Graduation Year

2025

Document Type

Thesis

Degree

M.S.

Degree Name

Master of Science (M.S.)

Degree Granting Department

Geosciences

Major Professor

Joni Downs, Ph.D.

Committee Member

Yi Qiang, Ph.D.

Committee Member

Christopher F. Meindl, Ph.D.

Keywords

Springs, Maximum Entropy, Spatial Distribution, Topographic and climatic variables

Abstract

Freshwater springs are one of the most vital groundwater sources and they have been a reliable water source for mining, irrigation, drinking, and farming, particularly in hot and arid regions like Groundwater Management Area-9 (GMA-9), Texas. However, excessive groundwater extraction, climate change, and environmental degradation have created tremendous stress on the sustainability of freshwater springs and as a result, they might not be able to meet the future water demand. This study uses the Maximum Entropy (MaxEnt) model to predict the distribution of springs in GMA-9 and to identify the key topographic and climatic factors influencing their occurrence. This study utilized a total of 244 groundwater spring outlet records combined with topographic and climatic datasets to model spring occurrence. MaxEnt algorithms effectively developed a model by calculating the complex and nonlinear relationships of these variables. The model achieved good predictive accuracy with an AUC value of 0.852 for training and 0.831 for testing data. The results indicate that the distance from rivers (71.3%) has the most influence on the results, followed by isothermality (3.4%) and annual mean temperature (2.9%). The spatial distribution maps generated by MaxEnt modeling identify the most suitable areas for spring occurrence within GMA-9. Additionally, these findings contribute to GMA-9’s sustainable management of water resources and provide an imitable management framework for areas facing water scarcity problems with similar hydrological settings.

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