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The planning and management of decentralized technologies in water systems is one of the promising, yet overlooked, domains where artificial intelligence (AI) can be successfully applied. In this study, we develop and deploy a reinforcement learning (RL)-based ‘smart planning agent’ capable of designing alternative decentralized water systems under demanding operational contexts. The agent's aim is to identify optimal water infrastructure configurations (i.e., proposed decisions on water management options and interventions) for different conditions with regard to climate, occupancy and water technology availability in a demanding, off-grid setting, i.e., a water system with high requirements of independence from centralized infrastructure. The agent is coupled with a source-to-tap water cycle simulation model capable of assessing and stress-testing the proposed configurations under different conditions. The approach is demonstrated in the case of a military camp deployed abroad for peacekeeping operations. The agent is tasked with selecting optimal interventions from an array of real-world camp water management technologies and evaluating their efficiency under highly variable, operational conditions explored through simulation. The results show that RL can be a useful addition to the arsenal of decision support systems (DSS) for distributed water system planning and management, especially under challenging, highly variable conditions.

The identification and assessment of the cyber-physical-threat landscape that surrounds water systems in the digital era is governed by complex socio-technical dynamics and uncertainties that exceed the boundaries of traditional risk assessment. This work provides a remedy for those challenges by incorporating socio-technical modelling to account for the adaptive balance between goal-driven behaviours and available skills of adversaries, exploitable vulnerabilities of assets and utility’s security posture, as well as an uncertainty-aware multi-scenario analysis to assess the risk level of any utility against cyber-physical threats. The proposed risk assessment framework, underpinned by a dedicated modelling chain, deploys a modular sequence of processes for (a) the estimation of vulnerability-induced probabilities and attack characteristics of the threat landscape under a spectrum of adversaries, (b) its formulation to a representative set of stochastically generated threat scenarios, (c) the combined cyber-physical stress-testing of the system against the generated scenarios and (d) the inference of the system’s risk level at system and asset level. The proposed framework is demonstrated by exploring different configurations of a synthetic utility case study that investigate the effects and efficiency that different cyber-security practices and design traits can have over the modification of the risk level of the utility at various dimensions.

Establishing cooperation in transboundary rivers is challenging especially with the weak or non-existent river basin institutions. A nexus-based approach is developed to explore cooperation opportunities in transboundary river basins while considering system operation and coordination under uncertain hydrologic river regimes. The proposed approach is applied to the Nile river basin with a special focus on the Grand Ethiopian Renaissance Dam (GERD), assuming two possible governance positions: with or without cooperation. A cooperation mechanism is developed to allocate additional releases from the GERD when necessary, while a unilateral position assumes that the GERD is operated to maximize hydropower generation regardless of downstream users' needs. The GERD operation modes were analysed considering operation of downstream reservoirs and varying demands in Egypt. Results show that average basin-wide hydropower generation is likely to increase by about 547 GWh/year (1%) if cooperation is adopted when compared to the unilateral position. In Sudan, hydropower generation and water supply are expected to enhance in the unilateral position and would improve further with cooperation. Furthermore, elevated low flows by the GERD are likely to improve the WFE nexus outcomes in Egypt under full cooperation governance scenario with a small reduction in GERD hydropower generation (2,000 GWh/year (19%)).

A prediction model for shipping noise in range-dependent environments based on coupled-mode theory is presented, as an enhancement to existing adiabatic normal-mode approaches without a significant increase in computational effort. Emphasis is placed on the categorization of environmental changes and precalculation and storage of eigenvalues, eigenfunctions and coupling matrices, such that they can be looked up and restored to efficiently compute the acoustic field of arbitrary noise source distributions over a given sea area. Taking into account that the water depth is the primary factor determining the number of propagating modes for a particular frequency, coupling is applied only in the case of changing bathymetry, whereas changes in the water sound-speed profile and/or the geoacoustic characteristics are treated adiabatically. Examples of noise calculations are given for benchmark setups in the Eastern Mediterranean Sea and comparisons with fully adiabatic predictions are drawn. Moreover, the effect of applying range propagation limitations in a numerical propagation model for shipping noise predictions is demonstrated.

The purpose of this study was to analyze different human behavioral components and the data collected from several households regarding their water consumption to fathom public demeanor better and ...

Optimizing the design and operation of an Urban Water System (UWS) faces significant challenges over its lifespan to account for the uncertainties of important stressors that arise from population growth rates, climate change factors, or shifting demand patterns. The analysis of a UWS’s performance across interdependent subsystems benefits from a multi-model approach where different designs are tested against a variety of metrics and in different times scales for each subsystem. In this work, we present a stress-testing framework for UWSs that assesses the system’s resilience, i.e., the degree to which a UWS continues to perform under progressively increasing disturbance (deviation from normal operating conditions). The framework is underpinned by a modeling chain that covers the entire water cycle, in a source-to-tap manner, coupling a water resources management model, a hydraulic water distribution model, and a water demand generation model. An additional stochastic simulation module enables the representation and modeling of uncertainty throughout the water cycle. We demonstrate the framework by “stress-testing” a synthetic UWS case study with an ensemble of scenarios whose parameters are stochastically changing within the UWS simulation timeframe and quantify the uncertainty in the estimation of the system’s resilience.

