Flood protection authorities are not prepared for compound flood risk in estuaries-now and in the face of climate change. Climate projections are rarely downscaled appropriately to assess future changes in storm surge and concurrent river discharge extremes, and their interactions to exacerbate flooding. This is the first time that hourly and fine spatial resolution (7/2.2 km sea level/precipitation), physically consistent, climate projections are used to assess changes in storm surge and river discharge-driven compound events. The analysis, applied to the Dyfi estuary, western UK, uses 12 downscaled perturbed parameter ensembles for the high-emissions "RCP8.5" scenario from a global climate model (HadGEM3-GC3.0). Residual surge and river discharge projections are assessed independently to identify changes in magnitudes and return periods-then combined to identify changing patterns of dependence and timing of compound events. Under RCP8.5 scenario to 2080, river discharge is expected to increase by 28%-29% for 1/20 and 1/50-year events. Extreme (95th percentile) discharge events are more likely to occur concurrently with extreme surges, and compound events will occur more often, and with a shorter time lag between peak surge and peak discharge-potentially compounding flooding further. The analysis provided forcing conditions representative of future 1 in 20-year and 1 in 50-year events used to simulate a potential increased flood footprint in the estuary. The research raises the question of the wider pattern of future compound events throughout the UK, and worldwide, highlighting the critical need for downscaled, coastal and fluvial projections to futureproof flood management strategies.

Estuaries are crucial for freshwater and nutrient cycling throughout shelf seas that drives the biodiversity and ecology of coastal and marine wildlife, and provide ecosystem services that sustain the livelihoods and wellbeing of coastal communities. These ecosystems are, however, potential pollution corridors and sinks carrying sewage and other loads containing harmful pathogens and contaminants – a serious health issue that is worsening with littoralisation and population growth. Being at the interface between oceanographic and fluvial processes, estuaries are the most dynamic coastal system, where water quality processes and habitat dynamics are shaped by complex geo-physical, chemical, and biological interactions that change over small spatio-temporal scales and are unique to each estuary. It is essential that these systems maintain safe water quality standards and that we are prepared for future changes in water quality that will affect their ecological status and public health risk. This research aims to characterise variability and potential change in indicators of estuary health across the UK, using a robust analysis and modelling strategy, that can be built upon to evaluate a range of water quality degradation processes and used to inform future management strategies. We will present the first analysis of both riverine and marine climate projections for the 21st Century (UKCP18 RCP8.5 perturbed parameter ensemble), downscaled to hourly- and sub-meso-scales, and applied to all estuaries in England. In particular, characterising projected changes in hydrology, temperature, salinity, sea level, and coincident conditions. Additionally, we have developed fine-scale estuary hydrodynamic models (Delft3D) of all estuaries and present potential changes in simulated estuary residence times as a result of projected sea-level rise and changing hydrology. The analyses and simulations highlight estuaries and estuary types that are vulnerable to changes in the physical stressors of coastal water quality – where coastal management efforts and hazard response should be focused the coming decades.

The increasing frequency and intensity of heavy rainfall events driven by climate change poses challenges for flood risk management. In this study, we use a high-resolution, convection-permitting ensemble from the UK climate projections local dataset to explore how the spatiotemporal characteristics of heavy rainfall events may evolve across the UK. Adopting an event-based framework, we analyse 5 km hourly rainfall data from 12 ensemble members and compare changes in future rainfall events to those derived from applying intensity-based scaling factors alone. This comparison allows us to identify aspects of rainfall change that are not captured by shifts in intensity distributions. Our results show that short-duration winter events become increasingly localised, with peak intensities increasing by up to 47%, amplifying flash flood potential. In summer, rainfall events exhibit expanded spatial extents—expanding by 25%–40%—magnifying total precipitation volumes. While we find small changes in the number of clustered events (i.e. heavy rainfall events that occur within a 21 day window), there are large changes to the contribution these have to seasonal precipitation, particularly in summer (7%–11% in the baseline to 11%–16% in future period). These findings highlight new insights into how heavy rainfall may change under future climate conditions, identifying aspects of change beyond intensity increases alone that are relevant for informing current practice for flood risk estimation.

