Uncertainty Assessments of Flood Inundation Impacts: Using spatial climate change scenarios to drive ensembles of distributed models for extremes

洪水淹没影响的不确定性评估：利用空间气候变化情景驱动极端分布式模型集合

• 批准号: NE/E002293/1
• 负责人: Matthew Wilson
• 金额: $ 2.76万
• 依托单位: UNIVERSITY OF EXETER
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FE002293%2F1
• 关键词: Uncertainty; Assessments; Flood; Inundation; Impacts

Exploratory climate change studies for the UK indicate that an increase in the frequency of extreme events and associated flood risk is likely. Given that floods cause damage of over £1bn per year under present climatic conditions, climate change bears significant consequences for flood risk management. In order to evaluate these consequences, hydrological and flood inundation models are forced with projections of precipitation from atmospheric models for a range of Greenhouse gas emission scenarios to produce future flood predictions. However the validity and uncertainty of these model-based input precipitation fields are of key concern, as they potentially constitute a major source of ambiguity for hydrological and hydraulic modelling. Additionally, uncertainty is associated with the hydrological and inundation models themselves, such as for example the models ability to represent the dominating physical processes and to uniquely identify effective model factors (parameters and any other model variables) that will shape future forecasts. As the non-linear interaction of all model components will influence the total uncertainty associated with hydrological impact assessments these need to be comprehensively assessed. Therefore, a key and exciting challenge is to describe and quantify the origin and propagation of uncertainty from climate to hydrological to flood inundation models. This project aims to develop a novel holistic modelling approach for doing this. Our region of focus will be the River Severn catchment because of concerns about current and future flood risk. Specifically we will: (1) Quantify the 'top-end' uncertainties associated with climate change hydrological impact assessments by analyzing precipitation fields produced by two contrasting methods and assess how these affect the nature of flood and inundation predictions (2) Evaluate all uncertainties between and within a cascade modeling framework for flood inundation predictions in a fully coupled and dynamic way (3) Use novel techniques of uncertainty analysis including global sensitivity analysis and a new efficient functional similarity sampling approach to enable an effective evaluation of the uncertainties in the modeling cascade. (4) Assess the likely flood hazard change for the River Severn catchment over the next 100 years for various climate, landuse and soil moisture scenarios This project will deliver an insightful scientific methodology which can be used in future research assessments and catapult UK science to the forefront of an exciting, socially, and politically important international research area.

英国的探索性气候变化研究表明，极端事件和相关洪水风险的频率可能会增加。鉴于在目前的气候条件下，洪水每年造成的损失超过10亿GB，气候变化对洪水风险管理具有重大影响。为了评估这些后果，水文和洪水淹没模型被迫使用大气模型对一系列温室气体排放情景的降水预测，以产生未来的洪水预测。然而，这些基于模型的输入降水场的有效性和不确定性是一个关键问题，因为它们可能构成水文和水力模拟的一个主要模糊性来源。此外，不确定性与水文和洪水模型本身有关，例如，模型表示主要物理过程和唯一确定将影响未来预报的有效模型因素(参数和任何其他模型变量)的能力。由于所有模型组成部分之间的非线性相互作用将影响与水文影响评估有关的总不确定性，因此需要对其进行全面评估。因此，一个关键和令人兴奋的挑战是描述和量化从气候到水文再到洪水淹没模型的不确定性的来源和传播。该项目旨在开发一种新的整体建模方法来实现这一点。出于对当前和未来洪水风险的担忧，我们的重点地区将是塞文河流域。具体地说，我们将：(1)通过分析两种不同方法产生的降水场来量化与气候变化水文影响评估相关的“高端”不确定性，并评估这些不确定性如何影响洪水和淹没预测的性质；(2)以完全耦合和动态的方式评估洪水淹没预测的梯级模型框架之间和框架内的所有不确定性；(3)使用新的不确定性分析技术，包括全局敏感性分析和新的高效功能相似抽样方法，以便能够有效地评估模型梯级中的不确定性。(4)评估塞文河流域未来100年在各种气候、土地利用和土壤湿度情景下可能发生的洪水风险变化。该项目将提供一种富有洞察力的科学方法，可用于未来的研究评估，并将英国科学推向一个令人兴奋的、社会和政治上重要的国际研究领域的前沿。