Sensors used for wastewater flow measurements need to be robust and are, consequently, expensive pieces of hardware that must be maintained regularly to function correctly in the hazardous environment of sewers. Remote sensing can remedy these issues, as the lack of direct contact between sensor and sewage reduces the hardware demands and need for maintenance. This paper utilizes off-the-shelf cameras and machine learning algorithms to estimate the discharge in open sewer channels. We use convolutional neural networks to extract the water level and surface velocity from camera images directly, without the need for artificial markers in the sewage stream. Under optimal conditions, our method estimates the water level with an accuracy of ±2.48% and the surface velocity with an accuracy of ±2.08% in a laboratory setting—a performance comparable to other state-of-the-art solutions (e.g., in situ measurements).

The generation of hydrometeorological time series that exhibit a given probabilistic and stochastic behavior across multiple temporal levels, traditionally expressed in terms of specific statistical characteristics of the observed data, is a crucial task for risk-based water resources studies, and simultaneously a puzzle for the community of stochastics. The main challenge stems from the fact that the reproduction of a specific behavior at a certain temporal level does not imply the reproduction of the desirable behavior at any other level of aggregation. In this respect, we first introduce a pairwise coupling of Nataf-based stochastic models within a disaggregation scheme, and next we propose their puzzle-type configuration to provide a generic stochastic simulation framework for multivariate processes exhibiting any distribution and any correlation structure. Within case studies we demonstrate two characteristic configurations, i.e., a three-level one, operating at daily, monthly and annual basis, and a two-level one to disaggregate daily to hourly data. The first configuration is applied to generate correlated daily rainfall and runoff data at the river basin of Achelous, Western Greece, which preserves the stochastic behavior of the two processes at the three temporal levels. The second configuration disaggregates daily rainfall, obtained from a meteorological station at Germany, to hourly. The two studies reveal the ability of the proposed framework to represent the peculiar behavior of hydrometeorological processes at multiple temporal resolutions, as well as its flexibility on formulating generic simulation schemes.

Prompt detection of cyber–physical attacks (CPAs) on a water distribution system (WDS) is critical to avoid irreversible damage to the network infrastructure and disruption of water services. However, the complex interdependencies of the water network’s components make CPA detection challenging. To better capture the spatiotemporal dimensions of these interdependencies, we represented the WDS as a mathematical graph and approached the problem by utilizing graph neural networks. We presented an online, one-stage, prediction-based algorithm that implements the temporal graph convolutional network and makes use of the Mahalanobis distance. The algorithm exhibited strong detection performance and was capable of localizing the targeted network components for several benchmark attacks. We suggested that an important property of the proposed algorithm was its explainability, which allowed the extraction of useful information about how the model works and as such it is a step towards the creation of trustworthy AI algorithms for water applications. Additional insights into metrics commonly used to rank algorithm performance were also presented and discussed.

Water distribution networks (WDNs) are critical infrastructure for the welfare of society. Due to their spatial extent and difficulties in deployment of security measures, they are vulnerable to threat scenarios that include the rising concern of cyber-physical attacks. To protect WDNs against different kinds of water contamination, it is customary to deploy water quality (WQ) monitoring sensors. Cyber-attacks on the monitoring system that employs WQ sensors combined with deliberate contamination events via backflow attacks can lead to severe disruptions to water delivery or even potentially fatal consequences for consumers. As such, the water sector is in immediate need of tools and methodologies that can support cyber-physical quality attack simulation and vulnerability assessment of the WQ monitoring system under such attacks. In this study we demonstrate a novel methodology to assess the resilience of placement schemes generated with the Threat Ensemble Vulnerability Assessment and Sensor Placement Optimization Tool (TEVA-SPOT) and evaluated under cyber-physical attacks simulated using the stress-testing platform RISKNOUGHT, using multidimensional metrics and resilience profile graphs. The results of this study show that some sensor designs are inherently more resilient than others, and this trait can be exploited in risk management practices.

This work proposes a modeling framework to quantify the effects of deliberate organic contamination events on water distribution systems. A bacterial regrowth model and a first parallel chlorine decay model were combined to describe the biochemical water species kinetics during a deliberate contaminant injection consisting of organic (TOC) and bacterial loads. Several attacks were modeled using EPANET-MSX. The impact of each attack was quantified using vulnerability indexes measuring the number of consumers affected and other metrics. These vulnerability indexes were then used to depict the network’s response to a potential threat and identify the most critical areas of the network. A sensitivity analysis was also performed to determine the network’s sensitivity to three dynamic network variables (mass injection, injection duration, and organic carbon concertation in water). An increase on bacterial populations was observed during the contaminant spread to the network, which in turn signals additional health risk to consumers.

The interplay between cyber and physical elements of water systems and the corresponding security challenges are fast becoming a key topic for the water sector. Of special concern is the absence of...

