Groundwater is the largest freshwater storage in the world. Its quantity and quality are hence of importance for ecosystems and humans. Its annual renewal potential plays an important role when quantifying (future) available water resources and provides us with a basis for future decision making in water resource management. However, groundwater recharge cannot be measured directly, and several methods have been applied in the past, trying to summarize the complex processes. We are developing quantitative tools capable of predicting groundwater recharge on a global scale under a changing climate using state-of-the art AI approaches.

There have been great progresses in the past aiming to map groundwater recharge on a global scale using different global hydrological and surface models  (see for example the figure below illustrating a simplified hydrogeological map showing the global distribution of groundwater recharge).

Many hydrological, environmental, socio-economic and climate related parameters influence groundwater recharge. Integrating the effects of all these parameters on the groundwater recharge in a physically based model is a grand challenge and close to impossible with the current knowledge and available models. Therefore, AI-based solutions are utilized in this project to map groundwater recharge considering the effects of a variety of hydrologic, environmental, climate and socio-economics parameters. One of the key objectives is to predict groundwater recharge under a changing climate. In addition to the global scale analysis, we will put a particular emphasis on the city of Hamburg. This project is a part of the Cluster of Excellence “Climate, Climatic Change, and Society” (CLICCS) which aim to establish a long-term program spanning the range from basic research on climate dynamics and climate-related social dynamics to the transdisciplinary exploration of human–environment interactions.