Peer-reviewed publications by Texas A&M AgriLife and Texas A&M University System scientists
Enhancing community-based participatory flood imagery using an AI-based super-resolution framework
Images and videos from the community provide valuable perspective on disaster conditions. But, these images often have low resolutions, motion blur, and other limitations. In this study, Texas A&M University researchers assess the use of an AI-based super-resolution framework to improve the quality of flood-related images and videos. They found that using the framework consistently improved image and video quality. These findings may improve the review of community-submitted flood imagery, and support post-flood assessments, according to the researchers.
An assessment of contaminants and benthic condition in the Matagorda Bay system
In the Matagorda Bay system, benthic conditions have been declining since the 1980s. This study, from Texas A&M University Corpus Christi researchers, examined sediment contamination to assess if legacy pollutants, which remain in the ecosystem for long periods of time, may have contributed to ecosystem degradation in the system. They found that almost half of the study sites had concentrations of seven trace elements above sediment quality guidelines. They also observed low toxicity in areas with high diversity. But they determined that legacy pollutants are not driving ecosystem degradation, as there was no observed relationship between sediment chemistry and toxicity or benthic conditions.
Machine Learning Uncertainty Quantification for Extreme Cold Water Events
In this study, Texas A&M University Corpus Christi researchers compared three methods of using a machine learning uncertainty quantification model. The goal was to determine which method best estimates uncertainty in water temperature predictions. The models were made to improve decision making across South Texas and advisories for cold-stunning events, during which extreme cold can harm wildlife. Researchers found that the method that uses a continuous ranked probability score outperformed all other methods.
Recent research from other Texas Universities
Impacts of urban growth upon aquifer recharge: a case study of the Edwards aquifer in Central Texas
Degradation of the Edwards Aquifer has long been a subject of concern. In 1992, the Save Our Springs (SOS) Ordinance was passed to limit urban development in Austin and prevent degradation of the aquifer. In this study, Texas State University researchers use an urban growth model and a soil-water balance model to examine the effects of multiple future growth scenarios on aquifer recharge. The results suggest impervious cover limits and information that urban planners may use to balance aquifer recharge with urban growth.
Identifying impacts and causes of drinking water quality issues through community-based participatory research in a suburban neighborhood in Texas: The case of Austin’s Colony
This study, by University of Texas at Austin researchers, uses community research to investigate the extent and causes of water issues in Austin’s Colony, a suburban neighborhood in Austin, Texas. In a survey for the study, 70% of participants reported discolored water events. Only 7% reported drinking untreated tap water. The researchers found that concentrations of five metals exceeded regulatory limits but were within regulatory in-home standards. They identified groundwater mixing as the main control on water quality.
Enhancing Recharge in the Edwards Aquifer, Texas: Measures, Outcomes, and Lessons for Karst Aquifers
Managed aquifer recharge is not often used with karst aquifers due to their hydrogeological characteristics. But it has been applied to the Edwards Aquifer, a karst aquifer in south-central Texas. In this study, researchers from The Meadows Center at Texas State University review enhanced recharge measures in Edwards Aquifer from the 1960s to 2024. They assessed the benefits, limitations, and regulatory frameworks of these measures. The findings show that recharge from dams is small compared to natural recharge volumes. They also observed the use of aquifer storage and recovery strategies and initiatives to enhance quality of water entering the aquifer.
Spatiotemporal quantification of relative contributions of compound flood drivers using Shapley value-based approach
This study, from University of Texas at Austin researchers, uses a Shapley value-based approach to determine the contributions of four flood drivers (tide, storm surge, river discharge, and precipitation) when simulating Hurricane Beryl in the Houston-Galveston region of Texas. The researchers found that this approach provided new insights into the spatiotemporal dynamics of each flood driver’s contribution. This approach can inform targeted flood mitigation and risk management strategies, says the study.
