New model for measuring global water storage

In their recent publication in Nature Water, D-BAUG researchers Junyang Gou and Professor Benedikt Soja introduced a finely resolved model of terrestrial water storage using a novel deep learning approach. By integrating satellite observations with hydrological models, their method achieves remarkable accuracy even in smaller basins. This model promises significant benefits across various domains, including hydrology, climate science, sustainable water management, and hazard prediction.

by Editorial office
Image of satelite mission

Reference

Gou, Junyang; Soja, Benedikt
external pageGlobal high-resolution total water storage anomalies from self-supervised data assimilation using deep learning algorithms
Nature Water (2024), doi: 10.1038/s44221-024-00194-w

Commentary

Sun, Alexander
external pageLearning to downscale satellite gravimetry data through artificial intelligence
Nature Water (2024), doi: 10.1038/s44221-024-00199-5

Further information

About the study and the Chair of Professor Benedikt Soja

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