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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. 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. The authors show that topography and drainage areas correlate with flood sensitivities as well as with societal behavior.

\"On a freezing December night, Hannah Reindel leaps to her death from an old railway bridge into the rushing waters of the river below. Yet the real cause of death was trauma suffered twelve years earlier when Hannah was plucked from a crowd of freshman girls at a college fraternity party, drugged, and then viciously assaulted by six members of the college football team. Those responsible have never faced or feared justice--until now\"-- Provided by publisher.

Elevation data are fundamental to many applications, especially in geosciences. The latest global elevation data contains forest and building artifacts that limit its usefulness for applications that require precise terrain heights, in particular flood simulation. Here, we use machine learning to remove buildings and forests from the Copernicus Digital Elevation Model to produce, for the first time, a global map of elevation with buildings and forests removed at 1 arc second (∼30 m) grid spacing. We train our correction algorithm on a unique set of reference elevation data from 12 countries, covering a wide range of climate zones and urban extents. Hence, this approach has much wider applicability compared to previous DEMs trained on data from a single country. Our method reduces mean absolute vertical error in built-up areas from 1.61 to 1.12 m, and in forests from 5.15 to 2.88 m. The new elevation map is more accurate than existing global elevation maps and will strengthen applications and models where high quality global terrain information is required.

Global flood mapping has developed rapidly over the past decade, but previous approaches have limited scope, function, and accuracy. These limitations restrict the applicability and fundamental science questions that can be answered with existing model frameworks. Harnessing recently available data and modeling methods, this paper presents a new global ∼30 m resolution Global Flood Map (GFM) with complete coverage of fluvial, pluvial, and coastal perils, for any return period or climate scenario, including accounting for uncertainty. With an extensive compilation of global benchmark case studies—ranging from locally collected event water levels, to national inventories of engineering flood maps—we execute a comprehensive validation of the new GFM. For flood extent comparisons, we demonstrate that the GFM achieves a critical success index of ∼0.75. In the more discriminatory tests of flood water levels, the GFM deviates from observations by ∼0.6 m on average. Results indicating this level of global model fidelity are unprecedented in the literature. With an optimistic scenario of future warming (SSP1‐2.6), we show end‐of‐century global flood hazard (average annual inundation volume) increases are limited to 9% (likely range ‐6%–29%); this is within the likely climatological uncertainty of −8%–12% in the current hazard estimate. In contrast, pessimistic scenario (SSP5‐8.5) hazard changes emerge from the background noise in the 2040s, rising to a 49% (likely range of 7%–109%) increase by 2100. This work verifies the fitness‐for‐purpose of this new‐generation GFM for impact analyses with a variety of beneficial applications across policymaking, planning, and commercial risk assessment. Plain Language Summary Computer models use a variety of data and physical equations to estimate the extent and depth of possible flood events. Global applications of these tools have been developed over the past decade, but they are not very good at simulating the behavior of real floods. In this paper, we address some key problems to make a global model that does a lot better than past ones. We apply new techniques to better understand how much water we need to put into the model for a given flood probability. This movement of water is simulated by the model over a more accurate map of the Earth's terrain than has been available previously, with river channels represented in a smarter way. We look at the projected changes in rainfall, river discharge, and sea levels for given levels of warming simulated by available climate models and adjust the probabilities of a given magnitude flood accordingly. The model results suggest that the effect of future climate change might be small relative to our ability to understand flood hazards today, but this depends heavily on how much carbon we emit in the coming decades. Key Points New climate‐conditioned model framework represents fluvial, pluvial, and coastal flood hazards at high‐resolution globally Comprehensive validation studies suggest that the model is approaching local model skill in many cases Emissions reduction can hold flood hazards largely constant this century, though coastal flooding will increase drastically regardless

The ability to generate new meaning by rearranging combinations of meaningless sounds is a fundamental component of language. Although animal vocalizations often comprise combinations of meaningless acoustic elements, evidence that rearranging such combinations generates functionally distinct meaning is lacking. Here, we provide evidence for this basic ability in calls of the chestnut-crowned babbler (Pomatostomus ruficeps), a highly cooperative bird of the Australian arid zone. Using acoustic analyses, natural observations, and a series of controlled playback experiments, we demonstrate that this species uses the same acoustic elements (A and B) in different arrangements (AB or BAB) to create two functionally distinct vocalizations. Specifically, the addition or omission of a contextually meaningless acoustic element at a single position generates a phoneme-like contrast that is sufficient to distinguish the meaning between the two calls. Our results indicate that the capacity to rearrange meaningless sounds in order to create new signals occurs outside of humans. We suggest that phonemic contrasts represent a rudimentary form of phoneme structure and a potential early step towards the generative phonemic system of human language.

