A vine copula-based ensemble projection of precipitation intensity–duration–frequency curves at sub-daily to multi-day time scales

Precipitation intensity–duration–frequency (IDF) curves play a crucial role in the design and planning of urban infrastructure to reduce the risk of urban flooding and rainfall-triggered landslides. However, changing rainfall characteristics in a warming climate render conventional IDF curves inappropriate due to the statistical assumption of stationarity. In this study, we develop vine copula-based projections of future IDF curves at sub-daily to multi-day time scales with a multi-model ensemble of five regional climate simulations over China. Stochastic spatiotemporal downscaling of precipitation is achieved to generate extreme precipitation simulations at a high spatial (0.1°) and temporal (3 hourly) resolution. These downscaled simulations are combined by the vine copula to improve the reliability and accuracy of climate-model-based IDF curves relative to historical observations. Our findings reveal that climate-model-based stochastic downscaling of precipitation reproduces the IDF curves well based on historical observations in China. The vine copula multi-model ensemble approach outperforms Bayesian model averaging by generating more accurate and reliable IDF curves. The urban areas of 196 Chinese cities are projected to experience an increase in extreme precipitation of up to 30% in intensity and nearly two times the frequency of historical events under a high emission scenario (RCP8.5). The current urban infrastructure of more than half of the 196 cities would thus be inadequate to prevent losses caused by rainfall-triggered hazards if designed solely based on historical precipitation observations. Compared to the climate-model-based IDF curves, we find that statistical IDF curves based on a nonstationary time covariate (i.e., extrapolating historical trends) are likely to underestimate the risk of urban infrastructure failures under a warming climate. This work highlights that urban infrastructure design guidelines in China should be upgraded to adapt existing IDF curves to the changing climate

Estimation of PM2.5 concentrations in China using a spatial back propagation neural network

Methods for estimating the spatial distribution of PM2.5 concentrations have been developed but have not yet been able to effectively include spatial correlation. We report on the development of a spatial back-propagation neural network (S-BPNN) model designed specifically to make such correlations implicit by incorporating a spatial lag variable (SLV) as a virtual input variable. The S-BPNN fits the nonlinear relationship between ground-based air quality monitoring station measurements of PM2.5, satellite observations of aerosol optical depth, meteorological synoptic conditions data and emissions data that include auxiliary geographical parameters such as land use, normalized difference vegetation index, elevation, and population density. We trained and validated the S-BPNN for both yearly and seasonal mean PM2.5 concentrations. In addition, principal components analysis was employed to reduce the dimensionality of the data and a grid of neural network models was run to optimize the model design. The S-BPNN was cross-validated against an analogous but SLV-free BPNN model using the coefficient of determination (R2) and root mean squared error (RMSE) as statistical measures of goodness of fit. The inclusion of the SLV led to demonstrably superior performance of the S-BPNN over the BPNN with R2 values increasing from 0.80 to 0.89 and with the RMSE decreasing from 8.1 to 5.8 μg/m3. The yearly mean PM2.5 concentration in China during the study period was found to be 41.8 μg/m3 and the model estimated spatial distribution was found to exceed Level 2 of the China Ambient Air Quality Standards (CAAQS) enacted in 2012 (>35 μg/m3) in more than 70% of the Chinese territory. The inclusion of spatial correlation upgrades the performance of conventional BPNN models and provides a more accurate estimation of PM2.5 concentrations for air quality monitoring

Culture-level dimensions of social axioms and their correlates across 41 cultures

Leung and colleagues have revealed a five-dimensional structure of social axioms across individuals from five cultural groups. The present research was designed to reveal the culture level factor structure of social axioms and its correlates across 41 nations. An ecological factor analysis on the 60 items of the Social Axioms Survey extracted two factors: Dynamic Externality correlates with value measures tapping collectivism, hierarchy, and conservatism and with national indices indicative of lower social development. Societal Cynicism is less strongly and broadly correlated with previous values measures or other national indices and seems to define a novel cultural syndrome. Its national correlates suggest that it taps the cognitive component of a cultural constellation labeled maleficence, a cultural syndrome associated with a general mistrust of social systems and other people. Discussion focused on the meaning of these national level factors of beliefs and on their relationships with individual level factors of belief derived from the same data set.(undefined

Identification of beryllium-dependent peptides recognized by CD4+ T cells in chronic beryllium disease

Chronic beryllium disease (CBD) is a granulomatous disorder characterized by an influx of beryllium (Be)-specific CD4(+) T cells into the lung. The vast majority of these T cells recognize Be in an HLA-DP–restricted manner, and peptide is required for T cell recognition. However, the peptides that stimulate Be-specific T cells are unknown. Using positional scanning libraries and fibroblasts expressing HLA-DP2, the most prevalent HLA-DP molecule linked to disease, we identified mimotopes and endogenous self-peptides that bind to MHCII and Be, forming a complex recognized by pathogenic CD4(+) T cells in CBD. These peptides possess aspartic and glutamic acid residues at p4 and p7, respectively, that surround the putative Be-binding site and cooperate with HLA-DP2 in Be coordination. Endogenous plexin A peptides and proteins, which share the core motif and are expressed in lung, also stimulate these TCRs. Be-loaded HLA-DP2–mimotope and HLA-DP2–plexin A4 tetramers detected high frequencies of CD4(+) T cells specific for these ligands in all HLA-DP2(+) CBD patients tested. Thus, our findings identify the first ligand for a CD4(+) T cell involved in metal-induced hypersensitivity and suggest a unique role of these peptides in metal ion coordination and the generation of a common antigen specificity in CBD

