Statistical Modelling of Complex Spatial Extreme Phenomena

近年来，加拿大遭受了许多大规模的自然灾害，例如2017年魁北克的洪水，2018- 19年袭击大草原的干旱或2021年夏天不列颠哥伦比亚省前所未有的野火。关于全球变暖的报告往往侧重于平均气温的预期上升，但很少强调极端气候事件的频率和强度可能增加。这些变化，再加上历史记录中极端事件的稀少，限制了我们估计未来灾害发生概率的能力。极值理论为研究罕见事件提供了一个有充分依据的框架，其单变量设置也很好理解。然而，往往是极端事件的同时发生及其聚集加剧了对风险社区的影响。因此，近年来的许多研究项目都集中在多变量和时空极值建模上。尽管取得了这些进展，但目前的空间模型无法同时处理数百个地点，并导致空间分辨率差的风险图。由于样本量小，估计数也很不确定。这直接影响到我们减轻复杂的极端自然灾害影响的能力。该研究议程提出了使用条件空间极端模型改进大规模环境空间极端事件风险评估的策略，并侧重于提高其可扩展性和灵活性。三个项目描述了这种效果，将允许使用更多的网站同时，创建更逼真的模拟，并最终允许我们结合联合收割机再分析模型输出与历史观测。这些项目将使用太平洋气候影响联合会（PCIC）的气候再分析数据以及NOAA和加拿大环境部的历史数据。该研究的长期目标是能够同时处理数千个气象站的应用，并将气候模型的信息与历史记录联合收割机结合起来，通过从开发的模型模拟的潜在极端事件目录来改善风险管理。

Canada has been hit by many large scale natural catastrophes in recent years, such as the 2017 floods in Quebec, the drought that hit the Prairies in 2018--19 or the unprecedented wildfires in British Columbia in the summer 2021. Reports on global warming often focus on the expected increase in mean temperatures, but few highlight the potential increased frequency and intensity of climate extreme episodes. These changes, together with the scarcity of extreme events in historical records, limit our ability to estimate the probability of future disasters. Extreme value theory provides a well- founded framework for studying rare events and its univariate setting is well understood. However, it is often the co--occurrence of the extreme episodes and their aggregation that exacerbates the impact on communities at risk. Many research projects in recent years have therefore focused on multivariate and spatio--temporal extreme modelling. Despite these advances, current spatial models are incapable of handling hundreds of sites simultaneously and result in risk maps with poor spatial resolution. Estimates are also highly uncertain as a result of small sample sizes. This directly affects our ability to mitigate the impact of complex extreme natural hazards. This research agenda proposes strategies to improve risk assessment for large- scale environmental spatial extreme events using the conditional spatial extreme model and focusing on increasing both its scalability and flexibility. Three projects are described to this effect that would permit usage of more sites simultaneously, create more realistic simulations and finally allow us to combine reanalysis model output with historical observations. The projects will use climate reanalysis data from the Pacific Climate Impacts Consortium (PCIC) and historical data from NOAA and Environment Canada. The long- term objective of the research is to be able to tackle applications with thousands of weather stations simultaneously and to combine information from climate models with historical records to improve risk management through a catalogue of potential extreme events simulated from the developed models.