Abstract. A new rationale for deriving the probability distribution of floods and help in understanding the physical processes underlying the distribution itself is presented. On the basis of this a model that presents a number of new assumptions is developed. The basic ideas are as follows: (1) The peak direct streamflow Q can always be expressed as the product of two random variates, namely, the average runoff per unit area u• and the peak contributing area a; (2) the distribution of u• conditional on a can be related to that of the rainfall depth occurring in a duration equal to a characteristic response time •'• of the contributing part of the basin; and (3) •'• is assumed to vary with a according to a power law. Consequently, the probability density function of Q can be found as the integral, over the total basin area A, of that of a times the density function of u• given a. It is suggested that u• can be expressed as a fraction of the excess rainfall and that the annual flood distribution can be related to that of Q by the hypothesis that the flood occurrence process is Poissonian. In the proposed model it is assumed, as an exploratory attempt, that a and u a are gamma and Weibull distributed, respectively. The model was applied to the annual flood series of eight gauged basins in Basilicata (southern italy) with catchment areas ranging from 40 to 1600 km 2. The results showed strong physical consistence as the parameters tended to assume values in good agreement with well-consolidated geomorphologic knowledge and suggested a new key to understanding the climatic control of the probability distribution of floods.

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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.

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[1] We develop a pulse-based representation of temporal rainfall with multifractal properties in the small-scale limit. The representation combines a traditional model for the exterior process at the synoptic scale with a novel hierarchical pulse model for the event interiors. For validation we apply the model to a temporal rainfall record from Florence, Italy. Although the model has only six parameters (four for the exterior process and two for the event interiors), it accurately reproduces a wide range of empirical statistics, including the distribution of dry and wet periods, the distribution of rainfall intensity up to extreme fractiles, the spectral density, the moment scaling function K(q), and the distribution of the partition coefficients for rainfall disaggregation. The model also reproduces observed deviations of physical rainfall from perfect scaling/multiscaling behavior.