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The Q‐natural flood management project has co‐developed with the Environment Agency 18 monitored micro‐catchments (~1 km2) in Cumbria, UK installing calibrated flumes aimed at quantifying the potential shift in observed flows resulting from a range of nature‐based‐solutions installed by local organizations. The small‐scale reduces the influence of variability characterizing larger catchments that would otherwise mask any such shifts, which we attempt to relate to a shift in model parameters. This paper demonstrates an approach to applying donor‐parameter‐shifts obtained from modelling two of the paired micro‐catchments to a much larger scale, in order to understand the potential for improved distributed modelling of nature‐based solutions in the form of additional tree‐planting. The models include a rainfall‐runoff model, Dynamic Topmodel, and a 2D hydrodynamic model, JFlow, permitting analysis of changes in hillslope processes and channel hydrodynamics resulting from a range of distributed measures designed to emulate natural hydrological processes that evaporate, store or infiltrate flows. We report on attempts to detect shift in hydrological response using one of the paired‐micro‐catchment moorland versus forestry sites in Lorton using Dynamic Topmodel. A donor‐parameter‐shift approach is used in a hypothetical experiment to represent new woodland in a much larger catchment, although testing all combinations of spatial planting strategies, responses to multiple‐extremes, failure‐modes and changes to synchronization becomes intractable to support good decision making. We argue that the problem can be re‐framed to use donor‐parameter‐shifts at multi‐local‐scale catchments above communities known to be at risk, commensurate with most of the evidence of NbS impacts being effective at the small scale (ca. 10 km2). This might lead to more effective modelling to help catchment managers prioritize those communities‐at‐risk where there is more evidence that NbS might be effective. [ABSTRACT FROM AUTHOR]

Decision‐making in flood risk management is increasingly dependent on access to data, with the availability of data increasing dramatically in recent years. We are therefore moving towards an era of big data, with the added challenges that, in this area, data sources are highly heterogeneous, at a variety of scales, and include a mix of structured and unstructured data. The key requirement is therefore one of integration and subsequent analyses of this complex web of data. This paper examines the potential of a data‐driven approach to support decision‐making in flood risk management, with the goal of investigating a suitable software architecture and associated set of techniques to support a more data‐centric approach. The key contribution of the paper is a cloud‐based data hypercube that achieves the desired level of integration of highly complex data. This hypercube builds on innovations in cloud services for data storage, semantic enrichment and querying, and also features the use of notebook technologies to support open and collaborative scenario analyses in support of decision making. The paper also highlights the success of our agile methodology in weaving together cross‐disciplinary perspectives and in engaging a wide range of stakeholders in exploring possible technological futures for flood risk management. [ABSTRACT FROM AUTHOR]

An important issue in taking account of uncertainty in flood inundation mapping is the communication of the meaning of the outputs from an uncertainty analysis. In part this is because uncertainty estimation in this domain is not a simple statistical problem in that it involves knowledge uncertainties as well as statistical (aleatory) uncertainties in most of the important sources of uncertainty (estimated upstream discharges, effective roughness coefficients, flood plain and channel geometries and infrastructure, choice of model, fragility of defences, etc.). Thus, assumptions are required associated with the knowledge or lack of knowledge about these different sources of uncertainty. A framework has been developed in the form of a sequence of condition trees to help define these assumptions. Since stakeholders in the process can potentially be involved in making and recording decisions about those assumptions the framework also serves as a means of communicating the assumptions. Recording the decisions also serves to provide an audit trail for later evaluation of the decisions and hence the resulting analysis. Communication can also be helped in this type of spatial problem by effective visualization techniques and a visualization tool has been developed for both a web-based service using Google Maps™ and a desktop application using the Matlab™ numerical package. [ABSTRACT FROM PUBLISHER]