Small and Medium‐Sized Inland Waterbodies: Water Volume Predictions and Flood Implications

Abstract Small (<10 ha) and medium (10–100 ha) inland waterbodies are crucial for water storage and flood regulation, necessitating an improved understanding of their water volume. Traditional water volume measurement methods, modeling techniques, and advanced altimetry missions struggle to capture the non‐linear volume changes in these waterbodies, leading to inaccurate and temporally sparse volume estimates. Using 86 in situ bathymetry and spatio‐temporal water spread area data, the study addresses this gap by developing a machine learning model that estimates monthly volume changes (1988–2023) in 914 waterbodies of the Adyar‐Chennai basin, India. The machine learning model demonstrated superior performance ( R 2  = 0.94), outperforming global ( R 2  = 0.57) and regional models ( R 2  = 0.24). The water volume of small and medium waterbodies in the Adyar‐Chennai basin nearly halved from ∼102.28 (95% CI: 93.38–114.28) million cubic meters (MCM) in January 1988 to ∼40.13 (32.25–61.42) MCM in December 2023, which is possibly driven by urbanization, vegetation loss, and increasing vapor pressure. The first‐ever future volumes were projected for these waterbodies ( R 2  = 0.62). While analyzing small and medium waterbodies' flood mitigation potential, the peak flood rate in the basin increased by 50% in their absence, highlighting their crucial role in flood control. To completely mitigate floods in the basin, we propose (a) creating 90 new waterbodies and (b) deepening existing waterbodies by 1 m. Suitable sites for creating new waterbodies were identified using hydrology and a land‐use‐based novel tankshed overflow index. This research advances water volume estimation and flood mitigation strategies of small and medium inland waterbodies.