Background University students are increasingly recognized as a vulnerable population, suffering from higher levels of anxiety, depression, substance abuse, and disordered eating compared to the general population. Therefore, when the nature of their educational experience radically changes—such as sheltering in place during the COVID-19 pandemic—the burden on the mental health of this vulnerable population is amplified. The objectives of this study are to 1) identify the array of psychological impacts COVID-19 has on students, 2) develop profiles to characterize students' anticipated levels of psychological impact during the pandemic, and 3) evaluate potential sociodemographic, lifestyle-related, and awareness of people infected with COVID-19 risk factors that could make students more likely to experience these impacts. Methods Cross-sectional data were collected through web-based questionnaires from seven U.S. universities. Representative and convenience sampling was used to invite students to complete the questionnaires in mid-March to early-May 2020, when most coronavirus-related sheltering in place orders were in effect. We received 2,534 completed responses, of which 61% were from women, 79% from non-Hispanic Whites, and 20% from graduate students. Results Exploratory factor analysis on close-ended responses resulted in two latent constructs, which we used to identify profiles of students with latent profile analysis, including high (45% of sample), moderate (40%), and low (14%) levels of psychological impact. Bivariate associations showed students who were women, were non-Hispanic Asian, in fair/poor health, of below-average relative family income, or who knew someone infected with COVID-19 experienced higher levels of psychological impact. Students who were non-Hispanic White, above-average social class, spent at least two hours outside, or less than eight hours on electronic screens were likely to experience lower levels of psychological impact. Multivariate modeling (mixed-effects logistic regression) showed that being a woman, having fair/poor general health status, being 18 to 24 years old, spending 8 or more hours on screens daily, and knowing someone infected predicted higher levels of psychological impact when risk factors were considered simultaneously. Conclusion Inadequate efforts to recognize and address college students’ mental health challenges, especially during a pandemic, could have long-term consequences on their health and education.

No abstract

Precipitation is an important climate variable to investigate extreme events (e.g. drought and flood) as well as to develop robust strategies for water resources planning and management. Lack of adequate and robust information on precipitation poses great difficulties in understanding the observed climate as well as to validate climate model outputs. To overcome this limitation gridded precipitation data sets have been constructed to supplement the lack of in situ data. This study compares five popular gridded precipitation data sets to evaluate their performance in terms of drought and wetness over Vietnam. These five gridded data sets include: (1) Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE), (2) Climate Precipitation Center (CPC), (3) Climate Research Unit (CRU), (4) Global Precipitation Climatology Center (GPCC) and (5) University of Delaware (UDEL). The recently developed gridded precipitation observational data ‘VnGP’ from Vietnam is used as the reference data set to assess the performance of these five gridded precipitation products. The Standardized Precipitation Index (SPI) is used to quantify drought and wetness. GPCC and APHRODITE performed reasonably well in reproducing spatial and temporal precipitation patterns. GPCC performs consistently better than APHRODITE in all the statistical tests. Except for UDEL, other gridded data sets able to exhibit the characteristics of drought/wetness (e.g. the percentage of drought events and severity) during strong El Nino Southern Oscillation (ENSO) events. However, higher uncertainty exists to quantify drought inter‐arrival time in most of the data sets. Furthermore, trend analysis was performed to evaluate the comparative performance of gridded data sets to quantify drought (wet) spells at annual and seasonal time scales. Although the gauge‐based and hybrid satellite–gauge merged products use partly ground truth data, the different interpolation techniques and merging algorithms may contribute to large uncertainties.

No abstract

No abstract

Abstract:Understanding the teleconnections between hydro-meteorological data and the El Niño-Southern Oscillation cycle (ENSO) is an important step towards developing flood early warning systems. In this study, the concept of mutual information (MI) was applied using marginal and joint information entropy to quantify the linear and non-linear relationship between annual streamflow, extreme precipitation indices over Mekong river basin, and ENSO. We primarily used Pearson correlation as a linear association metric for comparison with mutual information. The analysis was performed at four hydro-meteorological stations located on the mainstream Mekong river basin. It was observed that the nonlinear correlation information is comparatively higher between the large-scale climate index and local hydro-meteorology data in comparison to the traditional linear correlation information. The spatial analysis was carried out using all the grid points in the river basin, which suggests a spatial dependence structure between precipitation extremes and ENSO. Overall, this study suggests that mutual information approach can further detect more meaningful connections between large-scale climate indices and hydro-meteorological variables at different spatio-temporal scales. Application of nonlinear mutual information metric can be an efficient tool to better understand hydro-climatic variables dynamics resulting in improved climate-informed adaptation strategies.

ABSTRACT. Indochina Peninsula has abundant water resources

Motivated from the increasing need to develop a science-based, predictive understanding of the dynamics and response of cities when subjected to natural hazards, in this paper, we apply concepts from statistical mechanics and microrheology to develop mechanical analogues for cities with predictive capabilities. We envision a city to be a matrix where cell-phone users are driven by the city’s economy and other incentives while using the collection of its infrastructure networks in a similar way that thermally driven Brownian particles are moving within a complex viscoelastic material. Mean-square displacements of thousands of cell-phone users are computed from GPS location data to establish the creep compliance and the resulting impulse response function of a city. The derivation of these time-response functions allows the synthesis of simple mechanical analogues that model satisfactorily the city’s behaviour under normal conditions. Our study concentrates on predicting the response of cities to acute shocks (natural hazards) that are approximated with a rectangular pulse; and we show that the derived solid-like mechanical networks predict that cities revert immediately to their pre-event response suggesting an inherent resilience. Our findings are in remarkable good agreement with the recorded response of the Dallas metroplex following the February 2021 North American winter storm.