Marine spatial planning techniques with a case study on wave-powered offshore aquaculture farms

TL;DR

This study develops an open-source MSP workflow using QGIS and Python to evaluate the siting of wave-powered offshore aquaculture farms (WPAFs) along the US Northeast. By modeling Atlantic salmon farms powered by the RM3 wave energy converter and applying environmental, depth, and conflict constraints, the authors identify 52 feasible sites and single out an optimal location southwest of Acadia National Park with a total 15‑year cost of $56.8M and a levelized cost of fish of $9.23/kg. The framework highlights how wave energy availability and distance to shore drive WPAF costs, showing that co-location can reduce energy-related expenditures but is often constrained by conflicts and carrying capacity. The work demonstrates a practical, data-driven MSP approach with direct implications for offshore energy and aquaculture planning, and outlines avenues for refinement including time-series data, storage, and multi-trophic design.

Abstract

As emerging marine technologies lead to the development of new infrastructure across the ocean, they enter an environment that existing ecosystems and industries already rely on. Although necessary to provide sustainable sources of energy and food, careful planning will be important to make informed decisions and avoid conflicts. This paper examines several techniques used for marine spatial planning, an approach for analyzing and planning the use of marine resources. Using open source software including QGIS and Python, the potential for developing wave-powered offshore aquaculture farms using the RM3 wave energy converter along the Northeast coast of the United States is assessed and several feasible sites are identified. The optimal site, located at 43.7°N, 68.9°W along the coast of Maine, has a total cost for a 5-pen farm of \$56.8M, annual fish yield of 676 tonnes, and a levelized cost of fish of \$9.23 per kilogram. Overall trends indicate that the cost greatly decreases with distance to shore due to the greater availability of wave energy and that conflicts and environmental constraints significantly limit the number of feasible sites in this region.

Figures (12)

• Figure 1: Flowchart describing the marine spatial planning process used, including selecting the scope and data, preprocessing data sets, analysis in both QGIS and Python, and visualizing results.
• Figure 2: Reference model 3 (RM3) wave energy converter neary_methodology_2014, and Innovasea submersible net pen considered in this study innovasea_innovasea_2023.
• Figure 3: Selected region of Northeastern United States including state and federal waters, along with the 0.1° grid of sampled points (points over land are excluded early in analysis).
• Figure 4: Raster data sets of conditions considered in the analysis with generated contours overlaid. Environmental conditions and bathymetry are used to identify feasible sites, while the wave climate and distance to port are used in the cost model.
• Figure 5: Data sets for all regions with conflicting industries or restricted areas that are excluded from the analysis, including shipping lanes, marine protected areas, and more.