Extreme precipitation is projected to intensify and occur more frequently under climate change. However, the effect of global warming on the spatial and temporal structure of extreme rainfall events at the local scale is uncertain. In the UK, the current method for estimating changes in flood hazard under climate change involves applying a simple multiplicative uplift to spatially uniform catchment rainfall. This approach neglects spatio-temporal characteristics of rainfall, which are known to be important for flood hazards. The UCKP Local Convection Permitting Model (CPM) has for the first time provided the capacity to assess these characteristics of rainfall at the local scale. Here, we use an ensemble of 2.2km hourly convection-permitting transient projections from UKCP Local to identify changes in the spatial and temporal characteristics of precipitation extremes over 100-years (1981-2080) across the UK. The analysis uses an ‘event-based’ approach, exploring seasonal changes in the peak intensity, total rainfall, and duration of events, but also changes in the spatial extent and temporal clustering of events through time. We identify ~13000 extreme rainfall events across the UK over the 100-year period. Event peaks are identified using a seasonal and time-varying threshold (99th percentile) on hourly rainfall rates, and event start and stop times are extracted using a lower threshold (20th percentile). We identify seasonal differences in how spatial extents of rainfall extremes will change, with winter and spring events growing, but summer and autumn events reducing in areal coverage. We also identify changes in the sub-seasonal timing of rainfall extremes, with events becoming more clustered, particularly during the winter months. Understanding changes in the spatial and temporal characteristics of rainfall events is critical as they may compound with increases in rainfall intensity, exacerbating the impacts of flooding.

Estuarine flooding is driven by extreme sea-levels and river discharge, either occurring independently or at the same time, or in close succession to exacerbate the hazard, known as compound events. Understanding compound flooding in the face of climate change is crucial for anticipating and mitigating heightened risks. Rising sea levels, increased storm intensity, and changing precipitation patterns can amplify the simultaneous occurrence of extreme storm surges and river flows. It is necessary to assess changing patterns of timing and intensity in extreme storm-driven compound events to inform future incident and hazard management strategies. Understanding whether these events will intensify or diminish is crucial for adapting and developing effective mitigation measures. This research represents the first time that projections of future sea-level, storm surge, and river discharge to assess changes in the magnitude and timing of storm-driven compound events in an estuary particularly vulnerable to compound flooding (Dyfi, west Wales). Sub-daily projections of river discharge from a hydrological model and sea level and residual surge from a shelf sea model are assessed independently to identify changes in their magnitude and return periods. Projections are then assessed in combination to identify future extreme dependence and timing of compound events. The analysis provides forcing conditions representative of a 1 in 20-year and 1 in 50-year event to simulate the impacts of future return periods in the Dyfi Estuary. The research shows that more extreme river discharge and storm surges will occur up to 2100, and the severity of a 1 in 1-year to a 1 in 5-year event will become more severe into the future. There is a stronger likelihood of an extreme river discharge occurring at the same time as an extreme skew surge in the future, more often per storm season, and with greater dependence. Further to this, as storm-driven compound events become more prevalent in the future, the associated flood impacts are anticipated extend over larger areas and occur with increased severity. This research presents a scalable methodology for comprehensive assessment and analysis of the future likelihood and impacts of storm-driven compound events, that can be applied worldwide where sub-daily river and sea level projection forced by the same global climate model are available.

Abstract. East Africa has recently experienced a series of devastating tropical cyclone landfalls characterised by hundreds of fatalities, millions of displaced people and substantial economic damage. Forecasting the impact of these tropical cyclones can, in theory, better motivate anticipatory action compared to only forecasting the hazard. This paper describes an approach to forecasting the number of people directly exposed to flooding from tropical cyclones and documents experience gained communicating these forecasts to practitioners via emergency bulletins. Forecasting flood exposure requires a complex cascade of meteorological, hydrological, hydraulic and population models. Interpretation of forecasts was difficult, even for the scientific experts developing the systems, due to uncertainties brought in at each stage of the modelling cascade. Thus, producing interpretable forecast messaging was challenging and often required extensive discussion between forecasters with expertise on different elements of the system. This paper uses practical experience gained from several tropical cyclone events to highlight essential requirements for interpreting and disseminating tropical cyclone flood impact forecasts. We also analyse how forecasts evolved with lead-time and compare them to observed flooding in the case of Tropical Cyclone Freddy. Overall, we aim to synthesise our experience into actionable learning that might inform future use of forecasting in humanitarian response. Exposure estimates were most sensitive to storm track location, even when exposure was aggregated to districts. Uncertainty from track location remained substantial even in the days before landfall, meaning a recipient of these forecasts needs to understand and interpret the distribution of exposure. For the second landfall of Tropical Cyclone Freddy, nationwide exposure estimates were remarkably similar between remotely sensed flood extents and the best estimate from the forecast system. However, this overall similarity results from the averaging of substantial uncertainty at the district scale.