Water reuse and recycling is gaining momentum as a way to improve the circularity of cities, while recognizing the central role of water within a circular economy (CE) context. However, such interventions often depend on the location of wastewater treatment plants and the treatment technologies installed in their premises, while relying on an expensive piped network to ensure that treated wastewater gets transported from the treatment plant to the point of demand. Thus, the penetration level of treated wastewater as a source of non-potable supply in dense urban environments is limited. This paper focuses on the demonstration of a sewer mining (SM) unit as a source of treated wastewater, as part of a larger and more holistic configuration that examines all three ‘streams’ associated with water in CE: water, energy and materials. The application area is the Athens Plant Nursery, in the (water stressed) city of Athens, Greece. SM technology is in fact a mobile wastewater treatment unit in containers able to extract wastewater from local sewers, treat it directly and reuse at the point of demand even in urban environments with limited space. The unit consists of a membrane bioreactor unit (MBR) and a UV disinfection unit and produces high quality reclaimed water for irrigation and also for aquifer recharge during the winter. Furthermore, a short overview of the integrated nutrient and energy recovery subsystem is presented in order to conceptualise the holistic approach and circularity of the whole configuration. The SM technology demonstrates flexibility, scalability and replicability, which are important characteristics for innovation uptake within the emerging CE context and market.

Achieving a water, food, and energy (WFE) nexus balance through policy interventions is challenging in a transboundary river basin because of the dynamic nature and intersectoral complexity...

Cities at risk of extreme hydro-meteorological events need to be prepared to decrease the extent of the impacts. However, sometimes, authorities only react to catastrophes failing to proactively prepare against extremes. This can be a result of both absent structural protection measures and problematic governance. While for the first, models exist that can simulate the effect, the effect of the latter is difficult to quantify. This work aims to explore the effects that typical authorities&rsquo, behaviour has on the decisions for preparing and protecting a city against floods. This behaviour includes how the different authorities decide, for example, on whether or not to cooperate with each other, build something, assign funding to something, etc. These decisions affect directly the preparedness against and the protection from flood events. For that matter, the institutional analysis framework was used to conceptualise the decision-making processes of authorities responsible for flood risk management. Based on this, an agent-based modelling tool has been created, enabling the exploration of the system&rsquo, s behaviour under different scenarios. The tool is used as a case study of the responsible authorities for flood protection in the city of Rethymno on the island of Crete, Greece. The tool has a user-friendly interface enabling the end-users to explore the drivers of decision-making processes under different conditions.

Stochastic simulation has a prominent position in a variety of scientific domains including those of environmental and water resources sciences. This is due to the numerous applications that can benefit from it, such as risk-related studies. In such domains, stochastic models are typically used to generate synthetic weather data with the desired properties, often resembling those of hydrometeorological observations, which are then used to drive deterministic models of the understudy system. However, generating synthetic weather data with the desired properties is not an easy task. This is due to the peculiarities of such processes, i.e., non-Gaussianity, intermittency, dependence, and periodicity, and the limited availability of open-source software for such purposes. This work aims to simplify the synthetic data generation procedure by providing an R-package called anySim, specifically designed for the simulation of non-Gaussian correlated random variables, stochastic processes at single and multiple temporal scales, and random fields. The functionality of the package is demonstrated through seven simulation studies, accompanied by code snippets, which resemble real-world cases of stochastic simulation (i.e., generation of synthetic weather data) of hydrometeorological processes and fields (e.g., rainfall, streamflow, temperature, etc.), across several spatial and temporal scales (ranging from annual down to 10-min simulations).

We propose a novel probabilistic approach to flood hazard assessment, aiming to address the major shortcomings of everyday deterministic engineering practices in a computationally efficient manner. In this context, the principal sources of uncertainty are defined across the overall modeling procedure, namely, the statistical uncertainty of inferring annual rainfall maxima through distribution models that are fitted to empirical data, and the inherently stochastic nature of the underlying hydrometeorological and hydrodynamic processes. Our work focuses on three key facets, i.e., the temporal profile of storm events, the dependence of flood generation mechanisms on antecedent soil moisture conditions, and the dependence of runoff propagation over the terrain and the stream network on the intensity of the flood event. These are addressed through the implementation of a series of cascade modules, based on publicly available and open-source software. Moreover, the hydrodynamic processes are simulated by a hybrid 1D/2D modeling approach, which offers a good compromise between computational efficiency and accuracy. The proposed framework enables the estimation of the uncertainty of all flood-related quantities, by means of empirically derived quantiles for given return periods. Lastly, a set of easily applicable flood hazard metrics are introduced for the quantification of flood hazard.

Many hydrological applications, such as flood studies, require the use of long rainfall data at fine time scales varying from daily down to 1 min 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 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-min time scale. The applicability of the methodology was assessed on a 5-min 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.

The challenging task of generating a synthetic time series at finer temporal scales than the observed data, embeds the reconstruction of a number of essential statistical quantities at the desirable (i.e., lower) scale of interest. This paper introduces a parsimonious and general framework for the downscaling of statistical quantities based solely on available information at coarser time scales. The methodology is based on three key elements: (a) the analysis of statistics’ behaviour across multiple temporal scales; (b) the use of parametric functions to model this behaviour; and (c) the exploitation of extrapolation capabilities of the functions to downscale the associated statistical quantities at finer scales. Herein, we demonstrate the methodology using residential water demand records and focus on the downscaling of the following key quantities: variance, L-variation, L-skewness and probability of zero value (no demand; intermittency), which are typically used to parameterise a stochastic simulation model. Specifically, we downscale the above statistics down to a 1 min scale, assuming two scenarios of initial data resolution, i.e., 5 and 10 min. The evaluation of the methodology on several cases indicates that the four statistics can be well reconstructed. Going one step further, we place the downscaling methodology in a more integrated modelling framework for a cost-effective enhancement of fine-resolution records with synthetic ones, embracing the current limited availability of fine-resolution water demand measurements.

