Climate

Within the U.S., there are racial–ethnic and regional disparities in climate event experiences. For example, the West region has experienced increased frequencies of wildfires, whereas minoritized racial–ethnic groups have experienced more climate events. There is limited research investigating the intersection between race–ethnicity and region in relation to multiple climate events, particularly with a national U.S. sample. We aimed to examine regional (Northeast, Midwest, South, and West) differences in five climate event exposures (wildfire, drought, sea level rise, severe weather, and heat wave), and assess whether race–ethnicity (White, Black, Hispanic, and Asian) moderates this relationship. Our study utilized the 2022 American Trends Panel data, a nationally representative sample of 9799 U.S. adults. Regional and climate associations were analyzed using chi-square tests, while moderation was tested using interactions between race–ethnicity and region in separate logistic regression models that adjusted for sociodemographic factors. We found elevated frequencies of wildfires, drought, and heat waves in the West, sea level rise in all coastal regions except the inland Midwest, and severe weather in the South. Within the Northeast, Black adults were less exposed to sea level rise, while Asian adults were less exposed to wildfires and sea level rise. Within the Midwest, Black adults were less exposed to drought. Within the South, Hispanic adults were more exposed to drought. These findings provide insights into tailoring emergency preparedness efforts by region and prompt further investigation into reasons why some racial–ethnic groups are less likely to experience certain climate events.

The escalating frequency and severity of extreme weather events globally have underscored the critical importance of addressing anthropogenic climate change. Countries that contribute disproportionately to global warming relative to their population size bear an urgent responsibility to mitigate climate risks. However, effecting substantive policy change requires a broad public consensus to compel legislative action, a process fundamentally dependent on risk perception. It is theorized that individuals, households, and communities with higher levels of climate change risk perception are more inclined to adopt mitigation behaviors and support collective action. Such perception, however, varies considerably across social dimensions. This study aims to examine how sociodemographic factors shape climate change risk perception among Americans and how intersectionality reveals nuanced patterns beyond single-axis analysis. Using data from the 2023 National Survey of Health Attitudes, the analysis demonstrates that gender, race/ethnicity, educational attainment, religiosity, marital status, and geographic region serve as strong predictors of climate risk perception. Further intersectional analysis reveals that individuals with multiple marginalized social identities, such as race, gender, and socioeconomic status, perceive climate risk distinctly from those without such compounded identities. The study concludes that effective climate communication and policy interventions must prioritize sociodemographic diversity and integrate an intersectional lens to address differential vulnerabilities and perceptions equitably.

This study presents a comparative flood frequency analysis of annual maximum discharges for major Romanian river basins, assessing the performance of the Halphen-A distribution relative to the Pearson Type III distribution, the reference model in Romanian hydrological practice. Four long-term discharge series from the Siret, Ialomița, and Danube rivers are analyzed, covering diverse hydroclimatic conditions. Distribution parameters are estimated using the method of moments and maximum likelihood estimation. Model performance is evaluated using RMSE and MAE, complemented by an analysis of extreme quantile behavior. The results show that both distributions fit the observed data well, with only minor differences in global error metrics. However, for high return periods (T > 100 years), Halphen-A exhibits smoother extrapolation and yields more stable extreme quantile estimates, particularly when estimated by MLE. Although Pearson III often achieves slightly lower metrics values, its upper tail is more constrained and sensitive to skewness and record length. The study concludes that classical goodness-of-fit measures alone are insufficient for selecting models for design floods and that Halphen-A provides a robust complementary alternative for extreme flood estimation.