:
- collision risk,
- underwater noise,
- electromagnetic fields,
- changes to habitat,
- displacement of marine animals, and
- changes to oceanographic systems
The advancement of MRE has been hindered by a lack of information about the potential environmental impacts of these stressors. MRE devices include technologies like tidal turbines, floating wave energy converters, point absorbers, and others. Because of the locations designated for these devices, monitoring environmental effects is expensive and determining best practices is challenging due to the wide-range of impacts these dynamic structures may have on the ocean’s ecosystems.
One of the primary goals of TFiT is to determine the best and most cost-effective methods and technologies that can be used to monitor environmental stressors. Predicting the environmental effects of MRE, deciding what to monitor, and for how long are costly challenges for developers. The lack of recommendations for monitoring environmental stressors causes uncertainty among regulators who are responsible for permitting potential sites. Since there are few MRE devices installed in U.S. waters, stakeholders must rely on predictive models to address questions about risk and impacts related to device deployment. When TFiT field researchers are not able to study actual devices, they rely on the help of scientists like Buenau who are able to use predictive modeling to determine what needs to be monitored, estimate potential impacts in the environment and to specific species, and assess effectiveness of existing environmental monitoring methods. Creating and establishing trust in a model used for management decisions takes time, especially when considerable uncertainty remains. Some uncertainties, like weather, cannot be eliminated no matter how many numbers are crunched. Understanding model applications can help decision makers make value judgments about acceptable risk.
Buenau looks forward to future work disentangling the complex interactions between MRE devices and the environment for the Triton Initiative. There are countless possibilities for future modeling in the world of MRE applications, which is largely untapped. Specifically, Buenau is excited to work directly with the data generated by the TFiT field deployments to further explore and refine modeling approaches.
Written by Cailene Gunn.