Rapid urbanization affecting demand patterns, coupled with potential water shortages due to supply side impacts of climatic changes, has led to the emergence of new technologies for water and wastewater reuse. Sewer mining (SM) is a novel decentralized option that could potentially provide non-potable water for urban uses, including for example the irrigation of urban green spaces, providing a mid-scale solution to effective wastewater reuse. SM is based on extracting wastewater from local sewers and treatment at the point of demand and entails in some cases the return of treatment residuals back to the sewer system. Several challenges are currently in the way of such applications in Europe, including public perception, inadequate regulatory frameworks and engineering issues. In this paper we consider some of these engineering challenges, looking at the sewer network as a system where multiple physical, biological and chemical processes take place. We argue that prior to implementing SM, the dynamics of the sewer system should be investigated in order to identify optimum ways of deploying SM without endangering the reliability of the system. Specifically, both wastewater extraction and sludge return could result in altering the biochemical process of the network, thus unintentionally leading to degradation of the sewer infrastructure. We propose a novel Monte-Carlo based method that takes into account both spatial properties and water demand characteristics of a given area of SM deployment while simultaneously accounting for the variability of sewer network dynamics in order to identify potential locations for SM implementation. The outcomes of this study suggest that the method can provide rational results and useful guidelines for upscale SM technologies at a city level.

Storm events lead to sudden and drastic impacts on coastal areas, causing serious damages, flooding and severe coastal erosion. These extreme weather conditions seriously affect the nearshore system by reshaping the shoreline and moving large amounts of sand cross-shore. For the assessment of the consequences of such extreme storm events, an interdisciplinary approach simulating atmospheric, wave, hydraulic and geological processes is required, necessitating the use of sophisticated numerical models. Within this study, MIKE 21 and XBeach numerical models are employed for the estimation of the prevailing wave, hydrodynamic and morphodynamic characteristics in a coastal area. The results of the two models are utilized in a comparative approach to assess coastal processes. Furthermore, an alternative solution is also proposed for effective protection of a nearshore area. The approach is demonstrated in the coastal zone of the city of Rethymno in Greece, an area significantly affected by extreme storm events in the recent past, and results are presented and discussed.

Hydroinformatics, as an interdisciplinary domain that blurs boundaries between water science, data science and computer science, is constantly evolving and reinventing itself. At the heart of this evolution, lies a continuous process of critical (self) appraisal of the discipline&rsquo, s past, present and potential for further evolution, that creates a positive feedback loop between legacy, reality and aspirations. The power of this process is attested by the successful story of hydroinformatics thus far, which has arguably been able to mobilize wide ranging research and development and get the water sector more in tune with the digital revolution of the past 30 years. In this context, this paper attempts to trace the evolution of the discipline, from its computational hydraulics origins to its present focus on the complete socio-technical system, by providing at the same time, a functional framework to improve the understanding and highlight the links between different strands of the state-of-art hydroinformatic research and innovation. Building on this state-of-art landscape, the paper then attempts to provide an overview of key developments that are coming up, on the discipline&rsquo, s horizon, focusing on developments relevant to urban water management, while at the same time, highlighting important legal, ethical and technical challenges that need to be addressed to ensure that the brightest aspects of this potential future are realized. Despite obvious limitations imposed by a single paper&rsquo, s ability to report on such a diverse and dynamic field, it is hoped that this work contributes to a better understanding of both the current state of hydroinformatics and to a shared vision on the most exciting prospects for the future evolution of the discipline and the water sector it serves.

Centralized urban water management currently faces multiple challenges, both at the supply side and the demand side. These challenges underpin the need to progress to the decentralization of urban water, where multiple distributed technologies (water-aware appliances, rainwater harvesting, greywater recycling, sustainable urban drainage) are applied in an integrated fashion and as a supplement to centralized systems to design more resilient neighborhoods. However, the methods and tools to assess the performance of these distributed solutions and provide management support for integrated projects are still few and mostly untested in real, combined cases. This study presents a simulation-based framework for the quantitative performance assessment of decentralized systems at a neighborhood scale, where different technologies can be linked together to provide beneficial effects across multiple urban water cycle domains. This framework links an urban water cycle model, which provides a scenario-based simulation testbed for the response of the whole system, with key performance indicators that evaluate the performance of integrated decentralized solutions at a neighborhood scale. The demonstrated framework is applied to provide an ex ante evaluation of SUPERLOCAL, a newly developed area in Limburg, the Netherlands, designed as a circular, water-wise neighborhood where multiple decentralized technologies are combined.

