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There are growing concerns regarding the increase in flood risk due to climate change and land use/land cover changes. In light of these changes, levees play an increasingly critical role in safeguarding communities, infrastructure, and the natural environment. However, the average age of levees in the United States is 60 years, with the majority operating under marginal conditions. The most common failure mode of earthen levees is breach due to overtopping. Existing methodologies evaluate the site-specific probability of levee overtopping and do not provide a holistic view of flood risk across a wider area. Here we present a regional-scale overtopping model for levees using a data-driven overtopping model that uses five variables: (1) levee construction classification, (2) overtopping depth, (3) overtopping duration, (4) erosion resistance classification, and (5) duration of levee loading before overtopping. The overtopping model is applied to levee systems in Wilton, California. The probability of breach due to overtopping is presented for three distinct scenarios (overtopping duration) during a 50-year flood event. The results show the probability of breach for the Wilton Levee ranges from 0.32 to 0.91 for overtopping durations of 6 hours and between 6 to 24 hours. For durations exceeding 24 hours, the probability of breach increases to a range of 0.73 to 0.98. The proposed framework offers a viable tool for performing regional-scale levee risk assessment, offering broader implications for enhancing preparedness, response, and recovery strategies in the face of escalating flood risks. 

Despite major improvements in weather and climate modelling and substantial increases in remotely sensed observations, drought prediction remains a major challenge. After a review of the existing methods, we discuss major research gaps and opportunities to improve drought prediction. We argue that current approaches are top-down, assuming that the process(es) and/or driver(s) are known—i.e. starting with a model and then imposing it on the observed events (reality). With the help of an experiment, we show that there are opportunities to develop bottom-up drought prediction models—i.e. starting from the reality (here, observed events) and searching for model(s) and driver(s) that work. Recent advances in artificial intelligence and machine learning provide significant opportunities for developing bottom-up drought forecasting models. Regardless of the type of drought forecasting model (e.g. machine learning, dynamical simulations, analogue based), we need to shift our attention to robustness of theories and outputs rather than event-based verification. A shift in our focus towards quantifying the stability of uncertainty in drought prediction models, rather than the goodness of fit or reproducing the past, could be the first step towards this goal. Finally, we highlight the advantages of hybrid dynamical and statistical models for improving current drought prediction models. This article is part of the Royal Society Science+ meeting issue ‘Drought risk in the Anthropocene’. 

Abstract Sea‐level rise (SLR) increasingly threatens coastal communities around the world. However, not all coastal communities are equally threatened, and realistic estimation of hazard is difficult. Understanding SLR impacts on extreme sea level is challenging due to interactions between multiple tidal and non‐tidal flood drivers. We here use global hourly tidal data to show how and why tides and surges interact with mean sea level (MSL) fluctuations. At most locations around the world, the amplitude of at least one tidal constituent and/or amplitude of non‐tidal residual have changed in response to MSL variation over the past few decades. In 37% of studied locations, “Potential Maximum Storm Tide” (PMST), a proxy for extreme sea level dynamics, co‐varies with MSL variations. Over all stations, the median PMST will be 20% larger by the mid‐century, and conventional approaches that simply shift the current storm tide regime up at the rate of projected SLR may underestimate the flooding hazard at these locations by up to a factor of four. Micro‐ and meso‐tidal systems and those with diurnal tidal regime are generally more susceptible to altered MSL than other categories. The nonlinear interactions of MSL and storm tide captured in PMST statistics contribute, along with projected SLR, to the estimated increase in flood hazard at three‐fourth of studied locations by mid‐21st century. PMST is a threshold that captures nonlinear interactions between extreme sea level components and their co‐evolution over time. Thus, use of this statistic can help direct assessment and design of critical coastal infrastructure.