Uncertainty analysis and detection of structural changes in environmental systems dynamics for a better understanding of climatic and anthropogenic risks

环境系统动力学结构变化的不确定性分析和检测，以更好地了解气候和人为风险

• 批准号: 355495-2008
• 负责人: Seidou, Ousmane
• 金额: $ 1.31万
• 依托单位: University of Ottawa
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=505154
• 关键词: Uncertainty; analysis; detection; structural; changes

Progress in numerical technologies, as well as greater processing speed, has prompted the use of complex environmental models for research, design and planning purposes. Nowadays, powerful computer codes are available to describe nearly any environmental process of interest. As evidence for catastrophic impact of global warming grows at an alarming rate, the current practice is to use these models for impacts estimation after calibration using observed data. However, the issue of consistency is rarely considered: will a model that is performing relatively well in the current climate provide trustworthy results in a changed climate? In hydrology, nearly all categories of models have been used, unfortunately with very little attention if any to the suitability of the chosen environmental model under future climatic conditions. Black-box models should be used with extreme caution for such studies, since it is well known that the parameters of black-box models capture a lot of information related to the calibration conditions. In this case, the implicit assumption of consistency in a watershed's response to climate does not hold. The latter statement applies, in principle at a lesser extent, to physics-driven and conceptual models, which always involve some parameterisation and calibration. Since global warming is driving earth's climate in an unprecedented manner, insight is needed as to how a particular model can be expected to perform in the future climate.

数字技术的进步，以及更快的处理速度，促使人们使用复杂的环境模型进行研究，设计和规划。如今，强大的计算机代码可以用来描述几乎所有感兴趣的环境过程。由于全球变暖的灾难性影响的证据以惊人的速度增长，目前的做法是在使用观测数据进行校准后使用这些模型进行影响估计。 然而，一致性问题很少被考虑：在当前气候中表现相对较好的模型在变化的气候中是否会提供值得信赖的结果？在水文学中，几乎所有类别的模型都被使用，不幸的是，很少注意到所选择的环境模型在未来气候条件下的适用性。在这类研究中应极其谨慎地使用黑盒模型，因为众所周知，黑盒模型的参数捕获了与校准条件有关的大量信息。在这种情况下，流域对气候的反应的一致性的隐含假设并不成立。 后一种说法原则上在较小程度上适用于物理驱动和概念模型，这些模型总是涉及一些参数化和校准。由于全球变暖正在以前所未有的方式推动地球气候，因此需要了解特定模型在未来气候中的表现。