Uncertainty-aware design and management of urban water systems lies on the generation of synthetic series that should precisely reproduce the distributional and dependence properties of residential water demand process (i.e., significant deviation from Gaussianity, intermittent behaviour, high spatial and temporal variability and a variety of dependence structures) at various temporal and spatial scales of operational interest. This is of high importance since these properties govern the dynamics of the overall system, while prominent simulation methods, such as pulse-based schemes, address partially this issue by preserving part of the marginal behaviour of the process (e.g., low-order statistics) or neglecting the significant aspect of temporal dependence. In this work, we present a single stochastic modelling strategy, applicable at any fine time scale to explicitly preserve both the distributional and dependence properties of the process. The strategy builds upon the Nataf&rsquo, s joint distribution model and particularly on the quantile mapping of an auxiliary Gaussian process, generated by a suitable linear stochastic model, to establish processes with the target marginal distribution and correlation structure. The three real-world case studies examined, reveal the efficiency (suitability) of the simulation strategy in terms of reproducing the variety of marginal and dependence properties encountered in water demand records from 1-min up to 1-h.

The water sector is, currently and for the foreseeable future, challenged by rising levels of uncertainty in demand and availability of water, in a context of aging infrastructure and limited investment. In order to support strategic planning, water companies need a way to assess how their system behaves when faced with a range of changing conditions (climatic trends, asset deterioration, behavioral patterns, etc.) as well as accidents/incidents and/or extreme events (wildcards). In this study, a resilience assessment methodology was demonstrated, with &lsquo, stress tests&rsquo, alternative water system configurations (including systems designed with decentralized or distributed philosophies) under a range of scenarios and extreme events. A &lsquo, resilience profile graph&rsquo, was developed to quantify the performance of each configuration. The methodology was applied to the real-world urban water system of Oasen, which supplies the eastern part of the Province of South Holland, where the current system configuration and two potential future configurations were tested (one decentralized and one distributed). We show how the concept of resilience, operationalized through this methodology, can assist long term decision making and support strategic infrastructure planning.

The urban water system's sustainable evolution requires managing both water supply and water demand within a complete urban water cycle framework. Such an approach, however, requires tools to analyse and simulate the complete system including both physical and cultural environments. One of the main challenges, in this regard, is the design and development of tools able to simulate the society's water demand behaviour and the way policy measures affect it. The effects of these policy measures are a function of personal attitudes that subsequently lead to the formation of people's behaviours. This work focuses on the exploration of social impact theory on water conservation attitudes of urban households. A model is designed and implemented using agent based modelling. The developed model's ability to represent social structure and mechanisms of social influences is tested against historical data from the 1988–1994 drought of Athens, Greece as a case study.

The implementation of decentralized water management (DWM) systems as a mainstream practice is impeded by the knowledge gaps on their actual performance in a range of development types and settings. On the other hand, wireless sensor networks (WSN) provide a capable platform for low cost, high performance and real-time monitoring. By bringing together the strengths of WSN technology and distributed, reactive, knowledge management and representation, implemented as a dual-layer ontology framework, this work provides a holistic approach to the management of DWM systems. Low-level real-time knowledge such as sensor observations (water consumption, water quality and soil quality) is represented directly in the ontology while high-level knowledge, e.g. about scheduling precision irrigation and about meeting grey water demand in the household, is inferred through low-level knowledge by means of rule-based reasoning. Ultimately, the proposed system aims to: control the grey water reuse process, detect an...

Modern distributed water-aware technologies (including, for example, greywater recycling and rainwater harvesting) enable water reuse at the scale of household or neighbourhood. Nevertheless, even though these technologies are, in some cases, economically advantageous, they have a significant handicap compared to the centralized urban water management options: it is not easy to estimate a priori the extent and the rate of the technology spread. This disadvantage is amplified in the case of additional uncertainty due to expansion of an urban area. This overall incertitude is one of the basic reasons the stakeholders involved in urban water are sceptical about the distributed technologies, even in the cases where these appear to have lower cost. In this study, we suggest a methodology that attempts to cope with this uncertainty by coupling a cellular automata (CA) and a system dynamics (SD) model. The CA model is used to create scenarios of urban expansion including the suitability of installing water-aware technologies for each new urban area. Then, the SD model is used to estimate the adoption rate of the technologies. Various scenarios based on different economic conditions and water prices are assessed. The suggested methodology is applied to an urban area in Attica, Greece.

The sustainable evolution of the urban water system requires the recognition of uncertainty embedded in both climate and human behaviour. A challenge that water managers and policy makers need to tackle, is to understand the way the society’s water demand behaviour is affected. The inaccuracy between attitudes and behaviours and the cognitive association of water use to living standards, hinders the projection of society’s response to management’s measures. Thus, it is necessary to identify leverage points, where water demand management policies should aim their efforts. This work presents two parts of a social research held in Athens: quantitative questionnaire gathering information regarding the domestic water demand attitudes and behaviours; and a series of qualitative interviews aimed at exploring in-depth, the domestic water use attitudes, and behaviours. This work presents the design and results of both methods and the combination of the quantitative results with insights from the qualitativ...

