Being the most widely used generators of multifractal measures, multiplicative cascade models have been extensively applied in the field of geophysics, and particularly in hydrometeorology. As in any modeling effort, solving the "inverse problem" is essential, and in this case, it can be described as finding the appropriate cascade model that generates a given multifractal measure. Direct measurement of a generated field (e.g., a rainfall field, or a time series thereof) results in an immediate decomposition into breakdown coefficients, producing a microcanonical (strictly normalized) multiplicative cascade over a limited range of scales. Yet, the canonical (expectation-normalized) phenomenology at underlying scales may generate statistical properties that are non-trivial to reproduce. The present work analyzes such properties for the simplified case of a one-dimensional, beta-lognormal discrete multiplicative cascade.

Multifractal cascades, as the generic constructors of multifractal measures, have been widely used in hydrometeorology and other areas of geophysics. In order to parametrize the cascade models, the inverse problem in this case consists in finding the generator parameters, given a realization of the multifractal measure. From the direct measurement of a field or time series (e.g. rainfall), its decomposition into breakdown coefficients is immediate, producing a microcanonical (deterministically normalized) multiplicative cascade over a certain range of scales. However, the canonical (stochastically normalized) construction at underlying scales may generate statistical properties that are not easy to reproduce. This work analyzes those properties for the case of a one-dimensional, beta–lognormal discrete multiplicative cascade.

The development and regular revision of the Greek National Flood Risk Management Plans (FRMPs) serve as direct response to the guidelines introduced by the Floods Directive (Directive 2007/60/EC) of the European Parliament and of the Council, in order to effectively mitigate and manage potential risks related to extreme precipitation events. The current study presents a comparison between: a) the Intensity-Duration-Frequency (IDF) curves obtained in 2016 over Greece using the Koutsoyiannis et. al (1998) methodology, and b) their 2023 revised version using a more recent approach (Koutsoyiannis, 2022; Iliopoulou et al., 2022).Through a comparative analysis of the two distinct IDF sets, we assess the inherent statistical variability of rainfall fields and its probable influence on extreme rainfall estimation. Focus is on determining both the nature and extent of potential spatiotemporal alternations, while identifying emerging trends and possible abnormalities that indicate substantial shifts in precipitation patterns, thus enhancing understanding of the evolution of flood risk over Greece.As the IDF curves form the cornerstone of Flood Risk Management Plans, it is crucial to identify significant variations in their profiles over short periods of time. Consequently, the current work highlights the necessity for regular updates of the national Flood Risk Management Plans, in accordance with the Floods Directive guidelines, while identifying areas that exhibit substantial statistical variability. Ultimately, the obtained results will allow for the development of robust decision-making frameworks, enabling stakeholders and policymakers to develop flexible and compliant mitigation strategies against potential hydrological hazards to protect the community and infrastructural assets. ReferencesIliopoulou, T., Malamos, N. and Koutsoyiannis, D. (2022) Regional ombrian curves: design rainfall estimation for a spatially diverse rainfall regime, Hydrology, 9(5), 67, https://doi.org/10.3390/hydrology9050067.Koutsoyiannis, D., Kozonis, D. and Manetas, A. (1998) A mathematical framework for studying rainfall intensity-duration-frequency relationships, Journal of Hydrology, 206 (1-2), pp 118-135, https://doi.org/10.1016/S0022-1694(98)00097-3.Koutsoyiannis, D. (2022) Stochastics of Hydroclimatic Extremes - A Cool Look at Risk, 2nd Edition, ISBN: 978-618-85370-0-2, 346 pages, Kallipos Open Academic Editions, Athens, 2022, https://doi.org/10.57713/kallipos-1.

All urban and agricultural water distribution networks (WDNs), irrespective of their physical and operational characteristics, encounter substantial leakages which result in significant water losses, environmental degradation through increased carbon emissions, and noteworthy economic burdens. The current work aims to quantify both the environmental impact, estimated in terms of CO2 emissions, and the economic implications associated with leakages and evaluate the effect of the most widely used leakage reduction strategies. The current approach is applied to the water distribution network of the city of Patras in Western Greece.

Study region: The Arno River basin (Tuscany, Italy), located across the Northern Apennine chain. Study focus: A parsimonious approach for calibration of a multiplicative random cascade model for multifractal rainfall disaggregation is tested and investigated. The performance evaluation in the case study area revealed a particular behavior of the applied procedure for canonical disaggregation of daily rainrates to sub-daily levels; consequently, we introduce an improvement to the suggested approach, and we evaluate its effectiveness. New hydrological insights for the region: The key advancement of the proposed approach is that it allows for parsimonious estimation of the canonical multiplicative random cascade framework parameters (specifically, Cβ and CLN) using only daily rainfall time series data and the power-law exponent n of commonly used intensity-duration-frequency curves. The case study analysis suggests that the proposed method can be effectively adopted in the Arno River basin, simplifying and improving the simulation of sub-daily rainfall time series while also enhancing its practical applicability, as the required inputs are generally available to most users.

