Topography is critical information for water resources management in lakes, and remote sensing provides a unique opportunity to estimate it in ungauged regions. We introduce here a new method which estimates near shore topography of water bodies based on a flood frequency map and time series of water levels by assuming the equivalence between flood frequency and water level exceedance probability at a given area. Test cases are performed for two lakes and 12 hydropower reservoirs in Brazil using the proposed Flood2Topo app. This new application generates the bottom level pixel by pixel and a level-area-active storage relationships directly from the topography map, without the need to fit functions. Flood extent estimates from the Landsat based JRC Global Surface Water (GSW) dataset, current state-of-the-art, were used to run Flood2Topo, together with water levels from satellite altimetry and in-situ gauges. Results show bottom level root mean square deviation (RMSD) values of 18.5 cm and 146 cm for Lake Poopó (Bolivia) and Lake Curuai (Amazon basin), respectively. For reservoir active storage, RMSD normalized values ranged from 2% to 11.09% for 11 reservoirs (average NRMSD of 6.39 %). The method can be applied to any area seasonally flooded, for instance, it is applicable in 35.8 % (86%) of the global water surface area mapped by occurrence map from GSW dataset, when considering the number of pixels with occurrence between 0 and 95% (99%) over 35 years. This is a promising tool for obtaining data for hydrodynamic simulations and monitoring of ungauged water bodies.