Residential water demand consists one of the most uncertain factors posing extra difficulties in the efficient planning and management of urban water systems. Currently, high resolution data from smart meters provide the means for a better understanding and modelling of this variable at a household level and fine temporal scales. Having this in mind, this paper examines the statistical and distributional properties of residential water demand at a 15-minute and hourly scale, which are the temporal scales of interest for the majority of urban water modeling applications. Towards this, we investigate large residential water demand records of different characteristics. The analysis indicates that the studied characteristics of the marginal distribution of water demand vary among households as well as on the basis of different time intervals. Both month-to-month and hour-to-hour analysis reveal that the mean value and the probability of no demand exhibit high variability while the changes in the shape characteristics of the marginal distributions of the nonzero values are significantly less. The investigation of performance of 10 probabilistic models reveals that Gamma and Weibull distributions can be used to adequately describe the nonzero water demand records of different characteristics at both time scales.

Since the prime days of stochastic hydrology back in 1960s, autoregressive (AR) and moving average (MA) models (as well as their extensions) have been widely used to simulate hydrometeorological processes. Initially, AR(1) or Markovian models with Gaussian noise prevailed due to their conceptual and mathematical simplicity. However, the ubiquitous skewed behavior of most hydrometeorological processes, particularly at fine time scales, necessitated the generation of synthetic time series to also reproduce higher-order moments. In this respect, the former schemes were enhanced to preserve skewness through the use of non-Gaussian white noise&mdash, a modification attributed to Thomas and Fiering (TF). Although preserving higher-order moments to approximate a distribution is a limited and potentially risky solution, the TF approach has become a common choice in operational practice. In this study, almost half a century after its introduction, we reveal an important flaw that spans over all popular linear stochastic models that employ non-Gaussian white noise. Focusing on the Markovian case, we prove mathematically that this generating scheme provides bounded dependence patterns, which are both unrealistic and inconsistent with the observed data. This so-called &ldquo, envelope behavior&rdquo, is amplified as the skewness and correlation increases, as demonstrated on the basis of real-world and hypothetical simulation examples.

Infrastructure planning for Urban Water Systems (UWSs) is challenged by, inter alia, increasing uncertainty in both demand and availability of water and aging infrastructure, and this is already impacting the climate-proofing of cities. In this context, the idea of resilience has been gradually embraced by the water sector, but the term itself is not yet universally defined, nor operationalised. Here, we propose a methodology to assess the resilience of a UWS, defining it as the degree to which the UWS continues to perform under increasing stress. A resilience assessment method is then proposed as a ‘stress-test’ of UWS configurations, under increasingly more stressful scenarios. We then demonstrate a toolbox assembled for the proposed analysis using, as a proof of concept, a semi-synthetic case study. Results are promising, suggesting that the approach could assist in the uptake and evolution of resilience thinking in strategic water infrastructure decision making, leading to water-wiser cities.

Summary Floods and forest fires are two of the most devastating natural hazards with severe socioeconomic, environmental as well as aesthetic impacts on the affected areas. Traditionally, these hazards are examined from different perspectives and are thus investigated through different, independent systems, overlooking the fact that they are tightly interrelated phenomena. In fact, the same flood event is more severe, i.e. associated with increased runoff discharge and peak flow and decreased time to peak, if it occurs over a burnt area than that occurring over a land not affected by fire. Mediterranean periurban areas, where forests covered with flammable vegetation coexist with agricultural land and urban zones, are typical areas particularly prone to the combined impact of floods and forest fires. Hence, the accurate assessment and effective management of post-fire flood risk becomes an issue of priority. The research presented in this paper aims to develop a robust methodological framework, using state of art tools and modern technologies to support the estimation of the change in time of five representative hydrological parameters for post-fire conditions. The proposed methodology considers both longer- and short-term initial conditions in order to assess the dynamic evolution of the selected parameters. The research focuses on typical Mediterranean periurban areas that are subjected to both hazards and concludes with a set of equations that associate post-fire and pre-fire conditions for five Fire Severity (FS) classes and three soil moisture states. The methodology has been tested for several flood events on the Rafina catchment, a periurban catchment in Eastern Attica (Greece). In order to validate the methodology, simulated hydrographs were produced and compared against available observed data. Results indicate a close convergence of observed and simulated flows. The proposed methodology is particularly flexible and thus easily adaptable to catchments with similar hydrometeorological and geomorphological features.

Operation of large-scale hydropower reservoirs is a complex problem that involves conflicting objectives, such as hydropower generation and water supply. Deriving optimal operational rules is a challenging task due to the non-linearity of the system dynamics and the uncertainty of future inflows and water demands. A common approach to derive optimal control policies is to couple simulation models with optimization algorithms. This paper in order to investigate the performance of a future reservoir and safely infer about its significance employs stochastic simulation, thus long synthetically generated time-series and a multi-objective version of the Parameterization-Simulation-Optimization (PSO) framework to develop uncertainty-aware operational rules. Furthermore, in order to handle the high computational effort that ensues from that coupling we investigate the potential of a surrogate-based multi-objective optimization algorithm, ParEGO. The PSO framework is deployed with WEAP21 water resources management model as simulation engine and MATLAB for the implementation of optimization algorithms. A comparison between NSGAII and ParEGO optimization algorithms is performed to assess the effectiveness of the proposed algorithm. The aforementioned comparison showed that ParEGO provides efficient approximations of the Pareto front while reducing the computational effort required. Finally, the potential benefit and the significance of the future reservoir is underlined.

