Search

In coastal regions, compound flooding can arise from a combination of different drivers, such as storm surges, high tides, excess river discharge, and rainfall. Compound flood potential is often assessed by quantifying the dependence and joint probabilities of flood drivers using multivariate models. However, most of these studies assume that all extreme events originate from a single population. This assumption may not be valid for regions where flooding can arise from different generation processes, e.g., tropical cyclones (TCs) and extratropical cyclones (ETCs). Here we present a flexible copula-based statistical framework to assess compound flood potential from multiple flood drivers while explicitly accounting for different storm types. The proposed framework is applied to Gloucester City, New Jersey, and St. Petersburg, Florida, as case studies. Our results highlight the importance of characterizing the contributions from TCs and non-TCs separately to avoid potential underestimation of the compound flood potential. In both study regions, TCs modulate the tails of the joint distributions (events with higher return periods), while non-TC events have a strong effect on events with low to moderate joint return periods. We show that relying solely on TCs may be inadequate when estimating compound flood risk in coastal catchments that are also exposed to other storm types. We also assess the impact of non-classified storms that are not linked to either TCs or ETCs in the region (such as locally generated convective rainfall events and remotely forced storm surges). The presented study utilizes historical data and analyzes two populations, but the framework is flexible and can be extended to account for additional storm types (e.g., storms with certain tracks or other characteristics) or can be used with model output data including hindcasts or future projections.

IntroductionHelicobacter pylori (Hp) infection is highly prevalent globally and poses a significant public health challenge due to its link with chronic gastritis, peptic ulcers, and gastric malignancies. Hp’s persistence within the gastric environment, particularly in case of infection with virulent strains, triggers chronic inflammatory responses and mucosal damage. Antibiotic therapy is the primary approach for Hp eradication, but antibiotic resistance and adverse effects hinder treatment efficacy. Emerging evidence suggests that Brassicaceae-derived metabolites could serve as adjunctive therapy for Hp infection, offering potential antimicrobial and anti-inflammatory benefits.MethodsA systematic literature review was conducted following PRISMA guidelines to assess the impact of Brassicaceae-rich diets on Hp infection control. Searches were performed in MEDLINE PubMed, Web of Science, and the Cochrane Library until 18 October 2023, without language or date restrictions. Eligible studies meeting PICOS criteria were included, encompassing populations infected with Hp or Hp-infected human cell cultures, interventions involving Brassicaceae consumption or its bioactive molecules, and outcomes related to Hp infection control, antibiotic therapy interactions, reduction of antibiotic side effects, and inflammation mitigation. Animal studies, cell line experiments, reviews unrelated to the research objectives, and studies on Hp-related gastric cancer were excluded.ResultsAvailable evidence indicates that Brassicaceae consumption exhibits the potential to reduce Hp colonization but achieving complete eradication of the pathogen remains challenging. Conflicting results regarding the efficacy of broccoli in Hp treatment emerge, with certain investigations suggesting limited effectiveness. Other studies point to a potential for heightened eradication rates when combined with standard triple therapy. Furthermore, promising outcomes are observed with broccoli extract supplements, indicating their role in mitigating Hp-induced gastric mucosal damage. In fact, it is noteworthy that sulforaphane and its derivatives manifest notable reductions in pro-inflammatory markers, indicative of their anti-inflammatory properties. Adverse events associated with antibiotic therapy seem unaffected by sulforaphane derivatives or probiotics. However, individual responses to these treatments vary, underscoring the unpredictability of their efficacy in ameliorating antibiotic therapy-related side effects.ConclusionOur systematic review highlights the potential of Brassicaceae-rich diets as adjunctive therapy for Hp infection, offering synergistic interactions with antibiotics and possibly mitigating antibiotic side effects and inflammation. Further research, particularly well-designed randomized trials, is warranted to elucidate the therapeutic efficacy and optimal utilization of Brassicaceae-derived metabolites in managing human Hp-related diseases.

Acting as ‘antioxidants’, phenolic compounds have been shown to have a potential protective effect against a wide range of noncommunicable diseases. However, their content in plant-based products – and, in turn, their potential benefits – might be affected by the thermal procedures used in food cooking. In light of this, the aim of this work was a characterization of Patata Rossa di Colfiorito (PRC, an Italian PGI red-skinned potato) and an investigation of the impact of cooking and storage on its bioactive compounds, phenolic content, and antioxidant activity. After the harvesting (T0), samples were analyzed using instrumental analysis (e.g., HPLC-ESI-MS/MS) and a set of chemical assays (total phenolic content, 2,2-Diphenyl-1-picrylhydrazyl – DPPH, 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) – ABTS, and oxygen radical absorbance capacity – ORAC assays). Analyses were also performed on boiled, pressure-cooked and one-month-stored (T1) products. A commercial red-skinned potato (RSP) was also analyzed.Overall, compared with the raw product, both boiling and pressure-cooking led to a significant increase in bioactive compounds (raw: 168.15 mg kg−1 of fresh matter, FM; boiled: 398.92 mg kg−1 FM; pressure-cooked: 309.24 mg kg−1 FM), total phenolic content and antioxidant activity measured by DPPH and ABTS assays.PRC samples showed higher content in bioactive compounds, TPC values and antioxidant activity compared with commercial RSP (with some sporadic exceptions in ORAC values). Interestingly, one month of domestic storage did not affect the freshly harvested product’s features.In conclusion, these results highlighted the quality of this local product and its property to withstand thermal processing and storage. We believe that the results we have obtained should be taken into consideration by health professionals, at both local and national levels, and that PRC deserves to be further studied to investigate its potential human health benefits, as well.

