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Quantifying the likely magnitude of nature-based flood mitigation effects across large catchments (Q-NFM)

量化大型流域基于自然的防洪效果的可能程度 (Q-NFM)

基本信息

批准号：
NE/R004722/1
负责人：
Nick Chappell
金额：
$ 174.36万
依托单位：
Lancaster University
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=NE%2FR004722%2F1
关键词：
Quantifying likely magnitude nature based

项目摘要

The 2007 floods prompted the UK Government's "Pitt review", which came up with the idea that we need to start to deal with the causes of flooding upstream of the affected communities, rather than rely solely on the downstream engineering solutions. This stimulated a range of organisations to introduce "natural" features into the landscape that may have benefits in terms of reducing flooding (so called "Natural Flood Management, NFM"). Having introduced features these organisations, and local stakeholders working with them, are increasingly asking "Are these features working?" This has highlighted to funders, those implementing the features and scientists alike that there are gaps in the evidence of how individual features (e.g. a single farm pond or a small area of tree planting) work and what are potential downstream benefits for communities at risk of flooding. Stakeholders want both questions answered at the same time, making this one of the most important academic challenges for hydrological scientists in recent years. The only way to quantify the effects of many individual features at larger scales is to use computer models. To be credible, these models also need to produce believable results at individual feature scales. Meeting this challenge is the focus of this research project. Consequently, our primary objective is to quantify the likely effectiveness of these NFM features for mitigating flood risk at large catchment scales in the most credible way. In this context, credibility means being transparent and rigorous in the way that we deal with what we do know and what we don't know when addressing this problem using models. In doing this we need to address particular scientific challenges in the following ways:* We need to show that our models are capable of reproducing downstream floods while at the same time matching observed local hydrological phenomena, such as patterns of soil saturation. Integral to our methodology are observations of these local phenomena to further strengthen the credibility of the modelling.* We use the same models to predict NFM effects by changing key model components. These changes to the components are made in a rigorous way, initially based upon the current evidence. * As evidence of change is so critical, our project necessarily includes targeted experimental work to address some of the serious evidence gaps, to significantly improve the confidence in the model results.* This rigorous strategy provides us with a platform for quantifying the magnitude of benefit that can be offered by different spatial extents of NFM implementation across large areas.By addressing these scientific goals we believe that we can deliver a step change in the confidence of our quantification of the likely effectiveness of NFM measure for mitigating flood risk at large catchment scales.

2007年的洪水促使英国政府的“皮特审查”，提出了这样一个想法，即我们需要开始处理受影响社区上游的洪水原因，而不是仅仅依靠下游的工程解决方案。这刺激了一系列组织将“自然”特征引入景观中，这可能有助于减少洪水（所谓的“自然洪水管理，NFM”）。在引入了这些功能之后，这些组织以及与他们合作的当地利益相关者越来越多地问“这些功能是否有效？“这向资助者、实施这些功能的人和科学家们强调，在个别功能（例如单个农场池塘或小面积植树）如何工作以及对面临洪水风险的社区有哪些潜在下游利益的证据方面存在差距。利益相关者希望同时回答这两个问题，这使得这成为近年来水文科学家面临的最重要的学术挑战之一。要在更大的尺度上量化许多单个特征的影响，唯一的方法是使用计算机模型。为了可信，这些模型还需要在各个特征尺度上产生可信的结果。迎接这一挑战是本研究项目的重点。因此，我们的主要目标是量化这些NFM功能的可能有效性，以最可靠的方式在大流域尺度上减轻洪水风险。在这种情况下，可信性意味着在使用模型解决这个问题时，我们处理我们所知道的和我们不知道的事情的方式是透明和严格的。在这样做的过程中，我们需要通过以下方式解决特定的科学挑战：* 我们需要证明我们的模型能够再现下游洪水，同时匹配观察到的当地水文现象，如土壤饱和度模式。我们的方法不可或缺的是对这些局部现象的观察，以进一步加强建模的可信度。我们使用相同的模型来预测NFM的影响，通过改变关键的模型组件。这些组件的更改是以严格的方式进行的，最初是基于当前的证据。* 由于变化的证据至关重要，我们的项目必须包括有针对性的实验工作，以解决一些严重的证据差距，以显着提高对模型结果的信心。*这一严格的战略为我们提供了一个平台，量化的好处，可以通过不同的空间范围内的NFM实施跨越大区域提供的幅度。通过解决这些科学目标，我们相信，我们可以提供一个步骤的变化，我们的信心量化的可能有效性NFM措施，以减轻洪水风险在大流域尺度。