[EN] There is significant potential for energy recovery through the use of micro-hydropower installations in water supply systems (WSS). To exploit the full potential of hydro energy in balance with the optimal hydraulic performance and water supply service, multi-objective management tools are needed. This paper presents the application of four management tools: (1) an energy audit to evaluate the potential hydro energy in the water pressurised systems of Alcoy; (2) multi-criteria decision-making methods for the selection of the preferred energy-efficient operation of a system with a pump-storage reservoir and hydro-turbines in the Algarve; (3) a numerical dynamic tool for optimal turbine operation in the water distribution of Langhirano; and (4) an urban water optioneering tool to estimate the hydropower potential of the external aqueduct network in Athens. These methods showed that through an integrated approach the WSS can be optimised for both hydraulic performance and hydro energy production., The research results presented have been executed within the framework of the TRUST project: Transitions to the Urban Water Services of Tomorrow. The TRUST project has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° 265122.

Urban water management is currently understood as a socio-technical problem, including both technologies and engineering interventions as well as socioeconomic dimensions and contexts vis-à-vis both end users and institutions. In this framework, perhaps the most important driver of urban water demand, at the intersection between engineering, social and economic domains, is urban growth. This paper examines aspects of the interplay between the dynamics of urban growth and the urban water cycle. Specifically, a cellular automata urban growth model is re-engineered to provide growth patterns at the level of detail needed by an urban water cycle model. The resulting toolkit is able to simulate spatial changes in urban areas while simultaneously estimating their water demand impact under different water demand management scenarios, with an emphasis on distributed technologies whose applicability depends on urban form. The method and tools are tested in the case study of Mesogeia, Greece, and conclusions are drawn, regarding both the performance of the urban growth model and the effectiveness of different urban water management practices.

Recent years have seen a growing interest in more distributed approaches towards stormwater management, often integrated with other forms of distributed management of urban water such as water demand management technologies. This paper focuses on the role of green roofs (GR), rainwater harvesting (RWH) and greywater reuse and their integration at the building level. A number of models were developed to simulate these systems, and provide design curves able to simultaneously minimise both total runoff volumes and the amount of potable water used in the building (for irrigation and toilet flushing). The models developed were applied to the design of stormwater infrastructure for the building of the National Gallery, in Athens, Greece. A sensitivity analysis of various model parameters was conducted, with results suggesting, inter alia: (i) a significant decrease of total runoff volumes for rainfalls of medium-to-small return periods; (ii) a significant influence of the plant factor on water requirements (with implications for selecting vegetation for GR in a Mediterranean climate); and (iii) a significant impact of latent heat peaking during the months of June and July. The trade-off, on runoff volumes, between percentage of green roof area and the dimensions of the water storage tank was also investigated. The results suggest that the most preferable solution for conserving potable water was RWH combined with greywater recycling, while for runoff minimisation the best option was the combination of green roof and greywater recycling.

Agricultural water use in the Mediterranean region is significant, causing serious threats to water bodies that may not be able to reach the ‘good status’ required by the Water Framework Directive. This study uses the Soil and Water Assessment Tool (SWAT) model and a simple economic component developed in order to estimate the cost-effectiveness (CE) of six agricultural Best Management Practices (BMPs) in reducing irrigation water abstractions in the water scarce Pinios catchment in central Greece. Deficit irrigation, precision agriculture, waste water reuse, conveyance efficiency improvement and their combinations were evaluated and their CE was calculated for each Hydrologic Response Unit (HRU) separately and for the entire catchment. The results at the HRU scale are presented comprehensively on a map, demonstrating the spatial differentiation of CE ratios across the catchment. Based on the analysis, a catchment management solution of affordable total cost would include waste water reuse in areas adjacent to treatment installations, deficit irrigation in the least water deficient areas as well as precision agriculture in the most deficient ones. Conveyance losses reduction through the construction of piped irrigation networks was necessary for considerably decreasing groundwater overexploitation. However, since conveyance loss reduction entails significant costs, the resulting CE is not favorable. The methodology presented aims to facilitate decision making for agricultural water management by enabling modelers to combine process-based hydrological models with rapid and reliable cost estimations and use cost effectiveness metrics to identify and prioritize suitable irrigation water management practices. However, existing SWAT limitations are also discussed and the need for improving the accuracy of the representation of such practices in the future is highlighted.

Integrated urban water management calls for tools that can analyze and simulate the complete cycle including the physical, technical, and social dimensions. Scientific advances created simulation tools able to simulate the urban water cycle as realistically as possible. However, even these tools cannot effectively simulate the social component and quantify how behaviors are shaped by external stress factors, such as climate and policies. In this work, an agent-based modeling tool, urban water agents' behavior (UWAB) is used to simulate the water demand behavior of households and how it is influenced by water demand management strategies and drought conditions. UWAB was applied in Athens, Greece to explore the effect of different water demand management strategies to the reliability of the Athens hydrosystem. The results illustrate the usability of UWAB to support decision makers in identifying how &ldquo, strict&rdquo, water demand management measures are needed and when and for how long to deploy them in order to alleviate potential water supply issues.

