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Strategies are proposed to cope with uncertainties in a way that all possible kinds of uncertainty are named, recognized, statistically quantified as far as possible, and utilised in efficient decision making in flood risk management (FRM). We elaborated on the metaphor of uncertainty as a monster. We recommend two strategies to cope with the uncertainty monster to support efficient decision making in FRM: monster adaptation and monster assimilation. We present three cases to illustrate these strategies. We argue that these strategies benefit from improving structure and reducing complexity of decision problems. We discuss ways to involve decision makers in FRM, and how communication strategies can be responsive to their informational needs.

Bridget, Kathryn, Rob and Alex describe how the use of interactive physical demonstration models can help to reinforce geographical learning outcomes relating to rivers and coasts for secondary students.

A ‘roadmap’ for the future of UK flood hydrology over the next 25 years has been published, based on a wide-ranging and inclusive co-creation process involving more than 270 individuals and 50 organisations from different sectors and disciplines. This paper highlights key features of the roadmap and its development as a community-owned initiative. The roadmap's relationship with hydrological research and practice is discussed, as is its context within the wider flood risk management innovation landscape, including funding. While the paper has a focus on UK flood hydrology, reflecting the scope of the roadmap, it is also considered in the context of advances in hydrology internationally.

Bridges are critical-infrastructure components of road and rail transport networks. A large number of these critical assets cross or are adjacent to waterways and floodplains and are therefore exposed to flood actions such as scour, hydrodynamic loading, and inundation, all of which are exacerbated by debris accumulations. These stressors are widely recognized as responsible for the vast majority of bridge failures around the world, and they are expected to be exacerbated due to climate change. While efforts have been made to increase the robustness of bridges to the flood hazard, many scientific and technical gaps remain. These gaps were explored during an expert workshop that took place in April 2021 with the participation of academics, consultants, and decision makers operating mainly in the United Kingdom and specializing in the fields of bridge risk assessment and management and flood resilience. The objective of the workshop was to identify and prioritize the most urgent and significant impediments to bridge flood resilience. In particular, the following issues, established at different levels and scales of bridge flood resilience, were identified and analysed in depth: (i) characterization of the effects of floods on different bridge typologies, (ii) uncertainties in formulae for scour depth assessment, (iii) evaluation of consequences of damage, (iv) recovery process after flood damage, (v) decision-making under uncertainty for flood-critical bridges, and (vi) use of event forecasting and monitoring data for increasing the reliability of bridge flood risk estimations. These issues are discussed in this paper to inform other researchers and stakeholders worldwide, guide the directions of future research in the field, and influence policies for risk mitigation and rapid response to flood warnings, ultimately increasing bridge resilience.

In this study, we utilise Artificial Neural Network (ANN) models to estimate the 100- and 1500-year return levels for around 900,000 ungauged catchments in the contiguous USA. The models were trained and validated using 4,079 gauges and several selected catchment descriptors out of a total of 25 available. The study area was split into 15 regions, which represent major watersheds. ANN models were developed for each region and evaluated by calculating several performance metrics such as root-mean-squared error (RMSE), coefficient of determination (R2) and absolute percent error. The availability of a large dataset of gauges made it possible to test different model architectures and assess the regional performance of the models. The results indicate that ANN models with only one hidden layer are sufficient to describe the relationship between flood quantiles and catchment descriptors. The regional performance depends on climate type as models perform worse in arid and humid continental climates. Overall, the study suggests that ANN models are particularly applicable for predicting ungauged flood quantiles across a large geographic area. The paper presents recommendations about future application of ANN in regional flood frequency analysis.

