A new approach to navigating uncertainty in climate-related hydrologic risk

应对气候相关水文风险不确定性的新方法

• 批准号: 2284748
• 金额: --
• 依托单位: University of Reading
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2284748
• 关键词: new; approach; navigating; uncertainty; climate

Projections of future climate at the local scale are highly uncertain. This is partly because future emissions of greenhouse gases are uncertain (and to a degree unknowable), but largely because different climate models simulate quantitatively and qualitatively different changes in weather for a given forcing. The conventional approach to assessing uncertainty has been to use increasingly large ensembles of scenarios derived from multiple models. At the extreme, the UKCP09 climate projections are based on 10,000 scenarios. Additional uncertainty is added by boundary conditions, downscaling methods, choice of impacts models and model parameterization. The propagating uncertainties that result from these decisions has been conceptualized as the "cascade of uncertainty" (Wilby & Dessai 2010 Weather). It can be challenging to apply such big data to real-world risk assessments and adaptation decisions, and attitudes to confronting this "uncertainty monster" (Van der Sluijs 2005 Water Sci. Technol.) are varied. Navigating the cascade of uncertainty is an overwhelming task (Smith et al 2018 J. Extreme Events), and there are increasing calls for new approaches to organize climate risk information in ways that align better with policy needs (e.g. Kennel et al. 2016 Science). One such approach develops and uses a small number of 'storylines' to characterise risk (Clark et al. 2016 Curr. Clim. Change Rep.). A storyline is a plausible pathway, without any a priori probability attached. Storylines offer the benefit of allowing an end-to-end quantitative analysis and thereby incorporating compound risk, which is difficult to do within a probabilistic framework in the face of the cascade of uncertainty. Storylines are also easy for lay people to understand, and so provide a natural language for communication in the policy arena.This project develops the storyline approach, focusing on future drought risk in the United Kingdom. The proposed concept is to navigate the cascade of uncertainty to analyse and bound the system components contributing to climate influence on drought risk, and develop storylines that crystallize that risk (see details in Section 1d). The storylines will be developed to characterize the full range of potential changes in climate that are relevant to drought occurrence (such as change in the frequency of successive dry winters, and delays to the start of the winter recharge season). UK climate projections 2018 (UKCP18) will contain probabilistic projections, as well as ensembles of simulations, at global, regional, and national scale. In this project, using expertise from supervisors at the University of Reading and the Centre for Ecology & Hydrology, the storyline approach will be applied to these climate projections and propagated through hydrological models. Working with Anglian Water, these results will then be applied to water resources and reservoir yield models in order to stress test current water resource management plans, and develop drought risk assessments. This work has the potential for direct application in policy via drought risk management plans in the Anglian region, and can provide widely transferable methods to help better manage climate change impacts on drought risk across the UK.

在局部尺度上对未来气候的预估是高度不确定的。这部分是因为未来的温室气体排放是不确定的（在一定程度上是不可知的），但主要是因为不同的气候模式对给定强迫的天气变化进行了不同的定量和定性模拟。评估不确定性的传统方法是使用来自多个模型的越来越大的情景集合。在极端情况下，UKCP09的气候预测是基于1万种情景。边界条件、降尺度方法、影响模型的选择和模型参数化增加了额外的不确定性。这些决策导致的不确定性传播被概念化为“不确定性级联”（Wilby & Dessai 2010 Weather）。将这样的大数据应用于现实世界的风险评估和适应决策，以及面对这个“不确定性怪物”的态度，可能是具有挑战性的。技术)是多种多样的。在不确定性的级联中导航是一项艰巨的任务（Smith et al. 2018 J. Extreme Events），越来越多的人呼吁采用新的方法来组织气候风险信息，使其更符合政策需求（例如Kennel et al. 2016 Science）。其中一种方法是开发并使用少量的“故事情节”来描述风险(Clark et al. 2016 Curr。爬。改变众议员)。故事情节是一种看似合理的途径，没有任何先验的可能性。故事情节提供了允许端到端定量分析的好处，从而结合了复合风险，这在面对不确定性级联的概率框架中很难做到。故事情节对外行来说也很容易理解，因此为政策领域的沟通提供了一种自然的语言。本项目发展了故事线方法，重点关注英国未来的干旱风险。所提出的概念是在不确定性级联中进行导航，分析和结合导致气候对干旱风险影响的系统组成部分，并制定将风险具体化的故事情节（详情见第1d节）。将编写故事情节，以描述与干旱发生有关的所有潜在气候变化（例如连续干旱冬季频率的变化，以及冬季补给季节开始的延迟）。英国气候预测2018 （UKCP18）将包含全球、区域和国家尺度的概率预测和模拟集合。在这个项目中，利用雷丁大学和生态与水文学中心的导师的专业知识，故事线方法将应用于这些气候预测，并通过水文模型进行推广。与Anglian Water合作，这些结果将应用于水资源和水库产量模型，以便对当前的水资源管理计划进行压力测试，并开发干旱风险评估。这项工作有可能通过在盎格鲁地区的干旱风险管理计划直接应用于政策，并可以提供广泛可转移的方法，以帮助更好地管理气候变化对整个英国干旱风险的影响。