Advanced Risk Identification
Advanced risk identification combines geospatial analysis, hazard modeling, and machine learning to proactively detect and assess threats across environmental, agricultural, and energy infrastructure sectors. By integrating past, current, and future data, real-time monitoring, and predictive modeling, this approach enhances the ability to anticipate and mitigate risks related to permafrost degradation, flooding, wildfire hazards, and extreme weather events.
NOAA
The Levelock Village Council, in collaboration with NOAA’s Coastal Habitat Restoration and Resilience Grant program, is conducting a Community Impact Statement (CIS) to assess and mitigate the effects of shoreline erosion along the Kvichak River in Levelock, Alaska. This initiative integrates advanced hazard modeling, local observations, and stakeholder engagement to develop a comprehensive strategy.
Given the region’s reliance on the Kvichak River for subsistence fishing and infrastructure stability, the CIS will provide critical insights into how climate change, including permafrost melt, extreme weather, and erosion, impacts the community. The project will offer targeted mitigation strategies and explore nature-based solutions to enhance coastal resilience. Through this effort, Levelock aims to safeguard critical infrastructure, sustain ecological resources, and preserve traditional ways of life for future generations.
NASA
TBEC is partnered with the Ketchikan Indian Community (KIC), an Alaska Native Village and federally recognized sovereign nation, to conduct a CIS to assess climate impacts on Southeast Alaska region and co-develop a tailored and robust ICARIS funded by NASA.
​
The primary research objective of this proposal is to integrate co-generated Traditional Ecological Knowledge (TEK) with cutting edge climate modeling and projections to create a meaningful, actionable, and holistic assessment of climate impacts on the KIC. This research will build upon the previous CIS and ICARIS research, enabling the KIC to identify climate vulnerabilities and opportunities, develop plans for mitigation of climate risks, evaluate management options for adapting to these risks, and implement successful strategies that result in increased community resilience.
Downscaled Slow Onset and Extreme Weather Events
Downscaled Slow Onset and Extreme Weather Events refines global hazard projections to local and regional scales, enabling more precise assessments of risks and trends. This process enhances decision-making by integrating high-resolution data on temperature, precipitation, extreme weather events, and sea-level rise tailored to specific geographic areas. TBEC specializes in statistical and dynamical downscaling, ensuring that projections are actionable for energy security, agricultural planning, and infrastructure adaptation.
NSF SBIR Support
We received a National Science Foundation (NSF) Small Business Innovative Research (SBIR) Phase I award (#2014633) to develop proof-of-concept techniques using projected hazards overlayed with biophysical processes to project how risk hazards will change, e.g., erosion, flooding, drought, permafrost thaw, etc. This work was the foundation for an NSF SBIR Phase II award (#2126742). The Phase II award used machine learning to combine geometrics, future hazard risks, and geodesign to identify current and potential hazard susceptibility.
​
Future Energy Services
This project serves as the foundational phase of turbine development, identifying optimal rotor configurations that are accessible, transportable, and affordable along with the performance thresholds needed to achieve a competitive Levelized Cost of Energy (LCOE) for future implementation. The work will establish a strong technical foundation and accelerate the deployment of efficient and scalable future energy solutions in remote and resource-constrained areas.
By integrating local knowledge, advanced modeling, and scalable design, TBEC is supporting the transition to affordable, efficient, and reliable energy systems for Alaskan communities.