Floods related to extreme precipitation events, especially intense, short-duration precipitation, may cause significant damage in urbanized areas, including transport infrastructure, electricity networks, and property. These events are expected to increase in frequency with climate change but their characteristics, at either hourly or multi-hourly timescales, have been little studied due to short and poor quality data records. We examine annual maximum (AMAX) hourly and multi-hourly (3, 6, 12, and 24 hr) precipitation accumulations in the United Kingdom using a quality-controlled hourly precipitation data set for the period 1992-2014. We describe their seasonality and diurnal cycle and use a regional frequency analysis (RFA) approach with L-moments to produce at-site return level estimates, and then use existing extreme precipitation regions to provide regional-scale return levels. The analysis shows a clear seasonality and the dominant occurrence of shortduration AMAX in summer with similar seasonality for 1 and 3 hr accumulation periods in some regions, while longer-duration AMAX (12 and 24 hr) behave similarly to each other in all regions but are distributed over a longer period including late autumn and winter. The diurnal cycle of 1 hr AMAX indicates that most extremes occur during the afternoon, with a peak typically between 1400 and 1700, especially in southern and eastern regions. However, we also demonstrate that existing regions for UK daily extremes are not able to adequately reflect differences at shorter durations and that new regions should be created. These results provide new insights to help in designing urban drainage systems and infrastructure, including the need for new scaling relationships between sub-daily and design accumulation periods. K E Y W O R D Sextreme precipitation, hourly precipitation, regional frequency analysis, UK

Recent flooding related to extreme precipitation in the UK has highlighted the importance of better understanding these events. Many studies have quantified annual exceedance probabilities (or return periods) for UK extreme daily precipitation using fixed regions (e.g., HadUKP) and region of interest (ROI) (e.g., Flood Estimation Handbook) approaches, although fewer have evaluated short-duration events, which are important for flash flooding. Existing UK extreme precipitation regions are based on daily datasets which have different characteristics compared to sub-daily extremes, and their application to quantify short-duration extremes may therefore be inappropriate. We use a recently available, quality-controlled hourly precipitation dataset for the UK from 1992 to 2014 to derive various extreme precipitation indices (e.g., annual maxima, 0.99 quantile) which are combined with additional climatological variables (e.g., temperature), geographical characteristics (e.g., latitude), and weather patterns (WPs) to characterize the UK hourly extreme precipitation climatology and to define five new hourly extreme regions. These regions fulfil regional homogeneity and discordancy statistical measures, and reflect the dynamical processes associated with the weather pattern categorisation defined over the UK and surrounding European area. Thereafter, we use regional frequency analysis (RFA) to fit generalized extreme value (GEV) and generalized Pareto (GP) distributions to 1 hr annual maxima (AMAX) and 0.99 quantile (Q99) precipitation, respectively, to calculate regional annual probability estimates (AEP) for 20%, 10%, and 2% (i.e., 5-, 10-, and 50-year return periods). The new regions capture the spatial variation of hourly precipitation across the UK. Furthermore, the AEP estimates using both distributions are similar for each region. Finally, the WPs associated with the frequency and intensity of the most extreme hourly precipitation accumulations are not identical to results reported by others for daily precipitation.

We aimed to assess the prevalence of psychological reactions (depression, anxiety, and stress) and their correlates among Jordanian nurses. This study was conducted using an online survey from March 22, 2020, to March 27, 2020. The Arabic version of Depression, Anxiety, and Stress Scale (DASS) was used. Depression, anxiety, and stress were highly prevalent among nurses (57.8%, 42.4%, and 50.1%, respectively). Those who had close contact with a coronavirus disease 2019 (COVID-19) patient showed stronger psychological reactions than their counterparts (partial = 0.264, part = 0.254). Moreover, female gender and number of children were the main significant predictors of depression [(B = 0.176), (B = 0.232), (B = 0.255)], anxiety [(B = 0.155), (B = 0.232), (B = 0.268)], and stress [(B = 0.148), (B = 0.218), (B = 0.258)]. Hence, the mental health status of nurses should be given priority, especially those who are in contact with COVID-19 patients, female nurses, and those who have children.

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