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

洪水模型和预报是为基础设施和应急规划提供信息的基本工具。然而，准确的预测是很难实现的，即使在发达国家拥有几十年的经验和用于校准和验证的详细地形和水文数据集，最近的坎布里亚洪水就证明了这一点。在大型热带地区，预报更具挑战性，因为这些地区的数据可获得性有限(例如，河流测量站很少)，气象预报能力有限。更令人担忧的是，其庞大的人口、重要的经济产业(如石油/天然气、农业)和重要的生态栖息地意味着洪水与其他多种重大风险有关。该博士项目将尝试通过以下方式克服其中一些挑战：(1)使用新的近实时、高分辨率卫星数据集来改进对数据匮乏的热带地区的概率集合洪水预报所产生的中短期洪水风险评估，以及(2)将洪水模型应用于洪水引起的污染风险评估。该项目的案例研究将是墨西哥的塔巴斯科州，该州地势低洼，地形复杂，几条大河流(如格里贾尔瓦-乌苏马辛塔系统，1,911公里长的网络，128,390平方公里的集水区)发生洪水

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