Woody plant encroachment has profound impacts on the sustainable management of water resources in water-limited ecosystems. However, our understanding of the effects of this global phenomenon on groundwater recharge at local and regional scales is limited. Here, we reviewed studies related to (i) recharge estimation methods; (ii) mechanisms by which woody plants impact groundwater recharge; (iii) impacts of woody plant on recharge across different soil and geology; (iv) hydrological repercussions of woody plant removal; and (v) research gaps and needs for groundwater studies. We identified six different methods: water balance, water table, isotopes, chloride mass balance, electrical geophysical imaging, and modeling were used to study the impact of woody encroachment on groundwater. Woody plant encroachment could alter soil infiltration rates, soil water storage, transpiration, interception, and subsurface pathways to affect groundwater recharge. The impact is highly variable, with the extent and the magnitude varying across the soil, substrate, plant cover, and topographic locations. Our review revealed mixed effects of woody plant removal on groundwater recharge. Studies of litter interception, root water uptake, soil moisture dynamics, and deep percolation along with the progression of woody plant encroachment are still limited, warranting further experimental studies focusing on groundwater recharge. Overall, information about woody plant encroachment impacts on groundwater resources across a range of scales is essential for long-range planning of water resources.

Groundwater models are valuable tools to quantify the response of groundwater level to hydrological stresses induced by climate variability and groundwater extraction. These models strive for sustainable groundwater management by balancing recharge, discharge, and natural processes, with groundwater level serving as a critical response variable. While traditional numerical models are labour-intensive, machine learning and deep learning offer a data-driven alternative, learning from historical data to predict groundwater level variations. The groundwater level in wells is typically recorded as continuous groundwater level time series data and is essential for implementing managed aquifer recharge within a particular region. Machine learning and deep learning are essential tools to generate a data-driven approach to modeling an area, and there is a need to understand if they are the most suitable tools to improve model prediction. To address this objective, the study evaluates two machine learning algorithms - Random Forest (RF) and Support Vector Machine (SVM); and two deep learning algorithms - Simple Recurrent Neural Network (SimpleRNN) and Long Short-Term Memory (LSTM) for modeling groundwater level changes in the West Coast Aquifer System of South Africa. Analysis of regression error metrics on the test dataset revealed that SVM outperformed the other models in terms of the root mean square error, whereas random forest had the best performance in terms of the MAE. In the accuracy analysis of predicted groundwater levels, SVM achieved the highest accuracy with an MAE of 0.356 m and an RMSE of 0.372 m. The study concludes that machine learning and deep learning are effective tools for improved modeling and prediction of groundwater level. Further research can incorporate more detailed geologic information of the study area for enhanced interpretation.

Information on the spatio-temporal variability of soil moisture in the vadose zone is important to assess groundwater recharge and solute transport in unconsolidated substrate as influenced by biological processes. Time-lapse electrical resistivity imaging (ERI) was used to monitor soil moisture dynamics to a depth of 9 m in a grassland, a grassland encroached by a juniper species (eastern redcedar,Juniperus virginiana), a juniper woodland and an oak forest in the south-central Great Plains, Oklahoma, USA. A site-specific relationship between moisture content and electrical conductivity data was developed for the soil zone, and a perched water zone was monitored at two of the sites. Results showed that (a) change in soil moisture content was linearly correlated to change in electric conductivity in the soil zone; (b) vegetation cover type induced differences in vertical bulk electrical resistivity (ER) profiles and influenced the temporal evolution of soil moisture profiles; and (c) juniper encroachment lowered the water level in the perched groundwater aquifer. Our results suggest land use and vegetation cover type, as opposed to rock properties, controls deep water drainage for the vegetation transition zone. Methods used to measure hydrogeophysical changes, such as ERI, can be used for broader understanding of geological, physical, and biological processes and their links in Earth’s critical zones.