Abstract The results of probabilistic event attribution studies depend on the choice of the extreme value statistics used in the analysis, particularly with the arbitrariness in the selection of appropriate thresholds to define extremes. We bypass this issue by using the Extended Generalized Pareto Distribution (ExtGPD), which jointly models low precipitation with a generalized Pareto distribution and extremes with a different Pareto tail, to conduct daily precipitation attribution across the contiguous United States (CONUS). We apply the ExtGPD to 12 general circulation models from the Coupled Model Intercomparison Project Phase 6 and compare counterfactual scenarios with and without anthropogenic emissions. Observed precipitation by the Climate Prediction Center is used for evaluating the GCMs. We find that greenhouse gases rather than natural variability can explain the observed magnitude of extreme daily precipitation, especially in the temperate regions. Our results highlight an unambiguous linkage of anthropogenic emissions to daily precipitation extremes across CONUS.

The early and precise identification of a brain tumour is imperative for enhancing a patient’s life expectancy; this can be facilitated by quick and efficient tumour segmentation in medical imaging. Automatic brain tumour segmentation tools in computer vision have integrated powerful deep learning architectures to enable accurate tumour boundary delineation. Our study aims to demonstrate improved segmentation accuracy and higher statistical stability, using datasets obtained from diverse imaging acquisition parameters. This paper introduces a novel, fully automated model called Enhanced Channel Attention Transformer (E-CATBraTS) for Brain Tumour Semantic Segmentation; this model builds upon 3D CATBraTS, a vision transformer employed in magnetic resonance imaging (MRI) brain tumour segmentation tasks. E-CATBraTS integrates convolutional neural networks and Swin Transformer, incorporating channel shuffling and attention mechanisms to effectively segment brain tumours in multi-modal MRI. The model was evaluated on four datasets containing 3137 brain MRI scans. Through the adoption of E-CATBraTS, the accuracy of the results improved significantly on two datasets, outperforming the current state-of-the-art models by a mean DSC of 2.6% while maintaining a high accuracy that is comparable to the top-performing models on the other datasets. The results demonstrate that E-CATBraTS achieves both high segmentation accuracy and elevated generalisation abilities, ensuring the model is robust to dataset variation.

Abstract Urban areas have been shown to impact rainfall by altering both its intensity and spatial structure at sub‐hourly and sub‐kilometer scales. However, there is currently no clear understanding of the precise pattern of change and the mechanisms that drive these changes. Since the hydrological response in urban areas is highly sensitive to such rainfall properties, understanding these changes is critical to improving our ability to assess urban flood risk. We use 7 years of high‐resolution weather radar data (4‐ or 5‐min and 1 km) to analyze changes in patterns of rainfall intensity, spatial structure, and storm evolution across eight urban areas within Europe and the United States. The use of the same methodology across the different cities enables a consistent comparison among them. We track convective rainfall events using a storm tracking algorithm and assess changes in rainfall properties in the upwind, center, and downwind regions of each city. We also investigate changes in the frequency of storm initiations, terminations, splitting, and merging. Our results show that urban areas act to intensify rainfall—mostly over them, but sometimes on their peripheries. Overall, larger cities tend to show the largest rainfall enhancements. Our findings highlight that rainfall spatial structure is altered over the urban core; usually resulting in more spatially concentrated rainfall. We also observe increased storm initiations over most cities and increased storm splitting over one. Given that demographic projections show that future urban population will increase, our results point toward an increased future flood risk in growing cities.

In response to flood risk, design flood estimation is a cornerstone of planning, infrastructure design, setting of insurance premiums, and emergency response planning. Under stationary assumptions, flood guidance and the methods used in design flood estimation are firmly established in practice and mature in their theoretical foundations, but under climate change, guidance is still in its infancy. Human-caused climate change is influencing factors that contribute to flood risk such as rainfall extremes and soil moisture, and there is a need for updated flood guidance. However, a barrier to updating flood guidance is the translation of the science into practical application. For example, most science pertaining to historical changes to flood risk focuses on examining trends in annual maximum flood events or the application of non-stationary flood frequency analysis. Although this science is valuable, in practice, design flood estimation focuses on exceedance probabilities much rarer than annual maximum events, such as the 1 % annual exceedance probability event or even rarer, using rainfall-based procedures, at locations where there are few to no observations of streamflow. Here, we perform a systematic review to summarize the state-of-the-art understanding of the impact of climate change on design flood estimation in the Australian context, while also drawing on international literature. In addition, a meta-analysis, whereby results from multiple studies are combined, is conducted for extreme rainfall to provide quantitative estimates of possible future changes. This information is described in the context of contemporary design flood estimation practice to facilitate the inclusion of climate science into design flood estimation practice.

