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

基本信息

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：
2010
资助国家：
加拿大
起止时间：
2010-01-01 至 2011-12-31
项目状态：
已结题

来源：
https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=459411
关键词：
Uncertainty analysis detection structural changes

项目摘要

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.

数值技术的进展以及更高的处理速度，促使使用复杂的环境模型用于研究，设计和计划目的。如今，强大的计算机代码可用于描述几乎所有感兴趣的环境过程。随着全球变暖的灾难性影响的证据以惊人的速度增长，目前的做法是使用这些模型使用观察到的数据来校准后的估计。但是，很少考虑一致性的问题：在当前气候下表现较好的模型是否会在变化的气候中提供值得信赖的结果？在水文学中，几乎所有类别的模型都已被使用，不幸的是，如果在未来的气候条件下所选择的环境模型的适合性，则很少关注。黑框模型应在此类研究中谨慎使用，因为众所周知，黑盒模型的参数捕获了许多与校准条件有关的信息。在这种情况下，在流域对气候的反应中的一致性的隐含假设不存在。后一种陈述原则上适用于物理驱动和概念模型，这些模型始终涉及一些参数化和校准。由于全球变暖以前所未有的方式推动了地球的气候，因此需要洞察力就可以预期在未来气候中如何执行特定模型。