Peer-reviewed publications by Texas A&M AgriLife and Texas A&M University System scientists
Broadcasting cover crops at corn harvest can maximize biomass and reduce nitrogen leaching: In this study, coauthored by Texas Water Resources Institute (TWRI) Program Specialist Mary Michael Zahed, researchers sought to identify cover crop species and establishment techniques that allow for effective nitrogen uptake and biomass production. They also assessed the impacts on corn yield and how nitrogen leaching is affected by planting at harvest compared to delayed planting. They found that planting cover crops at corn harvest can reduce nitrogen leaching and maximize biomass compared to delayed planting.
What Lies Below: A Theory of Planned Behavior Study of Septic System Owners’ Practices in the Attoyac Bayou Watershed: Coauthored by TWRI’s Audrey McCrary and Allen Berthold, Ph.D., this study assesses the influences on septic system owners’ intentions to improve septic system maintenance and protect watershed health in Attoyac Bayou in East Texas. They found that attitude toward septic system maintenance was the biggest factor influencing maintenance behavior, whereas social norms and perceived behavioral control were less influential.
Cross-border groundwater impacts and joint management interventions: An overview of case studies: TWRI’s Rosario Sanchez, Ph.D. coauthored this review paper of transboundary aquifer cases from around the world to provide insights into cross-border groundwater impacts and joint management interventions. The researchers observed that most cross-border groundwater impacts are quantity issues due to over withdrawal. They also observed that joint management interventions are reactive and focused on border zones.
Deriving hydrological inferences from a machine learning model to understand the physical drivers of flow duration curves: This study, coauthored by TWRI’s Michael Schramm, uses a machine learning model with a large sample of watersheds to explore the interactions between watershed attributes and flow duration curves. The team found that flow duration curves were highly driven by climate attributes, such as precipitation, followed by baseflow index and geological attributes.
Validation and Psychological Correlates of a Water Provider Trust Scale for the United States: Coauthored by a Texas A&M University researcher, this research assesses water provider trust and the use of a four-item water provider trust scale. They found that antiestablishment orientations, perceived stress, and water insecurity negatively affected water provider trust.
Hydrogeochemical Processes Driving Elevated Arsenic and Fluoride in the Texas Gulf Coast Aquifer: In this study, Texas A&M researchers aim to identify the hydrological and geochemical processes that mobilize arsenic and fluoride dissolved in groundwater in aquifers. One important finding from this paper was that silicate weathering drives fluorite dissolution and fluoride release in Gulf Coast aquifers.
Recent research from other Texas universities
Perceptions on Climate Change and Resilience by Water-Resource Decision Makers in Texas: Texas State University researchers surveyed water professionals in Texas on their perceptions on climate change and resilience. By evaluating the responses, they hope to develop effective engagement frameworks. They found that professionals and the public have similar perceptions of climate change. In future studies, they recommend investigating frameworks that use the language of resiliency.
Untangling the myth of flood risk and mitigation in affluent inland urban neighbourhood – A case study of the Onion Creek Neighbourhood in Austin, Texas: This study, also from Texas State researchers, aims to improve understanding of how affluent inland urban neighborhoods respond to flood risk. They looked at the Onion Creek Neighborhood, which is flood-prone and high-income. They found that barriers to relocation include a lack of alternate housing options, attachment to the neighborhood and environment, and other diverse factors. They call for tailored strategies and policies for sustainable development and disaster preparedness in similar communities.
Predicting river turbidity in Pine Island Bayou using machine learning techniques coupled with variational mode decomposition: In this study, Lamar University researchers used water monitoring data to develop a sequence-to-sequence model to predict turbidity. They found that water temperature has the greatest influence on seasonal turbidity patterns, whereas hourly rainfall greatly influences short-term variability. This model could provide an early warning for public recreational water use.
