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

通过监测和模型预测（包括不确定性）量化现场自然洪水管理 (NFM) 的功效

• 批准号: 2474845
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 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=studentship-2474845
• 关键词: Quantifying; efficacy; situ; Natural; Flood

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.

自然洪水管理（NFM）是一种通过保护、恢复和模拟集水区、河流、洪泛区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，以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作为管理洪水风险的标准方法，需要更好的证据基础。这需要验证现有不同方法的有效性及其在景观中的部署，以最大限度地提高成本效益。气候预测表明，英国冬季降雨量将增加，极端事件和降雨强度将增加。需要采取新的办法来科普气候变化带来的预计洪水增加。因此，基础研究是必要的，以通知正在进行的全国辩论的有效性NFM在一个整体战略的综合流域管理，以减轻洪水风险脆弱community.This项目的目的是提供量化的NFM措施的有效性的证据基础，以便提供更好的成本效益分析和战略部署的NFM方法。该项目将通过在Parrett河和Tone河流域对广泛的NFM基础设施实施新的实地监测来实现这一目标。这些证据将支持这些功能的发展和表征，在空间分布的径流模型，可用于测试不同的配置和空间连通性在landscape.The NERC CASE合作伙伴自然英格兰（NE）和FWAG SW投资大量安装NFM措施在Parrett和Tone集水区。然而，这些合并结构的效力尚未量化。这项研究将利用这一广泛的基础设施，通过监测他们的动态在风暴事件中使用新的传感器的相机和水准仪。这一证据将使动态TOPMODEL，一个集水区的所有径流模型，模拟水文连接使用空间派生的水文响应单元（HRU的）的进一步发展。该模型可以解决景观功能下降到几米，如NFM控制。这些发展将导致一个动态的集水模拟系统，将测试不同类型和配置的NFM措施的景观。这将首次确保NFM的证据基础与建模和量化的固有不确定性相结合。