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Growing Threats From Swings Between Hot and Wet Extremes in a Warmer World

The abrupt alternation between hot and wet extremes can lead to more severe societal impacts than isolated extremes. However, despite an understanding of hot and wet extremes separately, their temporally compounding characteristics are not well examined yet. Our study presents a comprehensive assessment of successive heat-pluvial and pluvial-heat events globally. We find that these successive extremes within a week occur every 6–7 years on average within warm seasons during 1956–2015, about 15% more often than would be expected by chance, and that they have a significant increase in frequency of about 22% per decade due to warming. We further investigate the role of vapor pressure deficit (VPD) and find that heat-pluvial (pluvial-heat) events are linked to negative (positive) VPD anomalies. Our results are statistically significant based on moving-blocks bootstrap resampling and field significance tests, highlighting these methods' importance in robustly identifying compound events under autocorrelation and multiple-testing conditions

Improving Risk Projection and Mapping of Coastal Flood Hazards Caused by Typhoon-Induced Storm Surges and Extreme Sea Levels

Seawater inundation mapping plays a crucial role in climate change adaptation and flooding risk reduction for coastal low-lying areas. This study presents a new elevation model called the digital impermeable surface model (DISM) based on the topographical data acquired by unmanned aerial vehicle (UAVs) for improving seawater inundation mapping. The proposed DISM model, along with the bathtub model, was used to assess coastal vulnerability to flooding in significant tropical cyclone events in a low-lying region of Victoria Harbor in Hong Kong. The inundation simulations were evaluated based on the typhoon news and reports which indicated the actual storm surge flooding conditions. Our findings revealed that the proposed DISM obtains a higher accuracy than the existing digital elevation model (DEM) and the digital surface model (DSM) with a RMSE of 0.035 m. The DISM demonstrated a higher skill than the DEM and the DSM by better accounting for the water-repellent functionality of each geospatial feature and the water inflow under real-life conditions. The inundation simulations affirmed that at least 88.3% of the inundated areas could be recognized successfully in this newly-designed model. Our findings also revealed that accelerating sea level rise in Victoria Harbor may pose a flooding threat comparable to those induced by super typhoons by the end of the 21st century under two representative emission scenarios (RCP4.5 and RCP8.5). The seawater may overtop the existing protective measures and facilities, making it susceptible to flood-related hazards

Accelerated soil drying linked to increasing evaporative demand in wet regions

Abstract The rapid decline in soil water affects water resources, plant physiology, and agricultural development. However, the changes in soil drying rate and associated climatic mechanisms behind such changes remain poorly understood. Here, we find that wet regions have witnessed a significant increasing trend in the soil drying rate during 1980−2020, with an average increase of 6.01 − 9.90% per decade, whereas there is no consistent trend in dry regions. We also identify a near-linear relationship between the annual soil drying rate and its influencing factors associated with atmospheric aridity and high temperatures. Further, enhanced evapotranspiration by atmospheric aridity and high temperatures is the dominant factor increasing the soil drying rate in wet regions. Our results highlight the accelerated soil drying in the recent four decades in wet regions, which implies an increased risk of rapidly developing droughts, posing a serious challenge for the adaptability of ecosystems and agriculture to rapid drying

Examining the Impacts of Land Use on Air Quality from a Spatio-Temporal Perspective in Wuhan, China

Air pollution is one of the key environmental problems associated with urbanization and land use. Taking Wuhan city, Central China, as a case example, we explore the quantitative relationship between land use (built-up land, water bodies, and vegetation) and air quality (SO2, NO2, and PM10) based on nine ground-level monitoring sites from a long-term spatio-temporal perspective in 2007–2014. Five buffers with radiuses from 0.5 to 4 km are created at each site in geographical information system (GIS) and areas of land use categories within different buffers at each site are calculated. Socio-economic development, energy use, traffic emission, industrial emission, and meteorological condition are taken into consideration to control the influences of those factors on air quality. Results of bivariate correlation analysis between land use variables and annual average concentrations of air pollutants indicate that land use categories have discriminatory effects on different air pollutants, whether for the direction of correlation, the magnitude of correlation or the spatial scale effect of correlation. Stepwise linear regressions are used to quantitatively model their relationships and the results reveal that land use significantly influence air quality. Built-up land with one standard deviation growth will cause 2% increases in NO2 concentration while vegetation will cause 5% decreases. The increases of water bodies with one standard deviation are associated with 3%–6% decreases of SO2 or PM10 concentration, which is comparable to the mitigation effect of meteorology factor such as precipitation. Land use strategies should be paid much more attention while making air pollution reduction policies