Machine Learning Insights into Aquifer Recharge: Site suitability analysis in season water availability scenarios

“Groundwater models are valuable tools for optimising decisions that influence groundwater flow. Spatially distributed models represent groundwater levels across the entire area from where essential information can be extracted, directly aiding in the decision-making process. In our previous study, we explored different machine learning (ML) models as faster alternatives to predict the increase in stationary groundwater head due to artificial recharge in unconfined aquifers while considering a wider spatial extent (832 columns x 1472 rows, totalling 765 km2) than previous ML groundwater models. The trained ML model accurately estimates the increase in groundwater head within 0.24 seconds, achieving a Nash-Sutcliffe efficiency of 0.95. This allows quick analysis of site suitability at potential recharge rates. This study extends the approach to incorporate seasonal variation in water availability, illustrating the concept of storing excess water during winter to meet heightened demands during summer, when water availability is minimal. Additionally, we quantify the impacts of the local properties, geohydrological and surface water network properties, on the storage capacity by training ML models on estimating the summer decay rate of stored water in hypothetical aquifer recharge sites.”

Citation: Fernandes, V., de Louw, P., Ritsema, C., and Bartholomeus, R.: Machine Learning Insights into Aquifer Recharge: Site suitability analysis in season water availability scenarios, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21725, https://doi.org/10.5194/egusphere-egu24-21725, 2024)

© Author(s) 2024. This work is distributed under the Creative Commons Attribution 4.0 License.