The Earth’s terrain is linked to many physical processes, and gaining the most accurate representation is key to work in many sectors from engineering to natural hazards modeling and ecology. Existing global digital elevation models (DEMs) are widely used, however often suffer from systematic biases caused by trees, buildings and instrumentation error, ultimately limiting their effectiveness. We present here, FathomDEM, a new global 30 m DEM produced using a novel application of a hybrid vision transformer model. This model removes surface artifacts from a global radar DEM, Copernicus DEM, aligning it more closely with true topography. In addition to improving on other global DEMs, FathomDEM also has reduced error compared to coastal-focussed DEMs such as the recent DeltaDTM. This demonstrates its impressive capacity to perform for specific landscapes, while being trained globally to model a wide range of terrain types. FathomDEM has been tested on the downstream task of flood modeling, showing increased accuracy compared to those run with the previous best global DEM, FABDEM, approaching the performance of LiDAR based flood modeling. This improvement is attributed to FathomDEM’s smaller error and substantial reduction in artifacts. This shows the suitability of FathomDEM for applied tasks and strengthens our evaluation compared to one based on vertical error alone.

We present a transparent and validated climate-conditioned catastrophe flood model for the UK, that simulates pluvial, fluvial and coastal flood risks at 1 arcsec spatial resolution (∼ 20–25 m). Hazard layers for 10 different return periods are produced over the whole UK for historic, 2020, 2030, 2050 and 2070 conditions using the UK Climate Projections 2018 (UKCP18) climate simulations. From these, monetary losses are computed for five specific global warming levels above pre-industrial values (0.6, 1.1, 1.8, 2.5 and 3.3 ∘C). The analysis contains a greater level of detail and nuance compared to previous work, and represents our current best understanding of the UK's changing flood risk landscape. Validation against historical national return period flood maps yielded critical success index values of 0.65 and 0.76 for England and Wales, respectively, and maximum water levels for the Carlisle 2005 flood were replicated to a root mean square error (RMSE) of 0.41 m without calibration. This level of skill is similar to local modelling with site-specific data. Expected annual damage in 2020 was GBP 730 million, which compares favourably to the observed value of GBP 714 million reported by the Association of British Insurers. Previous UK flood loss estimates based on government data are ∼ 3× higher, and lie well outside our modelled loss distribution, which is plausibly centred on the observations. We estimate that UK 1 % annual probability flood losses were ∼ 6 % greater for the average climate conditions of 2020 (∼ 1.1 ∘C of warming) compared to those of 1990 (∼ 0.6 ∘C of warming), and this increase can be kept to around ∼ 8 % if all countries' COP26 2030 carbon emission reduction pledges and “net zero” commitments are implemented in full. Implementing only the COP26 pledges increases UK 1 % annual probability flood losses by 23 % above average 1990 values, and potentially 37 % in a “worst case” scenario where carbon reduction targets are missed and climate sensitivity is high.

For individuals collaborating to rear offspring, effective organization of resource delivery is difficult because each carer benefits when the others provide a greater share of the total investment required. When investment is provided in discrete events, one possible solution is to adopt a turn-taking strategy whereby each individual reduces its contribution rate after investing, only increasing its rate again once another carer contributes. To test whether turn-taking occurs in a natural cooperative care system, here we use a continuous time Markov model to deduce the provisioning behavior of the chestnut-crowned babbler (Pomatostomus ruficeps), a cooperatively breeding Australian bird with variable number of carers. Our analysis suggests that turn-taking occurs across a range of group sizes (2-6), with individual birds being more likely to visit following other individuals than to make repeat visits. We show using a randomization test that some of this apparent turn-taking arises as a by-product of the distribution of individual inter-visit intervals (\"passive\" turn-taking) but that individuals also respond actively to the investment of others over and above this effect (\"active\" turn-taking). We conclude that turn-taking in babblers is a consequence of both their individual provisioning behavior and deliberate response rules, with the former effect arising through a minimum interval required to forage and travel to and from the nest. Our results reinforce the importance of considering fine-scale investment dynamics when studying parental care and suggest that behavioral rules such as turn-taking may be more common than previously thought.

In chronic kidney disease it is hypothesised that the association between loss of lean tissue mass (LLTM) and mortality is purely a function of multimorbidity. We conducted a systematic review in CKD patients to quantify the strength of association between LLTM and mortality or frailty surrogates, including hospitalisation and quality of life (QoL). Muscle mass was estimated using different whole-body bioimpedance methods (BI-MM). Searches of electronic databases identified 132 studies for inclusion (147542 dialysis patients; 15378 CKD G3−5 patients; 356 kidney transplant recipients [KTR], with 14429 deaths). From 67 studies reporting unadjusted analyses, 52 (78%) demonstrated associations between LLTM and mortality. In 80 studies reporting analyses adjusting for age, sex, and multimorbidity, 59 (74% overall: 74, 67 and 100% in dialysis, CKD, and KTR studies respectively) reported an association. Meta-analysis of dialysis studies reporting adjusted survival analyses found each degree decrease in phase angle or a lean tissue index < 10th percentile was associated with a 92 and 49% higher hazard of mortality respectively. In studies reporting hospitalisation and QoL measures, 63 and 76% reported associations with BI-MM respectively. In conclusion, having accounted for multimorbidity, LLTM remained associated with mortality and frailty surrogates in CKD, irrespective of the BI-MM method used.