Scientists are frequently asked to provide models and simulations about complex systems — from weather and climate to disease transmission — but they may not accurately reflect on-the-ground circumstances. Research scientists in the AIM Flagship are developing new technologies that allow us to combine and analyze data across multiple modalities to make more accurate forecasts and archivable collections of hazard-related data products.
Using an artificial intelligence-enabled modeling framework, we connect observations from various data sources, such as sensor observations and lived experience narratives, providing ground truth information for integrated models. These models interlace hurricane projections with storm surge, flooding, and infectious disease to provide actionable information to communities.
The AIM project has multiple sub-projects targeting different but related challenges. The team leverages interdisciplinary expertise and computational/technological assets of UT Austin and the Texas Advanced Computing Center to fill gaps in existing knowledge and to create user-oriented tools to advance climate justice.
Central Research Questions:
- Can we develop computational models that predict the effects of climate-related events (flooding/storm surge/health issues/disaster response)?
- Can we link/integrate these models in a way that is useful to society?
What problem(s) is this project attempting to solve?
Coastal storm surge and inland flooding causes immense damage in Texas – low-income communities and communities of color are often disproportionately impacted by these hazards.
Further, increased flooding coupled with rising temperatures in Texas creates suitable environments for neglected tropical diseases, which have not received adequate attention by public health officials or the public. Currently, there are no existing open-sourced tools to model flood’s influence on vector-borne, water-borne, and soil-borne diseases.
Alongside partners at the Museum of South Texas History in Edinburg and UT Rio Grande Valley, the AIM team is currently focused on developing hurricane storm surge models covering the RGV to:
(1) better understand the dynamics of surge in the region;
(2) use model outputs as inputs in disease models for coupled flood-disease modeling; (3) combine scientific data with historical/cultural memory and residents’ lived experiences to inform public discourse and mitigate impacts hazards locally and;
(4) share what is developed as open-source software and tools that can be used globally in areas facing similar challenges.
Anticipated Outcomes and Impacts:
- Increased awareness and vigilance among vulnerable communities in South Texas.
- Increased understanding of storm surge dynamics at the outflow of the Rio Grande River that will aid in planning, preparedness, and emergency response efforts in South Texas.
- South Texas residents gain a more democratic voice in telling stories of natural disasters; Residents feel better informed and more historically grounded
- Open-sourced data infrastructures available to researchers world-wide, including a database for all neglected tropical diseases.
- Better policies and more acts on protecting the vulnerable groups from infected neglected tropical diseases under climate change.
- Minimize infectious disease spread in humans due to climate change, by making real-prediction with sufficient information, computing powers, and collaboration with health departments and medical groups (e.g. vaccine developers).
Geographic Areas of Interest: Rio Grande Valley, Southeast Texas, Mexico, coastal areas more broadly. The AIM team is creating open-source software and data/model integration methodologies that can be implemented globally
Key Partners: Texas Advanced Computing Center, Texas Libraries, Museum of South Texas History, University of Texas Rio Grande Valley, Texas Department of Health and Human Services, Balcones Canyonlands Preserve, American Forests, National Tropical Disease Institute
