Regional Water Stock Monitoring Hybridizing Remote Sensing and Ai Approaches in the Guadalquivir Basin, Spain

Operational monitoring of total water stocks in reservoirs at a large scale suffers from a lack of knowledge on the bathymetry of such water bodies. When known, a measured water area can be directly linked to the total water volume. Digital Elevation Models can be very useful to this regard, but often reservoirs bathymetry is not visible since they are filled with water. In such a context, recent developments in Artificial Intelligence might be helpful. Using an inpainting approach, a U-Net algorithm was trained on several thousands of virtual lakes to estimate reservoirs bathymetry, using a true DEM as inputs. Its capacity to determine accurate bathymetry and derived surface to volume relationship has been tested and assessed over the Guadalquivir basin in Southern Spain. The R
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score of 0.79 obtained over one reservoir on volume estimation along time is encouraging for the future operational monitoring of the whole basin.