摘要
Flood inundation modelling in developing countries is severely limited by the lack of high resolution terrain data and suitable imagery to map flood extents. This study assessed the predictive uncertainty of modelled flood extents generated from TELEMAC2D model using low-cost, sparse input data commonly available in developing countries. We studied a river reach characterised by anabranching channels and river islands in eastern India. In this complex fluvial setting, we analysed computational uncertainty as a function of error in both satellite-derived flood-extent maps using a Generalised Likelihood Uncertainty Estimation (GLUE)-based approach. The model performance was quite sensitive to the uncertainty in the inflow hydrograph, particularly close to the flood peak. Evaluation of the flood inundation probability map, conditioned upon deterministic and probabilistic observed flood extents, reveals that the effect of using probabilistic observed data is only evident for portions of the model domain where the model output is free from consistent bias (over or under prediction) likely created by the imperfect terrain data.