Inferring floodplain bathymetry using inundation frequency - Park, Edward (2020)
This study proposes a new method to retrieve the bathymetry of turbid-water floodplains from the inundation frequency (IF) data derived from over 32 years of composite optical remote sensing data. The new method was tested and validated over the Curuai floodplain in the lower Amazon River, where the entire bathymetry was surveyed in 2004, and water level gauge data has been available since 1960. The depth was estimated based on the relationship derived from IF and surveyed depth data, and the results were compared to those retrieved from bare-Earth DEM. We further assessed the sensitivity of the approach by analyzing the deepest part of the lake (i.e., permanent water body similar to 8m) with high IF, as well as the effect of gradual sedimentation in the lake over time. The results showed that the model is highly accurate and sensitive to IF changes even in the permanent water body areas, suggesting that this model can be used in other seasonal lakes worldwide with turbid-waters, where large-scale bathymetry surveys are not feasible due to high operation costs.