45 Hydrogeology (Posters)

Added due to amount of discipline abstracts submitted.

Wednesday, 11 March 2026
9:00 AM - 12:00 PM
RCC, Lower Level Hall

Using Wavelet Analysis to Isolate the Signal of Tree Groundwater Use in Humid Environments with Sandy Soils

Paper number: 45-3
Program: Technical Sessions
Time: 9:00 AM – 9:00 AM

Authors

Abstract

In arid environments, trees will access groundwater during times of low precipitation to get the moisture they need. However, tree-groundwater interactions in humid environments remain understudied. Understanding when trees can and will access groundwater can inform us as to what conditions will cause them stress, allowing us to better mitigate the effects of climate change on forest health. This work explores the use of wavelet analysis to isolate the diurnal signal of tree groundwater use from depth to groundwater data and employs multiple linear regression (MLR) and random forest (RF) models to determine important features dictating tree groundwater use in humid environments. Depth to groundwater (DTG) data was obtained from 10 wells located in northern and central Wisconsin. These data were collected in 15- or 30-minute increments spanning 2015-2019. Meteorologic data such as humidity, solar radiation, temperature, and precipitation were compiled from weather stations near the well sites. Reference evapotranspiration (ETo) was estimated using the Penman-Monteith equation and tree groundwater use (ETg) was approximated using the White method. A continuous wavelet transform (CWT) was applied to DTG data to isolate data points that truly represent ETg, and to detect the power of a tree’s influence on groundwater levels across various depths. DTG data, ETo, ETg, and meteorologic variables were input into a multiple linear regression and random forest model to find major drivers of ETg. CWT and ETg estimates reveal tree groundwater usage and influence on groundwater levels decreases as DTG increases. Despite relatively low R2 (0.14-0.46), both models were able to identify important determinants of ETg. The MLR cited more meteorologic variables such as vapor pressure deficit, dewpoint temperature, and air temperature, while the RF cited DTG as the greatest influence. Wavelet analysis can be used to isolate the signal of tree groundwater use from noisy depth to groundwater data and as a method of quality control when selecting training data to model ETg, allowing future models to be more accurate. The ETg-depth gradient implies that DTG can dictate tree groundwater access and use, and therefore ecosystem health. Machine learning approaches highlight the role of both meteorologic and hydrologic variables in understanding the mechanisms behind ETg and stress the need for a physically based groundwater flow model.

Geological Society of America Abstracts with Programs. Vol. 58, No. 1

doi: 10.1130/abs/2026SE-411542

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