Large-scale groundwater drought recovery assessment using a 1km global groundwater model

Assessing human influence on groundwater resources globally is a complex challenge, particularly when attempting to disentangle human impacts on groundwater drought dynamics. These impacts may also have a strong influence on groundwater recovery after drought periods, where intensification of groundwater pumping could lead to longer recovery periods. With the GLOBGM v1.0, a 1km global groundwater model (1), we investigate the groundwater drought recovery at different spatial scales and various locations, with and without human influences to see if we can disentangle these signals.

While such large-scale physically-based models are valuable for simulating underlying processes, they are often computationally intensive, especially when simulating at high spatial resolutions up to 1km globally, and rely on more coarser information than locally informed models. To improve future drought recovery insights, a groundwater surrogate model is created that can reproduce groundwater fields as generated by GLOBGM. Integrating machine learning and physically-based models (hybrid approaches) offer a promising solution to not only reduce computational demands but also allow for the integration of observational data. Specifically, we will merge information from observations and the hybrid model to enhance the model's accuracy in representing human influences on drought recovery.

Ultimately, the surrogate model will help us extend the current groundwater drought recovery analysis in the future by enabling the analysis of drought dynamics and human impacts using scenario analyses under different socio-economic forcings.

References:

• Verkaik, J., Sutanudjaja, E. H., Oude Essink, G. H. P., Lin, H. X., and Bierkens, M. F. P.: GLOBGM v1.0: a parallel implementation of a 30 arcsec PCR-GLOBWB-MODFLOW global-scale groundwater model, Geosci. Model Dev., 17, 275–300, https://doi.org/10.5194/gmd-17-275-2024, 2024.