Quantifying the efficacy of in-situ Natural Flood Management (NFM) through monitoring and model predictions including uncertainty

自然洪水管理（NFM）是一种旨在通过保护、恢复和模拟集水区、河流、洪泛平原和海岸的自然功能来降低洪水风险的方法。NFM是正在考虑的一系列缓解措施之一，旨在提高受洪水影响的下游社区的复原力。人们普遍认为，为了将NFM部署为管理洪水风险的标准方法，需要更好的证据基础。这需要描述可用的不同方法的功效及其在景观中的部署，以最大限度地提高成本效益。气候预测显示，英国冬季降雨量将增加，极端天气事件将增多，降雨强度也将增加。需要新的方法来应对与气候变化有关的预计会增加的洪水。因此，需要进行基础研究，以便为正在进行的关于NFM在综合流域管理总体战略中减轻脆弱社区洪水风险的有效性的全国性辩论提供信息。本项目旨在为量化NFM措施的有效性提供证据基础，从而为NFM方法提供更好的成本效益分析和战略部署。该项目将通过对Parrett河和Tone河流域广泛的NFM基础设施实施新颖的现场监测来实现这一目标。这些证据将在空间分布的降雨径流模型中支持这些特征的发展和特征，这些模型可用于测试景观中的不同配置和空间连通性。NERC CASE的合作伙伴Natural England （NE）和FWAG SW已经投入了大量资金在Parrett和Tone流域安装NFM措施。然而，迄今为止，这些组合结构的有效性尚未量化。这项研究将利用这种广泛的基础设施，通过使用照相机和液位计的新型传感器监测风暴事件期间的动态。这一证据将推动动态TOPMODEL的进一步发展，这是一个集水区降雨径流模型，利用空间衍生的水文响应单元（HRU）模拟水文连通性。该模型可以解决景观特征到几米，如NFM控制。这些发展将导致一个动态的集水区建模系统，该系统将在景观中测试不同类型和配置的NFM措施。这将首次确保NFM的证据基础与建模相结合，并对固有的不确定性进行量化。

Natural Flood Management (NFM) is an approach that seeks to reduce flood risk by protecting, restoring and emulating the natural function of catchments, rivers, floodplains and coast. NFM is one of a range of mitigation measures that is being considered to improve the resilience of downstream communities affected by flooding. It is widely accepted that a better evidence base is required in order to deploy NFM as a standard approach to managing flood risk. This needs to characterise the efficacy of the different approaches available and their deployment in the landscape to maximise cost-effectiveness. Climate predictions suggest the UK will receive an increase in winter rainfall, more extreme events and increased rainfall intensities . New approaches are needed to cope with the anticipated increased flooding associated with the changing climate. Therefore fundamental research is needed to inform the on-going national debate on the efficacy of NFM within an overall strategy of integrated catchment management for alleviating flood risk to vulnerable communities.This project aims to provide the evidence base for quantifying the effectiveness of NFM measures so provide better cost-benefit analyses and strategic deployment of NFM approaches. The project will achieve this through the implementation of novel field based monitoring of extensive NFM infrastructure in the River Parrett and Tone catchments. This evidence will then underpin the development and characterisation of these features in spatially distributed rainfall-runoff models that can be used to test different configurations and spatial connectivities in the landscape.The NERC CASE partners Natural England (NE) and FWAG SW have invested substantially in the installation of NFM measures in the Parrett and Tone catchments. However as yet the effectiveness of these combined structures has not been quantified. This research will take advantage of this extensive infrastructure by monitoring their dynamics during storm events using novel sensors of cameras and level gauges.This evidence will enable the further development of Dynamic TOPMODEL, a catchment rainfall-runoff model that simulates hydrological connectivity using spatially derived Hydrological Response Units (HRU's). The model can resolve landscape features down to a few meters such as NFM controls. These developments will result in a dynamic catchment modelling system that will test different types and configurations of NFM measures in the landscape. For the first time this will ensure the evidence base for NFM is coupled to the modeling and the inherent uncertainties quantified.