The need for long-term synthetic sub-daily rainfall time series is crucial in various hydrological applications, particularly in flood frequency analysis. Traditional sub-daily rainfall simulation models rely on high time-resolution data, typically spanning only 20–30 years, which is insufficient for generating the long synthetic time series required for high return period design value estimation. In contrast, longer datasets of daily rainfall records and annual maximum values are more widely available, often covering 50–80 years. These datasets underpin the derivation of Intensity-Duration-Frequency (IDF) curves, a cornerstone of current hydrological practice.This study introduces an innovative framework for simulating sub-daily rainfall time series using only daily rainfall records and IDF curves, thus eliminating the need for sub-daily observational data. The approach integrates a daily rainfall simulation model, Complete Stochastic Modelling Solution, calibrated with observed daily data, with a multifractal disaggregation scheme informed by IDF curves. The resulting framework offers a robust and parsimonious solution for generating sub-daily rainfall data.By leveraging readily available datasets, this method expands the applicability of sub-daily rainfall simulations to a broader range of hydrological and climate modeling contexts, providing a valuable tool for advancing flood frequency analysis and related applications.

Climate change is expected to impact the maritime sector, including the port industry. Ports are on the frontline when it comes to experiencing operational challenges from the increased sea levels and extreme weather conditions, associated with increased infrastructure investments. For instance, rising sea water levels are expected to change the accessibility of channels and increase the need for higher quay walls, while the increased intensity or/and frequency of events, such as fog, high winds, and waves, may increase the frequency of port operation disruptions; but changes are uncertain, and with regional variation.The present study focuses on the Port of Heraklion, one of the main ports of national importance in the Greek Maritime Network, located in the North side of the island of Crete, and aims at assessing the impacts of climate change on port operations associated with: Changes in mean sea level, storm surges and wave characteristics (i.e. wave height, period, frequency of occurrence). Reduced visibility caused by intense precipitation and/or fog. Disruption of port operations due to high wind speeds, drainage system induced flooding, as well as river discharges and sediment transfer in the harbor basin. To assess the effects of climate change on winds we use climate change factors (CCFs) obtained using climate model data at 3-hourly temporal resolution over the Island of Crete (i.e. sub-country level) from EURO-CORDEX ensemble, and more in particular from HIRHAM5 RCM (Regional Climate Model) nested in (downscaled from) EC-EARTH GCM (Global Climate model), for two Representative Concentration Pathways of future emissions: RCP 4.5 for the period 2071-2100 and RCP 8.5 for the period 2041-2070. These are also the RCM-GCM combination and time periods used to assess the effects of climate change on the sea state and wave characteristics.For rainfall, we make direct use of the climate change factors reported in the context of SWICCA program (Service for Water Indicators in Climate Change Adaption, 2015 - 2018), which was financed by the European Centre for Medium-Range Weather Forecasts (ECMWF) on behalf of the European Copernicus Agency within the framework of the Copernicus climate change service (C3S). Over the island of Crete, the corresponding factors are available for 9 GCM - RCM combinations (i.e. 5 for the RCP 4.5 scenario and 4 for the RCP 8.5 scenario).We find that the increase of the mean sea level, as well as the increase in the frequency of intense storms significantly affect the frequency of port operation disruptions, particularly due to breakwater overtopping, storm induced flooding, as well sediment deposition in the harbor basin.Acknowledgements The presented work has been conducted under the project Climate Risk and Vulnerability Assessment (CRVA) for the Heraklion Port Authority" (project code: AA 011391-002/CC15302), which has been financed by the EIB under the InvestEU Advisory Hub.

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.

Hydraulic modeling of water distribution networks (WDNs) is a vital step for all water-related professionals towards the development of management practices and strategies that aim for the reduction of water losses and the associated financial cost and environmental footprint. In the current work, we develop an easy-to-implement methodology for the effective modeling of WDNs, which seeks to minimize the computational load without undermining the analysis's accuracy, using the open access EPANET (Environmental Protection Agency Network Evaluation Tool) software package. The effectiveness of the proposed methodology is tested via a large-scale, real-world application for the city of Patras.

