Climate+ Projects
A team of students led by researchers at the Duke Marine Lab will explore the changing distribution of krill around the Antarctic Peninsula. Krill are a key prey species in this ecosystem, supporting a number of animals including whales, seals, and penguins, but they are dependent on winter sea ice and may be in trouble […]
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This project is also part of Duke’s first Climate+ cohort. A team of students led by researchers at Duke and abroad will develop and evaluate machine learning solutions to model behavioral patterns of electric use, emphasizing data privacy. Data collected in different parts of the world will be analyzed to understand the electric patterns that […]
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This project is also part of Duke’s first Climate+ cohort. A student team working with the Energy Data Analytics Lab will work to democratize access to data relevant to climate change mitigation and adaptation planning as well as the underlying models to acquire those data. This project will work towards building the first “foundation model” specifically for […]
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This project is also part of Duke’s first Climate+ cohort. A team of students led by researchers in the Hydroclimatological Lab will comprehensively quantify the wetland carbon emissions in the entire Southeast (SE) US using machine learning techniques and various climate datasets—including in situ measurements, remote sensing data, climate observations, and hydrological model (PIHM-Wetland) outputs. […]
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This project is also part of Duke’s first Climate+ cohort. Duke Data+ students, in collaboration with Dr. Emily Bernhardt (faculty advisor) and Audrey Thellman (graduate student) will evaluate how changing ice and snow conditions are impacting river ecosystems through classified ice imagery. Currently, our team has data from 7 field cameras that have been taking […]
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A team of students led by Physics professor Dan Scolnic collaborated with Duke Dining leadership to provide an in-depth, quantitative accounting of the carbon footprint of the Duke Dining program. Students used the latest research quantifying CO2 equivalent greenhouse gas emissions for various food types, meals, and sources to produce calculations of the effective CO2 […]
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A team of students led by Prof. Zuchuan Li and co-led by Prof. Nicolas Cassar developed means to estimate the amount of CO2 transferred from the ocean surface to the deep ocean through machine learning techniques applied to satellite data and automatic observations. The team identified variables that can be used to derive the estimate, […]
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A team of students led by researchers in the Hydroclimatological Lab created a workflow/pipeline for comprehensively estimating the carbon emissions from the Southeastern (SE) United States (US) wetlands using machine learning techniques applied to multi-source data, including field measurements, remote sensing products, and biophysical model outputs. The team first applied machine learning (ML) techniques to […]
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Students collaborated with CEE Professors David Carlson and Mike Bergin to model the effects of land use on the urban heat island effect using satellite imagery and ground-level temperature measurements. Students used machine learning to segment satellite images of Durham, North Carolina by land use. They then paired land use classifications with ground-level temperature measurements […]
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This project helped to build a globally scalable foundation model to enable near real-time tracking of climate change causes and impacts. A foundation model is a model (usually a deep neural network) that has been trained on a large and diverse set of data, after which it can be adapted to a variety of different […]
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Researchers with the Duke River Center and the Watershed Biogeochemistry Lab investigate patterns of anoxia, or periods of little to no oxygen, in rivers. Oxygen is a necessary element for many organisms to live in rivers, but researchers know little about the timing, duration, and magnitude of low oxygen time periods in rivers, and when […]
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A team of students led by Civil & Environmental Engineering Professor Helen Hsu-Kim developed a resource reserves database of coal ash wastes stored in hundreds of legacy disposal sites in the United States. The team extracted key information from historical datasets on coal energy production, incorporated geochemical information of coal ash wastes, and applied geospatial […]
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A team of students led by researchers within the Saltwater Intrusion and Sea Level Rise (SWISLR) Research Coordination Network created a geospatial database summarizing the current extent of SWISLR and the current knowledge on SWISLR within the North American Coastal Plain. Students were responsible for mapping scholarly articles, news stories, social media, and interviews to […]
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