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.  

Among 720 hypothetical recharge sites, we vary its location, recharge rate and size to capture various combinations of local properties in the catchment. Artificial recharge is modeled using a MODFLOW-based groundwater model, representing the geo-hydrological properties and the surface water network in the Baakse Beek catchment in the Netherlands. The recharge is simulated from October 2011 till February 2012 with the remainder of the year simulated without any artificial recharge. Based on the modeled heads, the decay rate of stored water is calculated for the period until October. This calculated decay rate, in combination with the local properties are used to train and evaluate the ML model. The relative contributions of properties to the decay rate are quantified using the latest developments in explainable AI techniques. Techniques such as permutation importance and Ceteris paribus profiles not only help categorize the suitability of potential recharge sites but also quantify the relative contribution of each property. By leveraging these insights, water managers can make informed decisions regarding site improvement measures.