<p>Assessments of potential future flood hazards under climate change using model cascades are subject to large uncertainties, severely limiting our ability to make robust decisions. The mitigation of impacts of disastrous flood events requires the development of complementary approaches to risk management that can identify exposure of populations and assets across a range of plausible events to derive actionable information under large uncertainty. Therefore, here we provide a high-resolution stress test of the global river network and quantify the sensitivity of inundated areas and population exposure to varying flood event magnitudes for 1.2 million river reaches for the first time. Our analysis reveals regional differences in settlement patterns with respect to flood hazard. We find clear settlement patterns in which floodplains that are most sensitive to flooding from frequent, low magnitudes events have evenly distributed exposure across hazard zones, which suggests that people have found ways to adapt to this risk. In contrast, floodplains most sensitive to extreme magnitudes events have tendency for populations to be most densely settled in these rarely flooded zones, being in significant danger from potentially increasing hazard magnitudes.</p>

Flooding is one of the most common natural hazards, causing disastrous impacts worldwide. Stress-testing the global human-Earth system to understand the sensitivity of floodplains and population exposure to a range of plausible conditions is one strategy to identify where future changes to flooding or exposure might be most critical. This study presents a global analysis of the sensitivity of inundated areas and population exposure to varying flood event magnitudes globally for 1.2 million river reaches. Here we show that topography and drainage areas correlate with flood sensitivities as well as with societal behaviour. We find clear settlement patterns in which floodplains most sensitive to frequent, low magnitude events, reveal evenly distributed exposure across hazard zones, suggesting that people have adapted to this risk. In contrast, floodplains most sensitive to extreme magnitude events have a tendency for populations to be most densely settled in these rarely flooded zones, being in significant danger from potentially increasing hazard magnitudes given climate change.

<p>Understanding global river flood risk is fundamental for impact assessment of future climate and socio-economic change. There is a growing interest in understanding future flood risk using alternative methods that are independent of the uncertainties associated with the common approach based on a model cascade of global climate models coupled with hydrological and inundation models. Here, we propose a new sensitivity index that quantifies whether river reaches are more sensitive to flooding from low or high return periods using flood hazard data from a global flood model. We assess the sensitivity of flood extents and population exposure to increasing river flow magnitudes of 1.1 million river reaches globally. The dominant control on the sensitivity of reaches is the local topography and upstream drainage area. We find that steep bedrock and low slope alluvial streams are sensitive to high return periods, while intermediate and transitional streams are sensitive to low return periods. We find a clear spatial pattern in where the largest proportions of populations have settled on floodplains, which are found in North Africa, South America and South and East Asia. This analysis allows us to identify regions where river reaches and populations might be most affected by climate change and an increase in frequency and magnitude of flood events. </p>

The growing worldwide impact of flood events has motivated the development and application of global flood hazard models (GFHMs). These models have become useful tools for flood risk assessment and management, especially in regions where little local hazard information is available. One of the key uncertainties associated with GFHMs is the estimation of extreme flood magnitudes to generate flood hazard maps. In this study, the 1-in-100 year flood (Q100) magnitude was estimated using flow outputs from four global hydrological models (GHMs) and two global flood frequency analysis datasets for 1350 gauges across the conterminous US. The annual maximum flows of the observed and modelled timeseries of streamflow were bootstrapped to evaluate the sensitivity of the underlying data to extrapolation. Results show that there are clear spatial patterns of bias associated with each method. GHMs show a general tendency to overpredict Western US gauges and underpredict Eastern US gauges. The GloFAS and HYPE models underpredict Q100 by more than 25% in 68% and 52% of gauges, respectively. The PCR-GLOBWB and CaMa-Flood models overestimate Q100 by more than 25% at 60% and 65% of gauges in West and Central US, respectively. The global frequency analysis datasets have spatial variabilities that differ from the GHMs. We found that river basin area and topographic elevation explain some of the spatial variability in predictive performance found in this study. However, there is no single model or method that performs best everywhere, and therefore we recommend a weighted ensemble of predictions of extreme flood magnitudes should be used for large-scale flood hazard assessment.