title: A question of scale: How turbulence around aerial roots shapes the seabed morphology in mangrove forests of the Mekong Delta

authors: Mullarney, Julia C.

abstract: Mangrove forests are highly productive ecosystems that provide many physical, societal, and ecological services in tropical and subtropical regions. Accurate prediction of the morphological evolution for these areas, in the face of global sea level rise and changes in sediment supply, requires understanding of interactions between vegetation growth, water flows, and sediment transport. Data presented from a wave-exposed mangrove forest in the Mekong Delta, Vietnam, include unique measurements that resolved water flows in and around the aerial mangrove roots (known as pneumatophores) over scales from a few millimeters to hundreds of meters. Flows were highly turbulent, with turbulence values sometimes as large as those measured in surf zones. These energetic processes appear to stir up sediments, with small scour troughs observed around individual pneumatophores, and larger-scale scour around clusters of pneumatophores. The vegetation fringe (the boundary between forest and mudflat) was a particularly dynamic area, with elevated turbulence levels, greater vegetation densities, coarser sediments, and occasional wave breaking. Intense turbulent dissipation at the fringe then reduces the energy of shoreward-propagating waves, sheltering the forest interior. The small-scale processes appear to be linked with forest-wide patterns of sediment transport and deposition. We discuss these links in the context of the biophysical interactions that control the changing shapes of deltas worldwide.

text: A question of scale: How turbulence around aerial roots shapes the seabed morphology in mangrove forests of the Mekong Delta Mangrove forests are highly productive ecosystems that provide many physical, societal, and ecological services in tropical and subtropical regions. Accurate prediction of the morphological evolution for these areas, in the face of global sea level rise and changes in sediment supply, requires understanding of interactions between vegetation growth, water flows, and sediment transport. Data presented from a wave-exposed mangrove forest in the Mekong Delta, Vietnam, include unique measurements that resolved water flows in and around the aerial mangrove roots (known as pneumatophores) over scales from a few millimeters to hundreds of meters. Flows were highly turbulent, with turbulence values sometimes as large as those measured in surf zones. These energetic processes appear to stir up sediments, with small scour troughs observed around individual pneumatophores, and larger-scale scour around clusters of pneumatophores. The vegetation fringe (the boundary between forest and mudflat) was a particularly dynamic area, with elevated turbulence levels, greater vegetation densities, coarser sediments, and occasional wave breaking. Intense turbulent dissipation at the fringe then reduces the energy of shoreward-propagating waves, sheltering the forest interior. The small-scale processes appear to be linked with forest-wide patterns of sediment transport and deposition. We discuss these links in the context of the biophysical interactions that control the changing shapes of deltas worldwide.

year: 2017

title: Machine learning techniques for accurate prediction and improved matching of renewable energy production, storage, and consumption

authors: Pasandideh, Seyedmostafa

abstract: In recent decades, the electricity industry has undergone considerable transformation, characterized by the expansion of competitive electricity markets, integration of renewable energy sources, and the deployment of digital systems. These developments have contributed to increased complexity and variability within power systems, highlighting the need for more accurate, adaptive, and intelligent methods for managing electricity generation, storage, and consumption. This thesis focuses on addressing these challenges, with a particular emphasis on advancing New Zealand’s renewable energy ambitions through the application of machine learning (ML) and digital twin technologies for optimizing renewable energy forecasting and management in both industrial and residential contexts.
In the industrial domain, the research develops real-time solar power monitoring systems and predictive models employing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. These models are designed to enhance forecasting precision and system efficiency by accounting for the inherent variability in renewable energy generation. Complementing this, fuzzy logic-based control strategies are implemented to manage electricity consumption adaptively in industrial processes, including applications within the meat processing industry. The incorporation of digital twin technologies further facilitates real-time monitoring, predictive maintenance, and agile response to evolving operational conditions, thereby supporting system resilience and efficiency.
In residential and community energy systems, the thesis introduces an innovative adaptive energy management framework combining streaming machine learning (SML) with a fractal-structured microgrid architecture. This framework utilizes incremental learning algorithms and digital twin principles to enable real-time adaptive control of energy flows, local trading of electricity, and dynamic adjustments in response to fluctuating supply and demand. The approach aims to enhance battery management, support local energy autonomy, and potentially reduce dependency on centralized grids. Comparative evaluations of centralized, distributed, and hybrid battery storage configurations provide insights into the trade-offs between grid interaction, storage longevity, and operational flexibility, offering practical solutions for optimizing residential microgrids.
In conclusion, this thesis explores a combined, case-study based approach to enhancing renewable energy utilization through the combined application of advanced machine learning and digital twin technologies. By addressing both industrial and residential energy systems, the research contributes to the broader vision of developing reliable, efficient, and sustainable power infrastructures. The findings align with New Zealand’s commitment to reducing carbon emissions and advancing renewable energy technologies, offering case-study evidence and potential approaches to support the transition toward a low-carbon energy future.

