Examination of Climatic Controls on Groundwater Elevation in Unconfined Aquifers Within the Piedmont of Georgia

Four monitoring wells in the Metro Atlanta region were used in this study to investigate how climate influences groundwater variability in unconfined aquifers in the Piedmont Physiographic Province. Antecedent groundwater levels and short- and long-term antecedent climatic variables (temperature and precipitation) were used as predictors in the development of multiple linear regression models. Variable selection was governed by the Bayesian Information Criterion (BIC), which balanced prediction accuracy and model complexity. Strong model performance is demonstrated by the results, with R2 values exceeding 0.9 for all models which show that both short-term rainfall events and long-term climatic patterns play a crucial role in predicting groundwater levels. The results highlight the importance of antecedent groundwater levels and climatic factors in capturing groundwater dynamics under various site settings.

Publication Details

Journal
ScholarWorks - Georgia State University (Georgia State University)
Published
2025-07-15
DOI
https://doi.org/10.57709/f4qp-fc04
Primary Topic
Hydrological Forecasting Using AI
Type
article
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article

Examination of Climatic Controls on Groundwater Elevation in Unconfined Aquifers Within the Piedmont of Georgia

ScholarWorks - Georgia State University (Georgia State University)
Hydrological Forecasting Using AI
article

Examination of Climatic Controls on Groundwater Elevation in Unconfined Aquifers Within the Piedmont of Georgia

article en

Abstract

Four monitoring wells in the Metro Atlanta region were used in this study to investigate how climate influences groundwater variability in unconfined aquifers in the Piedmont Physiographic Province. Antecedent groundwater levels and short- and long-term antecedent climatic variables (temperature and precipitation) were used as predictors in the development of multiple linear regression models. Variable selection was governed by the Bayesian Information Criterion (BIC), which balanced prediction accuracy and model complexity. Strong model performance is demonstrated by the results, with R2 values exceeding 0.9 for all models which show that both short-term rainfall events and long-term climatic patterns play a crucial role in predicting groundwater levels. The results highlight the importance of antecedent groundwater levels and climatic factors in capturing groundwater dynamics under various site settings.

ScholarWorks - Georgia State University (Georgia State University)
Climate action
Openalex Percentile: Top 58%
Hydrological Forecasting Using AI
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