Search

Case Presentation: A 16-month-old boy presented with acute fever of 99° Fahrenheit (after receiving antipyretics), grunting, and tachypnea. On examination, he was tachycardic, tachypneic, and ill-appearing with abdominal distention and diffuse tenderness. A plain film abdominal radiograph showed moderate free air, and emergent laparoscopy revealed perforated Meckel’s diverticulitis with peritonitis.Discussion: Although tachypnea and grunting in preverbal or nonverbal patients are often considered to be signs of respiratory illness, these findings may reflect intra-abdominal emergencies. Perforated Meckel’s diverticulitis is an important differential consideration in patients with pneumoperitoneum.

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 globallyComprehensive validation studies suggest that the model is approaching local model skill in many casesEmissions reduction can hold flood hazards largely constant this century, though coastal flooding will increase drastically regardless [ABSTRACT FROM AUTHOR]

Precipitation is the most important driver of the hydrological cycle, but it is challenging to estimate it over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR, hereafter PERCCDR) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against 1825 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly, and daily timescales, MSWEP followed by ERA5 demonstrated a higher correlation (CC) and Kling–Gupta efficiency (KGE) than other datasets for more than 50 % of the stations, whilst ERA5 was the second-highest-performing dataset, and it showed the highest error and bias for about 20 % of the stations. PERCCDR is the least-well-performing dataset, with a bias of up to 99 % and a normalised root mean square error of up to 247 %. PERCCDR only show a higher KGE and CC than the other products for less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region, or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products. [ABSTRACT FROM AUTHOR]

New remote sensing technologies, and the meter‐scale geospatial data they create, now allow for detailed urban landscape characterization, thereby advancing grid‐based hydrodynamic models. However, using a uniform fine grid over urban catchments generally result in dense grids and can lead to prohibitive computational costs. Moreover, an inability to see below the water surface and measure river bathymetry in most terrain remote sensing can severely impact local‐scale river hydraulics calculations given the significant volume of water conveyed by the channel. This paper introduces a super grid channel model which allow river channels with any width above that of the grid resolution to be simulated in 1D manner. As an extension of a previous subgrid model, this integration facilitates a seamless transition between subgrid and super grid channels, accommodating situations where channel width may surpass or fall below the grid resolution. The key contribution is the integration of the novel 1D channel representation with a nonuniform structured 2D floodplain hydrodynamic model and then coding this for application on GPU. Compared with the previous pure 2D nonuniform structured approaches, the new model presents an efficient compromise for riverine urban flooding where we are less concerned about fine‐scale details of in‐channel flow. Three tests reveal that the proposed model maintains accuracy but with significantly reduced computational cost. By leveraging GPU architectures, a ∼10× speedup compared to CPU computations is achieved, and a typical 6‐day urban flooding problem (domain size 1.42 km2) at 1 m resolution can be achieved within 10 hr on a single 8 GB GPU. Key Points: A nonuniform structured grid with a super grid river channel model is implemented on GPU for efficient meter‐scale urban flood modelingAs an extension of the subgrid model approach, this integration allows a seamless transition between subgrid and super grid channelsA ∼10× speedup compared to CPU computations is achieved by leveraging GPU architectures [ABSTRACT FROM AUTHOR]

Flood event set generation, as employed in catastrophe risk models, relies on gauge information that is not available in data‐scarce regions. To overcome this limitation, we develop a stochastic fluvial and pluvial flood model of Southeast Asia, using freely and globally available discharge data from the global hydrological model GloFAS and rainfall from the ERA5 reanalysis. We use a conditional multivariate statistical model to produce a synthetic catalog of 10,000 years of flood events. We calculate the flood population exposure associated with each flood event using freely available population data from WorldPop and generate exposure probability exceedance curves. We validate the population exposure curves against observed flood disaster data from EM‐DAT, showing that our methodology provides exposure estimates that are in line with historical observations. We find that there is a 1% probability that more than 30 million people will be exposed to flooding in a given year according to our event set. This number is roughly half the population living in the 100‐year return period flood zone of Fathom's hazard maps, suggesting most studies based on static flood maps overestimate exposure. This analysis provides significant progress over previous non‐stochastic studies which are only able to compute total or average exposure within a given floodplain area and demonstrates that a reanalysis‐based stochastic flood model can be designed to generate reliable estimates of population exposure probability exceedance. This study is a step toward a fully global catastrophe model for floods capable of providing exposure and loss estimates worldwide. Key Points: Global hydrological models can be used to drive a large‐scale stochastic flood inundation model in Southeast AsiaA reanalysis‐based stochastic flood model generates realistic flood eventsThe computed flood exposure exceedance curve for Southeast Asia compares well to the EM‐DAT database [ABSTRACT FROM AUTHOR]

