A 10-year Metocean dataset for Laguna Madre, Texas, including for the Study of Extreme Cold Events.

Coastal observations along the Texas coast are valuable for many stakeholders in diverse domains. However, the management of the collected data has been limited, creating gaps in hydrological and atmospheric datasets. Among these, water and air temperature measurements are particularly crucial for water temperature predictions, especially during freeze events. These events can pose a serious threat to endangered sea turtles and economically valuable fish, which can succumb to hypothermic stunning, making them vulnerable to cold-related illness or death. Reliable and complete water and air temperature measurements are needed to provide accurate predictions of when cold-stunning events occur. To address these concerns, the focus of this paper is to describe the method used to create a complete 10-year dataset that is representative of the upper Laguna Madre, TX using multiple stations and various gap-filling methods. The raw datasets consist of a decade's worth of air and water temperature measurements within the Upper Laguna Madre from 2012 to 2022 extracted from the archives of the Texas Coastal Ocean Observation Network and the National Park Service. Large portions of data from the multiple stations were missing from the raw datasets, therefore a systematic gap-filling approach was designed and applied to create a near-continuous dataset. The proposed imputation method consists of three steps, starting with a short gap interpolation method, followed by a long gap-filling process using nearby stations, and finalized by a second short gap interpolation method. This systematic data imputation approach was evaluated by creating random artificial gaps within the original datasets, filling them using the proposed data imputation method, and assessing the viability of the proposed methods using various performance metrics. The evaluation results help to ensure the reliability of the newly imputed dataset and the effectiveness of the data imputation method. The newly created dataset is a valuable resource that transcends the local cold-stunning issue, offering viable utility for analyzing temporal variability of air and water temperatures, exploring temperature interdependencies, reducing forecasting uncertainties, and refining natural resource and weather advisory decision-making processes. The cleaned dataset with minimal gaps (<2%) is ready and convenient for artificial intelligence and machine learning applications.

Trust and trustworthy artificial intelligence: A research agenda for AI in the environmental sciences.

Demands to manage the risks of artificial intelligence (AI) are growing. These demands and the government standards arising from them both call for trustworthy AI. In response, we adopt a convergent approach to review, evaluate, and synthesize research on the trust and trustworthiness of AI in the environmental sciences and propose a research agenda. Evidential and conceptual histories of research on trust and trustworthiness reveal persisting ambiguities and measurement shortcomings related to inconsistent attention to the contextual and social dependencies and dynamics of trust. Potentially underappreciated in the development of trustworthy AI for environmental sciences is the importance of engaging AI users and other stakeholders, which human-AI teaming perspectives on AI development similarly underscore. Co-development strategies may also help reconcile efforts to develop performance-based trustworthiness standards with dynamic and contextual notions of trust. We illustrate the importance of these themes with applied examples and show how insights from research on trust and the communication of risk and uncertainty can help advance the understanding of trust and trustworthiness of AI in the environmental sciences.

Hypothermic stunning of green sea turtles in a western Gulf of Mexico foraging habitat.

Texas waters provide one of the most important developmental and foraging habitats for juvenile green turtles (Chelonia mydas) in the western Gulf of Mexico, but hypothermic stunning is a significant threat and was the largest cause of green turtle strandings in Texas from 1980 through 2015; of the 8,107 green turtles found stranded, 4,529 (55.9%) were victims of hypothermic stunning. Additionally, during this time, 203 hypothermic stunned green turtles were found incidentally captured due to power plant water intake entrapment. Overall, 63.9% of 4,529 hypothermic stunned turtles were found alive, and 92.0% of those survived rehabilitation and were released. Numbers of green turtles recorded as stranded and as affected by hypothermic stunning increased over time, and were most numerous from 2007 through 2015. Large hypothermic stunning events (with more than 450 turtles documented) occurred during the winters of 2009-2010, 2010-2011, 2013-2014, and 2014-2015. Hypothermic stunning was documented between November and March, but peaked at various times depending on passage of severe weather systems. Hypothermic stunning occurred state-wide, but was most prevalent in South Texas, particularly the Laguna Madre. In the Laguna Madre, hypothermic stunning was associated with an abrupt drop in water temperatures strong northerly winds, and a threshold mean water temperature of 8.0°C predicted large turtle hypothermic stunning events. Knowledge of environmental parameters contributing to hypothermic stunning and the temporal and spatial distribution of turtles affected in the past, can aid with formulation of proactive, targeted search and rescue efforts that can ultimately save the lives of many affected individuals, and aid with recovery efforts for this bi-national stock. Such rescue efforts are required under the U.S. Endangered Species Act and respond to humanitarian concerns of the public.