The COVID-19 pandemic affected the lives of millions of people, radically changing their habits in just a few days. In many countries, containment measures prescribed by national governments restricted the movements of entire communities, with the impossibility of attending schools, universities, workplaces, and no longer allowing for traveling or leading a normal social life. People were then compelled to revise their habits and lifestyles. In such a situation, the availability of drinking water plays a crucial role in ensuring adequate health conditions for people and tackling the spread of the pandemic. Lifestyle of the population, climate, water scarcity and water price are influent factors on water drinking demand and its daily pattern. To analyze the effect of restriction measures on water demand, the instantaneous flow data of five Apulian towns (Italy) during the lockdown have been analyzed highlighting the important role of users’ habits and the not negligible effect of commuters on the water demand pattern besides daily volume requested.

The present paper deals with a performance assessment of the ERA5 wave dataset in an ocean basin where local wind waves superimpose on swell waves. The evaluation framework relies on observed wave data collected during a coastal experimental campaign carried out offshore of the southern Oman coast in the Western Arabian Sea. The applied procedure requires a detailed investigation on the observed waves, and aims at classifying wave regimes: observed wave spectra have been split using a 2D partition scheme and wave characteristics have been evaluated for each wave component. Once the wave climate was defined, a detailed wave model assessment was performed. The results revealed that during the analyzed time span the ERA5 wave model overestimates the swell wave heights, whereas the wind waves’ height prediction is highly influenced by the wave developing conditions. The collected field dataset is also useful for a discussion on spectral wave characteristics during monsoon and post-monsoon season in the examined region; the recorded wave data do not suffice yet to adequately describe wave fields generated by the interaction of monsoon and local winds.

Over the last few years, several socio-hydrological studies have investigated the risk dynamics generated by the complex interactions between floods and societies, with a focus on either changing reservoir operation rules or raising levees. In this study, we propose a new socio-hydrological model of human–flood interactions that represents both changes in the reservoir management strategies and updating of the levee system. Our model is applied to simulate three prototypes of floodplain management strategies to cope with flood risk: green systems, in which societies resettle outside the flood-prone area; technological systems, in which societies implement structural measures, such as levees; and green-to-techno systems, in which societies shift from green to technological approaches. Floodplain dynamics are explored simulating possible future scenarios in the city of Brisbane, Australia. Results show that flood risk is strongly influenced by changes in flood and drought memory of reservoir operators, while risk-awareness levels shape the urbanisation of floodplains. Furthermore, scenarios of more frequent and higher magnitude events prove to enhance social flood memory in green systems, while technological systems experience much higher losses. Interestingly, green-to-techno systems may also evolve toward green floodplain management systems in response to large losses and technical/economical unfeasibility of larger structural measures.

The need to fit time series characterized by the presence of a trend or change points has generated increased interest in the investigation of nonstationary probability distributions in recent years. Considering that the available hydrological time series can be recognized as the observable part of a stochastic process with a definite probability distribution, two main topics can be tackled in this context: the first is related to the definition of an objective criterion for choosing whether the stationary hypothesis can be adopted, whereas the second regards the effects of nonstationarity on the estimation of distribution parameters and quantiles for an assigned return period and flood risk evaluation. Although the time series trend or change points are usually detected using nonparametric tests available in the literature (e.g., Mann–Kendall or CUSUM test), the correct selection of the stationary or nonstationary probability distribution is still required for design purposes. In this light, the focus is shifted toward model selection criteria; this implies the use of parametric methods, including all of the issues related to parameter estimation. The aim of this study is to compare the performance of parametric and nonparametric methods for trend detection, analyzing their power and focusing on the use of traditional model selection tools (e.g., the Akaike information criterion and the likelihood ratio test) within this context. The power and efficiency of parameter estimation, including the trend coefficient, were investigated via Monte Carlo simulations using the generalized extreme value distribution as the parent with selected parameter sets.