Flooding associated with Hurricane Maria in 2017 had devastating consequences for lives and livelihoods in Puerto Rico. Yet, an understanding of current and future flood risk on small islands like Puerto Rico is limited. Thus, efforts to build resilience to flooding associated with hurricanes remain constrained. Here, we take an event set of hurricane rainfall estimates from a synthetic hurricane rainfall simulator as the input to an event-based rainfall-driven flood inundation model using the hydrodynamic code LISFLOOD-FP. Validation of our model against high-water-mark data for Hurricane Maria demonstrates the suitability of this model for estimating flood hazard in Puerto Rico. We produce event-based flood hazard and population exposure estimates for the present day and the future under the 1.5 and 2 ∘C Paris Agreement goals. Population exposure to flooding from hurricane rainfall in Puerto Rico for the present-day climate is approximately 8 %–10 % of the current population for a 5-year return period, with an increase in population exposure to flooding by 2 %–15 % and 1 %–20 % under 1.5 and 2 ∘C futures (5-year return period). This research demonstrates the significance of the 1.5 ∘C Paris Agreement goal for Small Island Developing States, providing the first event-based estimates of flooding from hurricane rainfall under climate change for a small island. [ABSTRACT FROM AUTHOR]

Flooding associated with Hurricane Maria in 2017 had devastating consequences for lives and livelihoods in Puerto Rico. Yet, an understanding of current and future flood risk in small islands like Puerto Rico is limited. Thus, efforts to build resilience to flooding associated with hurricanes remain constrained. Here, we take an event set of hurricane rainfall estimates from a synthetic hurricane rainfall simulator as the input to an event-based rainfall-driven flood inundation model using hydrodynamic code LISFLOOD-FP. Validation of our model against High Water Mark data for Hurricane Maria demonstrates the suitability of this model for estimating flood hazard in Puerto Rico. We produce event-based flood hazard and population exposure estimates for the present day, and the future under the 1.5 °C and 2 °C Paris Agreement goals. Population exposure to flooding from hurricane rainfall in Puerto Rico for the present day climate is approximately 8–10 % of the current population for 5-year return period, with an increase in population exposure to flooding by 2–15 % and 1–20 % under 1.5 °C and 2 °C futures (5-year return period). This research demonstrates the significance of the 1.5 °C Paris Agreement goal for Small Island Developing States, providing the first event-based estimates of flooding from hurricane rainfall under climate change in a small island. [ABSTRACT FROM AUTHOR]

Flooding associated with Hurricane Maria in 2017 had devastating consequences for lives and livelihoods in Puerto Rico. Yet, an understanding of current and future flood risk in small islands like Puerto Rico is limited. Thus, efforts to build resilience to flooding associated with hurricanes remain constrained. Here, we take an event set of hurricane rainfall estimates from a synthetic hurricane rainfall simulator as the input to an event- based rainfall-driven flood inundation model using hydrodynamic code LISFLOOD-FP. Validation of our model against High Water Mark data for Hurricane Maria demonstrates the suitability of this model for estimating flood hazard in Puerto Rico. We produce event-based flood hazard and population exposure estimates for the present day, and the future under the 1.5°C and 2°C Paris Agreement goals. Population exposure to flooding from hurricane rainfall in Puerto Rico for the present day climate is approximately 8-10% of the current population for 5-year return period, with an increase in population exposure to flooding by 2-15% and 1-20% under 1.5°C and 2°C futures (5-year return period). This research demonstrates the significance of the 1.5°C Paris Agreement goal for Small Island Developing States, providing the first event-based estimates of flooding from hurricane rainfall under climate change in a small island. [ABSTRACT FROM AUTHOR]