Photo by Ed Rhodes, TWRI.
Peer-reviewed publications by Texas A&M AgriLife scientists
Simulating the Effect of Climate Change Scenarios on Surface Water Quality in the Bosque Watershed, Central Texas, United States: Coauthored by Texas A&M AgriLife’s Vijay P. Singh, Ph.D., this research used robust climate change scenarios and hydrological modeling to simulate the impacts of climate change on the Bosque River watershed’s water quality. Streamflow, organic nitrogen, organic phosphorus, mineral phosphorus, and nitrate concentration were simulated under different climate change scenarios using the Soil and Water Assessment Tool (SWAT). The results showed significant water quality impacts, especially on nitrate concentrations, under climate change scenarios.
Comparative evaluation of daily streamflow prediction by ANN and SWAT models in two karst watersheds in central south Texas: This recent research compared the accuracy of streamflow estimated by a data-driven Artificial Neural Network (ANN) and the physically based Soil and Water Assessment Tool (SWAT). Coauthored by Texas A&M AgriLife’s Patricia K. Smith, Ph.D., the study included one urbanized watershed and one nonurban, both with prevalent karst geologic features, and it found that the models both predicted streamflow poorly in the nonurban, forested watershed. The results suggest that an ANN model may be useful for short-term streamflow forecasting in watersheds with many karst features where the complex processes of rapid groundwater recharge and discharge strongly influence surface water flow.
Water-related research and news from universities around Texas
Linear and nonlinear ensemble deep learning models for karst spring discharge forecasting: Coauthored by Texas A&M University and University of Sam Houston researchers, this paper examines the forecasting of karst spring discharge, an important tool for groundwater resource management in karst aquifers. The researchers used three novel deep learning models to forecast daily spring discharge across various lead times and time steps at Barton Springs, Texas. They found that ensemble models outperformed individual base models in prediction accuracy and consistency.
A 10-year Metocean dataset for Laguna Madre, Texas, including for the Study of Extreme Cold Events: Conducted by Texas A&M University-Corpus Christi scientists, this study sought to fill gaps in hydrological and atmospheric datasets for the Upper Laguna Madre. The newly created dataset offers 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, as well as machine learning applications.