Primary Supervisor: Kristin Poinar, PhD

Secondary Supervisor: Bruce Pitman, PhD

GLOFs endanger people (and can destroy infrastructure) who live downstream of glacier-dammed lakes. This includes American and Canadian communities (Alaska, Yukon) as well as Nepalese and Indian communities (High Mountain Asia). Students on this project will undertake detection of GLOFs and detect water volumes and fluxes using a combination of Google Earth Engine (remote sensing) or application of a basic beam-bending model for a partially floating glacier (Matlab).

Primary Supervisor: Stuart Evans, PhD

Secondary Supervisor: Elizabeth Thomas, PhD

Lake effect snow is the major climate hazard of WNY. Winter of 2022 was an exceptional demonstration of the scale of human impacts and need for planning around this annual hazard. Students on this project will relate ice cover on Lake Erie to snowfall records, especially extreme snowfall, in WNY using data processing (Matlab or Python) to relate maps of lake ice coverage and water temperature to observed snowfall around WNY, with a particular focus on major events

Primary Supervisor: Irina Benedyk, PhD

Secondary Supervisor: Andrew Crooks, PhD

Snowy and icy conditions on roads pose a hazard as drivers may slide, lose control of their vehicles, or get into accidents. Students on this project will explore driving behavior change under snowy weather conditions (from light snow, freezing to snow storm) and different traffic conditions (light, medium, heavy traffic) using data collected at driving simulator experiments on driving performance, environmental conditions, and reported subjective experience to model driving behavior. Students will use unsupervised machine learning methods in Python.

Primary Supervisor: Beata Csatho, PhD

Secondary Supervisor: Richard Marinos, PhD

Improving the knowledge of soil fertility is crucial for optimizing orchard and vineyard management. Near-surface geophysics and remote sensing provide non-invasive, cost-effective monitoring of the spatiotemporal evolution of soil properties to assist farm management. Students in this project will visit vineyards and orchards in WNY to learn about the soils, bedrock geology, and management practices. They will interpret remote sensing and geophysical data using soil sample analysis and various map layers (geology, topography, climate) to understand the complex interplay between environmental factors and agricultural practices.

Primary Supervisor: Stephan Kolzenburg, PhD

Secondary Supervisor: Sophie Nowicki, PhD

Glaciers and ice sheets are the largest uncertainty in future sea level projections, therefore accurate models of their behavior are needed to produce accurate estimates. One such improvement to the accuracy of models is the need for better friction coefficients for glacier flow models. Students on this project will prepare sediment tracks of varying granulometry (characterized in sieving lab) and drag large blocks of ice across various substrates at the UB geohazards field station in order to determine a range of feasible friction coefficients.

Primary Supervisor: Susan Clark, PhD

Secondary Supervisor: Michael Shelly, PhD

Disruptions to critical infrastructure services have severe implications for vulnerable populations, however there is a lack of equity-focused metrics that quantify the performance of infrastructure in terms of its ability to serve people and communities before, during, and after a disaster. Quantifying the social impacts allows for resilience planning that reflects the needs and preferences of vulnerable populations in an equitable manner. Students on this project will collect empirical data to quantify and map the burdens experienced by vulnerable communities in Bualo due to the Winter 2022 Blizzard. We will utilize and advance an equity-focused social burden metric aimed to capture the “well-being” consequences of disruptions on households and communities.

Primary Supervisor: Kang Sun, PhD

Secondary Supervisor: Stuart Evans, PhD

Buffalo in June 2023 had the most polluted days in recorded history when weighted by the US population. Understanding what surface conditions lead to the Canadian wildfires and the meteorological conditions that lead to the cross-border transport of wildfire smoke would aid in understanding how likely this is to happen in future. Students on this project will extract meteorological patterns from reanalysis and smoke distributions from EPA AirNow/NOAA Hazard Mapping System Fire and Smoke Product (HMS) and correlate fuel conditions that lead to the historical fire pollution in June/July 2023.

Primary Supervisor: Elizabeth Thomas, PhD

Secondary Supervisor: Stuart Evans, PhD

The Great Lakes region is home to 10% of the US population and produces >20% of US GDP. With abundant freshwater and a mild climate, this region has been billed as a haven for climate refugees. Yet, this century, the Great Lakes region will likely experience increasingly wet winters and dry summers, impacting socioeconomic and ecological systems. Quantifying historical climate and investigating whether there are changes in extremes are key to adaptation in a changing climate. Students on this project will use meteorological data, including large citizen science database of precipitation observations and precipitation isotopes, to examine patterns in  rain/snowfall and temperature, examine seasonal, interannual, and long-term variability by extract observations and model data from public websites, and performing statistical analyses to determine trends.

Primary Supervisor: D. Scott Mackay, PhD

Secondary Supervisor: James Boyle, PhD

With climate change, forests are at increased risk due to warm droughts, increased pathogen outbreaks, and wildfire. Forests are essential to ecosystem and societal health, and so an improved ability to predict forest responses to rapidly changing climate will help us develop solutions for the future. Students on this project will use plant ecophysiological data along with analytical tools to examine and explain patterns of forest die-off by extract data from local and global vegetation databases, parameterize and run predictive models of woody plant responses to abiotic and biotic stressors, and perform statistical analyses to assess predictive ability of the models.

Primary Supervisor: James Boyle, PhD

Secondary Supervisor: Jason Briner, PhD

The lithology of the rocks in the Buffalo area are such that some are more or less vulnerable to erosion and water transport. Characterizing these differences will aid in understanding the risk of hazards like rock falls or mass wasting events. Students on this project will examine outcrops along Lake Erie to test how much local rock units control what the coastline looks like and the hazards associated with them (i.e. rock falls). A combination of excursions to localities along the lakeshore to make field observations of exposed rock characteristics and nature of fallen blocks and cross-checking the collected data with recent mass wasting events from local news sources.