Musaab A. A. Mohammed, Norbert P. Szabó, Joseph O. Alao, Péter Szűcs

Our recent research, developed within the Sustainable Development and Technologies National Program (FFT NP FTA), presents a satellite- and machine learning–based framework for monitoring groundwater storage changes in Hungary. It integrates GRACE/GRACE-FO gravimetry with GLDAS land surface data to overcome the limitations of sparse in-situ monitoring networks and to quantify regional groundwater storage anomalies over time.

Results show a clear nationwide pattern of long-term groundwater decline, especially in the Great Hungarian Plain, driven by intensive agricultural and urban abstraction. Western karst regions exhibit highly variable recharge–discharge dynamics, while northern mountainous areas remain comparatively stable due to higher natural recharge.

Within the broader project, the work supports the development of scalable, data-driven tools for water resource assessment under climate variability. It provides a practical basis for improving groundwater management strategies, supporting policy decisions, and extending similar monitoring approaches to other data-scarce regions.

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