Near real-time correction of flood forecasting using high-resolution satellite data for multi-hazard risk assessment in lowland tropical regions.

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Flood modelling and forecasting are essential tools to inform infrastructure and emergency planning. Accurate forecasts, though, are difficult to achieve, even in developed countries with decades of experience and detailed topographical and hydrological datasets for calibration and validation, as demonstrated by the recent Cumbria floods. Forecasting is even more challenging in large tropical regions, which have limited data availability (e.g. few river gauging stations), and modest meteorological forecasting capabilities. More worryingly, their large human populations, economically-important industries (e.g. oil/gas, agriculture), and ecologically-important habitats mean that flooding is connected to multiple other significant risks. This PhD project will attempt to overcome some of these challenges by (1) using new near real-time, high resolution satellite datasets to improve the medium- and short-term flood risk assessment generated by probabilistic ensemble flood forecasting for data-poor tropical regions, and (2) applying the flood model to the assessment of flood-induced pollution risk. The case study for the project will be the Mexican State of Tabasco, which occupies a large, low-lying, topographically-complex area that experiences flooding from several large rivers (e.g. Grijalva-Usumacinta systems, 1,911 km long network, 128,390 km2 catchment area) which are affected