- Helmholtz-Zentrum Hereon
For some time, large scale analyses and data-driven approaches have become increasingly popular in all research fields of hydrology. Many advantages are seen in the ability to achieve good predictive accuracy with comparatively little time and financial investment. It has been shown by previous studies that complex hydrogeological processes can be learned from artificial neural networks, whereby Deep Learning demonstrates its strengths particularly in combination with large data sets. However, there are limitations in the interpretability of the predictions and the transferability with such methods. Furthermore, most groundwater data are not yet ready for data-driven applications and the data availability often remains insufficient for training neural networks. The larger the scale, the more difficult it becomes to obtain sufficient information and data on local processes and environmental drivers in addition to groundwater data. For example, groundwater dynamics are very sensitive to pumping activities, but information on their local effects and magnitude – especially in combination with natural fluctuations – is often missing or inaccurate. Coastal regions are often particularly water-stressed. Exemplified by the important coastal aquifers, novel data-driven approaches are presented that have the potential to both contribute to process understanding of groundwater dynamics and groundwater level prediction on large scales considering local processes.