An automated approach for near-real time simulation and geo-visualization of flooding, including estimation of affected infrastructures, in Lake Mainit, considered the Philippines' deepest lake is presented. Perennial flooding in several areas around the lake due to increase in the lake's water level during the rainy season and during the passing of tropical storms exemplified the need for rapid determination of the lake's current and future water levels, and more importantly, the depth and extent of flooding that will result from the increase in water level. The approach made use of LiDAR-derived topography of the lake's coastal zone, lake bathymetry, near-real time lake water level and rainfall information from monitoring stations, and a hydrological model. The synergistic combination of these datasets and techniques resulted to automated and near-real time generation of current and future (forecasted) flood depths and extents, which can be viewed in a web-based geo-visualization platform. It also allows estimation of infrastructures that are affected by a current or future flooding scenario. This platform can be used as an early warning system for communities residing near the lake.
['Jojene R. Santillan', 'Meriam Makinano-Santillan', 'Arthur M. Amora', 'Edsel Matt O. Morales', 'Linbert C. Cutamora', 'Lorie Cris S. Asube']
Near-real time simulation and geo-visualization of flooding in the Philippines' deepest lake