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 short‐duration 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.

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.

In general, construction projects are vulnerable to various risks during their life cycle. These risks may negatively affect the project cost and duration, in addition to other factors, including environmental impact, health and safety, and quality of construction work. The COVID-19 pandemic has brought more challenges to construction projects. This study presents an assessment of 47 major risks affecting construction projects. The research investigates and assesses the risk factors associated with a special type of construction project, namely, projects funded by external sources, due to their importance, characteristics, and sensitivity. Furthermore, the study was carried out under the impact of the special conditions and constraints associated with the COVID-19 health pandemic. These conditions provided specific types of risks, results of risk assessment, and recommended responses to the risks. The studied risks were categorized using the PESTLE technique, an external factor analysis technique, which includes six groups of Political, Economic, Social, Technological, Legal, and Environmental risks. A questionnaire survey was conducted on 34 construction organizations that implemented or supervised projects funded by external sources. The aim of the survey was to assess the risks affecting the construction projects in terms of probability of occurrence and severity on the projects’ cost and time schedule. Then, the importance of each risk was calculated, and the risks were ranked according to their importance. The results showed the high importance of environmental and legal risks and indicated that the most important risk factors are the difficulty of issuing licenses and permits and the inappropriate definition of the scope of work. This study would help managers and fund providers make decisions regarding risks during uncontrolled pandemic or disastrous circumstances. Although the current study was conducted in Jordan, its procedures and results can be useful in other locations with different properties and conditions.

This study aimed to assess the quality of Bus Rapid Transit (BRT) service in a developing country, implementing this service for the first time, based on service users’ perception. A convenience sample of 425 BRT users were surveyed using a structured QR-based survey. Moreover, to avoid sampling bias toward any specific age group or level of education, participants who had limited familiarity with filling out QR-based surveys received individualized assistance. Structural Equation Modelling (SEM) was used to test the proposed model and assess the relationships between the identified factors and the overall perceived quality of the BRT service. BRT users reported a positive perception of the quality of the service, with over 53% expressing satisfaction. Moreover, 63% and 67% reported feeling safe on the bus and bus stations, respectively. Furthermore, 71% reported appropriate service hours, while over 65% reported adequate frequency. Besides, 62% indicated the availability of services (comfortability) in bus stations. The SEM results indicated accessibility and operational, and safety are essential for predicting the quality of the Amman BRT service. The model goodness fit results indicated a good fit to the data and a reliable factor loadings, where χ²/d.f = 3.513, Root Mean Squared Error (RMSEA) = 0.079, Standardized Root Mean Square Residual (SRMR) = 0.057, Comparative Fit Index (CFI) = 0.905, and Tucker-Lewis Index (TLI) = 0.8. These findings highlighted the users related quality factors. Furthermore, the results showed that 80% of the users are student universities within the age group of 18–24, which indicates the need to promote using BRT across other age groups and sectors.