Waves in the ocean can be as dangerous as they are impressive. In order to study the behaviour of such waves, buoys are commonly deployed to collect recordings of the ocean surface over time. This results in large quantities of high-frequency multivariate time series data. The statistical analysis of such data is of great importance in a variety of engineering and scientific contexts, from the design of coastal flood defences to offshore structures. We develop methodology for analysing such buoy data, investigating two key questions. Firstly, how should we perform parameter inference for models of the frequency domain behaviour of the surface, given recorded buoy data? Secondly, how can we detect statistically significant non-linearities present in these time series? For parameter inference, we find that pseudo-likelihood approaches greatly outperform state-of-the-art methodologies. As a result, not only can we obtain more reliable parameter estimates, but we can also perform inference for more complicated models, allowing for a more intricate description of the waves. Due to the improved performance of such estimates, we are able to see the evolution of these parameters throughout storm events, using recorded buoy data from both California and the North Sea. For detecting non-linearities, we develop a robust testing procedure by evaluating the bispectrum of the observed time series against the bispectrum of bootstrap simulated Gaussian processes with similar characteristics. We explore the performance of this technique in simulation studies, and apply the approach to buoy data from California.

Digital technology is having a major impact on many areas of society, and there is equal opportunity for impact on science. This is particularly true in the environmental sciences as we seek to understand the complexities of the natural environment under climate change. This perspective presents the outcomes of a summit in this area, a unique cross-disciplinary gathering bringing together environmental scientists, data scientists, computer scientists, social scientists, and representatives of the creative arts. The key output of this workshop is an agreed vision in the form of a framework and associated roadmap, captured in the Windermere Accord. This accord envisions a new kind of environmental science underpinned by unprecedented amounts of data, with technological advances leading to breakthroughs in taming uncertainty and complexity, and also supporting openness, transparency, and reproducibility in science. The perspective also includes a call to build an international community working in this important area.

There is a no lack of significant open questions in the field of hydrology. How will hydrological connectivity between freshwater bodies be altered by future human alterations to the hydrological cycle? Where does water go when it rains? Or what is the future space–time variability of flood and drought events? However, the answers to these questions will vary with location due to the specific and often poorly understood local boundary conditions and system properties that control the functional behaviour of a catchment or any other hydrologic control volume. We suggest that an open, shared and evolving perceptual model of a region's hydrology is critical to tailor our science questions, as it would be for any other study domain from the plot to the continental scale. In this opinion piece, we begin to discuss the elements of and point out some knowledge gaps in the perceptual model of the terrestrial water cycle of Great Britain. We discuss six major knowledge gaps and propose four key ways to reduce them. While the specific knowledge gaps in our perceptual model do not necessarily transfer to other places, we believe that the development of such perceptual models should be at the core of the debate for all hydrologic communities, and we encourage others to have a similar debate for their hydrologic domain.

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 m

Digital technology is having a major impact on many areas of society and there is equal opportunity for impact on science. This is particularly true in the environmental sciences as we seek to understand the complexities of the natural environment under climate change. This perspective presents the outcomes of a summit in this area, a unique cross-disciplinary gathering bringing together environmental scientists, data scientists, computer scientists, social scientists and the representatives of the creative arts. The key output of this workshop is an agreed vision in the form of a framework and associated roadmap, captured in the Windermere Accord. This accord envisions a new kind of environmental science underpinned by unprecedented amounts of data, with technological advances leading to breakthroughs in taming uncertainty and complexity, and also supporting openness, transparency and reproducibility in science. The retrospective also includes a call to build an international community working on this important area.

Digital technology is having a major impact on many areas of society and there is equal opportunity for impact on science. This is particularly true in the environmental sciences as we seek to understand the complexities of the natural environment under climate change. This perspective presents the outcomes of a summit in this area, a unique cross-disciplinary gathering bringing together environmental scientists, data scientists, computer scientists, social scientists and the representatives of the creative arts. The key output of this workshop is an agreed vision in the form of a framework and associated roadmap, captured in the Windermere Accord. This accord envisions a new kind of environmental science underpinned by unprecedented amounts of data, with technological advances leading to breakthroughs in taming uncertainty and complexity, and also supporting openness, transparency and reproducibility in science. The retrospective also includes a call to build an international community working on this important area.