Deriving storage of small and medium-sized reservoirs with elevation datasets and medium-resolution satellite imagery

Small and medium-sized reservoirs play an important role in water systems that help cope with climate variability. Although reservoirs and dams are criticized for their negative social and environmental impacts by reducing natural flow variability and obstructing river connections, they are also recognized as important for social and economic development and climate change adaptation. These reservoirs are crucial to the well-being of many societies worldwide, but regular monitoring records of their water dynamics are mostly missing. Multiple studies exist which look into the quantification of water stored in the reservoirs behind these dams. Still, very few studies focus on small and medium-sized reservoirs globally. In this research, we present the current status of the research focusing on the derivation of storage for small and medium-sized reservoirs. We use multi-annual multi-sensor satellite data with up to daily observation frequency, combined with cloud analytics, derive dynamics and storage of small (10-100ha) to medium-sized (>100ha) artificial water reservoirs globally. We derive storage by combining multiple datasets such as water occurrence, surface water area dynamics observed from space, and several elevation datasets available globally such as ALOS, NASADEM, EU-DEM. We evaluate the applicability of ICESat-2 and GEDI LiDAR sensors to estimate water storage in these reservoirs, perform validation for more than 700 reservoirs globally, and assess the applicability of these datasets to monitor water storage for more than 70 000 reservoirs globally.