Peer-reviewed publications by Texas A&M AgriLife and Texas A&M University System scientists
Soil health indicator variability and management sensitivity across soils, bioregions, and agricultural systems: This study, coauthored by Texas A&M AgriLife researchers, measured twelve soil health indicators across different climates, soils, and agricultural production systems. They found that soil health indicator values varied widely across soil types and regions.
An upgraded GIS-based multi-criteria decision-making approach for flood control prioritization mapping: Case study of West Dallas-Fort Worth metroplex: In this study, Texas A&M AgriLife Researchers propose an upgraded decision-making framework to develop flood control prioritization maps. This framework is based on green stormwater infrastructure and includes a wide range of factors. This study shows the framework’s ability to streamline flood mitigation planning and support decision-making in urban developments.
Effects of rising CO2 concentrations on water dynamics and yields for C3 and C4 crops under both irrigated and dryland conditions in the Texas High Plains: This study, coauthored by a Texas A&M AgriLife researcher, evaluates the impact of elevated CO2 on evapotranspiration, using a modified version of the Soil and Water Assessment Tool (SWAT) model. One key finding is that elevated CO2 reduced evapotranspiration by 6.8% – 20.7% in irrigated conditions.
Assessment of rainfall-derived inflow and infiltration using smart sewer sensors and ensemble optimization: This study from Texas A&M University Corpus Christi researchers uses smart sewer monitoring with machine learning to automate the assessment of rainfall-derived inflow and infiltration. They found that using ensemble optimization, an approach that involves combining multiple models to improve performance, improved accuracy by 5% in calibration and 17% in validation.
Enhancing coastal resilience: AI-driven seasonal to multi-year water level predictions for the Texas Gulf Coast: This study, authored by Texas A&M University Corpus Christi researchers, introduces a multilayer perceptron model for forecasting water levels across different time scales. This model offers forecasts from seasonal to multi-year and covers the entire Texas coastline. This model can provide stakeholders with lead time to better plan and implement mitigation measures.
From wells to waves: Evaluating fecal contamination sources using FIB and MST markers along Texas coastal waters: This study, coauthored by Texas A&M University Corpus Christi researchers, investigates sources of fecal contamination along the Texas coast. To do so, they use fecal indicator bacteria enumeration and microbial source tracking markers. They found that 65% of surface water had elevated fecal contamination levels, making it unsafe for swimming.
Recent research from other Texas Universities
Triple oxygen isotopes in Texas precipitation reveal variability in the nature and timing of secondary evaporation: This study from University of Houston researchers examines triple oxygen isotopes in monthly precipitation across Texas from 2021 to 2023. They identified secondary evaporation as the main factor influencing isotopic variability. They also found that closer to the coast, re-evaporation occurs locally during warm season rainfall, and further from the coast, re-evaporation occurs during moisture transport year-round.
Biofiltration, seasonality, and distribution system factors influence nitrifier communities in a full-scale chloraminated drinking water system: Coauthored by a University of Texas at Austin researcher, this study investigates how biofiltration affects nitrifier communities in a drinking water system where chloramine is a secondary disinfectant. The same nitrifier populations were found in bioeffluent and across the study sites. They also found that site-specific factors influenced individual nitrifier populations.
Towards smart PFAS management: Integrating artificial intelligence in water and wastewater systems: This article, coauthored by a University of Texas Rio Grande Valley researcher, provides a synthesis of artificial intelligence and machine learning methods related to PFAS management. According to the authors, this article provides a practical reference for researchers and regulators in PFAS contamination management.
Evaluating trade-offs among cotton yield, groundwater extraction, and future projections for sustainable water management in the Texas High Plains: In this study, Texas Tech University researchers present a framework to quantify groundwater extraction and evaluate trade-offs with crop water productivity in irrigated cotton fields of the Texas High Plains. This data-driven framework combines remote sensing and in-situ observations. They found that from 2008-2023 in the central and northern Texas High Plains, overuse and inefficiency posed a risk to groundwater sustainability.
