Investigating biophysical linkages at tidal energy candidate sites: a case study for combining environmental assessment and resource characterisation

Scherelis, Constantin; Penesis, Irene; Hemer, Mark; Cossu, Remo; Wright, Jeffrey T.; Guihen, Damien

doi:10.1016/j.renene.2020.05.109

Cited by 27 publications

(11 citation statements)

References 35 publications

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“…The distribution of human activity in coastal areas is influenced by geomorphological factors (Yi et al, 2020). Preliminary observations of the coast must be carried out with the aim of maintaining environmental quality (Scherelis et al, 2020). Tidal energy site selection must consider the characteristics of coastal resources (Mejia-Olivares et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

The Geomorphological Factors and Its Implications for The Tidal Energy Installations in Java, Indonesia

Kurnianto¹,

Ikhsan²,

Apriyanto³

et al. 2021

J. Geoscience Eng. Environ. Technol.

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This study aims to place the tidal energy installation effectively in Indonesia based on geomorphological factors. The survey method was used to analyze the characteristics of beaches in Indonesia. Mathematical physics model was implemented to find the new formulas based on geomorphological factors. Tides are the result of gravitational attraction and the centrifugal effect, which is the drive In the earth-moon system, tidal generating forces are the resultant forces that cause tides, namely: the earth-moon system (FS) centrifugal force and the moon's gravitational force (FB). FS works in the center of the mass of the earth-moon system whose mass point is located on the 3/4 radius of the earth. The style of tidal generator caused by the moon can be calculated by combining Newton's universal gravitational law .The results of this study consist of F = m ac, where the style of the tidal generator caused by the moon can be calculated by combining newton's universal gravitational law in equation and newton's second law of motion in Equation. The another results is tan = , where the formula takes into account constants (K) based on slopes. The last result is the constants (K) for each land form starting on 0,00 untill 1,00. The north coast of Java is more suitable for tidal energy installations because the land form is dominated by alluvium plains of the quaternary age with a lower risk than the southern region of Java. The effectiveness of tidal energy installation depends on the characteristics of the land form. In alluvial plains, the quaternary age of the alluvial plains is more suitable than the hill form volcanic quaternary, tertiary volcanic, and tertiary holokarst.

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Section: Resultsmentioning

confidence: 99%

The Geomorphological Factors and Its Implications for The Tidal Energy Installations in Java, Indonesia

Kurnianto¹,

Ikhsan²,

Apriyanto³

et al. 2021

J. Geoscience Eng. Environ. Technol.

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“…This makes possible detection of the scattered sound with transducer and suitable receiver electronics. Advantages linked to hydroacoustic sampling techniques include, high spatial and temporal resolution, autonomous long-term sampling duration, range (especially during poor visibility when visual-based methods tend to fail), and a non-invasive surveying approach (Scherelis et al, 2020). Given these advantages, hydroacoustics is increasingly used to characterise animal behaviour in the marine environment, and considered a promising system to improve management of aquaculture farms (Bjordal et al, 1993;.…”

Section: Methodsmentioning

confidence: 99%

“…In this study, hydroacoustic data were collected by one of two sensors "CageEye" (Scherelis et al, 2020) or "Aquaculture Biomass Monitor" ABM (2020). Broadly speaking, processed hydroacoustic data generates two metrics: volume backscattering strength (S v ), is often considered as a proxy for fish biomass; while target strength (TS) is an acoustic measure of fish length (Simmonds and MacLennan, 2008).…”

Section: Methodsmentioning

confidence: 99%

Data Driven Insight Into Fish Behaviour and Their Use for Precision Aquaculture

et al. 2021

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Aquaculture, or the farmed production of fish and shellfish, has grown rapidly, from supplying just 7% of fish for human consumption in 1974 to more than half in 2016. This rapid expansion has led to the growth of Precision Aquaculture concept that aims to exploit data-driven management of fish production, thereby improving the farmer's ability to monitor, control, and document biological processes in farms. Fundamental to this paradigm is monitoring of environmental and animal processes within a cage, and processing those data toward farm insight using models and analytics. This paper presents an analysis of environmental and fish behaviour datasets collected at three salmon farms in Norway, Scotland, and Canada. Information on fish behaviour were collected using hydroacoustic sensors that sampled the vertical distribution of fish in a cage at high spatial and temporal resolution, while a network of environmental sensors characterised local site conditions. We present an analysis of the hydroacoustic datasets using AutoML (or automatic machine learning) tools that enables developers with limited data science expertise to train high-quality models specific to the data at hand. We demonstrate how AutoML pipelines can be readily applied to aquaculture datasets to interrogate the data and quantify the primary features that explains data variance. Results demonstrate that variables such as temperature, wind conditions, and hour-of-day were important drivers of fish motion at all sites. Further, there were distinct differences in factors that influenced in-cage variations driven by local variables such as water depth and ambient environmental conditions (particularly dissolved oxygen). The framework offers a transferable approach to interrogate fish behaviour within farm systems, and quantify differences between sites.

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“…Species distribution and habitat suitability models can be used to select ME sites that minimize species impacts. Simple linear regression models estimate the association between species observations and a relatively small number of environmental parameters (see [142] for an example involving fish at a tidal energy site). Ecologists have developed more sophisticated methods that include a larger number of environmental variables and identify which are most relevant [143,144], though the ability of a habitat model to predict species distribution does not guarantee that the relationships in the model are biologically meaningful [145].…”

Section: Statistical Species Distribution Modelsmentioning

confidence: 99%

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

Buenau

Garavelli

Hemery

et al. 2022

JMSE

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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.

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Investigating biophysical linkages at tidal energy candidate sites: a case study for combining environmental assessment and resource characterisation

Cited by 27 publications

References 35 publications

The Geomorphological Factors and Its Implications for The Tidal Energy Installations in Java, Indonesia

The Geomorphological Factors and Its Implications for The Tidal Energy Installations in Java, Indonesia

Data Driven Insight Into Fish Behaviour and Their Use for Precision Aquaculture

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

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