<p>Effective management of water losses in water distribution networks (WDNs) still remains a demanding task, as the temporal and spatial variability of water resources under changing climatic conditions and the increasing needs for drinking water may lead to freshwater shortages. In this context, pressure management strategies are widely adopted in an effort to reduce the water losses in the supply and distribution parts of water networks and, consequently, deescalate their environmental footprint. Installation of pressure reducing valves (PRVs) at critical locations of WDNs plays a central role in pressure regulation strategies, as PRVs reduce the upstream pressure to a set outlet pressure (i.e., downstream of the PRV), usually referred to as set point. Perdios et al. (2022) developed a novel statistical framework and applied it to an existing pressure management area (PMA) of the city of Patras in western Greece, aiming at early detection of PRV malfunctions that may significantly influence network’s operation and the corresponding lifetime of related infrastructure. The results showed that the suggested methodology allows reliable detection of critical malfunctions at least 2 days prior to flow disruptions. Ιn this study, we calibrate and implement Perdios et al. (2022) statistical framework, using pressure data for a 4-year period from 01/Jan./2017 to 26/Nov./2020 from several important PMAs of the WDN of the city of Patras, aiming towards better understanding of the causes of the malfunctions, by decomposing the observed pressure deviations from the set point to systematic and random error components.</p> <p><strong>Acknowledgements </strong></p> <p>The research work has been conducted within the project PerManeNt, which has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation under the call RESEARCH – CREATE – INNOVATE (project code: T2EDK-04177).</p> <p><strong>Reference</strong></p> <p>Perdios A., G. Kokosalakis, N. Th. Fourniotis, I. Karathanasi and A. Langousis (2022) Statistical framework for the detection of pressure regulation malfunctions and issuance of alerts in water distribution networks, <em>Stoch. Env. Res. Risk Asses.</em>, https://doi.org/10.1007/s00477-022-02256-5</p>

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.

As the outflow velocity from a pipe crack increases with increasing hydraulic pressure, pressure management concepts have been widely applied to reduce water losses in the delivering and distribution parts of water networks. In this context, pressure reducing valves (PRVs) have been commonly used to regulate pressures and therefore reduce water losses, in both water supply and water distribution 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 exhibit malfunctions affecting pressure regulation, which can be defined as events when the outlet pressure does not match the set point. These events can be classified in two categories: a) high frequency fluctuations around the set point, and b) prolonged systematic deviations from the set point. Since PRV malfunctions result in systematic or random deviations of the outlet pressure from the set point, their detection can be approached in a statistical context. In this study, we develop a novel framework for detection of PRV malfunctions in water supply and water distribution networks, which uses: a) the root mean squared error (RMSE) as a proper statistical metric for monitoring the performance of a PRV by detecting individual malfunctions (i.e. malfunction occurrences) in the 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 (the third largest city in Greece), for a 3 year period from 01 January 2017 to 31 December 2019. The obtained results show that the developed statistical approach effectively detects major PRV malfunctions (as reported by the Municipal Water Supply Company and Sewerage of Patras, DEYAP), allowing it to be used for operational purposes. Acknowledgments: This research is co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T2EDK-4177).

Research project “PerManeNt” aims at developing an integrated platform for operational monitoring, smart control, and sustainable energy management of the external aqueduct system of the city of Patras in western Greece, which consists of more than 60 km of pressurized pipeline, 44 pumping wells, 3 springs, 22 regulating tanks, and 14 pumping stations. Given the significance of the existing infrastructure, 5 main pipelines, 7 pumping wells, 9 reservoirs, and 5 pumping stations were selected to be monitored in the context of: a) real-time data collection, processing and visualization, b) near real-time detection of system malfunctioning and automatic alarm generation, and c) generation of short and longer term forecasts for the water demand and corresponding energy consumption rates, based on hydrometeorological data and environmental indices. The development of the integrated platform is expected to have significant scientific, financial, societal and environmental impacts including: i) efficient water resources management and environmental protection, ii) reduction of the operational costs and regulator expenses for system maintenance and management, iii) promotion of citizens’ awareness regarding environmental issues, and iv) significant improvement of the quality of services offered, including pricing and emergency planning. Acknowledgments: This research is co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T2EDK-4177.

Over the years, several studies have been carried out to investigate how the statistics of peak annual discharges 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, two important questions arise: a) how rainfall climatology affects the scaling of peak annual discharges, and b) 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 these two questions, using daily discharges from 805 catchments located in different parts of the United Kingdom, with at least 30 years of recordings. In doing 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 annual peak 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 of the underlying rainfall process, 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 to the approximate type of scaling of peak annual discharges with the basin size.

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.