Abstract Structural connectivity describes how landscapes facilitate the transfer of matter and plays a critical role in the flux of water, solutes, and sediment across the Earth’s surface. The strength of a landscape’s connectivity is a function of climatic and tectonic processes, but the importance of these drivers is poorly understood, particularly in the context of climate change. Here, we provide global estimates of structural connectivity at the hillslope level and develop a model to describe connectivity accounting for tectonic and climate processes. We find that connectivity is primarily controlled by tectonics, with climate as a second order control. However, we show climate change is projected to alter global-scale connectivity at the end of the century (2070 to 2100) by up to 4% for increasing greenhouse gas emission scenarios. Notably, the Ganges River, the world’s most populated basin, is projected to experience a large increase in connectivity. Conversely, the Amazon River and the Pacific coast of Patagonia are projected to experience the largest decreases in connectivity. Modeling suggests that, as the climate warms, it could lead to increased erosion in source areas, while decreased rainfall may hinder sediment flow downstream, affecting landscape connectivity with implications for human and environmental health.

Abstract Understanding changes in the hazard component of climate risk is important to inform societal resilience planning in a changing climate. Here, we examine local changes in wind speed, rainfall, and flooding related to tropical cyclones (TCs) and compare them across statistical and dynamical modeling approaches. Our focus region is the Delaware River Basin, located in the northeastern United States. We pair event‐based downscaling with large ensemble climate model information to capture the details of extreme TC wind, rain, and flooding, and their likelihood, in a changing climate. We identify local TCs in the Community Earth System Model 2 Large Ensemble (CESM2‐LENS). We find fewer TCs in the future, but these future storms have higher wind speeds and are wetter. We also find that TCs produce heavier 3‐day precipitation distributions than all other summertime weather events, with TCs constituting a larger percentage of the upper tail of the full precipitation distribution. With this information, we identify a small collection of 200‐year return events and compare the resulting TC rain and wind across dynamical and statistical downscaling methods. We find that dynamical downscaling produces peak rain rates far higher than CESM or the statistical downscaling method. It can also produce quite different future changes in precipitation totals for the small set of events considered here. This leads to vastly different flood responses. Overall, our results highlight the need to interpret future changes of event‐based simulations in the context of downscaling method limitations.

Abstract This study evaluates five bias correction and statistical downscaling (BCSD) techniques for daily precipitation and examines their impacts on the projected changes in flood extremes (i.e., 1%, 0.5%, and 0.2% floods). We use climate model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to conduct hydrologic simulations across watersheds in Iowa and determine historical and future flood extreme estimates based on generalized extreme value distribution fitting. Projected changes in these extremes are examined with respect to four Shared Socioeconomic Pathways (SSPs) alongside five BCSD techniques. We find the magnitude of the estimates of future annual exceedance probabilities (AEPs) are expected to increase under all SSPs, especially for the emission scenarios with higher greenhouse gases concentrations (i.e., SSP370 and SSP585). Our results also suggest the choice of BCSD impacts the magnitude of the projected changes, with the SSPs that play a more limited role compared to the choice of downscaling method. The variability in projected flood changes across Iowa is similar across the downscaling technique but increases as the AEP increases. Our findings provide insights into the impact of downscaling techniques on flood extremes' projections and useful information for climate planning across the state.

Abstract The detection of nonstationarities in partial duration time series (PDS) depends on several factors, including the length of the time series, the selected statistical test, and the heaviness of the tail of the distribution. Because of the more limited attention received in the literature when compared to the trend detection on block maxima variables, we perform a Monte Carlo simulation study to evaluate the performance of different approaches: Spearman's rho, Mann–Kendall, ordinary least squares (OLS), Sen's slope estimator (SEN), and the nonstationary generalized Pareto distribution fit to identify the presence of trends in PDS records characterized by different sample sizes (n), shape parameter (ξ) and degrees of nonstationarity. The results point to a power gain for all tests by increasing n and the degree of nonstationarity and by reducing ξ. The use of a nonparametric test is recommended in samples with a high positive skew. Furthermore, the use of sampling rates greater than one to increase the PDS sample size is encouraged, especially when dealing with small records. The use of SEN to estimate the magnitude of a trend is preferable over OLS due to its slightly smaller probability of occurrence of type S error when ξ is positive.

Abstract Extreme flood events have regional differences in their generating mechanisms due to the complex interaction of different climate and catchment processes. This study aims to examine the capability of climate drivers to capture year‐to‐year variability in global flood extremes. Here, we use a statistical attribution approach to model seasonal and annual maximum daily discharge for 7,886 stations worldwide, using season‐ and basin‐averaged precipitation and temperature as predictors. The results show robust performance of our seasonal climate‐informed models in describing the inter‐annual variability in seasonal and annual maximum discharges regardless of the geographical region, climate type, basin size, degree of regulation, and impervious area. The developed models enable the assessment of the sensitivity of flood discharge to precipitation and temperature changes, indicating their potential to reliably project changes in the magnitude of flood extremes.