Real-time monitoring of river discharge and lake water levels is critical for effective water resource management and disaster mitigation. However, the global scarcity of in-situ hydrological stations and underwater terrain data significantly limits our understanding of hydrological processes, especially in the context of climate change. This study introduces innovative remote sensing-based methods to simulate underwater topography, estimate river discharge, and assess lake water storage, addressing key challenges in hydrology.
For lakes, a three-dimensional (3D) topography simulation approach is developed based on the assumption of topographical continuity above and below the water surface, utilizing digital elevation models (DEM). This method enables large-scale water storage estimation in regions with limited or no subsurface data. Validation using underwater measurements from twelve lakes on the Qinghai-Tibet Plateau demonstrates a relative error of −11.99% in total water storage estimation, with average and maximum relative errors in water depth of −19.50% and −5.64%, respectively. These results confirm the method’s feasibility for large-scale hydrological applications.
For rivers, a novel remote sensing-based discharge estimation method is proposed, independent of in-situ hydrological parameters such as flow velocity and cross-sectional topography. The approach employs Manning’s formula with remotely sensed dynamic water surface area and 3D above-water topography data. Experimental validation at the Yellow River Guide hydrological station shows a strong correlation with measured discharge (R² = 0.72), and high accuracy (relative accuracy of 84.4%).
By constructing a global dataset for lakes over 50 km² and deploying virtual hydrological stations in key Chinese river basins, this study provides scalable, data-driven solutions for hydrological monitoring. These advancements hold significant potential for improving water resource management and disaster preparedness in data-scarce regions worldwide.