text: Machine learning techniques for accurate prediction and improved matching of renewable energy production, storage, and consumption In recent decades, the electricity industry has undergone considerable transformation, characterized by the expansion of competitive electricity markets, integration of renewable energy sources, and the deployment of digital systems. These developments have contributed to increased complexity and variability within power systems, highlighting the need for more accurate, adaptive, and intelligent methods for managing electricity generation, storage, and consumption. This thesis focuses on addressing these challenges, with a particular emphasis on advancing New Zealand’s renewable energy ambitions through the application of machine learning (ML) and digital twin technologies for optimizing renewable energy forecasting and management in both industrial and residential contexts.
In the industrial domain, the research develops real-time solar power monitoring systems and predictive models employing convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks. These models are designed to enhance forecasting precision and system efficiency by accounting for the inherent variability in renewable energy generation. Complementing this, fuzzy logic-based control strategies are implemented to manage electricity consumption adaptively in industrial processes, including applications within the meat processing industry. The incorporation of digital twin technologies further facilitates real-time monitoring, predictive maintenance, and agile response to evolving operational conditions, thereby supporting system resilience and efficiency.
In residential and community energy systems, the thesis introduces an innovative adaptive energy management framework combining streaming machine learning (SML) with a fractal-structured microgrid architecture. This framework utilizes incremental learning algorithms and digital twin principles to enable real-time adaptive control of energy flows, local trading of electricity, and dynamic adjustments in response to fluctuating supply and demand. The approach aims to enhance battery management, support local energy autonomy, and potentially reduce dependency on centralized grids. Comparative evaluations of centralized, distributed, and hybrid battery storage configurations provide insights into the trade-offs between grid interaction, storage longevity, and operational flexibility, offering practical solutions for optimizing residential microgrids.
In conclusion, this thesis explores a combined, case-study based approach to enhancing renewable energy utilization through the combined application of advanced machine learning and digital twin technologies. By addressing both industrial and residential energy systems, the research contributes to the broader vision of developing reliable, efficient, and sustainable power infrastructures. The findings align with New Zealand’s commitment to reducing carbon emissions and advancing renewable energy technologies, offering case-study evidence and potential approaches to support the transition toward a low-carbon energy future.

year: 2025

title: Indigenous-washing and colonial amnesia: how New Zealand’s nation brand depoliticizes climate change

authors: Hellmann, Olli

abstract: New Zealand’s nation brand has drawn ever louder accusations of “greenwashing” the country’s image in recent years. Through a visual discourse analysis of Air New Zealand’s Tiaki & The Guardians safety briefing video, this article shows that brand managers have responded with a strategy of “Indigenous-washing,” appropriating the Māori worldview to deflect attention from intensive farming’s carbon footprint and other environmentally unfriendly activities. More broadly, this article makes an important contribution to the growing critical-cultural literature on nation branding by revealing how New Zealand’s latest marketing initiative contributes to the depoliticization of climate change. The “Tiaki” campaign not only positions New Zealand as an “untouched land,” but also closes the space for democratic debate about climate change by obscuring the role of colonialism in causing the planet’s ecological crisis and by silencing alternative socioecological futures proposed by Indigenous peoples.

text: Indigenous-washing and colonial amnesia: how New Zealand’s nation brand depoliticizes climate change New Zealand’s nation brand has drawn ever louder accusations of “greenwashing” the country’s image in recent years. Through a visual discourse analysis of Air New Zealand’s Tiaki & The Guardians safety briefing video, this article shows that brand managers have responded with a strategy of “Indigenous-washing,” appropriating the Māori worldview to deflect attention from intensive farming’s carbon footprint and other environmentally unfriendly activities. More broadly, this article makes an important contribution to the growing critical-cultural literature on nation branding by revealing how New Zealand’s latest marketing initiative contributes to the depoliticization of climate change. The “Tiaki” campaign not only positions New Zealand as an “untouched land,” but also closes the space for democratic debate about climate change by obscuring the role of colonialism in causing the planet’s ecological crisis and by silencing alternative socioecological futures proposed by Indigenous peoples.

year: 2023-07-27