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

2007年的洪灾引发了英国政府的“PIT审查”，它提出了一个想法，即我们需要开始处理受影响社区上游的洪灾原因，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在景观中引入可能在减少洪水方面有好处的“自然”特征(所谓的“自然洪水管理，NFM”)。在引入这些功能后，这些组织和与它们合作的当地利益相关者越来越多地问道：“这些功能起作用了吗？”这向资助者、实施这些特征的人和科学家们强调，在单个特征(例如，单个农场池塘或一小块植树区域)如何发挥作用以及面临洪水风险的社区的潜在下游好处的证据方面存在差距。利益相关者希望这两个问题同时得到回答，这使这成为近年来水文科学家面临的最重要的学术挑战之一。在更大的尺度上量化许多单个特征的影响的唯一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。迎接这一挑战是本课题研究的重点。因此，我们的主要目标是以最可信的方式量化这些NFM特征在大流域范围内缓解洪水风险的可能有效性。在这种情况下，可信度意味着我们在使用模型解决这个问题时，在处理我们确实知道的和我们不知道的事情时保持透明和严格。为此，我们需要通过以下方式应对特殊的科学挑战：*我们需要证明，我们的模型能够再现下游洪水，同时与观察到的当地水文现象相匹配，例如土壤饱和模式。我们的方法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，通过改变关键的模型组件来预测NFM效应。对组件的这些更改是以严格的方式进行的，最初是基于当前的证据。*由于变化的证据如此关键，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，显著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，用于量化在大范围内实施NFM的不同空间范围所能提供的益处的大小。通过实现这些科学目标，我们相信我们可以在量化NFM措施在大流域范围内缓解洪水风险的可能有效性的信心方面实现一步变化。

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.