The provision of high quality urban water services, the assets of which are often conceptualised as ‘blue infrastructure’, is essential for public health and quality of life in the cities. On the other hand, parks, recreation grounds, gardens, green roofs and in general ‘green infrastructure’, provide a range of (urban) ecosystem services (including quality of life and aesthetics) and could also be thought of as inter alia contributors to the mitigation of floods, droughts, noise, air pollution and urban heat island (UHI) effects, improvement of biodiversity, amenity values and human health. Currently, these ‘blue’ and ‘green’ assets/infrastructure are planned to operate as two separate systems despite the obvious interactions between them (for example, low runoff coefficient of green areas resulting in reduction of stormwater flows, and irrigation of green areas by potable water in increasing pressure on water supply systems). This study explores the prospects of a more integrated ‘blue-green’ approach – tested at the scale of a household. Specifically, UWOT (the Urban Water Optioneering Tool) was extended and used to assess the potential benefits of a scheme that employed locally treated greywater along with harvested rainwater for irrigating a green roof. The results of the simulations indicated that the blue-green approach combined the benefits of both ‘green’ and ‘blue’ technologies/services and at the same time minimised the disadvantages of each when installed separately.

While flood risk is evolving as one of the most imminent natural hazards and the shift from a reactive decision environment to a proactive one sets the basis of the latest thinking in flood management, the need to equip decision makers with necessary tools to think about and intelligently select options and strategies for flood management is becoming ever more pressing. Within this context, the Preparing for Extreme and Rare Events in Coastal Regions (PEARL) intelligent knowledge-base (PEARL KB) of resilience strategies is presented here as an environment that allows end-users to navigate from their observed problem to a selection of possible options and interventions worth considering within an intuitive visual web interface assisting advanced interactivity. Incorporation of real case studies within the PEARL KB enables the extraction of (evidence-based) lessons from all over the word, while the KB’s collection of methods and tools directly supports the optimal selection of suitable interventions. The Knowledge-Base also gives access to the PEARL KB Flood Resilience Index (FRI) tool, which is an online tool for resilience assessment at a city level available to authorities and citizens. We argue that the PEARL KB equips authorities with tangible and operational tools that can improve strategic and operational flood risk management by assessing and eventually increasing resilience, while building towards the strengthening of risk governance. The online tools that the PEARL KB gives access to were demonstrated and tested in the city of Rethymno, Greece.

This study investigates the potential benefits of new technologies, modern appliances, and innovative techniques that help to improve the performance of the urban water cycle. Urbanisation is a major source of additional pressures (both qualitative and quantitative) on the environment. For example abstractions to cover the increased demands for water supply or alterations of the topographic and geomorphologic properties of the land cover result in considerable changes to the dynamics of the hydrosystem (change of average and maximum values of flows). Sustainable, water-aware technologies, like SUstainable Drainage Systems (SUDS) and rainwater harvesting schemes, can be implemented to reduce these adverse effects. These technologies introduce interactions between the components of the urban water cycle. Rainwater harvesting for example, apart from the potable water demand reduction, may have a significant influence on the generated runoff. Consequently, an integrated modelling of the urban water cycle is n...

Complex spatio-temporal environmental problems call for an integrated assessment of environmental systems due to their interdisciplinary nature. Within this context, the approach of integrated water resource management (IWRM) at the catchment scale, which is at the heart of advanced European environmental policy such as the water framework directive (WFD), argues for integrated modelling tools and approaches. This paper examines recent technological developments that facilitate integrated modelling and focuses on a novel approach, the open modelling interface (OpenMI), which allows models built by different developers to communicate at runtime. The individual models, when made compliant with the OpenMI standard, can be considered as ‘components' of an integrated model and seamlessly exchange data. Two OpenMI-enabled integrated modelling case studies are presented and their results critically discussed. The paper concludes with opportunities and challenges towards the design of flexible, component-based models supporting integrated environmental policy in general and the WFD implementation in particular.

Flood mapping, especially in urban areas, is a demanding task requiring substantial (and usually unavailable) data. However, with the recent introduction of the EU Floods Directive (2007/60/EC), the need for reliable, but cost effective, risk mapping at the regional scale is rising in the policy agenda. Methods are therefore required to allow for efficiently undertaking what the Directive terms “preliminary flood risk assessment,” in other words a screening of areas that could potentially be at risk of flooding and that consequently merit more detailed attention and analysis. Such methods cannot rely on modeling, as this would require more data and effort that is reasonable for this high-level, screening phase. This is especially true in urban areas, where modeling requires knowledge of the detailed urban terrain, the drainage networks, and their interactions. A GIS-based multicriteria flood risk assessment methodology was therefore developed and applied for the mapping of flood risk in urban areas. This approach quantifies the spatial distribution of flood risk and is able to deal with uncertainties in criteria values and to examine their influence on the overall flood risk assessment. It can further assess the spatially variable reliability of the resulting maps on the basis of the choice of method used to develop the maps. The approach is applied to the Greater Athens area and validated for its central and most urban part. A GIS database of economic, social, and environmental criteria contributing to flood risk was created. Three different multicriteria decision rules (Analytical Hierarchy Process, Weighted Linear Combination and Ordered Weighting Averaging) were applied, to produce the overall flood risk map of the area. To implement this methodology, the IDRISI Andes GIS software was customized and used. It is concluded that the results of the analysis are a reasonable representation of actual flood risk, on the basis of their comparison with historical flood events.