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Over the years, several studies have been carried out to investigate how the statistics of annual discharge maxima vary with the size of basins, with diverse findings regarding the observed type of scaling (i.e., simple scaling vs. multiscaling), especially in cases where the data originated from regions with significantly different hydroclimatic characteristics. In this context, an important question arises on how one can effectively conclude on an approximate type of statistical scaling of annual discharge maxima with respect to the basin size. The present study aims at addressing this question, using daily discharges from 805 catchments located in different parts of the United Kingdom, with at least 30 years of recordings. To do so, we isolate the effects of the catchment area and the local rainfall climatology, and examine how the statistics of the standardized discharge maxima vary with the basin scale. The obtained results show that: (a) the local rainfall climatology is an important contributor to the observed statistics of peak annual discharges, and (b) when the effects of the local rainfall climatology are properly isolated, the scaling of the standardized annual discharge maxima with the area of the catchment closely follows that commonly met in actual rainfields, deviating significantly from the simple scaling rule. The aforementioned findings explain to a large extent the diverse results obtained by previous studies in the absence of rainfall information, shedding light on the approximate type of scaling of annual discharge maxima with the basin size.

Synthetic rainfall scenarios at high temporal resolutions are pivotal in numerous environmental applications. Despite the abundance of available simulation methods, their practical utilization among practitioners remains limited, often due to challenges in model calibration stemming from sample size constraints. We introduce a novel parsimonious approach for estimating parameters of multifractal disaggregation models, based solely on available Intensity-Duration-Frequency curves, which are widely and readily accessible within the practitioner community. The performance of the proposed approach is assessed using three case studies, wherein detailed statistical properties of the simulated time series are compared against observed benchmarks. Our results indicate the potential of our approach to facilitate the straightforward application of complex models.

Pressure reducing valves (PRVs) are widely used to regulate pressures in the supply and distribution parts of water networks, by reducing the upstream pressure to a set outlet pressure (i.e., downstream of the PRV), usually referred to as set point. As all types of mechanical equipment, PRVs may exhibit malfunctions affecting pressure regulation, such as high frequency fluctuations around the set point and/or prolonged systematic deviations from the set point, allowing their detection to be approached in a statistical context. In this study, we develop a statistical framework for detection of PRV malfunctions in water supply and water distribution networks, which uses: (a) the root mean squared error as a proper statistical metric for monitoring the performance of PRVs by detecting individual malfunctions in high-resolution pressure time series, and (b) the hazard function concept to identify a proper duration of sequential events from (a) to issue alerts. The suggested methodology is implemented using pressure data at 1-min temporal resolution from pressure management area Diagora of the water distribution network of the city of Patras in Greece, for the 3-year period from 01/Jan./2017 to 31/Dec./2019. The obtained results show that the developed statistical approach effectively detects major PRV malfunctions as the issuance of alerts agrees well with the reported repair dates by the Municipal Enterprise of Water Supply and Sewerage of the City of Patras, allowing it to be used for operational purposes, while making it suitable for possible extensions to continuous monitoring and fault diagnosis of other types of mechanical equipment.