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Abstract. The upper part of a probability distribution, usually known as the tail, governs both the magnitude and the frequency of extreme events. The tail behaviour of all probability distributions may be, loosely speaking, categorized into two families: heavy-tailed and light-tailed distributions, with the latter generating "milder" and less frequent extremes compared to the former. This emphasizes how important for hydrological design it is to assess the tail behaviour correctly. Traditionally, the wet-day daily rainfall has been described by light-tailed distributions like the Gamma distribution, although heavier-tailed distributions have also been proposed and used, e.g., the Lognormal, the Pareto, the Kappa, and other distributions. Here we investigate the distribution tails for daily rainfall by comparing the upper part of empirical distributions of thousands of records with four common theoretical tails: those of the Pareto, Lognormal, Weibull and Gamma distributions. Specifically, we use 15 029 daily rainfall records from around the world with record lengths from 50 to 172 yr. The analysis shows that heavier-tailed distributions are in better agreement with the observed rainfall extremes than the more often used lighter tailed distributions. This result has clear implications on extreme event modelling and engineering design.

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The continuous expansion of urban areas is associated with increased water demand, both for domestic and non-domestic uses. To cover this additional demand, centralised infrastructure, such as water supply and distribution networks tend to become more and more complicated and are eventually over-extended with adverse effects on their reliability. To address this, there exist two main strategies: (a) Tools and algorithms are employed to optimise the operation of the external water supply system, in an effort to minimise risk of failure to cover the demand (either due to the limited availability of water resources or due to the limited capacity of the transmission system and treatment plants) and (b) demand management is employed to reduce the water demand per capita. Dedicated tools do exist to support the implementation of these two strategies separately. However, there is currently no tool capable of handling the complete urban water system, from source to tap, allowing for an investigation of these two strategies at the same time and thus exploring synergies between the two. This paper presents a new version of the UWOT model (Makropoulos et al., 2008), which adopts a metabolism modelling approach and is now capable of simulating the complete urban water cycle from source to tap and back again: the tool simulates the whole water supply network from the generation of demand at the household level to the water reservoirs and tracks wastewater generation from the household through the wastewater system and the treatment plants to the water bodies. UWOT functionality is demonstrated in the case of the water system of Athens and outputs are compared against the current operational tool used by the Water Company of Athens. Results are presented and discussed: The discussion highlights the conditions under which a single source-to-tap model is more advantageous than dedicated subsystem models.

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Distributed water infrastructure (located at the community or the household level) is relatively untried and unproven, compared with technologies for managing urban water at higher (e.g. regional) levels. This work presents a review of currently available options for distributed water infrastructure and illustrates the potential impact of their deployment through a number of indicative infrastructure strategies. The paper summarises the main categories of both centralised and decentralised water infrastructure, covering all three flows (water supply, wastewater and drainage) and their integration through recycling and reuse. The potential impact of the identified infrastructure options for urban water management is examined. The desirability of the strategies examined, is dependent on (case specific) constraints to urban development, including for example regional or local water resource availability, treatment plant capacity, cost of upgrading infrastructure, potential for (distributed) energy (micro) generation and climatic changes (and other nonstationary processes). The results are presented and discussed. It is concluded that there is currently a significant potential for a range of improvements in urban water management which could result from the context-aware deployment of a portfolio of technological infrastructure options. It is also suggested that there are trade-offs between water use, energy use and land use, and these have an equilibrium point that is associated with the technological state-of-art. At a given technological state-of-art, further reductions in water savings signify increase either energy consumption (for high-tech solutions) or land use (for low-tech solutions). The strategies' evaluation indicates however, that until this equilibrium point is reached there can be significant gains in all three aspects. After this equilibrium, improvements in one aspect inevitably signify costs in others. The choice of desired trade-off then depends on the specific constraints of the problem at hand.

Many hydrological applications, such as flood studies, require the use of long rainfall data at fine time scales varying from daily down to 1 minute time step. However, in the real world there is limited availability of data at sub-hourly scales. To cope with this issue, stochastic disaggregation techniques are typically employed to produce possible, statistically consistent, rainfall events that aggregate up to the field data collected at coarser scales. A methodology for the stochastic disaggregation of rainfall at fine time scales was recently introduced, combining the Bartlett-Lewis process to generate rainfall events along with adjusting procedures to modify the lower-level variables (i.e., hourly) so as to be consistent with the higher-level one (i.e., daily). In the present paper, we extend the aforementioned scheme, initially designed and tested for the disaggregation of daily rainfall into hourly depths, for any sub-hourly time scale. In addition, we take advantage of the recent developments in Poisson-cluster processes incorporating in the methodology a 2 Bartlett-Lewis model variant that introduces dependence between cell intensity and duration in order to capture the variability of rainfall at sub-hourly time scales. The disaggregation scheme is implemented in an R package, named HyetosMinute, to support disaggregation from daily down to 1-minute time scale. The applicability of the methodology was assessed on a 5-minute rainfall records collected in Bochum, Germany, comparing the performance of the above mentioned model variant against the original Bartlett-Lewis process (non-random with 5 parameters). The analysis shows that the disaggregation process reproduces adequately the most important statistical characteristics of rainfall at wide range of time scales, while the introduction of the model with dependent intensity-duration results in a better performance in terms of skewness, rainfall extremes and dry proportions.