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

使用高分辨率卫星数据对洪水预报进行近实时修正，以进行低地热带地区的多灾种风险评估。

基本信息

批准号：
1819435
负责人：
金额：
--
依托单位：
CRANFIELD UNIVERSITY
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1819435
关键词：
Near real time correction flood

项目摘要

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

洪水建模和预测是基础设施和应急规划的重要工具。然而，准确的预测是很难实现的，即使是在发达国家，有几十年的经验和详细的地形和水文数据集的校准和验证，最近的坎布里亚郡洪水证明。在大面积热带地区，预报更具挑战性，因为这些地区的数据有限（例如河流测量站很少），气象预报能力也不强。更令人担忧的是，它们庞大的人口、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。该博士项目将试图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，以改善数据匮乏的热带地区概率集合洪水预报所产生的中期和短期洪水风险评估，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塔巴斯科州，该州占地面积大，地势低洼，地形复杂，遭受几条受影响的大河（例如Grijalva-Usumacinta系统，1，911公里长的网络，128，390平方公里的集水区）的洪水